diff --git a/.gitignore b/.gitignore index 3471e5feb..d38e99e97 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,6 @@ cloud-harness .idea -**/helm +deployment/helm /build .vscode skaffold.yaml @@ -8,4 +8,7 @@ skaffold.yaml .pytest_cache /.overrides .hypothesis -skaffold-overrides.yaml \ No newline at end of file +/libraries/client/__pycache__ +/libraries/client/github.auth +skaffold-overrides.yaml +/libraries/client/netpyne-web/ diff --git a/applications/accounts-api/backend/README.md b/applications/accounts-api/backend/README.md index dd356bcee..307f30e53 100644 --- a/applications/accounts-api/backend/README.md +++ b/applications/accounts-api/backend/README.md @@ -8,7 +8,7 @@ is an example of building a OpenAPI-enabled Flask server. This example uses the [Connexion](https://github.com/zalando/connexion) library on top of Flask. ## Requirements -Python 3.5.2+ +Python 3.9+ ## Usage To run the server, please execute the following from the root directory: diff --git a/applications/accounts-api/backend/accounts_api/services/user_service.py b/applications/accounts-api/backend/accounts_api/services/user_service.py index a5b3dcb3b..f043639f4 100644 --- a/applications/accounts-api/backend/accounts_api/services/user_service.py +++ b/applications/accounts-api/backend/accounts_api/services/user_service.py @@ -95,6 +95,7 @@ def update_user(userid, user: User): updated_user = { 'firstName': user.first_name or current_user['firstName'], 'lastName': user.last_name or current_user['lastName'], + 'email': user.email or current_user['email'], 'attributes': { **(current_user.get('attributes') or {}), **({('profile--' + k): user.profiles[k] for k in user.profiles} if user.profiles else {}), diff --git a/applications/accounts/deploy/values.yaml b/applications/accounts/deploy/values.yaml new file mode 100644 index 000000000..564cbca86 --- /dev/null +++ b/applications/accounts/deploy/values.yaml @@ -0,0 +1,6 @@ +harness: + database: + resources: + requests: + memory: 128Mi + cpu: 10m \ No newline at end of file diff --git a/applications/accounts/themes/custom/login/login.ftl b/applications/accounts/themes/custom/login/login.ftl index c4161003e..d51b5c798 100644 --- a/applications/accounts/themes/custom/login/login.ftl +++ b/applications/accounts/themes/custom/login/login.ftl @@ -5,7 +5,7 @@ ${msg("doLogIn")} <#if realm.password && realm.registrationAllowed && !registrationDisabled??>
- ${msg("noAccount")} ${msg("doRegister")} + ${msg("noAccount")} ${msg("doRegister")}
diff --git a/applications/accounts/themes/custom/login/register.ftl b/applications/accounts/themes/custom/login/register.ftl index 216b51202..402b420cf 100644 --- a/applications/accounts/themes/custom/login/register.ftl +++ b/applications/accounts/themes/custom/login/register.ftl @@ -5,7 +5,7 @@ ${msg("registerTitle")}
- Already have an account? ${kcSanitize(msg("Log In"))?no_esc} + Already have an account? ${kcSanitize(msg("Log In"))?no_esc}
diff --git a/applications/jupyterhub/deploy/values-prod.yaml b/applications/jupyterhub/deploy/values-prod.yaml index 63318d25f..11e18b30c 100644 --- a/applications/jupyterhub/deploy/values-prod.yaml +++ b/applications/jupyterhub/deploy/values-prod.yaml @@ -23,9 +23,9 @@ singleuser: capacity: 2Gi cpu: limit: 1 - guarantee: 0.2 + guarantee: 0.02 memory: - limit: 1.5G - guarantee: 0.5G + limit: 0.5G + guarantee: 0.1G legacyusermax: 445 legacyworkspacemax: 465 \ No newline at end of file diff --git a/applications/jupyterhub/deploy/values.yaml b/applications/jupyterhub/deploy/values.yaml index e8a8e8f08..08dd72d12 100755 --- a/applications/jupyterhub/deploy/values.yaml +++ b/applications/jupyterhub/deploy/values.yaml @@ -46,7 +46,7 @@ singleuser: storage: type: dynamic capacity: 2Mi - + startTimeout: 3000 dynamic: pvcNameTemplate: osb-user-{userid} volumeNameTemplate: osb-user-{userid} @@ -57,10 +57,10 @@ singleuser: extraLabels: {} cpu: limit: 1 - guarantee: 0.2 + guarantee: 0.02 memory: - limit: 1G - guarantee: 0.5G + limit: 0.5G + guarantee: 0.1G prePuller: # hook relates to the hook-image-awaiter Job and hook-image-puller DaemonSet @@ -69,3 +69,8 @@ prePuller: continuous: enabled: false pullProfileListImages: false +scheduling: + userPods: + nodeAffinity: + matchNodePurpose: require + \ No newline at end of file diff --git a/applications/jupyterhub/src/osb_jupyter/osb_jupyter/osb_jupyterhub.py b/applications/jupyterhub/src/osb_jupyter/osb_jupyter/osb_jupyterhub.py index 02b109344..0778fdabf 100644 --- a/applications/jupyterhub/src/osb_jupyter/osb_jupyter/osb_jupyterhub.py +++ b/applications/jupyterhub/src/osb_jupyter/osb_jupyter/osb_jupyterhub.py @@ -8,6 +8,8 @@ from cloudharness.auth.exceptions import UserNotFound from urllib.parse import parse_qs, urlparse +from harness_jupyter.jupyterhub import set_key_value + allowed_chars = set( "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ") @@ -33,6 +35,7 @@ class CookieNotFound(Exception): pass + def change_pod_manifest(self: KubeSpawner): """ Application Hook to change the manifest of the notebook image @@ -135,8 +138,30 @@ def workspace_volume_is_legacy(workspace_id): self.storage_pvc_ensure = False self.volumes = [] self.volume_mounts = [] - print("Starting anonymoous session with no volumes") + print("Setting user quota cpu/mem usage") + from cloudharness.applications import get_current_configuration, get_configuration + try: + subdomain = self.handler.request.host.split( + str(self.config['domain']))[0][0:-1] + appname = next(app["name"] for app in self.config['apps'].values( + ) if app["harness"]["subdomain"] == subdomain) + app_conf = get_configuration(appname).to_dict() + cpu_conf = app_conf.get("singleuser", {}).get("cpu", {}) + mem_conf = app_conf.get("singleuser", {}).get("memory", {}) + except StopIteration: + cpu_conf = {} + mem_conf = {} + + jh_conf = get_configuration('jupyterhub') + cpu_conf = {**jh_conf.singleuser["cpu"].to_dict(), **cpu_conf} + mem_conf = {**jh_conf.singleuser["memory"].to_dict(), **mem_conf} + set_key_value(self, key="cpu_guarantee", value=cpu_conf["guarantee"]) + set_key_value(self, key="cpu_limit", value=cpu_conf["limit"]) + set_key_value(self, key="mem_guarantee", + value=mem_conf["guarantee"]) + set_key_value(self, key="mem_limit", value=mem_conf["limit"]) + print("Starting anonymous session with no volumes") except Exception as e: log.error('Change pod manifest failed due to an error.', exc_info=True) @@ -159,8 +184,6 @@ def workspace_volume_is_legacy(workspace_id): }) - - def get_app_user(user: User): auth_client = AuthClient() kc_user = auth_client.get_user(user.name) diff --git a/applications/jupyterhub/theming/page.html b/applications/jupyterhub/theming/page.html index 04457f38b..3096c0acc 100644 --- a/applications/jupyterhub/theming/page.html +++ b/applications/jupyterhub/theming/page.html @@ -131,6 +131,11 @@ margin-bottom: 57px; } + #warning-message { + color: #ffcc00; + margin: 10px; + } + .spawn-container { display: flex; align-items: center; diff --git a/applications/jupyterhub/theming/spawn_pending.html b/applications/jupyterhub/theming/spawn_pending.html index 609f20fb5..11e596231 100644 --- a/applications/jupyterhub/theming/spawn_pending.html +++ b/applications/jupyterhub/theming/spawn_pending.html @@ -8,6 +8,7 @@ {% block message %}

We are loading the workspace…

{% endblock %} +

log in as an OSBv2 user to get more resources and persisted workspaces.`); + } html_message = progressMessage.html(); } if (html_message) { diff --git a/applications/jupyterlab-minimal/deploy/values.yaml b/applications/jupyterlab-minimal/deploy/values.yaml index fc99dbec9..0779371e7 100644 --- a/applications/jupyterlab-minimal/deploy/values.yaml +++ b/applications/jupyterlab-minimal/deploy/values.yaml @@ -22,3 +22,9 @@ harness: c.JupyterHub.tornado_settings = { "headers": { "Content-Security-Policy": "frame-ancestors *"}} +singleuser: + storage: + capacity: 2Mi + cpu: + limit: 3 + guarantee: 0.13 \ No newline at end of file diff --git a/applications/jupyterlab/Dockerfile b/applications/jupyterlab/Dockerfile index 151473ad1..ecf6a4e40 100644 --- a/applications/jupyterlab/Dockerfile +++ b/applications/jupyterlab/Dockerfile @@ -31,7 +31,6 @@ RUN pip install -r requirements.txt --upgrade --no-cache-dir RUN jupyter labextension install plotlywidget - ############## USER root @@ -44,6 +43,10 @@ RUN echo -e '#!/bin/bash\n#Reusing the jNeuroML jar from the pip installed pyNeu RUN chmod +x /opt/conda/bin/jnml ENV JNML_HOME=/opt/conda/bin +### Set up lems, reusing pynml jar +RUN echo -e '#!/bin/bash\n#Reusing the jNeuroML jar from the pip installed pyNeuroML for the lems command\n\njava -classpath /opt/conda/lib/python3.9/site-packages/pyneuroml/lib/jNeuroML-*-jar-with-dependencies.jar org.lemsml.jlems.viz.VizMain $@' >> /opt/conda/bin/lems +RUN chmod +x /opt/conda/bin/lems + RUN cat ~/.bashrc @@ -84,13 +87,34 @@ RUN apt-get update && apt-get install libopenmpi-dev -y # Octave etc. RUN apt-get update && apt-get install octave octave-statistics -y +# NEST +ENV NEST_VER=3.5 +ENV NEST_HOME=/opt/conda/nest +RUN cd /tmp && \ + wget -nv https://github.com/nest/nest-simulator/archive/v$NEST_VER.tar.gz && \ + tar xvzf v$NEST_VER.tar.gz && \ + mv nest-simulator-$NEST_VER nest && \ + cd nest && \ + mkdir $NEST_HOME && \ + apt-get install libgsl-dev -y && \ + cmake -DCMAKE_INSTALL_PREFIX:PATH=$NEST_HOME -DPYTHON_EXECUTABLE:FILEPATH=/opt/conda/bin/python -DPYTHON_INCLUDE_DIR=/opt/conda/include/python3.9 . && \ + make -j7 && \ + make install +ENV PYTHONPATH=$NEST_HOME/lib/python3.9/site-packages +ENV PATH=$PATH:$NEST_HOME/bin + USER jovyan #### Install Python packages RUN pip install -r requirements.txt --upgrade --no-cache-dir +# Compile NEURON mod files for PyNN +RUN cd /opt/conda/lib/python3.9/site-packages/pyNN/neuron/nmodl && nrnivmodl +# See https://github.com/OpenSourceBrain/osb-model-validation/issues/91 for status of this +RUN pip install 'numpy<=1.23.0' # temp fix for https://levelup.gitconnected.com/fix-attributeerror-module-numpy-has-no-attribute-float-d7d68c5a4971 + ######################################################################### # fix for https://github.com/jupyter/notebook/issues/7048 - RUN pip install traitlets==5.9.0 + COPY --chown=jovyan:users overrides/* /opt/conda/share/jupyter/lab/static/ WORKDIR /opt/workspace diff --git a/applications/jupyterlab/deploy/values.yaml b/applications/jupyterlab/deploy/values.yaml index 838cbce41..20f1675cb 100755 --- a/applications/jupyterlab/deploy/values.yaml +++ b/applications/jupyterlab/deploy/values.yaml @@ -22,3 +22,10 @@ harness: c.JupyterHub.tornado_settings = { "headers": { "Content-Security-Policy": "frame-ancestors 'self' localhost:3000 *.osb.local osb.local localhost *.metacell.us *.opensourcebrain.org "}} +singleuser: + cpu: + limit: 0.1 + guarantee: 0.02 + memory: + limit: 0.5G + guarantee: 0.1G \ No newline at end of file diff --git a/applications/jupyterlab/requirements.txt b/applications/jupyterlab/requirements.txt index c822378c1..9f0bb0c57 100644 --- a/applications/jupyterlab/requirements.txt +++ b/applications/jupyterlab/requirements.txt @@ -11,7 +11,7 @@ git+https://github.com/Neurosim-lab/netpyne.git@osbv2#egg=netpyne #### Other simulators # Arbor -arbor==0.6.0 +arbor==0.9.0 # EDEN eden-simulator==0.2.1 @@ -42,10 +42,10 @@ seaborn # Note: this just installs pytorch for cpu, the default install adds a v large cuda lib -f https://download.pytorch.org/whl/torch_stable.html -torch==1.11.0+cpu +torch==2.1.2+cpu # For MDF -modeci_mdf==0.4.5 # big jump in size of image... +modeci_mdf==0.4.8 # big jump in size of image... scikit-learn # Required for some Neuromatch Academy material fasttext # Required for some Neuromatch Academy material @@ -57,8 +57,18 @@ tvb-data ipympl +#### SBML/COMBINE packages +python-libsbml +tellurium + + #### General Python packages plotly + +#### Install OMV +git+https://github.com/OpenSourceBrain/osb-model-validation.git@v0.2.15 + + #### Final updates numpy # Removes some issues with LFPy... \ No newline at end of file diff --git a/applications/netpyne/Dockerfile b/applications/netpyne/Dockerfile index 1b8f38a48..a1bccee03 100644 --- a/applications/netpyne/Dockerfile +++ b/applications/netpyne/Dockerfile @@ -1,4 +1,4 @@ -FROM gcr.io/metacellllc/netpyne-ui:1.0.0-osb +FROM gcr.io/metacellllc/netpyne-ui:1.1.0 COPY overrides/requirements.txt overrides/requirements.txt RUN --mount=type=cache,target=/root/.cache python -m pip install --upgrade pip &&\ diff --git a/applications/netpyne/deploy/values.yaml b/applications/netpyne/deploy/values.yaml index de881486a..ed0426133 100644 --- a/applications/netpyne/deploy/values.yaml +++ b/applications/netpyne/deploy/values.yaml @@ -18,4 +18,11 @@ harness: c.JupyterHub.tornado_settings = { "headers": { "Content-Security-Policy": "frame-ancestors 'self' localhost:3000 *.osb.local localhost *.metacell.us *.opensourcebrain.org "}} dependencies: hard: - - jupyterhub \ No newline at end of file + - jupyterhub +singleuser: + cpu: + limit: 1 + guarantee: 0.02 + memory: + limit: 1G + guarantee: 0.5G \ No newline at end of file diff --git a/applications/nfsserver/deploy/values.yaml b/applications/nfsserver/deploy/values.yaml index 067ce3c48..e6e80d7d1 100644 --- a/applications/nfsserver/deploy/values.yaml +++ b/applications/nfsserver/deploy/values.yaml @@ -1,4 +1,3 @@ - # nfs server pvc disk size (/exports) server: - diskSize: 90Gi + diskSize: 120Gi diff --git a/applications/nwb-explorer/.dockerignore b/applications/nwb-explorer/.dockerignore index b8cad24a2..e97689c6f 100644 --- a/applications/nwb-explorer/.dockerignore +++ b/applications/nwb-explorer/.dockerignore @@ -1 +1,2 @@ -/deploy \ No newline at end of file +/deploy +.git \ No newline at end of file diff --git a/applications/nwb-explorer/Dockerfile b/applications/nwb-explorer/Dockerfile index c1ed7f37b..5871da33b 100644 --- a/applications/nwb-explorer/Dockerfile +++ b/applications/nwb-explorer/Dockerfile @@ -1,23 +1,14 @@ -FROM node:15 as clone -ENV BRANCH_TAG="v0.7.0" -ENV REPO=https://github.com/MetaCell/nwb-explorer.git -RUN echo "cache 2023-12-23" -RUN git clone $REPO -b $BRANCH_TAG -RUN rm -Rf .git - -FROM node:15 as jsbuild +FROM node:20 as jsbuild ENV FOLDER=nwb-explorer - - WORKDIR $FOLDER/webapp -COPY --from=clone nwb-explorer/webapp/package.json . -COPY --from=clone nwb-explorer/webapp/package-lock.json . +COPY dependencies/nwb-explorer/webapp/package.json . +COPY dependencies/nwb-explorer/webapp/yarn.lock . RUN yarn install --network-timeout 1000000000 -COPY --from=clone nwb-explorer/webapp/ . +COPY dependencies/nwb-explorer/webapp/ . COPY geppetto/GeppettoConfiguration.json GeppettoConfiguration.json RUN yarn build @@ -40,23 +31,21 @@ RUN apt-get update -qq &&\ RUN chown $NB_UID /opt RUN chown $NB_UID . - - USER $NB_UID -COPY --from=clone $FOLDER/requirements.txt requirements.txt +COPY dependencies/nwb-explorer/requirements.txt requirements.txt RUN --mount=type=cache,target=/root/.cache python -m pip install --upgrade pip &&\ pip install -r requirements.txt RUN --mount=type=cache,target=/root/.cache python -m pip install --upgrade pip &&\ pip install cython --no-cache-dir -COPY --from=clone $FOLDER . +COPY dependencies/nwb-explorer/ . RUN mkdir -p /opt/workspace RUN ln -s /opt/workspace workspace RUN mkdir -p /opt/home RUN python utilities/install.py --npm-skip --no-test -COPY --from=clone $FOLDER/utilities/custom.css /home/jovyan/.jupyter/custom/custom.css +COPY dependencies/nwb-explorer/utilities/custom.css /home/jovyan/.jupyter/custom/custom.css # this removes the frame ancestor default cors settings RUN rm -f ~/.jupyter/*.json diff --git a/applications/nwb-explorer/deploy/values.yaml b/applications/nwb-explorer/deploy/values.yaml index ee5269d54..369a7dfca 100644 --- a/applications/nwb-explorer/deploy/values.yaml +++ b/applications/nwb-explorer/deploy/values.yaml @@ -4,6 +4,10 @@ harness: auto: false port: 80 name: proxy-public + dependencies: + git: + - url: https://github.com/MetaCell/nwb-explorer.git + branch_tag: development jupyterhub: args: ["--debug", "--NotebookApp.default_url=/geppetto", "--library=nwb_explorer", "--NotebookApp.notebook_dir=/opt/workspace"] applicationHook: "osb_jupyter.change_pod_manifest" diff --git a/applications/osb-portal/deploy/values-minimal.yaml b/applications/osb-portal/deploy/values-minimal.yaml index 1b6a8b04c..91ac27755 100644 --- a/applications/osb-portal/deploy/values-minimal.yaml +++ b/applications/osb-portal/deploy/values-minimal.yaml @@ -6,5 +6,5 @@ harness: - workspaces - jupyterhub - notifications - - jupyterlab-minimal + - nwb-explorer diff --git a/applications/osb-portal/package-lock.json b/applications/osb-portal/package-lock.json index 5dead15e8..536f91b49 100644 --- a/applications/osb-portal/package-lock.json +++ b/applications/osb-portal/package-lock.json @@ -19,7 +19,7 @@ "@reduxjs/toolkit": "^1.9.3", "@sentry/react": "^6.3.5", "assert": "^2.0.0", - "axios": "^0.21.4", + "axios": "^1.6.5", "keycloak-js": "^12.0.0", "less-vars-to-js": "^1.3.0", "lodash": "^4.17.21", @@ -122,11 +122,15 @@ } }, "node_modules/@babel/code-frame": { - "version": "7.10.4", - "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.10.4.tgz", - "integrity": "sha512-vG6SvB6oYEhvgisZNFRmRCUkLz11c7rp+tbNTynGqc6mS1d5ATd/sGyV6W0KZZnXRKMTzZDRgQT3Ou9jhpAfUg==", + "version": "7.23.5", + "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.23.5.tgz", + "integrity": "sha512-CgH3s1a96LipHCmSUmYFPwY7MNx8C3avkq7i4Wl3cfa662ldtUe4VM1TPXX70pfmrlWTb6jLqTYrZyT2ZTJBgA==", "dependencies": { - "@babel/highlight": "^7.10.4" + "@babel/highlight": "^7.23.4", + "chalk": "^2.4.2" + }, + "engines": { + "node": ">=6.9.0" } }, "node_modules/@babel/compat-data": { @@ -166,42 +170,10 @@ "url": "https://opencollective.com/babel" } }, - "node_modules/@babel/core/node_modules/@babel/code-frame": { - "version": "7.18.6", - "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.18.6.tgz", - "integrity": "sha512-TDCmlK5eOvH+eH7cdAFlNXeVJqWIQ7gW9tY1GJIpUtFb6CmjVyq2VM3u71bOyR8CRihcCgMUYoDNyLXao3+70Q==", - "dependencies": { - "@babel/highlight": "^7.18.6" 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true, - "requires": { - "@babel/helper-validator-identifier": "^7.18.6", - "chalk": "^2.0.0", - "js-tokens": "^4.0.0" - }, - "dependencies": { - "chalk": { - "version": "2.4.2", - "resolved": "https://registry.npmjs.org/chalk/-/chalk-2.4.2.tgz", - "integrity": "sha512-Mti+f9lpJNcwF4tWV8/OrTTtF1gZi+f8FqlyAdouralcFWFQWF2+NgCHShjkCb+IFBLq9buZwE1xckQU4peSuQ==", - "dev": true, - "requires": { - "ansi-styles": "^3.2.1", - "escape-string-regexp": "^1.0.5", - "supports-color": "^5.3.0" - } - } - } - }, "chalk": { "version": "4.1.2", "resolved": "https://registry.npmjs.org/chalk/-/chalk-4.1.2.tgz", @@ -24061,15 +23812,6 @@ "resolved": "https://registry.npmjs.org/color-name/-/color-name-1.1.4.tgz", "integrity": "sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==", "dev": true - }, - "supports-color": { - "version": "5.5.0", - "resolved": "https://registry.npmjs.org/supports-color/-/supports-color-5.5.0.tgz", - "integrity": "sha512-QjVjwdXIt408MIiAqCX4oUKsgU2EqAGzs2Ppkm4aQYbjm+ZEWEcW4SfFNTr4uMNZma0ey4f5lgLrkB0aX0QMow==", - "dev": true, - "requires": { - "has-flag": "^3.0.0" - } } } }, @@ -24450,9 +24192,9 @@ "dev": true }, "semver": { - "version": "7.3.7", - "resolved": "https://registry.npmjs.org/semver/-/semver-7.3.7.tgz", - "integrity": "sha512-QlYTucUYOews+WeEujDoEGziz4K6c47V/Bd+LjSSYcA94p+DmINdf7ncaUinThfvZyu13lN9OY1XDxt8C0Tw0g==", + "version": "7.5.4", + "resolved": "https://registry.npmjs.org/semver/-/semver-7.5.4.tgz", + "integrity": "sha512-1bCSESV6Pv+i21Hvpxp3Dx+pSD8lIPt8uVjRrxAUt/nbswYc+tK6Y2btiULjd4+fnq15PX+nqQDC7Oft7WkwcA==", "dev": true, "requires": { "lru-cache": "^6.0.0" @@ -25323,14 +25065,12 @@ "mime-db": { "version": "1.52.0", "resolved": "https://registry.npmjs.org/mime-db/-/mime-db-1.52.0.tgz", - "integrity": "sha512-sPU4uV7dYlvtWJxwwxHD0PuihVNiE7TyAbQ5SWxDCB9mUYvOgroQOwYQQOKPJ8CIbE+1ETVlOoK1UC2nU3gYvg==", - "dev": true + "integrity": "sha512-sPU4uV7dYlvtWJxwwxHD0PuihVNiE7TyAbQ5SWxDCB9mUYvOgroQOwYQQOKPJ8CIbE+1ETVlOoK1UC2nU3gYvg==" }, "mime-types": { "version": "2.1.35", "resolved": "https://registry.npmjs.org/mime-types/-/mime-types-2.1.35.tgz", "integrity": "sha512-ZDY+bPm5zTTF+YpCrAU9nK0UgICYPT0QtT1NZWFv4s++TNkcgVaT0g6+4R2uI4MjQjzysHB1zxuWL50hzaeXiw==", - "dev": true, "requires": { "mime-db": "1.52.0" } @@ -25394,10 +25134,9 @@ "dev": true }, "nanoid": { - "version": "3.3.1", - "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.1.tgz", - "integrity": "sha512-n6Vs/3KGyxPQd6uO0eH4Bv0ojGSUvuLlIHtC3Y0kEO23YRge8H9x1GCzLn28YX0H66pMkxuaeESFq4tKISKwdw==", - "dev": true + "version": "3.3.7", + "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.7.tgz", + "integrity": "sha512-eSRppjcPIatRIMC1U6UngP8XFcz8MQWGQdt1MTBQ7NaAmvXDfvNxbvWV3x2y6CdEUciCSsDHDQZbhYaB8QEo2g==" }, "native-request": { "version": "1.1.0", @@ -25964,14 +25703,20 @@ } }, "postcss": { - "version": "8.2.13", - "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.2.13.tgz", - "integrity": "sha512-FCE5xLH+hjbzRdpbRb1IMCvPv9yZx2QnDarBEYSN0N0HYk+TcXsEhwdFcFb+SRWOKzKGErhIEbBK2ogyLdTtfQ==", - "dev": true, + "version": "8.4.33", + "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.33.tgz", + "integrity": "sha512-Kkpbhhdjw2qQs2O2DGX+8m5OVqEcbB9HRBvuYM9pgrjEFUg30A9LmXNlTAUj4S9kgtGyrMbTzVjH7E+s5Re2yg==", "requires": { - "colorette": "^1.2.2", - "nanoid": "^3.1.22", - "source-map": "^0.6.1" + "nanoid": "^3.3.7", + "picocolors": "^1.0.0", + "source-map-js": "^1.0.2" + }, + "dependencies": { + "source-map-js": { + "version": "1.0.2", + "resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.0.2.tgz", + "integrity": "sha512-R0XvVJ9WusLiqTCEiGCmICCMplcCkIwwR11mOSD9CR5u+IXYdiseeEuXCVAjS54zqwkLcPNnmU4OeJ6tUrWhDw==" + } } }, "postcss-modules-extract-imports": { @@ -26139,6 +25884,11 @@ } } }, + "proxy-from-env": { + "version": "1.1.0", + "resolved": "https://registry.npmjs.org/proxy-from-env/-/proxy-from-env-1.1.0.tgz", + "integrity": "sha512-D+zkORCbA9f1tdWRK0RaCR3GPv50cMxcrz4X8k5LTSUD1Dkw47mKJEZQNunItRTkWwgtaUSo1RVFRIG9ZXiFYg==" + }, "prr": { "version": "1.0.1", "resolved": "https://registry.npmjs.org/prr/-/prr-1.0.1.tgz", @@ -26762,26 +26512,6 @@ "version": "5.0.0", "resolved": "https://registry.npmjs.org/is-plain-object/-/is-plain-object-5.0.0.tgz", "integrity": "sha512-VRSzKkbMm5jMDoKLbltAkFQ5Qr7VDiTFGXxYFXXowVj387GeGNOCsOH6Msy00SGZ3Fp84b1Naa1psqgcCIEP5Q==" - }, - "nanoid": { - "version": "3.3.4", - "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.4.tgz", - "integrity": "sha512-MqBkQh/OHTS2egovRtLk45wEyNXwF+cokD+1YPf9u5VfJiRdAiRwB2froX5Co9Rh20xs4siNPm8naNotSD6RBw==" - }, - "postcss": { - "version": "8.4.17", - "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.17.tgz", - "integrity": "sha512-UNxNOLQydcOFi41yHNMcKRZ39NeXlr8AxGuZJsdub8vIb12fHzcq37DTU/QtbI6WLxNg2gF9Z+8qtRwTj1UI1Q==", - "requires": { - "nanoid": "^3.3.4", - "picocolors": "^1.0.0", - "source-map-js": "^1.0.2" - } - }, - "source-map-js": { - "version": "1.0.2", - "resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.0.2.tgz", - "integrity": "sha512-R0XvVJ9WusLiqTCEiGCmICCMplcCkIwwR11mOSD9CR5u+IXYdiseeEuXCVAjS54zqwkLcPNnmU4OeJ6tUrWhDw==" } } }, @@ -26829,9 +26559,9 @@ } }, "semver": { - "version": "5.7.1", - "resolved": "https://registry.npmjs.org/semver/-/semver-5.7.1.tgz", - "integrity": "sha512-sauaDf/PZdVgrLTNYHRtpXa1iRiKcaebiKQ1BJdpQlWH2lCvexQdX55snPFyK7QzpudqbCI0qXFfOasHdyNDGQ==", + "version": "5.7.2", + "resolved": "https://registry.npmjs.org/semver/-/semver-5.7.2.tgz", + "integrity": "sha512-cBznnQ9KjJqU67B52RMC65CMarK2600WFnbkcaiwWq3xy/5haFJlshgnpjovMVJ+Hff49d8GEn0b87C5pDQ10g==", "dev": true }, "send": { @@ -27522,9 +27252,9 @@ "dev": true }, "semver": { - "version": "7.3.5", - "resolved": "https://registry.npmjs.org/semver/-/semver-7.3.5.tgz", - "integrity": "sha512-PoeGJYh8HK4BTO/a9Tf6ZG3veo/A7ZVsYrSA6J8ny9nb3B1VrpkuN+z9OE5wfE5p6H4LchYZsegiQgbJD94ZFQ==", + "version": "7.5.4", + "resolved": "https://registry.npmjs.org/semver/-/semver-7.5.4.tgz", + "integrity": "sha512-1bCSESV6Pv+i21Hvpxp3Dx+pSD8lIPt8uVjRrxAUt/nbswYc+tK6Y2btiULjd4+fnq15PX+nqQDC7Oft7WkwcA==", "dev": true, "requires": { "lru-cache": "^6.0.0" @@ -28209,9 +27939,9 @@ "dev": true }, "word-wrap": { - "version": "1.2.3", - "resolved": "https://registry.npmjs.org/word-wrap/-/word-wrap-1.2.3.tgz", - "integrity": "sha512-Hz/mrNwitNRh/HUAtM/VT/5VH+ygD6DV7mYKZAtHOrbs8U7lvPS6xf7EJKMF0uW1KJCl0H701g3ZGus+muE5vQ==", + "version": "1.2.5", + "resolved": "https://registry.npmjs.org/word-wrap/-/word-wrap-1.2.5.tgz", + "integrity": "sha512-BN22B5eaMMI9UMtjrGd5g5eCYPpCPDUy0FJXbYsaT5zYxjFOckS53SQDE3pWkVoWpHXVb3BrYcEN4Twa55B5cA==", "dev": true }, "wrap-ansi": { diff --git a/applications/osb-portal/package.json b/applications/osb-portal/package.json index d332027d9..ec3bfa62e 100644 --- a/applications/osb-portal/package.json +++ b/applications/osb-portal/package.json @@ -8,6 +8,7 @@ "start:dev": "webpack serve --progress --config webpack.dev.js --env DOMAIN=https://v2dev.opensourcebrain.org --env NAMESPACE=osb2dev --env NODE_OPTIONS='--max-old-space-size=12048'", "start:prod": "webpack serve --progress --config webpack.dev.js --env DOMAIN=https://v2.opensourcebrain.org --env NAMESPACE=osb2 --env NODE_OPTIONS='--max-old-space-size=12048'", "start:local": "webpack serve --progress --config webpack.dev.js --env DOMAIN=http://osb.local --env NAMESPACE=osblocal --env NODE_OPTIONS='--max-old-space-size=12048'", + "start:local-be": "webpack serve --progress --config webpack.dev.js --env APP_DOMAIN=http://localhost:8888 --env DOMAIN=https://osb.local --env WORKSPACES_DOMAIN=http://localhost:5001 --env NAMESPACE=osblocal --env NODE_OPTIONS='--max-old-space-size=12048'", "start:dev-be": "webpack serve --progress --config webpack.dev.js --env DOMAIN=https://v2dev.opensourcebrain.org --env ACCOUNTS_API_DOMAIN=localhost:5001 --env WORKSPACES_DOMAIN=http://localhost:5001 --env NAMESPACE=osblocal --env NODE_OPTIONS='--max-old-space-size=12048'", "start:dev-app": "webpack serve --progress --config webpack.dev.js --env APP_DOMAIN=https://localhost:8888 --env DOMAIN=https://v2.opensourcebrain.org --env NAMESPACE=osblocal --env NODE_OPTIONS='--max-old-space-size=12048'", "start:local-app": "webpack serve --progress --config webpack.dev.js --env APP_DOMAIN=http://localhost:8888 --env DOMAIN=http://osb.local --env NAMESPACE=osblocal --env NODE_OPTIONS='--max-old-space-size=12048'", @@ -34,7 +35,7 @@ "@reduxjs/toolkit": "^1.9.3", "@sentry/react": "^6.3.5", "assert": "^2.0.0", - "axios": "^0.21.4", + "axios": "^1.6.5", "keycloak-js": "^12.0.0", "less-vars-to-js": "^1.3.0", "lodash": "^4.17.21", diff --git a/applications/osb-portal/src/App.tsx b/applications/osb-portal/src/App.tsx index 3296855de..55969787a 100644 --- a/applications/osb-portal/src/App.tsx +++ b/applications/osb-portal/src/App.tsx @@ -32,6 +32,7 @@ import { import Box from "@mui/material/Box"; import { UserInfo } from "./types/user"; import SampleIframePage from "./pages/SampleIframePage"; +import NotFoundPage from "./pages/NotFoundPage"; declare module "@mui/styles/defaultTheme" { // eslint-disable-next-line @typescript-eslint/no-empty-interface @@ -43,9 +44,7 @@ const styles = { minHeight: "100vh", display: "flex", flexDirection: "column", - height: { - md: "100vh", - }, + height: "100vh", overflow: { xs: "auto", md: "hidden", @@ -75,7 +74,7 @@ export const App = (props: AppProps) => { return ( - + {!props.error && ( @@ -94,6 +93,34 @@ export const App = (props: AppProps) => { } /> + + + + } + /> + + + + } + /> + } + /> + + + + } + /> { /> } /> + } /> diff --git a/applications/osb-portal/src/apiclient/workspaces/apis/RestApi.ts b/applications/osb-portal/src/apiclient/workspaces/apis/RestApi.ts index 4cf0143f5..3b0123fb8 100644 --- a/applications/osb-portal/src/apiclient/workspaces/apis/RestApi.ts +++ b/applications/osb-portal/src/apiclient/workspaces/apis/RestApi.ts @@ -185,6 +185,11 @@ export interface WorkspaceresourcePostRequest { workspaceResource: WorkspaceResource; } +export interface WorkspacesControllersOsbrepositoryControllerSetthumbnailRequest { + id: number; + thumbNail?: Blob; +} + export interface WorkspacesControllersWorkspaceControllerAddimageRequest { id: number; image?: Blob; @@ -1323,6 +1328,64 @@ export class RestApi extends runtime.BaseAPI { return await response.value(); } + /** + * Sets the thumbnail of the workspace. + */ + async workspacesControllersOsbrepositoryControllerSetthumbnailRaw(requestParameters: WorkspacesControllersOsbrepositoryControllerSetthumbnailRequest): Promise> { + if (requestParameters.id === null || requestParameters.id === undefined) { + throw new runtime.RequiredError('id','Required parameter requestParameters.id was null or undefined when calling workspacesControllersOsbrepositoryControllerSetthumbnail.'); + } + + const queryParameters: any = {}; + + const headerParameters: runtime.HTTPHeaders = {}; + + if (this.configuration && this.configuration.accessToken) { + const token = this.configuration.accessToken; + const tokenString = typeof token === 'function' ? token("bearerAuth", []) : token; + + if (tokenString) { + headerParameters["Authorization"] = `Bearer ${tokenString}`; + } + } + const consumes: runtime.Consume[] = [ + { contentType: 'multipart/form-data' }, + ]; + // @ts-ignore: canConsumeForm may be unused + const canConsumeForm = runtime.canConsumeForm(consumes); + + let formParams: { append(param: string, value: any): any }; + let useForm = false; + // use FormData to transmit files using content-type "multipart/form-data" + useForm = canConsumeForm; + if (useForm) { + formParams = new FormData(); + } else { + formParams = new URLSearchParams(); + } + + if (requestParameters.thumbNail !== undefined) { + formParams.append('thumbNail', requestParameters.thumbNail as any); + } + + const response = await this.request({ + path: `/osbrepository/{id}/thumbnail/`.replace(`{${"id"}}`, encodeURIComponent(String(requestParameters.id))), + method: 'POST', + headers: headerParameters, + query: queryParameters, + body: formParams, + }); + + return new runtime.VoidApiResponse(response); + } + + /** + * Sets the thumbnail of the workspace. + */ + async workspacesControllersOsbrepositoryControllerSetthumbnail(requestParameters: WorkspacesControllersOsbrepositoryControllerSetthumbnailRequest): Promise { + await this.workspacesControllersOsbrepositoryControllerSetthumbnailRaw(requestParameters); + } + /** * Adds and image to the workspace. */ diff --git a/applications/osb-portal/src/apiclient/workspaces/models/OSBRepository.ts b/applications/osb-portal/src/apiclient/workspaces/models/OSBRepository.ts index 5c961b9a9..1c920e7bc 100644 --- a/applications/osb-portal/src/apiclient/workspaces/models/OSBRepository.ts +++ b/applications/osb-portal/src/apiclient/workspaces/models/OSBRepository.ts @@ -98,6 +98,12 @@ export interface OSBRepository { * @memberof OSBRepository */ defaultContext?: string; + /** + * + * @type {string} + * @memberof OSBRepository + */ + thumbnail?: string; /** * OSBRepository keycloak user id, will be automatically be set to the logged in user * @type {string} @@ -178,6 +184,7 @@ export function OSBRepositoryFromJSONTyped(json: any, ignoreDiscriminator: boole 'autoSync': !exists(json, 'auto_sync') ? undefined : json['auto_sync'], 'uri': json['uri'], 'defaultContext': !exists(json, 'default_context') ? undefined : json['default_context'], + 'thumbnail': !exists(json, 'thumbnail') ? undefined : json['thumbnail'], 'userId': !exists(json, 'user_id') ? undefined : json['user_id'], 'timestampCreated': !exists(json, 'timestamp_created') ? undefined : (new Date(json['timestamp_created'])), 'timestampUpdated': !exists(json, 'timestamp_updated') ? undefined : (new Date(json['timestamp_updated'])), @@ -208,6 +215,7 @@ export function OSBRepositoryToJSON(value?: OSBRepository | null): any { 'auto_sync': value.autoSync, 'uri': value.uri, 'default_context': value.defaultContext, + 'thumbnail': value.thumbnail, 'user_id': value.userId, 'timestamp_created': value.timestampCreated === undefined ? undefined : (value.timestampCreated.toISOString()), 'timestamp_updated': value.timestampUpdated === undefined ? undefined : (value.timestampUpdated.toISOString()), diff --git a/applications/osb-portal/src/apiclient/workspaces/models/OSBRepositoryBase.ts b/applications/osb-portal/src/apiclient/workspaces/models/OSBRepositoryBase.ts index 99d3352b7..5b256def4 100644 --- a/applications/osb-portal/src/apiclient/workspaces/models/OSBRepositoryBase.ts +++ b/applications/osb-portal/src/apiclient/workspaces/models/OSBRepositoryBase.ts @@ -78,6 +78,12 @@ export interface OSBRepositoryBase { * @memberof OSBRepositoryBase */ defaultContext?: string; + /** + * + * @type {string} + * @memberof OSBRepositoryBase + */ + thumbnail?: string; /** * OSBRepository keycloak user id, will be automatically be set to the logged in user * @type {string} @@ -122,6 +128,7 @@ export function OSBRepositoryBaseFromJSONTyped(json: any, ignoreDiscriminator: b 'autoSync': !exists(json, 'auto_sync') ? undefined : json['auto_sync'], 'uri': json['uri'], 'defaultContext': !exists(json, 'default_context') ? undefined : json['default_context'], + 'thumbnail': !exists(json, 'thumbnail') ? undefined : json['thumbnail'], 'userId': !exists(json, 'user_id') ? undefined : json['user_id'], 'timestampCreated': !exists(json, 'timestamp_created') ? undefined : (new Date(json['timestamp_created'])), 'timestampUpdated': !exists(json, 'timestamp_updated') ? undefined : (new Date(json['timestamp_updated'])), @@ -146,6 +153,7 @@ export function OSBRepositoryBaseToJSON(value?: OSBRepositoryBase | null): any { 'auto_sync': value.autoSync, 'uri': value.uri, 'default_context': value.defaultContext, + 'thumbnail': value.thumbnail, 'user_id': value.userId, 'timestamp_created': value.timestampCreated === undefined ? undefined : (value.timestampCreated.toISOString()), 'timestamp_updated': value.timestampUpdated === undefined ? undefined : (value.timestampUpdated.toISOString()), diff --git a/applications/osb-portal/src/apiclient/workspaces/models/OSBRepositoryEntity.ts b/applications/osb-portal/src/apiclient/workspaces/models/OSBRepositoryEntity.ts index 90814e4c6..315b869e9 100644 --- a/applications/osb-portal/src/apiclient/workspaces/models/OSBRepositoryEntity.ts +++ b/applications/osb-portal/src/apiclient/workspaces/models/OSBRepositoryEntity.ts @@ -82,6 +82,12 @@ export interface OSBRepositoryEntity { * @memberof OSBRepositoryEntity */ defaultContext?: string; + /** + * + * @type {string} + * @memberof OSBRepositoryEntity + */ + thumbnail?: string; /** * OSBRepository keycloak user id, will be automatically be set to the logged in user * @type {string} @@ -126,6 +132,7 @@ export function OSBRepositoryEntityFromJSONTyped(json: any, ignoreDiscriminator: 'autoSync': !exists(json, 'auto_sync') ? undefined : json['auto_sync'], 'uri': json['uri'], 'defaultContext': !exists(json, 'default_context') ? undefined : json['default_context'], + 'thumbnail': !exists(json, 'thumbnail') ? undefined : json['thumbnail'], 'userId': !exists(json, 'user_id') ? undefined : json['user_id'], 'timestampCreated': !exists(json, 'timestamp_created') ? undefined : (new Date(json['timestamp_created'])), 'timestampUpdated': !exists(json, 'timestamp_updated') ? undefined : (new Date(json['timestamp_updated'])), @@ -150,6 +157,7 @@ export function OSBRepositoryEntityToJSON(value?: OSBRepositoryEntity | null): a 'auto_sync': value.autoSync, 'uri': value.uri, 'default_context': value.defaultContext, + 'thumbnail': value.thumbnail, 'user_id': value.userId, 'timestamp_created': value.timestampCreated === undefined ? undefined : (value.timestampCreated.toISOString()), 'timestamp_updated': value.timestampUpdated === undefined ? undefined : (value.timestampUpdated.toISOString()), diff --git a/applications/osb-portal/src/components/common/RepositoriesWorkspacesSearchField.tsx b/applications/osb-portal/src/components/common/RepositoriesWorkspacesSearchField.tsx index 7f4d92b21..0e5fbe552 100644 --- a/applications/osb-portal/src/components/common/RepositoriesWorkspacesSearchField.tsx +++ b/applications/osb-portal/src/components/common/RepositoriesWorkspacesSearchField.tsx @@ -40,6 +40,7 @@ const StyledTextField = styled(TextField)(({ theme }) => ({ interface RepositoriesSearchProps { filterChanged: (newFilter: string) => void; borderRadius?: number; + value?: string; } export default (props: RepositoriesSearchProps) => { @@ -48,6 +49,7 @@ export default (props: RepositoriesSearchProps) => { variant="standard" id="standard-start-adornment" fullWidth={true} + value={props?.value} sx={{ borderRadius: props?.borderRadius ? `${props?.borderRadius}px` diff --git a/applications/osb-portal/src/components/common/SearchFilterReposWorkspaces.tsx b/applications/osb-portal/src/components/common/SearchFilterReposWorkspaces.tsx index 0e4286872..65eec78c1 100644 --- a/applications/osb-portal/src/components/common/SearchFilterReposWorkspaces.tsx +++ b/applications/osb-portal/src/components/common/SearchFilterReposWorkspaces.tsx @@ -34,6 +34,7 @@ import searchFilter from "../../types/searchFilter"; //services import RepositoryService from "../../service/RepositoryService"; +import { Badge } from "@mui/material"; interface SearchReposWorkspacesProps { searchFilterValues: searchFilter; @@ -185,13 +186,13 @@ export const SearchFilterReposWorkspaces = ( return ( <> - + } + startIcon={} > Filter @@ -244,6 +245,7 @@ export const SearchFilterReposWorkspaces = ( <> diff --git a/applications/osb-portal/src/components/common/ThumbnailUploadArea.tsx b/applications/osb-portal/src/components/common/ThumbnailUploadArea.tsx new file mode 100644 index 000000000..e6920ea2c --- /dev/null +++ b/applications/osb-portal/src/components/common/ThumbnailUploadArea.tsx @@ -0,0 +1,193 @@ +import * as React from "react"; +import Dropzone from "react-dropzone"; +import { Box, Button, Grid, IconButton, Typography } from "@mui/material"; + +//icons +import DeleteForeverIcon from "@mui/icons-material/DeleteForever"; + +//style +import styled from "@mui/system/styled"; +import { radius, gutter, bgInputs } from "../../theme"; + +// import style manually +import { alpha } from "@mui/material/styles"; + + +const MAX_ALLOWED_THUMBNAIL_SIZE = 1024 * 1024; // 1MB + +export const StyledDropZoneBox = styled(Box)(({ theme }) => ({ + color: bgInputs, + border: `2px dashed ${bgInputs}`, + borderRadius: 5, + padding: 4, + "& .MuiTypography-subtitle2": { + marginTop: theme.spacing(1), + marginBottom: theme.spacing(2), + }, + "& .MuiButton-outlined": { + margin: "0 auto", + display: "flex", + justifyContent: "center", + color: bgInputs, + borderRadius: radius, + border: `2px solid ${bgInputs}`, + }, +})); + +export const StyledImagePreviewSection = styled("section")(() => ({ + display: "flex", + minHeight: "15em", + alignItems: "stretch", + backgroundPosition: "center", + backgroundSize: "cover", + flex: 1, +})); + +export const dropAreaStyle = (error: any) => ({ + flex: 1, + display: "flex", + alignItems: "center", + borderRadius: radius, + padding: gutter, + borderColor: error ? "red" : alpha(bgInputs, 1), +}); + + +interface UploadAreaProps { + setThumbnail: any; + thumbnail: any; + thumbnailError: any; + setThumbnailError: any; + workspace: any; +} + +export const OSBDialog: React.FunctionComponent = ({ + setThumbnail, + thumbnail, + thumbnailError, + setThumbnailError, + workspace +}) => { + const [thumbnailPreview, setThumbnailPreview] = React.useState( + workspace?.thumbnail ? "/proxy/workspaces/" + workspace.thumbnail : null + ); + + + const dropThumbnail = (uploadedThumbnail: any) => { + setThumbnail(uploadedThumbnail); + previewFile(uploadedThumbnail); + }; + + const previewFile = (file: Blob) => { + if (!file) { + setThumbnailError(null); + setThumbnailPreview(null); + return; + } + + if (!file.type.includes("image")) { + setThumbnailError("Not an image file"); + return; + } + if (file.size > MAX_ALLOWED_THUMBNAIL_SIZE) { + setThumbnailError("File exceeds allowed size (1MB)"); + return; + } + + setThumbnailError(null); + + const fileReader: FileReader = new FileReader(); + + fileReader.onload = () => { + setThumbnailPreview(fileReader.result); + }; + + fileReader.readAsDataURL(file); + }; + return ( + + { + dropThumbnail(acceptedFiles[0]); + }} + > + {({ + getRootProps, + getInputProps, + acceptedFiles, + }: { + getRootProps: (p: any) => any; + getInputProps: () => any; + acceptedFiles: any[]; + }) => ( + +
+ + + {thumbnail && ( + + {!thumbnail ? ( + "" + ) : ( + { + e.preventDefault(); + dropThumbnail(null); + }} + size="large" + > + + + )} + + )} + + + {!thumbnail ? ( + <> + Drop file here to upload... + + + ) : null} + + {thumbnailError && ( + + {thumbnailError} + + )} + + + +
+ + )} + + + + ); +}; + +export default OSBDialog; diff --git a/applications/osb-portal/src/components/dialogs/DeleteDialog.tsx b/applications/osb-portal/src/components/dialogs/PrimaryDialog.tsx similarity index 60% rename from applications/osb-portal/src/components/dialogs/DeleteDialog.tsx rename to applications/osb-portal/src/components/dialogs/PrimaryDialog.tsx index e8392a4c2..b64c0bd91 100644 --- a/applications/osb-portal/src/components/dialogs/DeleteDialog.tsx +++ b/applications/osb-portal/src/components/dialogs/PrimaryDialog.tsx @@ -3,29 +3,18 @@ import DialogTitle from "@mui/material/DialogTitle"; import DialogContent from "@mui/material/DialogContent"; import DialogActions from "@mui/material/DialogActions"; import Dialog from "@mui/material/Dialog"; -import { - useNavigate, -} from "react-router-dom"; import { Button } from "@mui/material"; -const DeleteDialog = ({ +const PrimaryDialog = ({ open, setOpen, title, description, - handleDeleteCallback, - navigateToPath + handleCallback, + actionButtonText, + cancelButtonText, }) => { - const navigate = useNavigate(); - - const handleDelete = () => { - handleDeleteCallback(); - if (navigateToPath && window.location.pathname !== "/") { - navigate(navigateToPath) - } - } - return ( - CANCEL + {cancelButtonText || "CANCEL"} - ) } -export default DeleteDialog; \ No newline at end of file +export default PrimaryDialog; \ No newline at end of file diff --git a/applications/osb-portal/src/components/handlers/OSBErrorBoundary.tsx b/applications/osb-portal/src/components/handlers/OSBErrorBoundary.tsx index f3a41d5f2..21ff4a5a6 100644 --- a/applications/osb-portal/src/components/handlers/OSBErrorBoundary.tsx +++ b/applications/osb-portal/src/components/handlers/OSBErrorBoundary.tsx @@ -7,6 +7,10 @@ import DialogContent from "@mui/material/DialogContent"; import DialogTitle from "@mui/material/DialogTitle"; import Button from "@mui/material/Button"; +interface OSBErrorBoundaryProps { + error?: any; +} + interface OwnState { eventId: string; hasError: string; @@ -23,9 +27,12 @@ const ERROR_MESSAGES: any = { // 404 "NOT FOUND": "Oops. This resource could not be found. Please check the URL you are trying to access and try again.", + // User 404 + "USER_NOT_FOUND": + "Oops. This user could not be found. Please check the URL you are trying to access and try again.", }; -class OSBErrorBoundary extends React.Component<{}, OwnState> { +class OSBErrorBoundary extends React.Component { public state: OwnState = { eventId: null, hasError: null, @@ -46,7 +53,9 @@ class OSBErrorBoundary extends React.Component<{}, OwnState> { let message = "Oops. Something went wrong. Please report this error to us."; - if (error.status && error.status < 500) { + if (window.location.pathname.startsWith("/user/")) { + message = ERROR_MESSAGES["USER_NOT_FOUND"]; + } else if (error.status && error.status < 500) { message = ERROR_MESSAGES[error.statusText] || error.statusText; } else { Sentry.captureException(error); @@ -55,6 +64,13 @@ class OSBErrorBoundary extends React.Component<{}, OwnState> { }); } + static getDerivedStateFromProps(nextProps: any, prevState: any) { + if (nextProps.error) { + return { ...prevState, hasError: 'true', message: nextProps.error }; + } + return { ...prevState }; + } + render() { if (this.state.hasError) { // render fallback UI diff --git a/applications/osb-portal/src/components/icons.tsx b/applications/osb-portal/src/components/icons.tsx index a16115149..1ccbfbc35 100644 --- a/applications/osb-portal/src/components/icons.tsx +++ b/applications/osb-portal/src/components/icons.tsx @@ -162,6 +162,37 @@ export const RepositoriesCardIcon = (props: SvgIconProps) => ( ); +export const GithubCardIcon = (props: SvgIconProps) => ( + + + +); + +export const FigshareCardIcon = (props: SvgIconProps) => ( + + + + + + + + + + +); + +export const DandiArchiveCardIcon = (props: SvgIconProps) => ( + + + +); + export const ContextIcon = (props: SvgIconProps) => ( (); const [uri, setUri] = useState(repository?.uri); + const [thumbnail, setThumbnail] = React.useState(null); + const [thumbnailError, setThumbnailError] = React.useState(null); const [error, setError] = useState({ uri: "", @@ -269,6 +273,31 @@ export const EditRepoDialog = ({ setFormValues({ ...formValues, defaultContext: value }); }; + async function submitRepositoryThumbnail(obj: any) { + if (thumbnail && !thumbnailError) { + const fileThumbnail: any = await readFile(thumbnail); + RepositoryService.updateRepostioryThumbnail( + obj.id, + new Blob([fileThumbnail]) + ).then( + () => { + refreshRepositories(); + // Computed fields are not updated: remove so that the repo can be merged by the caller + Object.keys(obj).forEach((key) => obj[key] === undefined ? delete obj[key] : {} + ); + onSubmit(obj); + }, + (e) => { + console.error(e); + throw new Error("Error updating the repository thumbnail"); + }); + setThumbnail(null); + } else { + setLoading(true); + refreshRepositories(); + } + } + const addOrUpdateRepository = () => { console.log("original repository", repository); const errors = { @@ -288,16 +317,11 @@ export const EditRepoDialog = ({ setLoading(true); if (repository === RepositoryService.EMPTY_REPOSITORY) { RepositoryService.addRepository(formValues).then( - (r) => { + async (returnedRepository) => { setLoading(false); handleClose(false); - refreshRepositories(); - const obj: any = r; - // Computed fields are not updated: remove so that the repo can be merged by the caller - Object.keys(r).forEach((key) => - obj[key] === undefined ? delete obj[key] : {} - ); - onSubmit(r); + const obj: any = returnedRepository; + await submitRepositoryThumbnail(obj); }, (e) => { setLoading(false); @@ -313,16 +337,16 @@ export const EditRepoDialog = ({ console.log("sending this repository", putRequestRepository); RepositoryService.updateRepository(putRequestRepository) .then( - (r: OSBRepository) => { + async (returnedRepository) => { setLoading(false); handleClose(false); - refreshRepositories(); - const obj: any = r; + const obj: any = returnedRepository; // Computed fields are not updated: remove so that the repo can be merged by the caller - Object.keys(r).forEach((key) => + Object.keys(returnedRepository).forEach((key) => obj[key] === undefined ? delete obj[key] : {} ); - onSubmit(r); + onSubmit(returnedRepository); + await submitRepositoryThumbnail(obj); }, (e) => { setLoading(false); @@ -615,6 +639,15 @@ export const EditRepoDialog = ({ renderHTML={(text: string) => } /> + + + + { + row?.user && + + } } - - ) : null} - - {thumbnailError && ( - - {thumbnailError} - - )} - - - -
- - )} - - + diff --git a/applications/osb-portal/src/components/workspace/WorkspaceFromRepository.tsx b/applications/osb-portal/src/components/workspace/WorkspaceFromRepository.tsx index bdbb3ec27..00a02a48f 100644 --- a/applications/osb-portal/src/components/workspace/WorkspaceFromRepository.tsx +++ b/applications/osb-portal/src/components/workspace/WorkspaceFromRepository.tsx @@ -186,7 +186,7 @@ export const WorkspaceFromRepository = ({ ws.id, checked.map((c) => c.resource) ).then(() => { - setWorkspaceLink(`/workspace/${ws.id}`); + setWorkspaceLink(`/workspaces/${ws.id}`); setCreatedWorkspaceConfirmationContent((prevContent) => ({ ...prevContent, title: "Success!", diff --git a/applications/osb-portal/src/components/workspace/WorkspacesTable.tsx b/applications/osb-portal/src/components/workspace/WorkspacesTable.tsx index 723f7ff44..e2c0e30e2 100644 --- a/applications/osb-portal/src/components/workspace/WorkspacesTable.tsx +++ b/applications/osb-portal/src/components/workspace/WorkspacesTable.tsx @@ -154,9 +154,8 @@ export const WorkspacesList = (props: WorkspacesProps) => { label={tagObject.tag} clickable={true} onDelete={ - searchFilterValues?.tags?.includes( - tagObject.tag - ) + searchFilterValues?.tags?.map((tag) => + tag.toLowerCase()).includes(tagObject.tag.toLowerCase()) ? () => handleTagUnclick(tagObject) : null } diff --git a/applications/osb-portal/src/components/workspace/drawer/WorkspaceDrawer.tsx b/applications/osb-portal/src/components/workspace/drawer/WorkspaceDrawer.tsx index 43ac44248..73e18298f 100644 --- a/applications/osb-portal/src/components/workspace/drawer/WorkspaceDrawer.tsx +++ b/applications/osb-portal/src/components/workspace/drawer/WorkspaceDrawer.tsx @@ -158,6 +158,7 @@ export const WorkspaceDrawer: React.FunctionComponent = ({ alignItems="stretch" flex="1" className="verticalFill" + height={1} > void; currentResource: WorkspaceResource; hideTabs: Boolean; + staticPage: Boolean; } const SidebarBox = styled(Box)(({ theme }) => ({ @@ -129,6 +130,7 @@ function TabPanel(props) { hidden={value !== index} id={`simple-tabpanel-${index}`} aria-labelledby={`simple-tab-${index}`} + className="verticalFit" {...other} > {value === index && ( @@ -200,7 +202,7 @@ export default (props: WorkspaceProps | any) => { return ( <> {props.open ? ( - + {!hideTabs && ( { currentResource={props.currentResource} refreshWorkspace={handleWorkspaceRefresh} user={props.user} + staticPage={props.staticPage} /> diff --git a/applications/osb-portal/src/components/workspace/drawer/WorkspaceResourceBrowser.tsx b/applications/osb-portal/src/components/workspace/drawer/WorkspaceResourceBrowser.tsx index 7049131f3..232b1fc4e 100644 --- a/applications/osb-portal/src/components/workspace/drawer/WorkspaceResourceBrowser.tsx +++ b/applications/osb-portal/src/components/workspace/drawer/WorkspaceResourceBrowser.tsx @@ -46,7 +46,7 @@ const SidebarIconButton = styled(IconButton)(({ theme }) => ({ padding: 0, "& .MuiSvgIcon-root": { color: paragraph, - width: '1rem', + width: '1rem', height: '1rem' }, "&:hover": { @@ -98,6 +98,7 @@ const OSBResourceItem = (props: { currentResourceId: number; Icon: JSX.Element; workspaceId: number; + currentResource: WorkspaceResource; }) => { const { resource, @@ -105,6 +106,7 @@ const OSBResourceItem = (props: { refreshWorkspace, workspaceId, Icon, + currentResource } = props; const canOpenFile: boolean = resource.status === ResourceStatus.available; @@ -130,13 +132,22 @@ const OSBResourceItem = (props: { }; const handleOpenResource = - (e: any | Event) => { - e.preventDefault(); - navigate( - {pathname: `/workspace/open/${workspaceId}/${resource.type.application.code}`, - search: `?resource=${encodeURIComponent(resource.name)}`}, - ) - }; + (e: any | Event) => { + e.preventDefault(); + const isApplicationChanged = currentResource && currentResource.type.application.code !== resource.type.application.code; + if(isApplicationChanged && window.confirm("Unsaved changes will be lost: are you sure you want to change application?")){ + navigate( + {pathname: `/workspaces/open/${workspaceId}/${resource.type.application.code}`, + search: `?resource=${encodeURIComponent(resource.name)}`}, + ) + } + if(!isApplicationChanged){ + navigate( + {pathname: `/workspaces/open/${workspaceId}/${resource.type.application.code}`, + search: `?resource=${encodeURIComponent(resource.name)}`}, + ) + } + }; return ( {!active && - - } + + } } > @@ -170,8 +181,8 @@ const OSBResourceItem = (props: { title={`${workspaceResourceService.getResourcePath(resource)}. ${ active - ? "This is the resource currently opened on" - : "Click on this resource to open with" + ? "This is the resource currently opened on" + : "Click on this resource to open with" } ${resource.type.application.name}.`} > @@ -186,6 +197,7 @@ interface WorkspaceProps { refreshWorkspace: () => void; currentResource: WorkspaceResource; user: UserInfo; + staticPage: Boolean; } const WorkspaceResourceBrowser = (props: WorkspaceProps) => { @@ -201,7 +213,7 @@ const WorkspaceResourceBrowser = (props: WorkspaceProps) => { if (!workspace.resources || workspace.resources.length === 0) { return null; } - + const resources = workspace.resources.filter( (resource) => resource.id !== undefined && resource.id !== -1 ); @@ -231,8 +243,9 @@ const WorkspaceResourceBrowser = (props: WorkspaceProps) => { open?: boolean; resources: WorkspaceResource[]; onToggle: () => void; + currentResource: WorkspaceResource; }) => { - const { label, Icon, open, resources, onToggle } = tprops; + const { label, Icon, open, resources, onToggle, currentResource } = tprops; return ( <> @@ -254,7 +267,7 @@ const WorkspaceResourceBrowser = (props: WorkspaceProps) => { /> {resources.length} - + {open && resources.map((resource) => ( { currentResourceId={currentResourceId} Icon={Icon} workspaceId={workspace.id} + currentResource={props.staticPage ? null: currentResource} + /> ))} @@ -273,64 +288,65 @@ const WorkspaceResourceBrowser = (props: WorkspaceProps) => { }; return ( - - - - {experimentalResources.length > 0 && ( - } - onToggle={() => toggleResourceGroup('experimental')} - /> - )} - {modelResources.length > 0 && ( - } - onToggle={() => toggleResourceGroup('model')} - /> - )} - {notebookResources.length > 0 && ( - } - onToggle={() => toggleResourceGroup('notebook')} - /> - )} - - {pendingResource && ( - - - - - - - - + + + {experimentalResources.length > 0 && ( + } + onToggle={() => toggleResourceGroup('experimental')} + currentResource={currentResource} + /> + )} + {modelResources.length > 0 && ( + } + onToggle={() => toggleResourceGroup('model')} + currentResource={currentResource} + /> + )} + {notebookResources.length > 0 && ( + } + onToggle={() => toggleResourceGroup('notebook')} + currentResource={currentResource} + /> )} - + + {pendingResource && ( + + + + + + + + + )} ); }; diff --git a/applications/osb-portal/src/pages/NotFoundPage.tsx b/applications/osb-portal/src/pages/NotFoundPage.tsx new file mode 100644 index 000000000..7d63d2d5f --- /dev/null +++ b/applications/osb-portal/src/pages/NotFoundPage.tsx @@ -0,0 +1,43 @@ +import React from 'react' +import { Alert, Button, Dialog, DialogActions, DialogContent, DialogTitle } from '@mui/material' +import { getCleanPath } from '../utils' + +const NotFoundPage = () => { + const [message, setMessage] = React.useState("") + const pathParts = getCleanPath(window.location.pathname) + React.useEffect(() => { + setMessage("This path doesn't exist") + }, [pathParts]) + return ( + <> + + + + +
+ {message} +
+ + + + + + + + + ) + + +} + +export default NotFoundPage \ No newline at end of file diff --git a/applications/osb-portal/src/pages/Repositories/RepositoriesCards.tsx b/applications/osb-portal/src/pages/Repositories/RepositoriesCards.tsx index c3d65c43a..e78a0233b 100644 --- a/applications/osb-portal/src/pages/Repositories/RepositoriesCards.tsx +++ b/applications/osb-portal/src/pages/Repositories/RepositoriesCards.tsx @@ -22,10 +22,10 @@ import styled from "@mui/system/styled"; import OpenInNewIcon from "@mui/icons-material/OpenInNew"; import LocalOfferIcon from "@mui/icons-material/LocalOffer"; -import { CodeBranchIcon, RepositoriesCardIcon } from "../../components/icons"; +import { CodeBranchIcon, RepositoriesCardIcon, GithubCardIcon, FigshareCardIcon, DandiArchiveCardIcon } from "../../components/icons"; //types -import { OSBRepository } from "../../apiclient/workspaces"; +import { OSBRepository, RepositoryType } from "../../apiclient/workspaces"; import { UserInfo } from "../../types/user"; //utils @@ -60,8 +60,6 @@ export const RepositoriesListCards = (props: RepositoriesProps) => { props; const openRepoUrl = (uri: string) => window.open(uri, "_blank"); - const navigate = useNavigate(); - return ( <> {loading ? ( @@ -88,13 +86,12 @@ export const RepositoriesListCards = (props: RepositoriesProps) => { return ( - - + { alignItems="center" display="flex" mb={2} + overflow="hidden" + flex="1" + sx={{ + backgroundImage: repository.thumbnail && `url(/proxy/workspaces/${repository.thumbnail}?v=${repository.timestampUpdated.getMilliseconds()})`, + backgroundSize: "cover", + }} onClick={() => handleRepositoryClick(repository)} > - + { + !repository.thumbnail && ( + repository.repositoryType === RepositoryType.Github ? ( + + ) : repository.repositoryType === RepositoryType.Figshare ? ( + + ) : repository.repositoryType === RepositoryType.Dandi ? ( + + ) : () + ) + } diff --git a/applications/osb-portal/src/pages/Repositories/RepositoriesPage.tsx b/applications/osb-portal/src/pages/Repositories/RepositoriesPage.tsx index c11d1491a..be680744d 100644 --- a/applications/osb-portal/src/pages/Repositories/RepositoriesPage.tsx +++ b/applications/osb-portal/src/pages/Repositories/RepositoriesPage.tsx @@ -27,7 +27,7 @@ import { OSBRepository, Tag } from "../../apiclient/workspaces"; import searchFilter from "../../types/searchFilter"; //hooks -import { useNavigate } from "react-router-dom"; +import { useNavigate, useSearchParams } from "react-router-dom"; //api import RepositoryService from "../../service/RepositoryService"; @@ -89,12 +89,13 @@ export const RepositoriesPage = ({ user: UserInfo; counter: number; }) => { + const [searchParams, ] = useSearchParams(); const navigate = useNavigate(); const [searchFilterValues, setSearchFilterValues] = React.useState({ - text: undefined, - tags: [], - types: [], + text: searchParams.get("q"), + tags: searchParams.getAll("tags"), + types: searchParams.getAll("types"), }); const [repositories, setRepositories] = React.useState([]); const [page, setPage] = React.useState(1); @@ -110,9 +111,10 @@ export const RepositoriesPage = ({ navigate(`/repositories/${repositoryId}`); }; - const debouncedHandleSearchFilter = debounce((newTextFilter: string) => { + const debouncedHandleSearchFilter = (newTextFilter: string) => { setSearchFilterValues({ ...searchFilterValues, text: newTextFilter }); - }, 500); + debounce(() => updateReposList(newTextFilter), 500)(); + }; const setReposValues = (reposDetails) => { setRepositories(reposDetails.osbrepositories); @@ -121,15 +123,15 @@ export const RepositoriesPage = ({ setLoading(false); }; - const updateReposList = () => { + const updateReposList = (updatedSearchFilterValues) => { const isSearchFieldsEmpty = - searchFilterValues.tags.length === 0 && - searchFilterValues.types.length === 0 && - (typeof searchFilterValues.text === "undefined" || - searchFilterValues.text === ""); + updatedSearchFilterValues.tags.length === 0 && + updatedSearchFilterValues.types.length === 0 && + (typeof updatedSearchFilterValues.text === "undefined" || + updatedSearchFilterValues.text === ""); setLoading(true); if (!isSearchFieldsEmpty) { - const myReposFilter = tabValue ? {...searchFilterValues, user_id: user.id} : searchFilterValues + const myReposFilter = tabValue ? { ...updatedSearchFilterValues, user_id: user.id } : updatedSearchFilterValues RepositoryService.getRepositoriesByFilter( page, @@ -151,12 +153,13 @@ export const RepositoriesPage = ({ }; const handleRefreshRepositories = () => { - updateReposList(); + updateReposList(searchFilterValues); } const handleTabChange = (event: any, newValue: RepositoriesTab) => { setTotal(0); setTabValue(newValue); + setPage(1) }; const changeListView = (type: string) => { @@ -188,12 +191,12 @@ export const RepositoriesPage = ({ ); } }; - React.useEffect(updateReposList, [page, searchFilterValues, tabValue, counter]); + React.useEffect(() => updateReposList(searchFilterValues), [page, searchFilterValues, tabValue, counter]); return ( <> -
+ )} -
+ {repositories && totalPages > 1 && ( ({ padding: 0, @@ -190,7 +191,7 @@ export const RepositoryPage = (props: any) => { ) .then(() => { setShowWorkspaceEditor(false); - setWorkspaceLink(`/workspace/${ws.id}`); + setWorkspaceLink(`/workspaces/${ws.id}`); confirmAction("Success", "New workspace created!", true); }) .catch((e) => { @@ -219,7 +220,7 @@ export const RepositoryPage = (props: any) => { .then(() => { setSelectedWorkspace(null); confirmAction("Success", "Resources added to workspace!", true); - setWorkspaceLink(`/workspace/${selectedWorkspace.id}`); + setWorkspaceLink(`/workspaces/${selectedWorkspace.id}`); setLoading(false); setShowExisitngWorkspaceEditor(false); }) @@ -452,10 +453,18 @@ export const RepositoryPage = (props: any) => { } > {checked.length > 0 && ( - handleChipDelete(chipPath)} - /> +
+ handleChipDelete(chipPath)} + /> + + Please note that adding a file when a file with the same name already exists will overwrite the previous version of the file in the workspace. + +
)} setWorkspace(ws)} diff --git a/applications/osb-portal/src/pages/UserPage.tsx b/applications/osb-portal/src/pages/UserPage.tsx index 44b069e2a..05cac6398 100644 --- a/applications/osb-portal/src/pages/UserPage.tsx +++ b/applications/osb-portal/src/pages/UserPage.tsx @@ -182,7 +182,8 @@ export const UserPage = (props: any) => { React.useEffect(() => { getUser(userName).then((u) => { setUser(u); - + }).catch((e) => { + setError(e); }); }, [userName, props.workspacesCounter]); @@ -282,7 +283,7 @@ export const UserPage = (props: any) => { setProfileEditDialogOpen(false); updateUser(u) .then((updatedUser) => { - setUser(updatedUser); + setUser({ ...updatedUser, ...u }); setLoading(false); }) .catch((err) => { @@ -308,7 +309,7 @@ export const UserPage = (props: any) => { } const openWorkspaceUrl = (workspaceId: number) => { - navigate(`/workspace/${workspaceId}`); + navigate(`/workspaces/${workspaceId}`); }; const canEdit = @@ -793,7 +794,7 @@ export const UserPage = (props: any) => { {canEdit && profileEditDialogOpen && ( setProfileEditDialogOpen(false)} /> diff --git a/applications/osb-portal/src/pages/WorkspaceOpenPage.tsx b/applications/osb-portal/src/pages/WorkspaceOpenPage.tsx index 383857d91..c58473395 100644 --- a/applications/osb-portal/src/pages/WorkspaceOpenPage.tsx +++ b/applications/osb-portal/src/pages/WorkspaceOpenPage.tsx @@ -19,7 +19,7 @@ export default (props: any) => { }, [workspaceId]); return ( - + diff --git a/applications/osb-portal/src/pages/WorkspacePage.tsx b/applications/osb-portal/src/pages/WorkspacePage.tsx index 541b3c040..ec296f96b 100644 --- a/applications/osb-portal/src/pages/WorkspacePage.tsx +++ b/applications/osb-portal/src/pages/WorkspacePage.tsx @@ -80,7 +80,7 @@ export const WorkspacePage = (props: any) => { }; const openWithApp = (selectedOption: OSBApplication) => { - navigate(`/workspace/open/${workspaceId}/${selectedOption.code}`); + navigate(`/workspaces/open/${workspaceId}/${selectedOption.code}`); }; const canEdit = canEditWorkspace(props.user, workspace); @@ -97,16 +97,13 @@ export const WorkspacePage = (props: any) => { { @@ -240,6 +233,7 @@ export const WorkspacePage = (props: any) => { user={user} open={true} hideTabs={true} + staticPage={true} refreshWorkspace={props.refreshWorkspace} currentResource={ workspace.lastOpen || workspace.resources[0] @@ -248,7 +242,7 @@ export const WorkspacePage = (props: any) => { { }, }} > - + {workspace?.thumbnail && ( { } title={workspace.name} alt={workspace.name} + style={{ + width: 'auto', + height: 'auto', + maxWidth: '100%', + maxHeight: '300px' + }} /> )} diff --git a/applications/osb-portal/src/pages/WorkspacesPage.tsx b/applications/osb-portal/src/pages/WorkspacesPage.tsx index da0a94545..8726c7dab 100644 --- a/applications/osb-portal/src/pages/WorkspacesPage.tsx +++ b/applications/osb-portal/src/pages/WorkspacesPage.tsx @@ -1,7 +1,7 @@ import * as React from "react"; import { ReactElement } from "react"; import debounce from "lodash/debounce"; -import { useNavigate } from "react-router-dom"; +import { useNavigate, useSearchParams } from "react-router-dom"; //components import Box from "@mui/material/Box"; @@ -46,12 +46,13 @@ interface WorkspacesPageProps { export const WorkspacesPage = (props: WorkspacesPageProps) => { const navigate = useNavigate(); + const [searchParams, ] = useSearchParams(); const [searchFilterValues, setSearchFilterValues] = React.useState({ - text: undefined, - tags: [], - types: [], + text: searchParams.get("q"), + tags: searchParams.getAll("tags"), + types: searchParams.getAll("types"), }); const [workspaces, setWorkspaces] = React.useState(null); @@ -81,15 +82,23 @@ export const WorkspacesPage = (props: WorkspacesPageProps) => { searchFilterValues.text === ""); const openWorkspaceUrl = (workspaceId: number) => { - navigate(`/workspace/${workspaceId}`); + navigate(`/workspaces/${workspaceId}`); }; - const debouncedHandleSearchFilter = debounce((newTextFilter: string) => { + + const debouncedHandleSearchFilter = (newTextFilter: string) => { setSearchFilterValues({ ...searchFilterValues, text: newTextFilter, }); - }, 500); + + debounce(() => { + getWorkspacesList({ searchFilterValues: { ...searchFilterValues, text: newTextFilter } }); + }, 500); + }; + + + const setWorkspacesValues = (workspacesDetails) => { setWorkspaces(workspacesDetails.items); diff --git a/applications/osb-portal/src/service/RepositoryService.tsx b/applications/osb-portal/src/service/RepositoryService.tsx index 7d78be45d..2b1d99f2d 100644 --- a/applications/osb-portal/src/service/RepositoryService.tsx +++ b/applications/osb-portal/src/service/RepositoryService.tsx @@ -151,6 +151,17 @@ class RepositoryService { return this.workspacesApi.osbrepositoryIdPut(requestParameters); } + async updateRepostioryThumbnail( + repositoryId: number, + thumbNailBlob: Blob + ): Promise { + const repoRequest: workspaceApi.WorkspacesControllersOsbrepositoryControllerSetthumbnailRequest = + { id: repositoryId, thumbNail: thumbNailBlob }; + await this.workspacesApi.workspacesControllersOsbrepositoryControllerSetthumbnail( + repoRequest + ); + } + async getAllTags( page?: number, perPage?: number, diff --git a/applications/osb-portal/src/utils.ts b/applications/osb-portal/src/utils.ts index a59aba8c2..1feacfc2f 100644 --- a/applications/osb-portal/src/utils.ts +++ b/applications/osb-portal/src/utils.ts @@ -32,4 +32,26 @@ export function getNotebooksNamedServerLink() { return null; } return `//${OSBAllApplications.jupyter.subdomain}.${getBaseDomain()}/hub/home` +} + +export function getCleanPath(path: string) { + const pathParts = path.split("/") + if (pathParts[pathParts.length - 1] === "") { + pathParts.pop() + } + return pathParts +} + +export async function readFile(file: Blob) { + return new Promise((resolve, reject) => { + const fileReader: FileReader = new FileReader(); + + fileReader.onload = () => { + resolve(fileReader.result); + }; + + fileReader.onerror = reject; + + fileReader.readAsArrayBuffer(file); + }); } \ No newline at end of file diff --git a/applications/osb-portal/test/e2e/main_flows.spec.ts b/applications/osb-portal/test/e2e/main_flows.spec.ts index c40289435..82bb6fe2e 100644 --- a/applications/osb-portal/test/e2e/main_flows.spec.ts +++ b/applications/osb-portal/test/e2e/main_flows.spec.ts @@ -59,7 +59,7 @@ const testApplication = } await page.waitForSelector(selectors.OPENED_WORKSPACE); - expect(page.url()).toContain("/workspace/"); + expect(page.url()).toContain("/workspaces/"); await page.waitForSelector(selectors.SELECT_APPLICATION); await page.click(selectors.SELECT_APPLICATION); diff --git a/applications/workspaces/README.md b/applications/workspaces/README.md index 587f7815c..4c3d014bf 100644 --- a/applications/workspaces/README.md +++ b/applications/workspaces/README.md @@ -43,6 +43,8 @@ Configuration of the server can be changed in the config.py file ### Regenerate backend from openapi + +Check the workspaces/api/README.md for more information about the openapi generation. ``` openapi-generator-cli generate -i api/openapi.yaml -g python-flask -o server -c api/config.json ``` diff --git a/applications/workspaces/api/README.md b/applications/workspaces/api/README.md index 246a28553..71f7933a8 100644 --- a/applications/workspaces/api/README.md +++ b/applications/workspaces/api/README.md @@ -1,5 +1,7 @@ # Generate the frontend and backend openapi clients +You can use the script `generate_openapi_stubs.sh` to generate the openapi stubs for the frontend and backend. Ensure you are in the right directory and run the script. + ## Set openapi version ``` diff --git a/applications/workspaces/api/generate_openapi_stubs.sh b/applications/workspaces/api/generate_openapi_stubs.sh new file mode 100644 index 000000000..4673ed636 --- /dev/null +++ b/applications/workspaces/api/generate_openapi_stubs.sh @@ -0,0 +1,24 @@ + + +# Install OpenAPI Generator +npm install @openapitools/openapi-generator-cli -g + +# Set OpenAPI Generator Version +openapi-generator-cli version-manager set 5.1.0 + +# Generate OpenAPI Stubs - be in the api directory +if [[ $(pwd) == */applications/workspaces/api ]]; then + # Generate Backend Stubs + openapi-generator-cli generate -i openapi.yaml -g python-flask -o ../server -c config.json + + # Since /workspaces/api/openapi.yaml and /workspaces/api/server/workspaces/openapi/openapi.yaml are suppose to be same + # we need to copy the generated file to the correct location + cp openapi.yaml ../server/workspaces/openapi/openapi.yaml + + # Generate Frontend Stubs + openapi-generator-cli generate -i openapi.yaml -g typescript-fetch -o ../../osb-portal/src/apiclient/workspaces -c config.json + +else + echo "Please run this script from the api directory" +fi + diff --git a/applications/workspaces/api/openapi.yaml b/applications/workspaces/api/openapi.yaml index 060074f8f..295f37de1 100644 --- a/applications/workspaces/api/openapi.yaml +++ b/applications/workspaces/api/openapi.yaml @@ -932,6 +932,37 @@ paths: $ref: '#/components/schemas/RepositoryType' in: query required: true + '/osbrepository/{id}/thumbnail/': + post: + requestBody: + content: + multipart/form-data: + schema: + type: object + properties: + thumbNail: + format: binary + type: string + tags: + - rest + responses: + '200': + description: The repository was updated with the thumbnail. + '404': + description: The repository was not found or the thumbnail was not specified + security: + - + bearerAuth: [] + operationId: workspaces.controllers.osbrepository_controller.setthumbnail + summary: Sets the thumbnail of the workspace. + parameters: + - + name: id + description: 'Repository ID ' + schema: + type: integer + in: path + required: true components: schemas: Valid: @@ -1342,66 +1373,6 @@ components: type: array items: type: string - OSBRepositoryBase: - description: OSB Repository Base model - required: - - name - - repository_type - - content_types - - uri - type: object - properties: - id: - $ref: '#/components/schemas/PrimaryKey' - description: Unique identifier of the repository - name: - description: Repository name. - type: string - example: Repo One - summary: - description: Summary describing the OSB Repository - type: string - example: Sample repository with experimental data - repository_type: - $ref: '#/components/schemas/RepositoryType' - description: Repository type - content_types: - description: List of Repository Content Types - type: string - auto_sync: - description: Auto sync of the resources - default: true - type: boolean - example: true - uri: - description: URI of the repository - type: string - example: 'https://github.com/OpenSourceBrain/OSBv2' - default_context: - description: The default branch to show for this repository - type: string - example: develop - user_id: - description: 'OSBRepository keycloak user id, will be automatically be set to the logged in user' - type: string - timestamp_created: - format: date-time - description: Date/time the Workspace is created - type: string - example: '2019-11-23T01:23:45.000Z' - timestamp_updated: - format: date-time - description: Date/time the Workspace is last updated - type: string - example: '2019-11-23T01:23:45.000Z' - tags: - type: array - items: - allOf: - - - $ref: '#/components/schemas/Tag' - - - x-secondary: osbrepository_tag WorkspaceResourceBase: required: - name @@ -1441,6 +1412,7 @@ components: - $ref: '#/components/schemas/WorkspaceResourceBase' - + x-tablename: Workspaceresource properties: origin: description: Origin data JSON formatted of the WorkspaceResource @@ -1453,7 +1425,6 @@ components: description: WorkspaceResource path where the resource will stored in the pvc. type: string example: repo/sample.nwb - x-tablename: Workspaceresource WorkspaceResource: description: Workspace Resource item of a Workspace allOf: @@ -1471,6 +1442,69 @@ components: description: WorkspaceResource path where the resource will stored in the pvc. type: string example: repo/sample.nwb + OSBRepositoryBase: + description: OSB Repository Base model + required: + - name + - repository_type + - content_types + - uri + type: object + properties: + id: + $ref: '#/components/schemas/PrimaryKey' + description: Unique identifier of the repository + name: + description: Repository name. + type: string + example: Repo One + summary: + description: Summary describing the OSB Repository + type: string + example: Sample repository with experimental data + repository_type: + $ref: '#/components/schemas/RepositoryType' + description: Repository type + content_types: + description: List of Repository Content Types + type: string + auto_sync: + description: Auto sync of the resources + default: true + type: boolean + example: true + uri: + description: URI of the repository + type: string + example: 'https://github.com/OpenSourceBrain/OSBv2' + default_context: + description: The default branch to show for this repository + type: string + example: develop + thumbnail: + $ref: '#/components/schemas/ImageType' + description: Resource OSB thumbnail. + user_id: + description: 'OSBRepository keycloak user id, will be automatically be set to the logged in user' + type: string + timestamp_created: + format: date-time + description: Date/time the Workspace is created + type: string + example: '2019-11-23T01:23:45.000Z' + timestamp_updated: + format: date-time + description: Date/time the Workspace is last updated + type: string + example: '2019-11-23T01:23:45.000Z' + tags: + type: array + items: + allOf: + - + $ref: '#/components/schemas/Tag' + - + x-secondary: osbrepository_tag securitySchemes: bearerAuth: scheme: bearer @@ -1489,4 +1523,4 @@ tags: description: Consumer - name: Client - description: Client + description: Client \ No newline at end of file diff --git a/applications/workspaces/deploy/values.yaml b/applications/workspaces/deploy/values.yaml index 10bed4c35..66ade3d6c 100644 --- a/applications/workspaces/deploy/values.yaml +++ b/applications/workspaces/deploy/values.yaml @@ -25,7 +25,7 @@ harness: resources: requests: memory: 128Mi - cpu: 200m + cpu: 20m limits: memory: 512Mi cpu: 1500m @@ -64,14 +64,14 @@ harness: resources: requests: memory: 128Mi - cpu: 200m + cpu: 20m limits: memory: "256Mi" - cpu: "500m" + cpu: "2000m" test: unit: enabled: true commands: - pytest /usr/src/app/ -workspace_size: 2Gi \ No newline at end of file +workspace_size: 2Gi diff --git a/applications/workspaces/server/.openapi-generator-ignore b/applications/workspaces/server/.openapi-generator-ignore index 31ebaaafd..5faf9aa73 100644 --- a/applications/workspaces/server/.openapi-generator-ignore +++ b/applications/workspaces/server/.openapi-generator-ignore @@ -33,4 +33,7 @@ test-requirements.txt **/__init__.py **/encoder.py openapi -**/controllers \ No newline at end of file +**/controllers + +**/test/test_k8s_controller.py +**/test/test_rest_controller.py diff --git a/applications/workspaces/server/README.md b/applications/workspaces/server/README.md index 96cf07018..0b09f21d8 100644 --- a/applications/workspaces/server/README.md +++ b/applications/workspaces/server/README.md @@ -8,7 +8,7 @@ is an example of building a OpenAPI-enabled Flask server. This example uses the [Connexion](https://github.com/zalando/connexion) library on top of Flask. ## Requirements -Python 3.5.2+ +Python 3.9+ ## Usage To run the server, please execute the following from the root directory: diff --git a/applications/workspaces/server/requirements.txt b/applications/workspaces/server/requirements.txt index 4cd709b21..2e6977895 100644 --- a/applications/workspaces/server/requirements.txt +++ b/applications/workspaces/server/requirements.txt @@ -1,94 +1,72 @@ argo-workflows==5.0.0 -asn1crypto==0.24.0 -atomicwrites==1.4.0 -attrs==21.4.0 -autopep8==1.6.0 -black==22.1.0 -blinker==1.4 -cachetools==5.0.0 -certifi==2019.3.9 -cffi==1.15.0 -chardet==3.0.4 -charset-normalizer==2.0.12 -click==8.0.4 +attrs==23.2.0 +blinker==1.7.0 +cachetools==5.3.2 +certifi==2023.11.17 +cffi==1.16.0 +charset-normalizer==3.3.2 +click==8.1.7 clickclick==20.10.2 -connexion==2.6.0 -coverage==6.3.2 -cryptography==3.3.2 -distlib==0.3.4 -ecdsa==0.17.0 -filelock==3.6.0 -flake8==4.0.1 -Flask==1.1.2 -Flask-Cors==3.0.8 -flask-oidc==1.4.0 -Flask-SQLAlchemy==2.4.1 -Flask-Testing==0.6.1 -google-auth==2.6.2 -greenlet==1.1.2 -gunicorn==20.1.0 -httplib2==0.20.4 -idna==2.8 -importlib-metadata==4.2.0 -importlib-resources==5.4.0 +connexion==2.14.2 +cryptography==41.0.7 +deprecation==2.1.0 +ecdsa==0.18.0 +exceptiongroup==1.1.2 +Flask==2.2.5 +Flask-Cors==4.0.0 +Flask-SQLAlchemy==3.0.2 +frozendict==2.4.0 +google-auth==2.26.2 +greenlet==3.0.3 +gunicorn==21.2.0 +idna==3.6 +importlib-metadata==7.0.1 inflection==0.5.1 -isort==5.10.1 -itsdangerous==2.0.1 +iniconfig==2.0.0 +itsdangerous==2.1.2 Jinja2==3.0.3 jsonschema==4.4.0 kafka-python==2.0.2 -kazoo==2.5.0 -kubernetes==11.0.0 -MarkupSafe==2.1.1 -mccabe==0.6.1 -more-itertools==8.12.0 -mypy-extensions==0.4.3 -oauth2client==4.1.3 -oauthlib==3.2.0 -OpenAlchemy==1.1.0 +kubernetes==29.0.0 +MarkupSafe==2.1.3 +oauthlib==3.2.2 +openalchemy==1.1.0 openapi-schema-validator==0.2.3 openapi-spec-validator==0.4.0 -packaging==21.3 -pathspec==0.9.0 -pep517==0.12.0 -pip-tools==6.5.1 -platformdirs==2.5.1 -pluggy==0.13.1 -psycopg2-binary -py==1.11.0 -pyasn1==0.4.8 -pyasn1-modules==0.2.8 -pycodestyle==2.8.0 +oyaml==1.0 +packaging==23.2 +pluggy==1.2.0 +psutil==5.9.4 +psycopg2-binary==2.9.9 +pyaml==23.12.0 +pyasn1==0.5.1 +pyasn1-modules==0.3.0 pycparser==2.21 -pyflakes==2.4.0 -PyJWT>=2.0.0 -pykafka==2.8.0 -pyOpenSSL==19.0.0 -pyparsing==3.0.7 -pyrsistent==0.18.1 -PySocks==1.6.8 -pytest==4.6.11 -pytest-cov==3.0.0 -pytest-randomly==1.2.3 +pyhumps==3.8.0 +PyJWT==2.8.0 +pyrsistent==0.20.0 +pytest==7.4.0 python-dateutil==2.8.2 python-jose==3.3.0 -requests==2.27.1 +python-keycloak==3.7.0 +PyYAML==6.0.1 +referencing==0.32.1 +requests==2.31.0 requests-oauthlib==1.3.1 -rsa==4.8 -sentry-sdk==0.14.4 +requests-toolbelt==1.0.0 +responses==0.22.0 +rpds-py==0.17.1 +rsa==4.9 +sentry-sdk==1.39.2 six==1.16.0 -SQLAlchemy==1.4.32 -swagger-ui-bundle==0.0.8 -tabulate==0.8.9 +SQLAlchemy==1.4.51 +sqlalchemy-stubs==0.4 +swagger-ui-bundle==0.0.9 toml==0.10.2 tomli==2.0.1 -tox==3.24.5 -typed-ast==1.5.2 -typing_extensions==4.1.1 -urllib3==1.26.9 -virtualenv==20.13.4 -wcwidth==0.2.5 -websocket-client==1.3.1 -Werkzeug==2.0.3 -zipp==3.7.0 -responses==0.22.0 +types-toml==0.10.8.7 +typing_extensions==4.9.0 +urllib3==2.1.0 +websocket-client==1.7.0 +Werkzeug==2.2.3 +zipp==3.17.0 diff --git a/applications/workspaces/server/setup.py b/applications/workspaces/server/setup.py index 12f2a04f9..9053227d2 100644 --- a/applications/workspaces/server/setup.py +++ b/applications/workspaces/server/setup.py @@ -14,14 +14,17 @@ # http://pypi.python.org/pypi/setuptools REQUIRES = [ - "connexion>=2.0.2", + "connexion[swagger-ui]>=2.6.0", "swagger-ui-bundle>=0.0.2", "python_dateutil>=2.6.0", - "Flask-SQLAlchemy>=2.4.1", - "OpenAlchemy>=1.1.0", + "Flask-SQLAlchemy>3.0.0", + "OpenAlchemy==1.1.0", "psycopg2-binary", "cloudharness", - "flask_cors" + "flask_cors", + "sentry-sdk", + "responses==0.22.0", + "jinja2<3.1.0" ] setup( diff --git a/applications/workspaces/server/workspaces/__main__.py b/applications/workspaces/server/workspaces/__main__.py index d206e2536..b5d874145 100644 --- a/applications/workspaces/server/workspaces/__main__.py +++ b/applications/workspaces/server/workspaces/__main__.py @@ -32,8 +32,7 @@ def setup_static_router(app): # remove the static route (if exists) app.url_map._rules_by_endpoint["static"] = [] # add / as static route - app.add_url_rule(f"/", endpoint="static", view_func=app.send_static_file) - + app.add_url_rule(f"/", view_func=app.send_static_file) def init_app(app): cors = CORS(app, resources={r"/api/*": {"origins": "*"}}) @@ -56,7 +55,7 @@ def init_app(app): "Could not start kafka consumers. Some application functionality won't be available.", exc_info=True ) mkdirs() - setup_static_router(app) + setup_static_router(app) app.template_folder = Config.TEMPLATE_DIR @@ -70,7 +69,6 @@ def init_app(app): - if __name__ == "__main__": cloudharness.set_debug() main() diff --git a/applications/workspaces/server/workspaces/config.py b/applications/workspaces/server/workspaces/config.py index 0b3accf15..de44ae1cf 100644 --- a/applications/workspaces/server/workspaces/config.py +++ b/applications/workspaces/server/workspaces/config.py @@ -22,6 +22,7 @@ class Config(object): STATIC_DIR = os.path.join(BASE_DIR, "static") TEMPLATE_DIR = os.path.join(BASE_DIR, "templates") WORKSPACES_DIR = "workspaces" + REPOSITORY_DIR = "repositories" OPENAPI_DIR = os.path.join(BASE_DIR, "openapi") OPENAPI_FILE = "openapi.yaml" LOG_LEVEL = logging.DEBUG diff --git a/applications/workspaces/server/workspaces/controllers/crud/crud_controllers.py b/applications/workspaces/server/workspaces/controllers/crud/crud_controllers.py index e10baf987..68df4eaed 100644 --- a/applications/workspaces/server/workspaces/controllers/crud/crud_controllers.py +++ b/applications/workspaces/server/workspaces/controllers/crud/crud_controllers.py @@ -13,6 +13,8 @@ from workspaces.controllers.crud.base_model_controller import BaseModelView +from cloudharness.workflows.argo import delete_workflow + class WorkspaceView(BaseModelView): @@ -24,6 +26,17 @@ def post(self, body): except NotAllowed: return "Not allowed", 405 + + + def delete(self, id_): + """Delete an object from the repository.""" + for wf in WorkspaceService.get_workspace_workflows(id_): + delete_workflow(wf.name) + + return super().delete(id_) + + + class OsbrepositoryView(BaseModelView): service = OsbrepositoryService() diff --git a/applications/workspaces/server/workspaces/controllers/osbrepository_controller.py b/applications/workspaces/server/workspaces/controllers/osbrepository_controller.py index 943eb9ea6..78d901e9e 100644 --- a/applications/workspaces/server/workspaces/controllers/osbrepository_controller.py +++ b/applications/workspaces/server/workspaces/controllers/osbrepository_controller.py @@ -1,6 +1,10 @@ from cloudharness import log import workspaces.service.osbrepository as repository_service +from workspaces.persistence.crud_persistence import OSBRepositoryRepository, db +from .workspace_controller import _save_image +from workspaces.config import Config +from workspaces.controllers.workspace_controller import setthumbnail def get_contexts(uri=None, repository_type=None, **kwargs): # get the branches of the repository @@ -49,4 +53,18 @@ def get_info(uri=None, repository_type=None): return info, 200 except Exception as e: log.error("Error getting repo info", exc_info=True) - return str(e), 500 \ No newline at end of file + return str(e), 500 + + +def setthumbnail(id_=None, thumb_nail=None, body=None, **kwargs): + repository = OSBRepositoryRepository().get(id=id_) + if repository is None: + return f"Repository with id {id_} not found.", 404 + if not thumb_nail: + return f"Thumbnail is not specified.", 404 + + saved_filename = _save_image(id_=id_, image=thumb_nail, filename_base="thumbnail", dirname=Config.REPOSITORY_DIR) + repository.thumbnail = saved_filename + db.session.add(repository) + db.session.commit() + return "Saved", 200 \ No newline at end of file diff --git a/applications/workspaces/server/workspaces/controllers/rest_controller.py b/applications/workspaces/server/workspaces/controllers/rest_controller.py index 8c31bb4ba..b55e7858b 100644 --- a/applications/workspaces/server/workspaces/controllers/rest_controller.py +++ b/applications/workspaces/server/workspaces/controllers/rest_controller.py @@ -442,6 +442,21 @@ def workspaceresource_post(workspace_resource): # noqa: E501 return 'do some magic!' +def workspaces_controllers_osbrepository_controller_setthumbnail(id, thumb_nail=None): # noqa: E501 + """Sets the thumbnail of the workspace. + + # noqa: E501 + + :param id: Repository ID + :type id: int + :param thumb_nail: + :type thumb_nail: str + + :rtype: Union[None, Tuple[None, int], Tuple[None, int, Dict[str, str]] + """ + return 'do some magic!' + + def workspaces_controllers_workspace_controller_addimage(id, image=None): # noqa: E501 """Adds and image to the workspace. diff --git a/applications/workspaces/server/workspaces/controllers/workspace_controller.py b/applications/workspaces/server/workspaces/controllers/workspace_controller.py index 83ad1463f..d52e13d5a 100644 --- a/applications/workspaces/server/workspaces/controllers/workspace_controller.py +++ b/applications/workspaces/server/workspaces/controllers/workspace_controller.py @@ -12,9 +12,9 @@ from workspaces.helpers.etl_helpers import copy_origins from workspaces.service.crud_service import NotAuthorized, NotAllowed, WorkspaceService -def _save_image(id_=None, image=None, filename_base=None): +def _save_image(id_=None, image=None, filename_base=None, dirname=None): ext = mimetypes.guess_extension(image.mimetype) - folder = os.path.join(Config.WORKSPACES_DIR, f"{id_}") + folder = os.path.join(dirname, f"{id_}") Path(os.path.join(Config.STATIC_DIR, folder)).mkdir(parents=True, exist_ok=True) if filename_base is None: @@ -38,7 +38,7 @@ def setthumbnail(id_=None, thumb_nail=None, body=None, **kwargs): # filename = f"{folder}/thumbnail{ext}" # thumbNail.save(os.path.join(Config.STATIC_DIR,filename)) - saved_filename = _save_image(id_=id_, image=thumb_nail, filename_base="thumbnail") + saved_filename = _save_image(id_=id_, image=thumb_nail, filename_base="thumbnail", dirname=Config.WORKSPACES_DIR) workspace.thumbnail = saved_filename db.session.add(workspace) db.session.commit() @@ -58,7 +58,7 @@ def addimage(id_=None, image=None, body=None, **kwargs): # image.save(os.path.join(Config.STATIC_DIR, image.filename)) - saved_filename = _save_image(id=id_, image=image) + saved_filename = _save_image(id=id_, image=image, dirname=Config.WORKSPACES_DIR) workspace.gallery.append(WorkspaceImage(image=saved_filename)) db.session.add(workspace) db.session.commit() diff --git a/applications/workspaces/server/workspaces/models/base_model_.py b/applications/workspaces/server/workspaces/models/base_model_.py index eb41d484c..da965052c 100644 --- a/applications/workspaces/server/workspaces/models/base_model_.py +++ b/applications/workspaces/server/workspaces/models/base_model_.py @@ -2,7 +2,7 @@ import six import typing -import datetime + from workspaces import util T = typing.TypeVar('T') @@ -11,11 +11,11 @@ class Model(object): # openapiTypes: The key is attribute name and the # value is attribute type. - openapi_types: typing.Dict[str, type] = {} + openapi_types = {} # attributeMap: The key is attribute name and the # value is json key in definition. - attribute_map: typing.Dict[str, str] = {} + attribute_map = {} @classmethod def from_dict(cls: typing.Type[T], dikt) -> T: @@ -44,8 +44,6 @@ def to_dict(self): if hasattr(item[1], "to_dict") else item, value.items() )) - elif isinstance(value, datetime.datetime): - result[attr] = value.isoformat() else: result[attr] = value diff --git a/applications/workspaces/server/workspaces/models/dandi_repository_resource.py b/applications/workspaces/server/workspaces/models/dandi_repository_resource.py index 98babb44c..1f6a8d2af 100644 --- a/applications/workspaces/server/workspaces/models/dandi_repository_resource.py +++ b/applications/workspaces/server/workspaces/models/dandi_repository_resource.py @@ -6,8 +6,10 @@ from typing import List, Dict # noqa: F401 from workspaces.models.base_model_ import Model +from workspaces.models.repository_resource_base import RepositoryResourceBase from workspaces import util +from workspaces.models.repository_resource_base import RepositoryResourceBase # noqa: E501 class DandiRepositoryResource(Model): """NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech). diff --git a/applications/workspaces/server/workspaces/models/download_resource.py b/applications/workspaces/server/workspaces/models/download_resource.py index 85fb17b38..56b35e6fb 100644 --- a/applications/workspaces/server/workspaces/models/download_resource.py +++ b/applications/workspaces/server/workspaces/models/download_resource.py @@ -6,8 +6,10 @@ from typing import List, Dict # noqa: F401 from workspaces.models.base_model_ import Model +from workspaces.models.resource_base import ResourceBase from workspaces import util +from workspaces.models.resource_base import ResourceBase # noqa: E501 class DownloadResource(Model): """NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech). diff --git a/applications/workspaces/server/workspaces/models/figshare_repository_resource.py b/applications/workspaces/server/workspaces/models/figshare_repository_resource.py index eb1dab803..083b05c51 100644 --- a/applications/workspaces/server/workspaces/models/figshare_repository_resource.py +++ b/applications/workspaces/server/workspaces/models/figshare_repository_resource.py @@ -6,8 +6,10 @@ from typing import List, Dict # noqa: F401 from workspaces.models.base_model_ import Model +from workspaces.models.repository_resource_base import RepositoryResourceBase from workspaces import util +from workspaces.models.repository_resource_base import RepositoryResourceBase # noqa: E501 class FigshareRepositoryResource(Model): """NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech). diff --git a/applications/workspaces/server/workspaces/models/git_repository_resource.py b/applications/workspaces/server/workspaces/models/git_repository_resource.py index ead0b27f7..d090ea78d 100644 --- a/applications/workspaces/server/workspaces/models/git_repository_resource.py +++ b/applications/workspaces/server/workspaces/models/git_repository_resource.py @@ -6,8 +6,12 @@ from typing import List, Dict # noqa: F401 from workspaces.models.base_model_ import Model +from workspaces.models.git_repository_resource_all_of import GITRepositoryResourceAllOf +from workspaces.models.repository_resource_base import RepositoryResourceBase from workspaces import util +from workspaces.models.git_repository_resource_all_of import GITRepositoryResourceAllOf # noqa: E501 +from workspaces.models.repository_resource_base import RepositoryResourceBase # noqa: E501 class GITRepositoryResource(Model): """NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech). diff --git a/applications/workspaces/server/workspaces/models/osb_repository.py b/applications/workspaces/server/workspaces/models/osb_repository.py index 744953096..acf53f525 100644 --- a/applications/workspaces/server/workspaces/models/osb_repository.py +++ b/applications/workspaces/server/workspaces/models/osb_repository.py @@ -6,17 +6,21 @@ from typing import List, Dict # noqa: F401 from workspaces.models.base_model_ import Model -from workspaces.models.osb_repository_base_tags_inner import OSBRepositoryBaseTagsInner +from workspaces.models.osb_repository_all_of import OSBRepositoryAllOf +from workspaces.models.osb_repository_base import OSBRepositoryBase from workspaces.models.repository_content_type import RepositoryContentType from workspaces.models.repository_resource_node import RepositoryResourceNode from workspaces.models.repository_type import RepositoryType +from workspaces.models.tag import Tag from workspaces.models.user import User from workspaces import util -from workspaces.models.osb_repository_base_tags_inner import OSBRepositoryBaseTagsInner # noqa: E501 +from workspaces.models.osb_repository_all_of import OSBRepositoryAllOf # noqa: E501 +from workspaces.models.osb_repository_base import OSBRepositoryBase # noqa: E501 from workspaces.models.repository_content_type import RepositoryContentType # noqa: E501 from workspaces.models.repository_resource_node import RepositoryResourceNode # noqa: E501 from workspaces.models.repository_type import RepositoryType # noqa: E501 +from workspaces.models.tag import Tag # noqa: E501 from workspaces.models.user import User # noqa: E501 class OSBRepository(Model): @@ -25,7 +29,7 @@ class OSBRepository(Model): Do not edit the class manually. """ - def __init__(self, id=None, name=None, summary=None, repository_type=None, content_types=None, auto_sync=True, uri=None, default_context=None, user_id=None, timestamp_created=None, timestamp_updated=None, tags=None, context_resources=None, contexts=None, user=None, content_types_list=None, description=None, timestamp_modified=None): # noqa: E501 + def __init__(self, id=None, name=None, summary=None, repository_type=None, content_types=None, auto_sync=True, uri=None, default_context=None, thumbnail=None, user_id=None, timestamp_created=None, timestamp_updated=None, tags=None, context_resources=None, contexts=None, user=None, content_types_list=None, description=None, timestamp_modified=None): # noqa: E501 """OSBRepository - a model defined in OpenAPI :param id: The id of this OSBRepository. # noqa: E501 @@ -44,6 +48,8 @@ def __init__(self, id=None, name=None, summary=None, repository_type=None, conte :type uri: str :param default_context: The default_context of this OSBRepository. # noqa: E501 :type default_context: str + :param thumbnail: The thumbnail of this OSBRepository. # noqa: E501 + :type thumbnail: str :param user_id: The user_id of this OSBRepository. # noqa: E501 :type user_id: str :param timestamp_created: The timestamp_created of this OSBRepository. # noqa: E501 @@ -51,7 +57,7 @@ def __init__(self, id=None, name=None, summary=None, repository_type=None, conte :param timestamp_updated: The timestamp_updated of this OSBRepository. # noqa: E501 :type timestamp_updated: datetime :param tags: The tags of this OSBRepository. # noqa: E501 - :type tags: List[OSBRepositoryBaseTagsInner] + :type tags: List[Tag] :param context_resources: The context_resources of this OSBRepository. # noqa: E501 :type context_resources: RepositoryResourceNode :param contexts: The contexts of this OSBRepository. # noqa: E501 @@ -74,10 +80,11 @@ def __init__(self, id=None, name=None, summary=None, repository_type=None, conte 'auto_sync': bool, 'uri': str, 'default_context': str, + 'thumbnail': str, 'user_id': str, 'timestamp_created': datetime, 'timestamp_updated': datetime, - 'tags': List[OSBRepositoryBaseTagsInner], + 'tags': List[Tag], 'context_resources': RepositoryResourceNode, 'contexts': List[str], 'user': User, @@ -95,6 +102,7 @@ def __init__(self, id=None, name=None, summary=None, repository_type=None, conte 'auto_sync': 'auto_sync', 'uri': 'uri', 'default_context': 'default_context', + 'thumbnail': 'thumbnail', 'user_id': 'user_id', 'timestamp_created': 'timestamp_created', 'timestamp_updated': 'timestamp_updated', @@ -115,6 +123,7 @@ def __init__(self, id=None, name=None, summary=None, repository_type=None, conte self._auto_sync = auto_sync self._uri = uri self._default_context = default_context + self._thumbnail = thumbnail self._user_id = user_id self._timestamp_created = timestamp_created self._timestamp_updated = timestamp_updated @@ -325,6 +334,27 @@ def default_context(self, default_context): self._default_context = default_context + @property + def thumbnail(self): + """Gets the thumbnail of this OSBRepository. + + + :return: The thumbnail of this OSBRepository. + :rtype: str + """ + return self._thumbnail + + @thumbnail.setter + def thumbnail(self, thumbnail): + """Sets the thumbnail of this OSBRepository. + + + :param thumbnail: The thumbnail of this OSBRepository. + :type thumbnail: str + """ + + self._thumbnail = thumbnail + @property def user_id(self): """Gets the user_id of this OSBRepository. @@ -400,7 +430,7 @@ def tags(self): :return: The tags of this OSBRepository. - :rtype: List[OSBRepositoryBaseTagsInner] + :rtype: List[Tag] """ return self._tags @@ -410,7 +440,7 @@ def tags(self, tags): :param tags: The tags of this OSBRepository. - :type tags: List[OSBRepositoryBaseTagsInner] + :type tags: List[Tag] """ self._tags = tags diff --git a/applications/workspaces/server/workspaces/models/osb_repository_base.py b/applications/workspaces/server/workspaces/models/osb_repository_base.py index 70336e824..592189c19 100644 --- a/applications/workspaces/server/workspaces/models/osb_repository_base.py +++ b/applications/workspaces/server/workspaces/models/osb_repository_base.py @@ -6,12 +6,12 @@ from typing import List, Dict # noqa: F401 from workspaces.models.base_model_ import Model -from workspaces.models.osb_repository_base_tags_inner import OSBRepositoryBaseTagsInner from workspaces.models.repository_type import RepositoryType +from workspaces.models.tag import Tag from workspaces import util -from workspaces.models.osb_repository_base_tags_inner import OSBRepositoryBaseTagsInner # noqa: E501 from workspaces.models.repository_type import RepositoryType # noqa: E501 +from workspaces.models.tag import Tag # noqa: E501 class OSBRepositoryBase(Model): """NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech). @@ -19,7 +19,7 @@ class OSBRepositoryBase(Model): Do not edit the class manually. """ - def __init__(self, id=None, name=None, summary=None, repository_type=None, content_types=None, auto_sync=True, uri=None, default_context=None, user_id=None, timestamp_created=None, timestamp_updated=None, tags=None): # noqa: E501 + def __init__(self, id=None, name=None, summary=None, repository_type=None, content_types=None, auto_sync=True, uri=None, default_context=None, thumbnail=None, user_id=None, timestamp_created=None, timestamp_updated=None, tags=None): # noqa: E501 """OSBRepositoryBase - a model defined in OpenAPI :param id: The id of this OSBRepositoryBase. # noqa: E501 @@ -38,6 +38,8 @@ def __init__(self, id=None, name=None, summary=None, repository_type=None, conte :type uri: str :param default_context: The default_context of this OSBRepositoryBase. # noqa: E501 :type default_context: str + :param thumbnail: The thumbnail of this OSBRepositoryBase. # noqa: E501 + :type thumbnail: str :param user_id: The user_id of this OSBRepositoryBase. # noqa: E501 :type user_id: str :param timestamp_created: The timestamp_created of this OSBRepositoryBase. # noqa: E501 @@ -45,7 +47,7 @@ def __init__(self, id=None, name=None, summary=None, repository_type=None, conte :param timestamp_updated: The timestamp_updated of this OSBRepositoryBase. # noqa: E501 :type timestamp_updated: datetime :param tags: The tags of this OSBRepositoryBase. # noqa: E501 - :type tags: List[OSBRepositoryBaseTagsInner] + :type tags: List[Tag] """ self.openapi_types = { 'id': int, @@ -56,10 +58,11 @@ def __init__(self, id=None, name=None, summary=None, repository_type=None, conte 'auto_sync': bool, 'uri': str, 'default_context': str, + 'thumbnail': str, 'user_id': str, 'timestamp_created': datetime, 'timestamp_updated': datetime, - 'tags': List[OSBRepositoryBaseTagsInner] + 'tags': List[Tag] } self.attribute_map = { @@ -71,6 +74,7 @@ def __init__(self, id=None, name=None, summary=None, repository_type=None, conte 'auto_sync': 'auto_sync', 'uri': 'uri', 'default_context': 'default_context', + 'thumbnail': 'thumbnail', 'user_id': 'user_id', 'timestamp_created': 'timestamp_created', 'timestamp_updated': 'timestamp_updated', @@ -85,6 +89,7 @@ def __init__(self, id=None, name=None, summary=None, repository_type=None, conte self._auto_sync = auto_sync self._uri = uri self._default_context = default_context + self._thumbnail = thumbnail self._user_id = user_id self._timestamp_created = timestamp_created self._timestamp_updated = timestamp_updated @@ -289,6 +294,27 @@ def default_context(self, default_context): self._default_context = default_context + @property + def thumbnail(self): + """Gets the thumbnail of this OSBRepositoryBase. + + + :return: The thumbnail of this OSBRepositoryBase. + :rtype: str + """ + return self._thumbnail + + @thumbnail.setter + def thumbnail(self, thumbnail): + """Sets the thumbnail of this OSBRepositoryBase. + + + :param thumbnail: The thumbnail of this OSBRepositoryBase. + :type thumbnail: str + """ + + self._thumbnail = thumbnail + @property def user_id(self): """Gets the user_id of this OSBRepositoryBase. @@ -364,7 +390,7 @@ def tags(self): :return: The tags of this OSBRepositoryBase. - :rtype: List[OSBRepositoryBaseTagsInner] + :rtype: List[Tag] """ return self._tags @@ -374,7 +400,7 @@ def tags(self, tags): :param tags: The tags of this OSBRepositoryBase. - :type tags: List[OSBRepositoryBaseTagsInner] + :type tags: List[Tag] """ self._tags = tags diff --git a/applications/workspaces/server/workspaces/models/osb_repository_entity.py b/applications/workspaces/server/workspaces/models/osb_repository_entity.py index 78e46c054..525e914bc 100644 --- a/applications/workspaces/server/workspaces/models/osb_repository_entity.py +++ b/applications/workspaces/server/workspaces/models/osb_repository_entity.py @@ -6,12 +6,14 @@ from typing import List, Dict # noqa: F401 from workspaces.models.base_model_ import Model -from workspaces.models.osb_repository_base_tags_inner import OSBRepositoryBaseTagsInner +from workspaces.models.osb_repository_base import OSBRepositoryBase from workspaces.models.repository_type import RepositoryType +from workspaces.models.tag import Tag from workspaces import util -from workspaces.models.osb_repository_base_tags_inner import OSBRepositoryBaseTagsInner # noqa: E501 +from workspaces.models.osb_repository_base import OSBRepositoryBase # noqa: E501 from workspaces.models.repository_type import RepositoryType # noqa: E501 +from workspaces.models.tag import Tag # noqa: E501 class OSBRepositoryEntity(Model): """NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech). @@ -19,7 +21,7 @@ class OSBRepositoryEntity(Model): Do not edit the class manually. """ - def __init__(self, id=None, name=None, summary=None, repository_type=None, content_types=None, auto_sync=True, uri=None, default_context=None, user_id=None, timestamp_created=None, timestamp_updated=None, tags=None): # noqa: E501 + def __init__(self, id=None, name=None, summary=None, repository_type=None, content_types=None, auto_sync=True, uri=None, default_context=None, thumbnail=None, user_id=None, timestamp_created=None, timestamp_updated=None, tags=None): # noqa: E501 """OSBRepositoryEntity - a model defined in OpenAPI :param id: The id of this OSBRepositoryEntity. # noqa: E501 @@ -38,6 +40,8 @@ def __init__(self, id=None, name=None, summary=None, repository_type=None, conte :type uri: str :param default_context: The default_context of this OSBRepositoryEntity. # noqa: E501 :type default_context: str + :param thumbnail: The thumbnail of this OSBRepositoryEntity. # noqa: E501 + :type thumbnail: str :param user_id: The user_id of this OSBRepositoryEntity. # noqa: E501 :type user_id: str :param timestamp_created: The timestamp_created of this OSBRepositoryEntity. # noqa: E501 @@ -45,7 +49,7 @@ def __init__(self, id=None, name=None, summary=None, repository_type=None, conte :param timestamp_updated: The timestamp_updated of this OSBRepositoryEntity. # noqa: E501 :type timestamp_updated: datetime :param tags: The tags of this OSBRepositoryEntity. # noqa: E501 - :type tags: List[OSBRepositoryBaseTagsInner] + :type tags: List[Tag] """ self.openapi_types = { 'id': int, @@ -56,10 +60,11 @@ def __init__(self, id=None, name=None, summary=None, repository_type=None, conte 'auto_sync': bool, 'uri': str, 'default_context': str, + 'thumbnail': str, 'user_id': str, 'timestamp_created': datetime, 'timestamp_updated': datetime, - 'tags': List[OSBRepositoryBaseTagsInner] + 'tags': List[Tag] } self.attribute_map = { @@ -71,6 +76,7 @@ def __init__(self, id=None, name=None, summary=None, repository_type=None, conte 'auto_sync': 'auto_sync', 'uri': 'uri', 'default_context': 'default_context', + 'thumbnail': 'thumbnail', 'user_id': 'user_id', 'timestamp_created': 'timestamp_created', 'timestamp_updated': 'timestamp_updated', @@ -85,6 +91,7 @@ def __init__(self, id=None, name=None, summary=None, repository_type=None, conte self._auto_sync = auto_sync self._uri = uri self._default_context = default_context + self._thumbnail = thumbnail self._user_id = user_id self._timestamp_created = timestamp_created self._timestamp_updated = timestamp_updated @@ -289,6 +296,27 @@ def default_context(self, default_context): self._default_context = default_context + @property + def thumbnail(self): + """Gets the thumbnail of this OSBRepositoryEntity. + + + :return: The thumbnail of this OSBRepositoryEntity. + :rtype: str + """ + return self._thumbnail + + @thumbnail.setter + def thumbnail(self, thumbnail): + """Sets the thumbnail of this OSBRepositoryEntity. + + + :param thumbnail: The thumbnail of this OSBRepositoryEntity. + :type thumbnail: str + """ + + self._thumbnail = thumbnail + @property def user_id(self): """Gets the user_id of this OSBRepositoryEntity. @@ -364,7 +392,7 @@ def tags(self): :return: The tags of this OSBRepositoryEntity. - :rtype: List[OSBRepositoryBaseTagsInner] + :rtype: List[Tag] """ return self._tags @@ -374,7 +402,7 @@ def tags(self, tags): :param tags: The tags of this OSBRepositoryEntity. - :type tags: List[OSBRepositoryBaseTagsInner] + :type tags: List[Tag] """ self._tags = tags diff --git a/applications/workspaces/server/workspaces/models/repository_info.py b/applications/workspaces/server/workspaces/models/repository_info.py index 55b71fe54..821e0b966 100644 --- a/applications/workspaces/server/workspaces/models/repository_info.py +++ b/applications/workspaces/server/workspaces/models/repository_info.py @@ -61,7 +61,6 @@ def from_dict(cls, dikt) -> 'RepositoryInfo': def contexts(self): """Gets the contexts of this RepositoryInfo. - # noqa: E501 :return: The contexts of this RepositoryInfo. :rtype: List[str] @@ -72,7 +71,6 @@ def contexts(self): def contexts(self, contexts): """Sets the contexts of this RepositoryInfo. - # noqa: E501 :param contexts: The contexts of this RepositoryInfo. :type contexts: List[str] @@ -84,7 +82,6 @@ def contexts(self, contexts): def name(self): """Gets the name of this RepositoryInfo. - # noqa: E501 :return: The name of this RepositoryInfo. :rtype: str @@ -95,7 +92,6 @@ def name(self): def name(self, name): """Sets the name of this RepositoryInfo. - # noqa: E501 :param name: The name of this RepositoryInfo. :type name: str @@ -107,7 +103,6 @@ def name(self, name): def summary(self): """Gets the summary of this RepositoryInfo. - # noqa: E501 :return: The summary of this RepositoryInfo. :rtype: str @@ -118,7 +113,6 @@ def summary(self): def summary(self, summary): """Sets the summary of this RepositoryInfo. - # noqa: E501 :param summary: The summary of this RepositoryInfo. :type summary: str @@ -130,7 +124,6 @@ def summary(self, summary): def tags(self): """Gets the tags of this RepositoryInfo. - # noqa: E501 :return: The tags of this RepositoryInfo. :rtype: List[str] @@ -141,7 +134,6 @@ def tags(self): def tags(self, tags): """Sets the tags of this RepositoryInfo. - # noqa: E501 :param tags: The tags of this RepositoryInfo. :type tags: List[str] diff --git a/applications/workspaces/server/workspaces/models/repository_resource_base.py b/applications/workspaces/server/workspaces/models/repository_resource_base.py index 64197d3a0..72b13c690 100644 --- a/applications/workspaces/server/workspaces/models/repository_resource_base.py +++ b/applications/workspaces/server/workspaces/models/repository_resource_base.py @@ -6,8 +6,12 @@ from typing import List, Dict # noqa: F401 from workspaces.models.base_model_ import Model +from workspaces.models.repository_resource_base_all_of import RepositoryResourceBaseAllOf +from workspaces.models.resource_base import ResourceBase from workspaces import util +from workspaces.models.repository_resource_base_all_of import RepositoryResourceBaseAllOf # noqa: E501 +from workspaces.models.resource_base import ResourceBase # noqa: E501 class RepositoryResourceBase(Model): """NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech). diff --git a/applications/workspaces/server/workspaces/models/workspace.py b/applications/workspaces/server/workspaces/models/workspace.py index 92e5e6b53..52c7e5a96 100644 --- a/applications/workspaces/server/workspaces/models/workspace.py +++ b/applications/workspaces/server/workspaces/models/workspace.py @@ -6,19 +6,23 @@ from typing import List, Dict # noqa: F401 from workspaces.models.base_model_ import Model +from workspaces.models.tag import Tag from workspaces.models.user import User from workspaces.models.volume_storage import VolumeStorage -from workspaces.models.workspace_base_gallery_inner import WorkspaceBaseGalleryInner -from workspaces.models.workspace_base_tags_inner import WorkspaceBaseTagsInner +from workspaces.models.workspace_all_of import WorkspaceAllOf +from workspaces.models.workspace_base import WorkspaceBase from workspaces.models.workspace_collaborator import WorkspaceCollaborator +from workspaces.models.workspace_image import WorkspaceImage from workspaces.models.workspace_resource import WorkspaceResource from workspaces import util +from workspaces.models.tag import Tag # noqa: E501 from workspaces.models.user import User # noqa: E501 from workspaces.models.volume_storage import VolumeStorage # noqa: E501 -from workspaces.models.workspace_base_gallery_inner import WorkspaceBaseGalleryInner # noqa: E501 -from workspaces.models.workspace_base_tags_inner import WorkspaceBaseTagsInner # noqa: E501 +from workspaces.models.workspace_all_of import WorkspaceAllOf # noqa: E501 +from workspaces.models.workspace_base import WorkspaceBase # noqa: E501 from workspaces.models.workspace_collaborator import WorkspaceCollaborator # noqa: E501 +from workspaces.models.workspace_image import WorkspaceImage # noqa: E501 from workspaces.models.workspace_resource import WorkspaceResource # noqa: E501 class Workspace(Model): @@ -45,7 +49,7 @@ def __init__(self, id=None, name=None, description=None, timestamp_created=None, :param thumbnail: The thumbnail of this Workspace. # noqa: E501 :type thumbnail: str :param gallery: The gallery of this Workspace. # noqa: E501 - :type gallery: List[WorkspaceBaseGalleryInner] + :type gallery: List[WorkspaceImage] :param user_id: The user_id of this Workspace. # noqa: E501 :type user_id: str :param publicable: The publicable of this Workspace. # noqa: E501 @@ -59,7 +63,7 @@ def __init__(self, id=None, name=None, description=None, timestamp_created=None, :param storage: The storage of this Workspace. # noqa: E501 :type storage: VolumeStorage :param tags: The tags of this Workspace. # noqa: E501 - :type tags: List[WorkspaceBaseTagsInner] + :type tags: List[Tag] :param resources: The resources of this Workspace. # noqa: E501 :type resources: List[WorkspaceResource] :param user: The user of this Workspace. # noqa: E501 @@ -73,14 +77,14 @@ def __init__(self, id=None, name=None, description=None, timestamp_created=None, 'timestamp_updated': datetime, 'last_opened_resource_id': int, 'thumbnail': str, - 'gallery': List[WorkspaceBaseGalleryInner], + 'gallery': List[WorkspaceImage], 'user_id': str, 'publicable': bool, 'featured': bool, 'license': str, 'collaborators': List[WorkspaceCollaborator], 'storage': VolumeStorage, - 'tags': List[WorkspaceBaseTagsInner], + 'tags': List[Tag], 'resources': List[WorkspaceResource], 'user': User } @@ -302,7 +306,7 @@ def gallery(self): Gallery with images of the workspace # noqa: E501 :return: The gallery of this Workspace. - :rtype: List[WorkspaceBaseGalleryInner] + :rtype: List[WorkspaceImage] """ return self._gallery @@ -313,7 +317,7 @@ def gallery(self, gallery): Gallery with images of the workspace # noqa: E501 :param gallery: The gallery of this Workspace. - :type gallery: List[WorkspaceBaseGalleryInner] + :type gallery: List[WorkspaceImage] """ self._gallery = gallery @@ -460,7 +464,7 @@ def tags(self): :return: The tags of this Workspace. - :rtype: List[WorkspaceBaseTagsInner] + :rtype: List[Tag] """ return self._tags @@ -470,7 +474,7 @@ def tags(self, tags): :param tags: The tags of this Workspace. - :type tags: List[WorkspaceBaseTagsInner] + :type tags: List[Tag] """ self._tags = tags diff --git a/applications/workspaces/server/workspaces/models/workspace_base.py b/applications/workspaces/server/workspaces/models/workspace_base.py index b86cf0a3d..376b1abd7 100644 --- a/applications/workspaces/server/workspaces/models/workspace_base.py +++ b/applications/workspaces/server/workspaces/models/workspace_base.py @@ -6,16 +6,16 @@ from typing import List, Dict # noqa: F401 from workspaces.models.base_model_ import Model +from workspaces.models.tag import Tag from workspaces.models.volume_storage import VolumeStorage -from workspaces.models.workspace_base_gallery_inner import WorkspaceBaseGalleryInner -from workspaces.models.workspace_base_tags_inner import WorkspaceBaseTagsInner from workspaces.models.workspace_collaborator import WorkspaceCollaborator +from workspaces.models.workspace_image import WorkspaceImage from workspaces import util +from workspaces.models.tag import Tag # noqa: E501 from workspaces.models.volume_storage import VolumeStorage # noqa: E501 -from workspaces.models.workspace_base_gallery_inner import WorkspaceBaseGalleryInner # noqa: E501 -from workspaces.models.workspace_base_tags_inner import WorkspaceBaseTagsInner # noqa: E501 from workspaces.models.workspace_collaborator import WorkspaceCollaborator # noqa: E501 +from workspaces.models.workspace_image import WorkspaceImage # noqa: E501 class WorkspaceBase(Model): """NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech). @@ -41,7 +41,7 @@ def __init__(self, id=None, name=None, description=None, timestamp_created=None, :param thumbnail: The thumbnail of this WorkspaceBase. # noqa: E501 :type thumbnail: str :param gallery: The gallery of this WorkspaceBase. # noqa: E501 - :type gallery: List[WorkspaceBaseGalleryInner] + :type gallery: List[WorkspaceImage] :param user_id: The user_id of this WorkspaceBase. # noqa: E501 :type user_id: str :param publicable: The publicable of this WorkspaceBase. # noqa: E501 @@ -55,7 +55,7 @@ def __init__(self, id=None, name=None, description=None, timestamp_created=None, :param storage: The storage of this WorkspaceBase. # noqa: E501 :type storage: VolumeStorage :param tags: The tags of this WorkspaceBase. # noqa: E501 - :type tags: List[WorkspaceBaseTagsInner] + :type tags: List[Tag] """ self.openapi_types = { 'id': int, @@ -65,14 +65,14 @@ def __init__(self, id=None, name=None, description=None, timestamp_created=None, 'timestamp_updated': datetime, 'last_opened_resource_id': int, 'thumbnail': str, - 'gallery': List[WorkspaceBaseGalleryInner], + 'gallery': List[WorkspaceImage], 'user_id': str, 'publicable': bool, 'featured': bool, 'license': str, 'collaborators': List[WorkspaceCollaborator], 'storage': VolumeStorage, - 'tags': List[WorkspaceBaseTagsInner] + 'tags': List[Tag] } self.attribute_map = { @@ -288,7 +288,7 @@ def gallery(self): Gallery with images of the workspace # noqa: E501 :return: The gallery of this WorkspaceBase. - :rtype: List[WorkspaceBaseGalleryInner] + :rtype: List[WorkspaceImage] """ return self._gallery @@ -299,7 +299,7 @@ def gallery(self, gallery): Gallery with images of the workspace # noqa: E501 :param gallery: The gallery of this WorkspaceBase. - :type gallery: List[WorkspaceBaseGalleryInner] + :type gallery: List[WorkspaceImage] """ self._gallery = gallery @@ -446,7 +446,7 @@ def tags(self): :return: The tags of this WorkspaceBase. - :rtype: List[WorkspaceBaseTagsInner] + :rtype: List[Tag] """ return self._tags @@ -456,7 +456,7 @@ def tags(self, tags): :param tags: The tags of this WorkspaceBase. - :type tags: List[WorkspaceBaseTagsInner] + :type tags: List[Tag] """ self._tags = tags diff --git a/applications/workspaces/server/workspaces/models/workspace_entity.py b/applications/workspaces/server/workspaces/models/workspace_entity.py index c752e0ef4..9b9e2d2b5 100644 --- a/applications/workspaces/server/workspaces/models/workspace_entity.py +++ b/applications/workspaces/server/workspaces/models/workspace_entity.py @@ -6,17 +6,21 @@ from typing import List, Dict # noqa: F401 from workspaces.models.base_model_ import Model +from workspaces.models.tag import Tag from workspaces.models.volume_storage import VolumeStorage -from workspaces.models.workspace_base_gallery_inner import WorkspaceBaseGalleryInner -from workspaces.models.workspace_base_tags_inner import WorkspaceBaseTagsInner +from workspaces.models.workspace_base import WorkspaceBase from workspaces.models.workspace_collaborator import WorkspaceCollaborator +from workspaces.models.workspace_entity_all_of import WorkspaceEntityAllOf +from workspaces.models.workspace_image import WorkspaceImage from workspaces.models.workspace_resource_entity import WorkspaceResourceEntity from workspaces import util +from workspaces.models.tag import Tag # noqa: E501 from workspaces.models.volume_storage import VolumeStorage # noqa: E501 -from workspaces.models.workspace_base_gallery_inner import WorkspaceBaseGalleryInner # noqa: E501 -from workspaces.models.workspace_base_tags_inner import WorkspaceBaseTagsInner # noqa: E501 +from workspaces.models.workspace_base import WorkspaceBase # noqa: E501 from workspaces.models.workspace_collaborator import WorkspaceCollaborator # noqa: E501 +from workspaces.models.workspace_entity_all_of import WorkspaceEntityAllOf # noqa: E501 +from workspaces.models.workspace_image import WorkspaceImage # noqa: E501 from workspaces.models.workspace_resource_entity import WorkspaceResourceEntity # noqa: E501 class WorkspaceEntity(Model): @@ -43,7 +47,7 @@ def __init__(self, id=None, name=None, description=None, timestamp_created=None, :param thumbnail: The thumbnail of this WorkspaceEntity. # noqa: E501 :type thumbnail: str :param gallery: The gallery of this WorkspaceEntity. # noqa: E501 - :type gallery: List[WorkspaceBaseGalleryInner] + :type gallery: List[WorkspaceImage] :param user_id: The user_id of this WorkspaceEntity. # noqa: E501 :type user_id: str :param publicable: The publicable of this WorkspaceEntity. # noqa: E501 @@ -57,7 +61,7 @@ def __init__(self, id=None, name=None, description=None, timestamp_created=None, :param storage: The storage of this WorkspaceEntity. # noqa: E501 :type storage: VolumeStorage :param tags: The tags of this WorkspaceEntity. # noqa: E501 - :type tags: List[WorkspaceBaseTagsInner] + :type tags: List[Tag] :param resources: The resources of this WorkspaceEntity. # noqa: E501 :type resources: List[WorkspaceResourceEntity] """ @@ -69,14 +73,14 @@ def __init__(self, id=None, name=None, description=None, timestamp_created=None, 'timestamp_updated': datetime, 'last_opened_resource_id': int, 'thumbnail': str, - 'gallery': List[WorkspaceBaseGalleryInner], + 'gallery': List[WorkspaceImage], 'user_id': str, 'publicable': bool, 'featured': bool, 'license': str, 'collaborators': List[WorkspaceCollaborator], 'storage': VolumeStorage, - 'tags': List[WorkspaceBaseTagsInner], + 'tags': List[Tag], 'resources': List[WorkspaceResourceEntity] } @@ -295,7 +299,7 @@ def gallery(self): Gallery with images of the workspace # noqa: E501 :return: The gallery of this WorkspaceEntity. - :rtype: List[WorkspaceBaseGalleryInner] + :rtype: List[WorkspaceImage] """ return self._gallery @@ -306,7 +310,7 @@ def gallery(self, gallery): Gallery with images of the workspace # noqa: E501 :param gallery: The gallery of this WorkspaceEntity. - :type gallery: List[WorkspaceBaseGalleryInner] + :type gallery: List[WorkspaceImage] """ self._gallery = gallery @@ -453,7 +457,7 @@ def tags(self): :return: The tags of this WorkspaceEntity. - :rtype: List[WorkspaceBaseTagsInner] + :rtype: List[Tag] """ return self._tags @@ -463,7 +467,7 @@ def tags(self, tags): :param tags: The tags of this WorkspaceEntity. - :type tags: List[WorkspaceBaseTagsInner] + :type tags: List[Tag] """ self._tags = tags diff --git a/applications/workspaces/server/workspaces/models/workspace_resource.py b/applications/workspaces/server/workspaces/models/workspace_resource.py index ae4041979..0aab9a365 100644 --- a/applications/workspaces/server/workspaces/models/workspace_resource.py +++ b/applications/workspaces/server/workspaces/models/workspace_resource.py @@ -9,11 +9,13 @@ from workspaces.models.resource_origin import ResourceOrigin from workspaces.models.resource_status import ResourceStatus from workspaces.models.resource_type import ResourceType +from workspaces.models.workspace_resource_base import WorkspaceResourceBase from workspaces import util from workspaces.models.resource_origin import ResourceOrigin # noqa: E501 from workspaces.models.resource_status import ResourceStatus # noqa: E501 from workspaces.models.resource_type import ResourceType # noqa: E501 +from workspaces.models.workspace_resource_base import WorkspaceResourceBase # noqa: E501 class WorkspaceResource(Model): """NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech). @@ -21,9 +23,15 @@ class WorkspaceResource(Model): Do not edit the class manually. """ - def __init__(self, id=None, name=None, status=None, timestamp_created=None, timestamp_updated=None, timestamp_last_opened=None, resource_type=None, workspace_id=None, origin=None, path=None): # noqa: E501 + def __init__(self, workspace_id=None, origin=None, path=None, id=None, name=None, status=None, timestamp_created=None, timestamp_updated=None, timestamp_last_opened=None, resource_type=None): # noqa: E501 """WorkspaceResource - a model defined in OpenAPI + :param workspace_id: The workspace_id of this WorkspaceResource. # noqa: E501 + :type workspace_id: int + :param origin: The origin of this WorkspaceResource. # noqa: E501 + :type origin: ResourceOrigin + :param path: The path of this WorkspaceResource. # noqa: E501 + :type path: str :param id: The id of this WorkspaceResource. # noqa: E501 :type id: int :param name: The name of this WorkspaceResource. # noqa: E501 @@ -38,39 +46,36 @@ def __init__(self, id=None, name=None, status=None, timestamp_created=None, time :type timestamp_last_opened: datetime :param resource_type: The resource_type of this WorkspaceResource. # noqa: E501 :type resource_type: ResourceType - :param workspace_id: The workspace_id of this WorkspaceResource. # noqa: E501 - :type workspace_id: int - :param origin: The origin of this WorkspaceResource. # noqa: E501 - :type origin: ResourceOrigin - :param path: The path of this WorkspaceResource. # noqa: E501 - :type path: str """ self.openapi_types = { + 'workspace_id': int, + 'origin': ResourceOrigin, + 'path': str, 'id': int, 'name': str, 'status': ResourceStatus, 'timestamp_created': datetime, 'timestamp_updated': datetime, 'timestamp_last_opened': datetime, - 'resource_type': ResourceType, - 'workspace_id': int, - 'origin': ResourceOrigin, - 'path': str + 'resource_type': ResourceType } self.attribute_map = { + 'workspace_id': 'workspace_id', + 'origin': 'origin', + 'path': 'path', 'id': 'id', 'name': 'name', 'status': 'status', 'timestamp_created': 'timestamp_created', 'timestamp_updated': 'timestamp_updated', 'timestamp_last_opened': 'timestamp_last_opened', - 'resource_type': 'resource_type', - 'workspace_id': 'workspace_id', - 'origin': 'origin', - 'path': 'path' + 'resource_type': 'resource_type' } + self._workspace_id = workspace_id + self._origin = origin + self._path = path self._id = id self._name = name self._status = status @@ -78,9 +83,6 @@ def __init__(self, id=None, name=None, status=None, timestamp_created=None, time self._timestamp_updated = timestamp_updated self._timestamp_last_opened = timestamp_last_opened self._resource_type = resource_type - self._workspace_id = workspace_id - self._origin = origin - self._path = path @classmethod def from_dict(cls, dikt) -> 'WorkspaceResource': @@ -93,6 +95,73 @@ def from_dict(cls, dikt) -> 'WorkspaceResource': """ return util.deserialize_model(dikt, cls) + @property + def workspace_id(self): + """Gets the workspace_id of this WorkspaceResource. + + workspace_id # noqa: E501 + + :return: The workspace_id of this WorkspaceResource. + :rtype: int + """ + return self._workspace_id + + @workspace_id.setter + def workspace_id(self, workspace_id): + """Sets the workspace_id of this WorkspaceResource. + + workspace_id # noqa: E501 + + :param workspace_id: The workspace_id of this WorkspaceResource. + :type workspace_id: int + """ + + self._workspace_id = workspace_id + + @property + def origin(self): + """Gets the origin of this WorkspaceResource. + + + :return: The origin of this WorkspaceResource. + :rtype: ResourceOrigin + """ + return self._origin + + @origin.setter + def origin(self, origin): + """Sets the origin of this WorkspaceResource. + + + :param origin: The origin of this WorkspaceResource. + :type origin: ResourceOrigin + """ + + self._origin = origin + + @property + def path(self): + """Gets the path of this WorkspaceResource. + + WorkspaceResource path where the resource will stored in the pvc. # noqa: E501 + + :return: The path of this WorkspaceResource. + :rtype: str + """ + return self._path + + @path.setter + def path(self, path): + """Sets the path of this WorkspaceResource. + + WorkspaceResource path where the resource will stored in the pvc. # noqa: E501 + + :param path: The path of this WorkspaceResource. + :type path: str + """ + + self._path = path + @property def id(self): """Gets the id of this WorkspaceResource. @@ -251,70 +320,3 @@ def resource_type(self, resource_type): raise ValueError("Invalid value for `resource_type`, must not be `None`") # noqa: E501 self._resource_type = resource_type - - @property - def workspace_id(self): - """Gets the workspace_id of this WorkspaceResource. - - workspace_id # noqa: E501 - - :return: The workspace_id of this WorkspaceResource. - :rtype: int - """ - return self._workspace_id - - @workspace_id.setter - def workspace_id(self, workspace_id): - """Sets the workspace_id of this WorkspaceResource. - - workspace_id # noqa: E501 - - :param workspace_id: The workspace_id of this WorkspaceResource. - :type workspace_id: int - """ - - self._workspace_id = workspace_id - - @property - def origin(self): - """Gets the origin of this WorkspaceResource. - - - :return: The origin of this WorkspaceResource. - :rtype: ResourceOrigin - """ - return self._origin - - @origin.setter - def origin(self, origin): - """Sets the origin of this WorkspaceResource. - - - :param origin: The origin of this WorkspaceResource. - :type origin: ResourceOrigin - """ - - self._origin = origin - - @property - def path(self): - """Gets the path of this WorkspaceResource. - - WorkspaceResource path where the resource will stored in the pvc. # noqa: E501 - - :return: The path of this WorkspaceResource. - :rtype: str - """ - return self._path - - @path.setter - def path(self, path): - """Sets the path of this WorkspaceResource. - - WorkspaceResource path where the resource will stored in the pvc. # noqa: E501 - - :param path: The path of this WorkspaceResource. - :type path: str - """ - - self._path = path diff --git a/applications/workspaces/server/workspaces/models/workspace_resource_entity.py b/applications/workspaces/server/workspaces/models/workspace_resource_entity.py index 7431d4d4c..c696cbee7 100644 --- a/applications/workspaces/server/workspaces/models/workspace_resource_entity.py +++ b/applications/workspaces/server/workspaces/models/workspace_resource_entity.py @@ -8,10 +8,12 @@ from workspaces.models.base_model_ import Model from workspaces.models.resource_status import ResourceStatus from workspaces.models.resource_type import ResourceType +from workspaces.models.workspace_resource_base import WorkspaceResourceBase from workspaces import util from workspaces.models.resource_status import ResourceStatus # noqa: E501 from workspaces.models.resource_type import ResourceType # noqa: E501 +from workspaces.models.workspace_resource_base import WorkspaceResourceBase # noqa: E501 class WorkspaceResourceEntity(Model): """NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech). @@ -19,9 +21,15 @@ class WorkspaceResourceEntity(Model): Do not edit the class manually. """ - def __init__(self, id=None, name=None, status=None, timestamp_created=None, timestamp_updated=None, timestamp_last_opened=None, resource_type=None, origin=None, workspace_id=None, folder=None): # noqa: E501 + def __init__(self, origin=None, workspace_id=None, folder=None, id=None, name=None, status=None, timestamp_created=None, timestamp_updated=None, timestamp_last_opened=None, resource_type=None): # noqa: E501 """WorkspaceResourceEntity - a model defined in OpenAPI + :param origin: The origin of this WorkspaceResourceEntity. # noqa: E501 + :type origin: str + :param workspace_id: The workspace_id of this WorkspaceResourceEntity. # noqa: E501 + :type workspace_id: int + :param folder: The folder of this WorkspaceResourceEntity. # noqa: E501 + :type folder: str :param id: The id of this WorkspaceResourceEntity. # noqa: E501 :type id: int :param name: The name of this WorkspaceResourceEntity. # noqa: E501 @@ -36,39 +44,36 @@ def __init__(self, id=None, name=None, status=None, timestamp_created=None, time :type timestamp_last_opened: datetime :param resource_type: The resource_type of this WorkspaceResourceEntity. # noqa: E501 :type resource_type: ResourceType - :param origin: The origin of this WorkspaceResourceEntity. # noqa: E501 - :type origin: str - :param workspace_id: The workspace_id of this WorkspaceResourceEntity. # noqa: E501 - :type workspace_id: int - :param folder: The folder of this WorkspaceResourceEntity. # noqa: E501 - :type folder: str """ self.openapi_types = { + 'origin': str, + 'workspace_id': int, + 'folder': str, 'id': int, 'name': str, 'status': ResourceStatus, 'timestamp_created': datetime, 'timestamp_updated': datetime, 'timestamp_last_opened': datetime, - 'resource_type': ResourceType, - 'origin': str, - 'workspace_id': int, - 'folder': str + 'resource_type': ResourceType } self.attribute_map = { + 'origin': 'origin', + 'workspace_id': 'workspace_id', + 'folder': 'folder', 'id': 'id', 'name': 'name', 'status': 'status', 'timestamp_created': 'timestamp_created', 'timestamp_updated': 'timestamp_updated', 'timestamp_last_opened': 'timestamp_last_opened', - 'resource_type': 'resource_type', - 'origin': 'origin', - 'workspace_id': 'workspace_id', - 'folder': 'folder' + 'resource_type': 'resource_type' } + self._origin = origin + self._workspace_id = workspace_id + self._folder = folder self._id = id self._name = name self._status = status @@ -76,9 +81,6 @@ def __init__(self, id=None, name=None, status=None, timestamp_created=None, time self._timestamp_updated = timestamp_updated self._timestamp_last_opened = timestamp_last_opened self._resource_type = resource_type - self._origin = origin - self._workspace_id = workspace_id - self._folder = folder @classmethod def from_dict(cls, dikt) -> 'WorkspaceResourceEntity': @@ -91,6 +93,75 @@ def from_dict(cls, dikt) -> 'WorkspaceResourceEntity': """ return util.deserialize_model(dikt, cls) + @property + def origin(self): + """Gets the origin of this WorkspaceResourceEntity. + + Origin data JSON formatted of the WorkspaceResource # noqa: E501 + + :return: The origin of this WorkspaceResourceEntity. + :rtype: str + """ + return self._origin + + @origin.setter + def origin(self, origin): + """Sets the origin of this WorkspaceResourceEntity. + + Origin data JSON formatted of the WorkspaceResource # noqa: E501 + + :param origin: The origin of this WorkspaceResourceEntity. + :type origin: str + """ + + self._origin = origin + + @property + def workspace_id(self): + """Gets the workspace_id of this WorkspaceResourceEntity. + + workspace_id # noqa: E501 + + :return: The workspace_id of this WorkspaceResourceEntity. + :rtype: int + """ + return self._workspace_id + + @workspace_id.setter + def workspace_id(self, workspace_id): + """Sets the workspace_id of this WorkspaceResourceEntity. + + workspace_id # noqa: E501 + + :param workspace_id: The workspace_id of this WorkspaceResourceEntity. + :type workspace_id: int + """ + + self._workspace_id = workspace_id + + @property + def folder(self): + """Gets the folder of this WorkspaceResourceEntity. + + WorkspaceResource path where the resource will stored in the pvc. # noqa: E501 + + :return: The folder of this WorkspaceResourceEntity. + :rtype: str + """ + return self._folder + + @folder.setter + def folder(self, folder): + """Sets the folder of this WorkspaceResourceEntity. + + WorkspaceResource path where the resource will stored in the pvc. # noqa: E501 + + :param folder: The folder of this WorkspaceResourceEntity. + :type folder: str + """ + + self._folder = folder + @property def id(self): """Gets the id of this WorkspaceResourceEntity. @@ -249,72 +320,3 @@ def resource_type(self, resource_type): raise ValueError("Invalid value for `resource_type`, must not be `None`") # noqa: E501 self._resource_type = resource_type - - @property - def origin(self): - """Gets the origin of this WorkspaceResourceEntity. - - Origin data JSON formatted of the WorkspaceResource # noqa: E501 - - :return: The origin of this WorkspaceResourceEntity. - :rtype: str - """ - return self._origin - - @origin.setter - def origin(self, origin): - """Sets the origin of this WorkspaceResourceEntity. - - Origin data JSON formatted of the WorkspaceResource # noqa: E501 - - :param origin: The origin of this WorkspaceResourceEntity. - :type origin: str - """ - - self._origin = origin - - @property - def workspace_id(self): - """Gets the workspace_id of this WorkspaceResourceEntity. - - workspace_id # noqa: E501 - - :return: The workspace_id of this WorkspaceResourceEntity. - :rtype: int - """ - return self._workspace_id - - @workspace_id.setter - def workspace_id(self, workspace_id): - """Sets the workspace_id of this WorkspaceResourceEntity. - - workspace_id # noqa: E501 - - :param workspace_id: The workspace_id of this WorkspaceResourceEntity. - :type workspace_id: int - """ - - self._workspace_id = workspace_id - - @property - def folder(self): - """Gets the folder of this WorkspaceResourceEntity. - - WorkspaceResource path where the resource will stored in the pvc. # noqa: E501 - - :return: The folder of this WorkspaceResourceEntity. - :rtype: str - """ - return self._folder - - @folder.setter - def folder(self, folder): - """Sets the folder of this WorkspaceResourceEntity. - - WorkspaceResource path where the resource will stored in the pvc. # noqa: E501 - - :param folder: The folder of this WorkspaceResourceEntity. - :type folder: str - """ - - self._folder = folder diff --git a/applications/workspaces/server/workspaces/openapi/openapi.yaml b/applications/workspaces/server/workspaces/openapi/openapi.yaml index 060074f8f..295f37de1 100644 --- a/applications/workspaces/server/workspaces/openapi/openapi.yaml +++ b/applications/workspaces/server/workspaces/openapi/openapi.yaml @@ -932,6 +932,37 @@ paths: $ref: '#/components/schemas/RepositoryType' in: query required: true + '/osbrepository/{id}/thumbnail/': + post: + requestBody: + content: + multipart/form-data: + schema: + type: object + properties: + thumbNail: + format: binary + type: string + tags: + - rest + responses: + '200': + description: The repository was updated with the thumbnail. + '404': + description: The repository was not found or the thumbnail was not specified + security: + - + bearerAuth: [] + operationId: workspaces.controllers.osbrepository_controller.setthumbnail + summary: Sets the thumbnail of the workspace. + parameters: + - + name: id + description: 'Repository ID ' + schema: + type: integer + in: path + required: true components: schemas: Valid: @@ -1342,66 +1373,6 @@ components: type: array items: type: string - OSBRepositoryBase: - description: OSB Repository Base model - required: - - name - - repository_type - - content_types - - uri - type: object - properties: - id: - $ref: '#/components/schemas/PrimaryKey' - description: Unique identifier of the repository - name: - description: Repository name. - type: string - example: Repo One - summary: - description: Summary describing the OSB Repository - type: string - example: Sample repository with experimental data - repository_type: - $ref: '#/components/schemas/RepositoryType' - description: Repository type - content_types: - description: List of Repository Content Types - type: string - auto_sync: - description: Auto sync of the resources - default: true - type: boolean - example: true - uri: - description: URI of the repository - type: string - example: 'https://github.com/OpenSourceBrain/OSBv2' - default_context: - description: The default branch to show for this repository - type: string - example: develop - user_id: - description: 'OSBRepository keycloak user id, will be automatically be set to the logged in user' - type: string - timestamp_created: - format: date-time - description: Date/time the Workspace is created - type: string - example: '2019-11-23T01:23:45.000Z' - timestamp_updated: - format: date-time - description: Date/time the Workspace is last updated - type: string - example: '2019-11-23T01:23:45.000Z' - tags: - type: array - items: - allOf: - - - $ref: '#/components/schemas/Tag' - - - x-secondary: osbrepository_tag WorkspaceResourceBase: required: - name @@ -1441,6 +1412,7 @@ components: - $ref: '#/components/schemas/WorkspaceResourceBase' - + x-tablename: Workspaceresource properties: origin: description: Origin data JSON formatted of the WorkspaceResource @@ -1453,7 +1425,6 @@ components: description: WorkspaceResource path where the resource will stored in the pvc. type: string example: repo/sample.nwb - x-tablename: Workspaceresource WorkspaceResource: description: Workspace Resource item of a Workspace allOf: @@ -1471,6 +1442,69 @@ components: description: WorkspaceResource path where the resource will stored in the pvc. type: string example: repo/sample.nwb + OSBRepositoryBase: + description: OSB Repository Base model + required: + - name + - repository_type + - content_types + - uri + type: object + properties: + id: + $ref: '#/components/schemas/PrimaryKey' + description: Unique identifier of the repository + name: + description: Repository name. + type: string + example: Repo One + summary: + description: Summary describing the OSB Repository + type: string + example: Sample repository with experimental data + repository_type: + $ref: '#/components/schemas/RepositoryType' + description: Repository type + content_types: + description: List of Repository Content Types + type: string + auto_sync: + description: Auto sync of the resources + default: true + type: boolean + example: true + uri: + description: URI of the repository + type: string + example: 'https://github.com/OpenSourceBrain/OSBv2' + default_context: + description: The default branch to show for this repository + type: string + example: develop + thumbnail: + $ref: '#/components/schemas/ImageType' + description: Resource OSB thumbnail. + user_id: + description: 'OSBRepository keycloak user id, will be automatically be set to the logged in user' + type: string + timestamp_created: + format: date-time + description: Date/time the Workspace is created + type: string + example: '2019-11-23T01:23:45.000Z' + timestamp_updated: + format: date-time + description: Date/time the Workspace is last updated + type: string + example: '2019-11-23T01:23:45.000Z' + tags: + type: array + items: + allOf: + - + $ref: '#/components/schemas/Tag' + - + x-secondary: osbrepository_tag securitySchemes: bearerAuth: scheme: bearer @@ -1489,4 +1523,4 @@ tags: description: Consumer - name: Client - description: Client + description: Client \ No newline at end of file diff --git a/applications/workspaces/server/workspaces/persistence/base_crud_persistence.py b/applications/workspaces/server/workspaces/persistence/base_crud_persistence.py index 3ebc13f2e..dfedd1a5a 100644 --- a/applications/workspaces/server/workspaces/persistence/base_crud_persistence.py +++ b/applications/workspaces/server/workspaces/persistence/base_crud_persistence.py @@ -172,7 +172,7 @@ def search(self, page=1, per_page=20, q=None, *args, **kwargs): sqs = self._get_qs(filters, q, *args, **kwargs) else: sqs = self._get_qs(*args, **kwargs) - objects = sqs.paginate(page, per_page, True) + objects = sqs.paginate(page=page, per_page=per_page) return objects diff --git a/applications/workspaces/server/workspaces/persistence/crud_persistence.py b/applications/workspaces/server/workspaces/persistence/crud_persistence.py index b956ce3b1..c8ac1031c 100644 --- a/applications/workspaces/server/workspaces/persistence/crud_persistence.py +++ b/applications/workspaces/server/workspaces/persistence/crud_persistence.py @@ -98,8 +98,10 @@ def filter_by_user_and_fieldkey(self, filter, user_id, show_all, q_base): return q_base def filter_by_tags(self, tags, q_base): - q_base = q_base.join(self.model.tags).filter( - func.lower(Tag.tag).in_(func.lower(t) for t in tags.split("+"))) + q_base = q_base.join(self.model.tags) + q_base = q_base.intersect( + *[q_base.filter(func.lower(Tag.tag) == func.lower(i_tag)) for i_tag in tags.split("+")]) + return q_base def filter_by_search_tags(self, filter, q_base, q_base_by_name_description): @@ -162,8 +164,10 @@ def filter_by_qfilters(self, filter, q_base): return q_base def filter_by_tags(self, tags, q_base): - q_base = q_base.join(self.model.tags).filter( - func.lower(Tag.tag).in_(func.lower(t) for t in tags.split("+"))) + q_base = q_base.join(self.model.tags) + q_base = q_base.intersect( + *[q_base.filter(func.lower(Tag.tag) == func.lower(i_tag)) for i_tag in tags.split("+")]) + return q_base def filter_by_search_tags(self, filter, q_base, q_base_by_q): @@ -225,7 +229,7 @@ def update_workspace_resources(self, workspace_id, resources): # not found --> create a new wsr filename = os.path.basename(resource) wsr = WorkspaceResourceEntity( - name=filename, + name=filename if not "index" in filename else os.path.basename(os.path.dirname(resource)), folder=resource, origin='{"path": "' + resource + '"}', status=ResourceStatus.P, # default status diff --git a/applications/workspaces/server/workspaces/persistence/models.py b/applications/workspaces/server/workspaces/persistence/models.py index 9f1065531..2eaae5d81 100644 --- a/applications/workspaces/server/workspaces/persistence/models.py +++ b/applications/workspaces/server/workspaces/persistence/models.py @@ -524,6 +524,7 @@ class OSBRepositoryEntityDict(_OSBRepositoryEntityDictBase, total=False): summary: typing.Optional[str] auto_sync: bool default_context: typing.Optional[str] + thumbnail: typing.Optional[str] user_id: typing.Optional[str] timestamp_created: typing.Optional[datetime.datetime] timestamp_updated: typing.Optional[datetime.datetime] @@ -546,6 +547,7 @@ class TOSBRepositoryEntity(typing.Protocol): auto_sync: Auto sync of the resources uri: URI of the repository default_context: The default branch to show for this repository + thumbnail: The thumbnail of the OSBRepositoryEntity. user_id: OSBRepository keycloak user id, will be automatically be set to the logged in user timestamp_created: Date/time the Workspace is created @@ -568,6 +570,7 @@ class TOSBRepositoryEntity(typing.Protocol): auto_sync: bool uri: str default_context: typing.Optional[str] + thumbnail: typing.Optional[str] user_id: typing.Optional[str] timestamp_created: typing.Optional[datetime.datetime] timestamp_updated: typing.Optional[datetime.datetime] @@ -583,6 +586,7 @@ def __init__( summary: typing.Optional[str] = None, auto_sync: bool = True, default_context: typing.Optional[str] = None, + thumbnail: typing.Optional[str] = None, user_id: typing.Optional[str] = None, timestamp_created: typing.Optional[datetime.datetime] = None, timestamp_updated: typing.Optional[datetime.datetime] = None, @@ -601,6 +605,7 @@ def __init__( auto_sync: Auto sync of the resources uri: URI of the repository default_context: The default branch to show for this repository + thumbnail: The thumbnail of the OSBRepositoryEntity. user_id: OSBRepository keycloak user id, will be automatically be set to the logged in user timestamp_created: Date/time the Workspace is created @@ -621,6 +626,7 @@ def from_dict( summary: typing.Optional[str] = None, auto_sync: bool = True, default_context: typing.Optional[str] = None, + thumbnail: typing.Optional[str] = None, user_id: typing.Optional[str] = None, timestamp_created: typing.Optional[datetime.datetime] = None, timestamp_updated: typing.Optional[datetime.datetime] = None, @@ -639,6 +645,7 @@ def from_dict( auto_sync: Auto sync of the resources uri: URI of the repository default_context: The default branch to show for this repository + thumbnail: The thumbnail of the OSBRepositoryEntity. user_id: OSBRepository keycloak user id, will be automatically be set to the logged in user timestamp_created: Date/time the Workspace is created diff --git a/applications/workspaces/server/workspaces/service/crud_service.py b/applications/workspaces/server/workspaces/service/crud_service.py index 2e1893160..997bbfc0f 100644 --- a/applications/workspaces/server/workspaces/service/crud_service.py +++ b/applications/workspaces/server/workspaces/service/crud_service.py @@ -2,6 +2,7 @@ import json import os import shutil +from typing import List from cloudharness.service.pvc import create_persistent_volume_claim, delete_persistent_volume_claim from cloudharness import log @@ -34,6 +35,7 @@ from workspaces.utils import dao_entity2dict, guess_resource_type from ..database import db + def rm_null_values(dikt): tmp = {} for k, v in dikt.items(): # remove null fields from dict @@ -45,9 +47,11 @@ def rm_null_values(dikt): class NotAuthorized(Exception): pass + class NotFoundException(Exception): pass + class NotAllowed(Exception): pass @@ -66,7 +70,7 @@ def get(self, user_id): email=user.get("email", ""), ) except Exception as e: - return User() + return User(id="0") class BaseModelService: @@ -81,10 +85,11 @@ class BaseModelService: calculated_fields = set() - def _calculated_fields_populate(self, obj): - if self.calculated_fields: - for fld in self.calculated_fields: - setattr(obj, fld, getattr(self, fld)(obj)) + @classmethod + def _calculated_fields_populate(cls, obj): + if cls.calculated_fields: + for fld in cls.calculated_fields: + setattr(obj, fld, getattr(cls, fld)(obj)) return obj def search(self, page=1, per_page=20, *args, **kwargs) -> Pagination: @@ -105,8 +110,6 @@ def search(self, page=1, per_page=20, *args, **kwargs) -> Pagination: objects = self.repository.search( page=page, per_page=per_page, *args, **kwargs) - for obj in objects.items: - self._calculated_fields_populate(obj) objects.items = [self.to_dto(obj) for obj in objects.items] return objects @@ -117,7 +120,7 @@ def to_dao(cls, d: dict): @classmethod def to_dto(cls, dao) -> Model: - return cls.dict_to_dto(dao_entity2dict(dao)) + return cls._calculated_fields_populate(cls.dict_to_dto(dao_entity2dict(dao))) @classmethod def dict_to_dto(cls, d) -> Model: @@ -129,19 +132,19 @@ def post(self, body): """Save an object to the repository.""" dao = self.to_dao(body) - return self.to_dto(self._calculated_fields_populate(self.repository.post(dao))) + return self.to_dto(self.repository.post(dao)) def get(self, id_): """Get an object from the repository.""" res = self.repository.get(id_) if not self.is_authorized(res): raise NotAuthorized() - return self.to_dto(self._calculated_fields_populate(res)) + return self.to_dto(res) def put(self, body, id_): """Update an object in the repository.""" dao = self.to_dao(body) - return self._calculated_fields_populate(self.repository.put(dao, id_)) + return self.to_dto(self.repository.put(dao, id_)) def delete(self, id_): """Delete an object from the repository.""" @@ -153,10 +156,11 @@ def __str__(self): def is_authorized(self, object): raise NotImplementedError( f"Authorization not implemented for {self.__class__.__name__}") - + + @classmethod @cache - def get_user_cached(self, user_id): - return self.user_service.get(user_id) + def get_user_cached(cls, user_id): + return cls.user_service.get(user_id) class WorkspaceService(BaseModelService): @@ -184,20 +188,27 @@ def check_max_num_workspaces_per_user(self, user_id=None): "limit exceeded" ) + @staticmethod + def get_workspace_workflows(ws_id) -> List[argo.Workflow]: + try: + return [w for w in argo.get_workflows(status="Running", limit=9999).items + if w.status == "Running" and w.raw.spec.templates[0].metadata.labels.get( + "workspace" + ).strip() == str(ws_id)] + except AttributeError: + return [] + @send_event(message_type="workspace", operation="create") def post(self, body): if 'user_id' not in body: body['user_id'] = keycloak_user_id() self.check_max_num_workspaces_per_user(body['user_id']) - workspace = super().post(body) - create_volume(name=self.get_pvc_name(workspace.id), size=self.get_workspace_volume_size(workspace)) - - + for workspace_resource in workspace.resources: WorkspaceresourceService.handle_resource_data(workspace_resource) @@ -222,7 +233,7 @@ def clone(self, workspace_id): if workspace is None: raise NotFoundException( f"Cannot clone workspace with id {workspace_id}: not found.") - + workspace.name = f"Clone of {workspace.name}" workspace.user_id = user_id workspace.publicable = False @@ -230,13 +241,10 @@ def clone(self, workspace_id): workspace.timestamp_created = None workspace.resources = [] - - - cloned = self.repository.post(workspace) create_volume(name=self.get_pvc_name(cloned.id), - size=self.get_workspace_volume_size(workspace)) + size=self.get_workspace_volume_size(workspace)) clone_workspaces_content(workspace_id, cloned.id) return cloned @@ -277,33 +285,34 @@ def search(self, page=1, per_page=20, *args, **kwargs) -> Pagination: page=page, per_page=per_page, *args, **kwargs) with db.session.no_autoflush: paged_results.items = [self.to_dto(w) for w in paged_results.items] - - for obj in paged_results.items: - self._calculated_fields_populate(obj) return paged_results @classmethod def to_dto(cls, workspace_entity: TWorkspaceEntity) -> Workspace: - workspace = cls.dict_to_dto(dao_entity2dict(workspace_entity)) - for resource in workspace_entity.resources: - resource.origin = json.loads(resource.origin) - workspace.resources = [WorkspaceresourceService.to_dto(r) for r in workspace_entity.resources] if workspace_entity.resources else [] + workspace = super().to_dto(workspace_entity) + + + workspace.resources = [WorkspaceresourceService.to_dto( + r) for r in workspace_entity.resources] if workspace_entity.resources else [] return workspace @classmethod def to_dao(cls, d: dict) -> TWorkspaceEntity: - + resources = d.get("resources", []) d["resources"] = [] workspace: TWorkspaceEntity = super().to_dao(d) workspace.tags = TagRepository().get_tags_daos(workspace.tags) - workspace.resources = [WorkspaceresourceService.to_dao(r) for r in resources] + workspace.resources = [ + WorkspaceresourceService.to_dao(r) for r in resources] return workspace def get(self, id_): workspace: Workspace = super().get(id_) + if not self.is_authorized(workspace): + raise NotAuthorized() if any(wr.status == ResourceStatus.P for wr in workspace.resources): fake_path = f"Importing resources" @@ -322,42 +331,38 @@ def get(self, id_): logger.debug( "Post get, check workflows for workspace %s....", workspace.id) try: - workflows = argo.get_workflows(status="Running", limit=9999) - if workflows and workflows.items: - for workflow in workflows.items: - try: - if workflow.status == "Running" and workflow.raw.spec.templates[0].metadata.labels.get( - "workspace" - ).strip() == str(workspace.id): - fake_path = f"Importing resources, progress {workflow.raw.status.progress}".replace( - "/", " of ") - workspace.resources.append( - WorkspaceResource.from_dict( - { - "id": -1, - "name": "Refreshing resources", - "origin": {"path": fake_path}, - "resource_type": ResourceType.U, - "workspace_id": workspace.id, - } + for workflow in self.get_workspace_workflows(workspace.id): + try: + fake_path = f"Importing resources, progress {workflow.raw.status.progress}".replace( + "/", " of ") + workspace.resources.append( + WorkspaceResource.from_dict( + { + "id": -1, + "name": "Refreshing resources", + "origin": {"path": fake_path}, + "resource_type": ResourceType.U, + "workspace_id": workspace.id, + } + ) ) - ) - break - except Exception as e: + + except Exception as e: logger.exception("Error checking workflow for workspace %s: %s", - workspace.id, workflow.name) + workspace.id, workflow.name) from pprint import pprint # pprint(workflow.raw) # probably not a workspace import workflow job --> skip it pass except Exception as e: logger.exception("Error checking workflows for workspace %s: %s", - workspace.id, e) + workspace.id, e) return workspace - def user(self, workspace): - return self.get_user_cached(workspace.user_id) + @classmethod + def user(cls, workspace): + return cls.get_user_cached(workspace.user_id) def delete(self, id): resource_repository = WorkspaceResourceRepository() @@ -418,13 +423,13 @@ def is_authorized(self, repository): # All osbrepositories are public return True - def content_types_list(self, osb_repository): + @classmethod + def content_types_list(cls, osb_repository): return osb_repository.content_types.split(",") - - def user(self, osbrepo): - return self.get_user_cached(osbrepo.user_id) - + @classmethod + def user(cls, osbrepo): + return cls.get_user_cached(osbrepo.user_id) class VolumestorageService(BaseModelService): @@ -446,7 +451,7 @@ def to_dao(cls, ws_dict: dict) -> TWorkspaceResourceEntity: if "origin" in ws_dict: wro_dao_dict = dict(ws_dict.get("origin")) ws_dict.update({"origin": json.dumps(wro_dao_dict)}) - if 'path' in ws_dict: + if 'folder' in ws_dict: # path is folder in the db for legacy reasons ws_dict['folder'] = ws_dict['path'] del ws_dict['path'] workspace_resource: TWorkspaceResourceEntity = super().to_dao(ws_dict) @@ -458,20 +463,36 @@ def to_dao(cls, ws_dict: dict) -> TWorkspaceResourceEntity: workspace_resource.folder = workspace_resource.name return workspace_resource + @classmethod + def to_dto(cls, resource) -> Model: + resource_dict = dao_entity2dict(resource) + if hasattr(resource, "origin") and resource.origin: + resource_dict['origin'] = json.loads(resource.origin) + if 'folder' in resource_dict and resource_dict['folder']: # Legacy folder/path handling + + # Legacy folder/path handling + if resource_dict['origin'].get("folder", None) is not None: + resource_dict['origin']["path"] = resource_dict['origin'].get("folder") + del resource_dict['origin']["folder"] + dto = cls._calculated_fields_populate(cls.dict_to_dto(resource_dict)) + if hasattr(resource, "folder"): + dto.path = resource.folder + return dto + @classmethod def dict_to_dto(cls, d) -> WorkspaceResource: - if 'origin' in d and isinstance(d['origin'], str): - d['origin'] = json.loads(d['origin']) workspace_resource: WorkspaceResource = super().dict_to_dto(d) + # Legacy folder/path handling if workspace_resource.path is None: + logger.debug( f"Pre Commit for workspace resource id: {workspace_resource.id} setting folder from file name") workspace_resource.path = workspace_resource.name - if guess_resource_type(workspace_resource.path) is None: + if guess_resource_type(workspace_resource.path) is None and workspace_resource.origin is not None: workspace_resource.path = os.path.join(workspace_resource.path, os.path.basename( workspace_resource.origin.path.split("?")[0])) return workspace_resource @@ -484,7 +505,7 @@ def post(self, body) -> WorkspaceResource: @staticmethod def handle_resource_data(workspace_resource: WorkspaceResource) -> WorkspaceResource: - if workspace_resource.status == "p" and workspace_resource.origin: + if workspace_resource.status == "p" and workspace_resource.origin: from workspaces.helpers.etl_helpers import copy_workspace_resource copy_workspace_resource(workspace_resource) diff --git a/applications/workspaces/server/workspaces/utils.py b/applications/workspaces/server/workspaces/utils.py index 6c06661e8..aaa6cec0c 100644 --- a/applications/workspaces/server/workspaces/utils.py +++ b/applications/workspaces/server/workspaces/utils.py @@ -26,33 +26,29 @@ def get_pvc_name(workspace_id): disallowed_class_types = ["BaseQuery", "type", "registry", "MetaData"] -name_mappings = {WorkspaceResourceEntity.__name__: {"folder": "path"}} -exclude = { WorkspaceEntity.__name__: {"resources"} } def dao_entity2dict(obj): + if hasattr(obj, "to_dict"): + result = obj.to_dict() + else: + disallowed_names = {name for name, value in getmembers( + type(obj)) if isinstance(value, FunctionType)} + + result = {} - disallowed_names = {name for name, value in getmembers( - type(obj)) if isinstance(value, FunctionType) or name in exclude.get(type(obj).__name__, ())} - - result = {} - - for name in dir(obj): + for name in dir(obj): - if name not in disallowed_names and name[0] != "_" and hasattr(obj, name): - val = getattr(obj, name) - if not ismethod(val): - clas = val.__class__.__name__ - if hasattr(val.__class__, "to_dict"): - val = val.__class__.to_dict(val) - if clas == "InstrumentedList": - val = list(dao_entity2dict(r) for r in val) - if clas not in disallowed_class_types: - result.update({name: val}) - if type(obj).__name__ in name_mappings: - for src, dest in name_mappings[type(obj).__name__].items(): - result[dest] = result[src] - del result[src] + if name not in disallowed_names and name[0] != "_" and hasattr(obj, name): + val = getattr(obj, name) + if not ismethod(val): + clas = val.__class__.__name__ + if hasattr(val.__class__, "to_dict"): + val = val.__class__.to_dict(val) + if clas == "InstrumentedList": + val = list(dao_entity2dict(r) for r in val) + if clas not in disallowed_class_types: + result.update({name: val}) return result @@ -61,7 +57,7 @@ def guess_resource_type(resource_path): extension = resource_path.split(".")[-1] if extension == "nwb": return ResourceType.E - elif extension == "np": + elif extension == "npjson": return ResourceType.M elif extension == "ipynb": return ResourceType.G diff --git a/applications/workspaces/tasks/github-copy/run.sh b/applications/workspaces/tasks/github-copy/run.sh old mode 100644 new mode 100755 index b47e15b58..826ddbe19 --- a/applications/workspaces/tasks/github-copy/run.sh +++ b/applications/workspaces/tasks/github-copy/run.sh @@ -5,33 +5,112 @@ set -e # remove the pvc from the path (if it has one) # and append the folder export download_path=`echo $shared_directory | cut -d ":" -f 2`/"${folder}" +echo "Download path is ${download_path}" + +timestamp="$(date +"%Y%m%d%H%M%S-osbv2")" mkdir -p "${download_path}" cd "${download_path}" -echo GitHub copy "$paths" to "$download_path" +echo "GitHub copy $paths to $download_path" ls -la -echo "Fix permissions" + +echo "Git: fix permissions" git config --global --add safe.directory "$download_path" -# do the next command only if download path does not exist + +# do the next command only if download path does not exist (initial clone) if [ ! -d "${download_path}/.git" ]; then - echo Cloning $branch + echo "Git: cloning new repository branch: $branch" git clone -n "${url}" --branch $branch "${download_path}" -fi -# check is paths has a value. In that case, checkout everything -if [ -z "$paths" ]; then - echo "Checking out everything" - git checkout HEAD - exit 0 + # configure OSB user for this clone + echo "Git: setting user/email to OSBv2 for new repo" + git config user.name "OSBv2" + git config user.email "info@opensourcebrain.org" + + # check is paths has a value, otherwise check out everything + if [ -z "$paths" ]; then + echo "Git: checking out everything" + git checkout HEAD + else + # Split paths by ## and checkout each path + IFS='\' + for path in $paths; do + echo "Git: checking out $path" + git checkout HEAD "$path" + done + fi + + # create new branch, commit there + echo "Git: checking out new branch and committing" + git checkout -b "$timestamp" + echo "checked out branch $timestamp" + + # unset username: ensure that user has to set it manually if they + # do anything with git in the repo + echo "Git: unsetting user/email for repo" + git config --unset user.name + git config --unset user.email + +# on subsequent updates from same repo else - # Split paths by ## and checkout each path - IFS='\' - for path in $paths; do - echo "Checking out $path" - git checkout HEAD "$path" - done + echo "Updating existing repository" + # configure OSB user + echo "Git: saving current git user/email and setting to OSBv2" + gituser="$(git config user.name)" || true + git config user.name "OSBv2" + gitemail="$(git config user.email)" || true + git config user.email "info@opensourcebrain.org" + + # save state, but do not error if there's nothing to save + echo "Git: saving current state" + git diff-index --quiet HEAD || git commit -a -m "osbv2: saving state at $timestamp" || true + + # do not assume user has left the default remote name as "origin" + echo "Git: adding and fetching from temporary remote" + git remote add "$timestamp" "${url}" + git fetch "$timestamp" "$branch" + + if [ -z "$paths" ]; then + echo "Git: checking out everything" + git checkout $timestamp/$branch -- * + else + # Split paths by ## and checkout each path + IFS='\' + for path in $paths; do + echo "Git: checking out $path" + git checkout $timestamp/$branch -- "$path" + done + fi + # save new state but do not error if nothing changed + echo "Git: saving new state" + git diff-index --quiet HEAD || git commit -a -m "osbv2: updated state at $timestamp" || true + + echo "Git: removing temporary remote" + # remove temporary remote + git remote remove "$timestamp" + + # reset user to whatever it was before + # setting to "" is not the same as unsetting + # git still adds an entry to the config file + echo "Git: resetting user/email" + if [ -z "$gituser" ] + then + git config --unset user.name + else + git config user.name "$gituser" + fi + + if [ -z "$gitemail" ] + then + git config --unset user.email + else + git config user.name "$gitemail" + fi fi + + # fix permissions +echo "Fixing permissions on download path" chown -R 1000:100 "${download_path}" -ls -la \ No newline at end of file +ls -la diff --git a/applications/workspaces/tasks/scan-workspace/main.py b/applications/workspaces/tasks/scan-workspace/main.py index eb0365c17..74a46ef65 100644 --- a/applications/workspaces/tasks/scan-workspace/main.py +++ b/applications/workspaces/tasks/scan-workspace/main.py @@ -34,8 +34,9 @@ for file in files: full_file_name = os.path.join(root, file) filename, file_extension = os.path.splitext(full_file_name) - if file_extension.lower() in (".nwb", ".netpyne", ".ipynb"): + if file_extension.lower() in (".nwb", ".npjson", ".ipynb"): logger.info(f"Found resource: {full_file_name}") + resources.append(full_file_name) payload = { diff --git a/deployment-configuration/helm/templates/auto-gatekeepers.yaml b/deployment-configuration/helm/templates/auto-gatekeepers.yaml new file mode 100644 index 000000000..66465bb89 --- /dev/null +++ b/deployment-configuration/helm/templates/auto-gatekeepers.yaml @@ -0,0 +1,174 @@ +{{/* Secured Services/Deployments */}} +{{- define "deploy_utils.securedservice" }} +{{- $tls := not (not .root.Values.tls) }} +apiVersion: v1 +kind: ConfigMap +metadata: + name: "{{ .app.harness.service.name }}-gk" + labels: + app: "{{ .app.harness.service.name }}-gk" +data: + proxy.yml: |- + verbose: {{ .root.Values.debug }} + discovery-url: {{ ternary "https" "http" $tls}}://{{ .root.Values.apps.accounts.harness.subdomain }}.{{ .root.Values.domain }}/auth/realms/{{ .root.Values.namespace }} + client-id: {{ .root.Values.apps.accounts.webclient.id | quote }} + client-secret: {{ .root.Values.apps.accounts.webclient.secret }} + secure-cookie: {{ $tls }} + forbidden-page: /templates/access-denied.html.tmpl + enable-default-deny: {{ eq (.app.harness.secured | toString) "true" }} + listen: 0.0.0.0:8080 + enable-refresh-tokens: true + server-write-timeout: {{ .app.harness.proxy.timeout.send | default .root.Values.proxy.timeout.send | default 180 }}s + upstream-timeout: {{ .app.harness.proxy.timeout.read | default .root.Values.proxy.timeout.read | default 180 }}s + upstream-response-header-timeout: {{ .app.harness.proxy.timeout.read | default .root.Values.proxy.timeout.read | default 180 }}s + upstream-expect-continue-timeout: {{ .app.harness.proxy.timeout.read | default .root.Values.proxy.timeout.read | default 180 }}s + server-read-timeout: {{ .app.harness.proxy.timeout.read | default .root.Values.proxy.timeout.read | default 180 }}s + upstream-keepalive-timeout: {{ .app.harness.proxy.timeout.keepalive | default .root.Values.proxy.timeout.keepalive | default 180 }}s + http-only-cookie: false + tls-cert: + tls-private-key: + redirection-url: {{ ternary "https" "http" $tls }}://{{ .app.harness.subdomain }}.{{ .root.Values.domain }} + encryption-key: AgXa7xRcoClDEU0ZDSH4X0XhL5Qy2Z2j + upstream-url: http://{{ .app.harness.service.name }}.{{ .app.namespace | default .root.Release.Namespace }}:{{ .app.harness.service.port | default 80}} + {{ if .app.harness.secured }} + {{ with .app.harness.uri_role_mapping }} + resources: + {{. | toYaml | nindent 4 }} + {{- end }} + {{- end }} + {{ if or .root.Values.local (not $tls) }} + skip-openid-provider-tls-verify: true + skip-upstream-tls-verify: true + {{- end }} + cacert.crt: {{ .files.Get "resources/certs/cacert.crt" | quote }} + access-denied.html.tmpl: |- + + + + + 403 - Access Forbidden + + + + + + +
+
+
+
+

Oops!

+

403 Permission Denied

+
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+ + + +--- +apiVersion: v1 +kind: Service +metadata: + name: "{{ .app.harness.service.name }}-gk" + labels: + app: "{{ .app.harness.service.name }}-gk" +spec: + ports: + - name: http + port: 8080 + selector: + app: "{{ .app.harness.service.name }}-gk" + type: ClusterIP +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + name: "{{ .app.harness.service.name }}-gk" + labels: + app: "{{ .app.harness.service.name }}-gk" + +spec: + replicas: 1 + selector: + matchLabels: + app: "{{ .app.harness.service.name }}-gk" + template: + metadata: + annotations: + checksum/config: {{ .app.harness.uri_role_mapping | toString | sha256sum }} + labels: + app: "{{ .app.harness.service.name }}-gk" + spec: +{{ include "deploy_utils.etcHosts" .root | indent 6 }} + containers: + - name: {{ .app.harness.service.name | quote }} + image: "quay.io/gogatekeeper/gatekeeper:1.3.8" + imagePullPolicy: IfNotPresent + {{ if .root.Values.local }} + securityContext: + allowPrivilegeEscalation: false + runAsUser: 0 + {{- end }} + env: + - name: PROXY_CONFIG_FILE + value: /opt/proxy.yml + volumeMounts: + - name: "{{ .app.harness.service.name }}-gk-proxy-config" + mountPath: /opt/proxy.yml + subPath: proxy.yml + - name: "{{ .app.harness.service.name }}-gk-proxy-config" + mountPath: /etc/pki/ca-trust/source/anchors/cacert.crt + subPath: cacert.crt + - name: "{{ .app.harness.service.name }}-gk-proxy-config" + mountPath: /templates/access-denied.html.tmpl + subPath: access-denied.html.tmpl + ports: + - name: http + containerPort: 8080 + - name: https + containerPort: 8443 + resources: + requests: + memory: "32Mi" + cpu: "5m" + limits: + memory: "64Mi" + cpu: "100m" + volumes: + - name: "{{ .app.harness.service.name }}-gk-proxy-config" + configMap: + name: "{{ .app.harness.service.name }}-gk" +--- +{{- end }} +{{- if .Values.secured_gatekeepers }} +{{ $files := .Files }} +{{- range $app := .Values.apps }} + {{- if and (hasKey $app "port") ($app.harness.secured) }} +--- + {{ include "deploy_utils.securedservice" (dict "root" $ "app" $app "files" $files) }} + {{- end }} + {{- range $subapp := $app }} + {{- if contains "map" (typeOf $subapp) }} + {{- if and (hasKey $subapp "harness.port") (hasKey $subapp "harness.secured") }} + {{- if $subapp.harness.secured }} +--- + {{ include "deploy_utils.securedservice" (dict "root" $ "app" $subapp "files" $files) }} + {{- end }} + {{- end }} + {{- end }} + {{- end }} + {{- end }} +{{- end }} diff --git a/deployment/codefresh-dev.yaml b/deployment/codefresh-dev.yaml index b7533c9b9..0d45004ea 100644 --- a/deployment/codefresh-dev.yaml +++ b/deployment/codefresh-dev.yaml @@ -26,6 +26,12 @@ steps: revision: '${{CLOUDHARNESS_BRANCH}}' working_directory: . git: github + - title: Cloning nwb-explorer.git repository... + type: git-clone + repo: https://github.com/MetaCell/nwb-explorer.git + revision: development + working_directory: applications/nwb-explorer/dependencies/ + git: github prepare_deployment: title: Prepare helm chart image: python:3.9.10 @@ -702,6 +708,16 @@ steps: commands: - echo $APP_NAME scale: + volumemanager_api_test: + title: volumemanager api test + volumes: + - '${{CF_REPO_NAME}}/cloud-harness/applications/volumemanager:/home/test' + - '${{CF_REPO_NAME}}/deployment/helm/values.yaml:/opt/cloudharness/resources/allvalues.yaml' + environment: + - APP_URL=https://volumemanager.${{DOMAIN}}/api + commands: + - st --pre-run cloudharness_test.apitest_init run api/openapi.yaml --base-url + https://volumemanager.${{DOMAIN}}/api -c all common_api_test: title: common api test volumes: diff --git a/libraries/client/cached_info/dandishowcase_info.json b/libraries/client/cached_info/dandishowcase_info.json new file mode 100644 index 000000000..e58b5ce25 --- /dev/null +++ b/libraries/client/cached_info/dandishowcase_info.json @@ -0,0 +1,9695 @@ +[ + { + "citation": "Senzai, Yuta; Fernandez-Ruiz, Antonio; Buzs\u00e1ki, Gy\u00f6rgy (2023) Physiological Properties and Behavioral Correlates of Hippocampal Granule Cells and Mossy Cells (Version draft) [Data set]. DANDI archive. https://dandiarchive.org/dandiset/000003/draft", + "dandiset_schemaver": "0.6.0", + "data_type": "Neurodata Without Borders (NWB)", + "doi_link": "https://doi.org/10.1016/j.neuron.2016.12.011", + "file_0": "https://api.dandiarchive.org/api/assets/4153ba8e-5f24-41de-8b9b-2523f0d44821/download/", + "file_1": "https://api.dandiarchive.org/api/assets/cbbaf9d5-cb17-46af-9bc7-2109eeb720b0/download/", + "file_size_0": "5266942198.0", + "file_size_1": "6668136760.0", + "identifier": "DANDI:000003", + "keywords": "['cell types', 'current source density', 'laminar recordings', 'oscillations', 'mossy cells', 'granule cells', 'optogenetics']", + "name": "Physiological Properties and Behavioral Correlates of Hippocampal Granule Cells and Mossy Cells", + "num_bytes": "2559248010229", + "num_files": "101", + "numb_subjects": "16.0", + "nwb_version": "", + "nwbe_compatibility_0": "NI", + "nwbe_compatibility_1": "NI", + "parent_folder_0": "sub-YutaMouse37", + "parent_folder_1": "sub-YutaMouse42", + "schemaKey": "Dandiset", + "species": "House mouse", + "url": "https://dandiarchive.org/dandiset/000003/draft", + "validation_summary": "NULL_FILE_LIMIT", + "variableMeasured": "['DecompositionSeries', 'LFP', 'Units', 'Position', 'ElectricalSeries']", + "version": "draft" + }, + { + "citation": "Chandravadia, Nand; Liang, Dehua; Schjetnan, Andrea Gomez Palacio; Carlson, April; Faraut, Mailys; Chung, Jeffrey M.; Reed, Chrystal M.; Dichter, Ben; Maoz, Uri; Kalia, Suneil K.; Valiante, Taufik A.; Mamelak, Adam N.; Rutishauser, Ueli (2023) A NWB-based dataset and processing pipeline of human single-neuron activity during a declarative memory task (Version draft) [Data set]. 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For more details, see the reference below." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2730", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181170, + "object_name": "2730_2" + }, + { + "object_id": 181171, + "object_name": "2730_1" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 302 + }, + "ver_date": "2022-05-27T18:15:00.95", + "ver_number": 50 + }, + "2733": { + "class_id": 19, + "created": "2001-05-18T15:26:52", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 2733, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33739, + "object_name": "Davison, Andrew [Andrew.Davison at iaf.cnrs-gif.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 4675, + "object_name": "Bhalla US, Bower JM (1993)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "2733", + "name": "ModelView" + } + ], + "default": "2733" + }, + "name": "Olfactory Mitral Cell (Bhalla, Bower 1993)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a conversion to NEURON of the mitral cell model described in Bhalla \r\nand Bower (1993). \r\nThe original model was written in GENESIS and is available by joining BABEL, the GENESIS users' group here http://www.genesis-sim.org/GENESIS/babel.html" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2733", + "simPFid": { + "attr_id": 488, + "value": 303 + }, + "ver_date": "2022-05-27T18:16:56.667", + "ver_number": 29 + }, + "2796": { + "class_id": 19, + "created": "2001-06-05T17:25:25", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 2796, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 26749, + "object_name": "Migliore M, Hoffman DA, Magee JC, Johnston D (1999)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "CA1 pyramidal neuron (Migliore et al 1999)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Hippocampal CA1 pyramidal neuron model from the paper \r\nM.Migliore, D.A Hoffman, J.C. 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Instructions are provided in the below README file.Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2796", + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181645, + "object_name": "2796_2" + }, + { + "object_id": 181646, + "object_name": "2796_1" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 165 + }, + "ver_date": "2015-01-02T22:00:55", + "ver_number": 15 + }, + "2798": { + "class_id": 19, + "created": "2001-06-05T17:29:19", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 2798, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3542, + "object_name": "Pattern Recognition" + }, + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 83539, + "object_name": "Attractor Neural Network" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 4655, + "object_name": "Hopfield JJ, Brody CD (2001)" + }, + { + "object_id": 4656, + "object_name": "Hopfield JJ, Brody CD (2000)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "2798_1", + "name": "before training" + }, + { + "id": "2798_2", + "name": "after training" + }, + { + "id": "2798_3", + "name": "random activation" + } + ], + "default": "2798_1" + }, + "name": "Hopfield and Brody model (Hopfield, Brody 2000)", + "notes": { + "attr_id": 24, + "value": "NEURON implementation of the Hopfield and Brody model from the papers:\r\nJJ Hopfield and CD Brody (2000)\r\nJJ Hopfield and CD Brody (2001). Instructions are provided in the below readme.txt file." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2798", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115949, + "object_name": "Olfactory cortex" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181642, + "object_name": "2798_1" + }, + { + "object_id": 181643, + "object_name": "2798_3" + }, + { + "object_id": 181644, + "object_name": "2798_2" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 263 + }, + "ver_date": "2018-10-17T14:23:47.05", + "ver_number": 13 + }, + "2937": { + "class_id": 19, + "created": "2001-06-19T18:03:07", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 2937, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 4236, + "object_name": "Migliore M (1996)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "CA1 Pyramidal Neuron: slow Na+ inactivation (Migliore 1996)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Model files from the paper: \r\nM. Migliore, Modeling the attenuation and failure of action potentials in \r\nthe dendrites of hippocampal neurons, Biophys. J. 71:2394-403 (1996). Please see the below readme file for installation and use instructions. Contact michele.migliore@pa.ibf.cnr.it\r\n if you have any questions about the implementation of the model." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2937", + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181303, + "object_name": "2937_2" + }, + { + "object_id": 181304, + "object_name": "2937_3" + }, + { + "object_id": 181305, + "object_name": "2937_1" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 162 + }, + "ver_date": "2015-01-02T22:06:31", + "ver_number": 13 + }, + "3167": { + "class_id": 19, + "created": "2001-08-27T10:14:31", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 3167, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 3891, + "object_name": "Migliore M, Messineo L, Cardaci M, Ayala GF (2001)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "3167", + "name": "ModelView" + } + ], + "default": "3167" + }, + "name": "Estimation and Production of Time Intervals (Migliore et al 2001)", + "notes": { + "attr_id": 24, + "value": "NEURON model files from the paper \r\nM. Migliore, L. Messineo, M. Cardaci, G.F. Ayala, \r\nQuantitative modeling of perception and production of time intervals, J.Neurophysiol. 86, 2754-2760 (2001). Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/3167", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 239 + }, + "ver_date": "2015-07-26T07:44:54.557", + "ver_number": 12 + }, + "3263": { + "class_id": 19, + "created": "2001-09-20T14:50:19", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 3263, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 4307, + "object_name": "Migliore M, Cook EP, Jaffe DB, Turner DA, Johnston D (1995)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "3263_1", + "name": "burst" + }, + { + "id": "3263_2", + "name": "no-burst short" + }, + { + "id": "3263_3", + "name": "no-burst long" + } + ], + "default": "3263_1" + }, + "name": "CA3 Pyramidal Neuron (Migliore et al 1995)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Model files from the paper:\r\nM. Migliore, E. Cook, D.B. Jaffe, D.A. Turner and D. Johnston, Computer\r\nsimulations of morphologically reconstructed CA3 hippocampal neurons, J.\r\nNeurophysiol. 73, 1157-1168 (1995).\r\n\r\nDemonstrates how the same cell could be bursting or non bursting according to the Ca-independent conductance densities. Includes calculation of intracellular Calcium. Instructions are provided in the below README file. Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/3263", + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181579, + "object_name": "3263_1" + }, + { + "object_id": 181581, + "object_name": "3263_3" + }, + { + "object_id": 181582, + "object_name": "3263_2" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 180 + }, + "ver_date": "2015-01-02T22:07:07", + "ver_number": 14 + }, + "3264": { + "class_id": 19, + "created": "2001-09-20T17:17:59", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 3264, + "implemented_by": { + "attr_id": 299, + "value": [ + { + 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description of short-term plasticity at \r\n excitatory synapses in layer 2/3 of rat primary visual cortex.\r\n Journal of Neuroscience 17:7926-7940, 1997.\r\n\r\nContact ted.carnevale@yale.edu if you have questions \r\nabout this implementation of the model." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/3264", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 266986, + "object_name": "Visual cortex" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181523, + "object_name": "3264_2" + }, + { + "object_id": 181524, + "object_name": "3264_3" + }, + { + "object_id": 181525, + "object_name": "3264_1" + }, + { + "object_id": 181526, + "object_name": "3264_6" + }, + { + "object_id": 181527, + "object_name": "3264_7" + }, + { + "object_id": 181529, + "object_name": "3264_8" + } + ] + }, + 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Perform experiments from the book 'Electrophysiology of the Neuron, A Companion to Shepherd's Neurobiology, An Interactive Tutorial' by John Huguenard & David McCormick, Oxford University Press 1997, or design your own one or two cell simulation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/3808", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 288 + }, + "ver_date": "2023-01-30T09:58:07.433", + "ver_number": 21 + }, + "3810": { + "class_id": 19, + "created": "2002-03-05T15:20:23", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 3810, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33766, + "object_name": "MacIntyre, CC [ccm4 at case.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 84598, + "object_name": "Extracellular Fields" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 3874, + "object_name": "McIntyre CC, Richardson AG, Grill WM (2002)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "3810", + "name": "ModelView" + } + ], + "default": "3810" + }, + "name": "Spinal Motor Neuron (McIntyre et al 2002)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 276, + "object_name": "Spinal cord lumbar motor neuron alpha ACh cell" + }, + { + "object_id": 52425, + "object_name": "Myelinated neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Simulation of peripheral nervous system (PNS) mylelinated axon.\r\n\r\nThis model is described in detail in:\r\n\r\nMcIntyre CC, Richardson AG, and Grill WM.(2002)" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/3810", + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181797, + "object_name": "3810" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 490 + }, + "ver_date": "2018-09-20T17:27:37.327", + "ver_number": 12 + }, + "3812": { + "class_id": 19, + "created": "2002-03-06T09:23:09", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 3812, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33805, + "object_name": "Khana, Oz" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3542, + "object_name": "Pattern Recognition" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 4081, + "object_name": "Anderson JC, Binzegger T, Kahana O, Martin KA, Segev I (1999)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "3812", + "name": "ModelView" + } + ], + "default": "3812" + }, + "name": "Visual Cortex Neurons: Dendritic study (Anderson et al 1999)", + "notes": { + "attr_id": 24, + "value": "Neuron mod and hoc files for the paper: Anderson, J.C. 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Further information and reprint requests are available from Dr T.H. 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Neurosci. 16:5-13 (2004).\r\nThe model demonstrates how the dendritic Ih in pyramidal neurons could selectively suppress AP generation for a volley of excitatory afferents \r\nwhen they are asynchronously and distally activated.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/32992", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181245, + "object_name": "32992" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 172 + }, + "ver_date": "2013-12-10T14:38:15", + "ver_number": 10 + }, + "33728": { + "class_id": 19, + "created": "2003-11-14T11:26:50", + "gitrepo": false, + "id": 33728, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33735, + "object_name": "Carnevale, Ted [Ted.Carnevale at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 33729, + "object_name": "Friesen WO, Friesen JA (1994)" + }, + { + "object_id": 33732, + "object_name": "SZEKELY G (1965)" + }, + { + "object_id": 138204, + "object_name": "Kling U, Sz\u00e9kely G (1968)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Irregular oscillations produced by cyclic recurrent inhibition (Friesen, Friesen 1994)", + "notes": { + "attr_id": 24, + "value": "Model of recurrent cyclic inhibition as described on p.119 of Friesen and Friesen (1994), which was slightly modified from Szekely's model (1965) of a network for producing alternating limb movements." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/33728", + "simPFid": { + "attr_id": 488, + "value": 267 + }, + "ver_date": "2011-03-22T15:34:28", + "ver_number": 15 + }, + "33975": { + "class_id": 19, + "created": "2003-11-24T11:52:41", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 33975, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33736, + "object_name": "Hines, Michael [Michael.Hines at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 49249, + "object_name": "Lytton WW, Hines ML (2005)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "33975_1", + "name": "Fig 1" + }, + { + "id": "33975_2", + "name": "Fig 5 Ring Performance" + } + ], + "default": "33975_1" + }, + "name": "Local variable time step method (Lytton, Hines 2005)", + "notes": { + "attr_id": 24, + "value": "The local variable time-step method utilizes separate variable step integrators for individual neurons in the network. It is most suitable for medium size networks in which average synaptic input intervals to a single cell are much greater than a fixed step dt." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/33975", + "other_concept": { + "attr_id": 306, + "value": "methods" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181680, + "object_name": "33975_2" + }, + { + "object_id": 181795, + "object_name": "33975_1" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 274 + }, + "ver_date": "2015-01-02T22:30:35", + "ver_number": 13 + }, + "33986": { + "class_id": 19, + "created": "2003-12-05T15:21:26", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "id": 33986, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33985, + "object_name": "Baxter, Douglas" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 33987, + "object_name": "Baxter DA, Canavier CC, Clark JW, Byrne JH (1999)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "Serotonergic modulation of Aplysia sensory neurons (Baxter et al 1999)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52413, + "object_name": "Aplysia sensory neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 239, + "object_name": "Serotonin" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The present study investigated how the modulation of these currents altered the spike duration\r\nand excitability of sensory neurons and examined the relative contributions\r\nof PKA- and PKC-mediated effects to the actions of 5-HT. A\r\nHodgkin-Huxley type model was developed that described the ionic\r\nconductances in the somata of sensory neurons. The descriptions of\r\nthese currents and their modulation were based largely on voltageclamp\r\ndata from sensory neurons. Simulations were preformed with\r\nthe program SNNAP (Simulator for Neural Networks and Action\r\nPotentials). The model was sufficient to replicate empirical data that\r\ndescribes the membrane currents, action potential waveform and\r\nexcitability as well as their modulation by application of 5-HT,\r\nincreased levels of adenosine cyclic monophosphate or application of\r\nactive phorbol esters. The results provide\r\nseveral predictions that warrant additional experimental investigation\r\nand illustrate the importance of considering indirect as well as direct\r\neffects of modulatory agents on the modulation of membrane currents. See paper for more details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/33986", + "ver_date": "2007-04-13T15:10:02", + "ver_number": 9 + }, + "34163": { + "class_id": 19, + "created": "2003-12-05T17:22:35", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "id": 34163, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 34164, + "object_name": "Cai, Yidao" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 34126, + "object_name": "Cai Y, Baxter DA, Crow T (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "Enhanced Excitability in Hermissenda: modulation by 5-HT (Cai et al 2003)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114290, + "object_name": "Hermissenda photoreceptor Type B" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 239, + "object_name": "Serotonin" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Serotonin (5-HT) applied to the exposed but otherwise intact nervous system results in enhanced excitability\r\nof Hermissenda type-B photoreceptors. Several ion currents in the type-B photoreceptors are modulated\r\nby 5-HT, including the A-type K+ current (IK,A), sustained Ca2+ current (ICa,S), Ca-dependent K+ current (IK,Ca),\r\nand a hyperpolarization-activated inward rectifier current (Ih). In this study,we developed a computational model that\r\nreproduces physiological characteristics of type B photoreceptors, e.g. resting membrane potential, dark-adapted\r\nspike activity, spike width, and the amplitude difference between somatic and axonal spikes. We then used the\r\nmodel to investigate the contribution of different ion currents modulated by 5-HT to the magnitudes of enhanced\r\nexcitability produced by 5-HT. See paper for results and more details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/34163", + "ver_date": "2008-06-18T14:41:03", + "ver_number": 15 + }, + "34168": { + "class_id": 19, + "created": "2003-12-07T11:11:58", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 34168, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 34607, + "object_name": "Phares, Gregg A [gregg.a.phares at uth.tmc.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 22010, + "object_name": "Facilitation" + }, + { + "object_id": 22012, + "object_name": "Depression" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 34167, + "object_name": "Phares GA, Antzoulatos EG, Baxter DA, Byrne JH (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "Burst induced synaptic plasticity in Apysia sensorimotor neurons (Phares et al 2003)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52413, + "object_name": "Aplysia sensory neuron" + }, + { + "object_id": 52415, + "object_name": "Aplysia motor neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The Aplysia sensorimotor synapse is a key site of plasticity for several simple forms of learning. Intracellular stimulation of sensory neurons to fire a burst of action potentials at 10 Hz for 1 sec led to significant\r\nhomosynaptic depression of postsynaptic responses. During the burst, the steady-state depressed phase of the postsynaptic response, which was only 20% of the initial EPSP of the burst, still contributed to firing the motor neuron. To explore the functional contribution of transient homosynaptic depression to the response of the motor neuron, computer simulations of the sensorimotor synapse with and without depression were compared. Depression allowed the motor\r\nneuron to produce graded responses over a wide range of presynaptic input strength. \r\nThus, synaptic depression increased the dynamic range of the sensorimotor synapse and can, in principle, have a profound effect on\r\ninformation processing. Please see paper for results and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/34168", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115956, + "object_name": "Aplysia" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 249921, + "object_name": "Aplysia" + } + ] + }, + "ver_date": "2019-02-26T10:06:02.16", + "ver_number": 26 + }, + "34558": { + "class_id": 19, + "created": "2003-12-08T10:03:42", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 34558, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 34559, + "object_name": "Komendantov, Alexander O [akomenda at tulane.edu]" + }, + { + "object_id": 34561, + "object_name": "Kononenko, Nikolai I [nik137 at lamar.colostate.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 34510, + "object_name": "Komendantov AO, Kononenko NI (2000)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "Caffeine-induced electrical oscillations in Aplysia neurons (Komendantov, Kononenko 2000)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88109, + "object_name": "Aplysia cultured neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "It has been found that in cultured Aplysia neurons bath applications of 40 mM cafffeine evokes oscillations of the membrane potential with about a 40 mV amplitude with a frequency of 0.2 to 0.5 Hz. The most probable mechanism of these caffeine-induced oscillations is inhibition of voltage-activated outward potassium current and, as can be seen from our mathematical modeling, slowdown of inactivation of inward sodium current. It seems likely that these oscillations have a purely membrane origin. Please see paper for results and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/34558", + "ver_date": "2007-04-13T14:14:18", + "ver_number": 14 + }, + "34560": { + "class_id": 19, + "created": "2003-12-08T11:13:27", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 34560, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33985, + "object_name": "Baxter, Douglas" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 34172, + "object_name": "Pelz C, Jander J, Rosenboom H, Hammer M, Menzel R (1999)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "I A in Kenyon cells resemble Shaker currents (Pelz et al 1999)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114295, + "object_name": "Honeybee kenyon cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Cultured Kenyon cells from the mushroom body of the honeybee,\r\nApis mellifera, show a voltage-gated, fast transient K1 current that is\r\nsensitive to 4-aminopyridine, an A current. The kinetic properties of\r\nthis A current and its modulation by extracellular K1 ions were\r\ninvestigated in vitro with the whole cell patch-clamp technique. The\r\nA current was isolated from other voltage-gated currents either pharmacologically\r\nor with suitable voltage-clamp protocols. Hodgkin- and\r\nHuxley-style mathematical equations were used for the description of\r\nthis current and for the simulation of action potentials in a Kenyon cell\r\nmodel. The data of the A\r\ncurrent were incorporated into a reduced computational model of the\r\nvoltage-gated currents of Kenyon cells. In addition, the model contained\r\na delayed rectifier K current, a Na current, and a leakage\r\ncurrent. The model reproduces several experimental features and makes \r\npredictions. See paper for details and results." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/34560", + "ver_date": "2008-06-18T14:47:33", + "ver_number": 10 + }, + "34606": { + "class_id": 19, + "created": "2003-12-08T17:59:15", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 34606, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33985, + "object_name": "Baxter, Douglas" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 22010, + "object_name": "Facilitation" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 34177, + "object_name": "White JA, Ziv I, Cleary LJ, Baxter DA, Byrne JH (1993)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "A network model of tail withdrawal in Aplysia (White et al 1993)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52413, + "object_name": "Aplysia sensory neuron" + }, + { + "object_id": 52414, + "object_name": "Aplysia interneuron" + }, + { + "object_id": 52415, + "object_name": "Aplysia motor neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The contributions of monosynaptic and polysynaptic circuitry to the tail-withdrawal reflex in the marine mollusk Aplysia californica were assessed by the use of physiologically based neural network models. Effects of monosynaptic circuitry were examined by the use of a two-layer network model with four sensory neurons in the input layer and one motor neuron in the output layer. Results of these simulations indicated that the monosynaptic circuit could not account fully for long-duration responses of tail motor neurons elicited by tail stimulation.\r\nA three-layer network model was constructed by interposing a layer of two excitatory interneurons between the input and output layers of the two-layer network model. The three-layer model could account for long-duration responses in motor neurons. Sensory neurons are a known site of plasticity in Aplysia. Synaptic plasticity at more than one locus modified dramatically the input-output relationship of the three-layer network model. This feature gave the model redundancy in its plastic properties and points to the possibility of distributed memory in the circuitry mediating withdrawal reflexes in Aplysia.\r\nPlease see paper for more results and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/34606", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115956, + "object_name": "Aplysia" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 249921, + "object_name": "Aplysia" + } + ] + }, + "ver_date": "2019-02-26T10:07:40.943", + "ver_number": 13 + }, + "35358": { + "biosimulations": { + "value": "modeldb-35358" + }, + "class_id": 19, + "created": "2004-02-09T17:12:24", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 35358, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33738, + "object_name": "Lytton, William [bill.lytton at downstate.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 5225, + "object_name": "Pinsky PF, Rinzel J (1994)" + }, + { + "object_id": 29029, + "object_name": "Pinsky PF, Rinzel J (1995)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "CA3 pyramidal cell: rhythmogenesis in a reduced Traub model (Pinsky, Rinzel 1994)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Fig. 2A and 3 are reproduced in this simulation of Pinsky PF, Rinzel J (1994)." + }, + "opensourcebrain": { + "value": "pinskyrinzelmodel" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/35358", + "other_simulator": { + "attr_id": 307, + "value": "xppaut" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181605, + "object_name": "35358" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 181 + }, + "ver_date": "2022-05-27T09:37:41.877", + "ver_number": 22 + }, + "35781": { + "class_id": 19, + "created": "2004-03-08T13:27:22", + "gitrepo": false, + "id": 35781, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 22010, + "object_name": "Facilitation" + }, + { + "object_id": 22011, + "object_name": "Post-Tetanic Potentiation" + }, + { + "object_id": 22012, + "object_name": "Depression" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 6021, + "object_name": "Migliore M, Lansky P (1999)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 58965, + "object_name": "QBasic/QuickBasic/Turbo Basic/VBA" + } + ] + }, + "name": "Stochastic LTP/LTD conditioning of a synapse (Migliore and Lansky 1999)", + "notes": { + "attr_id": 24, + "value": "Protracted presynaptic activity can induce long-term potentiation\r\n(LTP) or long-term depression (LTD) of the synaptic strength. However,\r\nvirtually all the experiments testing how LTP and LTD depend on the\r\nconditioning input are carried out with trains of stimuli at constant\r\nfrequencies, whereas neurons in vivo most likely experience a stochastic\r\nvariation of interstimulus intervals. We used a computational model of\r\nsynaptic transmission to test if and to what extent the stochastic\r\nfluctuations of an input signal could alter the probability to change the\r\nstate of a synapse. See paper for conclusions." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/35781", + "ver_date": "2005-11-16T13:38:42", + "ver_number": 14 + }, + "36834": { + "class_id": 19, + "created": "2004-04-01T11:29:15", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 36834, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 36860, + "object_name": "Zhang, Xuedong " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 36800, + "object_name": "Heinz MG, Zhang X, Bruce IC, Carney LH (2001)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Auditory nerve model for predicting performance limits (Heinz et al 2001)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52419, + "object_name": "Auditory nerve" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A computational auditory nerve (AN) model was developed for\r\nuse in modeling psychophysical experiments with normal and impaired\r\nhuman listeners. In this phenomenological model, many physiologically\r\nvulnerable response properties associated with the cochlear amplifier are\r\nrepresented by a single nonlinear control mechanism, see paper for details. Several model versions are described that\r\ncan be used to evaluate the relative effects of these nonlinear properties." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/36834", + "other_type": { + "attr_id": 305, + "value": "phenomenological" + }, + "ver_date": "2018-10-18T15:11:49.8", + "ver_number": 15 + }, + "36861": { + "class_id": 19, + "created": "2004-04-01T16:09:05", + "gitrepo": false, + "id": 36861, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 36865, + "object_name": "Naidu, R " + }, + { + "object_id": 36866, + "object_name": "Mountain, David [dcm at bu.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 36864, + "object_name": "Peterson LC, Bogert BP (1950)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Model of cochlear membrane adapted (Peterson, Bogert 1950)", + "notes": { + "attr_id": 24, + "value": "This model, adapted from Peterson and Bogert (1950), simulates the response of the gerbil basilar membrane to a pure tone stimulus. This model does not attempt to simulate the effect of outer hair cell motility. The program prompts the user for the stimulus frequency and the Q (quality factor) for the basilar membrane impedance. It then plots cochlear partition volume velocity, the pressure difference across the partition and the cochlear partition impedance as a function of cochlear location. More information on the actual computations are contained in comments within the m-file." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/36861", + "simPFid": { + "attr_id": 488, + "value": 121 + }, + "ver_date": "2005-04-10T15:57:38", + "ver_number": 9 + }, + "36869": { + "class_id": 19, + "created": "2004-04-01T18:07:06", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 36869, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 36866, + "object_name": "Mountain, David [dcm at bu.edu]" + }, + { + "object_id": 36873, + "object_name": "Deligeorges, S" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 36870, + "object_name": "Deligeorges S, Mountain DC (1997)" + }, + { + "object_id": 36874, + "object_name": "Singh S, Mountain DC (1997)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Inner hair cell auditory nerve synapse model (Deligeorges, Mountain 1997)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 1965, + "object_name": "Cochlea hair outer GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model simulates the response of the synapse between the inner hair cell and an auditory nerve fiber to a square voltage pulse applied to the IHC membrane. The model output is average firing rate. More details of this model can be found in: Deligeorges and Mountain." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/36869", + "simPFid": { + "attr_id": 488, + "value": 114 + }, + "ver_date": "2018-10-18T15:12:17.347", + "ver_number": 13 + }, + "36956": { + "class_id": 19, + "created": "2004-04-02T10:13:11", + "gitrepo": false, + "id": 36956, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 36866, + "object_name": "Mountain, David [dcm at bu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 36876, + "object_name": "Mountain DC, Cody AR (1999)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Multiple modes of inner hair cell stimulation (Mountain, Cody 1999)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 1965, + "object_name": "Cochlea hair outer GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model simulates the membrane potential of an inner hair cell for a sinusoidal stimulus to the hair bundle. It uses a 2-state Boltzmann model for the tension-gated conductance in the stereocilia and a linear model for the basolateral membrane. This model is based on the IHC model used in Mountain and Cody (1999)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/36956", + "simPFid": { + "attr_id": 488, + "value": 124 + }, + "ver_date": "2005-04-10T15:59:59", + "ver_number": 5 + }, + "37103": { + "class_id": 19, + "created": "2004-04-02T11:45:32", + "gitrepo": false, + "id": 37103, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 36860, + "object_name": "Zhang, Xuedong " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 33465, + "object_name": "Zhang X, Heinz MG, Bruce IC, Carney LH (2001)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Auditory nerve response model (Zhang et al 2001)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 1965, + "object_name": "Cochlea hair outer GLU cell" + }, + { + "object_id": 52419, + "object_name": "Auditory nerve" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A phenomenological model was developed to describe responses of high-spontaneous-rate auditory-nerve (AN) fibers, including several nonlinear response properties. The implementation of this model represents a relatively simple phenomenological description of a single mechanism that underlies several important nonlinear response properties of AN fibers. The model provides a tool for studying the roles of these nonlinearities in the encoding of simple and complex sounds in the responses of populations of AN fibers." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/37103", + "ver_date": "2007-04-13T15:26:36", + "ver_number": 9 + }, + "37129": { + "class_id": 19, + "created": "2004-04-02T16:44:48", + "gitrepo": false, + "id": 37129, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 37130, + "object_name": "Tan, Qing " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 37105, + "object_name": "Tan Q, Carney LH (2003)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Auditory nerve response model (Tan, Carney 2003)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 1965, + "object_name": "Cochlea hair outer GLU cell" + }, + { + "object_id": 52419, + "object_name": "Auditory nerve" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A computational model was developed to simulate the responses of auditory-nerve (AN) fibers in cat. The incorporation of both the level-independent frequency glide and the level-dependent compressive nonlinearity into a phenomenological model for the AN was the primary focus of this work. The ability of this model to process arbitrary sound inputs makes it a useful tool for studying peripheral auditory processing." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/37129", + "ver_date": "2007-04-13T15:25:56", + "ver_number": 9 + }, + "37436": { + "class_id": 19, + "created": "2004-04-08T18:13:56", + "gitrepo": false, + "id": 37436, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 37571, + "object_name": "Heinz, Michael G [mgheinz at alum.mit.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 37110, + "object_name": "Heinz MG, Colburn HS, Carney LH (2001)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Auditory nerve model with linear tuning (Heinz et al 2001)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 1965, + "object_name": "Cochlea hair outer GLU cell" + }, + { + "object_id": 52419, + "object_name": "Auditory nerve" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A method for calculating psychophysical performance limits based on stochastic \r\nneural responses is introduced and compared to previous analytical methods for \r\nevaluating auditory discrimination of tone frequency and level. The method uses \r\nsignal detection theory and a computational model for a population of auditory \r\nnerve (AN) fiber responses. Please see paper for details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/37436", + "simPFid": { + "attr_id": 488, + "value": 59 + }, + "ver_date": "2010-02-12T19:50:46", + "ver_number": 11 + }, + "37819": { + "class_id": 19, + "created": "2004-04-26T16:54:51", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 37819, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33738, + "object_name": "Lytton, William [bill.lytton at downstate.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 14214, + "object_name": "Bazhenov M, Timofeev I, Steriade M, Sejnowski TJ (1998)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "37819", + "name": "ModelView" + } + ], + "default": "37819" + }, + "name": "Thalamocortical augmenting response (Bazhenov et al 1998)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + }, + { + "object_id": 263, + "object_name": "Thalamus reticular nucleus GABA cell" + }, + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In the cortical model, augmenting responses were more powerful in the \"input\" layer compared with those in the \"output\" layer. Cortical stimulation of the network model produced augmenting responses in cortical neurons in distant cortical areas through corticothalamocortical loops and low-threshold intrathalamic augmentation. ... The predictions of the model were compared with in vivo recordings from neurons in cortical area 4 and thalamic ventrolateral nucleus of anesthetized cats. The known intrinsic properties of thalamic cells and thalamocortical interconnections can account for the basic properties of cortical augmenting responses. See reference for details. NEURON implementation note: cortical SU cells are getting slightly too little stimulation - reason unknown." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/37819", + "other_concept": { + "attr_id": 306, + "value": "activity augmentation" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181852, + "object_name": "37819" + } + ] + }, + "ver_date": "2022-05-27T09:39:47.07", + "ver_number": 22 + }, + "37856": { + "class_id": 19, + "created": "2004-05-06T16:52:02", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 37856, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33781, + "object_name": "Manis, Paul B [PManis at med.unc.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 3977, + "object_name": "Kanold PO, Manis PB (2001)" + }, + { + "object_id": 12730, + "object_name": "Kanold PO, Manis PB (1999)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "CN pyramidal fusiform cell (Kanold, Manis 2001)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 274, + "object_name": "Cochlear nucleus pyramidal/fusiform GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Pyramidal cells in the dorsal cochlear nucleus (DCN) show three characteristic discharge patterns in response tones: pauser, buildup, and regular firing. Experimental evidence suggests that a rapidly inactivating K+ current (I(KIF)) plays a critical role in generating these discharge patterns. To explore the role of I(KIF), we used a computational model based on the biophysical data. The model replicated the dependence of the discharge pattern on the magnitude and duration of hyperpolarizing prepulses, and I(KIF) was necessary to convey this dependence. Experimentally, half-inactivation voltage and kinetics of I(KIF) show wide variability. Varying these parameters in the model ... suggests that pyramidal cells can adjust their sensitivity to different temporal patterns of inhibition and excitation by modulating the kinetics of I(KIF). Overall, I(KIF) is a critical conductance controlling the excitability of DCN pyramidal cells. (See readme.txt and paper for details).\r\n\r\nAny questions regarding these implementations should be directed to:\r\npmanis@med.unc.edu\r\n\r\n2 April 2004\r\nPaul B Manis, Ph.D." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/37856", + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181730, + "object_name": "37856_1" + }, + { + "object_id": 181731, + "object_name": "37856_3" + }, + { + "object_id": 181732, + "object_name": "37856_2" + }, + { + "object_id": 181733, + "object_name": "37856_5" + }, + { + "object_id": 181734, + "object_name": "37856_4" + }, + { + "object_id": 181735, + "object_name": "37856_7" + }, + { + "object_id": 181736, + "object_name": "37856_6" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 187 + }, + "ver_date": "2015-01-02T21:47:11", + "ver_number": 17 + }, + "37857": { + "class_id": 19, + "created": "2004-05-07T11:18:49", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 37857, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33781, + "object_name": "Manis, Paul B [PManis at med.unc.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 37966, + "object_name": "Rothman JS, Manis PB (2003)" + }, + { + "object_id": 37967, + "object_name": "Rothman JS, Manis PB (2003)" + }, + { + "object_id": 37968, + "object_name": "Rothman JS, Manis PB (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "CN bushy, stellate neurons (Rothman, Manis 2003)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 273, + "object_name": "Cochlear nucleus bushy GLU cell" + }, + { + "object_id": 88113, + "object_name": "CN stellate cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Using kinetic data from three different K+ currents in acutely isolated neurons, a single electrical compartment model representing the soma of a ventral cochlear nucleus (VCN) neuron was created. The K+ currents include a fast transient current (IA), a slow-inactivating low-threshold current (ILT), and a noninactivating high-threshold current (IHT). The model also includes a fast-inactivating Na+ current, a hyperpolarization-activated cation current (Ih), and 1-50 auditory nerve synapses. With this model, the role IA, ILT, and IHT play in shaping the discharge patterns of VCN cells is explored. Simulation results indicate these currents have specific roles in shaping the firing patterns of stellate and bushy CN cells. (see readme.txt and the papers, esp 2003c, for details). Any questions regarding these implementations should be directed to: pmanis@med.unc.edu 2 April 2004 Paul B Manis, Ph.D." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/37857", + "other_neurons": { + "attr_id": 301, + "value": "CN stellate cell" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181383, + "object_name": "37857_12" + }, + { + "object_id": 181384, + "object_name": "37857_13" + }, + { + "object_id": 181385, + "object_name": "37857_10" + }, + { + "object_id": 181386, + "object_name": "37857_11" + }, + { + "object_id": 181387, + "object_name": "37857_16" + }, + { + "object_id": 181388, + "object_name": "37857_17" + }, + { + "object_id": 181389, + "object_name": "37857_14" + }, + { + "object_id": 181390, + "object_name": "37857_15" + }, + { + "object_id": 181392, + "object_name": "37857_18" + }, + { + "object_id": 181729, + "object_name": "37857_9" + }, + { + "object_id": 181800, + "object_name": "37857_2" + }, + { + "object_id": 181885, + "object_name": "37857_8" + }, + { + "object_id": 181887, + "object_name": "37857_1" + }, + { + "object_id": 181889, + "object_name": "37857_3" + }, + { + "object_id": 181890, + "object_name": "37857_4" + }, + { + "object_id": 181891, + "object_name": "37857_5" + }, + { + "object_id": 181892, + "object_name": "37857_6" + }, + { + "object_id": 181893, + "object_name": "37857_7" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 186 + }, + "ver_date": "2018-10-18T15:12:38.187", + "ver_number": 20 + }, + "39948": { + "class_id": 19, + "created": "2004-07-30T17:30:22", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 39948, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33738, + "object_name": "Lytton, William [bill.lytton at downstate.edu]" + }, + { + "object_id": 182905, + "object_name": "Dura-Bernal, Salvador [salvadordura at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3642, + "object_name": "Tutorial/Teaching" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 40104, + "object_name": "Izhikevich EM (2007)" + }, + { + "object_id": 70557, + "object_name": "Izhikevich EM (2004)" + }, + { + "object_id": 116892, + "object_name": "Izhikevich EM (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Artificial neuron model (Izhikevich 2003, 2004, 2007)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A set of models is presented based on 2 related\r\nparameterizations to reproduce spiking and bursting behavior of multiple\r\ntypes of cortical neurons and thalamic neurons. These models combine the\r\nbiologically plausibility of Hodgkin Huxley-type dynamics and the\r\ncomputational efficiency of integrate-and-fire neurons. Using these\r\nmodel, one can simulate tens of thousands of spiking cortical neurons in\r\nreal time (1 ms resolution) using a desktop PC." + }, + "opensourcebrain": { + "value": "izhikevichmodel" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/39948", + "other_neurons": { + "attr_id": 301, + "value": "generic" + }, + "ver_date": "2023-01-30T13:46:18.41", + "ver_number": 19 + }, + "39949": { + "class_id": 19, + "created": "2004-07-31T16:52:18", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122812, + "object_name": "Cav1.3 CACNA1D" + }, + { + "object_id": 122828, + "object_name": "D1 DRD1A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 39949, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33735, + "object_name": "Carnevale, Ted [Ted.Carnevale at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 41934, + "object_name": "Intrinsic plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 35784, + "object_name": "Gruber AJ, Solla SA, Surmeier DJ, Houk JC (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "39949", + "name": "ModelView" + } + ], + "default": "39949" + }, + "name": "Spiny neuron model with dopamine-induced bistability (Gruber et al 2003)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 260, + "object_name": "Neostriatum medium spiny direct pathway GABA cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "These files implement a model of dopaminergic modulation of voltage-gated currents (called kir2 and caL in the original paper). See spinycell.html for details of usage and implementation. For questions about this implementation, contact Ted Carnevale (ted.carnevale@yale.edu)" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/39949", + "other_currents": { + "attr_id": 303, + "value": "I CaL" + }, + "other_implementer": { + "attr_id": 308, + "value": "Original (published) implementation by Aaron Gruber" + }, + "other_type": { + "attr_id": 305, + "value": "Synapse, Network" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 196, + "object_name": "D1" + }, + { + "object_id": 223, + "object_name": "Dopaminergic Receptor" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181741, + "object_name": "39949" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 496 + }, + "ver_date": "2014-02-25T01:10:37", + "ver_number": 17 + }, + "42020": { + "class_id": 19, + "created": "2004-08-26T15:48:19", + "gitrepo": false, + "id": 42020, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 76455, + "object_name": "Gewaltig, Marc-Oliver [marc-oliver.gewaltig@epfl.ch]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 29380, + "object_name": "Brunel N (2000)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 2832, + "object_name": "NEST" + } + ] + }, + "name": "Sparsely connected networks of spiking neurons (Brunel 2000)", + "notes": { + "attr_id": 24, + "value": "The dynamics of networks of sparsely connected excitatory and inhibitory integrate-and-fire neurons\r\nare studied analytically (and with simulations). The analysis reveals a rich repertoire of states, including synchronous states in which\r\nneurons fire regularly; asynchronous states with stationary global activity and very irregular individual cell activity;\r\nand states in which the global activity oscillates but individual cells fire irregularly, typically at rates lower than\r\nthe global oscillation frequency. See paper for more and details." + }, + "opensourcebrain": { + "value": "brunel2000" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/42020", + "other_concept": { + "attr_id": 306, + "value": "Sparse network activity" + }, + "other_implementer": { + "attr_id": 308, + "value": "Marc-Oliver Gewaltig" + }, + "ver_date": "2012-01-05T14:13:17", + "ver_number": 8 + }, + "42022": { + "class_id": 19, + "created": "2004-08-30T16:54:36", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 42022, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33985, + "object_name": "Baxter, Douglas" + }, + { + "object_id": 42042, + "object_name": "Boytcheva, Milena [milena at cs.tu-berlin.de]" + }, + { + "object_id": 42043, + "object_name": "Wustenberg, Daniel [Daniel.Wustenberg at uth.tmc.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 42041, + "object_name": "W\u00fcstenberg DG, Boytcheva M, Gr\u00fcnewald B, Byrne JH, Menzel R, Baxter DA (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "Kenyon cells in the honeybee (Wustenberg et al 2004)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114295, + "object_name": "Honeybee kenyon cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The mushroom body of the insect brain is an important locus for olfactory information\r\nprocessing and associative learning. ... Current- and voltage-clamp analyses were\r\nperformed on cultured Kenyon cells from honeybees. ... Voltage-clamp analyses characterized a\r\nfast transient Na+ current (INa), a delayed rectifier K+ current (IK,V) and a fast transient K+ current\r\n(IK,A). Using the neurosimulator SNNAP, a Hodgkin-Huxley type model was developed and\r\nused to investigate the roles of the different currents during spiking. The model led to the\r\nprediction of a slow transient outward current (IK,ST) that was subsequently identified by reevaluating\r\nthe voltage-clamp data. Simulations indicated that the primary currents that underlie\r\nspiking are INa and IK,V, whereas IK,A and IK,ST primarily determined the responsiveness of the\r\nmodel to stimuli such constant or oscillatory injections of current. See paper for more details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/42022", + "other_neurons": { + "attr_id": 301, + "value": "Kenyon cell" + }, + "ver_date": "2018-10-17T15:40:57.22", + "ver_number": 10 + }, + "42036": { + "class_id": 19, + "created": "2004-09-06T05:11:35", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 42036, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 114306, + "object_name": "Cataldo, Enrico" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + }, + { + "object_id": 114307, + "object_name": "Conduction failure" + }, + { + "object_id": 120695, + "object_name": "Sodium pump" + }, + { + "object_id": 245798, + "object_name": "Touch" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 42057, + "object_name": "Cataldo E, Brunelli M, Byrne JH, Av-Ron E, Cai Y, Baxter DA (2005)" + }, + { + "object_id": 89177, + "object_name": "Scuri R, Lombardo P, Cataldo E, Ristori C, Brunelli M (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 120694, + "object_name": "Electrogenic pump" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "Touch Sensory Cells (T Cells) of the Leech (Cataldo et al. 2004) (Scuri et al. 2007)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114293, + "object_name": "Leech T segmental sensory neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Bursts of spikes in leech T cells produce an AHP, which results from activation of a Na+/K+ pump and \r\na Ca2+-dependent K+ current. Activity-dependent increases in the AHP are believed to induce conduction \r\nblock of spikes in several regions of the neuron, which in turn, may decrease presynaptic invasion of spikes and \r\nthereby decrease transmitter release. To explore this possibility, we used the neurosimulator SNNAP to develop \r\na multi-compartmental model of the T cell. Each compartment was modeled as an equivalent electrical circuit, \r\nin which some currents were regulated by intracellular Ca2+ and Na+. The membrane model consisted of \r\na membrane capacitance (Cm), for which we used the value 1 uF/cm2, in parallel with \r\ntwo inward currents (Na+ and Ca2+), two K+ currents, a leak current and pump current. \r\nThe model incorporated empirical data that describe the geometry of the cell and activity-dependent changes of the\r\nAHP (see paper for details). \r\nSimulations indicated that at some branching points, activity-dependent increases of the AHP reduced the number \r\nof spikes transmitted from the minor receptive field to the soma and beyond. \r\nThese results suggest that the AHP can regulate spike conduction within the presynaptic arborizations of the cell and \r\ncould in principle contribute to the synaptic depression that is correlated with increases in the AHP." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/42036", + "other_concept": { + "attr_id": 306, + "value": "Conduction Failure" + }, + "other_currents": { + "attr_id": 303, + "value": "I Leak, I Na Pump" + }, + "other_implementer": { + "attr_id": 308, + "value": "Cataldo, Enrico" + }, + "other_neurons": { + "attr_id": 301, + "value": "Leech T cell" + }, + "ver_date": "2018-10-18T17:20:07.99", + "ver_number": 23 + }, + "42037": { + "class_id": 19, + "created": "2004-09-07T09:53:33", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "id": 42037, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + }, + { + "object_id": 120695, + "object_name": "Sodium pump" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 42056, + "object_name": "Lombardo P, Scuri R, Cataldo E, Calvani M, Nicolai R, Mosconi L, Brunelli M (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 120694, + "object_name": "Electrogenic pump" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "Effects of Acetyl-L-carnitine on neural transmission (Lombardo et al 2004)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114293, + "object_name": "Leech T segmental sensory neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Acetyl-L-carnitine is known to improve many aspects of the neural activity even if its exact role in neurotransmission is \r\nstill unknown. This study investigates the effects of acetyl-L-carnitine in T segmental sensory neurons of the leech Hirudo \r\nmedicinalis. These neurons are involved in some forms of neural plasticity associated with learning processes. \r\nTheir physiological firing is accompanied by a large afterhyperpolarization that is mainly due to the Na+/K+ ATPase \r\nactivity and partially to a Ca2+-dependent K+ current. A clear-cut hyperpolarization and a significant increase of the \r\nafterhyperpolarization have been recorded in T neurons of leeches injected with 2 mM acetyl-L-carnitine some days \r\nbefore. Acute treatments of 50 mM acetyl-L-carnitine induced similar effects in T cells of naive animals. \r\nMoreover, in these cells, widely arborized, the afterhyperpolarization seems to play an important role in determining \r\nthe action potential transmission at neuritic bifurcations. \r\nA computational model of a T cell has been previously developed considering detailed data for geometry and the \r\nmodulation of the pump current. Herein, we showed that to a larger afterhyperpolarization, due to the \r\nacetyl-L-carnitine-induced effects, corresponds a decrement in the number of action potentials \r\nreaching synaptic terminals." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/42037", + "other_currents": { + "attr_id": 303, + "value": "I Leak, I Na Pump" + }, + "other_implementer": { + "attr_id": 308, + "value": "Cataldo, Enrico" + }, + "other_neurons": { + "attr_id": 301, + "value": "Leech T cell" + }, + "ver_date": "2009-05-28T13:07:07", + "ver_number": 17 + }, + "42044": { + "class_id": 19, + "created": "2004-09-09T15:23:52", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 42044, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 42045, + "object_name": "Av-Ron, Evyatar [eav-ron at uth.tmc.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 29126, + "object_name": "HODGKIN AL, HUXLEY AF (1952)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "Squid axon (Hodgkin, Huxley 1952) (SNNAP)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52426, + "object_name": "Squid axon" + }, + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The classic HH model of squid axon membrane\r\nimplemented in SNNAP.\r\nHodgkin, A.L., Huxley, A.F. (1952)" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/42044", + "ver_date": "2008-09-18T15:04:10", + "ver_number": 8 + }, + "42046": { + "class_id": 19, + "created": "2004-09-09T18:12:30", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 42046, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 42045, + "object_name": "Av-Ron, Evyatar [eav-ron at uth.tmc.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 29018, + "object_name": "Morris C, Lecar H (1981)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "Morris-Lecar model of the barnacle giant muscle fiber (Morris, Lecar 1981)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114299, + "object_name": "Depressor scutorum rostralis muscle cell" + }, + { + "object_id": 150222, + "object_name": "Abstract Morris-Lecar neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "... This paper presents an analysis of the possible modes of behavior available to a system of two noninactivating conductance mechanisms, and indicates a good correspondence to the types of behavior exhibited by barnacle fiber. The differential equations of a simple equivalent circuit for the fiber are dealt with by means of some of the mathematical techniques of nonlinear mechanics. General features of the system are (a) a propensity to produce damped or sustained oscillations over a rather broad parameter range, and (b) considerable latitude in the shape of the oscillatory potentials. It is concluded that for cells subject to changeable parameters (either from cell to cell or with time during cellular activity), a system dominated by two noninactivating conductances can exhibit varied oscillatory and bistable behavior. See paper for details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/42046", + "ver_date": "2013-06-05T11:38:56", + "ver_number": 10 + }, + "42047": { + "class_id": 19, + "created": "2004-09-10T10:07:01", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "id": 42047, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 42045, + "object_name": "Av-Ron, Evyatar [eav-ron at uth.tmc.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 42048, + "object_name": "Av-Ron E, Parnas H, Segel LA (1991)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "Minimal cell model (Av-Ron et al 1991)", + "notes": { + "attr_id": 24, + "value": "The minimal cell model (MCM) is a reduced Hodgkin-Huxley model that can\r\nexhibit excitable and oscillatory behavior. It consists of two\r\nordinary differential equations, dV/dt for membrane voltage and dW/dt\r\nfor potassium activation and sodium inactivation. The MCM has a stable\r\nmembrane potential of -60mV. With constant input current of 10uA/cm2, it\r\nexhibits oscillations of 150Hz. It is based on the work by FitzHugh and\r\nRinzel." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/42047", + "ver_date": "2015-07-26T07:54:16.89", + "ver_number": 8 + }, + "42321": { + "class_id": 19, + "created": "2004-09-13T09:54:48", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 240, + "object_name": "I Chloride" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "id": 42321, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33985, + "object_name": "Baxter, Douglas" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 34948, + "object_name": "Epstein IR, Marder E (1990)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "Multiple modes of a conditional neural oscillator (Epstein, Marder 1990)", + "notes": { + "attr_id": 24, + "value": "We present a model for a conditional bursting neuron consisting of five conductances: Hodgkin-Huxley type time- and voltage-dependent Na+ and K+ conductances, a calcium activated voltage-dependent K+ conductance, a calcium-inhibited time- and voltage-dependent Ca++ conductance, and a leakage Cl- conductance.\r\nDifferent bursting and silent modes and transitions between them are analyzed in the model and compared to bursting modes in experiment. See the paper for details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/42321", + "ver_date": "2005-04-10T15:59:25", + "ver_number": 6 + }, + "42322": { + "class_id": 19, + "created": "2004-09-14T09:53:49", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "id": 42322, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33985, + "object_name": "Baxter, Douglas" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 7958, + "object_name": "L\u00fcscher HR, Shiner JS (1990)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "Spike propagation and bouton activation in terminal arborizations (Luscher, Shiner 1990)", + "notes": { + "attr_id": 24, + "value": "Action potential propagation in axons with bifurcations involving short collaterals with synaptic boutons has been simulated ... The architecture of the terminal arborizations has a profound effect on the activation pattern of synapses, suggesting that terminal arborizations not only distribute neural information to postsynaptic cells but may also be able to process neural information presynaptically. Please see paper for details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/42322", + "ver_date": "2017-06-21T14:28:39.443", + "ver_number": 8 + }, + "42323": { + "class_id": 19, + "created": "2004-09-14T10:07:37", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "id": 42323, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33985, + "object_name": "Baxter, Douglas" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 27059, + "object_name": "Butera RJ, Clark JW, Canavier CC, Baxter DA, Byrne JH (1995)" + }, + { + "object_id": 34010, + "object_name": "Canavier CC, Clark JW, Byrne JH (1991)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "Bursting activity of neuron R15 in Aplysia (Canavier et al 1991, Butera et al 1995)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52420, + "object_name": "Aplysia R15 bursting neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "An equivalent circuit model of the R15 bursting neuron in Aplysia has been combined with a fluid compartment model, resulting in a model that incorporates descriptions of most of the membrane ion channels that are known to exist in the somata of R15, as well as providing a Ca2+ balance on the cell. ... (from the second paper) we have implemented proposed mechanisms for the modulation of two ionic currents (IR and ISI) that play key roles in regulating its spontaneous electrical activity. The model was sufficient to simulate a wide range of endogenous activity in the presence of various concentrations of 5-HT or DA. 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To the extent possible, both electrophysiology and synaptic connectivity were\r\nbased on published data, although many arbitrary choices were necessary." + }, + "opensourcebrain": { + "value": "thalamocortical" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/45539", + "other_concept": { + "attr_id": 306, + "value": "Epilepsy, Sleep spindles" + }, + "other_neurons": { + "attr_id": 301, + "value": "deep cortical interneuron" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2017-07-03T22:08:21.927", + "ver_number": 41 + }, + "46839": { + "class_id": 19, + "created": "2004-12-10T04:21:35", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 46839, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 46840, + "object_name": "D'Angelo, Egidio [dangelo at unipv.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 3989, + "object_name": "D'Angelo E, Nieus T, Maffei A, Armano S, Rossi P, Taglietti V, Fontana A, Naldi G (2001)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Bursting and resonance in cerebellar granule cells (D'Angelo et al. 2001)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 7346, + "object_name": "Cerebellum interneuron granule GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In this study we report theta-frequency (3-12 Hz)\r\nbursting and resonance in rat cerebellar granule cells and show that these neurons express a previously unidentified slow repolarizing K1 current (IK-slow ). Our experimental and modeling results indicate that IK-slow was necessary for both bursting and resonance. See paper for more." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/46839", + "simPFid": { + "attr_id": 488, + "value": 161 + }, + "ver_date": "2015-07-26T07:59:59.673", + "ver_number": 15 + }, + "48332": { + "class_id": 19, + "created": "2005-01-13T10:03:28", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92087, + "object_name": "Nav1.6 SCN8A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 48332, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 48422, + "object_name": "Raman, Indira M [i-raman at northwestern.edu]" + }, + { + "object_id": 48423, + "object_name": "Bean, Bruce [bruce_bean at hms.harvard.edu]" + }, + { + "object_id": 48424, + "object_name": "Gouwens, Nathan W [nathan_gouwens at hms.harvard.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 43936, + "object_name": "Khaliq ZM, Gouwens NW, Raman IM (2003)" + }, + { + "object_id": 52479, + "object_name": "Raman IM, Bean BP (2001)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "48332", + "name": "ModelView" + } + ], + "default": "48332" + }, + "name": "Cerebellar Purkinje Cell: resurgent Na current and high frequency firing (Khaliq et al 2003)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "These mod files supplied by Dr Raman are for the below two references. ... we modeled action potential firing by simulating eight currents directly recorded from Purkinje cells in both wild-type and (mutant) med mice.\r\nRegular, high-frequency firing was slowed in med Purkinje neurons. In addition to disrupted sodium currents, med neurons had small\r\nbut significant changes in potassium and leak currents. Simulations indicated that these modified non-sodium currents could not\r\naccount for the reduced excitability of med cells but instead slightly facilitated spiking. The loss of NaV1.6-specific kinetics, however,\r\nslowed simulated spontaneous activity. Together, the data suggest that across a range of conditions, sodium currents with a resurgent\r\ncomponent promote and accelerate firing. See papers for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/48332", + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181907, + "object_name": "48332" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 196 + }, + "ver_date": "2014-02-06T16:21:16", + "ver_number": 17 + }, + "48506": { + "class_id": 19, + "created": "2005-01-17T13:38:21", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 88117, + "object_name": "I_KHT" + }, + { + "object_id": 88118, + "object_name": "I_KLT" + } + ] + }, + "gitrepo": false, + "id": 48506, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 48507, + "object_name": "Fernandez FR [ffernand at ucalgary.ca]" + }, + { + "object_id": 48508, + "object_name": "Mehaffey WH " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 48505, + "object_name": "Fernandez FR, Mehaffey WH, Molineux ML, Turner RW (2005)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Pyramidal neurons: IKHT offsets activation of IKLT to increase gain (Fernandez et al 2005)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88116, + "object_name": "ELL pyramidal cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This matlab model was supplied by Dr Fernandez. It provides the model specification for the below paper. The influence of a high threshold K current on low threshold K and Na currents (especially frequency-current relationships) are studied in the paper with both experiments and modeling. Please see the reference for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/48506", + "other_currents": { + "attr_id": 303, + "value": "IKHT, ILHT" + }, + "other_neurons": { + "attr_id": 301, + "value": "ELL Pyramidal cells" + }, + "simPFid": { + "attr_id": 488, + "value": 133 + }, + "ver_date": "2007-04-13T15:58:54", + "ver_number": 14 + }, + "49305": { + "class_id": 19, + "created": "2005-01-27T15:52:37", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 49305, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 22012, + "object_name": "Depression" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 102181, + "object_name": "Doi T, Kuroda S, Michikawa T, Kawato M (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 18747, + "object_name": "GENESIS (web link to model)" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Spike timing detection in different forms of LTD (Doi et al 2005)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "To understand the spike-timing detection mechanisms in cerebellar long-term depression (LTD), we developed a kinetic model of Ca dynamics within a Purkinje dendritic spine. In our kinetic simulation, IP3 was first produced via the metabotropic pathway of parallel fiber (PF) inputs, and the Ca influx in response to the climbing fiber (CF) input triggered regenerative Ca-induced Ca release from the internal stores via the IP3\r\nreceptors activated by the increased IP3. The delay in IP3 increase caused by the PF metabotropic pathway generated the optimal PF\u2013CF\r\ninterval. The Ca dynamics revealed a threshold for large Ca2 release that decreased as IP3 increased, and it coherently explained the\r\ndifferent forms of LTD. See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/49305", + "other_receptors": { + "attr_id": 302, + "value": "IP3" + }, + "other_simulator": { + "attr_id": 307, + "value": "excel" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 153715, + "object_name": "IP3" + } + ] + }, + "ver_date": "2015-07-01T13:50:22.293", + "ver_number": 14 + }, + "50207": { + "class_id": 19, + "created": "2005-02-13T20:01:31", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 50207, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 50209, + "object_name": "Kampa, Bjorn M [Bjoern.Kampa at anu.edu.au]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 50212, + "object_name": "Kampa BM, Clements J, Jonas P, Stuart GJ (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "50207", + "name": "ModelView" + } + ], + "default": "50207" + }, + "name": "Kinetic NMDA receptor model (Kampa et al 2004)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This kinetic NMDA receptor model is based on voltage-clamp recordings of NMDA receptor-mediated currents in nucleated patches of rat neocortical layer 5 pyramidal neurons (Kampa et al 2004 J Physiol), this model was fit with AxoGraph directly to experimental recordings in order to obtain the optimal values for the parameters. The demo shows the behaviour of a kinetic NMDA receptor model reproducing the data in figure 2.\r\nThe NMDA receptor model uses realistic rates of magnesium block and its effects on channel desensitisation. Presynaptic transmitter release is necessary for glutamate binding to the receptor. This model was written by Bjoern Kampa, Canberra, 2004." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/50207", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181629, + "object_name": "50207" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 269 + }, + "ver_date": "2014-02-15T23:21:53", + "ver_number": 12 + }, + "50210": { + "class_id": 19, + "created": "2005-02-15T04:20:13", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 50210, + 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reduction in the dendritic length of newborn GC\r\ncould independently explain the observed increase in excitability. \r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/50210", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181725, + "object_name": "50210_2" + }, + { + "object_id": 181726, + "object_name": "50210_3" + }, + { + "object_id": 181728, + "object_name": "50210_1" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 310 + }, + "ver_date": "2018-10-17T15:41:42.843", + "ver_number": 10 + }, + "50219": { + "class_id": 19, + "created": "2005-02-18T17:17:42", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 247, + 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Within a few tens of milliseconds after the start of random mossy fiber input, the populations of Golgi and granule cells became entrained in a single synchronous oscillation, the basic frequency of which ranged from 10 to 40 Hz depending on the average rate of firing in the mossy fiber population. ... The synchronous, rhythmic firing pattern was robust over a broad range of biologically realistic parameter values and to parameter randomization. Three conditions, however, made the oscillations more transient and could desynchronize the entire network in the end: a very low mossy fiber activity, a very dominant excitation of Golgi cells through mossy fiber synapses (rather than through parallel fiber synapses), and a tonic activation of granule cell GABAA receptors (with an almost complete absence of synaptically induced inhibitory postsynaptic currents). The model predicts that, under conditions of strong mossy fiber input to the cerebellum, Golgi cells do not only control the strength of parallel fiber activity but also the timing of the individual spikes. Provided that their parallel fiber synapses constitute an important source of excitation, Golgi cells fire rhythmically and synchronized with granule cells over large distances along the parallel fiber axis. 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Qualitatively reproduces the phenomena described in \r\nthe reference." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/50391", + "other_concept": { + "attr_id": 306, + "value": "Stochastic activity" + }, + "other_currents": { + "attr_id": 303, + "value": "Leak" + }, + "other_implementer": { + "attr_id": 308, + "value": "Donnelly, David" + }, + "other_neurons": { + "attr_id": 301, + "value": "squid axon" + }, + "other_type": { + "attr_id": 305, + "value": "Membrane action potential" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181912, + "object_name": "50391" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 497 + }, + "ver_date": "2014-02-25T01:11:06", + "ver_number": 13 + }, + "50392": { + "class_id": 19, + "created": "2005-02-22T15:10:54", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "id": 50392, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 50400, + "object_name": "Maex, Reinoud [reinoud at bbf.uia.ac.be]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 50220, + "object_name": "Maex R, De Schutter E (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 18747, + "object_name": "GENESIS (web link to model)" + } + ] + }, + "name": "Fast oscillations in inhibitory networks (Maex, De Schutter 2003)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 7346, + "object_name": "Cerebellum interneuron granule GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We observed a new phenomenon of resonant synchronization in computer-simulated networks of inhibitory neurons in which the synaptic current has a delayed onset, reflecting finite spike propagation and synaptic transmission times. 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See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/50997", + "other_concept": { + "attr_id": 306, + "value": "Ribbon Synapse" + }, + "other_receptors": { + "attr_id": 302, + "value": "AMPA" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181834, + "object_name": "50997_2" + }, + { + "object_id": 181835, + "object_name": "50997_3" + }, + { + "object_id": 181836, + "object_name": "50997_1" + }, + { + "object_id": 181837, + "object_name": "50997_6" + }, + { + "object_id": 181839, + "object_name": "50997_4" + }, + { + "object_id": 181840, + "object_name": "50997_5" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 330 + }, + "ver_date": "2015-01-02T22:30:47", + "ver_number": 23 + }, + "51022": { + "class_id": 19, + "created": "2005-03-28T10:03:04", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 51022, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 51023, + "object_name": "Amir, Ron [ronamir at pob.huji.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 24714, + "object_name": "Amir R, Devor M (2003)" + }, + { + "object_id": 24715, + "object_name": "Amir R, Devor M (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "51022", + "name": "ModelView" + } + ], + "default": "51022" + }, + "name": "Dorsal root ganglion (DRG) neuronal model (Amir, Devor 2003)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52425, + "object_name": "Myelinated neuron" + }, + { + "object_id": 136307, + "object_name": "Dorsal Root Ganglion (DRG) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model shows that an electrically excitable soma is not necessary for spike through-conduction in the t-shaped geometry of a dorsal root ganglion neuron axon. Electrical excitability of the soma is required, however, for soma spike invasion. 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We, therefore, suggest that these models have a high predictive value." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/53876", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181160, + "object_name": "53876_1" + }, + { + "object_id": 181162, + "object_name": "53876_3" + }, + { + "object_id": 181163, + "object_name": "53876_2" + }, + { + "object_id": 181164, + "object_name": "53876_5" + }, + { + "object_id": 181165, + "object_name": "53876_4" + }, + { + "object_id": 181166, + "object_name": "53876_6" + } + ] + }, + "ver_date": "2015-01-02T22:39:06", + "ver_number": 23 + }, + "53893": { + "class_id": 19, + "created": "2005-06-08T18:42:05", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 245, + "object_name": "I T low threshold" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 53893, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33772, + "object_name": "Morse, Tom [Tom.Morse at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 23054, + "object_name": "Wang XJ, Rinzel J, Rogawski MA (1991)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "T-type Ca current in thalamic neurons (Wang et al 1991)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A model of the transient, low-threshold voltage-dependent (T-type)\r\nCa2+ current is constructed using whole-cell voltage-clamp \r\ndata from enzymatically isolated rat thalamocortical relay neurons. \r\nThe T-type Ca2+ current is described according to the Hodgkin-Huxley \r\nscheme, using the m3h format, with rate constants determined from the experimental data." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/53893", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2022-11-04T13:21:03.173", + "ver_number": 5 + }, + "53894": { + "class_id": 19, + "created": "2005-06-10T12:33:25", + "gitrepo": false, + "id": 53894, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53898, + "object_name": "Avery, Leon [leon at eatworms.swmed.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 53897, + "object_name": "Avery L, Shtonda BB (2003)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53895, + "object_name": "Java" + } + ] + }, + "name": "C elegans pharynx simulation (Avery and Shtonda 2003)", + "notes": { + "attr_id": 24, + "value": "Experimental obervations, measurements, and theoretical analysis of C. elegans pharynx feeding behavior function are reported in the paper. See the paper and the model files for more." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/53894", + "other_simulator": { + "attr_id": 307, + "value": "Java" + }, + "other_type": { + "attr_id": 305, + "value": "Hydrodynamic" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "leon@eatworms.swmed.edu" + }, + "ver_date": "2014-07-18T15:18:22", + "ver_number": 11 + }, + "53965": { + "class_id": 19, + "created": "2005-06-30T17:45:59", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 245, + "object_name": "I T low threshold" + } + ] + }, + "gitrepo": true, + "id": 53965, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 53548, + "object_name": "Vitko I, Chen Y, Arias JM, Shen Y, Wu XR, Perez-Reyes E (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "T channel currents (Vitko et al 2005)", + "notes": { + "attr_id": 24, + "value": "Computer simulations predict that seven of the SNPs would increase firing of neurons, with three of them inducing oscillations at similar frequencises. 3 representative models from the paper have been submited: a wild-type (WT) recombinant Cav3.2 T-channel, and two of the\r\nmutants described in the Vitko et al., 2005 paper (C456S and R788C). See the paper for more and details.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/53965", + "ver_date": "2012-01-13T14:16:11", + "ver_number": 23 + }, + "54141": { + "class_id": 19, + "created": "2005-07-19T15:51:06", + "gitrepo": false, + "id": 54141, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 54151, + "object_name": "Willms, Allan [AWillms at uoguelph.ca]" + }, + { + "object_id": 54152, + "object_name": "Kuo, Howie " + }, + { + "object_id": 54153, + "object_name": "Ewing, Greg" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 27044, + "object_name": "Willms AR, Baro DJ, Harris-Warrick RM, Guckenheimer J (1999)" + }, + { + "object_id": 54142, + "object_name": "Willms AR (2002)" + }, + { + "object_id": 54143, + "object_name": "T\u00f3th TI, Crunelli V (1995)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 45540, + "object_name": "FORTRAN" + }, + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "NEUROFIT: fitting HH models to voltage clamp data (Willms 2002)", + "notes": { + "attr_id": 24, + "value": "Publicly available software for accurate fitting of Hodgkin-Huxley models to voltage-clamp data... The set of parameter values for the model determined by this software yield current traces that are substantially closer to the observed data than those determined from the usual fitting method. This improvement is due to the fact that the software fits all of the parameters simultaneously utilizing all of the data rather than fitting steady-state and time constant parameters disjointly using peak currents and portions of the rising and falling phases... The software also incorporates a linear pre-estimation procedure to help in determining reasonable initial values for the full non-linear algorithm. See references for details and more." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/54141", + "public_submitter_email": { + "attr_id": 309, + "value": "Tom.Morse@Yale.edu" + }, + "ver_date": "2005-07-20T11:12:11", + "ver_number": 7 + }, + "54154": { + "class_id": 19, + "created": "2005-07-21T11:06:32", + "gitrepo": false, + "id": 54154, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 54155, + "object_name": "Toth, T.I [toth at cardiff.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 54157, + "object_name": "T\u00f3th TI, Crunelli V (2001)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 54156, + "object_name": "Octave" + } + ] + }, + "name": "Channel parameter estimation from current clamp and neuronal properties (Toth, Crunelli 2001)", + "notes": { + "attr_id": 24, + "value": "In this paper, we present a method by which the activation and kinetic properties of INa, IK can be estimated from current-clamp data, more precisely from the time course of the action potential, provided some additional electrophysiological properties of the neurone are a priori known. See reference for details and more." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/54154", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2015-07-26T08:05:35.747", + "ver_number": 4 + }, + "54896": { + "class_id": 19, + "created": "2005-08-08T13:25:01", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 252, + "object_name": "I Cl,Ca" + }, + { + "object_id": 254, + "object_name": "I CNG" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 54896, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 54900, + "object_name": "Dougherty, Daniel P [dpdoughe at mbi.ohio-state.edu]" + }, + { + "object_id": 54901, + "object_name": "Wright, Geraldine A [wright.571 at osu.edu]" + }, + { + "object_id": 54902, + "object_name": "Yew, Alice C [yew at math.ohio-state.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 54899, + "object_name": "Dougherty DP, Wright GA, Yew AC (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Olfactory receptor neuron model (Dougherty et al 2005)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 268, + "object_name": "Olfactory receptor GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Demonstration of ORN model by Dougherty, Wright and Yew (2005) PNAS 102: 10415-10420. This program, dwy_pnas_demo2, simulates the transduction current response of a single olfactory receptor neuron being stimulated by an odorant plume. The program is interactive in that a user can tweak parameter values and stimulus conditions. Also, users can save a configuration in a mat-file or export all aspects to a directory of text files. These text files can be read by other programs. There is also an import facility for importing text files from a directory that allows the user to specify their own data, pulses and parameters." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/54896", + "other_implementer": { + "attr_id": 308, + "value": "Daniel P. Dougherty, Geraldine A. Wright, Alice C. 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Specific antibodies were\r\nused to map the distribution of voltage-dependent sodium channels along\r\nthe two unmyelinated axon-like processes of the bipolar ganglion cells.\r\nThree distinct hot spots were detected. A high density of sodium\r\nchannels was present over the entire trajectory of sensory endings\r\nbeneath the outer hair cells (the most distal portion of the peripheral\r\naxon). The other two hot spots were localized in the initial segments of\r\nboth of the axons that flank the unmyelinated bipolar ganglion cell bodies.\r\n\r\nA biophysical model indicates that all three hot spots might play\r\nimportant roles in action potential initiation and propagation. For\r\ninstance, the hot spot in the receptor segment is important for\r\ntransforming the receptor potentials into a full blown action potential\r\n(Supplemental Fig. 1). The hot spots in the two paraganglionic axon\r\ninitial segments are there to ensure the successful propagation of\r\naction potentials from the peripheral to the central axon through the\r\ncell body.\r\n\r\nThe Readme.txt file provides step by step instructions on how to\r\nrecreate Figures 6 and 7 of Hossain et al., 2005 paper." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/54903", + "other_neurons": { + "attr_id": 301, + "value": "Cochlear Ganglion Cell Type II" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "antic@neuron.uchc.edu" + }, + "simPFid": { + "attr_id": 488, + "value": 145 + }, + "ver_date": "2018-10-18T15:17:05.36", + "ver_number": 20 + }, + "55035": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 127354, + "object_name": "neuroConstruct" + } + ] + }, + "class_id": 19, + "created": "2005-08-25T03:56:25", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "default_file_notes": { + "attr_id": 392, + "value": "NEURON" + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 55035, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 58973, + "object_name": "Migliore M, Ferrante M, Ascoli GA (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 116943, + "object_name": "neuroConstruct (web link to model)" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "55035", + "name": "ModelView" + } + ], + "default": "55035" + }, + "name": "CA1 pyramidal neuron: signal propagation in oblique dendrites (Migliore et al 2005)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "NEURON mod files from the paper:\r\nM. 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Ferrante, GA Ascoli (2005).\r\nThe model shows how the back- and forward propagation of action potentials in the oblique dendrites of CA1 neurons could be modulated by local properties such as morphology or active conductances." + }, + "opensourcebrain": { + "value": "ca1pyramidalcell" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/55035", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181613, + "object_name": "55035" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 178 + }, + "ver_date": "2014-02-06T16:13:58", + "ver_number": 11 + }, + "55273": { + "class_id": 19, + "created": "2005-09-12T15:10:34", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": false, + "id": 55273, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 136106, + "object_name": "G-protein coupled" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 55272, + "object_name": "Suh BC, Horowitz LF, Hirdes W, Mackie K, Hille B (2004)" + }, + { + "object_id": 55275, + "object_name": "Shapiro MS (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 55276, + "object_name": "Virtual Cell (web link to model)" + } + ] + }, + "name": "Regulation of KCNQ2/KCNQ3 current by G protein cycling (Suh et al 2004)", + "notes": { + "attr_id": 24, + "value": "Receptor-mediated modulation of KCNQ channels regulates neuronal excitability. This study concerns\r\nthe kinetics and mechanism of M1 muscarinic receptor-mediated regulation of the cloned neuronal M channel, KCNQ2/KCNQ3 (Kv7.2/Kv7.3). ... observations were successfully described by a kinetic model representing biochemical steps of the signaling\r\ncascade using published rate constants where available. The model supports the following sequence of events for\r\nthis Gq-coupled signaling: A classical G-protein cycle, including competition for nucleotide-free G-protein by all\r\nnucleotide forms and an activation step requiring Mg2, followed by G-protein-stimulated phospholipase C and\r\nhydrolysis of PIP2, and finally PIP2 dissociation from binding sites for inositol lipid on the channels so that KCNQ\r\ncurrent was suppressed. See paper for details and more." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/55273", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 204, + "object_name": "Muscarinic" + }, + { + "object_id": 218, + "object_name": "Cholinergic Receptors" + } + ] + }, + "ver_date": "2010-11-02T08:03:32", + "ver_number": 15 + }, + "55748": { + "class_id": 19, + "created": "2005-10-04T16:08:48", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 88212, + "object_name": "KCNQ1" + }, + { + "object_id": 88215, + "object_name": "I_Ks" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122822, + "object_name": "Kv1.9 Kv7.1 KCNQ1" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 55748, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 55751, + "object_name": "Silva, Jonathan [jonsilva at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52407, + "object_name": "Heart disease" + }, + { + "object_id": 88214, + "object_name": "Long-QT" + }, + { + "object_id": 238957, + "object_name": "Markov-type model" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 55676, + "object_name": "Silva J, Rudy Y (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Role of KCNQ1 and IKs in cardiac repolarization (Silva, Rudy 2005)", + "notes": { + "attr_id": 24, + "value": "Detailed Markov models of IKs (the slow delayed rectifier K+ current) and its alpha-subunit KCNQ1 were developed. The model is compared to experiment in the paper. The role of IKs in disease and drug treatments is elucidated (the prevention of excessive action potential prolongation and development of arrhythmogenic early afterdepolarizations). See paper for more and details." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/55748", + "ver_date": "2018-02-26T11:00:30.433", + "ver_number": 14 + }, + "55749": { + "class_id": 19, + "created": "2005-10-05T13:30:18", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 55749, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 55750, + "object_name": "Hennings, Kristian [krist at hst.auc.dk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 64175, + "object_name": "Electrotonus" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 55755, + "object_name": "Hennings K, Arendt-Nielsen L, Andersen OK (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Breakdown of accmmodation in nerve: a possible role for INAp (Hennings et al 2005)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 276, + "object_name": "Spinal cord lumbar motor neuron alpha ACh cell" + }, + { + "object_id": 52425, + "object_name": "Myelinated neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The present modeling study suggests that persistent, low-threshold, rapidly activating\r\nsodium currents have a key role in breakdown of accommodation, and that breakdown of\r\naccommodation can be used as a tool for studying persistent sodium current under normal and\r\npathological conditions. See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/55749", + "ver_date": "2007-04-13T18:11:53", + "ver_number": 8 + }, + "55756": { + "class_id": 19, + "created": "2005-10-05T14:32:56", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 240, + "object_name": "I Chloride" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gitrepo": false, + "id": 55756, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 56009, + "object_name": "Decker, Keith [keithforestdecker at gmail.com]" + }, + { + "object_id": 56010, + "object_name": "Livshitz, Leonid [livshitz at wustl.edu]" + }, + { + "object_id": 56011, + "object_name": "Hund, Tom [thund at pathology.wustl.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52407, + "object_name": "Heart disease" + }, + { + "object_id": 120695, + "object_name": "Sodium pump" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 55691, + "object_name": "Hund TJ, Rudy Y (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 120694, + "object_name": "Electrogenic pump" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + }, + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "A dynamic model of the canine ventricular myocyte (Hund, Rudy 2004)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52423, + "object_name": "Heart cell" + }, + { + "object_id": 88205, + "object_name": "Cardiac ventricular cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The Hund-Rudy dynamic (HRd) model is based on data from the canine epicardial ventricular myocyte. Rate-dependent phenomena associated with ion channel kinetics, action potential properties and Ca2+ handling are simulated by the model. See paper for more and details." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/55756", + "ver_date": "2009-05-28T14:21:03", + "ver_number": 8 + }, + "55859": { + "class_id": 19, + "created": "2005-10-05T18:07:09", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 88215, + "object_name": "I_Ks" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 55859, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52407, + "object_name": "Heart disease" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 5176, + "object_name": "Zeng J, Laurita KR, Rosenbaum DS, Rudy Y (1995)" + }, + { + "object_id": 5177, + "object_name": "Zeng J, Rudy Y (1995)" + }, + { + "object_id": 5192, + "object_name": "Luo CH, Rudy Y (1994)" + }, + { + "object_id": 55689, + "object_name": "Viswanathan PC, Shaw RM, Rudy Y (1999)" + }, + { + "object_id": 55860, + "object_name": "Luo CH, Rudy Y (1991)" + }, + { + "object_id": 56013, + "object_name": "Luo CH, Rudy Y (1994)" + }, + { + "object_id": 56014, + "object_name": "Shaw RM, Rudy Y (1997)" + }, + { + "object_id": 56015, + "object_name": "Viswanathan PC, Rudy Y (1999)" + }, + { + "object_id": 56017, + "object_name": "Clancy CE, Rudy Y (1999)" + }, + { + "object_id": 56018, + "object_name": "Viswanathan PC, Rudy Y (2000)" + }, + { + "object_id": 56020, + "object_name": "Faber GM, Rudy Y (2000)" + }, + { + "object_id": 112806, + "object_name": "Wang YJ, Sung RJ, Lin MW, Wu SN (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Ventricular cell model (Guinea-pig-type) (Luo, Rudy 1991, +11 other papers!) 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See paper for more and details." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/55859", + "ver_date": "2015-08-04T10:28:44.327", + "ver_number": 17 + }, + "56012": { + "class_id": 19, + "created": "2005-10-08T17:23:49", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "id": 56012, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 58024, + "object_name": "Sperelakis N, Kalloor B, Ramasamy L (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 57924, + "object_name": "PSpice" + } + ] + }, + "name": "Boundary effects influence velocity in transverse propagation of cardiac APs (Sperelakis et al 2005)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52423, + "object_name": "Heart cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "... earlier experiments were carried out with 2-dimensional\r\nsheets of cells: 2 \u00c3\u2014 3, 3 \u00c3\u2014 4, and 5 \u00c3\u2014 5 models (where the first number is the number of parallel\r\nchains and the second is the number of cells in each chain). The purpose of the present study was\r\nto enlarge the model size to 7 \u00c3\u2014 7, thus enabling the transverse velocities to be compared in models\r\nof different sizes (where all circuit parameters are identical in all models). This procedure should\r\nenable the significance of the role of edge (boundary) effects in transverse propagation to be\r\ndetermined. See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/56012", + "public_submitter_email": { + "attr_id": 309, + "value": "laksnarayana@yahoo.com" + }, + "ver_date": "2015-07-26T11:11:55.24", + "ver_number": 17 + }, + "57905": { + "class_id": 19, + "created": "2005-10-21T04:26:22", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": false, + "id": 57905, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57906, + "object_name": "Golomb, David [golomb at bgu.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 57779, + "object_name": "Golomb D, Shedmi A, Curtu R, Ermentrout GB (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Persistent synchronized bursting activity in cortical tissues (Golomb et al 2005)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 146805, + "object_name": "Abstract Wang-Buzsaki neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The program simulates a one-dimensional model of a cortical tissue with excitatory and inhibitory populations.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/57905", + "other_neurons": { + "attr_id": 301, + "value": "Neocortical inhibitory neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "golomb@bgu.ac.il" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 74 + }, + "ver_date": "2012-11-20T12:24:18", + "ver_number": 6 + }, + "57910": { + "biosimulations": { + "value": "modeldb-57910" + }, + "class_id": 19, + "created": "2005-10-23T09:38:01", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 88206, + "object_name": "I_HERG" + } + ] + }, + "gitrepo": false, + "id": 57910, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + }, + { + "object_id": 58176, + "object_name": "Chang, Han-Dong" + }, + { + "object_id": 58177, + "object_name": "Wu, Jiun-Shian [coolneon at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 88214, + "object_name": "Long-QT" + }, + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 57915, + "object_name": "Chiesa N, Rosati B, Arcangeli A, Olivotto M, Wanke E (1997)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "HERG K+ channels spike-frequency adaptation (Chiesa et al 1997)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88207, + "object_name": "Neuroblastoma" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Spike frequency adaptation has contributions from the IHERG current (encoded by the human eag-related gene\r\n(HERG); Warmke & Ganetzky, 1994), which develops with\r\nslow kinetics during depolarization and contributes to the\r\nrepolarization of the long action potentials typically present\r\nin the heart. IHERG is one of the delayed rectifier currents\r\n(IK(r)) of the heart, and HERG mutations are associated\r\nwith one of the cardiac arrhythmia LQT syndromes (LQT2).\r\nSee paper for more and details.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/57910", + "other_currents": { + "attr_id": 303, + "value": "IK(erg)" + }, + "other_neurons": { + "attr_id": 301, + "value": "Neuroblastoma cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2009-01-26T16:26:03", + "ver_number": 10 + }, + "58172": { + "biosimulations": { + "value": "modeldb-58172" + }, + "class_id": 19, + "created": "2005-10-27T01:59:06", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 88206, + "object_name": "I_HERG" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122807, + "object_name": "HERG KCNH2" + } + ] + }, + "gitrepo": false, + "id": 58172, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + }, + { + "object_id": 58176, + "object_name": "Chang, Han-Dong" + }, + { + "object_id": 58177, + "object_name": "Wu, Jiun-Shian [coolneon at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 52407, + "object_name": "Heart disease" + }, + { + "object_id": 88214, + "object_name": "Long-QT" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 58174, + "object_name": "Clancy CE, Rudy Y (2001)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Consequences of HERG mutations in the long QT syndrome (Clancy, Rudy 2001)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52423, + "object_name": "Heart cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This study demonstrates which mutations can prolong APD sufficiently to generate early \r\nafterdepolarizations (EADs), which may trigger life-threatening arrhythmias. The severity of the phenotype is shown to \r\ndepend on the specific kinetic changes and how they affect I(Kr) during the time course of the action potential. 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We further show that a reduction in dendritic Na(+) current increases excitability by decreasing the interspike interval (ISI) and promoting burst firing." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/59480", + "other_neurons": { + "attr_id": 301, + "value": "ELL pyramidal neurons" + }, + "simPFid": { + "attr_id": 488, + "value": 82 + }, + "ver_date": "2007-04-13T16:01:13", + "ver_number": 14 + }, + "59581": { + "class_id": 19, + "created": "2005-12-03T11:28:06", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 59581, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33787, + "object_name": "Graham, Bruce [B.Graham at cs.stir.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 128875, + "object_name": "Development" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 59590, + "object_name": "Graham BP, Lauchlan K, Mclean DR (2006)" + }, + { + "object_id": 83586, + "object_name": "Graham BP, van Ooyen A (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Continuum model of tubulin-driven neurite elongation (Graham et al 2006)", + "notes": { + "attr_id": 24, + "value": "This model investigates the elongation over time of a single developing neurite (axon or dendrite). 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The cytoskeleton is not explicitly modelled, but its construction is assumed to depend on the\r\navailable free tubulin at the growing neurite tip." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/59581", + "other_concept": { + "attr_id": 306, + "value": "Development" + }, + "other_neurons": { + "attr_id": 301, + "value": "Generic" + }, + "other_type": { + "attr_id": 305, + "value": "Neurite" + }, + "simPFid": { + "attr_id": 488, + "value": 81 + }, + "ver_date": "2018-10-17T15:42:47.86", + "ver_number": 8 + }, + "59582": { + "class_id": 19, + "created": "2005-12-03T11:38:33", + "gitrepo": false, + "id": 59582, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33787, + "object_name": "Graham, Bruce [B.Graham at cs.stir.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 128875, + "object_name": "Development" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 59589, + "object_name": "Graham BP, van Ooyen A (2004)" + }, + { + "object_id": 83586, + "object_name": "Graham BP, van Ooyen A (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53895, + "object_name": "Java" + } + ] + }, + "name": "Compartmental models of growing neurites (Graham and van Ooyen 2004)", + "notes": { + "attr_id": 24, + "value": "Simulator for models of neurite outgrowth. The principle model is a biophysical model of neurite outgrowth described in Graham and van Ooyen (2004). In the model, branching depends on the concentration of a branch-determining substance in each terminal segment. The substance is produced in the cell body and is transported by active transport and diffusion to the terminals. The model reveals that transport-limited effects may give rise to the same modulation of branching as indicated by the stochastic BESTL model. Different limitations arise if transport is dominated by active transport or by diffusion." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/59582", + "other_concept": { + "attr_id": 306, + "value": "Development" + }, + "other_type": { + "attr_id": 305, + "value": "Neurite" + }, + "ver_date": "2015-07-26T11:21:20.857", + "ver_number": 8 + }, + "62266": { + "class_id": 19, + "created": "2005-12-28T17:11:38", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 62266, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 62267, + "object_name": "Safronov, Boris [safronov at ibmc.up.pt]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 4019, + "object_name": "Safronov BV, Wolff M, Vogel W (2000)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "62266", + "name": "ModelView" + } + ], + "default": "62266" + }, + "name": "Excitability of the soma in central nervous system neurons (Safronov et al 2000)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 276, + "object_name": "Spinal cord lumbar motor neuron alpha ACh cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The ability of the soma of a spinal dorsal horn neuron, a spinal ventral horn neuron, and a hippocampal pyramidal neuron to generate action potentials was studied using experiments and computer simulations. By comparing recordings ... of a dorsal horn neuron with simulated responses, it was shown that computer models can be adequate for the study of somatic excitability. The modeled somata of both spinal neurons were unable to generate action potentials, showing only passive and local responses to current injections. ... In contrast to spinal neurons, the modeled soma of the hippocampal pyramidal neuron generated spikes with an overshoot of +9 mV. It is concluded that the somata of spinal neurons cannot generate action potentials and seem to resist their propagation from the axon to dendrites. ... See paper for more and details.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/62266", + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181635, + "object_name": "62266" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 241 + }, + "ver_date": "2014-02-07T11:51:17", + "ver_number": 10 + }, + "62268": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 118518, + "object_name": "backup of web link model" + } + ] + }, + "class_id": 19, + "created": "2006-01-03T14:46:21", + "gitrepo": false, + "id": 62268, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 62269, + "object_name": "Miller, Paul [pmiller at brandeis.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3542, + "object_name": "Pattern Recognition" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 82787, + "object_name": "Working memory" + }, + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 62270, + "object_name": "Miller P, Wang XJ (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + }, + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Inhibitory control by an integral feedback signal in prefrontal cortex (Miller and Wang 2006)", + "notes": { + "attr_id": 24, + "value": "The prefrontal cortex (PFC) is known to be critical for inhibitory\r\ncontrol of behavior, but the underlying mechanisms are unclear.\r\nHere, we propose that inhibitory control can be instantiated by an\r\nintegral signal derived from working memory, another key function of the PFC. Specifically, we assume that an integrator converts\r\nexcitatory input into a graded mnemonic activity that provides an\r\ninhibitory signal (integral feedback control) to upstream afferent\r\nneurons. We demonstrate this scenario in a neuronal-network\r\nmodel for a temporal discrimination task... See paper for details\r\nand more.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/62268", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "ver_date": "2013-01-15T10:35:45", + "ver_number": 17 + }, + "62272": { + "biosimulations": { + "value": "modeldb-62272" + }, + "class_id": 19, + "created": "2006-01-04T10:59:30", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 87769, + "object_name": "ATP-senstive potassium current" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122823, + "object_name": "Kir6.2 KCNJ11" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 62272, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + }, + { + "object_id": 58176, + "object_name": "Chang, Han-Dong" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 62276, + "object_name": "Wu SN, Chang HD (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "A model for pituitary GH(3) lactotroph (Wu and Chang 2005)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 62277, + "object_name": "Pituitary cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The ATP-sensitive K(+) (K(ATP)) channels are composed of sulfonylurea receptor and inwardly rectifying K(+) channel (Kir6.2) subunit. These channels are regulated by intracellular ADP/ATP ratio and play a role in cellular metabolism. ... The objective of this study was to determine whether Diethyl pyrocarbonate (DEPC) modifies K(ATP)-channel activity in pituitary GH(3) cells. ... Simulation studies also demonstrated that the increased conductance of K(ATP)-channels used to mimic DEPC actions reduced the frequency of spontaneous action potentials and fluctuation of intracellular Ca(2+). The results indicate that chemical modification with DEPC enhances K(ATP)-channel activity and influences functional activities of pituitary GH(3) cells. See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/62272", + "other_currents": { + "attr_id": 303, + "value": "IK(ATP)" + }, + "other_implementer": { + "attr_id": 308, + "value": "Sheng-Nan Wu, Han-Dong Chang" + }, + "other_neurons": { + "attr_id": 301, + "value": "Pituitary cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 153715, + "object_name": "IP3" + } + ] + }, + "ver_date": "2015-07-26T11:25:08.303", + "ver_number": 16 + }, + "62284": { + "class_id": 19, + "created": "2006-01-05T11:28:07", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 62284, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 62267, + "object_name": "Safronov, Boris [safronov at ibmc.up.pt]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + }, + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 62283, + "object_name": "Melnick IV, Santos SF, Safronov BV (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "62284", + "name": "ModelView" + } + ], + "default": "62284" + }, + "name": "Spike frequency adaptation in spinal sensory neurones (Melnick et al 2004)", + "notes": { + "attr_id": 24, + "value": "Using tight-seal recordings from rat spinal cord slices, intracellular\r\nlabelling and computer simulation, we analysed the mechanisms of spike\r\nfrequency adaptation in substantia gelatinosa (SG)\r\nneurones. Adapting-firing neurones (AFNs) generated short bursts of\r\nspikes during sustained depolarization and were mostly found in\r\nlateral SG. ... Ca2 + -dependent conductances do not contribute to\r\nadapting firing. Transient KA current was small and completely\r\ninactivated at resting potential suggesting that adapting firing was\r\nmainly generated by voltage-gated Na+ and delayed-rectifier K+ (KDR )\r\ncurrents. ... Computer simulation has further revealed that\r\ndown-regulation of Na+ conductance represents an effective mechanism\r\nfor the induction of firing adaptation. It is suggested that the\r\ncell-specific regulation of Na+ channel expression can be an important\r\nfactor underlying the diversity of firing patterns in SG neurones.\r\nSee paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/62284", + "other_neurons": { + "attr_id": 301, + "value": "Spinal sensory neuron" + }, + "simPFid": { + "attr_id": 488, + "value": 486 + }, + "ver_date": "2017-05-22T12:21:00.493", + "ver_number": 13 + }, + "62285": { + "class_id": 19, + "created": "2006-01-05T14:20:37", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 62285, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 62267, + "object_name": "Safronov, Boris [safronov at ibmc.up.pt]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 46382, + "object_name": "Melnick IV, Santos SF, Szokol K, Sz\u00fbcs P, Safronov BV (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "62285", + "name": "ModelView" + } + ], + "default": "62285" + }, + "name": "Tonic firing in substantia gelatinosa neurons (Melnick et al 2004)", + "notes": { + "attr_id": 24, + "value": "Ionic conductances underlying excitability in tonically firing neurons\r\n(TFNs) from substantia gelatinosa (SG) were studied by the patch-clamp\r\nmethod in rat spinal cord slices. ... Suppression of Ca2+ and KCA currents ... did not\r\nabolish the basic pattern of tonic firing, indicating that it was\r\ngenerated by voltage-gated Na+ and K+ currents. ...\r\n on the basis of present data, we created a model of TFN\r\nand showed that Na+ and KDR currents are sufficient to generate a\r\nbasic pattern of tonic firing. It is concluded that the balanced\r\ncontribution of all ionic conductances described here is important for\r\ngeneration and modulation of tonic firing in SG neurons. See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/62285", + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181603, + "object_name": "62285" + } + ] + }, + "ver_date": "2017-05-22T12:29:43.85", + "ver_number": 16 + }, + "62286": { + "class_id": 19, + "created": "2006-01-05T21:04:33", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 62286, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 60186, + "object_name": "Greenstein JL, Hinch R, Winslow RL (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Excitation-contraction coupling in an integrative heart cell model (Greenstein et al 2006)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52423, + "object_name": "Heart cell" + }, + { + "object_id": 88205, + "object_name": "Cardiac ventricular cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... In this study, we generalize a recently developed analytical\r\napproach for deriving simplified mechanistic models of CICR\r\n(Ca(2+)-induced Ca(2+) release) to formulate an integrative model of\r\nthe canine cardiac myocyte which is computationally efficient. The\r\nresulting model faithfully reproduces experimentally measured\r\nproperties of EC (excitation-contraction) coupling and whole cell\r\nphenomena. The model is used to study the role of local redundancy in\r\nL-type Ca(2+) channel gating and the role of dyad configuration on EC\r\ncoupling. Simulations suggest that the characteristic steep rise in EC\r\ncoupling gain observed at hyperpolarized potentials is a result of\r\nincreased functional coupling between LCCs (L-type Ca(2+) channels)\r\nand RyRs (ryanodine-sensitive Ca(2+) release channels). We also\r\ndemonstrate mechanisms by which alterations in the early\r\nrepolarization phase of the action potential, resulting from reduction\r\nof the transient outward potassium current, alters properties of EC\r\ncoupling.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/62286", + "other_receptors": { + "attr_id": 302, + "value": "Ryanodine" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2017-07-21T18:20:25.67", + "ver_number": 16 + }, + "62287": { + "biosimulations": { + "value": "modeldb-62287" + }, + "class_id": 19, + "created": "2006-01-07T05:29:50", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122803, + "object_name": "Kv4.3 KCND3" + }, + { + "object_id": 122804, + "object_name": "Kv1.4 KCNA4" + } + ] + }, + "gitrepo": false, + "id": 62287, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + }, + { + "object_id": 58177, + "object_name": "Wu, Jiun-Shian [coolneon at gmail.com]" + }, + { + "object_id": 62652, + "object_name": "Sung, Ruey J " + }, + { + "object_id": 62653, + "object_name": "Wang, Ya-Jean " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 52407, + "object_name": "Heart disease" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 60284, + "object_name": "Greenstein JL, Wu R, Po S, Tomaselli GF, Winslow RL (2000)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Kv4.3, Kv1.4 encoded K(+) channel in heart cells (Greenstein et al 2000) (XPP)", + "notes": { + "attr_id": 24, + "value": "A model of canine I:(to1) (the Ca(2+)-independent transient outward current) is formulated as the combination of Kv4.3 and Kv1.4 \r\ncurrents and is incorporated into an existing canine ventricular myocyte model. Simulations demonstrate strong \r\ncoupling between L-type Ca(2+) current and I:(Kv4.3) and predict a bimodal relationship between I:(Kv4.3) \r\ndensity and APD whereby perturbations in I:(Kv4.3) density may produce either prolongation or shortening of APD, \r\ndepending on baseline I:(to1) current level.\r\nThe model files were submitted by:\r\nDr. Sheng-Nan Wu, Dr. Ruey J. Sung, Ya-Jean Wang and Jiun-Shian Wu\r\ne-mail: snwu@mail.ncku.edu.tw\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/62287", + "other_implementer": { + "attr_id": 308, + "value": "Dr. Ruey J. Sung, Ya-Jean Wang and Jiun-Shian Wu" + }, + "other_neurons": { + "attr_id": 301, + "value": "heart cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2009-07-13T10:53:49", + "ver_number": 6 + }, + "62654": { + "class_id": 19, + "created": "2006-01-09T15:11:17", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "id": 62654, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 62655, + "object_name": "Bassingthwaighte, James" + }, + { + "object_id": 62656, + "object_name": "Li, Zheng " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 52407, + "object_name": "Heart disease" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 60284, + "object_name": "Greenstein JL, Wu R, Po S, Tomaselli GF, Winslow RL (2000)" + }, + { + "object_id": 62288, + "object_name": "Winslow RL, Rice J, Jafri S, Marb\u00e1n E, O'Rourke B (1999)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53895, + "object_name": "Java" + } + ] + }, + "name": "Kv4.3, Kv1.4 encoded K channel in heart cells & tachy. (Winslow et al 1999, Greenstein et al 2000)", + "notes": { + "attr_id": 24, + "value": "(1999) We present a model of the canine midmyocardial ventricular action potential and Ca2+ transient. The model is used to estimate the degree of functional upregulation and downregulation of Na/Ca exchanger protein and sarcoplasmic reticulum Ca ATPase in heart failure using data obtained from 2 different experimental protocols.\r\n(2000): A model of canine I:(to1) (the Ca(2+)-independent transient outward current) is formulated as the combination of Kv4.3 and Kv1.4 \r\ncurrents and is incorporated into an existing canine ventricular myocyte model. Simulations demonstrate strong \r\ncoupling between L-type Ca(2+) current and I:(Kv4.3) and predict a bimodal relationship between I:(Kv4.3) \r\ndensity and APD whereby perturbations in I:(Kv4.3) density may produce either prolongation or shortening of APD, \r\ndepending on baseline I:(to1) current level.\r\nSee each paper for more and details.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/62654", + "other_currents": { + "attr_id": 303, + "value": "Kv4.3, Kv1.4" + }, + "ver_date": "2006-01-09T18:29:49", + "ver_number": 16 + }, + "62661": { + "biosimulations": { + "value": "modeldb-62661" + }, + "class_id": 19, + "created": "2006-01-11T22:42:35", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 62661, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + }, + { + "object_id": 58176, + "object_name": "Chang, Han-Dong" + }, + { + "object_id": 58177, + "object_name": "Wu, Jiun-Shian [coolneon at gmail.com]" + }, + { + "object_id": 62652, + "object_name": "Sung, Ruey J " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 52407, + "object_name": "Heart disease" + }, + { + "object_id": 88213, + "object_name": "Brugada" + }, + { + "object_id": 88214, + "object_name": "Long-QT" + }, + { + "object_id": 238957, + "object_name": "Markov-type model" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 62664, + "object_name": "Clancy CE, Rudy Y (2002)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Markovian model for cardiac sodium channel (Clancy, Rudy 2002)", + "notes": { + "attr_id": 24, + "value": "Complex physiological interactions determine the functional consequences of gene abnormalities and make mechanistic interpretation of phenotypes extremely difficult. A recent example is a single mutation in the C terminus of the cardiac Na(+) channel, 1795insD. The mutation causes two distinct clinical syndromes, long QT (LQT) and Brugada, leading to life-threatening cardiac arrhythmias. Coexistence of these syndromes is seemingly paradoxical; LQT is associated with enhanced Na(+) channel function, and Brugada with reduced function. Using a computational approach, we demonstrate that the 1795insD mutation exerts variable effects depending on the myocardial substrate. We develop Markov models of the wild-type and 1795insD cardiac Na(+) channels. See reference for more and details. The model files were submitted by: Dr. Jiun-Shian Wu, Dr. Sheng-Nan Wu, Dr. Ruey J. Sung, Han-Dong Chang." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/62661", + "other_implementer": { + "attr_id": 308, + "value": "Jiun-Shian Wu, Sheng-Nan Wu, Ruey J. Sung, Han-Dong Chang" + }, + "other_neurons": { + "attr_id": 301, + "value": "Heart cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "snwu" + }, + "ver_date": "2018-02-26T11:01:27.257", + "ver_number": 11 + }, + "62673": { + "class_id": 19, + "created": "2006-01-16T10:43:50", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 62673, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 62674, + "object_name": "Baranauskas, Gytis [baranauskas at elet.polimi.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 62662, + "object_name": "Baranauskas G, Martina M (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "62673", + "name": "ModelView" + } + ], + "default": "62673" + }, + "name": "Sodium currents activate without a delay (Baranauskas and Martina 2006)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + }, + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Hodgkin and Huxley established that sodium currents in the squid giant\r\naxons activate after a delay, which is explained by the model of a\r\nchannel with three identical independent gates that all have to open\r\nbefore the channel can pass current (the HH model). It is assumed that\r\nthis model can adequately describe the sodium current activation time\r\ncourse in all mammalian central neurons, although there is no\r\nexperimental evidence to support such a conjecture. We performed high\r\ntemporal resolution studies of sodium currents gating in three types\r\nof central neurons. ... These results can be explained by a model with\r\ntwo closed states and one open state. ... This\r\nmodel captures all major properties of the sodium current\r\nactivation. In addition, the proposed model reproduces the observed\r\naction potential shape more accurately than the traditional HH model.\r\nSee paper for more and details.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/62673", + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181828, + "object_name": "62673" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 609 + }, + "ver_date": "2014-02-25T01:09:02", + "ver_number": 14 + }, + "62676": { + "biosimulations": { + "value": "modeldb-62676" + }, + "class_id": 19, + "created": "2006-01-20T04:36:01", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 62676, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + }, + { + "object_id": 58176, + "object_name": "Chang, Han-Dong" + }, + { + "object_id": 58177, + "object_name": "Wu, Jiun-Shian [coolneon at gmail.com]" + }, + { + "object_id": 62652, + "object_name": "Sung, Ruey J " + }, + { + "object_id": 62678, + "object_name": "Lai, Hsing-Jung " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 52407, + "object_name": "Heart disease" + }, + { + "object_id": 141450, + "object_name": "Cardiac pacemaking" + }, + { + "object_id": 238957, + "object_name": "Markov-type model" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 62677, + "object_name": "Matsuoka S, Sarai N, Kuratomi S, Ono K, Noma A (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Markovian model for single-channel recordings of Ik_1 in ventricular cells (Matsuoka et al 2003)", + "notes": { + "attr_id": 24, + "value": "The interaction between many currents in a cardiac ventricular model are examined in this paper. One of the main contributions come from a current called IK_1. An XPP version of this model was supplied by\r\nHsieng-Jung Lai, Jiun-Shian Wu, Sheng-Nan Wu, Ruey J. Sung, Han-Dong\r\nChang. Please see paper and model for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/62676", + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2018-02-26T11:01:57.51", + "ver_number": 16 + }, + "64167": { + "class_id": 19, + "created": "2006-02-17T10:34:04", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 64167, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33779, + "object_name": "Spruston, Nelson [Spruston at Northwestern.edu]" + }, + { + "object_id": 64168, + "object_name": "Golding, Nace L [golding at mail.utexas.edu]" + }, + { + "object_id": 64169, + "object_name": "Kath, William L [kath at northwestern.edu]" + }, + { + "object_id": 64172, + "object_name": "Mickus, Timothy " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 3886, + "object_name": "Golding NL, Kath WL, Spruston N (2001)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "Dichotomy of action-potential backpropagation in CA1 pyramidal neuron dendrites (Golding et al 2001)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "From reference below and \r\nCorrigendum: J Neurophysiol 87:1a, 2002 (better versions of figures 2, 3, 5 and 7 because of poor print quality in the original article; as of 2/2006, these figures are perfectly fine in the PDF of the original article that is currently available from the publisher's WWW site).\r\nExamines the anatomical and biophysical factors that account for the fact that retrograde invasion of spikes into the apical dendritic tree past 300 um succeeds in some CA1 pyramidal neurons but fails in others." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/64167", + "other_implementer": { + "attr_id": 308, + "value": "Golding, Nace L., Kath, William L." + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ted.carnevale@yale.edu" + }, + "ver_date": "2012-09-05T16:57:31", + "ver_number": 12 + }, + "64170": { + "class_id": 19, + "created": "2006-02-19T20:09:38", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "id": 64170, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33779, + "object_name": "Spruston, Nelson [Spruston at Northwestern.edu]" + }, + { + "object_id": 64168, + "object_name": "Golding, Nace L [golding at mail.utexas.edu]" + }, + { + "object_id": 64169, + "object_name": "Kath, William L [kath at northwestern.edu]" + }, + { + "object_id": 64172, + "object_name": "Mickus, Timothy " + }, + { + "object_id": 64173, + "object_name": "Katz, Yael [y-katz at northwestern.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 64175, + "object_name": "Electrotonus" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 64166, + "object_name": "Golding NL, Mickus TJ, Katz Y, Kath WL, Spruston N (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "Voltage attenuation in CA1 pyramidal neuron dendrites (Golding et al 2005)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Voltage attenuation in the apical dendritic field of CA1 pyramidal neurons is particularly strong for epsps spreading toward the soma. High cytoplasmic resistivity and high membrane (leak) conductance appear to be the major determinants of voltage attenuation over most of the apical field, but H current may be responsible for as much as half of the attenuation of distal apical epsps." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/64170", + "other_concept": { + "attr_id": 306, + "value": "see my email" + }, + "other_implementer": { + "attr_id": 308, + "value": "Mickus, Tim\r\nKatz, Yael" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ted.carnevale@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2012-09-05T16:45:42", + "ver_number": 11 + }, + "64171": { + "biosimulations": { + "value": "modeldb-64171" + }, + "class_id": 19, + "created": "2006-02-19T21:44:35", + "gitrepo": false, + "id": 64171, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + }, + { + "object_id": 62652, + "object_name": "Sung, Ruey J " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 52407, + "object_name": "Heart disease" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 55860, + "object_name": "Luo CH, Rudy Y (1991)" + }, + { + "object_id": 64176, + "object_name": "Wu SN (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Cardiac action potential based on Luo-Rudy phase 1 model (Luo and Rudy 1991), (Wu 2004)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52423, + "object_name": "Heart cell" + }, + { + "object_id": 88205, + "object_name": "Cardiac ventricular cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A mathematical model of the membrane action potential of the mammalian \r\nventricular cell is introduced. The model is based, whenever possible, \r\non recent single-cell and single-channel data and incorporates the \r\npossibility of changing extracellular potassium concentration [K]o. The \r\nfast sodium current, INa, is characterized by fast upstroke velocity (Vmax \r\n= 400 V/sec) and slow recovery from inactivation. The time-independent \r\npotassium current, IK1, includes a negative-slope phase and displays \r\nsignificant crossover phenomenon as [K]o is varied. The time-dependent \r\npotassium current, IK, shows only a minimal degree of crossover. A novel \r\npotassium current that activates at plateau potentials is included in \r\nthe model. The simulated action potential duplicates the experimentally \r\nobserved effects of changes in [K]o on action potential duration and rest \r\npotential. See papers for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/64171", + "other_implementer": { + "attr_id": 308, + "value": "Drs. Sheng-Nan Wu and Ruey J Sung" + }, + "other_neurons": { + "attr_id": 301, + "value": "Heart cells" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "snwu" + }, + "ver_date": "2007-04-13T16:44:34", + "ver_number": 8 + }, + "64195": { + "class_id": 19, + "created": "2006-03-07T14:52:38", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 64195, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 64249, + "object_name": "Hallermann, Stefan [hallermann at medizin.uni-leipzig.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 64233, + "object_name": "Kole MH, Hallermann S, Stuart GJ (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "64195_1", + "name": "fig6B simulation" + }, + { + "id": "64195_2", + "name": "Short run simulation" + } + ], + "default": "64195_1" + }, + "name": "Stochastic Ih and Na-channels in pyramidal neuron dendrites (Kole et al 2006)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The hyperpolarization-activated cation current (Ih) plays an important role in regulating neuronal excitability, yet its native single-channel properties in the brain are essentially unknown. Here we use variance-mean analysis to study the properties of single Ih channels in the apical dendrites of cortical layer 5 pyramidal neurons in vitro. ... In contrast to the uniformly distributed single-channel conductance, Ih channel number increases exponentially with distance, reaching densities as high as approximately 550 channels/microm2 at distal dendritic sites. These high channel densities generate significant membrane voltage noise. By incorporating a stochastic model of Ih single-channel gating into a morphologically realistic model of a layer 5 neuron, we show that this channel noise is higher in distal dendritic compartments and increased threefold with a 10-fold increased single-channel conductance (6.8 pS) but constant Ih current density. ... These data suggest that, in the face of high current densities, the small single-channel conductance of Ih is critical for maintaining the fidelity of action potential output. See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/64195", + "other_currents": { + "attr_id": 303, + "value": "I h" + }, + "other_implementer": { + "attr_id": 308, + "value": "Hallermann, Stefan" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181668, + "object_name": "64195_2" + }, + { + "object_id": 181669, + "object_name": "64195_1" + } + ] + }, + "ver_date": "2015-01-02T22:06:54", + "ver_number": 20 + }, + "64212": { + "class_id": 19, + "created": "2006-03-14T02:23:01", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 64212, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 64213, + "object_name": "Shimazaki, Ranken " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 64236, + "object_name": "Shimazaki R, Boccaccio A, Mazzatenta A, Pinato G, Migliore M, Menini A (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "64212", + "name": "ModelView" + } + ], + "default": "64212" + }, + "name": "Vomeronasal sensory neuron (Shimazaki et al 2006)", + "notes": { + "attr_id": 24, + "value": "NEURON model files from the papers:\r\nShimazaki et al, Chem. 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The results reported in this work further establish the importance of Kx channels in rod photoreceptor function." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/64228", + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181815, + "object_name": "64228_1" + }, + { + "object_id": 181816, + "object_name": "64228_2" + }, + { + "object_id": 181817, + "object_name": "64228_3" + }, + { + "object_id": 181818, + "object_name": "64228_4" + }, + { + "object_id": 181819, + "object_name": "64228_5" + }, + { + "object_id": 181820, + "object_name": "64228_6" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 340 + }, + "ver_date": "2015-01-02T22:30:43", + "ver_number": 9 + }, + "64229": { + "class_id": 19, + "created": "2006-03-18T20:42:02", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 64229, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33736, + "object_name": "Hines, Michael [Michael.Hines at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 64230, + "object_name": "Migliore M, Cannia C, Lytton WW, Markram H, Hines ML (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Parallel network simulations with NEURON (Migliore et al 2006)", + "notes": { + "attr_id": 24, + "value": "The NEURON simulation environment has been extended to support parallel network simulations.\r\nThe performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/64229", + "public_submitter_email": { + "attr_id": 309, + "value": "michael.hines@yale.edu" + }, + "ver_date": "2022-05-27T18:19:13.74", + "ver_number": 15 + }, + "64242": { + "class_id": 19, + "created": "2006-03-27T17:11:46", + "gitrepo": false, + "id": 64242, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 64246, + "object_name": "Cannon, Robert C [Robert.C.Cannon at gmail.com]" + }, + { + "object_id": 64247, + "object_name": "Koene, Randal A [randalk at bu.edu]" + }, + { + "object_id": 64248, + "object_name": "Hasselmo, Michael E [hasselmo at bu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 98011, + "object_name": "Place cell/field" + }, + { + "object_id": 182690, + "object_name": "Spatial Navigation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 64244, + "object_name": "Cannon RC, Hasselmo ME, Koene RA (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 64245, + "object_name": "Catacomb (web link to model)" + } + ] + }, + "name": "Biologically-plausible models for spatial navigation (Cannon et al 2003)", + "notes": { + "attr_id": 24, + "value": "Hypotheses about how parahippocampal and hippocampal structures may be involved in spatial navigation tasks are implemented in a model of a virtual rat navigating through a virtual environment in search of a food reward. The model incorporates theta oscillations to separate encoding from retrieval and yields testable predictions about the phase relations of spiking activity to theta oscillations in different parts of the hippocampal formation at various stages of the behavioral task. See paper for more and details." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/64242", + "ver_date": "2017-06-02T17:00:49.513", + "ver_number": 16 + }, + "64255": { + "class_id": 19, + "created": "2006-03-30T03:30:41", + "gitrepo": false, + "id": 64255, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 64256, + "object_name": "Leblois, Arthur [leblois at clipper.ens.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 64251, + "object_name": "Leblois A, Boraud T, Meissner W, Bergman H, Hansel D (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "A dynamical model of the basal ganglia (Leblois et al 2006)", + "notes": { + "attr_id": 24, + "value": "We propose a new model for the function and dysfunction of the basal ganglia (BG). \r\nThe basal ganglia are a set of cerebral structures involved in motor control which \r\ndysfunction causes high-incidence pathologies such as Parkinson's disease (PD). \r\nTheir precise motor functions remain unknown. \r\nThe classical model of the BG that allowed for the discovery of new treatments \r\nfor PD seems today outdated in several respects. \r\nBased on experimental observations, our model proposes a simple dynamical framework \r\nfor the understanding of how BG may select motor programs to be executed. Moreover, \r\nwe explain how this ability is lost and how tremor-related oscillations in neuronal \r\nactivity may emerge in PD. \r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/64255", + "other_implementer": { + "attr_id": 308, + "value": "Leblois, arthur " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "leblois@clipper.ens.fr" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 58 + }, + "ver_date": "2008-10-07T09:19:15", + "ver_number": 8 + }, + "64259": { + "class_id": 19, + "created": "2006-04-01T03:16:05", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 64259, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33744, + "object_name": "Destexhe, Alain [Destexhe at iaf.cnrs-gif.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 183719, + "object_name": "Synaptic noise" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 64260, + "object_name": "Rudolph M, Destexhe A (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "64259", + "name": "ModelView" + } + ], + "default": "64259" + }, + "name": "Steady-state Vm distribution of neurons subject to synaptic noise (Rudolph, Destexhe 2005)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This package simulates synaptic background activity similar to in vivo measurements using a model of fluctuating synaptic conductances, and compares the simulations with analytic estimates. The steady-state membrane potential (Vm) distribution is calculated numerically and compared with the \"extended\" analytic expression provided in the reference (see this paper for details)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/64259", + "other_concept": { + "attr_id": 306, + "value": "Synaptic noise" + }, + "other_neurons": { + "attr_id": 301, + "value": "Neocortical pyramidal neuron (generic)" + }, + "other_receptors": { + "attr_id": 302, + "value": "AMPA and GabaA" + }, + "other_type": { + "attr_id": 305, + "value": "passive 1cpt model" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2022-12-16T10:40:00.913", + "ver_number": 9 + }, + "64261": { + "class_id": 19, + "created": "2006-04-12T09:39:20", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 64261, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33739, + "object_name": "Davison, Andrew [Andrew.Davison at iaf.cnrs-gif.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 83538, + "object_name": "Unsupervised Learning" + }, + { + "object_id": 87578, + "object_name": "STDP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 64294, + "object_name": "Davison AP, Fr\u00e9gnac Y (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "64261", + "name": "ModelView" + } + ], + "default": "64261" + }, + "name": "Learning spatial transformations through STDP (Davison, Fr\u00e9gnac 2006)", + "notes": { + "attr_id": 24, + "value": "A common problem in tasks involving the integration of spatial information from multiple senses, or in sensorimotor coordination, is that different modalities represent space in different frames of reference. Coordinate transformations between different reference frames are therefore required. One way to achieve this relies on the encoding of spatial information using population codes. The set of network responses to stimuli in different locations (tuning curves) constitute a basis set of functions which can be combined\r\nlinearly through weighted synaptic connections in order to approximate non-linear transformations of the input variables. The question then arises how the appropriate synaptic connectivity is obtained. \r\n\r\nThis model shows that a network of spiking neurons can learn the coordinate transformation from one frame of reference to another, with connectivity that develops continuously in an unsupervised manner, based only on the correlations available in the environment, and with a biologically-realistic plasticity mechanism (spike timing-dependent plasticity)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/64261", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115960, + "object_name": "Generic" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181877, + "object_name": "64261" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 272 + }, + "ver_date": "2014-02-15T23:23:23", + "ver_number": 14 + }, + "64266": { + "class_id": 19, + "created": "2006-04-26T12:05:47", + "gitrepo": false, + "id": 64266, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 64267, + "object_name": "Butts, Dan [daniel_butts at hms.harvard.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 64204, + "object_name": "Lu HC, Butts DA, Kaeser PS, She WC, Janz R, Crair MC (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Simple model of barrel-specific segregation in cortex (Lu et al 2006)", + "notes": { + "attr_id": 24, + "value": "Mice with a loss-of-function mutation of calcium/calmodulin-activated adenylyl cyclase I (AC1) - barrelless mice - have strikingly abherrent cortical development: the thalamic afferents into the barrel cortex do not segregate into whisker-specific barrels. Our paper investigates the link between this mutation and the \"barrelless\" phenotype, and demonstrates that the loss-of-function mutation leads to deficits in presynaptic mechanisms at the thalamocortical synapse.\r\n\r\nHow might presynaptic deficits disrupt whisker-specific segregation in the barrel cortex? We used a model to demonstrate one possibility: decrease in the release probability at the thalamocortical synapse (which is observed in the barrelless mutant) can influence the balance between LTP and LTD (in favor of LTD), which can disrupt whisker segregaton. Though how this occurs is easily explained with a conceptual model (described succinctly in the associated paper), we also produced a computational simulation of this phenomenon." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/64266", + "other_neurons": { + "attr_id": 301, + "value": "Generic layer 4 cortical neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "dbutts@deas.harvard.edu" + }, + "ver_date": "2006-04-26T13:37:52", + "ver_number": 8 + }, + "64285": { + "class_id": 19, + "created": "2006-05-17T21:56:50", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "id": 64285, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 56008, + "object_name": "Kuznetsov AS, Kopell NJ, Wilson CJ (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Dopaminergic cell bursting model (Kuznetsov et al 2006)", + "notes": { + "attr_id": 24, + "value": "Dopaminergic neurons of the midbrain fire spontaneously at rates\r\n<10/s and ordinarily will not exceed this range even when driven with\r\nsomatic current injection. During spontaneous\r\nbursting of dopaminergic neurons in vivo, bursts related to reward\r\nexpectation in behaving animals, and bursts generated by dendritic\r\napplication of N-methyl-D-aspartate (NMDA) agonists, transient firing\r\nattains rates well above this range. We suggest a way such highfrequency\r\nfiring may occur in response to dendritic NMDA receptor\r\nactivation. We have extended the coupled oscillator model of the\r\ndopaminergic neuron, which represents the soma and dendrites as\r\nelectrically coupled compartments with different natural spiking frequencies,\r\nby addition of dendritic AMPA (voltage-independent) or\r\nNMDA (voltage-dependent) synaptic conductance. Both soma and\r\ndendrites contain a simplified version of the calcium-potassium mechanism\r\nknown to be the mechanism for slow spontaneous oscillation\r\nand background firing in dopaminergic cells. We show that because of its voltage dependence,\r\nNMDA receptor activation acts to amplify the effect on the\r\nsoma of the high-frequency oscillation of the dendrites, which is\r\nnormally too weak to exert a large influence on the overall oscillation\r\nfrequency of the neuron." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/64285", + "public_submitter_email": { + "attr_id": 309, + "value": "tmm46@email.med.yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2006-05-17T21:58:55", + "ver_number": 2 + }, + "64296": { + "class_id": 19, + "created": "2006-05-31T14:27:01", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 64296, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 64298, + "object_name": "Rubin, Daniel B " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 97752, + "object_name": "Delay" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 64297, + "object_name": "Rubin DB, Cleland TA (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "64296_1", + "name": "Figure 2A Top Trace" + }, + { + "id": "64296_2", + "name": "Figure 2A Middle Trace" + }, + { + "id": "64296_3", + "name": "Figure 2A Bottom Trace" + }, + { + "id": "64296_4", + "name": "Figure 2B Top Trace" + }, + { + "id": "64296_5", + "name": "Figure 2B Second Trace" + }, + { + "id": "64296_6", + "name": "Figure 2B Third Trace" + }, + { + "id": "64296_7", + "name": "Figure 2B Bottom Trace" + }, + { + "id": "64296_8", + "name": "Figure 3A" + }, + { + "id": "64296_9", + "name": "Figure 3B" + }, + { + "id": "64296_10", + "name": "Figure 3C Top" + }, + { + "id": "64296_11", + "name": "Figure 3C Bottom" + }, + { + "id": "64296_12", + "name": "Figure 4A" + }, + { + "id": "64296_13", + "name": "Figure 5B" + }, + { + "id": "64296_14", + "name": "Figure 6A" + }, + { + "id": "64296_15", + "name": "Figure 6B" + }, + { + "id": "64296_16", + "name": "Figure 6C" + }, + { + "id": "64296_17", + "name": "Figure 6D" + }, + { + "id": "64296_18", + "name": "Figure 6E" + }, + { + "id": "64296_19", + "name": "Figure 7A" + }, + { + "id": "64296_20", + "name": "Figure 7B" + }, + { + "id": "64296_21", + "name": "Figure 8A" + }, + { + "id": "64296_22", + "name": "Figure 8B" + }, + { + "id": "64296_23", + "name": "Figure 8C" + } + ], + "default": "64296_1" + }, + "name": "Dynamical model of olfactory bulb mitral cell (Rubin, Cleland 2006)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This four-compartment mitral cell exhibits endogenous subthreshold oscillations, phase resetting, and evoked spike phasing properties as described in electrophysiological studies of mitral cells. It is derived from the prior work of Davison et al (2000) and Bhalla and Bower (1993). See readme.txt for details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/64296", + "other_implementer": { + "attr_id": 308, + "value": "Cleland, Thomas A\r\nRubin, Daniel B" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181309, + "object_name": "64296_13" + }, + { + "object_id": 181310, + "object_name": "64296_12" + }, + { + "object_id": 181311, + "object_name": "64296_11" + }, + { + "object_id": 181312, + "object_name": "64296_10" + }, + { + "object_id": 181313, + "object_name": "64296_17" + }, + { + "object_id": 181314, + "object_name": "64296_16" + }, + { + "object_id": 181315, + "object_name": "64296_15" + }, + { + "object_id": 181316, + "object_name": "64296_14" + }, + { + "object_id": 181317, + "object_name": "64296_19" + }, + { + "object_id": 181318, + "object_name": "64296_18" + }, + { + "object_id": 181556, + "object_name": "64296_22" + }, + { + "object_id": 181557, + "object_name": "64296_23" + }, + { + "object_id": 181558, + "object_name": "64296_20" + }, + { + "object_id": 181559, + "object_name": "64296_21" + }, + { + "object_id": 181688, + "object_name": "64296_9" + }, + { + "object_id": 181689, + "object_name": "64296_8" + }, + { + "object_id": 181690, + "object_name": "64296_7" + }, + { + "object_id": 181691, + "object_name": "64296_6" + }, + { + "object_id": 181692, + "object_name": "64296_5" + }, + { + "object_id": 181693, + "object_name": "64296_4" + }, + { + "object_id": 181694, + "object_name": "64296_3" + }, + { + "object_id": 181695, + "object_name": "64296_2" + }, + { + "object_id": 181696, + "object_name": "64296_1" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 227 + }, + "ver_date": "2022-05-27T18:21:02.397", + "ver_number": 11 + }, + "65412": { + "class_id": 19, + "created": "2006-06-21T07:04:49", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 240, + "object_name": "I Chloride" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 65412, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 65413, + "object_name": "Cataldo E, Byrne JH, Baxter DA (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "Computational Model of a Central Pattern Generator (Cataldo et al 2006)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 65418, + "object_name": "Aplysia feeding CPG neurons" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The buccal ganglia of Aplysia contain a central pattern generator (CPG) that mediates rhythmic movements of the foregut during feeding. This CPG is a multifunctional circuit and generates at least two types of buccal motor patterns (BMPs), one that mediates ingestion (iBMP) and another that mediates rejection (rBMP). The present study used a computational approach to examine the ways in which an ensemble of identified cells and synaptic connections function as a CPG. Hodgkin-Huxley-type models were developed that mimicked the biophysical properties of these cells and synaptic connections. The results suggest that the currently identified ensemble of cells is inadequate to produce rhythmic neural activity and that several key elements of the CPG remain to be identified." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/65412", + "other_neurons": { + "attr_id": 301, + "value": "Aplysia feeding CPG neurons" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "Enrico.Cataldo@uth.tmc.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115956, + "object_name": "Aplysia" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 249921, + "object_name": "Aplysia" + } + ] + }, + "ver_date": "2019-02-26T10:07:57.11", + "ver_number": 8 + }, + "66268": { + "biosimulations": { + "value": "modeldb-66268" + }, + "class_id": 19, + "created": "2006-06-28T14:40:21", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": false, + "id": 66268, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57906, + "object_name": "Golomb, David [golomb at bgu.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 66269, + "object_name": "Golomb D, Yue C, Yaari Y (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "CA1 pyramidal cell: I_NaP and I_M contributions to somatic bursting (Golomb et al 2006)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "To study the mechanisms of bursting, we have constructed a\r\nconductance-based, one-compartment model of CA1 pyramidal neurons. In this neuron model,\r\nreduced [Ca2+]o is simulated by negatively shifting the activation curve of the persistent Na+ current\r\n(INaP), as indicated by recent experimental results. The neuron model accounts, with different\r\nparameter sets, for the diversity of firing patterns observed experimentally in both zero and normal\r\n[Ca2+]o. Increasing INaP in the neuron model induces bursting and increases the number of spikes\r\nwithin a burst, but is neither necessary nor sufficient for bursting. We show, using fast-slow analysis\r\nand bifurcation theory, that the M-type K+ current (IM) allows bursting by shifting neuronal behavior\r\nbetween a silent and a tonically-active state, provided the kinetics of the spike generating currents are\r\nsufficiently, though not extremely, fast. We suggest that bursting in CA1 pyramidal cells can be\r\nexplained by a single compartment *square bursting* mechanism with one slow variable, the\r\nactivation of IM. See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/66268", + "ver_date": "2012-01-18T16:33:05", + "ver_number": 9 + }, + "71312": { + "class_id": 19, + "created": "2006-07-18T10:22:48", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "id": 71312, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 71314, + "object_name": "Li, Xiaoshen [xsli2 at yahoo.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 71313, + "object_name": "Li X, Ascoli GA (2006)" + }, + { + "object_id": 113958, + "object_name": "Li X, Ascoli GA (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "CA1 pyramidal neuron synaptic integration (Li and Ascoli 2006, 2008)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model shows how different input patterns (irregular & asynchronous,\r\nirregular & synchronous, regular & asynchronous, regular & synchronous)\r\naffect the neuron's output rate when 1000 synapses are distributed in\r\nthe proximal apical dendritic tree of a hippocampus CA1 pyramidal neuron." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/71312", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2008-05-28T15:10:15", + "ver_number": 12 + }, + "71317": { + "class_id": 19, + "created": "2006-07-18T13:39:24", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "id": 71317, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 71315, + "object_name": "Dai, Yue [ydai at scrc.umanitoba.ca]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3642, + "object_name": "Tutorial/Teaching" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 29798, + "object_name": "Dai Y, Jones KE, Fedirchuk B, McCrea DA, Jordan LM (2002)" + }, + { + "object_id": 64323, + "object_name": "Gardiner P, Dai Y, Heckman CJ (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "Activity dependent changes in motoneurones (Dai Y et al 2002, Gardiner et al 2002)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 276, + "object_name": "Spinal cord lumbar motor neuron alpha ACh cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "These two papers review various experimental papers and examine the effects of activity on motoneurons in a similar 5 compartment model with 10 active conductances. Included are slow (S) and fast (F) type and fast fatigue resistant (FR) and fast fatigable (FF) models corresponding to the types of motoneurons. See papers for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/71317", + "ver_date": "2006-07-19T10:06:40", + "ver_number": 9 + }, + "74298": { + "class_id": 19, + "created": "2006-07-25T08:51:58", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2397, + "object_name": "I Mixed" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122812, + "object_name": "Cav1.3 CACNA1D" + }, + { + "object_id": 122814, + "object_name": "Cav1.2 CACNA1C" + }, + { + "object_id": 122815, + "object_name": "KCa2.1 KCNN1" + }, + { + "object_id": 122816, + "object_name": "Kv2.1 KCNB1" + }, + { + "object_id": 122817, + "object_name": "Kv3.1 KCNC1" + }, + { + "object_id": 122818, + "object_name": "HCN Cnga1" + }, + { + "object_id": 122820, + "object_name": "Cav2.2 CACNA1B" + }, + { + "object_id": 122821, + "object_name": "KCa2.2 KCNN2" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 74298, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 74299, + "object_name": "Gillies, Andrew [andrew at anc.ed.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 64336, + "object_name": "Gillies A, Willshaw D (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "74298", + "name": "ModelView" + } + ], + "default": "74298" + }, + "name": "Rat subthalamic projection neuron (Gillies and Willshaw 2006)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 74300, + "object_name": "Subthalamus nucleus projection neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A computational model of the rat subthalamic nucleus projection neuron is constructed using electrophysiological and morphological data and a restricted set of channel specifications. The model cell exhibits a wide range of electrophysiological behaviors characteristic of rat subthalamic neurons. It reveals that a key set of three channels play a primary role in distinguishing behaviors: a high-voltage-activated calcium channel (Cav 1.2.-1.3), a low-voltage-activated calcium channel (Cav 3.-), and a small current calcium-activated potassium channel (KCa 2.1-2.3). See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/74298", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181854, + "object_name": "74298" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 319 + }, + "ver_date": "2014-02-25T01:15:23", + "ver_number": 12 + }, + "76879": { + "biosimulations": { + "value": "modeldb-76879" + }, + "class_id": 19, + "created": "2006-07-28T13:53:34", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 76879, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33738, + "object_name": "Lytton, William [bill.lytton at downstate.edu]" + }, + { + "object_id": 76884, + "object_name": "Ermentrout, Bard [bard_at_pitt.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 5830, + "object_name": "Wilson HR, Cowan JD (1972)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "76879_1", + "name": "No drive (slow)" + }, + { + "id": "76879_2", + "name": "Driven (slow)" + }, + { + "id": "76879_3", + "name": "As set (slow)" + }, + { + "id": "76879_4", + "name": "No drive (fast)" + }, + { + "id": "76879_5", + "name": "Driven (fast)" + }, + { + "id": "76879_6", + "name": "As set (fast)" + } + ], + "default": "76879_1" + }, + "name": "Excitatory and inhibitory interactions in populations of model neurons (Wilson and Cowan 1972)", + "notes": { + "attr_id": 24, + "value": "Coupled nonlinear differential equations are derived for the dynamics\r\nof spatially localized populations containing both excitatory and inhibitory model\r\nneurons. Phase plane methods and numerical solutions are then used to investigate\r\npopulation responses to various types of stimuli. The results obtained show simple\r\nand multiple hysteresis phenomena and limit cycle activity. The latter is particularly\r\ninteresting since the frequency of the limit cycle oscillation is found to be a monotonic\r\nfunction of stimulus intensity. Finally, it is proved that the existence of limit cycle\r\ndynamics in response to one class of stimuli implies the existence of multiple stable\r\nstates and hysteresis in response to a different class of stimuli. The relation between\r\nthese findings and a number of experiments is discussed." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/76879", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115960, + "object_name": "Generic" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181403, + "object_name": "76879_6" + }, + { + "object_id": 181405, + "object_name": "76879_4" + }, + { + "object_id": 181408, + "object_name": "76879_1" + }, + { + "object_id": 181897, + "object_name": "76879_5" + }, + { + "object_id": 181903, + "object_name": "76879_3" + }, + { + "object_id": 181905, + "object_name": "76879_2" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 242 + }, + "ver_date": "2015-01-02T22:00:10", + "ver_number": 9 + }, + "76883": { + "class_id": 19, + "created": "2006-07-28T15:52:04", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 76883, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 77203, + "object_name": "Bednar, James [jbednar at alumni.utexas.net]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 83538, + "object_name": "Unsupervised Learning" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 76880, + "object_name": "Bednar JA, Kelkar A, Miikkulainen R (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 76882, + "object_name": "Topographica (web link to model)" + } + ] + }, + "name": "Scaling self-organizing maps to model large cortical networks (Bednar et al 2004)", + "notes": { + "attr_id": 24, + "value": "Self-organizing computational models\r\nwith specific intracortical connections can\r\nexplain many functional features of visual\r\ncortex, such as topographic orientation and\r\nocular dominance maps. ... This article\r\nintroduces two techniques that make large simulations\r\npractical. \r\n\r\nFirst, we show how parameter\r\nscaling equations can be derived for\r\nlaterally connected self-organizing models.\r\nThese equations result in quantitatively equivalent\r\nmaps over a wide range of simulation\r\nsizes, making it possible to debug small simulations\r\nand then scale them up only when\r\nneeded. ...\r\nSecond, we use parameter\r\nscaling to implement a new growing map\r\nmethod called GLISSOM, which dramatically\r\nreduces the memory and computational\r\nrequirements of large self-organizing networks.\r\n\r\nSee paper for more and details." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/76883", + "ver_date": "2018-10-17T16:46:25.2", + "ver_number": 11 + }, + "79145": { + "class_id": 19, + "created": "2006-08-01T16:37:26", + "gitrepo": false, + "id": 79145, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 79304, + "object_name": "Kazanovich, Yakov [yakov_k at impb.psn.ru]" + }, + { + "object_id": 79305, + "object_name": "Borisyuk, Roman [rborisyuk at plymouth.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 67388, + "object_name": "Kazanovich Y, Borisyuk R (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "An oscillatory neural model of multiple object tracking (Kazanovich and Borisyuk 2006)", + "notes": { + "attr_id": 24, + "value": "An oscillatory neural network model of multiple object tracking is described. The model works with a set of identical visual objects moving around the screen. At the initial stage, the model selects into the focus of attention a subset of objects initially marked as targets. Other objects are used as distractors. The model aims to preserve the initial separation between targets and distractors while objects are moving. This is achieved by a proper interplay of synchronizing and desynchronizing interactions in a multilayer network, where each layer is responsible for tracking a single target. The results of the model simulation are presented and compared with experimental data. In agreement with experimental evidence, simulations with a larger number of targets have shown higher error rates. Also, the functioning of the model in the case of temporarily overlapping objects is presented." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/79145", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2007-11-02T17:14:01", + "ver_number": 3 + }, + "79237": { + "class_id": 19, + "created": "2006-08-02T08:58:04", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2397, + "object_name": "I Mixed" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gitrepo": false, + "id": 79237, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + }, + { + "object_id": 58176, + "object_name": "Chang, Han-Dong" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52407, + "object_name": "Heart disease" + }, + { + "object_id": 88214, + "object_name": "Long-QT" + }, + { + "object_id": 120695, + "object_name": "Sodium pump" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 79470, + "object_name": "Sung RJ, Wu SN, Wu JS, Chang HD, Luo CH (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 120694, + "object_name": "Electrogenic pump" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Simulation study of Andersen-Tawil syndrome (Sung et al 2006)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52423, + "object_name": "Heart cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 236, + "object_name": "Ions" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Patients with Andersen-Tawil syndrome (ATS) mostly have mutations on the KCNJ2 gene producing loss of function or \r\ndominant-negative suppression of the inward rectifier K(+) channel Kir2.1. However, clinical manifestations of ATS \r\nincluding dysmorphic features, periodic paralysis (hypo-, hyper-, or normokalemic), long QT, and ventricular arrhythmias \r\n(VA) are considerably variable. Using a modified dynamic Luo-Rudy simulation model of cardiac ventricular myocyte, we \r\nelucidate the mechanisms of VA in ATS. We adopted a kinetic model of KCNJ2 in which channel block by Mg(+2) and \r\nspermine was incorporated. In this study, we attempt to examine the effects of KCNJ2 mutations on the ventricular action \r\npotential (AP), single-channel Markovian models were reformulated and incorporated into the dynamic Luo-Rudy model \r\nfor rapidly and slowly delayed rectifying K(+) currents and KCNJ2 channel. During pacing at 1.0 Hz with [K(+)]o at 5.4 \r\nmM, a stepwise 10% reduction of Kir2.1 channel conductance progressively prolonged the terminal repolarization phase \r\nof AP along with gradual depolarization of the resting membrane potential (RMP). At 90% reduction, early after-\r\ndepolarizations (EADs) became inducible and RMP was depolarized to -55.0 mV (control: -90.1 mV) followed by \r\nemergence of spontaneous action potentials (SAP). Both EADs and SAP were facilitated by a decrease in [K(+)]o and \r\nsuppressed by increase in [K(+)]o. beta-adrenergic stimulation enhanced delayed after-depolarizations (DADs) and could \r\nalso facilitate EADs as well as SAP in the setting of low [K(+)]o and reduced Kir2.1 channel conductance. In conclusion, \r\nthe spectrum of VA in ATS includes (1) triggered activity mediated by EADs and/or DADs, and (2) abnormal automaticity \r\nmanifested as SAP. These VA can be aggravated by a decrease in [K(+)]o and beta-adrenergic stimulation, and may \r\npotentially induce torsades de pointes and cause sudden death. In patients with ATS, the hypokalemic form of periodic \r\nparalysis should have the highest propensity to VA especially during physical activities." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/79237", + "other_implementer": { + "attr_id": 308, + "value": "Han-Dong Chang" + }, + "other_neurons": { + "attr_id": 301, + "value": "heart cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2009-05-28T14:30:11", + "ver_number": 10 + }, + "79238": { + "class_id": 19, + "created": "2006-08-02T09:43:28", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "id": 79238, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + }, + { + "object_id": 79302, + "object_name": "Chen, Chien-Liang" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 22188, + "object_name": "Adrian RH, Chandler WK, Hodgkin AL (1970)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Action potential of striated muscle fiber (Adrian et al 1970)", + "notes": { + "attr_id": 24, + "value": "1. Membrane currents during step depolarizations were determined by \r\na method in which three electrodes were inserted near the end of a \r\nfibre in the frog's sartorius muscle. The theoretical basis and \r\nlimitations of the method are discussed.\r\n2. Measurements of the membrane capacity (CM) and resting resistance \r\n(RM) derived from the current during a step change in membrane \r\npotential are consistent with values found by other methods.\r\n3. In fibres made mechanically inactive with hypertonic solutions \r\n(Ringer solution plus 350 mM sucrose) step depolarizations produced \r\nionic currents which resembled those of nerve in showing (a) an early \r\ntransient inward current, abolished by tetrodotoxin, which reversed \r\nwhen the depolarization was carried beyond an internal potential of \r\nabout +20 mV, (b) a delayed outward current, with a linear instantaneous \r\ncurrent\u00a1Xvoltage relation, and a mean equilibrium potential with a normal \r\npotassium concentration (2\u00a1P5 mM) of -85 mV.\r\n4. The reversal potential for the early current appears to be consistent\r\nwith the sodium equilibrium potential expected in hypertonic solutions.\r\n5. The variation of the equilibrium potential for the delayed current \r\n(V\u00a1\u00acK) with external potassium concentration suggests that the channel \r\nfor delayed current has a ratio of potassium to sodium permeability of \r\n30:1; this is less than the resting membrane where the ratio appears \r\nto be 100:1. V\u00a1\u00acK corresponds well with the membrane potential at the \r\nbeginning of the negative after-potential observed under similar conditions.\r\n6. The variation of V\u00a1\u00acK with the amount of current which has passed \r\nthrough the delayed channel suggests that potassium ions accumulate in a \r\nspace of between 1/3 and 1/6 of the fibre volume. If potassium accumulates in \r\nthe transverse tubular system (T system) much greater variation in V\u00a1\u00acK \r\nwould be expected.\r\n7. The delayed current is not maintained but is inactivated like the early \r\ncurrent. The inactivation is approximately exponential with a time constant \r\nof 0\u00a1P5 to 1 sec at 20\u00a2X C. The steady-state inactivation of the potassium \r\ncurrent is similar to that for the sodium current, but its voltage \r\ndependence is less steep and the potential for half inactivation is 20 mV \r\nrate more positive.\r\n8. Reconstructions of ionic currents were made in terms of the parameters\r\n(m, n, h) of the Hodgkin\u00a1XHuxley model for the squid axon, using constants \r\nwhich showed a similar dependence on voltage.\r\n9. Propagated action potentials and conduction velocities were computed for \r\nvarious conditions on the assumption that the T system behaves as if it were \r\na series resistance and capacity in parallel with surface capacity and the \r\nchannels for sodium, potassium and leak current. There was reasonable \r\nagreement with observed values, the main difference being that the \r\ncalculated velocities and rates of rise were somewhat less than those \r\nobserved experimentally." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/79238", + "other_implementer": { + "attr_id": 308, + "value": "Chien-Liang Chen" + }, + "other_neurons": { + "attr_id": 301, + "value": "skeletal muscle cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2015-09-02T13:27:02.21", + "ver_number": 8 + }, + "79461": { + "class_id": 19, + "created": "2006-08-02T22:12:32", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gitrepo": false, + "id": 79461, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + }, + { + "object_id": 62678, + "object_name": "Lai, Hsing-Jung " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 52407, + "object_name": "Heart disease" + }, + { + "object_id": 120695, + "object_name": "Sodium pump" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 3870, + "object_name": "Courtemanche M, Ramirez RJ, Nattel S (1998)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 120694, + "object_name": "Electrogenic pump" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Cardiac Atrial Cell (Courtemanche et al 1998) (C++)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The mechanisms underlying many important properties of the human atrial action potential (AP) \r\nare poorly understood. Using specific formulations of the K+, Na+, and Ca2+ currents based on \r\ndata recorded from human atrial myocytes, along with representations of pump, exchange, and \r\nbackground currents, we developed a mathematical model of the AP. The model AP resembles APs \r\nrecorded from human atrial samples and responds to rate changes, L-type Ca2+ current blockade, \r\nNa+/Ca2+ exchanger inhibition, and variations in transient outward current amplitude in a \r\nfashion similar to experimental recordings. Rate-dependent adaptation of AP duration, an \r\nimportant determinant of susceptibility to atrial fibrillation, was attributable to \r\nincomplete L-type Ca2+ current recovery from inactivation and incomplete delayed rectifier \r\ncurrent deactivation at rapid rates. Experimental observations of variable AP morphology \r\ncould be accounted for by changes in transient outward current density, as suggested \r\nexperimentally. We conclude that this mathematical model of the human atrial AP reproduces \r\na variety of observed AP behaviors and provides insights into the mechanisms of clinically \r\nimportant AP properties.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/79461", + "other_implementer": { + "attr_id": 308, + "value": "Hsing-Jung Lai" + }, + "other_neurons": { + "attr_id": 301, + "value": "heart cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2017-06-15T11:04:12.013", + "ver_number": 8 + }, + "79465": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 79467, + "object_name": "locally available GENESIS" + } + ] + }, + "class_id": 19, + "created": "2006-08-03T11:41:23", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "default_file_notes": { + "attr_id": 392, + "value": "web-link to externallly available GENESIS" + }, + "gitrepo": false, + "id": 79465, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53429, + "object_name": "Blackwell, Avrama [avrama at gmu.edu]" + }, + { + "object_id": 79466, + "object_name": "Kotaleski, Jeanette [jeanette at csc.kth.se]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 79463, + "object_name": "Kotaleski JH, Plenz D, Blackwell KT (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "FS Striatal interneuron: K currents solve signal-to-noise problems (Kotaleski et al 2006)", + "notes": { + "attr_id": 24, + "value": "... We show that a transient\r\npotassium (KA) current allows the Fast Spiking (FS) interneuron to strike a balance\r\nbetween sensitivity to correlated input and robustness to noise,\r\nthereby increasing its signal-to-noise ratio (SNR). First, a compartmental\r\nFS neuron model was created to match experimental data from\r\nstriatal FS interneurons in cortex\u2013striatum\u2013substantia nigra organotypic\r\ncultures. Densities of sodium, delayed rectifier, and KA channels\r\nwere optimized to replicate responses to somatic current injection.\r\nSpontaneous AMPA and GABA synaptic currents were\r\nadjusted to the experimentally measured amplitude, rise time, and\r\ninterevent interval histograms. Second, two additional adjustments\r\nwere required to emulate the remaining experimental observations.\r\nGABA channels were localized closer to the soma than AMPA\r\nchannels to match the synaptic population reversal potential. Correlation\r\namong inputs was required to produce the observed firing rate\r\nduring up-states. In this final model, KA channels were essential for\r\nsuppressing down-state spikes while allowing reliable spike generation\r\nduring up-states. ... Our results suggest that KA\r\nchannels allow FS interneurons to operate without a decrease in SNR\r\nduring conditions of increased dopamine, as occurs in response to\r\nreward or anticipated reward. See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/79465", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 138633, + "object_name": "Striatum" + } + ] + }, + "ver_date": "2016-12-19T17:41:12.66", + "ver_number": 13 + }, + "79471": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 79474, + "object_name": "local" + } + ] + }, + "class_id": 19, + "created": "2006-08-03T14:53:14", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "default_file_notes": { + "attr_id": 392, + "value": "current" + }, + "gitrepo": false, + "id": 79471, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53429, + "object_name": "Blackwell, Avrama [avrama at gmu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 75415, + "object_name": "Blackwell KT (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53428, + "object_name": "Chemesis" + } + ] + }, + "name": "Differences between type A and B photoreceptors (Blackwell 2006)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114289, + "object_name": "Hermissenda photoreceptor Type A" + }, + { + "object_id": 114290, + "object_name": "Hermissenda photoreceptor Type B" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In Hermissenda crassicornis, the memory of light associated with turbulence\r\nis stored as changes in intrinsic and synaptic currents in both\r\ntype A and type B photoreceptors. These photoreceptor types exhibit\r\nqualitatively different responses to light and current injection, and\r\nthese differences shape the spatiotemporal firing patterns that control\r\nbehavior. Thus the objective of the study was to identify the mechanisms\r\nunderlying these differences. The approach was to develop a\r\ntype B model that reproduced characteristics of type B photoreceptors\r\nrecorded in vitro, and then to create a type A model by modifying a\r\nselect number of ionic currents. Comparison of type A models with\r\ncharacteristics of type A photoreceptors recorded in vitro revealed that\r\ntype A and type B photoreceptors have five main differences, three\r\nthat have been characterized experimentally and two that constitute\r\nhypotheses to be tested with experiments in the future. See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/79471", + "ver_date": "2016-12-19T17:07:10.087", + "ver_number": 10 + }, + "79488": { + "class_id": 19, + "created": "2006-08-08T19:27:59", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 252, + "object_name": "I Cl,Ca" + } + ] + }, + "gitrepo": false, + "id": 79488, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 79489, + "object_name": "Frank, Michael [mfrank at u.arizona.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + 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"name": "Short demo run simulation" + } + ], + "default": "80769_1" + }, + "name": "Cerebellar purkinje cell: interacting Kv3 and Na currents influence firing (Akemann, Knopfel 2006)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Purkinje neurons spontaneously generate action potentials in the absence of synaptic drive and thereby exert a tonic, yet plastic, input to their target cells in the deep cerebellar nuclei. Purkinje neurons express two ionic currents with biophysical properties that are specialized for high-frequency firing: resurgent sodium currents and potassium currents mediated by Kv3.3. Numerical simulations indicated that Kv3.3 increases the spontaneous firing rate via cooperation with resurgent sodium currents. We conclude that the rate of spontaneous action potential firing of Purkinje neurons is controlled by the interaction of Kv3.3 potassium currents and resurgent sodium currents. See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/80769", + "simPFid": { + "attr_id": 488, + "value": 198 + }, + "ver_date": "2017-03-18T10:07:35.34", + "ver_number": 15 + }, + "82364": { + "class_id": 19, + "created": "2006-08-28T18:10:30", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 82364, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 67382, + "object_name": "van Welie I, Remme MW, van Hooft JA, Wadman WJ (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "82364_1", + "name": "regular - 20 Hz" + }, + { + "id": "82364_2", + "name": "regular - 50 Hz" + }, + { + "id": "82364_3", + "name": "burster - 20 Hz" + }, + { + "id": "82364_4", + "name": "burster - 50 Hz" + } + ], + "default": "82364_1" + }, + "name": "Ih levels roles in bursting and regular-spiking subiculum pyramidal neurons (van Welie et al 2006)", + "notes": { + "attr_id": 24, + "value": "Pyramidal neurons in the subiculum typically display either bursting\r\nor regular-spiking behavior. ... Here we report that bursting neurons\r\nposses a hyperpolarization-activated cation current (Ih) that is\r\ntwo-fold larger (conductance: 5.3 \u00c2\u00b1 0.5 nS) than in regularspiking\r\nneurons (2.2 \u00c2\u00b1 0.6 nS), while Ih exhibits similar voltage-dependent\r\nand kinetic properties in both classes of neurons. Bursting and\r\nregular-spiking neurons display similar morphology. The difference in\r\nIh between the two classes is not responsible for the distinct firing\r\npatterns, since neither pharmacological blockade of Ih nor enhancement\r\nof Ih using a dynamic clamp affects the qualitative firing\r\npatterns. Instead, the difference in Ih between bursting and\r\nregular-spiking neurons determines the temporal integration of evoked\r\nsynaptic input from the CA1 area. In response to 50 Hz stimulation,\r\nbursting neurons, with a large Ih, show ~50% less temporal summation\r\nthan regular-spiking neurons. ... A computer simulation model of a\r\nsubicular neuron with the properties of either a bursting or a\r\nregular-spiking neuron confirmed the pivotal role of Ih in temporal\r\nintegration of synaptic input. These data suggest that in the\r\nsubicular network, bursting neurons are better suited to discriminate\r\nthe content of high frequency input, such as that occurring during\r\ngamma oscillations, compared to regular-spiking neurons. See paper for more and details.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/82364", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181357, + "object_name": "82364_1" + }, + { + "object_id": 181359, + "object_name": "82364_3" + }, + { + "object_id": 181360, + "object_name": "82364_2" + }, + { + "object_id": 181362, + "object_name": "82364_4" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 264 + }, + "ver_date": "2015-01-02T22:40:12", + "ver_number": 11 + }, + "82385": { + "class_id": 19, + "created": "2006-09-06T11:14:19", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "id": 82385, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 82382, + "object_name": "Haeusler S, Maass W (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 82384, + "object_name": "CSIM (web link to model)" + } + ] + }, + "name": "Information-processing in lamina-specific cortical microcircuits (Haeusler and Maass 2006)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A major challenge for computational neuroscience is to understand\r\nthe computational function of lamina-specific synaptic connection\r\npatterns in stereotypical cortical microcircuits.We approach this problem by studying ...\r\nthe dynamical system defined by more realistic cortical microcircuit\r\nmodels as a whole and by investigating the influence that its\r\nlaminar structure has on the transmission and fusion of information\r\nwithin this dynamical system. The circuit models that we examine\r\nconsist of Hodgkin--Huxley neurons with dynamic synapses... We investigate to what\r\nextent this cortical microcircuit template supports the accumulation\r\nand fusion of information contained in generic spike inputs into\r\nlayer 4 and layers 2/3 and how well it makes this information\r\naccessible to projection neurons in layers 2/3 and layer 5. ... We conclude that computer simulations\r\nof detailed lamina-specific cortical microcircuit models\r\nprovide new insight into computational consequences of anatomical\r\nand physiological data. See paper for more and details." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/82385", + "other_currents": { + "attr_id": 303, + "value": "non inactivating K" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2008-10-08T18:40:41", + "ver_number": 10 + }, + "82392": { + "class_id": 19, + "created": "2006-09-06T16:36:01", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": false, + "id": 82392, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 82391, + "object_name": "Maass W, Joshi P, Sontag ED (2007)" + }, + { + "object_id": 82393, + "object_name": "Maass W, Joshi P, Sontag ED (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 82384, + "object_name": "CSIM (web link to model)" + } + ] + }, + "name": "Computational aspects of feedback in neural circuits (Maass et al 2006)", + "notes": { + "attr_id": 24, + "value": "It had previously been shown that generic cortical microcircuit models\r\ncan perform complex real-time computations on continuous input\r\nstreams, provided that these computations can be carried out with a\r\nrapidly fading memory. We investigate ... the computational\r\ncapability of such circuits in the more realistic case where not only\r\nreadout neurons, but in addition a few neurons within the circuit have\r\nbeen trained for specific tasks. This is essentially equivalent to\r\nthe case where the output of trained readout neurons is fed back into\r\nthe circuit. We show that this new model overcomes the limitation of\r\na rapidly fading memory. In fact, we prove that in the idealized case\r\nwithout noise it can carry out any conceiv- able digital or analog\r\ncomputation on time-varying inputs. But even with noise the resulting\r\ncomputational model can perform a large class of biologically relevant\r\nreal-time computations that require a non-fading memory. ... In\r\nparticular we show that ... generic cortical microcircuits with\r\nfeedback provide a new model for working memory that is consistent\r\nwith a large set of biological constraints. See paper for more and details." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/82392", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2017-10-25T23:20:50.317", + "ver_number": 11 + }, + "82394": { + "class_id": 19, + "created": "2006-09-06T17:48:02", + "gitrepo": false, + "id": 82394, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 83539, + "object_name": "Attractor Neural Network" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 82395, + "object_name": "Haeusler S, Markram H, Maass W (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 82384, + "object_name": "CSIM (web link to model)" + } + ] + }, + "name": "High dimensional dynamics and low dimensional readouts in neural microcircuits (Haeusler et al 2006)", + "notes": { + "attr_id": 24, + "value": "We investigate generic models for cortical microcircuits,\r\ni.e. recurrent circuits of integrate-and fire neurons with dynamic\r\nsynapses. These complex dynamic systems subserve the amazing\r\ninformation processing capabilities of the cortex, but are at the\r\npresent time very little understood. We analyze the transient dynamics\r\nof models for neural microcircuits from the point of view of one or\r\ntwo readout neurons that collapse the high dimensional transient\r\ndynamics of a neural circuit into a 1- or 2--dimensional output\r\nstream. See paper for more and details.\r\n" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/82394", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2016-05-26T15:38:22.917", + "ver_number": 9 + }, + "82758": { + "class_id": 19, + "created": "2006-09-18T10:16:35", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 226058, + "object_name": "HCN2" + }, + { + "object_id": 226060, + "object_name": "HCN4" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 82758, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 82759, + "object_name": "Elinder, Fredrik [fredrik.elinder at ibk.liu.se]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52407, + "object_name": "Heart disease" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 64331, + "object_name": "Elinder F, M\u00e4nnikk\u00f6 R, Pandey S, Larsson HP (2006)" + }, + { + "object_id": 65951, + "object_name": "M\u00e4nnikk\u00f6 R, Pandey S, Larsson HP, Elinder F (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 58965, + "object_name": "QBasic/QuickBasic/Turbo Basic/VBA" + } + ] + }, + "name": "Hysteresis in voltage gating of HCN channels (Elinder et al 2006, Mannikko et al 2005)", + "notes": { + "attr_id": 24, + "value": "We found that HCN2 and HCN4 channels\r\nexpressed in oocytes from the frog Xenopus laevis do not display the\r\nactivation kinetic changes that we (previously) observed in spHCN and\r\nHCN1. However, HCN2 and HCN4 channels display changes in their tail\r\ncurrents, suggesting that these channels also undergo mode shifts and\r\nthat the conformational changes underlying the mode shifts are due to\r\nconserved aspects of HCN channels. With computer modelling, we show\r\nthat in channels with relatively slow opening kinetics and fast\r\nmode-shift transitions, such as HCN2 and HCN4 channels, the mode shift\r\neffects are not readily observable, except in the tail\r\nkinetics. Computer simulations of sino-atrial node action potentials\r\nsuggest that the HCN2 channel, together with the HCN1 channel, are\r\nimportant regulators of the heart firing frequency and that the mode\r\nshift is an important property to prevent arrhythmic firing. We\r\nconclude that although all HCN channels appear to undergo mode shifts\r\n\u2013 and thus may serve to prevent arrhythmic firing\r\n\u2013 it is mainly observable in ionic currents\r\nfrom HCN channels with faster kinetics. See papers for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/82758", + "other_neurons": { + "attr_id": 301, + "value": "cardiac" + }, + "ver_date": "2017-03-18T10:09:34.847", + "ver_number": 11 + }, + "82784": { + "class_id": 19, + "created": "2006-10-03T16:01:39", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 82784, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 82786, + "object_name": "Durstewitz, Daniel [daniel.durstewitz at plymouth.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 82787, + "object_name": "Working memory" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 82783, + "object_name": "Durstewitz D, Gabriel T (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "82784", + "name": "ModelView" + } + ], + "default": "82784" + }, + "name": "Irregular spiking in NMDA-driven prefrontal cortex neurons (Durstewitz and Gabriel 2006)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Slow N-Methyl-D-aspartic acid (NMDA) synaptic currents are assumed to strongly contribute to the persistently elevated firing rates observed in prefrontal cortex (PFC) during working memory. During persistent activity, spiking of many neurons is highly irregular. ... The highest interspike-interval (ISI) variability occurred in a transition regime where the subthreshold membrane potential distribution shifts from mono- to bimodality, ... Predictability within irregular ISI series was significantly higher than expected from a noise-driven linear process, indicating that it might best be described through complex (potentially chaotic) nonlinear deterministic processes. Accordingly, the phenomena observed in vitro could be reproduced in purely deterministic biophysical model neurons. High spiking irregularity in these models emerged within a chaotic, close-to-bifurcation regime characterized by a shift of the membrane potential distribution from mono- to bimodality and by similar ISI return maps as observed in vitro. ... NMDA-induced irregular dynamics may have important implications for computational processes during working memory and neural coding." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/82784", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181248, + "object_name": "82784" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 116 + }, + "ver_date": "2014-02-15T14:34:02", + "ver_number": 16 + }, + "82849": { + "class_id": 19, + "created": "2006-10-06T14:23:46", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 82849, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 82786, + "object_name": "Durstewitz, Daniel [daniel.durstewitz at plymouth.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 32013, + "object_name": "Durstewitz D, Seamans JK, Sejnowski TJ (2000)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "82849", + "name": "ModelView" + } + ], + "default": "82849" + }, + "name": "Neocortical pyramidal neuron: deep; effects of dopamine (Durstewitz et al 2000)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... Simulated dopamine strongly enhanced high, delay-type activity but not low, spontaneous activity in the model network. Furthermore the strength of an afferent stimulation needed to disrupt delay-type activity increased with the magnitude of the dopamine-induced shifts in network parameters, making the currently active representation much more stable. Stability could be increased by dopamine-induced enhancements of the persistent Na(+) and N-methyl-D-aspartate (NMDA) conductances. Stability also was enhanced by a reduction in AMPA conductances. The increase in GABA(A) conductances that occurs after stimulation of dopaminergic D1 receptors was necessary in this context to prevent uncontrolled, spontaneous switches into high-activity states (i.e., spontaneous activation of task-irrelevant representations). In conclusion, the dopamine-induced changes in the biophysical properties of intrinsic ionic and synaptic conductances conjointly acted to highly increase stability of activated representations in PFC networks and at the same time retain control over network behavior and thus preserve its ability to adequately respond to task-related stimuli. ...\" See paper and references for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/82849", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181566, + "object_name": "82849" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 296 + }, + "ver_date": "2014-02-25T01:02:23", + "ver_number": 11 + }, + "82891": { + "class_id": 19, + "created": "2006-10-26T09:41:25", + "gitrepo": false, + "id": 82891, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 82893, + "object_name": "Calin-Jageman, Robert [rcalinjageman at gsu dot edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 82890, + "object_name": "Calin-Jageman RJ, Katz PS (2006)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 82892, + "object_name": "NEURONPM (web link to tool)" + } + ] + }, + "name": "Distributed computing tool for NEURON, NEURONPM (screensaver) (Calin-Jageman and Katz 2006)", + "notes": { + "attr_id": 24, + "value": "\"... To lower the barrier for large-scale model analysis, we have developedNeuronPM,\r\na client/server application that creates a \u201cscreen-saver\u201d cluster\r\nfor running simulations in NEURON (Hines & Carnevale, 1997). ... The NeuronPM client is\r\na Windows-based screen saver, and the NeuronPM server can be hosted\r\non any Apache/PHP/MySQL server. ... Administrative\r\npanels make it simple to upload model files, define the parameters and\r\nconditions to vary, and then monitor client status and work progress.\r\nNeuronPM is open-source freeware and is available for download at\r\nhttp://neuronpm.homeip.net. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/82891", + "ver_date": "2019-02-28T18:23:16.663", + "ver_number": 6 + }, + "83319": { + "class_id": 19, + "created": "2006-11-21T07:19:17", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 83319, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33735, + "object_name": "Carnevale, Ted [Ted.Carnevale at Yale.edu]" + }, + { + "object_id": 33736, + "object_name": "Hines, Michael [Michael.Hines at Yale.edu]" + }, + { + "object_id": 33739, + "object_name": "Davison, Andrew [Andrew.Davison at iaf.cnrs-gif.fr]" + }, + { + "object_id": 33744, + "object_name": "Destexhe, Alain [Destexhe at iaf.cnrs-gif.fr]" + }, + { + "object_id": 76884, + "object_name": "Ermentrout, Bard [bard_at_pitt.edu]" + }, + { + "object_id": 83453, + "object_name": "Brette R" + }, + { + "object_id": 83454, + "object_name": "Bower, James" + }, + { + "object_id": 83455, + "object_name": "Beeman, Dave" + }, + { + "object_id": 83456, + "object_name": "Diesmann M" + }, + { + "object_id": 83457, + "object_name": "Morrison A " + }, + { + "object_id": 83458, + "object_name": "Goodman PH" + }, + { + "object_id": 83459, + "object_name": "Harris Jr, FC" + }, + { + "object_id": 83460, + "object_name": "Zirpe M " + }, + { + "object_id": 83461, + "object_name": "Natschlager T " + }, + { + "object_id": 83462, + "object_name": "Pecevski D " + }, + { + "object_id": 83464, + "object_name": "Djurfeldt M" + }, + { + "object_id": 83465, + "object_name": "Lansner, Anders [ala at kth.se]" + }, + { + "object_id": 83466, + "object_name": "Rochel O " + }, + { + "object_id": 83467, + "object_name": "Vieville T " + }, + { + "object_id": 83468, + "object_name": "Muller E " + }, + { + "object_id": 83470, + "object_name": "El Boustani, Sami [elboustani at unic.cnrs-gif.fr]" + }, + { + "object_id": 83471, + "object_name": "Rudolph M " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 58969, + "object_name": "Vogels TP, Abbott LF (2005)" + }, + { + "object_id": 83443, + "object_name": "Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Morrison A, Goodman PH, Harris FC, Zirpe M, Natschl\u00e4ger T, Pecevski D, Ermentrout B, Djurfeldt M, Lansner A, Rochel O, Vieville T, Muller E, Davison AP, El Boustani S, Destexhe A (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 1883, + "object_name": "GENESIS" + }, + { + "object_id": 2832, + "object_name": "NEST" + }, + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 33977, + "object_name": "XPPAUT" + }, + { + "object_id": 82383, + "object_name": "CSIM" + }, + { + "object_id": 83448, + "object_name": "NCS" + }, + { + "object_id": 83449, + "object_name": "SPLIT" + }, + { + "object_id": 83451, + "object_name": "MVASpike" + }, + { + "object_id": 83452, + "object_name": "SciLab" + }, + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 116010, + "object_name": "PyNN" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "83319_2", + "name": "Coba" + } + ], + "default": "83319_2" + }, + "name": "Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This package provides a series of codes that simulate networks of spiking neurons (excitatory and inhibitory, integrate-and-fire or Hodgkin-Huxley type, current-based or conductance-based synapses; some of them are event-based). The same networks are implemented in different simulators (NEURON, GENESIS, NEST, NCS, CSIM, XPP, SPLIT, MVAspike; there is also a couple of implementations in SciLab and C++).\r\nThe codes included in this package are benchmark simulations; see\r\nthe associated review paper (Brette et al. 2007). The \r\nmain goal is to provide a series of benchmark simulations of\r\nnetworks of spiking neurons, and demonstrate how these are implemented in the\r\ndifferent simulators overviewed in the paper. See also details in the\r\nenclosed file Appendix2.pdf, which describes these different \r\nbenchmarks. Some of these benchmarks were based on the \r\nVogels-Abbott model (Vogels TP and Abbott LF 2005).\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83319", + "other_neurons": { + "attr_id": 301, + "value": "generic cortical neurons, integrate and fire, HH" + }, + "other_simulator": { + "attr_id": 307, + "value": "many different simulators" + }, + "ver_date": "2017-07-28T13:55:31.95", + "ver_number": 45 + }, + "83320": { + "class_id": 19, + "created": "2006-11-21T11:14:22", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 83320, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83321, + "object_name": "Jiang, Ning [ning.jiang at unb.ca]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83237, + "object_name": "Jiang N, Englehart KB, Parker PA (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A simulation method for the firing sequences of motor units (Jiang et al 2006)", + "notes": { + "attr_id": 24, + "value": "\" ... a novel model based on the Hodgkin\u2013Huxley (HH) system is proposed, which has the ability to simulate\r\nthe complex neurodynamics of the firing sequences of motor neurons. The model is presented at the cellular level and network level,\r\nand some simulation results from a simple 3-neuron network are presented to demonstrate its applications.\" See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83320", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115954, + "object_name": "Spinal motoneuron" + } + ] + }, + "ver_date": "2008-10-09T16:11:43", + "ver_number": 9 + }, + "83344": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 83440, + "object_name": "autolaunchable NEURON" + } + ] + }, + "class_id": 19, + "created": "2006-11-22T17:08:10", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "default_file_notes": { + "attr_id": 392, + "value": "current" + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 83344, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83441, + "object_name": "Schaefer, Andreas T [andreas.schaefer at crick.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3632, + "object_name": "Coincidence Detection" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 24607, + "object_name": "Schaefer AT, Larkum ME, Sakmann B, Roth A (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "83344_1", + "name": "a. Backpropagating AP" + }, + { + "id": "83344_2", + "name": "b. EPSP" + }, + { + "id": "83344_3", + "name": "c. Ca spike" + }, + { + "id": "83344_4", + "name": "d. BAC firing" + } + ], + "default": "83344_1" + }, + "name": "Pyramidal neuron coincidence detection tuned by dendritic branching pattern (Schaefer et al 2003)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... We examined the relationship between dendritic arborization\r\nand the coupling between somatic and dendritic action potential\r\n(AP) initiation sites in layer 5 (L5) neocortical pyramidal neurons.\r\nCoupling was defined as the relative reduction in threshold for\r\ninitiation of a dendritic calcium AP due to a coincident\r\nback-propagating AP. Simulations based on reconstructions of\r\nbiocytin-filled cells showed that addition of oblique branches of the\r\nmain apical dendrite in close proximity to the soma (d < 140 um)\r\nincreases the coupling between the apical and axosomatic AP initiation\r\nzones, whereas incorporation of distal branches decreases\r\ncoupling. ... We conclude that variation in dendritic arborization may\r\nbe a key determinant of variability in coupling (49+-17%; range\r\n19-83%; n = 37) and is likely to outweigh the contribution made by\r\nvariations in active membrane properties. Thus coincidence detection\r\nof inputs arriving from different cortical layers is strongly\r\nregulated by differences in dendritic arborization.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83344", + "ver_date": "2023-04-30T15:14:03.437", + "ver_number": 26 + }, + "83472": { + "citation": { + "attr_id": 391, + "value": "Baxter DA Byrne JH (2006) Short-term plasticity in a computational model of the tail-withdrawal circuit in Aplysia, Neurocomputing, doi:10.1016/j.neurcom.2006.10.080" + }, + "class_id": 19, + "created": "2006-11-29T09:51:55", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 83472, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33985, + "object_name": "Baxter, Douglas" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 22010, + "object_name": "Facilitation" + }, + { + "object_id": 22011, + "object_name": "Post-Tetanic Potentiation" + }, + { + "object_id": 22012, + "object_name": "Depression" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83473, + "object_name": "Baxter DA, Byrne JH (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "Homosynaptic plasticity in the tail withdrawal circuit (TWC) of Aplysia (Baxter and Byrne 2006)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52413, + "object_name": "Aplysia sensory neuron" + }, + { + "object_id": 52414, + "object_name": "Aplysia interneuron" + }, + { + "object_id": 52415, + "object_name": "Aplysia motor neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The tail-withdrawal circuit of Aplysia provides a useful model system for investigating synaptic dynamics. Sensory neurons within the circuit manifest several forms of synaptic plasticity. Here, we developed a model of the circuit and investigated the ways in which depression (DEP) and potentiation (POT) contributed to information processing. DEP limited the amount of motor neuron activity that could be elicited by the monosynaptic pathway alone. POT within the monosynaptic pathway did not compensate for DEP. There was, however, a synergistic interaction between POT and the polysynaptic pathway. This synergism extended the dynamic range of the network, and the interplay between DEP and POT made the circuit respond preferentially to long-duration, low-frequency inputs." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83472", + "other_neurons": { + "attr_id": 301, + "value": "Aplysia Sensory Neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "douglas.baxter@uth.tmc.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115956, + "object_name": "Aplysia" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 249921, + "object_name": "Aplysia" + } + ] + }, + "ver_date": "2019-02-26T10:08:11.487", + "ver_number": 13 + }, + "83491": { + "class_id": 19, + "created": "2006-12-04T10:28:30", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 88208, + "object_name": "Late Na" + }, + { + "object_id": 88209, + "object_name": "Na/Ca exchanger" + }, + { + "object_id": 88210, + "object_name": "I_Na,Ca" + }, + { + "object_id": 88211, + "object_name": "I_SERCA" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122803, + "object_name": "Kv4.3 KCND3" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 83491, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83534, + "object_name": "Flaim, Sarah [flaim at comlab.ox.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52407, + "object_name": "Heart disease" + }, + { + "object_id": 120695, + "object_name": "Sodium pump" + }, + { + "object_id": 238957, + "object_name": "Markov-type model" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83493, + "object_name": "Flaim SN, Giles WR, McCulloch AD (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 120694, + "object_name": "Electrogenic pump" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "INa and IKv4.3 heterogeneity in canine LV myocytes (Flaim et al 2006)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52423, + "object_name": "Heart cell" + }, + { + "object_id": 88205, + "object_name": "Cardiac ventricular cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The roles of sustained components of INa and IKv43 in shaping the\r\naction potentials (AP) of myocytes isolated from the canine left\r\nventricle (LV) have not been studied in detail. Here we investigate\r\nthe hypothesis that these two currents can contribute substantially to\r\nheterogeneity of early repolarization and arrhythmic\r\nrisk.... The resulting simulations illustrate ways in which KChIP2- and\r\nCa2+- dependent control of IKv43 can result in a sustained outward\r\ncurrent that can neutralize INaL in a rate- and myocyte\r\nsubtype-dependent manner. Both these currents appear to play\r\nsignificant roles in modulating AP duration and rate dependence in\r\nmidmyocardial myocytes. ... By design, these models allow upward\r\nintegration into organ models or may be used as a basis for further\r\ninvestigations into cellular heterogeneities.\" See paper for more\r\nand details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83491", + "other_currents": { + "attr_id": 303, + "value": "Late Na, Na/Ca exchanger, INaCa, ISERCA" + }, + "ver_date": "2018-02-26T11:05:54.493", + "ver_number": 24 + }, + "83508": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 83527, + "object_name": "IGOR Pro experiment file" + } + ] + }, + "class_id": 19, + "created": "2006-12-04T18:36:20", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 83508, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83510, + "object_name": "Powers, Randy [rkpowers at u.washington.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83495, + "object_name": "Powers RK, Dai Y, Bell BM, Percival DB, Binder MD (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 83509, + "object_name": "IGOR Pro" + } + ] + }, + "name": "Contibutions of input and history to motoneuron output (Powers et al 2005)", + "notes": { + "attr_id": 24, + "value": "\"The present study presents results based\r\non recordings of noise-driven discharge in rat hypoglossal motoneurones ... First, we show that the hyperpolarizing trough is larger in Average Current Trajectories (ACTs)\r\ncalculated from spikes preceded by long interspike intervals, and minimal or absent in those\r\nbased on short interspike intervals. Second, we show that the trough is present for ACTs\r\ncalculated from the discharge of a threshold-crossing neurone model with a postspike after-\r\nhyperpolarization (AHP), but absent from those calculated from the discharge of a model\r\nwithout an AHP. We show that it is possible to represent noise-driven discharge using a\r\ntwo-component linear model that predicts discharge probability based on the sum of a feedback\r\nkernel and a stimulus kernel. The feedback kernel reflects the influence of prior discharge\r\nmediated by the AHP, and it increases in amplitude when AHP amplitude is increased by\r\npharmacological manipulations. Finally, we show that the predictions of this model are virtually\r\nidentical to those based on the first-order Wiener kernel. This suggests that the Wiener kernel\r\nderived from standard white-noise analysis of noise-driven discharge in neurones actually\r\nreflect the effects of both stimulus and discharge history.\" See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83508", + "ver_date": "2017-07-31T16:43:22.467", + "ver_number": 10 + }, + "83512": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 83513, + "object_name": "local copy of scripts" + } + ] + }, + "class_id": 19, + "created": "2006-12-05T10:43:49", + "gitrepo": false, + "id": 83512, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 64248, + "object_name": "Hasselmo, Michael E [hasselmo at bu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83511, + "object_name": "Hasselmo ME (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Prefrontal cortical mechanisms for goal-directed behavior (Hasselmo 2005)", + "notes": { + "attr_id": 24, + "value": "\".. a model of prefrontal cortex function emphasizing\r\nthe influence of goal-related activity on the choice\r\nof the next motor output. ...\r\nDifferent neocortical minicolumns represent distinct sensory\r\ninput states and distinct motor output actions. The dynamics\r\nof each minicolumn include separate phases of encoding and\r\nretrieval. During encoding, strengthening of excitatory connections\r\nforms forward and reverse associations between each\r\nstate, the following action, and a subsequent state, which\r\nmay include reward. During retrieval, activity spreads from\r\nreward states throughout the network. The interaction of this\r\nspreading activity with a specific input state directs selection of\r\nthe next appropriate action. Simulations demonstrate how\r\nthese mechanisms can guide performance in a range of goal directed\r\ntasks, and provide a functional framework for some\r\nof the neuronal responses previously observed in the medial\r\nprefrontal cortex during performance of spatial memory tasks\r\nin rats.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83512", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "ver_date": "2013-01-15T10:38:09", + "ver_number": 10 + }, + "83514": { + "class_id": 19, + "created": "2006-12-06T11:08:39", + "gitrepo": false, + "id": 83514, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 64248, + "object_name": "Hasselmo, Michael E [hasselmo at bu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 72672, + "object_name": "Sutton RS, Barto AG (2002)" + }, + { + "object_id": 116475, + "object_name": "Sutton RS, Barto AG (1998)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "A reinforcement learning example (Sutton and Barto 1998)", + "notes": { + "attr_id": 24, + "value": "This MATLAB script demonstrates an example of reinforcement learning\r\nfunctions guiding the movements of an agent (a black square) in a\r\ngridworld environment. See at the top of the matlab script and the book for more details.\r\n" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83514", + "ver_date": "2007-11-02T17:13:13", + "ver_number": 8 + }, + "83516": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 83518, + "object_name": "backup for weblink" + } + ] + }, + "class_id": 19, + "created": "2006-12-06T11:42:26", + "gitrepo": false, + "id": 83516, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 64248, + "object_name": "Hasselmo, Michael E [hasselmo at bu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83515, + "object_name": "Hasselmo ME, Eichenbaum H (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Hippocampal context-dependent retrieval (Hasselmo and Eichenbaum 2005)", + "notes": { + "attr_id": 24, + "value": "\"... The model simulates the context-sensitive firing properties of hippocampal neurons including trial-specific firing during spatial\r\nalternation and trial by trial changes in theta phase precession on a linear track. ...\" See paper for more and details." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83516", + "ver_date": "2007-11-02T17:17:22", + "ver_number": 7 + }, + "83517": { + "class_id": 19, + "created": "2006-12-06T12:04:27", + "gitrepo": false, + "id": 83517, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 64248, + "object_name": "Hasselmo, Michael E [hasselmo at bu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + }, + { + "object_id": 83539, + "object_name": "Attractor Neural Network" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 27882, + "object_name": "Hasselmo ME, Schnell E, Barkai E (1995)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Fixed point attractor (Hasselmo et al 1995)", + "notes": { + "attr_id": 24, + "value": "\"... In the model, cholinergic suppression of synaptic transmission at excitatory feedback synapses is shown to determine the extent to which activity depends upon new features of the afferent input versus components of previously stored representations. ...\" See paper for more and details. The MATLAB script demonstrates the model of fixed point attractors mediated by excitatory feedback with subtractive inhibition in a continuous firing rate model.\r\n" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83517", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2008-10-09T10:31:54", + "ver_number": 7 + }, + "83520": { + "class_id": 19, + "created": "2006-12-06T14:24:25", + "gitrepo": false, + "id": 83520, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 182362, + "object_name": "Sensory coding" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83206, + "object_name": "Carlson BA, Kawasaki M (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Ambiguous Encoding and Distorted Perception (Carlson and Kawasaki 2006)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88119, + "object_name": "Electric fish P- and T-type primary afferent fibers" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... In\r\nthe weakly electric fish Eigenmannia, P- and T-type primary afferent fibers are specialized for encoding the amplitude and phase,\r\nrespectively, of electrosensory stimuli. We used a stimulus estimation technique to quantify the ability of P- and T-units to encode\r\nrandom modulations in amplitude and phase. As expected, P-units exhibited a clear preference for encoding amplitude modulations,\r\nwhereas T-units exhibited a clear preference for encoding phase modulations. Surprisingly, both types of afferents also encoded their\r\nnonpreferred stimulus attribute when it was presented in isolation or when the preferred stimulus attribute was sufficiently weak.\r\nBecause afferent activity can be affected by modulations in either amplitude or phase, it is not possible to unambiguously distinguish\r\nbetween these two stimulus attributes by observing the activity of a single afferent fiber. Simple model neurons with a preference for\r\nencoding either amplitude or phase also encoded their nonpreferred stimulus attribute when it was presented in isolation, suggesting that\r\nsuch ambiguity is unavoidable. ... \" See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83520", + "ver_date": "2015-05-11T10:53:35.063", + "ver_number": 9 + }, + "83521": { + "class_id": 19, + "created": "2006-12-06T14:40:39", + "gitrepo": false, + "id": 83521, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83522, + "object_name": "Carlson BA, Kawasaki M (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Combination sensitivity and active conductances (Carlson and Kawasaki 2006)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88204, + "object_name": "Electric fish midbrain torus semicircularis neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... The weakly electric fish Gymnarchus discriminates the\r\nsign of the frequency difference (Df) between a neighbor\u2019s electric\r\norgan discharge (EOD) and its own EOD by comparing temporal\r\npatterns of amplitude modulation (AM) and phase modulation (PM).\r\nSign-selective neurons in the midbrain respond preferentially to either\r\npositive frequency differences (Df >0 selective) or negative frequency\r\ndifferences (Df <0 selective). To study the mechanisms of\r\ncombination sensitivity, we made whole cell intracellular recordings\r\nfrom sign-selective midbrain neurons in vivo and recorded postsynaptic\r\npotential (PSP) responses to AM, PM, Df >0, and Df <0.\r\n... Responses to the nonpreferred\r\nsign of Df, but not the preferred sign of Df, were substantially weaker\r\nthan linear predictions, causing a significant increase in the actual\r\ndegree of sign selectivity. By using various levels of current clamp\r\nand comparing our results to simple models of synaptic integration,\r\nwe demonstrate that this decreased response to the nonpreferred sign\r\nof Df is caused by a reduction in voltage-dependent excitatory\r\nconductances. This finding reveals that nonlinear decoders, in the\r\nform of voltage-dependent conductances, can enhance the selectivity\r\nof single neurons for particular combinations of stimulus attributes.\" See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83521", + "ver_date": "2015-07-26T11:49:15.303", + "ver_number": 12 + }, + "83523": { + "class_id": 19, + "created": "2006-12-06T16:39:02", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 83523, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 110661, + "object_name": "Korngreen, Alon [alon.korngreen at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83497, + "object_name": "Korngreen A, Kaiser KM, Zilberter Y (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Subthreshold inact. of K channels modulates APs in bitufted interneurons (Korngreen et al 2005)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 93384, + "object_name": "Neocortex bitufted interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "... In this study we show that in bitufted interneurones\r\nfrom layer 2/3 of the somatosensory cortex, the height and width of APs recorded at the\r\nsoma are sensitive to changes in the resting membrane potential, suggesting subthreshold\r\nactivity of voltage-gated conductances. Attributes of K+ currents examined in nucleated\r\npatches revealed a fast subthreshold-inactivating K+ conductance (Kf ) and a slow\r\nsuprathreshold-inactivating K+ conductance (Ks ). Simulations of these K+ conductances,\r\nincorporated into a Hodgkin\u2013Huxley-type model, suggested that during a single AP or during\r\nlow frequency trains of APs, subthreshold inactivation of Kf was the primary modulator of AP\r\nshape, whereas during trains of APs the shape was governed to a larger degree by Ks resulting\r\nin the generation of smaller and broader APs. ... Compartmental simulation\r\nof the back-propagating AP suggested a mechanism for the modulation of the back-propagating\r\nAP height and width by subthreshold activation of Kf . We speculate that this signal may\r\nmodulate retrograde GABA release and consequently depression of synaptic efficacy of excitatory\r\ninput from neighbouring pyramidal neurones." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83523", + "ver_date": "2008-01-14T14:54:34", + "ver_number": 8 + }, + "83528": { + "citation": { + "attr_id": 391, + "value": "Weber C, Wermter S, Elshaw M (2006) A hybrid generative and \r\npredictive model of the motor cortex. Neural Netw 19:339\u201453" + }, + "class_id": 19, + "created": "2006-12-11T16:57:38", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 83528, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83529, + "object_name": "Weber, Cornelius [cweber at fias.uni-frankfurt.de]" + }, + { + "object_id": 83530, + "object_name": "Elshaw, Mark [mark.elshaw at sunderland.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + }, + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + }, + { + "object_id": 83538, + "object_name": "Unsupervised Learning" + }, + { + "object_id": 83539, + "object_name": "Attractor Neural Network" + }, + { + "object_id": 83540, + "object_name": "Winner-take-all" + }, + { + "object_id": 180821, + "object_name": "Hebbian plasticity" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83265, + "object_name": "Weber C, Wermter S, Elshaw M (2006)" + }, + { + "object_id": 83532, + "object_name": "Weber C, Triesch J (2006)" + }, + { + "object_id": 83533, + "object_name": "Weber C, Wermter S (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Cortex learning models (Weber at al. 2006, Weber and Triesch, 2006, Weber and Wermter 2006/7)", + "notes": { + "attr_id": 24, + "value": "A simulator and the configuration files for three publications are\r\nprovided. First, \"A hybrid generative and predictive model of the motor\r\ncortex\" (Weber at al. 2006) which uses reinforcement learning to set up a\r\ntoy action scheme, then uses unsupervised learning to \"copy\" the learnt\r\naction, and an attractor network to predict the hidden code of the\r\nunsupervised network. Second, \"A Self-Organizing Map of Sigma-Pi Units\"\r\n(Weber and Wermter 2006/7) learns frame of reference transformations on\r\npopulation codes in an unsupervised manner. Third, \"A possible\r\nrepresentation of reward in the learning of saccades\" (Weber and Triesch,\r\n2006) implements saccade learning with two possible learning schemes for\r\nhorizontal and vertical saccades, respectively." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83528", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2018-10-17T16:47:00.917", + "ver_number": 15 + }, + "83547": { + "citation": { + "attr_id": 391, + "value": "Komendantov A.O., Komendantova O.G., Johnson S.W., Canavier C.C. (2004) A modeling study suggests complementary roles for GABAA and NMDA receptors and the SK channel in regulating the firing pattern in midbrain dopamine neurons. J Neurophysiol 91: 346-357, 2004." + }, + "class_id": 19, + "created": "2006-12-19T13:02:36", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 83547, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83550, + "object_name": "Kuznetsova, Anna [anna.kuznetsova at utsa.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 120695, + "object_name": "Sodium pump" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83549, + "object_name": "Komendantov AO, Komendantova OG, Johnson SW, Canavier CC (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 120694, + "object_name": "Electrogenic pump" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "83547", + "name": "ModelView" + } + ], + "default": "83547" + }, + "name": "Regulation of the firing pattern in dopamine neurons (Komendantov et al 2004)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 261, + "object_name": "Substantia nigra pars compacta DA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Midbrain dopaminergic (DA) neurons in vivo exhibit two major firing patterns: single-spike firing and burst firing. The firing pattern expressed is dependent on both the intrinsic properties of the neurons and their excitatory and inhibitory synaptic inputs. Experimental data suggest that the activation of NMDA and GABAA receptors is crucial contributor to the initiation and suppression of burst firing, respectively, and that blocking calcium-activated potassium channels can facilitate burst firing. This multi-compartmental model of a DA neuron with a branching structure was developed and calibrated based on in vitro experimental data to explore the effects of different levels of activation of NMDA and GABAA receptors as well as the modulation of the SK current on the firing activity." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83547", + "other_implementer": { + "attr_id": 308, + "value": "Kuznetsova Anna akuzne@lsuhsc.edu" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "akuzne@lsuhsc.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181417, + "object_name": "83547" + } + ] + }, + "ver_date": "2014-02-25T01:16:39", + "ver_number": 12 + }, + "83558": { + "biosimulations": { + "value": "modeldb-83558" + }, + "citation": { + "attr_id": 391, + "value": "Goldberg JA, Deister CA, Wilson CJ (2006)" + }, + "class_id": 19, + "created": "2006-12-31T16:52:30", + "gitrepo": false, + "id": 83558, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83567, + "object_name": "Goldberg, Joshua [JoshG at ekmd.huji.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 137847, + "object_name": "Phase Response Curves" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83212, + "object_name": "Goldberg JA, Deister CA, Wilson CJ (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Role of active dendrites in rhythmically-firing neurons (Goldberg et al 2006)", + "notes": { + "attr_id": 24, + "value": "\"The responsiveness of rhythmically-firing neurons to synaptic inputs is characterized by their phase response curve (PRC), which relates how weak somatic perturbations affect the timing of the next action potential. The shape of the somatic PRC is an important determinant of collective network dynamics. Here we study theoretically and experimentally the impact of distally-located synapses and dendritic nonlinearities on the synchronization properties of rhythmically firing neurons. Combining the theories of quasi-active cables and phase-coupled oscillators we derive an approximation for the dendritic responsiveness, captured by the neuron's dendritic PRC (dPRC). This closed-form expression indicates that the dPRCs are linearly-filtered versions of the somatic PRC, and that the filter characteristics are determined by the passive and active properties of the dendrite. ... collective dynamics can be qualitatively different depending on the location of the synapse, the neuronal firing rates and the dendritic nonlinearities.\" See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83558", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2011-03-10T10:20:06", + "ver_number": 6 + }, + "83559": { + "citation": { + "attr_id": 391, + "value": "17167083" + }, + "class_id": 19, + "created": "2007-01-03T07:21:43", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 83559, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83561, + "object_name": "Humphries, Mark D [m.d.humphries at shef.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + }, + { + "object_id": 93420, + "object_name": "Sleep" + }, + { + "object_id": 136252, + "object_name": "Rebound firing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83557, + "object_name": "Humphries MD, Stewart RD, Gurney KN (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Spiking neuron model of the basal ganglia (Humphries et al 2006)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 260, + "object_name": "Neostriatum medium spiny direct pathway GABA cell" + }, + { + "object_id": 74300, + "object_name": "Subthalamus nucleus projection neuron" + }, + { + "object_id": 88217, + "object_name": "Globus pallidus neuron" + }, + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A spiking neuron model of the basal ganglia (BG) circuit (striatum, STN, GP, SNr). Includes: parallel anatomical channels; tonic dopamine; dopamine receptors in striatum, STN, and GP; burst-firing in STN; GABAa, AMPA, and NMDA currents; effects of synaptic location. Model demonstrates selection and switching of input signals. Replicates experimental data on changes in slow-wave (<1 Hz) and gamma-band oscillations within BG nuclei following lesions and pharmacological manipulations." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83559", + "other_concept": { + "attr_id": 306, + "value": "Action selection" + }, + "other_neurons": { + "attr_id": 301, + "value": "Subthalamic nucleus; globus pallidus" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "m.d.humphries@shef.ac.uk" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 223, + "object_name": "Dopaminergic Receptor" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2014-05-21T11:05:16", + "ver_number": 14 + }, + "83560": { + "citation": { + "attr_id": 391, + "value": "11417053, 15600234" + }, + "class_id": 19, + "created": "2007-01-03T10:05:48", + "gitrepo": false, + "id": 83560, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83561, + "object_name": "Humphries, Mark D [m.d.humphries at shef.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 65222, + "object_name": "Gurney K, Prescott TJ, Redgrave P (2001)" + }, + { + "object_id": 83565, + "object_name": "Gurney KN, Humphries M, Wood R, Prescott TJ, Redgrave P (2004)" + }, + { + "object_id": 83566, + "object_name": "Gurney K, Prescott TJ, Redgrave P (2001)" + }, + { + "object_id": 83568, + "object_name": "Humphries MD (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 83572, + "object_name": "Simulink" + } + ] + }, + "name": "Population-level model of the basal ganglia and action selection (Gurney et al 2001, 2004)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We proposed a new functional architecture for the basal ganglia (BG) based on the premise that these brain structures play a central role in behavioural action selection. The papers quantitatively describes the properties of the model using analysis and simulation. In the first paper, we show that the decomposition of the BG into selection and control pathways is supported in several ways. First, several elegant features are exposed--capacity scaling, enhanced selectivity and synergistic dopamine modulation--which might be expected to exist in a well designed action selection mechanism. Second, good matches between model GPe output and GPi and SNr output, and neurophysiological data, have been found. Third, the behaviour of the model as a signal selection mechanism has parallels with some kinds of action selection observed in animals under various levels of dopaminergic modulation.\r\n\r\nIn the second paper, we extend the BG model to include new connections, and show that action selection is maintained. In addition, we provide quantitative measures for defining different forms of selection, and methods for assessing performance changes in computational neuroscience models. \r\n\r\n\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83560", + "other_concept": { + "attr_id": 306, + "value": "Action selection" + }, + "other_simulator": { + "attr_id": 307, + "value": "Simulink" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "m.d.humphries@shef.ac.uk" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 132 + }, + "ver_date": "2008-10-09T15:43:50", + "ver_number": 8 + }, + "83562": { + "citation": { + "attr_id": 391, + "value": "11873842" + }, + "class_id": 19, + "created": "2007-01-03T11:06:37", + "gitrepo": false, + "id": 83562, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83561, + "object_name": "Humphries, Mark D [m.d.humphries at shef.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83568, + "object_name": "Humphries MD (2003)" + }, + { + "object_id": 83569, + "object_name": "Humphries MD, Gurney KN (2002)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 83572, + "object_name": "Simulink" + } + ] + }, + "name": "Basal ganglia-thalamocortical loop model of action selection (Humphries and Gurney 2002)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We embed our basal ganglia model into a wider circuit containing the motor thalamocortical loop and thalamic reticular nucleus (TRN). Simulation of this extended model showed that the additions gave five main results which are desirable in a selection/switching mechanism. First, low salience actions (i.e. those with low urgency) could be selected. Second, the range of salience values over which actions could be switched between was increased. Third, the contrast between the selected and non-selected actions was enhanced via improved differentiation of outputs from the BG. Fourth, transient increases in the salience of a non-selected action were prevented from interrupting the ongoing action, unless the transient was of sufficient magnitude. Finally, the selection of the ongoing action persisted when a new closely matched salience action became active. The first result was facilitated by the thalamocortical loop; the rest were dependent on the presence of the TRN. Thus, we conclude that the results are consistent with these structures having clearly defined functions in action selection." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83562", + "other_concept": { + "attr_id": 306, + "value": "Action selection" + }, + "other_simulator": { + "attr_id": 307, + "value": "Simulink" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "m.d.humphries@shef.ac.uk" + }, + "simPFid": { + "attr_id": 488, + "value": 79 + }, + "ver_date": "2007-11-02T14:01:02", + "ver_number": 9 + }, + "83570": { + "biosimulations": { + "value": "modeldb-83570" + }, + "class_id": 19, + "created": "2007-01-03T17:59:36", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 83570, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83567, + "object_name": "Goldberg, Joshua [JoshG at ekmd.huji.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 72561, + "object_name": "Goldberg JA, Rokni U, Sompolinsky H (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "A theory of ongoing activity in V1 (Goldberg et al 2004)", + "notes": { + "attr_id": 24, + "value": "Ongoing spontaneous activity in the cerebral cortex exhibits\r\ncomplex spatiotemporal patterns in the absence of sensory stimuli. To elucidate the nature of\r\nthis ongoing activity, we present a theoretical treatment of two contrasting scenarios of cortical dynamics: (1) fluctuations about a single background state\r\nand (2) wandering among multiple \u201cattractor\u201d states, which\r\nencode a single or several stimulus features.\r\nStudying simplified network rate models of the primary\r\nvisual cortex (V1), we show that the single state scenario\r\nis characterized by fast and high-dimensional\r\nGaussian-like fluctuations, whereas in the multiple\r\nstate scenario the fluctuations are slow, low dimensional,\r\nand highly non-Gaussian. Studying a more realistic model that incorporates correlations in the feedforward input, spatially restricted cortical interactions,\r\nand an experimentally derived layout of pinwheels,\r\nwe show that recent optical-imaging data of ongoing\r\nactivity in V1 are consistent with the presence of either\r\na single background state or multiple attractor states\r\nencoding many features." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83570", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2018-10-17T16:47:17.077", + "ver_number": 9 + }, + "83575": { + "biosimulations": { + "value": "modeldb-83575" + }, + "citation": { + "attr_id": 391, + "value": "PMID: 14702331" + }, + "class_id": 19, + "created": "2007-01-05T16:56:56", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122828, + "object_name": "D1 DRD1A" + }, + { + "object_id": 122829, + "object_name": "D2 DRD2" + } + ] + }, + "gitrepo": false, + "id": 83575, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83576, + "object_name": "Yu X, Byrne JH, Baxter DA (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Modeling interactions in Aplysia neuron R15 (Yu et al 2004)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52420, + "object_name": "Aplysia R15 bursting neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 239, + "object_name": "Serotonin" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The biophysical properties of neuron R15 in Aplysia endow it with the ability to express multiple modes of oscillatory electrical activity, such as beating and bursting. Previous modeling studies examined the ways in which membrane conductances contribute to the electrical activity of R15 and the ways in which extrinsic modulatory inputs alter the membrane conductances by biochemical cascades and influence the electrical activity. The goals of the present study were to examine the ways in which electrical activity influences the biochemical cascades and what dynamical properties emerge from the ongoing interactions between electrical activity and these cascades.\" See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83575", + "other_concept": { + "attr_id": 306, + "value": "second messenger" + }, + "other_neurons": { + "attr_id": 301, + "value": "R15" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "douglas.baxter@uth.tmc.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 196, + "object_name": "D1" + }, + { + "object_id": 197, + "object_name": "D2" + }, + { + "object_id": 198, + "object_name": "5-HT1" + }, + { + "object_id": 199, + "object_name": "5-HT2" + }, + { + "object_id": 200, + "object_name": "5-HT4" + }, + { + "object_id": 208, + "object_name": "5-HT3" + }, + { + "object_id": 223, + "object_name": "Dopaminergic Receptor" + } + ] + }, + "ver_date": "2014-07-18T15:55:46", + "ver_number": 10 + }, + "83590": { + "class_id": 19, + "created": "2007-01-09T06:45:00", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 83590, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 87550, + "object_name": "Arsiero M, L\u00fcscher HR, Lundstrom BN, Giugliano M (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "83590", + "name": "ModelView" + } + ], + "default": "83590" + }, + "name": "Input Fluctuations effects on f-I curves (Arsiero et al. 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... We examined in vitro frequency versus current (f-I) relationships of layer 5 (L5) pyramidal cells of the rat medial prefrontal cortex (mPFC) using fluctuating stimuli. ...our results show that mPFC L5 pyramidal neurons retain an increased sensitivity to input fluctuations, whereas their sensitivity to the input mean diminishes to near zero. This implies that the discharge properties of L5 mPFC neurons are well suited to encode input fluctuations rather than input mean in their firing rates, with important consequences for information processing and stability of persistent activity at the network level.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/83590", + "other_concept": { + "attr_id": 306, + "value": "noise" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181487, + "object_name": "83590" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 265 + }, + "ver_date": "2022-11-04T17:19:38.957", + "ver_number": 21 + }, + "84167": { + "class_id": 19, + "created": "2007-01-18T16:59:27", + "gitrepo": false, + "id": 84167, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 84168, + "object_name": "Izhikevich, Eugene [Eugene.Izhikevich at braincorporation.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 87578, + "object_name": "STDP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83595, + "object_name": "Izhikevich EM (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Linking STDP and Dopamine action to solve the distal reward problem (Izhikevich 2007)", + "notes": { + "attr_id": 24, + "value": "\"... How does the brain know what firing patterns of what neurons are responsible for the reward if 1) the patterns are no longer there when the reward arrives and 2) all\r\nneurons and synapses are active during the waiting period to the\r\nreward? Here, we show how the conundrum is resolved by a model network\r\nof cortical spiking neurons with spike-timing-dependent plasticity\r\n(STDP) modulated by dopamine (DA). Although STDP is triggered by\r\nnearly coincident firing patterns on a millisecond timescale, slow\r\nkinetics of subsequent synaptic plasticity is sensitive to changes in\r\nthe extracellular DA concentration during the critical period of a few\r\nseconds. ... This study emphasizes the importance of precise firing\r\npatterns in brain dynamics and suggests how a global diffusive\r\nreinforcement signal in the form of extracellular DA can selectively\r\ninfluence the right synapses at the right time.\" See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/84167", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 119 + }, + "ver_date": "2017-06-15T11:14:45.3", + "ver_number": 13 + }, + "84589": { + "citation": { + "attr_id": 391, + "value": "Gold C, Henze DA, Koch C. (2007). Using Extracellular Action Potential Recordings to Tune Compartmental Models. Journal of Computational Neuroscience. In Press." + }, + "class_id": 19, + "created": "2007-01-21T16:24:05", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 84589, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 84590, + "object_name": "Gold, Carl [carlg at caltech.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 84598, + "object_name": "Extracellular Fields" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 28766, + "object_name": "Holt GR, Koch C (1999)" + }, + { + "object_id": 75414, + "object_name": "Gold C, Henze DA, Koch C, Buzs\u00e1ki G (2006)" + }, + { + "object_id": 84591, + "object_name": "Holt G (1998)" + }, + { + "object_id": 84592, + "object_name": "Gold C, Henze DA, Koch C (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Extracellular Action Potential Simulations (Gold et al 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 52425, + "object_name": "Myelinated neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This package recreates the the principal experiments described in (Gold, Henze and Koch, 2007) and includes the core code necessary to create your own Extracellular Action Potential Simulations." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/84589", + "other_concept": { + "attr_id": 306, + "value": "Extracellular Action Pote" + }, + "other_implementer": { + "attr_id": 308, + "value": "Gold, Carl " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "carl@klab.caltech.edu" + }, + "simPFid": { + "attr_id": 488, + "value": 243 + }, + "ver_date": "2019-09-27T15:22:02.503", + "ver_number": 16 + }, + "84593": { + "class_id": 19, + "created": "2007-01-22T12:09:33", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 84593, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 84594, + "object_name": "Wang, Xiao-Jing [xjwang at yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 82035, + "object_name": "Wang XJ, Liu Y, Sanchez-Vives MV, McCormick DA (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Temporal decorrelation by intrinsic cellular dynamics (Wang et al 2003)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... Recent investigations in primary visual (V1) cortical neurons have\r\ndemonstrated that adaptation to prolonged changes in stimulus contrast\r\nis mediated in part through intrinsic ionic currents, a Ca2+\r\nactivated K+ current (IKCa) and especially a Na+ activated K+ current\r\n(IKNa). The present study was designed to test the hypothesis that\r\nthe activation of adaptation ionic currents may provide a cellular\r\nmechanism for temporal decorrelation in V1. A conductance-based\r\nneuron model was simulated, which included an IKCa and an IKNa. We\r\nshow that the model neuron reproduces the adaptive behavior of V1\r\nneurons in response to high contrast inputs. ...\". See paper for details and more." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/84593", + "other_concept": { + "attr_id": 306, + "value": "Correlation" + }, + "other_currents": { + "attr_id": 303, + "value": "IK, Na" + }, + "other_neurons": { + "attr_id": 301, + "value": "spiny stellate" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "simPFid": { + "attr_id": 488, + "value": 70 + }, + "ver_date": "2018-10-17T16:48:33.563", + "ver_number": 9 + }, + "84599": { + "citation": { + "attr_id": 391, + "value": "PMID: 16914611" + }, + "class_id": 19, + "created": "2007-01-23T20:39:11", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "id": 84599, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 84601, + "object_name": "Takahata, Masakazu [takahata at sci.hokudai.ac.jp]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83231, + "object_name": "Takashima A, Hikosaka R, Takahata M (2006)" + }, + { + "object_id": 83232, + "object_name": "Takahata M, Takashima A, Hikosaka R (2000)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "Synaptic integration of an identified nonspiking interneuron in crayfish (Takashima et al 2006)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 87588, + "object_name": "Crayfish identified nonspiking interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This GENESIS simulation shows how a single or compound excitatory synaptic potential evoked by mechanosensory stimulation spreads over the dendrites of the LDS interneuron that is one of the identified nonspiking interneurons in the central nervous system of crayfish Procambarus clarkii. The model is based on physiological experiments carried out by Akira Takashima using single-electrode voltage clamp techniques and also 3-D morphometry of the interneuron carried out by Ryou Hikosaka using confocal laser scanning microscopic techniques. The physiological and morphological studies were coordinated by Masakazu Takahata." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/84599", + "other_implementer": { + "attr_id": 308, + "value": "Takahata, Masakazu " + }, + "other_neurons": { + "attr_id": 301, + "value": "an identified nonspiking interneuron in crayfish" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "takashim@sci.hokudai.ac.jp" + }, + "ver_date": "2008-03-05T09:21:12", + "ver_number": 10 + }, + "84606": { + "biosimulations": { + "value": "modeldb-84606" + }, + "citation": { + "attr_id": 391, + "value": "Zonisamide-induced inhibition on the firing of action potentials in hippocampal neuron" + }, + "class_id": 19, + "created": "2007-01-27T03:51:43", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "id": 84606, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + }, + { + "object_id": 84610, + "object_name": "Huang, Chin-Wei" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 84609, + "object_name": "Huang CW, Huang CC, Wu SN (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Zonisamide-induced inhibition of the firing of APs in hippocampal neurons (Huang et al. 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Zonisamide (ZNS), a synthetic benzisoxazole derivative, has been used as an alternative choice in the treatment of epilepsy with a better efficacy and safety profile. However, little is known regarding the mechanism of ZNS actions on ion currents in neurons. We thus investigated its effect on ion currents in differentiated hippocampal 19-7 cells. The ZNS (30 uM) reversibly increased the amplitude of K+ outward currents and paxilline (1 uM) was effective in suppressing ZNS-induced increase of K+ outward currents. In inside-out configuration, ZNS (30 uM) applied to the intracellular face of the membrane did not alter single-channel conductance; however, it did enhance the activity of large-conductance Ca2+-activated K+ (BKCa) channels primarily by decreasing mean closed time. The EC50 value for ZNS-stimulated BKCa channels was 34 uM. This drug caused a left shift in the activation curve of BKCa channels with no change in the gating charge of these channels. ZNS at a concentration greater than 100 uM also reduced the amplitude of A-type K+ current in these cells. A simulation modeling based on hippocampal CA3 pyramidal neurons (Pinsky-Rinzel model) was also analyzed to investigate the inhibitory effect of ZNS on the firing of simulated action potentials. Taken together, this study suggests that in hippocampal neurons, during the exposure to ZNS, the ZNS-mediated effects on BKCa channels and IA could be one of the ionic mechanisms through which it affects neuronal excitability." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/84606", + "other_simulator": { + "attr_id": 307, + "value": "Dr. Chin-Wei Huang" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2015-07-26T13:28:16.64", + "ver_number": 11 + }, + "84612": { + "citation": { + "attr_id": 391, + "value": "Canavier and Landry 2006" + }, + "class_id": 19, + "created": "2007-01-31T12:24:06", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 84612, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83550, + "object_name": "Kuznetsova, Anna [anna.kuznetsova at utsa.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 41934, + "object_name": "Intrinsic plasticity" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 120695, + "object_name": "Sodium pump" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83592, + "object_name": "Canavier CC, Landry RS (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 120694, + "object_name": "Electrogenic pump" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Differential modulation of pattern and rate in a dopamine neuron model (Canavier and Landry 2006)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 261, + "object_name": "Substantia nigra pars compacta DA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"A stylized, symmetric, compartmental model of a dopamine neuron in vivo shows how rate and pattern can be modulated either concurrently or differentially. If two or more parameters in the model are varied concurrently, the baseline firing rate and the extent of bursting become decorrelated, which provides an explanation for the lack of a tight correlation in vivo and is consistent with some independence of the mechanisms that generate baseline firing rates versus bursting. ...\" See paper for more and details.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/84612", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 223 + }, + "ver_date": "2022-12-16T10:42:19.54", + "ver_number": 16 + }, + "84627": { + "class_id": 19, + "created": "2007-02-09T14:32:03", + "gitrepo": false, + "id": 84627, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 84630, + "object_name": "Major LA, Hegedus J, Weber DJ, Gordon T, Jones KE (2007)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Method for counting motor units in mice (Major et al 2007)", + "notes": { + "attr_id": 24, + "value": "\"... Our goal was to develop an efficient method\r\nto determine the number of motor neurons making functional connections\r\nto muscle in a transgenic mouse model of amyotrophic lateral\r\nsclerosis (ALS). We developed a novel protocol for motor unit\r\nnumber estimation (MUNE) using incremental stimulation. The\r\nmethod involves analysis of twitch waveforms using a new software\r\nprogram, ITS-MUNE, designed for interactive calculation of motor\r\nunit number. The method was validated by testing simulated twitch\r\ndata from a mathematical model of the neuromuscular system. Computer\r\nsimulations followed the same stimulus-response protocol and\r\nproduced waveform data that were indistinguishable from experiments.\r\n... The ITS-MUNE analysis method has the potential to quantitatively\r\nmeasure the progression of motor neuron diseases and therefore the\r\nefficacy of treatments designed to alleviate pathologic processes of\r\nmuscle denervation.\" The software is available for download under the \"ITS-MUNE software\" link at \r\n(see below for links).\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/84627", + "ver_date": "2015-12-14T13:08:35.4", + "ver_number": 8 + }, + "84641": { + "class_id": 19, + "created": "2007-02-12T13:28:29", + "gitrepo": false, + "id": 84641, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 84650, + "object_name": "Bers, Donald M" + }, + { + "object_id": 84651, + "object_name": "Puglisi, Jose " + }, + { + "object_id": 84652, + "object_name": "Salah, Wajeeh " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3642, + "object_name": "Tutorial/Teaching" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52407, + "object_name": "Heart disease" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 60348, + "object_name": "Puglisi JL, Bers DM (2001)" + }, + { + "object_id": 84648, + "object_name": "Busjahn A, Seebohm G, Maier G, Toliat MR, N\u00fcrnberg P, Aydin A, Luft FC, Lang F (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "A cardiac cell simulator (Puglisi and Bers 2001), applied to the QT interval (Busjahn et al 2004)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52423, + "object_name": "Heart cell" + }, + { + "object_id": 88205, + "object_name": "Cardiac ventricular cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"LabHEART is an easy to use program that simulates the cardiac action potential, calcium transient and ionic currents. Key parameters such as ionic concentration, stimulus waveform and channel conductance can easily be changed by a click on an icon or dragging a slider.\r\n\r\nIt is a powerfull tool for teaching and researching cardiac electrophysiology.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/84641", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2017-06-15T11:28:55.75", + "ver_number": 13 + }, + "84649": { + "class_id": 19, + "created": "2007-02-12T15:40:06", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 84649, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 84650, + "object_name": "Bers, Donald M" + }, + { + "object_id": 84651, + "object_name": "Puglisi, Jose " + }, + { + "object_id": 84652, + "object_name": "Salah, Wajeeh " + }, + { + "object_id": 84653, + "object_name": "Mejia-Alvarez, Rafael " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 29126, + "object_name": "HODGKIN AL, HUXLEY AF (1952)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Squid axon (Hodgkin, Huxley 1952) (LabAXON)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52426, + "object_name": "Squid axon" + }, + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The classic HH model of squid axon membrane implemented in LabAXON. Hodgkin, A.L., Huxley, A.F. (1952)" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/84649", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2008-09-18T15:03:16", + "ver_number": 4 + }, + "84655": { + "class_id": 19, + "created": "2007-02-13T16:39:29", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 84655, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 84656, + "object_name": "Oltedal, Leif [Leif.Oltedal at biomed.uib.no]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 84639, + "object_name": "Oltedal L, M\u00f8rkve SH, Veruki ML, Hartveit E (2007)" + }, + { + "object_id": 84654, + "object_name": "Mennerick S, Zenisek D, Matthews G (1997)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "84655", + "name": "ModelView" + } + ], + "default": "84655" + }, + "name": "Recording from rod bipolar axon terminals in situ (Oltedal et al 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 7020, + "object_name": "Retina bipolar GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... Whole cell\r\nrecordings from axon terminals and cell bodies were used to investigate\r\nthe passive membrane properties of rod bipolar cells and analyzed\r\nwith a two-compartment equivalent electrical circuit model\r\ndeveloped by Mennerick et al. For both terminal- and soma-end\r\nrecordings, capacitive current decays were well fitted by biexponential\r\nfunctions. Computer simulations of simplified models of rod bipolar\r\ncells demonstrated that estimates of the capacitance of the axon\r\nterminal compartment can depend critically on the recording location,\r\nwith terminal-end recordings giving the best estimates. Computer\r\nsimulations and whole cell recordings demonstrated that terminal-end\r\nrecordings can yield more accurate estimates of the peak amplitude\r\nand kinetic properties of postsynaptic currents generated at the axon\r\nterminals due to increased electrotonic filtering of these currents when\r\nrecorded at the soma. ...\" See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/84655", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "simPFid": { + "attr_id": 488, + "value": 321 + }, + "ver_date": "2015-07-26T13:29:57.907", + "ver_number": 10 + }, + "85112": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 87558, + "object_name": "under development for fig 9" + } + ] + }, + "citation": { + "attr_id": 391, + "value": "16002450" + }, + "class_id": 19, + "created": "2007-02-17T16:01:26", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "default_file_notes": { + "attr_id": 392, + "value": "current" + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92082, + "object_name": "Nav1.1 SCN1A" + }, + { + "object_id": 92087, + "object_name": "Nav1.6 SCN8A" + }, + { + "object_id": 92088, + "object_name": "Nav1.7 SCN9A" + }, + { + "object_id": 92089, + "object_name": "Nav1.8 SCN10A" + }, + { + "object_id": 92091, + "object_name": "Nav1.9 SCN11A SCN12A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 85112, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33772, + "object_name": "Morse, Tom [Tom.Morse at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 96767, + "object_name": "Nociception" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83248, + "object_name": "Baker MD (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "85112", + "name": "ModelView" + } + ], + "default": "85112" + }, + "name": "Models of Na channels from a paper on the PKC control of I Na,P (Baker 2005)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 136307, + "object_name": "Dorsal Root Ganglion (DRG) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The tetrodotoxin-resistant (TTX-r) persistent Na(+) current, attributed to Na(V)1.9, was recorded in small (< 25 mum apparent diameter) dorsal root ganglion (DRG) neurones cultured from P21 rats and from adult wild-type and Na(V)1.8 null mice. ... Numerical simulation of the up-regulation qualitatively reproduced changes in sensory neurone firing properties. ...\" Note: models of NaV1.8 and NaV1.9 and also persistent and transient Na channels that collectively model Nav 1.1, 1.6, and 1.7 are present in this model." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/85112", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181243, + "object_name": "85112" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 285 + }, + "ver_date": "2013-12-10T15:39:27", + "ver_number": 13 + }, + "85981": { + "citation": { + "attr_id": 391, + "value": "Postlethwaite M, Hennig MH, Steinert JR, Graham BP, Forsythe ID (2007) Acceleration of AMPA receptor kinetics underlies temperature-dependent changes in synaptic strength at the rat calyx of Held. J Physiol. 579: 69-84" + }, + "class_id": 19, + "created": "2007-02-20T05:50:22", + "gitrepo": false, + "id": 85981, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 139659, + "object_name": "Hennig, Matthias H [mhhennig at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 85984, + "object_name": "Postlethwaite M, Hennig MH, Steinert JR, Graham BP, Forsythe ID (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 2835, + "object_name": "MCell" + } + ] + }, + "name": "Modeling temperature changes in AMPAR kinetics (Postlethwaite et al 2007)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 85986, + "object_name": "Medial Nucleus of the Trapezoid Body (MNTB) neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model was used to simulate glutamatergic, AMPA receptor mediated mEPSCs (miniature EPSCs, resulting from spontaneous vesicular transmitter release) at the calyx of Held synapse. It was used to assess the influence of temperature (physiological vs. subphysiological) on the amplitude and time course of mEPSCs. In the related paper, simulation results were directly compared to the experimental data, and it was concluded that an increase of temperature accelerates AMPA receptor kinetics." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/85981", + "other_concept": { + "attr_id": 306, + "value": "Transmitter Release" + }, + "other_neurons": { + "attr_id": 301, + "value": "principal neurons in the MNTB" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "mhhennig@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2015-10-26T18:17:03.557", + "ver_number": 4 + }, + "86537": { + "class_id": 19, + "created": "2007-02-23T18:25:43", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92082, + "object_name": "Nav1.1 SCN1A" + }, + { + "object_id": 92087, + "object_name": "Nav1.6 SCN8A" + }, + { + "object_id": 92088, + "object_name": "Nav1.7 SCN9A" + }, + { + "object_id": 92089, + "object_name": "Nav1.8 SCN10A" + }, + { + "object_id": 92091, + "object_name": "Nav1.9 SCN11A SCN12A" + } + ] + }, + "gitrepo": false, + "id": 86537, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33772, + "object_name": "Morse, Tom [Tom.Morse at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 96767, + "object_name": "Nociception" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 3906, + "object_name": "Herzog RI, Cummins TR, Waxman SG (2001)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "TTX-R Na+ current effect on cell response (Herzog et al 2001)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 136307, + "object_name": "Dorsal Root Ganglion (DRG) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Small dorsal root ganglion (DRG) neurons, which include nociceptors,\r\nexpress multiple voltage-gated sodium currents. In addition to a\r\nclassical fast inactivating tetrodotoxin-sensitive (TTX-S) sodium\r\ncurrent, many of these cells express a TTX-resistant (TTX-R) sodium\r\ncurrent that activates near -70 mV and is persistent at negative\r\npotentials. To investigate the possible contributions of this TTX-R\r\npersistent (TTX-RP) current to neuronal excitability, we carried out\r\ncomputer simulations using the Neuron program with TTX-S and -RP\r\ncurrents, fit by the Hodgkin-Huxley model, that closely matched the\r\ncurrents recorded from small DRG neurons. ...\" See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/86537", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2011-03-21T14:45:00", + "ver_number": 13 + }, + "86538": { + "citation": { + "attr_id": 391, + "value": "16326896; P.Borowski, \"Stochastic dynamics in olfactory signal transduction and development\", Dissertation (2006): http://nbn-resolving.de/urn:nbn:de:swb:14-1159519135136-22697" + }, + "class_id": 19, + "created": "2007-02-23T19:15:31", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 254, + "object_name": "I CNG" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 86538, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 85108, + "object_name": "Reidl J, Borowski P, Sensse A, Starke J, Zapotocky M, Eiswirth M (2006)" + }, + { + "object_id": 86539, + "object_name": "Borowski P (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Model of calcium oscillations in olfactory cilia (Reidl et al. 2006)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 268, + "object_name": "Olfactory receptor GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Simulation of experiments on olfactory receptor neurons (ORNs). Focussing on the negative feedback that calcium (through calmodulin) has on its own influx through CNG channels, this model is able to reproduce both calcium oscillations as well as adaptation behaviour as seen in experiments done with ORNs." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/86538", + "other_implementer": { + "attr_id": 308, + "value": "Reidl, Juergen; Borowski, Peter; Sensse, Anke; Starke, Jens; Zapotocky, Martin; Eiswirth, Markus" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "borowski@math.ubc.ca" + }, + "simPFid": { + "attr_id": 488, + "value": 120 + }, + "ver_date": "2018-10-17T15:43:51.193", + "ver_number": 6 + }, + "87216": { + "citation": { + "attr_id": 391, + "value": "17267756" + }, + "class_id": 19, + "created": "2007-03-06T14:52:23", + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122826, + "object_name": "NR2A GRIN2A" + }, + { + "object_id": 122827, + "object_name": "NR2B GRIN2B" + } + ] + }, + "gitrepo": false, + "id": 87216, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 83540, + "object_name": "Winner-take-all" + }, + { + "object_id": 87578, + "object_name": "STDP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 84616, + "object_name": "Gerkin RC, Lau PM, Nauen DW, Wang YT, Bi GQ (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 83509, + "object_name": "IGOR Pro" + } + ] + }, + "name": "STDP and NMDAR Subunits (Gerkin et al. 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The paper argues for competing roles of NR2A- and NR2B-containing NMDARs in spike-timing-dependent plasticity. This simple dynamical model recapitulates the results of STDP experiments involving selective blockers of NR2A- and NR2B-containing NMDARs, for which the stimuli are pre- and postsynaptic spikes in varying combinations. Experiments were done using paired recordings from glutamatergic neurons in rat hippocampal cultures. This model focuses on the dynamics of the putative potentiation and depression modules themselves, and their interaction For detailed dynamics involving NMDARs and Ca2+ transients, see Rubin et al., J. Neurophys., 2005." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/87216", + "other_concept": { + "attr_id": 306, + "value": "STDP" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "rig4@pitt.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2009-07-14T13:48:30", + "ver_number": 5 + }, + "87278": { + "citation": { + "attr_id": 391, + "value": "Clancy CE, Kass RS (2004) Theoretical investigation of the neuronal Na+ channel SCN1A: abnormal gating and epilepsy. Biophys J 86:2606\u201414" + }, + "class_id": 19, + "created": "2007-03-07T21:55:17", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92082, + "object_name": "Nav1.1 SCN1A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 87278, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 87283, + "object_name": "Clancy, Colleen E [ceclancy at ucdavis.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 238957, + "object_name": "Markov-type model" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 84130, + "object_name": "Clancy CE, Kass RS (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Markov models of SCN1A (NaV1.1) applied to abnormal gating and epilepsy (Clancy and Kass 2004)", + "notes": { + "attr_id": 24, + "value": "\"Recently, some forms of idiopathic epilepsy have been causally related to\r\ngenetic mutations in neuronal ion channels. To understand disease\r\nmechanisms, it is crucial to understand how a gene defect can disrupt\r\nchannel gating, which in turn can affect complex cellular dynamic\r\nprocesses. We develop a theoretical Markovian model of the neuronal\r\nNa+ channel NaV1.1 to explore and explain gating mechanisms underlying\r\ncellular excitability and physiological and pathophysiological\r\nmechanisms of abnormal neuronal excitability in the context of\r\nepilepsy. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/87278", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2018-02-26T11:08:26.403", + "ver_number": 8 + }, + "87284": { + "class_id": 19, + "created": "2007-03-09T15:41:30", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 87284, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33735, + "object_name": "Carnevale, Ted [Ted.Carnevale at Yale.edu]" + }, + { + "object_id": 33772, + "object_name": "Morse, Tom [Tom.Morse at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 112047, + "object_name": "Aging/Alzheimer`s" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 126976, + "object_name": "Morse TM, Carnevale NT, Mutalik PG, Migliore M, Shepherd GM (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "87284_1", + "name": "Figure 1, 2" + }, + { + "id": "87284_2", + "name": "Figure 3" + }, + { + "id": "87284_3", + "name": "Figure 4" + }, + { + "id": "87284_4", + "name": "Figure 5" + }, + { + "id": "87284_5", + "name": "Figure 6" + } + ], + "default": "87284_1" + }, + "name": "Amyloid beta (IA block) effects on a model CA1 pyramidal cell (Morse et al. 2010)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model simulations provide evidence oblique dendrites in CA1 pyramidal neurons are susceptible to hyper-excitability by amyloid beta block of the transient K+ channel, IA. See paper for details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/87284", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "simPFid": { + "attr_id": 488, + "value": 153 + }, + "ver_date": "2017-03-13T17:25:23.043", + "ver_number": 111 + }, + "87450": { + "class_id": 19, + "created": "2007-03-12T15:02:56", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 87450, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 87451, + "object_name": "Schilstra, Maria [m.j.1.schilstra at herts.ac.uk]" + }, + { + "object_id": 87452, + "object_name": "Lloyd, Catherine [c.lloyd at auckland.ac.nz]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 29126, + "object_name": "HODGKIN AL, HUXLEY AF (1952)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 87474, + "object_name": "XML (web link to model)" + }, + { + "object_id": 91894, + "object_name": "XPP (web link to model)" + }, + { + "object_id": 125746, + "object_name": "SBML (web link to model)" + } + ] + }, + "name": "Squid axon (Hodgkin, Huxley 1952) (SBML, XPP, other)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52426, + "object_name": "Squid axon" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "An SBML (and related XPP and other formats) implementation of the classic HH paper is available in the BIOMODELS database. See far below for links." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/87450", + "ver_date": "2015-12-14T11:47:43.55", + "ver_number": 16 + }, + "87454": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 87517, + "object_name": "first submission" + } + ] + }, + "class_id": 19, + "created": "2007-03-13T09:21:54", + "default_file_notes": { + "attr_id": 392, + "value": "current" + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 87454, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 87533, + "object_name": "Bahmer, Andreas [Andreas.Bahmer at kgu.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 87211, + "object_name": "Bahmer A, Langner G (2006)" + }, + { + "object_id": 87455, + "object_name": "Bahmer A, Langner G (2006)" + }, + { + "object_id": 87518, + "object_name": "Bahmer A, Langner G (2007)" + }, + { + "object_id": 125393, + "object_name": "Bahmer A, Langner G (2009)" + }, + { + "object_id": 125394, + "object_name": "Bahmer A, Langner G (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "87454", + "name": "ModelView" + } + ], + "default": "87454" + }, + "name": "Oscillating neurons in the cochlear nucleus (Bahmer Langner 2006a, b, and 2007)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88112, + "object_name": "Cochlear ganglion cell Type II" + }, + { + "object_id": 88113, + "object_name": "CN stellate cell" + }, + { + "object_id": 88115, + "object_name": "Ventral cochlear nucleus T stellate (chopper) neuron" + }, + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Based on the physiological and anatomical data, we propose a model consisting of a minimum network of two choppers that are interconnected with a synaptic delay of 0.4 ms (Bahmer and Langner 2006a) . Such minimum delays have been found in different systems and in various animals (e.g. Hackett, Jackson, and Rubel 1982; Borst, Helmchen, and Sakmann 1995). The choppers receive input from both the auditory nerve and an onset neuron. This model can reproduce the mean, standard deviation, and coefficient of variation of the ISI and the dynamic features of AM coding of choppers.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/87454", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "simPFid": { + "attr_id": 488, + "value": 127 + }, + "ver_date": "2018-10-18T15:19:09.627", + "ver_number": 23 + }, + "87473": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 87514, + "object_name": "authors" + } + ] + }, + "class_id": 19, + "created": "2007-03-14T00:10:47", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "default_file_notes": { + "attr_id": 392, + "value": "current" + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 87473, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 87515, + "object_name": "Weaver, Christina [christina.weaver at fandm.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 80998, + "object_name": "Weaver CM, Wearne SL (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "AP shape and parameter constraints in optimization of compartment models (Weaver and Wearne 2006)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 139834, + "object_name": "Vestibular neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... We construct an\r\nobjective function that includes both time-aligned action potential shape error and errors in firing rate and firing regularity. We then\r\nimplement a variant of simulated annealing that introduces a recentering algorithm to handle infeasible points outside the boundary\r\nconstraints. We show how our objective function captures essential features of neuronal firing patterns, and why our boundary\r\nmanagement technique is superior to previous approaches.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/87473", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181335, + "object_name": "87473" + } + ] + }, + "ver_date": "2014-01-19T10:07:44", + "ver_number": 25 + }, + "87535": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 87545, + "object_name": "attempt to auto launch" + } + ] + }, + "class_id": 19, + "created": "2007-03-21T06:03:05", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "default_file_notes": { + "attr_id": 392, + "value": "current" + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 87535, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3542, + "object_name": "Pattern Recognition" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 82787, + "object_name": "Working memory" + }, + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 114668, + "object_name": "Migliore M, Novara G, Tegolo D (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "87535", + "name": "ModelView" + } + ], + "default": "87535" + }, + "name": "CA1 pyramidal neurons: binding properties and the magical number 7 (Migliore et al. 2008)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "NEURON files from the paper:\r\n\r\nSingle neuron binding properties and the magical number 7,\r\nby M. Migliore, G. Novara, D. Tegolo, Hippocampus, in press (2008).\r\n\r\nIn an extensive series of simulations with realistic morphologies and active properties, \r\nwe demonstrate how n radial (oblique) dendrites of these neurons may be used to bind n inputs \r\nto generate an output signal. \r\nThe results suggest a possible neural code as the most effective n-ple of dendrites that \r\ncan be used for short-term memory recollection of persons, objects, or places. \r\nOur analysis predicts a straightforward physiological explanation for the observed \r\npuzzling limit of about 7 short-term memory items that can be stored by humans.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/87535", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181231, + "object_name": "87535" + } + ] + }, + "ver_date": "2013-12-10T15:40:04", + "ver_number": 23 + }, + "87546": { + "class_id": 19, + "created": "2007-03-23T05:16:32", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 87546, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 101634, + "object_name": "Minneci F, Janahmadi M, Migliore M, Dragicevic N, Avossa D, Cherubini E (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "CA1 oriens alveus interneurons: signaling properties (Minneci et al. 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 35373, + "object_name": "Hippocampus CA1 interneuron oriens alveus GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model supports the experimental findings showing that the dynamic interaction between cells with various firing patterns could differently affect GABAergic signaling, leading to a wide range of interneuronal communication within the hippocampal network." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/87546", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181466, + "object_name": "87546" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 164 + }, + "ver_date": "2014-01-31T23:01:11", + "ver_number": 12 + }, + "87581": { + "citation": { + "attr_id": 391, + "value": "Hirst GD, Garcia-Londono AP, Edwards FR (2006) Propagation of slow waves in the guinea-pig gastric antrum. J Physiol 571:165\u201477 AND Edwards FR, Hirst GD (2006) An electrical analysis of slow wave propagation in the guinea-pig gastric antrum. J Physiol 571:179\u201489" + }, + "class_id": 19, + "created": "2007-03-30T00:21:44", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 87581, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 87583, + "object_name": "Edwards, Frank [frank.edwards at anu.edu.au]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 64588, + "object_name": "Edwards FR, Hirst GD (2006)" + }, + { + "object_id": 87213, + "object_name": "Hirst GD, Garcia-Londo\u00f1o AP, Edwards FR (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Slow wave propagation in the guinea-pig gastric antrum (Hirst et al. 2006, Edwards and Hirst 2006)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 156000, + "object_name": "Myenteric interstitial cell of Cajal (ICCMY)" + }, + { + "object_id": 156002, + "object_name": "Intramuscular interstitial cell of Cajal (ICCIM)" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"(Edwards and Hirst 2006) provides an electrical description of the propagation of slow waves and pacemaker potentials in the guinea-pig gastric antrum in anal and circumferential directions. As electrical conduction between laterally adjacent circular muscle bundles is regularly interrupted, anal conduction of pacemaker potentials was assumed to occur via an electrically interconnected chain of myenteric interstitial cells of Cajal (ICCMY). ICCMY were also connected resistively to serially connected compartments of longitudinal muscle. Circumferential conduction occurred in a circular smooth muscle bundle that was represented as a chain of electrically connected isopotential compartments: each compartment contained a proportion of intramuscular interstitial cells of Cajal (ICCIM) that are responsible for the regenerative component of the slow wave. The circular muscle layer, which contains ICCIM, and the ICCMY network incorporated a mechanism, modelled as a two-stage chemical reaction, which produces an intracellular messenger. ... The model generates pacemaker potentials and slow waves with propagation velocities similar to those determined in the physiological experiments described in the accompanying paper.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/87581", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115955, + "object_name": "Enteric nervous system" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 136 + }, + "ver_date": "2017-06-21T14:25:27.6", + "ver_number": 8 + }, + "87582": { + "citation": { + "attr_id": 391, + "value": "Self-influencing synaptic plasticity: Reccurent changes of synaptic weights can lead to specific functional properties. Minija Tamosiunaite, Bernd Porr and Florentin W\u00f6rg\u00f6tter, Journal of Computational Neuroscience. DOI 10.1007/s10827-007-0021-2" + }, + "class_id": 19, + "created": "2007-03-30T08:49:44", + "gitrepo": false, + "id": 87582, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 83540, + "object_name": "Winner-take-all" + }, + { + "object_id": 87578, + "object_name": "STDP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 84623, + "object_name": "Tamosiunaite M, Porr B, W\u00f6rg\u00f6tter F (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Self-influencing synaptic plasticity (Tamosiunaite et al. 2007)", + "notes": { + "attr_id": 24, + "value": "\"... Similar to a previous study (Saudargiene et al., 2004) we employ a differential\r\nHebbian learning rule to emulate spike-timing dependent\r\nplasticity and investigate how the interaction of dendritic\r\nand back-propagating spikes, as the post-synaptic signals,\r\ncould influence plasticity. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/87582", + "public_submitter_email": { + "attr_id": 309, + "value": "minija_tamosiunaite@fc.vdu.lt" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 135 + }, + "ver_date": "2009-03-05T16:04:49", + "ver_number": 5 + }, + "87585": { + "citation": { + "attr_id": 391, + "value": "16525050" + }, + "class_id": 19, + "created": "2007-03-30T20:44:46", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92082, + "object_name": "Nav1.1 SCN1A" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 87585, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 89667, + "object_name": "Lickfett, Jay " + }, + { + "object_id": 89672, + "object_name": "Goldin, Al [agoldin at uci.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 64196, + "object_name": "Barela AJ, Waddy SP, Lickfett JG, Hunter J, Anido A, Helmers SL, Goldin AL, Escayg A (2006)" + }, + { + "object_id": 82458, + "object_name": "Spampanato J, Aradi I, Soltesz I, Goldin AL (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "87585", + "name": "ModelView" + } + ], + "default": "87585" + }, + "name": "Sodium channel mutations causing generalized epilepsy with febrile seizures + (Barela et al. 2006)", + "notes": { + "attr_id": 24, + "value": "A novel mutation, R859C, in the Nav1.1 sodium channel was identified in a 4-generation, 33-member Caucasian family with a clinical presentation consistent with GEFS+. The mutation neutralizes a positively charged arginine in the domain 2 S4 voltage sensor of the Nav1.1 channel \u0192\u00d1 subunit. When the mutation was placed in the rat Nav1.1 channel and expressed in Xenopus oocytes, the mutant channel displayed a positive shift in the voltage-dependence of sodium channel activation, slower recovery from slow inactivation, and lower levels of current compared to the wild-type channel. Computational analysis suggests that neurons expressing the mutant channel have higher thresholds for firing a single action potential and for firing multiple action potentials, along with decreased repetitive firing. Therefore, this mutation should lead to decreased neuronal excitability, in contrast to most previous GEFS+ sodium channel mutations that have changes predicted to increase neuronal firing." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/87585", + "public_submitter_email": { + "attr_id": 309, + "value": "agoldin@uci.edu" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181173, + "object_name": "87585" + } + ] + }, + "ver_date": "2022-05-27T09:46:21.533", + "ver_number": 16 + }, + "87751": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 87752, + "object_name": "copy of authors version 1 code" + }, + { + "object_id": 88438, + "object_name": "copy of authors version 1.1 code" + }, + { + "object_id": 88439, + "object_name": "copy of version 1.1 targz" + } + ] + }, + "class_id": 19, + "created": "2007-04-05T15:06:55", + "gitrepo": false, + "id": 87751, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 87750, + "object_name": "Zilany MS, Bruce IC (2006)" + }, + { + "object_id": 88437, + "object_name": "Zilany MS, Bruce IC (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Cat auditory nerve model (Zilany and Bruce 2006, 2007)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52419, + "object_name": "Auditory nerve" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"This paper presents a computational model to simulate normal and impaired auditory-nerve (AN)\r\nfiber responses in cats. The model responses match physiological data over a wider dynamic range\r\nthan previous auditory models. This is achieved by providing two modes of basilar membrane\r\nexcitation to the inner hair cell (IHC) rather than one. ... The model responses are consistent with a wide range of\r\nphysiological data from both normal and impaired ears for stimuli presented at levels spanning the\r\ndynamic range of hearing.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/87751", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2007-12-20T12:17:08", + "ver_number": 9 + }, + "87760": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 87761, + "object_name": "backup of authors weblinked model" + } + ] + }, + "class_id": 19, + "created": "2007-04-05T16:21:12", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 87760, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 87755, + "object_name": "Bruce IC, Irlicht LS, White MW, O'Leary SJ, Clark GM (2000)" + }, + { + "object_id": 87756, + "object_name": "Bruce IC, White MW, Irlicht LS, O'Leary SJ, Clark GM (1999)" + }, + { + "object_id": 87757, + "object_name": "Bruce IC, Irlicht LS, White MW, O'Leary SJ, Dynes S, Javel E, Clark GM (1999)" + }, + { + "object_id": 87758, + "object_name": "Bruce IC, White MW, Irlicht LS, O'Leary SJ, Dynes S, Javel E, Clark GM (1999)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Cochlear implant models (Bruce et al. 1999a, b, c, 2000)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52419, + "object_name": "Auditory nerve" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"In a recent set of modeling studies we have developed a stochastic threshold model of auditory nerve response to single biphasic electrical pulses (Bruce et al., 1999c) and moderate rate (less than 800 pulses per second) pulse trains (Bruce et al., 1999a). In this article we derive an analytical approximation for the single-pulse model, which is then extended to describe the pulse-train model in the case of evenly timed, uniform pulses. This renewal-process description provides an accurate and computationally efficient model of electrical stimulation of single auditory nerve fibers by a cochlear implant that may be extended to other forms of electrical neural stimulation.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/87760", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2018-10-18T15:21:30.55", + "ver_number": 7 + }, + "87762": { + "biosimulations": { + "value": "modeldb-87762" + }, + "citation": { + "attr_id": 391, + "value": "17410601" + }, + "class_id": 19, + "created": "2007-04-06T11:31:09", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 87769, + "object_name": "ATP-senstive potassium current" + } + ] + }, + "gitrepo": false, + "id": 87762, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + }, + { + "object_id": 84610, + "object_name": "Huang, Chin-Wei" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 87763, + "object_name": "Huang CW, Huang CC, Cheng JT, Tsai JJ, Wu SN (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "The role of ATP-sensitive potassium channels in a hippocampal neuron (Huang et al. 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Hyperglycemia-related neuronal excitability and epileptic seizures \r\nare not uncommon in clinical practice. However, their underlying \r\nmechanism remains elusive. ATP-sensitive K(+) (K(ATP)) channels are \r\nfound in many excitable cells, including cardiac myocytes, \r\npancreatic beta cells, and neurons. These channels provide a link \r\nbetween the electrical activity of cell membranes and cellular \r\nmetabolism. We investigated the effects of higher extracellular \r\nglucose on hippocampal K(ATP) channel activities and neuronal \r\nexcitability. The cell-attached patch-clamp configuration on \r\ncultured hippocampal cells and a novel multielectrode recording \r\nsystem on hippocampal slices were employed. In addition, a \r\nsimulation modeling hippocampal CA3 pyramidal neurons (Pinsky-Rinzel \r\nmodel) was analyzed to investigate the role of K(ATP) channels in \r\nthe firing of simulated action potentials. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/87762", + "other_currents": { + "attr_id": 303, + "value": "ATP-senstive potassiun current" + }, + "other_implementer": { + "attr_id": 308, + "value": "Dr. Chin-Wei Huang" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2007-04-13T08:06:40", + "ver_number": 5 + }, + "91387": { + "citation": { + "attr_id": 391, + "value": "PMID: 16381804 PMID: 17428916" + }, + "class_id": 19, + "created": "2007-05-07T05:53:25", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 91387, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 97752, + "object_name": "Delay" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 90099, + "object_name": "Bathellier B, Lagier S, Faure P, Lledo PM (2006)" + }, + { + "object_id": 90103, + "object_name": "Lagier S, Panzanelli P, Russo RE, Nissant A, Bathellier B, Sasso\u00e8-Pognetto M, Fritschy JM, Lledo PM (2007)" + }, + { + "object_id": 113741, + "object_name": "Bathellier B, Lagier S, Faure P, Lledo PM (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Olfactory bulb network model of gamma oscillations (Bathellier et al. 2006; Lagier et al. 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model implements a network of 100 mitral cells connected with \r\nasynchronous inhibitory \"synapses\" that is meant to reproduce the \r\nGABAergic transmission of ensembles of connected granule cells. \r\nFor appropriate parameters of this special synapse the model generates \r\ngamma oscillations with properties very similar to what is observed \r\nin olfactory bulb slices (See Bathellier et al. 2006, Lagier et al. 2007).\r\nMitral cells are modeled as single compartment neurons with a small \r\nnumber of different voltage gated channels. Parameters were tuned to reproduce the\r\nfast subthreshold oscillation of the membrane potential observed experimentally\r\n(see Desmaisons et al. 1999).\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/91387", + "public_submitter_email": { + "attr_id": 309, + "value": "brice.bathellier@epfl.ch" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "ver_date": "2018-10-17T15:44:36.65", + "ver_number": 8 + }, + "91893": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 91895, + "object_name": "backup of web link to Egli et al. 2004" + }, + { + "object_id": 91896, + "object_name": "Egli et al.2006 backup of weblink" + }, + { + "object_id": 91897, + "object_name": "Bertram et al.2006 backup" + } + ] + }, + "class_id": 19, + "created": "2007-05-08T12:15:35", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 91893, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 91818, + "object_name": "Egli M, Bertram R, Toporikova N, Sellix MT, Blanco W, Freeman ME (2006)" + }, + { + "object_id": 91837, + "object_name": "Bertram R, Egli M, Toporikova N, Freeman ME (2006)" + }, + { + "object_id": 91890, + "object_name": "Egli M, Bertram R, Sellix MT, Freeman ME (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 91894, + "object_name": "XPP (web link to model)" + } + ] + }, + "name": "Oxytocin and VIP involvement in prolactin secretion (Egli et al. 2004,2006, Bertram et al. 2006)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 62277, + "object_name": "Pituitary cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 235, + "object_name": "Peptides" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Prolactin (PRL) is secreted from lactotrophs of the anterior\r\npituitary gland of rats in a unique pattern in response to\r\nuterine cervical stimulation (CS) during mating. Surges of\r\nPRL secretion occur in response to relief from hypothalamic\r\ndopaminergic inhibition and stimulation by hypothalamic releasing\r\nneurohormones. In this study, we characterized the\r\nrole of oxytocin (OT) in this system and the involvement of\r\nvasoactive intestinal polypeptide (VIP) from the suprachiasmatic\r\nnucleus (SCN) in controlling OT and PRL secretion of\r\nCS rats. ... OT\r\nmeasurements of serial blood samples obtained from ovariectomized\r\n(OVX) CS rats displayed a prominent increase at\r\nthe time of the afternoon PRL peak. The injection of VIP antisense\r\noligonucleotides into the SCN abolished the afternoon\r\nincrease of OT and PRL in CS-OVX animals. These findings\r\nsuggest that VIP from the SCN contributes to the regulation\r\nof OT and PRL secretion in CS rats. We propose that in CS rats\r\nthe regulatory mechanism(s) for PRL secretion comprise coordinated\r\naction of neuroendocrine dopaminergic and OT\r\ncells, both governed by the daily rhythm of VIP-ergic output\r\nfrom the SCN. This hypothesis is illustrated with a mathematical model.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/91893", + "ver_date": "2015-07-26T13:33:01.77", + "ver_number": 15 + }, + "91898": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 92001, + "object_name": "local copy of web link" + } + ] + }, + "class_id": 19, + "created": "2007-05-08T15:59:49", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "id": 91898, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 87372, + "object_name": "Tabak J, Toporikova N, Freeman ME, Bertram R (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 91894, + "object_name": "XPP (web link to model)" + } + ] + }, + "name": "Low dose of dopamine may stimulate prolactin secretion by increasing K currents (Tabak et al. 2006)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 62277, + "object_name": "Pituitary cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\".. We considered\r\nthe fast K+ currents flowing through large-conductance\r\nBK channels and through A-type channels. We developed a\r\nminimal lactotroph model to investigate the effects of these\r\ntwo currents. Both IBK and IA could transform the electrical\r\npattern of activity from spiking to bursting, but through\r\ndistinct mechanisms. IBK always increased the intracellular\r\nCa2+ concentration, while IA could either increase or\r\ndecrease it. Thus, the stimulatory effects of DA could be\r\nmediated by a fast K+ conductance which converts tonically\r\nspiking cells to bursters. In addition, the study illustrates that\r\na heterogeneous distribution of fast K+ conductances could\r\ncause heterogeneous lactotroph firing patterns.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/91898", + "ver_date": "2007-05-08T16:21:46", + "ver_number": 8 + }, + "91899": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 91983, + "object_name": "local copy of web link" + } + ] + }, + "class_id": 19, + "created": "2007-05-08T17:04:04", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 88211, + "object_name": "I_SERCA" + } + ] + }, + "gitrepo": false, + "id": 91899, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 91892, + "object_name": "Bertram R, Tabak J, Toporikova N, Freeman ME (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 91894, + "object_name": "XPP (web link to model)" + } + ] + }, + "name": "Endothelin action on pituitary latotrophs (Bertram et al. 2006)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 62277, + "object_name": "Pituitary cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 231, + "object_name": "Amino Acids" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Endothelin (ET-1, -2, and -3 designate three genes which produce different endothelin isopeptides) is a prolactin inhibiting substance of hypothalmic origin. ET-1 binding is part of at least four G protein signaling pathways in lactotrophs. The sequence of events in these pathways following the presentation of nano- and pico-molar concentrations of ET-1 is modeled in the paper." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/91899", + "ver_date": "2007-05-09T10:06:03", + "ver_number": 7 + }, + "93315": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 93316, + "object_name": "backup of Bruce IC (2007) weblink code" + } + ] + }, + "class_id": 19, + "created": "2007-05-14T11:35:42", + "gitrepo": false, + "id": 93315, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 87741, + "object_name": "Bruce IC (2007)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Implementation issues in approximate methods for stochastic Hodgkin-Huxley models (Bruce 2007)", + "notes": { + "attr_id": 24, + "value": "Four different algorithms for implementing Hodgkin\u2013Huxley models\r\nwith stochastic sodium channels: Strassberg and\r\nDeFelice (1993), Rubinstein (1995), Chow and White\r\n(1996), and Fox (1997) are compared." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/93315", + "ver_date": "2007-05-14T12:04:19", + "ver_number": 8 + }, + "93319": { + "citation": { + "attr_id": 391, + "value": "PMID 17287443" + }, + "class_id": 19, + "created": "2007-05-16T09:54:05", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 88215, + "object_name": "I_Ks" + } + ] + }, + "gitrepo": false, + "id": 93319, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 136252, + "object_name": "Rebound firing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 87746, + "object_name": "Huss M, Lansner A, Wall\u00e9n P, El Manira A, Grillner S, Kotaleski JH (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "Lamprey spinal CPG neuron (Huss et al. 2007)", + "notes": { + "attr_id": 24, + "value": "This is a model of a generic locomotor network neuron in the lamprey spinal cord. The given version is assumed to correspond to an interneuron; motoneurons can also be modelled by changing the dendritic tree morphology." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/93319", + "public_submitter_email": { + "attr_id": 309, + "value": "hussm@nada.kth.se" + }, + "ver_date": "2010-11-12T16:04:16", + "ver_number": 4 + }, + "93321": { + "class_id": 19, + "created": "2007-05-17T17:54:01", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 93321, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33772, + "object_name": "Morse, Tom [Tom.Morse at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 12739, + "object_name": "Liu Z, Golowasch J, Marder E, Abbott LF (1998)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Activity dependent conductances in a neuron model (Liu et al. 1998)", + "notes": { + "attr_id": 24, + "value": "\"... We present a model of a\r\nstomatogastric ganglion (STG) neuron in which several Ca2+-dependent\r\npathways are used to regulate the maximal conductances of membrane\r\ncurrents in an activity-dependent manner. Unlike previous models of\r\nthis type, the regulation and modification of maximal conductances by\r\nelectrical activity is unconstrained. The model has seven\r\nvoltage-dependent membrane currents and uses three Ca2+ sensors acting\r\non different time scales. ... The model suggests that neurons may regulate their\r\nconductances to maintain fixed patterns of electrical activity, rather\r\nthan fixed maximal conductances, and that the regulation process\r\nrequires feedback systems capable of reacting to changes of electrical\r\nactivity on a number of different time scales.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/93321", + "other_neurons": { + "attr_id": 301, + "value": "STG neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181626, + "object_name": "93321_2" + }, + { + "object_id": 181627, + "object_name": "93321_3" + }, + { + "object_id": 181628, + "object_name": "93321_1" + }, + { + "object_id": 181630, + "object_name": "93321_4" + }, + { + "object_id": 181631, + "object_name": "93321_5" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 149 + }, + "ver_date": "2022-05-27T18:22:41.153", + "ver_number": 21 + }, + "93325": { + "class_id": 19, + "created": "2007-05-21T12:34:52", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 93325, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 82893, + "object_name": "Calin-Jageman, Robert [rcalinjageman at gsu dot edu]" + }, + { + "object_id": 93331, + "object_name": "Mensh, Brett " + }, + { + "object_id": 93332, + "object_name": "Frost, William N" + }, + { + "object_id": 93333, + "object_name": "Katz, Paul S" + }, + { + "object_id": 97276, + "object_name": "Tunstall, Mark " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 97220, + "object_name": "Calin-Jageman RJ, Tunstall MJ, Mensh BD, Katz PS, Frost WN (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "93325", + "name": "ModelView" + } + ], + "default": "93325" + }, + "name": "Updated Tritonia Swim CPG (Calin-Jagemann et al. 2007)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 93328, + "object_name": "Tritonia swim interneuron dorsal" + }, + { + "object_id": 93334, + "object_name": "Tritonia cerebral cell" + }, + { + "object_id": 93335, + "object_name": "Tritonia swim interneuron ventral" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Model of the 3-cell core CPG (DSI, C2, and VSI-B) mediating escape swimming in Tritonia diomedea. Cells use a hybrid integrate-and-fire scheme pioneered by Peter Getting. Each model cell is reconstructed from extensive physiological measurements to precisely mimic I-F curves, synaptic waveforms, and functional connectivity." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/93325", + "other_implementer": { + "attr_id": 308, + "value": "Calin0Jageman, RJ; Tunstall M; Mensh B; Frost WN; Katz PS;" + }, + "other_neurons": { + "attr_id": 301, + "value": "Tritonia Dorsal Swim Interneuron (DSI)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "rcalinjageman@gsu.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115957, + "object_name": "Tritonia" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181200, + "object_name": "93325" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 114269, + "object_name": "Tritonia diomedea" + } + ] + }, + "ver_date": "2019-02-26T10:27:02.567", + "ver_number": 14 + }, + "93326": { + "citation": { + "attr_id": 391, + "value": "Getting PA. Reconstruction of small neural networks. In: Methods in neural modeling, edited by C. Koch and I. Segev. Cambridge: MIT Press, 1989b, p. 171-196; See Also Getting PA. Mechanisms of pattern generation underlying swimming in Tritonia. II. Network reconstruction. J Neurophysiol 49: 1017-1035, 1983a. PMID=6854355" + }, + "class_id": 19, + "created": "2007-05-21T12:45:43", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 93326, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 82893, + "object_name": "Calin-Jageman, Robert [rcalinjageman at gsu dot edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 34494, + "object_name": "Getting PA (1989)" + }, + { + "object_id": 93327, + "object_name": "Getting PA (1983)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "93326", + "name": "ModelView" + } + ], + "default": "93326" + }, + "name": "Classic model of the Tritonia Swim CPG (Getting, 1989)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 93328, + "object_name": "Tritonia swim interneuron dorsal" + }, + { + "object_id": 93334, + "object_name": "Tritonia cerebral cell" + }, + { + "object_id": 93335, + "object_name": "Tritonia swim interneuron ventral" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Classic model developed by Petter Getting of the 3-cell core CPG (DSI, C2, and VSI-B) mediating escape swimming in Tritonia diomedea. Cells use a hybrid integrate-and-fire scheme pioneered by Peter Getting. Each model cell is reconstructed from extensive physiological measurements to precisely mimic I-F curves, synaptic waveforms, and functional connectivity. **However, continued physiological measurements show that Getting may have inadvertently incorporated modulatory and or polysynaptic effects -- the properties of this model do *not* match physiological measurements in rested preparations.** This simulation reconstructs the Getting model as reported in: Getting (1989) 'Reconstruction of small neural networks' In Methods in Neural Modeling, 1st ed, p. 171-196. See also, an earlier version of this model reported in Getting (1983). Every attempt has been made to replicate the 1989 model as precisely as possible." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/93326", + "other_implementer": { + "attr_id": 308, + "value": "Calin-Jageman, Robert " + }, + "other_neurons": { + "attr_id": 301, + "value": "Tritionia DSI, C2, and VSI-B" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "rcalinjageman@gsu.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115957, + "object_name": "Tritonia" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181201, + "object_name": "93326" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 199 + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 114269, + "object_name": "Tritonia diomedea" + } + ] + }, + "ver_date": "2019-02-26T10:27:17.007", + "ver_number": 14 + }, + "93349": { + "citation": { + "attr_id": 391, + "value": "15661312" + }, + "class_id": 19, + "created": "2007-05-23T22:10:43", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "id": 93349, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 93354, + "object_name": "Dyhrfjeld-Johnsen, Jonas [jdyhrfje at uci.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 93352, + "object_name": "Dyhrfjeld-Johnsen J, Maier J, Schubert D, Staiger J, Luhmann HJ, Stephan KE, K\u00f6tter R (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "Pyramidal Neuron Deep: Constrained by experiment (Dyhrfjeld-Johnsen et al. 2005)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... As a practical demonstration of the use of CoCoDat we\r\nconstructed a detailed computer model of an intrinsically\r\nbursting (IB) layer V pyramidal neuron from the rat barrel\r\ncortex supplementing experimental data (Schubert et al.,\r\n2001) with information extracted from the database. The\r\npyramidal neuron morphology (Fig. 10B) was reconstructed\r\nfrom histological sections of a biocytin-stained IB neuron\r\nusing the NeuroLucida software package...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/93349", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2007-05-26T21:35:39", + "ver_number": 10 + }, + "93390": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 93392, + "object_name": "local backup copy of Huys et al. 2006" + } + ] + }, + "class_id": 19, + "created": "2007-05-29T14:58:20", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 93390, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 93389, + "object_name": "Huys QJ, Ahrens MB, Paninski L (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Efficient estimation of detailed single-neuron models (Huys et al. 2006)", + "notes": { + "attr_id": 24, + "value": "\"Biophysically accurate multicompartmental models of individual neurons ... depend on a large number of parameters that are difficult to estimate. ... We propose a statistical approach to the automatic estimation of various biologically relevant parameters, including 1) the distribution of channel densities, 2) the spatiotemporal pattern of synaptic input, and 3) axial resistances across extended dendrites. ... We demonstrate that the method leads to accurate estimations on a wide variety of challenging model data sets that include up to about 10,000 parameters (roughly two orders of magnitude more than previously feasible) and describe how the method gives insights into the functional interaction of groups of channels.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/93390", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2017-09-26T16:07:11.593", + "ver_number": 4 + }, + "93394": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 93395, + "object_name": "local copy backup of Huys et al. 2007" + } + ] + }, + "class_id": 19, + "created": "2007-05-29T15:41:50", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 93394, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 93393, + "object_name": "Huys QJ, Zemel RS, Natarajan R, Dayan P (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Fast population coding (Huys et al. 2007)", + "notes": { + "attr_id": 24, + "value": "\"Uncertainty coming from the noise in its neurons and the ill-posed nature of many tasks plagues neural computations. Maybe surprisingly, many studies show that the brain manipulates these forms of uncertainty in a probabilistically consistent and normative manner, and there is now a rich theoretical literature on the capabilities of populations of neurons to implement computations in the face of uncertainty. However, one major facet of uncertainty has received comparatively little attention: time. In a dynamic, rapidly changing world, data are only temporarily relevant. Here, we analyze the computational consequences of encoding stimulus trajectories in populations of neurons. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/93394", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2017-09-26T16:10:23.027", + "ver_number": 5 + }, + "93398": { + "citation": { + "attr_id": 391, + "value": "Stiefel KM, Sejnowski TJ (2007) Mapping Function onto Neuronal Morphology. J Neurophysiol" + }, + "class_id": 19, + "created": "2007-05-31T08:09:26", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 93398, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 93399, + "object_name": "Stiefel, Klaus [stiefel at salk.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 83538, + "object_name": "Unsupervised Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 90092, + "object_name": "Stiefel KM, Sejnowski TJ (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "93398_1", + "name": "Original GA settings" + }, + { + "id": "93398_2", + "name": "Short run test" + } + ], + "default": "93398_1" + }, + "name": "Mapping function onto neuronal morphology (Stiefel and Sejnowski 2007)", + "notes": { + "attr_id": 24, + "value": "\"... We used an optimization procedure to find neuronal morphological\r\nstructures for two computational tasks: First, neuronal morphologies were selected for\r\nlinearly summing excitatory synaptic potentials (EPSPs); second, structures were\r\nselected that distinguished the temporal order of EPSPs. The solutions resembled the\r\nmorphology of real neurons. In particular the neurons optimized for linear summation\r\nelectrotonically separated their synapses, as found in avian nucleus laminaris neurons,\r\nand neurons optimized for spike-order detection had primary dendrites of significantly\r\ndifferent diameter, as found in the basal and apical dendrites of cortical pyramidal\r\nneurons. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/93398", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181270, + "object_name": "93398_2" + }, + { + "object_id": 181271, + "object_name": "93398_1" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 279 + }, + "ver_date": "2015-01-02T22:39:47", + "ver_number": 12 + }, + "93416": { + "class_id": 19, + "created": "2007-06-11T19:00:59", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 93416, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 93417, + "object_name": "Majumdar S, Sikdar SK (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "Periodicity in Na channel properties alters model neuron excitability (Majumdar and Sikdar 2007)", + "notes": { + "attr_id": 24, + "value": "\"... We have shown earlier that the\r\nduration and amplitude of a prolonged depolarization alter all the steady state and kinetic parameters of rNav1.2a voltage gated Na\r\nchannel in a pseudo-oscillatory fashion. In the present study, we show that the Hodgkin\u2013Huxley voltage and time dependent rate constants\r\nof activation (am and bm) and fast inactivation (ah and bh), obtained from the analyses of Na currents and steady state activation\r\nand inactivation plots, following application of prepulses in both slow (1\u2013100 s) and fast (100\u20131000 ms) ranges, vary with the duration of\r\na prepulse in a pseudo-oscillatory manner. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/93416", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2007-06-12T15:53:09", + "ver_number": 6 + }, + "93422": { + "citation": { + "attr_id": 391, + "value": "Cutsuridis, V. (2007). Does Abnormal Spinal Reciprocal Inhibition Lead to Co-contraction of Antagonist Motor Units? A Modeling Study. International Journal of Neural Systems, in press; Cutsuridis, V., and Perantonis, S. (2006). A Neural Model of Parkinson's Disease Bradykinesia. Neural Networks, 19(4): 354-374; V. Cutsuridis (2006) Neural Model of Dopaminergic Control of Arm Movements in Parkinson's Disease Bradykinesia. In: Artificial Neural Networks - ICANN \u201806, Lecture Notes in Computer Science, LNCS 4131 (Springer-Verlag, Berlin) 583-591" + }, + "class_id": 19, + "created": "2007-06-25T05:52:54", + "gitrepo": false, + "id": 93422, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 93425, + "object_name": "Cutsuridis, Vassilis [vcutsuridis at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 82402, + "object_name": "Cutsuridis V, Perantonis S (2006)" + }, + { + "object_id": 93423, + "object_name": "Cutsuridis V (2006)" + }, + { + "object_id": 93424, + "object_name": "Cutsuridis V (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A neural model of Parkinson`s disease (Cutsuridis and Perantonis 2006, Cutsuridis 2006, 2007)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"A neural model of neuromodulatory (dopamine) control of arm movements in Parkinson\u2019s disease (PD) bradykinesia was recently introduced [1, 2]. The model is multi-modular consisting of a basal ganglia module capable of selecting the most appropriate motor command in a given context, a cortical module for coordinating and executing the final motor commands, and a spino-musculo-skeletal module for guiding the arm to its final target and providing proprioceptive (feedback) input of the current state of the muscle and arm to higher cortical and lower spinal centers.\r\n... The new (extended) model [3] predicted that the reduced reciprocal disynaptic Ia inhibition in the DA depleted case doesn\u2019t lead to the co-contraction of antagonist motor units.\" See below readme and papers for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/93422", + "other_implementer": { + "attr_id": 308, + "value": "Cutsuridis, Vassilis" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "vcu@cs.stir.ac.uk" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 223, + "object_name": "Dopaminergic Receptor" + } + ] + }, + "ver_date": "2007-11-02T17:12:55", + "ver_number": 27 + }, + "93449": { + "citation": { + "attr_id": 391, + "value": "17484043, 15114047, 11923461" + }, + "class_id": 19, + "created": "2007-06-29T18:23:02", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "id": 93449, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 97881, + "object_name": "Feng, Xiao-Jiang [xfeng at mahler.princeton.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 39981, + "object_name": "Terman D, Rubin JE, Yew AC, Wilson CJ (2002)" + }, + { + "object_id": 49493, + "object_name": "Rubin JE, Terman D (2004)" + }, + { + "object_id": 93412, + "object_name": "Feng XJ, Shea-Brown E, Greenwald B, Kosut R, Rabitz H (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Optimal deep brain stimulation of the subthalamic nucleus-a computational study (Feng et al. 2007)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88217, + "object_name": "Globus pallidus neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Here, we use a biophysically-based model of spiking cells in the basal ganglia (Terman et al., Journal of Neuroscience, 22, 2963-2976, 2002; Rubin and Terman, Journal of Computational Neuroscience, 16, 211-235, 2004) to provide computational evidence that alternative temporal patterns of DBS inputs might be equally effective as the standard high-frequency waveforms, but require lower amplitudes. Within this model, DBS performance is assessed in two ways. First, we determine the extent to which DBS causes Gpi (globus pallidus pars interna) synaptic outputs, which are burstlike and synchronized in the unstimulated Parkinsonian state, to cease their pathological modulation of simulated thalamocortical cells. Second, we evaluate how DBS affects the GPi cells' auto- and cross-correlograms." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/93449", + "other_implementer": { + "attr_id": 308, + "value": "Xiao-Jiang Feng " + }, + "other_neurons": { + "attr_id": 301, + "value": "basal ganglia" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ebrown@math.nyu.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 212, + "object_name": "Glutamate" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2009-01-20T15:19:51", + "ver_number": 9 + }, + "94321": { + "class_id": 19, + "created": "2007-07-12T12:29:34", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 94321, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 97968, + "object_name": "Giugliano, Michele [mgiugliano at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 97966, + "object_name": "Cal\u00ec C, Berger TK, Pignatelli M, Carleton A, Markram H, Giugliano M (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 120799, + "object_name": "Glia" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 57924, + "object_name": "PSpice" + }, + { + "object_id": 97969, + "object_name": "Sspice Symbolic SPICE" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "94321", + "name": "ModelView" + } + ], + "default": "94321" + }, + "name": "Inferring connection proximity in electrically coupled networks (Cali et al. 2007)", + "notes": { + "attr_id": 24, + "value": "In order to explore electrical coupling in the nervous system and its network-level organization, it is imperative to map the electrical synaptic microcircuits, in analogy with in vitro studies on monosynaptic and disynaptic chemical coupling. However, walking from cell to cell over large distances with a glass pipette is challenging, and microinjection of (fluorescent) dyes diffusing through gap-junctions remains so far the only method available to decipher such microcircuits even though technical limitations exist.\r\n\r\nBased on circuit theory, we derived analytical descriptions of the AC electrical coupling in networks of isopotential cells. We then proposed an operative electrophysiological protocol to distinguish between direct electrical connections and connections involving one or more intermediate cells. \r\n\r\nThis method allows inferring the number of intermediate cells, generalizing the conventional coupling coefficient, which provides limited information. \r\n\r\nWe provide here some analysis and simulation scripts that used to test our method through computer simulations, in vitro recordings, theoretical and numerical methods.\r\n\r\nKey words: Gap-Junctions; Electrical Coupling; Networks; ZAP current; Impedance.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/94321", + "other_implementer": { + "attr_id": 308, + "value": "Giugliano, Michele" + }, + "other_neurons": { + "attr_id": 301, + "value": "Isopotential generic neuron" + }, + "other_simulator": { + "attr_id": 307, + "value": "Sspice - Symbolic SPICE" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181753, + "object_name": "94321" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 87 + }, + "ver_date": "2015-01-05T16:43:02", + "ver_number": 31 + }, + "94845": { + "class_id": 19, + "created": "2007-07-13T19:04:01", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 94845, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 94869, + "object_name": "Robbins, Kay [krobbins at cs.utsa.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 38733, + "object_name": "Nenadic Z, Ghosh BK, Ulinski P (2003)" + }, + { + "object_id": 38793, + "object_name": "Nenadic Z, Ghosh BK, Ulinski PS (2002)" + }, + { + "object_id": 49481, + "object_name": "Robbins KA, Senseman DM (2004)" + }, + { + "object_id": 64241, + "object_name": "Wang W, Campaigne C, Ghosh BK, Ulinski PS (2005)" + }, + { + "object_id": 65101, + "object_name": "Wang W (2006)" + }, + { + "object_id": 94847, + "object_name": "Wang W, Luo S, Ghosh BK, Ulinski PS (2006)" + }, + { + "object_id": 94848, + "object_name": "Du X, Ghosh BK, Ulinski P (2006)" + }, + { + "object_id": 94849, + "object_name": "Du X, Ghosh BK, Ulinski P (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "Turtle visual cortex model (Nenadic et al. 2003, Wang et al. 2005, Wang et al. 2006)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 94850, + "object_name": "Turtle dorsal cortex lateral pyramidal cell" + }, + { + "object_id": 94851, + "object_name": "Turtle dorsal cortex medial pyramidal cell" + }, + { + "object_id": 94852, + "object_name": "Turtle dorsal cortex subpial cell" + }, + { + "object_id": 94853, + "object_name": "Turtle dorsal cortex stellate cell" + }, + { + "object_id": 94854, + "object_name": "Turtle dorsal cortex horizontal cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a model of the visual cortex of freshwater turtles that is based upon the\r\nknown anatomy and physiology of individual neurons. The model was published in three\r\npapers (Nenadic et al., 2003; Wang et al., 2005; Wang et al., 2006), which should be\r\nconsulted for full details on its construction. The model has also been used in several\r\npapers (Robbins and Senseman, 2004; Du et al., 2005; Du et al., 2006). It is\r\nimplemented in GENESIS (Bower and Beeman, 1998)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/94845", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115948, + "object_name": "Turtle cortex" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 249927, + "object_name": "Turtle" + } + ] + }, + "ver_date": "2019-02-26T10:27:59.943", + "ver_number": 10 + }, + "95870": { + "class_id": 19, + "created": "2007-07-31T22:54:10", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 252, + "object_name": "I Cl,Ca" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 95870, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 95873, + "object_name": "Shahali, Mahboubeh [shahali222 at yahoo.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 11059, + "object_name": "Barnes S, Hille B (1989)" + }, + { + "object_id": 41597, + "object_name": "Kourennyi DE, Liu XD, Hart J, Mahmud F, Baldridge WH, Barnes S (2004)" + }, + { + "object_id": 64215, + "object_name": "Liu XD, Kourennyi DE (2004)" + }, + { + "object_id": 80965, + "object_name": "Publio R, Oliveira RF, Roque AC (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "95870", + "name": "ModelView" + } + ], + "default": "95870" + }, + "name": "Rod photoreceptor (Barnes and Hille 1989, Publio et al. 2006, Kourennyi and Liu et al. 2004)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 1966, + "object_name": "Retina photoreceptor cone GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This a conductance-based model of a rod photoreceptor cell based on other modeling works\r\n(Barnes and Hille 1989 and Publio et al. 2006 and Kourennyi and Liu et al. 2004 ). In this\r\nmodel four types of ionic channels identified in the inner segment of the rod: nonselective cation channel (h), delayed rectifying potassium channel (Kv), noninactivating potassium channel (Kx) and calcium channel (Ca) was used. \r\nThe model accurately reproduces the rod response when stimulated with a simulated photocurrent signal. We can show the effect of nonselective cation channel. The absence of this channel cause increasing the peak amplitude and the time to \r\nreach the peak of voltage response and absence of transient mode in this response." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/95870", + "other_currents": { + "attr_id": 303, + "value": "non selective cation channel" + }, + "simPFid": { + "attr_id": 488, + "value": 331 + }, + "ver_date": "2015-07-26T13:33:58.477", + "ver_number": 21 + }, + "95960": { + "class_id": 19, + "created": "2007-08-08T11:29:24", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 95960, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 95963, + "object_name": "Schmidt-Hieber, Christoph [c.schmidt-hieber at ucl.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 95962, + "object_name": "Schmidt-Hieber C, Jonas P, Bischofberger J (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Dentate gyrus granule cell: subthreshold signal processing (Schmidt-Hieber et al. 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Detailed compartmental cable models of 8 hippocampal granule cells of adult mice were obtained from dual patch-clamp whole-cell recordings and subsequent 3D reconstructions. This code allows to reproduce figures 6-8 from the paper." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/95960", + "public_submitter_email": { + "attr_id": 309, + "value": "christoph.schmidt-hieber@uni-freiburg.de" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "ver_date": "2017-03-28T14:46:24.54", + "ver_number": 12 + }, + "95990": { + "citation": { + "attr_id": 391, + "value": "PMID: 17483156, PMID: 17189308" + }, + "class_id": 19, + "created": "2007-08-14T09:40:03", + "gitrepo": false, + "id": 95990, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 96767, + "object_name": "Nociception" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 94899, + "object_name": "Riedel T, Schmalzing G, Markwardt F (2007)" + }, + { + "object_id": 94901, + "object_name": "Riedel T, Lozinsky I, Schmalzing G, Markwardt F (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 62675, + "object_name": "QuB" + } + ] + }, + "name": "Kinetics of the P2X7 receptor as expressed in Xenopus oocytes (Riedel et al. 2007a,b)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 96242, + "object_name": "Microglia" + }, + { + "object_id": 96243, + "object_name": "Macrophage" + }, + { + "object_id": 96244, + "object_name": "B lymphocyte" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Human P2X7 receptors were expressed in Xenopus laevis oocytes and\r\nsingle channels were recorded using the patch-clamp technique in the\r\noutside-out configuration. ATP4- evoked two types of P2X7\r\nreceptor-mediated single channel currents characterized by short-lived\r\nand long-lived openings. ... The kinetics of the short channel\r\nopenings at negative membrane potentials fitted well to a linear\r\nC-C-C-O model with two ATP4- binding steps at equal binding sites\r\n....\" and \"Using the patch-clamp method, we studied the influence of\r\nexternal alkali and organic monovalent cations on the single-channel\r\nproperties of the adenosine triphosphate (ATP)-activated recombinant\r\nhuman P2X(7) receptor.\" See the references for more." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/95990", + "other_neurons": { + "attr_id": 301, + "value": "microglia, macrophage, B lymphocyte" + }, + "other_neurotransmitter": { + "attr_id": 304, + "value": "ATP" + }, + "other_receptors": { + "attr_id": 302, + "value": "P2X7" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "fritz.markwardt@medizin.uni-halle.de" + }, + "ver_date": "2007-08-16T11:14:14", + "ver_number": 9 + }, + "96444": { + "citation": { + "attr_id": 391, + "value": "Hines, M.L. and Carnevale, N.T. (2007) Translating network models to parallel hardware in NEURON. J. Neurosci. Meth. 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The simulations predict a new role for the dendrodendritic synapses in the multicolumnar organization of the granule cells." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/97263", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181656, + "object_name": "97263" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 312 + }, + "ver_date": "2018-10-17T16:05:18.263", + "ver_number": 10 + }, + "97274": { + "class_id": 19, + "created": "2007-08-24T17:42:45", + "gitrepo": false, + "id": 97274, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 94911, + "object_name": "Aristizabal F, Glavinovic MI (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 83572, + "object_name": "Simulink" + } + ] + }, + "name": "Vesicular pool simulations of synaptic depression (Aristizabal and Glavinovic 2004)", + "notes": { + "attr_id": 24, + "value": "\"Synaptic release was simulated using a Simulink sequential storage model with three vesicular pools. Modeling was modular and easily extendable to the systems with greater number of vesicular pools, parallel input, or time-varying parameters. ... Finally, the method was tested experimentally using the rat phrenic-diaphragm neuromuscular junction.\"\r\nSee paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/97274", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2007-08-24T17:55:52", + "ver_number": 2 + }, + "97743": { + "citation": { + "attr_id": 391, + "value": "Lindskog M, Kim M, Wikstrom M, Blackwell KT, Hellgren Kotaleski J (2006), PLoS Comput Biol 2(9):e119" + }, + "class_id": 19, + "created": "2007-08-30T10:34:14", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122828, + "object_name": "D1 DRD1A" + }, + { + "object_id": 122829, + "object_name": "D2 DRD2" + } + ] + }, + "gitrepo": false, + "id": 97743, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 97745, + "object_name": "Lindskog M, Kim M, Wikstr\u00f6m MA, Blackwell KT, Kotaleski JH (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Model of DARPP-32 phosphorylation in striatal medium spiny neurons (Lindskog et al. 2006)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 260, + "object_name": "Neostriatum medium spiny direct pathway GABA cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The work describes a model of how transient calcium and dopamine inputs might affect phosphorylation of DARPP-32 in the medium spiny neurons in the striatum. The model is relevant for understanding both the \"three-factor rule\" for synaptic plasticity in corticostriatal synapses, and also for relating reinforcement learning theories to biology." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/97743", + "other_neurons": { + "attr_id": 301, + "value": "medium spiny neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jeanette@csc.kth.se" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 196, + "object_name": "D1" + }, + { + "object_id": 197, + "object_name": "D2" + } + ] + }, + "ver_date": "2015-07-26T13:35:36.947", + "ver_number": 7 + }, + "97747": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 113435, + "object_name": "NEURON" + } + ] + }, + "citation": { + "attr_id": 391, + "value": "D. Golomb, K. Donner, L. Shacham, D. Shlosberg, Y. Amitai and D. Hansel (2007) Mechanisms of firing patterns in fast-spiking cortical interneurons. PLoS Comput. Biol. 3(8)}:e156, 1498-1512." + }, + "class_id": 19, + "created": "2007-08-31T00:15:02", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + }, + { + "object_id": 143445, + "object_name": "I_KD" + } + ] + }, + "default_file_notes": { + "attr_id": 392, + "value": "XPP (original)" + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 97747, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 97751, + "object_name": "Stuttering" + }, + { + "object_id": 97752, + "object_name": "Delay" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 97750, + "object_name": "Golomb D, Donner K, Shacham L, Shlosberg D, Amitai Y, Hansel D (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Fast-spiking cortical interneuron (Golomb et al. 2007)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Cortical fast-spiking (FS) interneurons display highly variable electrophysiological properties. We hypothesize that this variability emerges naturally if one assumes a continuous distribution of properties in a small set of active channels. We construct a minimal, single-compartment conductance-based model of FS cells that includes transient Na+, delayed-rectifier K+, and slowly inactivating d-type K+ conductances. The model may display delay to firing. Stuttering (elliptic bursting) and subthreshold oscillations may be observed for small Na+ window current." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/97747", + "other_concept": { + "attr_id": 306, + "value": "Delay, stuttering" + }, + "other_neurons": { + "attr_id": 301, + "value": "Fast-spiking cortical interneuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "golomb@bgu.ac.il" + }, + "simPFid": { + "attr_id": 488, + "value": 246 + }, + "ver_date": "2015-01-03T11:27:47", + "ver_number": 17 + }, + "97756": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 97759, + "object_name": "local copy" + } + ] + }, + "citation": { + "attr_id": 391, + "value": "A Numerical Approach to Ion Channel Modelling Using Whole-Cell Voltage-Clamp Recordings and a Genetic Algorithm Gurkiewicz M, Korngreen A PLoS Computational Biology Vol. 3, No. 8, e169 doi:10.1371/journal.pcbi.0030169" + }, + "class_id": 19, + "created": "2007-09-01T22:24:10", + "default_file_notes": { + "attr_id": 392, + "value": "links to the original paper and model at PLoS" + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 97756, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 110661, + "object_name": "Korngreen, Alon [alon.korngreen at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 238957, + "object_name": "Markov-type model" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 97757, + "object_name": "Gurkiewicz M, Korngreen A (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "97756", + "name": "ModelView" + } + ], + "default": "97756" + }, + "name": "Ion channel modeling with whole cell and a genetic algorithm (Gurkiewicz and Korngreen 2007)", + "notes": { + "attr_id": 24, + "value": "\"... Here we show that a genetic search algorithm in combination with a gradient descent algorithm can be used to\r\nfit whole-cell voltage-clamp data to kinetic models with a high degree of accuracy. Previously, ion channel stimulation\r\ntraces were analyzed one at a time, the results of these analyses being combined to produce a picture of channel\r\nkinetics. Here the entire set of traces from all stimulation protocols are analysed simultaneously. The algorithm was\r\ninitially tested on simulated current traces produced by several Hodgkin-Huxley\u2013like and Markov chain models of\r\nvoltage-gated potassium and sodium channels. ... Finally, the algorithm was used for finding the kinetic parameters of several voltage-gated\r\nsodium and potassium channels models by matching its results to data recorded from layer 5 pyramidal neurons\r\nof the rat cortex in the nucleated outside-out patch configuration. The minimization scheme gives electrophysiologists\r\na tool for reproducing and simulating voltage-gated ion channel kinetics at the cellular level.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/97756", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181866, + "object_name": "97756" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 266 + }, + "ver_date": "2019-02-15T16:04:59.947", + "ver_number": 28 + }, + "97850": { + "citation": { + "attr_id": 391, + "value": "Pascual A, Modolo J, Beuter A (2006) Is a computational model useful to\r\nunderstand the effect of deep brain stimulation in Parkinson's disease?\r\nJ Integr Neurosci 5:541-59" + }, + "class_id": 19, + "created": "2007-09-06T09:54:08", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "id": 97850, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 95257, + "object_name": "Pascual A, Modolo J, Beuter A (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Study of augmented Rubin and Terman 2004 deep brain stim. model in Parkinsons (Pascual et al. 2006)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + }, + { + "object_id": 74300, + "object_name": "Subthalamus nucleus projection neuron" + }, + { + "object_id": 88217, + "object_name": "Globus pallidus neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... The model by Rubin and Terman [31] represents one of the most comprehensive and biologically plausible models of DBS published recently. We examined the validity of the model, replicated its simulations and tested its robustness. While our simulations partially reproduced the results presented by Rubin and Terman [31], several issues were raised including the high complexity of the model in its non simplified form, the lack of robustness of the model with respect to small perturbations, the nonrealistic representation of the thalamus and the absence of time delays. Computational models are indeed necessary, but they may not be sufficient in their current forms to explain the effect of chronic electrical stimulation on the activity of the basal ganglia (BG) network in PD.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/97850", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2017-06-21T14:44:07.223", + "ver_number": 11 + }, + "97860": { + "class_id": 19, + "created": "2007-09-19T12:46:45", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122812, + "object_name": "Cav1.3 CACNA1D" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 97860, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 97862, + "object_name": "Held, Joshua [j-held at northwestern.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 97861, + "object_name": "Chan CS, Guzman JN, Ilijic E, Mercer JN, Rick C, Tkatch T, Meredith GE, Surmeier DJ (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "97860", + "name": "ModelView" + } + ], + "default": "97860" + }, + "name": "Rejuvenation model of dopamine neuron (Chan et al. 2007)", + "notes": { + "attr_id": 24, + "value": "Model files for the paper C. Savio Chan, et al. 'Rejuvenation' protects neurons in mouse models of Parkinson's disease, Nature 447, 1081-1086(28 June 2007)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/97860", + "other_implementer": { + "attr_id": 308, + "value": "Held, Joshua" + }, + "other_neurons": { + "attr_id": 301, + "value": "Substantia nigra" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181782, + "object_name": "97860" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 324 + }, + "ver_date": "2014-03-06T15:53:03", + "ver_number": 19 + }, + "97863": { + "citation": { + "attr_id": 391, + "value": "Reconstruction of the action potential of ventricular myocardial fibres, Beeler, G.W. and Reuter, H. 1977 Journal of Physiology, 268, 177-210. PubMed ID: 874889" + }, + "class_id": 19, + "created": "2007-09-21T11:50:08", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 97863, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 97864, + "object_name": "Gannier, Francois [francois.gannier at univ-tours.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 56568, + "object_name": "Beeler GW, Reuter H (1977)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "97863", + "name": "ModelView" + } + ], + "default": "97863" + }, + "name": "Mammalian Ventricular Cell (Beeler and Reuter 1977)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88205, + "object_name": "Cardiac ventricular cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This classic model of ventricular myocardial fibres was implemented by Francois Gannier. \"... Four individual components of ionic current were formulated mathematically\r\nin terms of Hodgkin-Huxley type equations. The model incorporates\r\ntwo voltage- and time-dependent inward currents, the excitatory\r\ninward sodium current, illa, and a secondary or slow inward current,\r\nis, primarily carried by calcium ions. A time-independent outward\r\npotassium current, iK1, exhibiting inward-going rectification, and a voltage-\r\nand time-dependent outward current, i.1, primarily carried by potassium\r\nions, are further elements of the model....\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/97863", + "other_implementer": { + "attr_id": 308, + "value": "Gannier, Francois\r\n" + }, + "other_type": { + "attr_id": 305, + "value": "Heart cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "francois.gannier@univ-tours.fr" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181781, + "object_name": "97863" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 278 + }, + "ver_date": "2014-02-20T17:11:03", + "ver_number": 10 + }, + "97868": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 115920, + "object_name": "version prior to 20081006" + }, + { + "object_id": 124525, + "object_name": "pre NEURON 7" + } + ] + }, + "class_id": 19, + "created": "2007-09-25T16:10:17", + "default_file_notes": { + "attr_id": 392, + "value": "current" + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 97868, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33738, + "object_name": "Lytton, William [bill.lytton at downstate.edu]" + }, + { + "object_id": 97869, + "object_name": "Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 97870, + "object_name": "Neymotin S, Uhlrich DJ, Manning KA, Lytton WW (2008)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "NEURON interfaces to MySQL and the SPUD feature extraction algorithm (Neymotin et al. 2008)", + "notes": { + "attr_id": 24, + "value": "See the readme.txt for information on setting up this interface to a MySQL server from the NEURON simulator. Note the SPUD feature extraction algorithm includes its own readme in the spud directory." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/97868", + "ver_date": "2022-05-27T09:50:10.94", + "ver_number": 16 + }, + "97874": { + "class_id": 19, + "created": "2007-09-26T10:49:48", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 97874, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33738, + "object_name": "Lytton, William [bill.lytton at downstate.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 80617, + "object_name": "Lytton WW (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Neural Query System NQS Data-Mining From Within the NEURON Simulator (Lytton 2006)", + "notes": { + "attr_id": 24, + "value": "NQS is a databasing program with a query command modeled loosely on the SQL select command.\r\nPlease see the manual NQS.pdf for details of use.\r\nAn NQS database must be populated with data to be used. This package includes MFP (model fingerprint) which provides an example of NQS use with the model provided in the modeldb folder (see readme for usage)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/97874", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2022-05-27T09:54:41.367", + "ver_number": 8 + }, + "97903": { + "class_id": 19, + "created": "2007-09-28T18:12:44", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 97903, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 97998, + "object_name": "van Elburg, Ronald A.J. [R.van.Elburg at ai.rug.nl]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 97992, + "object_name": "Cornelisse LN, van Elburg RA, Meredith RM, Yuste R, Mansvelder HD (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 97997, + "object_name": "CalC Calcium Calculator" + } + ] + }, + "name": "Determinants of fast calcium dynamics in dendritic spines and dendrites (Cornelisse et al. 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 167344, + "object_name": "Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... Calcium influx time course and calcium extrusion rate were both in the same range for spines and dendrites when fitted with a dynamic multi-compartment model that included calcium binding kinetics and diffusion. In a subsequent analysis we used this model to investigate which parameters are critical determinants in spine calcium dynamics. The model confirmed the experimental findings: a higher SVR (surface-to-volume ratio) is not sufficient by itself to explain the faster rise time kinetics in spines, but only when paired with a lower buffer capacity in spines. Simulations at zero calcium-dye conditions show that calmodulin is more efficiently activated in spines, which indicates that spine morphology and buffering conditions in neocortical spines favor synaptic plasticity. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/97903", + "other_concept": { + "attr_id": 306, + "value": "Calcium Dynamics" + }, + "other_implementer": { + "attr_id": 308, + "value": "van Elburg, Ronald A.J." + }, + "other_simulator": { + "attr_id": 307, + "value": "CalC" + }, + "ver_date": "2017-07-03T22:33:05.017", + "ver_number": 17 + }, + "97917": { + "class_id": 19, + "created": "2007-09-29T16:00:05", + "gitrepo": true, + "id": 97917, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33736, + "object_name": "Hines, Michael [Michael.Hines at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 107758, + "object_name": "Hines ML, Eichner H, Sch\u00fcrmann F (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Cell splitting in neural networks extends strong scaling (Hines et al. 2008)", + "notes": { + "attr_id": 24, + "value": "Neuron tree topology equations can be split into two subtrees and solved\r\non different processors with no change in accuracy, stability, or\r\ncomputational effort; communication costs involve only sending and\r\nreceiving two double precision values by each subtree at each time step.\r\nApplication of the cell splitting method to two published\r\nnetwork models exhibits good runtime scaling on twice as many\r\nprocessors as could be effectively used with whole-cell balancing.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/97917", + "public_submitter_email": { + "attr_id": 309, + "value": "michael.hines@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115960, + "object_name": "Generic" + } + ] + }, + "ver_date": "2023-04-30T16:00:08.05", + "ver_number": 18 + }, + "97972": { + "citation": { + "attr_id": 391, + "value": "Frank, M.J. (2006). Hold your horses: A dynamic computational role for the subthalamic nucleus in decision making. Neural Networks, 19, 1120-1136." + }, + "class_id": 19, + "created": "2007-10-16T21:36:30", + "gitrepo": false, + "id": 97972, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 79489, + "object_name": "Frank, Michael [mfrank at u.arizona.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + }, + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + }, + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + }, + { + "object_id": 136252, + "object_name": "Rebound firing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 83587, + "object_name": "Frank MJ (2006)" + }, + { + "object_id": 98000, + "object_name": "Frank MJ, Samanta J, Moustafa AA, Sherman SJ (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53430, + "object_name": "Emergent/PDP++" + } + ] + }, + "name": "Roles of subthalamic nucleus and DBS in reinforcement conflict-based decision making (Frank 2006)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 260, + "object_name": "Neostriatum medium spiny direct pathway GABA cell" + }, + { + "object_id": 261, + "object_name": "Substantia nigra pars compacta DA cell" + }, + { + "object_id": 74300, + "object_name": "Subthalamus nucleus projection neuron" + }, + { + "object_id": 88217, + "object_name": "Globus pallidus neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Deep brain stimulation (DBS) of the subthalamic nucleus dramatically improves the motor symptoms of Parkinson's disease, but causes cognitive side effects such as impulsivity. This model from Frank (2006) simulates the role of the subthalamic nucleus (STN) within the basal ganglia circuitry in decision making. The STN dynamically modulates network decision thresholds in proportion to decision conflict. The STN ``hold your horses'' signal adaptively allows the system more time to settle on the best choice when multiple options are valid. The model also replicates effects in Parkinson's patients on and off DBS in experiments designed to test the model (Frank et al, 2007)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/97972", + "other_implementer": { + "attr_id": 308, + "value": "Frank, Michael " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "mfrank@u.arizona.edu" + }, + "ver_date": "2010-11-12T16:03:07", + "ver_number": 10 + }, + "97983": { + "class_id": 19, + "created": "2007-10-19T09:40:19", + "gitrepo": false, + "id": 97983, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 97960, + "object_name": "Wendling F, Hernandez A, Bellanger JJ, Chauvel P, Bartolomei F (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 97995, + "object_name": "GNUstep NeXTStep/OpenStep" + } + ] + }, + "name": "Neuronal population models of intracerebral EEG (Wendling et al. 2005)", + "notes": { + "attr_id": 24, + "value": "\"... In this study, the authors relate electrophysiologic patterns typically observed during the transition from interictal to ictal activity in human mesial temporal lobe epilepsy (MTLE) to mechanisms (at a neuronal population level) involved in seizure generation through a computational model of EEG activity. Intracerebral EEG signals recorded from hippocampus in five patients with MTLE during four periods (during interictal activity, just before seizure onset, during seizure onset, and during ictal activity) were used to identify the three main parameters of a model of hippocampus EEG activity (related to excitation, slow dendritic inhibition and fast somatic inhibition). ... . Results demonstrated that the model generates very realistic signals for automatically identified parameters. They also showed that the transition from interictal to ictal activity cannot be simply explained by an increase in excitation and a decrease in inhibition but rather by time-varying ensemble interactions between pyramidal cells and local interneurons projecting to either their dendritic or perisomatic region (with slow and fast GABAA kinetics). Particularly, during preonset activity, an increasing dendritic GABAergic inhibition compensates a gradually increasing excitation up to a brutal drop at seizure onset when faster oscillations (beta and low gamma band, 15 to 40 Hz) are observed. ... These findings obtained from model identification in human temporal lobe epilepsy are in agreement with some results obtained experimentally, either on animal models of epilepsy or on the human epileptic tissue.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/97983", + "ver_date": "2016-10-27T22:32:52.107", + "ver_number": 10 + }, + "97985": { + "citation": { + "attr_id": 391, + "value": "Hines, M and Schuermann, F (2008) Fully Implicit Parallel Simulation of Single Neurons. (to be submitted)" + }, + "class_id": 19, + "created": "2007-10-20T14:15:33", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 97985, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 97989, + "object_name": "Hines ML, Markram H, Sch\u00fcrmann F (2008)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Fully Implicit Parallel Simulation of Single Neurons (Hines et al. 2008)", + "notes": { + "attr_id": 24, + "value": "A 3-d reconstructed\r\nneuron model can be simulated in parallel on a dozen or so processors and experience almost linear\r\nspeedup. Network models can be simulated when\r\nthere are more processors than cells.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/97985", + "public_submitter_email": { + "attr_id": 309, + "value": "michael.hines@yale.edu" + }, + "ver_date": "2021-08-26T16:30:14.207", + "ver_number": 11 + }, + "98003": { + "class_id": 19, + "created": "2007-10-28T22:51:53", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2397, + "object_name": "I Mixed" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122826, + "object_name": "NR2A GRIN2A" + } + ] + }, + "gitrepo": false, + "id": 98003, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 98006, + "object_name": "Baker, John L [jbakerb at gmu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + }, + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 98011, + "object_name": "Place cell/field" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 93427, + "object_name": "Baker JL, Olds JL (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Theta phase precession in a model CA3 place cell (Baker and Olds 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + }, + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... The present study concerns a neurobiologically based computational model of the emergence of theta phase precession in which the responses of a single model CA3 pyramidal cell are examined in the context of stimulation by realistic afferent spike trains including those of place cells in entorhinal cortex, dentate gyrus, and other CA3 pyramidal cells. \r\nSpike-timing dependent plasticity in the model CA3 pyramidal cell leads to a spatially correlated associational synaptic drive that subsequently creates a spatially asymmetric expansion of the model cell\u2019s place field. ...\r\nThrough selective manipulations of the model it is possible to decompose theta phase precession in CA3 into the separate contributing factors of inheritance from upstream afferents in the dentate gyrus and entorhinal cortex, the interaction of synaptically controlled increasing afferent drive with phasic inhibition, and the theta phase difference between dentate gyrus granule cell and CA3 pyramidal cell activity.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/98003", + "other_concept": { + "attr_id": 306, + "value": "Place Cell" + }, + "other_implementer": { + "attr_id": 308, + "value": "Baker, John L" + }, + "other_simulator": { + "attr_id": 307, + "value": "R" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2009-07-14T13:58:15", + "ver_number": 11 + }, + "98005": { + "class_id": 19, + "created": "2007-10-30T12:30:28", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122829, + "object_name": "D2 DRD2" + }, + { + "object_id": 226057, + "object_name": "HCN1" + }, + { + "object_id": 226058, + "object_name": "HCN2" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 98005, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 97862, + "object_name": "Held, Joshua [j-held at northwestern.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 80717, + "object_name": "Maurice N, Mercer J, Chan CS, Hernandez-Lopez S, Held J, Tkatch T, Surmeier DJ (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "98005", + "name": "ModelView" + } + ], + "default": "98005" + }, + "name": "D2 dopamine receptor modulation of interneuronal activity (Maurice et al. 2004)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 7021, + "object_name": "Neostriatum interneuron ACh cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... Using a combination of electrophysiological, molecular, and computational approaches, the studies reported here show that D2 dopamine receptor modulation of Na+ currents underlying autonomous spiking contributes to a slowing of discharge rate, such as that seen in vivo. Four lines of evidence support this conclusion. ... Fourth, simulation of cholinergic interneuron pacemaking revealed that a modest increase in the entry of Na+ channels into the slow-inactivated state was sufficient to account for the slowing of pacemaker discharge. These studies establish a cellular mechanism linking dopamine and the reduction in striatal cholinergic interneuron activity seen in the initial stages of associative learning.\" See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/98005", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 197, + "object_name": "D2" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 212 + }, + "ver_date": "2017-03-18T09:58:27.283", + "ver_number": 18 + }, + "98017": { + "citation": { + "attr_id": 391, + "value": "Edin F et al. J Cogn Neurosci. 2007 May;19(5):750-60., Edin F et al. 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It was concluded that inter-regional synaptic connection strength cause vsWM development.\r\n\r\n2) J Integr Neurosci: Given the importance of fronto-parietal connections, we tested whether connection asymmetry affected resistance to distraction. We drew the conclusion that stronger frontal connections are beneficial. By comparing model results to EEG, we concluded that the brain indeed has stronger frontal-to-parietal connections than vice versa." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/98017", + "public_submitter_email": { + "attr_id": 309, + "value": "fredrik.edin@ki.se" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2017-09-06T11:09:25.453", + "ver_number": 10 + }, + "98902": { + "citation": { + "attr_id": 391, + "value": "PMID: 17619199" + }, + "class_id": 19, + "created": "2007-11-10T18:49:56", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 98902, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 97901, + "object_name": "Anderson WS, Kudela P, Cho J, Bergey GK, Franaszczuk PJ (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Studies of stimulus parameters for seizure disruption using NN simulations (Anderson et al. 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + }, + { + "object_id": 7387, + "object_name": "Neocortex V1 interneuron basket PV GABA cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Architecturally realistic neocortical model using seven classes of excitatory and inhibitory single compartment Hodgkin-Huxley cells. Wiring is adapted to minicolumn hypothesis and incorporates visual and neocortical data. Simulation demonstrates spontaneous bursting onset and cessation, and activity can be altered with external electric field." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/98902", + "public_submitter_email": { + "attr_id": 309, + "value": "wanderso@jhmi.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "ver_date": "2018-10-17T16:49:07.137", + "ver_number": 9 + }, + "100603": { + "class_id": 19, + "created": "2007-11-19T17:48:59", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 88206, + "object_name": "I_HERG" + } + ] + }, + "gitrepo": false, + "id": 100603, + "implemented_by": 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also mimic the induction\r\nof plateau potentials by the calcium chelator BAPTA.\" See paper for more." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/100603", + "simPFid": { + "attr_id": 488, + "value": 61 + }, + "ver_date": "2015-07-26T13:37:48.513", + "ver_number": 12 + }, + "101629": { + "class_id": 19, + "created": "2007-11-28T03:20:04", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + 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Pharmacological and physiological data suggest that three currents contribute to the S cell's afterhyperpolarization: a charybdotoxin-sensitive, fast calcium-dependent potassium current (fAHP); a tubocurare-sensitive, calcium-dependent potassium current (mAHP); and, a saxitoxin-sensitive, afterdepolarization current (ADP). This single-compartment model of the S cell is constructed using fAHP, mAHP and ADP currents, and shows that reduction of the conductances to mimic the effects of serotonin is sufficient to enhance excitability (repetitive firing)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/102279", + "other_concept": { + "attr_id": 306, + "value": "Excitability" + }, + "other_implementer": { + "attr_id": 308, + "value": "Crisp, Kevin " + }, + "other_neurons": { + "attr_id": 301, + "value": "Leech S Cell" + }, + "ver_date": "2016-06-02T13:34:42.02", + "ver_number": 12 + }, + "102288": { + "citation": { + "attr_id": 391, + "value": "http://www.jneurosci.org/cgi/content/full/26/47/12325" + }, + "class_id": 19, + "created": "2007-12-05T14:18:33", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "default_file_notes": { + "attr_id": 392, + "value": "current" + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 102288, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 102353, + "object_name": "Lawrence JJ, Saraga F, Churchill JF, Statland JM, Travis KE, Skinner FK, McBain CJ (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "102288_1", + "name": "Create Fig 9 A1" + }, + { + "id": "102288_2", + "name": "Create Fig 9 B1" + }, + { + "id": "102288_3", + "name": "Create Fig 9 C1" + }, + { + "id": "102288_4", + "name": "Create Fig 9 A2" + }, + { + "id": "102288_5", + "name": "Create Fig 9 B2" + }, + { + "id": "102288_6", + "name": "Create Fig 9 C2" + }, + { + "id": "102288_7", + "name": "Create Fig 9 A3" + }, + { + "id": "102288_8", + "name": "Create Fig 9 B3" + }, + { + "id": "102288_9", + "name": "Create Fig 9 C3" + }, + { + "id": "102288_10", + "name": "Create all of Fig 9" + } + ], + "default": "102288_1" + }, + "name": "O-LM interneuron model (Lawrence et al. 2006)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 35373, + "object_name": "Hippocampus CA1 interneuron oriens alveus GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Exploring the kinetics and distribution of the muscarinic potassium channel, IM, in 2 O-LM interneuron morphologies. Modulation of the ion channel by drugs such as XE991 (antagonist) and retigabine (agonist) are simulated in the models to examine the role of IM in spiking properties." + }, + "opensourcebrain": { + "value": "ca1-oriens-lacunosum-moleculare-lawrence-et-al-2006" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/102288", + "public_submitter_email": { + "attr_id": 309, + "value": "fernanda.saraga@utoronto.ca" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 204, + "object_name": "Muscarinic" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181433, + "object_name": "102288_10" + }, + { + "object_id": 181703, + "object_name": "102288_2" + }, + { + "object_id": 181704, + "object_name": "102288_3" + }, + { + "object_id": 181706, + "object_name": "102288_1" + }, + { + "object_id": 181707, + "object_name": "102288_6" + }, + { + "object_id": 181708, + "object_name": "102288_7" + }, + { + "object_id": 181709, + "object_name": "102288_4" + }, + { + "object_id": 181710, + "object_name": "102288_5" + }, + { + "object_id": 181713, + "object_name": "102288_8" + }, + { + "object_id": 181714, + "object_name": "102288_9" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 301 + }, + "ver_date": "2015-01-02T22:01:24", + "ver_number": 8 + }, + "102871": { + "class_id": 19, + "created": "2007-12-06T18:06:46", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 246, + "object_name": "I p,q" + } + ] + }, + "gitrepo": false, + "id": 102871, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 22010, + "object_name": "Facilitation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 102873, + "object_name": "Chaudhuri D, Issa JB, Yue DT (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Elementary mechanisms producing facilitation of Cav2.1 (P/Q-type) channels", + "notes": { + "attr_id": 24, + "value": "\"The regulation of Ca(V)2.1 (P/Q-type) channels by calmodulin (CaM) showcases the powerful Ca(2+) decoding capabilities of CaM in complex with the family of Ca(V)1-2 Ca(2+) channels. \r\n\r\nThroughout this family, CaM does not simply exert a binary on/off regulatory effect; rather, Ca(2+) binding to either the C- or N-terminal lobe of CaM alone can selectively trigger a distinct form of channel modulation. \r\n...\r\nCa(2+) binding to the C-terminal lobe induces Ca(2+)-dependent facilitation of opening (CDF), whereas the N-terminal lobe yields Ca(2+)-dependent inactivation of opening (CDI). \r\n...\r\nFurthermore, direct single-channel determinations of channel open probability (P(o)) and kinetic simulations demonstrate that CDF represents a genuine enhancement of open probability, without appreciable change of activation kinetics. \r\nThis enhanced-opening mechanism suggests that the CDF evoked during action-potential trains would produce not only larger, but longer-lasting Ca(2+) responses, an outcome with potential ramifications for short-term synaptic plasticity.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/102871", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "simPFid": { + "attr_id": 488, + "value": 84 + }, + "ver_date": "2007-12-07T18:20:53", + "ver_number": 9 + }, + "104623": { + "class_id": 19, + "created": "2007-12-11T15:26:44", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 88209, + "object_name": "Na/Ca exchanger" + }, + { + "object_id": 88215, + "object_name": "I_Ks" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gitrepo": false, + "id": 104623, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 104625, + "object_name": "Zhu, Zheng I. 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Here we\r\nemploy theoretical simulations to examine the effects of a Timothy Syndrome (TS) mutation\r\nin the L-type Ca2+ channel on cardiac dynamics over multiple\r\nscales, from a gene mutation to protein, cell, tissue, and finally the\r\nECG, to connect a defective Ca2+ channel to arrhythmia susceptibility. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/104623", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "simPFid": { + "attr_id": 488, + "value": 64 + }, + "ver_date": "2009-05-28T15:14:54", + "ver_number": 9 + }, + "105383": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 105386, + "object_name": "cvode" + } + ] + }, + "class_id": 19, + "created": "2007-12-13T15:16:49", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 88209, + "object_name": "Na/Ca exchanger" + }, + { + "object_id": 88211, + "object_name": "I_SERCA" + } + ] + }, + "default_file_notes": { + "attr_id": 392, + "value": "current" + }, + "gitrepo": false, + "id": 105383, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 60334, + "object_name": "Cortassa S, Aon MA, Marb\u00e1n E, Winslow RL, O'Rourke B (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 120694, + "object_name": "Electrogenic pump" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Excitation-contraction coupling/mitochondrial energetics (ECME) model (Cortassa et al. 2006)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52423, + "object_name": "Heart cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"An intricate network of reactions is involved in matching energy supply with demand in the heart. This complexity\r\narises because energy production both modulates and is modulated by the electrophysiological and contractile activity of the\r\ncardiac myocyte. Here, we present an integrated mathematical model of the cardiac cell that links excitation-contraction\r\ncoupling with mitochondrial energy generation. The dynamics of the model are described by a system of 50 ordinary differential\r\nequations. The formulation explicitly incorporates cytoplasmic ATP-consuming processes associated with force generation and\r\nion transport, as well as the creatine kinase reaction. Changes in the electrical and contractile activity of the myocyte are\r\ncoupled to mitochondrial energetics through the ATP, Ca21, and Na1 concentrations in the myoplasmic and mitochondrial\r\nmatrix compartments. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/105383", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2009-05-28T15:15:39", + "ver_number": 6 + }, + "105385": { + "class_id": 19, + "created": "2007-12-13T15:48:55", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92087, + "object_name": "Nav1.6 SCN8A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 105385, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 97862, + "object_name": "Held, Joshua [j-held at northwestern.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 104518, + "object_name": "Mercer JN, Chan CS, Tkatch T, Held J, Surmeier DJ (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "105385_1", + "name": "Figure 9" + }, + { + "id": "105385_2", + "name": "Figure 10" + } + ], + "default": "105385_1" + }, + "name": "Nav1.6 sodium channel model in globus pallidus neurons (Mercer et al. 2007)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88217, + "object_name": "Globus pallidus neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Model files for the paper Mercer JN, Chan CS, Tkatch T, Held J, Surmeier DJ. Nav1.6 sodium channels are critical to pacemaking and fast spiking in globus pallidus neurons.,J Neurosci. 2007 Dec 5;27(49):13552-66." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/105385", + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181150, + "object_name": "105385_2" + }, + { + "object_id": 181157, + "object_name": "105385_1" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 294 + }, + "ver_date": "2017-06-20T17:18:20.913", + "ver_number": 12 + }, + "105501": { + "class_id": 19, + "created": "2007-12-14T18:58:55", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "id": 105501, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 97458, + "object_name": "Okamoto H, Isomura Y, Takada M, Fukai T (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Temporal integration by stochastic recurrent network (Okamoto et al. 2007)", + "notes": { + "attr_id": 24, + "value": "\"Temporal integration of externally or internally\r\ndriven information is required for a variety of cognitive\r\nprocesses. This computation is generally linked with graded rate\r\nchanges in cortical neurons, which typically appear during a delay\r\nperiod of cognitive task in the prefrontal and other cortical areas.\r\nHere, we present a neural network model to produce graded (climbing\r\nor descending) neuronal activity. Model neurons are interconnected\r\nrandomly by AMPA-receptor\u2013mediated fast excitatory synapses and\r\nare subject to noisy background excitatory and inhibitory synaptic\r\ninputs. In each neuron, a prolonged afterdepolarizing potential follows\r\nevery spike generation. Then, driven by an external input, the individual\r\nneurons display bimodal rate changes between a baseline state\r\nand an elevated firing state, with the latter being sustained by regenerated\r\nafterdepolarizing potentials. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/105501", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 71 + }, + "ver_date": "2008-10-09T16:52:22", + "ver_number": 6 + }, + "105506": { + "class_id": 19, + "created": "2007-12-17T19:07:01", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 105506, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 105511, + "object_name": "Sun J, Pang ZP, Qin D, Fahim AT, Adachi R, S\u00fcdhof TC (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 83509, + "object_name": "IGOR Pro" + } + ] + }, + "name": "A dual-Ca2+-sensor model for neurotransmitter release in a central synapse (Sun et al. 2007)", + "notes": { + "attr_id": 24, + "value": "\"Ca2+-triggered synchronous neurotransmitter release is well described, but asynchronous release-in fact, its very existence-remains enigmatic. \r\nHere we report a quantitative description of asynchronous neurotransmitter release in calyx-of-Held synapses. \r\n...\r\nOur results reveal that release triggered in wild-type synapses at low Ca2+ concentrations is physiologically asynchronous, and that asynchronous release completely empties the readily releasable pool of vesicles during sustained elevations of Ca2+. \r\nWe propose a dual-Ca2+-sensor model of release that quantitatively describes the contributions of synchronous and asynchronous release under conditions of different presynaptic Ca2+ dynamics.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/105506", + "ver_date": "2016-09-16T14:19:33.8", + "ver_number": 6 + }, + "105507": { + "class_id": 19, + "created": "2007-12-17T20:06:29", + "gitrepo": true, + "id": 105507, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33738, + "object_name": "Lytton, William [bill.lytton at downstate.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 105521, + "object_name": "Lytton WW, Omurtag A (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Tonic-clonic transitions in a seizure simulation (Lytton and Omurtag 2007)", + "notes": { + "attr_id": 24, + "value": "\"... The authors have ... computationally manageable networks of moderate size consisting of 1,000 to 3,000 neurons with multiple intrinsic\r\nand synaptic properties. \r\n\r\nExperiments on these simulations demonstrated the presence of epileptiform behavior in the form of\r\nrepetitive high-intensity population events (clonic behavior) or\r\nlatch-up with near maximal activity (tonic behavior). \r\n\r\n...\r\nSeveral simulations revealed the importance of random coincident inputs to shift a network from\r\na low-activation to a high-activation epileptiform state. Finally, a\r\nsimulated anticonvulsant acting on excitability tended to preferentially\r\ndecrease tonic activity.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/105507", + "ver_date": "2022-05-30T15:55:20.163", + "ver_number": 12 + }, + "105528": { + "citation": { + "attr_id": 391, + "value": "Wang et al., Life Sci 2007;82:11-20" + }, + "class_id": 19, + "created": "2007-12-22T06:25:06", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "id": 105528, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + }, + { + "object_id": 62653, + "object_name": "Wang, Ya-Jean " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 105530, + "object_name": "Wang YJ, Lin MW, Lin AA, Wu SN (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Effect of riluzole on action potential in cultured human skeletal muscle cells (Wang YJ et al. 2008)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 105531, + "object_name": "Skeletal muscle cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Simulation studies also unraveled that both decreased conductance of I(Na) and increased conductance of I(K(Ca)) utilized to mimic riluzole\r\nactions in skeletal muscle cells could combine to decrease the amplitude of action potentials and increase the repolarization of action potentials." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/105528", + "other_implementer": { + "attr_id": 308, + "value": "Sheng-Nan Wu\r\nYa-Jean Wang" + }, + "other_neurons": { + "attr_id": 301, + "value": "skeltal muscle cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2007-12-22T12:41:53", + "ver_number": 5 + }, + "106551": { + "class_id": 19, + "created": "2007-12-27T05:20:26", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 106551, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": 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"notes": { + "attr_id": 24, + "value": "NEURON mod files from the paper:\r\nCassar\u00e0 AM, Hagberg GE, Bianciardi M, Migliore M, Maraviglia B.\r\nRealistic simulations of neuronal activity: A contribution to the debate on direct detection of neuronal currents by MRI.\r\nNeuroimage. 39:87-106 (2008).\r\n\r\nIn this paper, we use a detailed calculation of the magnetic field produced by the neuronal \r\ncurrents propagating over a hippocampal CA1 pyramidal neuron placed inside a cubic MR voxel of \r\nlength 1.2 mm to estimate the Magnetic Resonance signal.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/106551", + "other_implementer": { + "attr_id": 308, + "value": "Antonio Cassara'" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181748, + "object_name": "106551" + } + ] + }, + "simPFid": { + "attr_id": 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"attr_id": 488, + "value": 268 + }, + "ver_date": "2023-04-30T15:16:17.49", + "ver_number": 27 + }, + "108458": { + "class_id": 19, + "created": "2008-01-04T05:57:51", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 108458, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 50209, + "object_name": "Kampa, Bjorn M [Bjoern.Kampa at anu.edu.au]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3639, 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"notes": { + "attr_id": 24, + "value": "This model was published in Kampa & Stuart (2006) J Neurosci 26(28):7424-32. The simulation creates two plots showing voltage and calcium changes in basal dendrites of layer 5 pyramidal neurons during action potential backpropagation. \r\n\r\ncreated by B. Kampa (2006)" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/108458", + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181541, + "object_name": "108458" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 190 + }, + "ver_date": "2017-07-03T22:23:48.73", + "ver_number": 12 + }, + "108459": { + "class_id": 19, + "created": "2008-01-04T06:25:57", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 108459, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 50209, + "object_name": "Kampa, Bjorn M [Bjoern.Kampa at anu.edu.au]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 82860, + "object_name": "Letzkus JJ, Kampa BM, Stuart GJ (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "108459_1", + "name": "3 AP burst" + }, + { + "id": "108459_2", + "name": "no APs" + } + ], + "default": "108459_1" + }, + "name": "STDP depends on dendritic synapse location (Letzkus et al. 2006)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model was published in Letzkus, Kampa & Stuart (2006) J Neurosci 26(41):10420-9. The simulation creates several plots showing voltage and NMDA current and conductance changes at different apical dendritic locations in layer 5 pyramidal neurons during STDP induction protocols. \r\n\r\nCreated by B. Kampa (2006)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/108459", + "other_currents": { + "attr_id": 303, + "value": "I h" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181458, + "object_name": "108459_1" + }, + { + "object_id": 181459, + "object_name": "108459_2" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 332 + }, + "ver_date": "2015-01-02T21:47:29", + "ver_number": 18 + }, + "110022": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 110023, + "object_name": "copy of web linked version" + } + ] + }, + "class_id": 19, + "created": "2008-01-09T16:02:16", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 110022, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 110025, + "object_name": "Heinzle J, Hepp K, Martin KA (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A microcircuit model of the frontal eye fields (Heinzle et al. 2007)", + "notes": { + "attr_id": 24, + "value": "\" ... we show that the canonical circuit (Douglas et al. 1989, Douglas and Martin 1991) can,\r\nwith a few modifications, model the primate FEF. The spike-based network of integrate-and-fire neurons was tested in tasks that were\r\nused in electrophysiological experiments in behaving macaque monkeys. The dynamics of the model matched those of neurons observed\r\nin the FEF, and the behavioral results matched those observed in psychophysical experiments. The close relationship between the model\r\nand the cortical architecture allows a detailed comparison of the simulation results with physiological data and predicts details of the\r\nanatomical circuit of the FEF.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/110022", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2018-10-17T16:49:23.293", + "ver_number": 11 + }, + "110560": { + "class_id": 19, + "created": "2008-01-13T16:28:19", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 110560, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 110661, + "object_name": "Korngreen, Alon [alon.korngreen at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 110561, + "object_name": "Bar-Yehuda D, Korngreen A (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "110560_1", + "name": "Potassium 1. 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The simulations also demonstrate that passive cable theory cannot be used to justify voltage-clamping of neurons, due to significant shunting to the reversal potential of the voltage-gated conductance during channel activation.\r\n... \"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/110560", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181468, + "object_name": "110560_4" + }, + { + "object_id": 181469, + "object_name": "110560_5" + }, + { + "object_id": 181470, + "object_name": "110560_6" + }, + { + "object_id": 181471, + "object_name": "110560_7" + }, + { + "object_id": 181472, + "object_name": "110560_1" + }, + { + "object_id": 181473, + "object_name": "110560_2" + }, + { + "object_id": 181474, + "object_name": "110560_3" + }, + { + "object_id": 181476, + "object_name": "110560_8" + } + ] + }, + "ver_date": "2015-01-02T22:06:46", 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low-frequency oscillations of the GPi neurons. ...\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/111870", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 139 + }, + "ver_date": "2008-10-09T10:56:52", + "ver_number": 14 + }, + "111877": { + "biosimulations": { + "value": "modeldb-111877" + }, + "citation": { + "attr_id": 391, + "value": "Moczydlowski & Latorre, J Gen Physiol 1983;82:511-42. & Wang et al., Life Sci 2008;82:11-20." + }, + "class_id": 19, + "created": "2008-02-03T23:07:51", + "currents": { + "attr_id": 27, + "value": [ 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The model exhibits bistability properties and stochastic fluctuations present in the network may flip the system between the two operational states. The predictions of the model were compared with real EEG data in rats and humans. A possibility to abort an ictal state by a single counter stimulus is suggested by the model. \r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/111880", + "other_type": { + "attr_id": 305, + "value": "Neural mass" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "suffa@fuw.edu.pl" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2017-04-04T13:53:28.92", + "ver_number": 10 + }, + "111967": { + "citation": { + "attr_id": 391, + "value": "Time dependent molecular memory in single voltage gated sodium channel (Nayak & Sikdar, 2007)" + }, + "class_id": 19, + "created": "2008-02-18T04:29:24", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 111967, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 112043, + "object_name": "Nayak, Tapan Kumar [tapan at mbu.iisc.ernet.in]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 238957, + "object_name": "Markov-type model" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 111964, + "object_name": "Nayak TK, Sikdar SK (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 112042, + "object_name": "Yale HMM" + } + ] + }, + "name": "Kinetic properties of voltage gated Na channel (Nayak and Sikdar 2007)", + "notes": { + "attr_id": 24, + "value": "Here we illustrate novel non-linear properties of voltage gated Na+ channel induced by sustained membrane depolarization. In cell-attached patch clamp recordings of rNav1.2 channels expressed in CHO cells, we found complex non-linear changes in the molecular kinetic properties, including channel dwell times and unitary conductance of single Na+ channels that were dependent on the extent of conditioning membrane depolarization.\r\nA \u201cmolecular memory\u201d phenomenon arises at longer depolarization characterized by clusters of dwell time events and strong autocorrelation in dwell times. Hidden Markov Modeling (HMM) ... suggested a possible explanation to the memory phenomenon.\r\nSee paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/111967", + "other_simulator": { + "attr_id": 307, + "value": "Yale HMM" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tapan@mbu.iisc.ernet.in" + }, + "ver_date": "2018-02-26T11:10:36.083", + "ver_number": 12 + }, + "112079": { + "citation": { + "attr_id": 391, + "value": "Lin MW, Wang YJ, Liu SI, Lin AA, Lo YC, Wu SN. Characterization of aconitine-" + }, + "class_id": 19, + "created": "2008-03-17T09:23:53", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 88117, + "object_name": "I_KHT" + } + ] + }, + "gitrepo": false, + "id": 112079, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + }, + { + "object_id": 112082, + "object_name": "Lin, Ming-Wei " + }, + { + "object_id": 112083, + "object_name": "Wan, Ya-Jean " + }, + { + "object_id": 112084, + "object_name": "Lin, An-An " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 112081, + "object_name": "Lin MW, Wang YJ, Liu SI, Lin AA, Lo YC, Wu SN (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Properties of aconitine-induced block of KDR current in NG108-15 neurons (Lin et al. 2008)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88207, + "object_name": "Neuroblastoma" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The effects of aconitine (ACO), a highly toxic alkaloid, on ion currents in differentiated NG108-15 neuronal cells were investigated in this study. ACO (0.3-30 microM) suppressed the amplitude of delayed rectifier K+ current (IK(DR)) in a concentration-dependent manner with an IC50 value of 3.1 microM. The presence of ACO enhanced the rate and extent of IK(DR) inactivation, although it had no effect on the initial activation phase of IK(DR). ... A modeled cell was designed to duplicate its inhibitory effect on spontaneous pacemaking. ... Taken together, the experimental data and simulations show that ACO can block delayed rectifier K+ channels of neurons in a concentration- and state-dependent manner. Changes in action potentials induced by ACO in neurons in vivo can be explained mainly by its blocking actions on IK(DR) and INa.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/112079", + "other_implementer": { + "attr_id": 308, + "value": "Ming-Wei Lin, Ya-Jean Wan, An-An Lin and Sheng-Nan Wu" + }, + "other_neurons": { + "attr_id": 301, + "value": "neuroblastoma cells" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2008-03-17T11:26:03", + "ver_number": 7 + }, + "112086": { + "class_id": 19, + "created": "2008-03-18T10:20:28", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 112086, + "implemented_by": { + 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We propose here a mathematical model of how spontaneous Ca2+ \r\noscillations arise in astrocytes. This model uses the calcium-induced calcium release and inositol cross-coupling \r\nmechanisms coupled with a receptor-independent method for producing inositol (1,4,5)-trisphosphate as the heart \r\nof the model. By computationally mimicking experimental constraints we have found that this model provides \r\nresults that are qualitatively similar to experiment.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/112547", + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 153715, + "object_name": "IP3" + } + ] + }, + "ver_date": "2014-04-21T10:47:27", + "ver_number": 7 + }, + "112633": { + "class_id": 19, + "created": "2008-04-04T10:48:49", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "id": 112633, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 112307, + "object_name": "Gansert J, Golowasch J, Nadim F (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 112634, + "object_name": "Network" + } + ] + }, + "name": "Gap-junction coupled network activity depends on coupled dendrites diameter (Gansert et al. 2007)", + "notes": { + "attr_id": 24, + "value": "\"... We have previously shown that the amplitude of electrical signals propagating across gap-junctionally coupled passive cables is maximized at a unique diameter. This suggests that threshold-dependent signals may propagate through gap junctions for a finite range of diameters around this optimal value. \r\nHere we examine the diameter dependence of action potential propagation across model networks of dendro-dendritically coupled neurons. The neurons in these models have passive soma and dendrites and an action potential-generating axon. We show that propagation of action potentials across gap junctions occurs only over a finite range of dendritic diameters and that propagation delay depends on this diameter. ...\". 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Our results suggest that a complex complement of ionic mechanisms is needed to fine-tune separate aspects of the neuronal response dynamics. Simulations also suggest that the Golgi cell may exploit these mechanisms to obtain a fine regulation of timing of incoming mossy fiber responses and granular layer circuit oscillation and bursting.\"" + }, + "opensourcebrain": { + "value": "cerebellum--cerebellar-golgi-cell--solinasetal-golgicell" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/112685", + "other_implementer": { + "attr_id": 308, + "value": "D'Angelo Egidio \r\nDe Schutter Erik " + }, + "other_neurons": { + "attr_id": 301, + "value": "Cerebellar Golgi cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "solinas@unipv.it" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 195 + }, + "ver_date": "2022-12-16T10:47:42.83", + "ver_number": 21 + }, + "112834": { + "class_id": 19, + "created": "2008-04-13T15:22:11", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 112841, + "object_name": "I Krp" + }, + { + "object_id": 112843, + "object_name": "I R" + }, + { + "object_id": 112844, + "object_name": "I Q" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 112834, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 112845, + "object_name": "Wolf, John A. [johnwolf at warpmail.net]" + }, + { + "object_id": 112846, + "object_name": "Moyer, Jason [jtmoyer at seas.upenn.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 112853, + "object_name": "Schizophrenia" + }, + { + "object_id": 112854, + "object_name": "Addiction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 86558, + "object_name": "Wolf JA, Moyer JT, Lazarewicz MT, Contreras D, Benoit-Marand M, O'Donnell P, Finkel LH (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Afferent Integration in the NAcb MSP Cell (Wolf et al. 2005)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 112837, + "object_name": "Nucleus accumbens spiny projection neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"We describe a computational model of the principal cell in the nucleus accumbens (NAcb), the medium spiny projection (MSP) neuron. \r\n\r\nThe model neuron, constructed in NEURON, includes all of the known ionic currents in these cells and receives synaptic input from simulated spike trains via NMDA, AMPA, and GABAA receptors. \r\n\r\n... results suggest that afferent information integration by the NAcb MSP cell may be compromised by pathology in which the NMDA current is altered or modulated, as has been proposed in both schizophrenia and addiction.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/112834", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181687, + "object_name": "112834" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 151 + }, + "ver_date": "2014-01-20T15:52:46", + "ver_number": 18 + }, + "112836": { + "biosimulations": { + "value": "modeldb-112836" + }, + "citation": { + "attr_id": 391, + "value": "Wang J, Yanqiu WS, Fei X." + }, + "class_id": 19, + "created": "2008-04-14T02:00:49", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "id": 112836, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + }, + { + "object_id": 112917, + "object_name": "Wu, Yung-Hun " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 112855, + "object_name": "Complementary and alternative medicine" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 112848, + "object_name": "Wang J,Si W, Che Y, Fei X (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Spike trains in Hodgkin\u2013Huxley model and ISIs of acupuncture manipulations (Wang et al. 2008)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52426, + "object_name": "Squid axon" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The Hodgkin-Huxley equations (HH) are parameterized by a number of parameters \r\nand shows a variety of qualitatively different behaviors depending on the \r\nparameter values. Under stimulation of an external periodic voltage, the \r\nISIs (interspike intervals) of a HH model are investigated in this work, \r\nwhile the frequency of the voltage is taken as the controlling parameter. \r\nAs well-known, the science of acupuncture and moxibustion is an important \r\ncomponent of Traditional Chinese Medicine with a long history. Although there \r\nare a number of different acupuncture manipulations, the method for \r\ndistinguishing them is rarely investigated. With the idea of ISI, we study \r\nthe electrical signal time series at the spinal dorsal horn produced by \r\nthree different acupuncture manipulations in Zusanli point and present an \r\neffective way to distinguish them.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/112836", + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2008-04-15T10:19:23", + "ver_number": 9 + }, + "112914": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 112915, + "object_name": "backup of web linked model" + } + ] + }, + "class_id": 19, + "created": "2008-04-14T17:12:11", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 245, + "object_name": "I T low threshold" + } + ] + }, + "gitrepo": false, + "id": 112914, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 102463, + "object_name": "Matveev V, Bose A, Nadim F (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Two-cell inhibitory network bursting dynamics captured in a one-dimensional map (Matveev et al 2007)", + "notes": { + "attr_id": 24, + "value": "\" ... Here\r\nwe describe a simple method that allows us to investigate\r\nthe existence and stability of anti-phase bursting\r\nsolutions in a network of two spiking neurons, each\r\npossessing a T-type calcium current and coupled by\r\nreciprocal inhibition. \r\nWe derive a one-dimensional map\r\nwhich fully characterizes the genesis and regulation of\r\nanti-phase bursting arising from the interaction of the\r\nT-current properties with the properties of synaptic\r\ninhibition. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/112914", + "ver_date": "2018-02-12T13:49:42.413", + "ver_number": 11 + }, + "112922": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 112923, + "object_name": "backup of web link" + } + ] + }, + "class_id": 19, + "created": "2008-04-15T14:35:26", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "default_file_notes": { + "attr_id": 392, + "value": "current" + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 112922, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3542, + "object_name": "Pattern Recognition" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 82787, + "object_name": "Working memory" + }, + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 182690, + "object_name": "Spatial Navigation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 112919, + "object_name": "Grossberg S and Versace M (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 83553, + "object_name": "KInNeSS (web link to model)" + }, + { + "object_id": 87474, + "object_name": "XML (web link to model)" + }, + { + "object_id": 154350, + "object_name": "NeuroML (web link to model)" + } + ] + }, + "name": "Spikes,synchrony,and attentive learning by laminar thalamocort. circuits (Grossberg & Versace 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + }, + { + "object_id": 263, + "object_name": "Thalamus reticular nucleus GABA cell" + }, + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... The model\r\nhereby clarifies, for the first time, how the following levels of brain organization coexist to realize\r\ncognitive processing properties that regulate fast learning and stable memory of brain representations:\r\nsingle cell properties, such as spiking dynamics, spike-timing-dependent plasticity (STDP), and\r\nacetylcholine modulation; detailed laminar thalamic and cortical circuit designs and their interactions;\r\naggregate cell recordings, such as current-source densities and local field potentials; and single cell and\r\nlarge-scale inter-areal oscillations in the gamma and beta frequency domains. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/112922", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 213, + "object_name": "Gaba" + }, + { + "object_id": 218, + "object_name": "Cholinergic Receptors" + } + ] + }, + "ver_date": "2015-05-29T14:57:47.28", + "ver_number": 10 + }, + "112968": { + "class_id": 19, + "created": "2008-04-21T17:05:29", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "id": 112968, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83550, + "object_name": "Kuznetsova, Anna [anna.kuznetsova at utsa.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 112959, + "object_name": "Kuznetsova AY, Deth RC (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Synchronization by D4 dopamine receptor-mediated phospholipid methylation (Kuznetsova, Deth 2008)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"We describe a new molecular mechanism of\r\ndopamine-induced membrane protein modulation that\r\ncan tune neuronal oscillation frequency to attention related\r\ngamma rhythm. This mechanism is based on\r\nthe unique ability of D4 dopamine receptors (D4R)\r\nto carry out phospholipid methylation (PLM) that\r\nmay affect the kinetics of ion channels.\r\nWe show that by deceasing the inertia of the delayed rectifier potassium channel, a transition to 40 Hz oscillations can be achieved. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/112968", + "ver_date": "2015-10-08T15:43:40.88", + "ver_number": 5 + }, + "113426": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 113427, + "object_name": "backup of web version" + } + ] + }, + "class_id": 19, + "created": "2008-04-24T18:07:29", + "gitrepo": false, + "id": 113426, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 87578, + "object_name": "STDP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 93367, + "object_name": "Roberts PD (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113428, + "object_name": "Java (web link to model)" + } + ] + }, + "name": "Stability of complex spike timing-dependent plasticity in cerebellar learning (Roberts 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Dynamics of spike-timing dependent synaptic\r\nplasticity are analyzed for excitatory and inhibitory synapses\r\nonto cerebellar Purkinje cells. \r\n\r\nThe purpose of this study is\r\nto place theoretical constraints on candidate synaptic learning\r\nrules that determine the changes in synaptic efficacy\r\ndue to pairing complex spikes with presynaptic spikes in\r\nparallel fibers and inhibitory interneurons. \r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/113426", + "ver_date": "2015-12-14T14:00:28.573", + "ver_number": 9 + }, + "113446": { + "citation": { + "attr_id": 391, + "value": "Computer simulations of neuron-glia interactions mediated by ion flux" + }, + "class_id": 19, + "created": "2008-04-28T15:44:47", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 88209, + "object_name": "Na/Ca exchanger" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 113446, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 120695, + "object_name": "Sodium pump" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 82503, + "object_name": "Somjen GG, Kager H, Wadman WJ (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 120694, + "object_name": "Electrogenic pump" + }, + { + "object_id": 120799, + "object_name": "Glia" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Computer simulations of neuron-glia interactions mediated by ion flux (Somjen et al. 2008)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 87589, + "object_name": "Astrocyte" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 236, + "object_name": "Ions" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"...\r\nTo examine the effect of glial K+ uptake, we used a model neuron equipped with Na+, K+, Ca2+ and Cl−\r\nconductances, ion pumps and ion exchangers, surrounded by interstitial space and glia. \r\nThe glial membrane was either \u201cpassive\u201d, incorporating only leak channels and an ion\r\nexchange pump, or it had rectifying K+ channels. We computed ion fluxes, concentration changes and osmotic\r\nvolume changes.\r\n...\r\nWe conclude that voltage gated K+ currents can boost the effectiveness of the glial \u201cpotassium buffer\u201d\r\nand that this buffer function is important even at moderate or low levels of excitation, but especially so in pathological states.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/113446", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181349, + "object_name": "113446" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 206 + }, + "ver_date": "2023-04-30T15:18:43.997", + "ver_number": 11 + }, + "113459": { + "citation": { + "attr_id": 391, + "value": "Kim, H. & Bao, S. (2007). Distributed representation of perceptual categories in the auditory cortex. J Comput Neurosci. DOI 10.1007/s10827-007-0055-5" + }, + "class_id": 19, + "created": "2008-04-29T19:46:06", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 113459, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 113460, + "object_name": "Bao, Shaowen [sbao at berkeley.edu]" + }, + { + "object_id": 113461, + "object_name": "Kim, Heesoo [heesoo at berkeley.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 113658, + "object_name": "Perceptual Categories" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 112952, + "object_name": "Kim H, Bao S (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Distributed representation of perceptual categories in the auditory cortex (Kim and Bao 2008)", + "notes": { + "attr_id": 24, + "value": "Examines the hypothesis that enlargement in cortical stimulus representation is a mechanism of categorical perception. 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Paper explores how distal gap junctions and active dendrites can tune network dynamics." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/114047", + "other_concept": { + "attr_id": 306, + "value": "Gap junctions" + }, + "other_neurons": { + "attr_id": 301, + "value": "CA1 basket cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "fernanda.saraga@gmail.com" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181667, + "object_name": "114047" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 259 + }, + "ver_date": "2014-02-14T17:06:12", + "ver_number": 10 + }, + "114108": { + "biosimulations": { + "value": "modeldb-114108" + }, + "citation": { + "attr_id": 391, + "value": "Wu SN, Chen BS, Lin MW, Liu YC. Contribution of slowly inactivating potassium current to delayed firing of action potentials in NG108-15 neuronal cells: Experimental and theoretical studies. J Theor Biol 2008;252:711-21" + }, + "class_id": 19, + "created": "2008-06-06T05:35:01", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "id": 114108, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + }, + { + "object_id": 114111, + "object_name": "Chen, Bing-Shuo" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 97752, + "object_name": "Delay" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 114110, + "object_name": "Wu SN, Chen BS, Lin MW, Liu YC (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Effect of slowly inactivating IKdr to delayed firing of action potentials (Wu et al. 2008)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88207, + "object_name": "Neuroblastoma" + }, + { + "object_id": 114112, + "object_name": "NG108-15 neuronal cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The properties of slowly inactivating delayed-rectifier K+ current (IKdr) were investigated in NG108-15 neuronal cells differentiated with long-term exposure to dibutyryl cyclic AMP. \r\n...\r\nThe computer model, in which state-dependent inactivation of IKdr was incorporated, was also implemented to predict the firing behavior present in NG108-15 cells. \r\n...\r\nOur theoretical work and the experimental results led us to propose a pivotal role of slowly inactivating IKdr in delayed firing of APs in NG108-15 cells. The results also suggest that aconitine modulation of IKdr gating is an important molecular mechanism through which it can contribute to neuronal firing.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/114108", + "other_implementer": { + "attr_id": 308, + "value": "Sheng-Nan Wu, Bing-Shuo Chen" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2008-06-06T10:12:48", + "ver_number": 8 + }, + "114230": { + "citation": { + "attr_id": 391, + "value": "Zheng M, Friesen WO, Iwasaki T (2007) Systems-level modeling of neuronal circuits for leech swimming.modeling of neuronal circuits for leech swimming.J Comput Neurosci 22:21-38" + }, + "class_id": 19, + "created": "2008-06-12T07:34:19", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 114230, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 93156, + "object_name": "Zheng M, Friesen WO, Iwasaki T (2007)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Systems-level modeling of neuronal circuits for leech swimming (Zheng et al. 2007)", + "notes": { + "attr_id": 24, + "value": "\"This paper describes a mathematical model of the neuronal central pattern generator (CPG) that controls the rhythmic body motion of the swimming leech. The systems approach is employed to capture the neuronal dynamics essential for generating coordinated oscillations of cell membrane potentials by a simple CPG architecture with a minimal number of parameters. \r\n... parameter estimation leads to predictions regarding the synaptic coupling strength and intrinsic period gradient along the nerve cord, the latter of which agrees qualitatively with experimental observations.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/114230", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115958, + "object_name": "Leech" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 114272, + "object_name": "Hirudinea (leech)" + } + ] + }, + "ver_date": "2019-02-26T10:21:46.24", + "ver_number": 9 + }, + "114242": { + "class_id": 19, + "created": "2008-06-17T12:24:19", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 114242, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83453, + "object_name": "Brette R" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 115423, + "object_name": "Touboul J, Brette R (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Brette-Gerstner model (Touboul and Brette 2008)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Brian code to simulate the Brette-Gerstner model and reproduce the figures of Touboul and Brette, Biol Cyber (2008)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/114242", + "public_submitter_email": { + "attr_id": 309, + "value": "brette@di.ens.fr" + }, + "ver_date": "2017-07-28T11:57:10.047", + "ver_number": 4 + }, + "114310": { + "citation": { + "attr_id": 391, + "value": "PMID: 18483611" + }, + "class_id": 19, + "created": "2008-06-25T10:50:05", + "gitrepo": false, + "id": 114310, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 114312, + "object_name": "Donohue, Duncan [duncan_donohue at hotmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 114311, + "object_name": "Donohue DE, Ascoli GA (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53895, + "object_name": "Java" + } + ] + }, + "name": "A comparative computer simulation of dendritic morphology (Donohue and Ascoli 2008)", + "notes": { + "attr_id": 24, + "value": "Morphological aspects of dendritic branching such branch lengths, taper rates,ratios of daughter radii, and bifurcation probabilities are measured from real cells. These morphometrics are then resampled to create virtual trees based on the current branch order, radius, path distance to the soma, or combination of the three." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/114310", + "other_implementer": { + "attr_id": 308, + "value": "Donohue, Duncan " + }, + "other_type": { + "attr_id": 305, + "value": "Morphological" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "duncan_donohue@hotmail.com" + }, + "ver_date": "2008-06-25T13:35:34", + "ver_number": 7 + }, + "114337": { + "class_id": 19, + "created": "2008-07-08T17:25:11", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92082, + "object_name": "Nav1.1 SCN1A" + }, + { + "object_id": 122809, + "object_name": "Cav3.2 CACNA1H" + }, + { + "object_id": 122810, + "object_name": "Cav3.1 CACNA1G" + }, + { + "object_id": 122811, + "object_name": "Cav3.3 CACNA1I" + } + ] + }, + "gitrepo": false, + "id": 114337, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 114338, + "object_name": "Xu J, Clancy CE (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Ionic mechanisms of bursting in CA3 pyramidal neurons (Xu and Clancy 2008)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... We present a single-compartment model of a CA3 hippocampal pyramidal neuron based on recent experimental data. We then use the model to determine the roles of primary depolarizing currents in burst generation.\r\nThe single compartment\r\nmodel incorporates accurate representations of sodium (Na+) channels (NaV1.1) and T-type calcium (Ca2+) channel subtypes\r\n(CaV3.1, CaV3.2, and CaV3.3).\r\nOur simulations predict the importance of Na+ and T-type Ca2+ channels in hippocampal\r\npyramidal cell bursting and reveal the distinct contribution of each subtype to burst morphology.\r\nWe also performed fastslow\r\nanalysis in a reduced comparable model, which shows that our model burst is generated as a result of the interaction\r\nof two slow variables, the T-type Ca2+ channel activation gate and the Ca2+-dependent potassium (K+) channel activation\r\ngate.\r\nThe model reproduces a range of experimentally observed phenomena including afterdepolarizing potentials, spike widening at the end of the burst, and rebound.\r\nFinally, we use the model to simulate the effects of two epilepsy-linked\r\nmutations: R1648H in NaV1.1 and C456S in CaV3.2, both of which result in increased cellular excitability.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/114337", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 203, + "object_name": "GabaB" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 65 + }, + "ver_date": "2009-07-13T11:31:33", + "ver_number": 4 + }, + "114342": { + "class_id": 19, + "created": "2008-07-09T16:35:18", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 114342, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 114341, + "object_name": "Crook SM, Dur-E-Ahmad M, Baer SM (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Activity dependent changes in dendritic spine density and spine structure (Crook et al. 2007)", + "notes": { + "attr_id": 24, + "value": "\"... In this work, we extend previous modeling studies [27] by combining a model for activity-dependent spine density with one for\r\ncalcium-mediated spine stem restructuring.\r\n... Additional equations characterize the change in spine density along\r\nthe dendrite, the current balance equation for an individual spine\r\nhead, the change in calcium concentration in the spine head, and the\r\ndynamics of spine stem resistance.\r\n\r\nWe use computational studies to investigate the changes in spine\r\ndensity and structure for differing synaptic inputs and demonstrate\r\nthe effects of these changes on the input-output properties of the\r\ndendritic branch.\r\n... \"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/114342", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 77 + }, + "ver_date": "2008-07-11T13:45:35", + "ver_number": 10 + }, + "114355": { + "class_id": 19, + "created": "2008-07-14T15:56:50", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 114355, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 84833, + "object_name": "Rabinowitch I, Segev I (2006)" + }, + { + "object_id": 114356, + "object_name": "Rabinowitch I, Segev I (2006)" + }, + { + "object_id": 114357, + "object_name": "Rabinowitch I, Segev I (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Homeostatic synaptic plasticity (Rabinowitch and Segev 2006a,b)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "(2006a): \"We investigated analytically and numerically the interplay between two opposing forms of synaptic plasticity: positive-feedback, long-term potentiation/depression (LTP/LTD), and negative-feedback, homeostatic synaptic plasticity (HSP). A detailed model of a CA1 pyramidal neuron, with numerous HSP-modifiable dendritic synapses, demonstrates that HSP may have an important role in selecting which spatial patterns of LTP/LTD are to last.\r\n...\r\nDespite the negative-feedback nature of HSP, under both local and global HSP, numerous synaptic \r\npotentiations/depressions can persist. These experimentally testable results imply that HSP could be significantly involved in shaping the spatial distribution of synaptic weights in the dendrites and not just normalizing it, as is currently believed.\"\r\n(2006b): \"Homeostatic synaptic plasticity (HSP) is an important mechanism attributed with the slow regulation of the neuron's activity. Whenever activity is chronically enhanced, HSP weakens the weights of the synapses in the dendrites and vice versa. Because dendritic morphology and its electrical properties partition the dendritic tree into functional compartments, we set out to explore the interplay between HSP and dendritic compartmentalization.\r\n...\r\nThe spatial distribution of synaptic weights throughout the dendrites will markedly differ under the local versus global HSP mechanisms. We suggest an experimental paradigm to unravel which type of HSP mechanism operates in the dendritic tree. The answer to this question will have important implications to our understanding of the functional organization of the neuron.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/114355", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "simPFid": { + "attr_id": 488, + "value": 85 + }, + "ver_date": "2013-12-10T15:24:50", + "ver_number": 17 + }, + "114359": { + "class_id": 19, + "created": "2008-07-16T05:20:17", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 114359, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 97998, + "object_name": "van Elburg, Ronald A.J. [R.van.Elburg at ai.rug.nl]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 127095, + "object_name": "van Elburg RA, van Ooyen A (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Impact of dendritic size and topology on pyramidal cell burst firing (van Elburg and van Ooyen 2010)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The code provided here was written to systematically investigate which of the\r\nphysical parameters controlled by dendritic morphology underlies the differences\r\nin spiking behaviour observed in different realizations of the\r\n'ping-pong'-model. Structurally varying dendritic topology and length in a\r\nsimplified model allows us to separate out the physical parameters derived from\r\nmorphology underlying burst firing.\r\nTo perform the parameter scans we created a new NEURON tool the\r\nMultipleRunControl which can be used to easily set up a parameter scan and write\r\nthe simulation results to file.\r\nUsing this code we found that not input conductance but the arrival time of the\r\nreturn current, as measured provisionally by the average electrotonic path\r\nlength, determines whether the pyramidal cell (with ping-pong model dynamics)\r\nwill burst or fire single spikes." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/114359", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2016-05-26T17:06:15.137", + "ver_number": 29 + }, + "114365": { + "class_id": 19, + "created": "2008-07-23T15:07:20", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "id": 114365, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 87578, + "object_name": "STDP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 114366, + "object_name": "Urakubo H, Honda M, Froemke RC, Kuroda S (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 18747, + "object_name": "GENESIS (web link to model)" + } + ] + }, + "name": "An allosteric kinetics of NMDARs in STDP (Urakubo et al. 2008)", + "notes": { + "attr_id": 24, + "value": "\"... We developed a detailed biophysical model of STDP and found\r\nthat the model required spike timing-dependent distinct suppression of NMDARs by Ca2+-calmodulin.\r\n\r\nThis led us to predict an allosteric\r\nkinetics of NMDARs: a slow and rapid suppression of NMDARs by Ca2+-calmodulin with prespiking -> postspiking and postspiking -> prespiking, respectively.\r\n\r\nWe found that the allosteric kinetics, but not the conventional kinetics, is consistent with specific features of\r\namplitudes and peak time of NMDAR-mediated EPSPs in experiments.\r\n...\" See paper for more and details." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/114365", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2015-07-26T13:45:01.107", + "ver_number": 5 + }, + "114394": { + "class_id": 19, + "created": "2008-07-28T08:01:21", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 226057, + "object_name": "HCN1" + }, + { + "object_id": 226058, + "object_name": "HCN2" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 114394, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 114397, + "object_name": "Kole, Maarten [m.kole at nin.knaw.nl]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 114396, + "object_name": "Kole MH, Ilschner SU, Kampa BM, Williams SR, Ruben PC, Stuart GJ (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "114394", + "name": "ModelView" + } + ], + "default": "114394" + }, + "name": "Na+ channel dependence of AP initiation in cortical pyramidal neuron (Kole et al. 2008)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In this simulation action potential initiation, action potential properties and the role of axon initial segment Na+ channels are investigated in a realistic model of a layer 5 pyramidal neuron axon initial segment. The main Na+ channel properties were constrained by experimental data and the axon initial segment was reconstructed. Model parameters were constrained by direct recordings at the axon initial segment." + }, + "opensourcebrain": { + "value": "rothmanetalkoleetalpyrcell" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/114394", + "other_implementer": { + "attr_id": 308, + "value": "Kole, Maarten " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "maarten.kole@anu.edu.au" + }, + "simPFid": { + "attr_id": 488, + "value": 293 + }, + "ver_date": "2017-03-18T10:03:31.647", + "ver_number": 13 + }, + "114424": { + "biosimulations": { + "value": "modeldb-114424" + }, + "citation": { + "attr_id": 391, + "value": "PMID 18080179" + }, + "class_id": 19, + "created": "2008-07-31T00:05:57", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "id": 114424, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 114386, + "object_name": "Huss M, Wang D, Tran\u00e9 C, Wikstr\u00f6m M, Hellgren Kotaleski J (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "A simplified model of NMDA oscillations in lamprey locomotor neurons (Huss et al. 2008)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 276, + "object_name": "Spinal cord lumbar motor neuron alpha ACh cell" + }, + { + "object_id": 114428, + "object_name": "Spinal lamprey neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Using experiments in conjunction with this simplified model, we sought to understand the basic mechanisms behind NMDA-induced oscillations in lamprey locomotor neurons, specifically (a) how the oscillation frequency depends on NMDA concentration and why, and (b) what the minimal number of components for generating NMDA oscillations is (in vitro and in the model)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/114424", + "other_implementer": { + "attr_id": 308, + "value": "Mikael Huss" + }, + "other_neurons": { + "attr_id": 301, + "value": "Spinal lamprey neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "mikael.huss@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2015-07-26T13:46:05.03", + "ver_number": 9 + }, + "114450": { + "biosimulations": { + "value": "modeldb-114450" + }, + "citation": { + "attr_id": 391, + "value": "Huang et al., The synergistic inhibitory actions of oxcarbazepine on voltage-gated sodium and potassium currents in differentiated NG108-15 neuronal cells and model neurons. Int J Neuropsychopharmacol 2008;11:597-610." + }, + "class_id": 19, + "created": "2008-08-05T01:49:11", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "id": 114450, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 114454, + "object_name": "Huang CW, Huang CC, Lin MW, Tsai JJ, Wu SN (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Synergistic inhibitory action of oxcarbazepine on INa and IK (Huang et al. 2008)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114112, + "object_name": "NG108-15 neuronal cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Oxcarbazepine (OXC), one of the newer anti-epileptic drugs, has been demonstrating its efficacy on wide-spectrum neuropsychiatric disorders. \r\n... With the aid of patch-clamp technology, we first investigated the effects of OXC on ion currents in NG108-15 neuronal cells differentiated with cyclic AMP. \r\n\r\nWe found OXC ... caused a reversible reduction in the amplitude of voltage-gated Na+ current (INa) ...\r\nand produce(d) a significant prolongation in the recovery of INa inactivation. \r\n\r\n...\r\nMoreover, OXC could suppress the amplitude of delayed rectifier K+ current (IK(DR)), with no effect on M-type K+ current (IK(M)). \r\n\r\n...\r\nFurthermore, the simulations, based on hippocampal pyramidal neurons (Pinsky-Rinzel model) and a network of the Hodgkin-Huxley model, were analysed to investigate the effect of OXC on action potentials. \r\n\r\nTaken together, our results suggest that the synergistic blocking effects on INa and IK(DR) may contribute to the underlying mechanisms through which OXC affects neuronal function in vivo.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/114450", + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2008-08-05T11:52:06", + "ver_number": 4 + }, + "114451": { + "citation": { + "attr_id": 391, + "value": "Yen-Ching Liu et al., The mechanisms of propofol-induced block on ion currents in differentiated H9c2 cardiac cells. Eur. J. Pharmacol. 2008;590:93-98." + }, + "class_id": 19, + "created": "2008-08-05T04:08:43", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 88215, + "object_name": "I_Ks" + } + ] + }, + "gitrepo": false, + "id": 114451, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 52407, + "object_name": "Heart disease" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 114456, + "object_name": "Liu YC, Wang YJ, Wu SN (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Efffect of propofol on potassium current in cardiac H9c2 cells (Liu et al. 2008)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52423, + "object_name": "Heart cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"...\r\nThe effects \r\nof propofol, an intravenous anesthetic agent with a distinct chemical structure, \r\non ion currents of differentiated clonal cardiac (H9c2) cells were investigated \r\nin this study. \r\n\r\nPropofol ... suppressed the amplitude of delayed \r\nrectifier K(+) current (I(K(DR))) in a concentration-dependent manner with \r\nan IC(50) value of 36 muM. \r\n...\r\nPropofol (30 muM) had no effect on erg-mediated K(+) \r\ncurrent in these cells; however, it suppressed L-type Ca(2+) current (I(Ca,L)) \r\nof cardiac and skeletal types to a similar extent. \r\n...\r\nNumerical \r\nsimulations of I(K(DR)) based on a Markovian model reproduce the \r\nexperimental results and show that propofol-induced blockade of I(K(DR)) \r\nis associated with an decrease in forward rate of the activation process and \r\nan increase in transitional rate into the inactivated state. \r\n\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/114451", + "other_implementer": { + "attr_id": 308, + "value": "Yen-Ching Liu" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2015-07-26T13:47:25.767", + "ver_number": 5 + }, + "114452": { + "biosimulations": { + "value": "modeldb-114452" + }, + "citation": { + "attr_id": 391, + "value": "18052535" + }, + "class_id": 19, + "created": "2008-08-05T08:48:12", + "gitrepo": false, + "id": 114452, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 114553, + "object_name": "Graupner, Michael [michael.graupner at ens.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 114458, + "object_name": "Graupner M, Brunel N (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "CaMKII system exhibiting bistability with respect to calcium (Graupner and Brunel 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... We present a detailed biochemical model of the CaMKII autophosphorylation and the protein signaling cascade governing the CaMKII dephosphorylation.\r\n... it is shown that the CaMKII system can qualitatively reproduce results of plasticity outcomes in response to spike-timing dependent plasticity (STDP) and presynaptic stimulation protocols.\r\nThis shows that the CaMKII protein network can account for both induction, through LTP/LTD-like transitions, and storage, due to its bistability, of synaptic changes.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/114452", + "other_implementer": { + "attr_id": 308, + "value": "Graupner, Michael" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "michael.graupner@ens.fr" + }, + "ver_date": "2008-08-21T10:04:03", + "ver_number": 14 + }, + "114637": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 114652, + "object_name": "2nd attempt to make a backup" + } + ] + }, + "class_id": 19, + "created": "2008-08-14T22:50:03", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 114637, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3542, + "object_name": "Pattern Recognition" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 114634, + "object_name": "Banitt Y, Martin KA, Segev I (2007)" + }, + { + "object_id": 114962, + "object_name": "Anderson JC, Douglas RJ, Martin KA, Nelson JC (1994)" + }, + { + "object_id": 114963, + "object_name": "Anderson JC, Douglas RJ, Martin KA, Nelson JC (1994)" + }, + { + "object_id": 121120, + "object_name": "Banitt Y, Martin KA, Segev I (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "114637", + "name": "ModelView" + } + ], + "default": "114637" + }, + "name": "Contrast invariance by LGN synaptic depression (Banitt et al. 2007)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114635, + "object_name": "Neocortex spiny stellate cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Simple cells in layer 4 of the primary visual cortex of the cat show contrast-invariant orientation tuning, in which the amplitude of the\r\npeak response is proportional to the stimulus contrast but the width of the tuning curve hardly changes with contrast. \r\n\r\nThis study uses a\r\ndetailed model of spiny stellate cells (SSCs) from cat area 17 to explain this property. \r\n\r\nThe model integrates our experimental data,\r\nincluding morphological and intrinsic membrane properties and the number and spatial distribution of four major synaptic input\r\nsources of the SSC: the dorsal lateral geniculate nucleus (dLGN) and three cortical sources. \r\n\r\n...\r\n\r\nThe model response is in close\r\nagreement with experimental results, in terms of both output spikes and membrane voltage (amplitude and fluctuations), with reasonable\r\nexceptions given that recurrent connections were 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millisecond precision, as in synfire braids. \r\n\r\nThe network consists\r\nof cortical spiking neurons with axonal conduction delays and spiketiming-\r\ndependent plasticity (STDP); a ready-to-use MATLAB code is\r\nincluded. \r\n\r\nIt exhibits sleeplike oscillations, gamma (40 Hz) rhythms,\r\nconversion of firing rates to spike timings, and other interesting regimes.\r\n\r\n...\r\nTo our surprise, the number of\r\ncoexisting polychronous groups far exceeds the number of neurons in\r\nthe network, resulting in an unprecedented memory capacity of the\r\nsystem. \r\n\r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/115968", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2014-08-29T08:48:53", + "ver_number": 9 + }, + "116053": { + "alternative_version": { + "attr_id": 314, 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A, Kath WL, Spruston N (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "CA1 pyramidal neuron synaptic integration (Jarsky et al. 2005)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The perforant-path projection to the hippocampus forms synapses in the apical tuft of CA1 pyramidal neurons. \r\n\r\nWe used computer modeling to examine the function of these distal synaptic inputs, which led to three predictions that we confirmed in experiments using rat hippocampal slices. \r\n\r\n...\r\n\r\nThis 'gating' of dendritic spike propagation may be an important activation mode of CA1 pyramidal neurons, and its modulation by neurotransmitters or long-term, activity-dependent plasticity may be an important feature of dendritic integration during mnemonic processing in the hippocampus.\"\r\n" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116084", + "ver_date": "2019-02-04T16:44:43.723", + "ver_number": 11 + }, + "116086": { + "class_id": 19, + "created": "2008-10-26T14:09:55", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 116086, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33744, + "object_name": "Destexhe, Alain [Destexhe at iaf.cnrs-gif.fr]" + }, + { + "object_id": 116941, + "object_name": "Pospischil, Martin " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 93420, + "object_name": "Sleep" + 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according to the method published by Pospischil et al., J Neurophysiol, 2007. The method consists of a maximum likelihood estimate of the conductance STA, from the voltage STA (which is calculated from the data). The method was tested using models and dynamic-clamp experiments; for details, see the original publication (Pospischil et al., 2007). The first application of this method to experimental data was from intracellular recordings in awake cat cerebral cortex (Rudolph et al., 2007)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116086", + "ver_date": "2016-09-16T16:24:09.297", + "ver_number": 11 + }, + "116094": { + "class_id": 19, + "created": "2008-10-28T17:23:46", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 116094, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 105523, + "object_name": "David F, Linster C, Cleland TA (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "116094_1", + "name": "Long Dendrite - Fig1bde" + }, + { + "id": "116094_2", + "name": "Long Dendrite - Fig1fg" + }, + { + "id": "116094_3", + "name": "Long Dendrite - Fig2ace" + }, + { + "id": "116094_4", + "name": "Short Dendrite - Fig1cde" + }, + { + "id": "116094_5", + "name": "Short Dendrite - Fig1fg" + }, + { + "id": "116094_6", + "name": "Short Dendrite - Fig2bf" + }, + { + "id": "116094_7", + "name": "Short Dendrite - Fig3" + }, + { + "id": "116094_8", + "name": "Short Dendrite - Fig4abcd" + }, + { + "id": "116094_9", + "name": "Short Dendrite - Fig4ef" + }, + { + "id": "116094_10", + "name": "Short Dendrite - Fig5" + }, + { + "id": "116094_11", + "name": "Short Dendrite - Fig6" + } + ], + "default": "116094_1" + }, + "name": "Lateral dendrodenditic inhibition in the Olfactory Bulb (David et al. 2008)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + }, + { + "object_id": 3979, + "object_name": "Olfactory bulb main interneuron granule MC GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Mitral cells, the principal output neurons of the olfactory bulb, receive direct synaptic activation from primary sensory neurons. Shunting inhibitory inputs delivered by granule cell interneurons onto mitral cell lateral dendrites are believed to influence spike timing and underlie coordinated field potential oscillations. Lateral dendritic shunt conductances delayed spiking to a degree dependent on both their electrotonic distance and phase of onset. Recurrent inhibition significantly narrowed the distribution of mitral cell spike times, illustrating a tendency towards coordinated synchronous activity. This result suggests an essential role for early mechanisms of temporal coordination in olfaction. The model was adapted from Davison et al, 2003, but include additional noise mechanisms, long lateral dendrite, and specific synaptic point processes." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116094", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181168, + "object_name": "116094_10" + }, + { + "object_id": 181169, + "object_name": "116094_11" + }, + { + "object_id": 181250, + "object_name": "116094_9" + }, + { + "object_id": 181252, + "object_name": "116094_7" + }, + { + "object_id": 181262, + "object_name": "116094_3" + }, + { + "object_id": 181560, + 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+ "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 87578, + "object_name": "STDP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 72076, + "object_name": "Badoual M, Zou Q, Davison AP, Rudolph M, Bal T, Fr\u00e9gnac Y, Destexhe A (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Biophysical and phenomenological models of spike-timing dependent plasticity (Badoual et al. 2006)", + "notes": { + "attr_id": 24, + "value": "\"Spike-timing dependent plasticity (STDP) is a form of associative synaptic modification which depends\r\non the respective timing of pre- and post-synaptic spikes. \r\n\r\nThe biophysical mechanisms underlying this\r\nform of plasticity are currently not known. \r\n\r\nWe present here a biophysical model which captures the\r\ncharacteristics of STDP, such as its frequency dependency, and the effects of spike pair or spike triplet\r\ninteractions. \r\n...\r\nA simplified phenomenological\r\nmodel is also derived...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116096", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181204, + "object_name": "116096_1" + }, + { + "object_id": 181543, + "object_name": "116096_2" + }, + { + "object_id": 181544, + "object_name": "116096_3" + }, + { + "object_id": 181545, + "object_name": "116096_4" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 159 + }, + "ver_date": "2015-01-02T21:48:02", + "ver_number": 11 + }, + "116123": { + "biosimulations": { + "value": "modeldb-116123" + }, + "class_id": 19, + "created": "2008-11-03T15:37:12", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 116123, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53570, + "object_name": "Prescott, Steven [steve.prescott at sickkids.ca]]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + }, + { + "object_id": 182362, + "object_name": "Sensory coding" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116122, + "object_name": "Prescott SA, De Koninck Y, Sejnowski TJ (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Dynamics of Spike Initiation (Prescott et al. 2008)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 150222, + "object_name": "Abstract Morris-Lecar neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Transduction of graded synaptic input into trains of all-or-none action potentials (spikes) is a crucial step in neural coding.\r\n\r\nHodgkin identified three classes of neurons with qualitatively different analog-to-digital transduction properties. \r\n\r\nDespite widespread use of this classification scheme, a generalizable explanation of its biophysical basis has not been described. \r\n\r\nWe recorded from spinal sensory neurons representing each class and reproduced their transduction properties in a minimal\r\nmodel. With phase plane and bifurcation analysis, each class of excitability was shown to derive from distinct spike initiating dynamics. \r\n\r\nExcitability could be converted between all three classes by varying single parameters; moreover, several\r\nparameters, when varied one at a time, had functionally equivalent effects on excitability. From this, we conclude that the\r\nspike-initiating dynamics associated with each of Hodgkin\u2019s classes represent different outcomes in a nonlinear competition\r\nbetween oppositely directed, kinetically mismatched currents. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116123", + "ver_date": "2015-05-11T10:54:34.893", + "ver_number": 11 + }, + "116312": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 116832, + "object_name": "local backup of web linked model" + } + ] + }, + "class_id": 19, + "created": "2008-11-13T10:57:44", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 116312, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116244, + "object_name": "Balkenius A, Kelber A, Balkenius C (2008)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Multimodal stimuli learning in hawkmoths (Balkenius et al. 2008)", + "notes": { + "attr_id": 24, + "value": "The moth Macroglossum stellatarum can learn the color and sometimes the odor of a rewarding food\r\nsource.\r\n\r\nWe present data from 20 different experiments with different combinations of blue and yellow\r\nartificial flowers and the two odors, honeysuckle and lavender. \r\n\r\n... \r\nThree computational models were tested in the same experimental situations as the real\r\nmoths and their predictions were compared with the experimental data. \r\n\r\n...\r\nNeither the\r\nRescorla\u2013Wagner model nor a learning model with independent learning for each stimulus component\r\nwere able to explain the experimental data. \r\n\r\nWe present the new hawkmoth learning model, which\r\nassumes that the moth learns a template for the sensory attributes of the rewarding stimulus. \r\n\r\nThis\r\nmodel produces behavior that closely matches that of the real moth in all 20 experiments." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116312", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 116370, + "object_name": "Hawkmoth Deilephila elpenor" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 249923, + "object_name": "Hawkmoth Deilephila elpenor" + } + ] + }, + "ver_date": "2019-02-26T10:18:05.67", + "ver_number": 6 + }, + "116313": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 116368, + "object_name": "backup of web link" + } + ] + }, + "class_id": 19, + "created": "2008-11-13T11:06:21", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 116313, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83535, + "object_name": "Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116262, + "object_name": "Balkenius A, Kelber A, Balkenius C (2004)" + }, + { + "object_id": 116369, + "object_name": "Balkenius A, Kelber A, Balkenius C (2002)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "A Model of Selection between Stimulus and Place Strategy in a Hawkmoth (Balkenius et al. 2004)", + "notes": { + "attr_id": 24, + "value": "\"In behavioral experiments, the hawkmoth Deilephila elpenor can learn both the \r\ncolor and the position of artificial flowers.\r\n\r\n...\r\n\r\nWe show how a computational model can reproduce the behavior in the experimental situation.\r\n\r\nThe aim of the model is to investigate which learning and behavior selection strategies are \r\nnecessary to reproduce the behavior observed in the experiment.\r\n\r\nThe model is based on behavioral data and the sensitivities of the moth photoreceptors. \r\n\r\nThe model consists of a number of interacting behavior systems that are triggered by \r\nspecific stimuli and control specific behaviors. \r\n\r\nThe ability of the moth to learn the colors of different flowers and the adaptive processes involved \r\nin the choice between stimulus-approach and place-approach strategies are reproduced very accurately by the model. \r\nThe model has implications both for further studies of the ecology of the animal and for robotic systems.\"\r\n" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116313", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 116370, + "object_name": "Hawkmoth Deilephila elpenor" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 72 + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 249923, + "object_name": "Hawkmoth Deilephila elpenor" + } + ] + }, + "ver_date": "2019-02-26T10:18:23.933", + "ver_number": 11 + }, + "116386": { + "biosimulations": { + "value": "modeldb-116386" + }, + "class_id": 19, + "created": "2008-11-14T12:24:31", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 116386, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53570, + "object_name": "Prescott, Steven [steve.prescott at sickkids.ca]]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ 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wakefulness, pyramidal neurons in the intact brain are bombarded by synaptic\r\ninput that causes tonic depolarization, increased membrane conductance (i.e. shunting), and\r\nnoisy fluctuations in membrane potential; by comparison, pyramidal neurons in acute slices\r\ntypically experience little background input.\r\n\r\nSuch differences in operating conditions can compromise extrapolation of in vitro data to explain \r\nneuronal operation in vivo.\r\n\r\n...\r\n\r\n in slice experiments, we show that\r\nCA1 hippocampal pyramidal cells switch from integrators to resonators, i.e. from class 1 to class\r\n2 excitability.\r\n\r\nThe switch is explained by increased outward current contributed by the M-type\r\npotassium current IM\r\n...\r\nThus, even so-called \u201cintrinsic\u201d properties may differ qualitatively between in vitro and in vivo conditions." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116386", + "ver_date": "2013-06-05T11:40:24", + "ver_number": 10 + }, + "116491": { + "class_id": 19, + "created": "2008-11-26T13:03:16", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 116491, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33736, + "object_name": "Hines, Michael [Michael.Hines at Yale.edu]" + }, + { + "object_id": 33739, + "object_name": "Davison, Andrew [Andrew.Davison at iaf.cnrs-gif.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116492, + "object_name": "Hines ML, Davison AP, Muller E (2009)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 154351, + "object_name": "NeuroML" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "NEURON + Python (Hines et al. 2009)", + "notes": { + "attr_id": 24, + "value": "The NEURON simulation program now allows Python to be used alone or in\r\ncombination with NEURON's traditional Hoc interpreter. Adding Python to\r\nNEURON has the immediate benefit of making available a very extensive\r\nsuite of analysis tools written for engineering and science. It also\r\ncatalyzes NEURON software development by offering users a modern programming\r\ntool that is recognized for its flexibility and power to create and \r\nmaintain complex programs. At the same time, nothing is lost because\r\nall existing models written in Hoc, including GUI tools, continue to\r\nwork without change and are also available within the Python context.\r\nAn example of the benefits of Python availability is the use of the xml\r\nmodule in implementing NEURON's Import3D and CellBuild tools to read MorphML and\r\nNeuroML model specifications." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116491", + "ver_date": "2016-09-16T16:28:05.023", + "ver_number": 10 + }, + "116567": { + "class_id": 19, + "created": "2008-12-01T17:52:03", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 116567, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116495, + "object_name": "Haj\u00f3s M, Hoffmann WE, Orb\u00e1n G, Kiss T, Erdi P (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "Modulation of septo-hippocampal theta activity by GABAA receptors (Hajos et al. 2004)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 35373, + "object_name": "Hippocampus CA1 interneuron oriens alveus GABA cell" + }, + { + "object_id": 114079, + "object_name": "Hippocampus CA1 basket cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Theta frequency oscillation of the septo-hippocampal system has been considered as a prominent activity associated with cognitive function and affective processes.\r\n\r\n...\r\n\r\nIn the present experiments we applied a combination of computational and physiological techniques to explore the functional role of GABAA receptors in theta oscillation.\r\n\r\n...\r\n\r\nIn parallel to these experimental observations, a computational model has been constructed by implementing a septal GABA neuron model with a CA1 hippocampal model containing three types of neurons (including oriens and basket interneurons and pyramidal cells; latter modeled by multicompartmental techniques; for detailed model description with network parameters see online addendum: http://geza.kzoo.edu/theta).\r\n\r\nThis connectivity made the network capable of simulating the responses of the septo-hippocampal circuitry to the modulation of GABAA transmission, and the presently described computational model proved suitable to reveal several aspects of pharmacological modulation of GABAA receptors.\r\n\r\n In addition, computational findings indicated different roles of distinctively located GABAA receptors in theta generation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116567", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2017-06-29T14:38:35.78", + "ver_number": 11 + }, + "116575": { + "citation": { + "attr_id": 391, + "value": "Crisp KM (2009) Multiple spike initiation zones in a neuron implicated in learning in the leech: a computational model. Invert Neurosci, in press" + }, + "class_id": 19, + "created": "2008-12-04T17:08:48", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "id": 116575, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 102284, + "object_name": "Crisp, Kevin [crisp at stolaf.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116578, + "object_name": "Crisp KM (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "A Model of Multiple Spike Initiation Zones in the Leech C-interneuron (Crisp 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 116579, + "object_name": "Leech C interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The leech C-interneuron and its electrical synapse with the S-interneuron exhibit unusual properties: an asymmetric delay when impulses travel from one soma to the other, and graded C-interneuron impulse amplitudes under elevated divalent cation concentrations. These properties have been simulated using a SNNAP model in which the C-interneuron has multiple, independent spike initiation zones associated with individual electrical junctions with the C-interneuron." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116575", + "other_concept": { + "attr_id": 306, + "value": "Electrical Synapse" + }, + "other_neurons": { + "attr_id": 301, + "value": "Leech C-interneuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "crisp@stolaf.edu" + }, + "ver_date": "2008-12-08T11:29:47", + "ver_number": 9 + }, + "116606": { + "citation": { + "attr_id": 391, + "value": "J Neurophysiol 99:814-30" + }, + "class_id": 19, + "created": "2008-12-07T22:23:14", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 116606, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116583, + "object_name": "Hurwitz I, Ophir A, Korngreen A, Koester J, Susswein AJ (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "Currents contributing to decision making in neurons B31-B32 of Aplysia (Hurwitz et al. 2008)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 116700, + "object_name": "Aplysia B31/B32 neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Biophysical properties of neurons contributing to the ability of an animal to decide whether or not to respond were examined. \r\n\r\nB31/B32, two pairs of bilaterally symmetrical Aplysia neurons, are major participants in deciding to initiate a buccal motor program, the neural correlate of a consummatory feeding response. \r\n\r\nB31/B32 respond to an adequate stimulus after a delay, during which time additional stimuli influence the decision to respond. \r\n\r\nB31/B32 then respond with a ramp depolarization followed by a sustained soma depolarization and axon spiking that is the expression of a commitment to respond to food. \r\n\r\nFour currents contributing to decision making in B31/B32 were characterized, and their functional effects were determined, in current- and voltage-clamp experiments and with simulations. ...\r\n\r\nHodgkin-Huxley kinetic analyses were performed on the outward currents. \r\n\r\nSimulations using equations from these analyses showed that IK-V and IK-A slow the ramp depolarization preceding the sustained depolarization. \r\n\r\nThe three outward currents contribute to braking the B31/B32 depolarization and keeping the sustained depolarization at a constant voltage. \r\n\r\nThe currents identified are sufficient to explain the properties of B31/B32 that play a role in generating the decision to feed.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116606", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115956, + "object_name": "Aplysia" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 249921, + "object_name": "Aplysia" + } + ] + }, + "ver_date": "2019-02-26T10:08:25.35", + "ver_number": 11 + }, + "116740": { + "class_id": 19, + "created": "2008-12-10T18:05:09", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high 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"object_name": "Aradi I, Holmes WR (1999)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Dentate gyrus granule cell: calcium and calcium-dependent conductances (Aradi and Holmes 1999)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We have constructed a detailed model of a hippocampal dentate granule (DG) cell that includes nine different channel types. \r\nChannel densities and distributions were chosen to reproduce reported physiological responses observed in normal solution and when blockers were applied. \r\n\r\nThe model was used to explore the contribution of each channel type to spiking behavior with particular emphasis on the mechanisms underlying postspike events. \r\n\r\n...\r\nThe model was used to predict changes in channel densities that could lead to epileptogenic burst discharges and to predict the effect of altered buffering capacity on firing behavior. \r\n\r\nWe conclude that the clustered spatial distributions of calcium related channels, the presence of slow delayed rectifier potassium currents in dendrites, and calcium buffering properties, together, might explain the resistance of DG cells to the development of epileptogenic burst discharges." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116740", + "other_implementer": { + "attr_id": 308, + "value": "Nakhoul, Hani" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115947, + "object_name": "Dentate gyrus" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181479, + "object_name": "116740_1" + }, + { + "object_id": 181711, 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"object_id": 116770, + "object_name": "Grunditz A, Holbro N, Tian L, Zuo Y, Oertner TG (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "116769", + "name": "ModelView" + } + ], + "default": "116769" + }, + "name": "Spine neck plasticity controls postsynaptic calcium signals (Grunditz et al. 2008)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model was set up to dissect the relative contribution of different channels to \r\nthe spine calcium transients measured at single spines.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116769", + "other_receptors": { + "attr_id": 302, + "value": "R-type VGCC" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "asa.mueller@fmi.ch" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181300, + "object_name": "116769" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 494 + }, + "ver_date": "2017-06-21T14:35:29.77", + "ver_number": 13 + }, + "116806": { + "citation": { + "attr_id": 391, + "value": "Tadashi Yamazaki and Shigeru Tanaka, Robust Reservoir Generation by Correlation-Based Learning, Advances in Artificial Neural Systems, vol. 2009, Article ID 467128, 7 pages, 2009. doi:10.1155/2009/467128 http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2009/467128" + }, + "class_id": 19, + "created": "2008-12-17T02:10:40", + "gitrepo": false, + "id": 116806, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + }, + { + "object_id": 83535, + "object_name": "Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116808, + "object_name": "Yamazaki T, Tanaka S (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Robust Reservoir Generation by Correlation-Based Learning (Yamazaki & Tanaka 2008)", + "notes": { + "attr_id": 24, + "value": "\"Reservoir computing (RC) is a new framework for neural computation. \r\nA reservoir is usually a recurrent neural network with fixed random connections. \r\n\r\nIn this article, we propose an RC model in which the connections in the reservoir are modifiable. \r\n\r\n...\r\nWe apply our RC model to trace eyeblink conditioning. \r\n\r\nThe reservoir bridged the gap of an interstimulus interval between the conditioned and unconditioned stimuli, and a readout neuron was able to learn and express the timed conditioned response.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116806", + "public_submitter_email": { + "attr_id": 309, + "value": "tyam@brain.riken.jp" + }, + "simPFid": { + "attr_id": 488, + "value": 73 + }, + "ver_date": "2011-05-24T13:36:01", + "ver_number": 4 + }, + "116830": { + "class_id": 19, + "created": "2008-12-23T17:37:05", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 116830, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33738, + "object_name": "Lytton, William [bill.lytton at downstate.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116827, + "object_name": "Lytton WW, Orman R, Stewart M (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Broadening of activity with flow across neural structures (Lytton et al. 2008)", + "notes": { + "attr_id": 24, + "value": "\"Synfire chains have long been suggested as a substrate for perception and information processing in the nervous system. \r\n\r\nHowever, embedding activation chains in a densely connected nervous matrix risks spread of signal that will obscure or obliterate the message. \r\n\r\nWe used computer modeling and physiological measurements in rat hippocampus to assess this problem of activity broadening.\r\n\r\nWe simulated a series of neural modules with feedforward propagation and random connectivity within each module and from one module to the next. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116830", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@YALE.EDU" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181440, + "object_name": "116830" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 160 + }, + "ver_date": "2022-05-27T10:12:31.607", + "ver_number": 9 + }, + "116835": { + "class_id": 19, + "created": "2009-01-07T10:52:15", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 116835, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 116853, + "object_name": "Diwakar, Shyam [shyam at amrita.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 41934, + "object_name": "Intrinsic plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116743, + "object_name": "Diwakar S, Magistretti J, Goldfarb M, Naldi G, D'Angelo E (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "116835", + "name": "ModelView" + } + ], + "default": "116835" + }, + "name": "Multicompartmental cerebellar granule cell model (Diwakar et al. 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 7346, + "object_name": "Cerebellum interneuron granule GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A detailed multicompartmental model was used to study neuronal electroresponsiveness of cerebellar granule cells in rats. Here we show that, in cerebellar granule cells, Na+ channels are enriched in the axon, especially in the hillock, but almost absent from soma and dendrites. Numerical simulations indicated that granule cells have a compact electrotonic structure allowing EPSPs to diffuse with little attenuation from dendrites to axon. The spike arose almost simultaneously along the whole axonal ascending branch and invaded the hillock, whose activation promoted spike back-propagation with marginal delay (<200 micros) and attenuation (<20 mV) into the somato-dendritic compartment. For details check the cited article." + }, + "opensourcebrain": { + "value": "multicompgrc" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116835", + "other_implementer": { + "attr_id": 308, + "value": "Diwakar, Shyam \r\nshyam@unipv.it" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181439, + "object_name": "116835" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 287 + }, + "ver_date": "2022-11-04T12:59:59.36", + "ver_number": 14 + }, + "116837": { + "class_id": 19, + "created": "2009-01-07T11:32:27", + "gitrepo": false, + "id": 116837, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3542, + "object_name": "Pattern Recognition" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + }, + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 116857, + "object_name": "Biofeedback" + }, + { + "object_id": 116858, + "object_name": "Reward-modulated STDP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116227, + "object_name": "Legenstein R, Pecevski D, Maass W (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 116856, + "object_name": "PCSIM" + } + ] + }, + "name": "Reward modulated STDP (Legenstein et al. 2008)", + "notes": { + "attr_id": 24, + "value": "\"...\r\nThis article provides tools for an analytic treatment of reward-modulated\r\nSTDP, which allows us to predict under which conditions reward-modulated STDP will achieve a desired learning\r\neffect.\r\n\r\nThese analytical results imply that neurons can learn through reward-modulated STDP to classify not only spatial but\r\nalso temporal firing patterns of presynaptic neurons. \r\n\r\nThey also can learn to respond to specific presynaptic firing patterns\r\nwith particular spike patterns. \r\n\r\nFinally, the resulting learning theory predicts that even difficult credit-assignment problems,\r\nwhere it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system,\r\ncan be solved in a self-organizing manner through reward-modulated STDP. \r\n\r\nThis yields an explanation for a fundamental\r\nexperimental result on biofeedback in monkeys by Fetz and Baker. \r\n\r\nIn this experiment monkeys were rewarded for\r\nincreasing the firing rate of a particular neuron in the cortex and were able to solve this extremely difficult credit assignment\r\nproblem. \r\n...\r\nIn addition our model\r\ndemonstrates that reward-modulated STDP can be applied to all synapses in a large recurrent neural network without\r\nendangering the stability of the network dynamics.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116837", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@YALE.EDU" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2009-02-16T14:02:18", + "ver_number": 8 + }, + "116838": { + "class_id": 19, + "created": "2009-01-07T13:34:08", + "gitrepo": true, + "id": 116838, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33738, + "object_name": "Lytton, William [bill.lytton at downstate.edu]" + }, + { + "object_id": 97869, + "object_name": "Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116840, + "object_name": "Lytton WW, Neymotin SA, Hines ML (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "The virtual slice setup (Lytton et al. 2008)", + "notes": { + "attr_id": 24, + "value": "\"In an effort to design a simulation environment that is more similar to that of neurophysiology, we introduce a virtual slice setup in the NEURON simulator. \r\n\r\nThe virtual slice setup runs continuously and permits parameter changes, including changes to synaptic weights and time course and to intrinsic cell properties. \r\n\r\nThe virtual slice setup permits shocks to be applied at chosen locations and activity to be sampled intra- or extracellularly from chosen locations. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116838", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@YALE.EDU" + }, + "ver_date": "2023-04-30T15:20:30.08", + "ver_number": 12 + }, + "116862": { + "class_id": 19, + "created": "2009-01-13T16:08:50", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 255, + "object_name": "I CAN" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 116862, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 116863, + "object_name": "Zhu, J. Julius [jjzhu at virginia.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 4092, + "object_name": "Zhu JJ, Uhlrich DJ, Lytton WW (1999)" + }, + { + "object_id": 10341, + "object_name": "Zhu JJ, Lytton WW, Xue JT, Uhlrich DJ (1999)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Thalamic interneuron multicompartment model (Zhu et al. 1999)", + "notes": { + "attr_id": 24, + "value": "This is an attempt to recreate a set of simulations originally performed in 1994 under NEURON version 3 and last tested in 1999. When I ran it now it did not behave exactly the same as previously which I suspect is due to some minor mod file changes on my side rather than due to any differences among versions. \r\n\r\nAfter playing around with the parameters a little bit I was able to get something that looks generally like a physiological trace in \r\nJ Neurophysiol, 81:702--711, 1999, fig. 8b top trace. \r\n\r\nThis sad preface is simply offered in order to encourage anyone who is interested in this model to make and post fixes. I'm happy to help out.\r\n\r\nSimulation by JJ Zhu\r\n\r\nTo run\r\nnrnivmodl\r\nnrngui.hoc\r\n\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116862", + "other_currents": { + "attr_id": 303, + "value": "IH" + }, + "other_implementer": { + "attr_id": 308, + "value": "JJ Zhu" + }, + "other_neurons": { + "attr_id": 301, + "value": "thalamic interneuron" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2022-05-27T10:19:11.143", + "ver_number": 13 + }, + "116867": { + "biosimulations": { + "value": "modeldb-116867" + }, + "class_id": 19, + "created": "2009-01-16T11:49:13", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "id": 116867, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 49493, + "object_name": "Rubin JE, Terman D (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "High frequency stimulation of the Subthalamic Nucleus (Rubin and Terman 2004)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + }, + { + "object_id": 74300, + "object_name": "Subthalamus nucleus projection neuron" + }, + { + "object_id": 88217, + "object_name": "Globus pallidus neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Using a computational model, this paper considers the hypothesis that DBS works by replacing pathologically rhythmic\r\nbasal ganglia output with tonic, high frequency firing.\r\n\r\nIn our simulations of parkinsonian conditions, rhythmic inhibition from GPi to the thalamus compromises the ability of thalamocortical relay (TC) cells to respond to depolarizing inputs, such as sensorimotor signals.\r\n\r\nHigh frequency stimulation of STN regularizes GPi firing, and this restores TC responsiveness, despite the increased frequency and amplitude of GPi inhibition to thalamus that result.\r\n\r\nWe provide a mathematical phase plane analysis of the mechanisms that determine TC relay capabilities in\r\nnormal, parkinsonian, and DBS states in a reduced model.\r\n\r\nThis analysis highlights the differences in deinactivation of the low-threshold calcium T -current that we observe in TC cells in these different conditions. ...\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116867", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + }, + { + "object_id": 115952, + "object_name": "Basal ganglia" + }, + { + "object_id": 116868, + "object_name": "Subthalamic Nucleus" + } + ] + }, + "ver_date": "2009-01-20T15:26:00", + "ver_number": 11 + }, + "116870": { + "class_id": 19, + "created": "2009-01-18T16:14:02", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 116870, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33744, + "object_name": "Destexhe, Alain [Destexhe at iaf.cnrs-gif.fr]" + }, + { + "object_id": 116941, + "object_name": "Pospischil, Martin " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116937, + "object_name": "Pospischil M, Piwkowska Z, Bal T, Destexhe A (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Python demo of the VmT method to extract conductances from single Vm traces (Pospischil et al. 2009)", + "notes": { + "attr_id": 24, + "value": "This python code implements a method to estimate synaptic conductances from single membrane potential traces (the \"VmT method\"), as described in Pospischil et al. (2009). The method uses a maximum likelihood procedure and was successfully tested using models and dynamic-clamp experiments in vitro (see paper for details)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116870", + "other_implementer": { + "attr_id": 308, + "value": "Pospischil, Martin and Destexhe, Alain " + }, + "ver_date": "2016-09-16T16:49:45.063", + "ver_number": 7 + }, + "116871": { + "class_id": 19, + "created": "2009-01-19T11:18:34", + "gitrepo": false, + "id": 116871, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 116872, + "object_name": "Shanahan, Murray [m.shanahan at imperial.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 82787, + "object_name": "Working memory" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116074, + "object_name": "Shanahan M (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A spiking model of cortical broadcast and competition (Shanahan 2008)", + "notes": { + "attr_id": 24, + "value": "\"This paper presents a computer model of cortical broadcast and competition based on spiking neurons and inspired by\r\nthe hypothesis of a global neuronal workspace underlying conscious information processing in the human brain. In the\r\nmodel, the hypothesised workspace is realised by a collection of recurrently interconnected regions capable of sustaining\r\nand disseminating a reverberating spatial pattern of activation. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116871", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 76 + }, + "ver_date": "2009-01-19T11:38:25", + "ver_number": 3 + }, + "116901": { + "class_id": 19, + "created": "2009-01-19T14:11:01", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 116901, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 116906, + "object_name": "Carver, Sean [sean.carver at jhu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 22012, + "object_name": "Depression" + }, + { + "object_id": 116907, + "object_name": "Direction Selectivity" + }, + { + "object_id": 242018, + "object_name": "Synaptic Convergence" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116905, + "object_name": "Carver S, Roth E, Cowan NJ, Fortune ES (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Synaptic plasticity can produce and enhance direction selectivity (Carver et al, 2008)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88204, + "object_name": "Electric fish midbrain torus semicircularis neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... We propose a parsimonious model of motion processing that generates direction selective responses using short-term synaptic depression and can reproduce salient features of direction selectivity found in a population of neurons in the midbrain of the weakly electric fish Eigenmannia virescens. \r\n\r\nThe model achieves direction selectivity with an elementary Reichardt motion detector: information from spatially separated receptive fields converges onto a neuron via dynamically different pathways. \r\n\r\nIn the model, these differences arise from convergence of information through distinct synapses that either exhibit or do not exhibit short-term synaptic depression\u2014short-term depression produces phase-advances relative to nondepressing synapses. ...\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116901", + "other_concept": { + "attr_id": 306, + "value": "Direction Selectivity" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "sean.carver@jhu.edu" + }, + "ver_date": "2018-06-06T17:24:19.097", + "ver_number": 6 + }, + "116945": { + "citation": { + "attr_id": 391, + "value": "Spike frequency adaptation mediates looming stimulus selectivity in a collision-detecting neuron; Peron and Gabbiani; Nature Neuroscience, in press (2009)." + }, + "class_id": 19, + "created": "2009-01-26T07:40:17", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 112841, + "object_name": "I Krp" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 116945, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33804, + "object_name": "Gabbiani, F" + }, + { + "object_id": 116947, + "object_name": "Peron, Simon [perons at janelia.hhmi.org]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116950, + "object_name": "Peron S, Gabbiani F (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Spike frequency adaptation in the LGMD (Peron and Gabbiani 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 116948, + "object_name": "Locust Lobula Giant Movement Detector (LGMD) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model is used in the referenced paper to demonstrate that a model of an SK-like calcium-sensitive potassium (KCa) conductance can replicate the spike frequency adaptation (SFA) of the locust lobula giant movement detector (LGMD) neuron. The model simulates current injection experiments with and without KCa block in the LGMD, as well as visual stimulation experiments with and without KCa block." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116945", + "other_concept": { + "attr_id": 306, + "value": "Spike Frequency Adaptatio" + }, + "other_implementer": { + "attr_id": 308, + "value": "Peron, Simon " + }, + "other_neurons": { + "attr_id": 301, + "value": "Lobula Giant Movement Detector (LGMD)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "perons@janelia.hhmi.org" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 218, + "object_name": "Cholinergic Receptors" + } + ] + }, + "ver_date": "2018-10-17T16:50:23.767", + "ver_number": 8 + }, + "116956": { + "class_id": 19, + "created": "2009-01-27T11:35:27", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 116966, + "object_name": "I_K,Na" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 116956, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33735, + "object_name": "Carnevale, Ted [Ted.Carnevale at Yale.edu]" + }, + { + "object_id": 116982, + "object_name": "Torben-Nielsen, Ben [btorbennielsen at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 150219, + "object_name": "Reliability" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 25002, + "object_name": "Borst A, Haag J (1996)" + }, + { + "object_id": 26661, + "object_name": "Haag J, Theunissen F, Borst A (1997)" + }, + { + "object_id": 26663, + "object_name": "Haag J, Vermeulen A, Borst A (1999)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "116956", + "name": "ModelView" + } + ], + "default": "116956" + }, + "name": "Fly lobular plate VS cell (Borst and Haag 1996, et al. 1997, et al. 1999)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 116967, + "object_name": "Fly lobular plate vertical system cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In a series of papers the authors conducted experiments to develop understanding and models of fly visual system HS, CS, and VS neurons. This model recreates the VS neurons from those papers with enough success to merit approval by Borst although some discrepancies remain (see readme)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116956", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 206363, + "object_name": "Drosophila" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 250 + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 120613, + "object_name": "Drosophila (fruit fly)" + } + ] + }, + "ver_date": "2019-02-26T10:09:45.693", + "ver_number": 17 + }, + "116957": { + "citation": { + "attr_id": 391, + "value": "T. Nowotny, R. Levi and A. I. Selverston, Probing the Dynamics of Identified Neurons with a Data-Driven Modeling Approach, PLoS ONE 3(7): e2627. DOI:10.1371/journal.pone.0002627 (2008)" + }, + "class_id": 19, + "created": "2009-01-27T13:18:27", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "id": 116957, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 116961, + "object_name": "Nowotny, Thomas [t.nowotny at sussex.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 116962, + "object_name": "Parameter sensitivity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116601, + "object_name": "Nowotny T, Levi R, Selverston AI (2008)" + }, + { + "object_id": 116960, + "object_name": "Ivanchenko MV, Thomas Nowotny , Selverston AI, Rabinovich MI (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Data-driven, HH-type model of the lateral pyloric (LP) cell in the STG (Nowotny et al. 2008)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + }, + { + "object_id": 116963, + "object_name": "Stomatogastric Ganglion (STG) Lateral Pyloric (LP) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model was developed using voltage clamp data and existing LP models to assemble an initial set of currents which were then adjusted by extensive fitting to a long data set of an isolated LP neuron. The main points of the work are\r\na) automatic fitting is difficult but works when the method is carefully adjusted to the problem (and the initial guess is good enough).\r\nb) The resulting model (in this case) made reasonable predictions for manipulations not included in the original data set, e.g., blocking some of the ionic currents.\r\nc) The model is reasonably robust against changes in parameters but the different parameters vary a lot in this respect.\r\nd) The model is suitable for use in a network and has been used for this purpose (Ivanchenko et al. 2008)" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116957", + "other_concept": { + "attr_id": 306, + "value": "Parameter sensitivity" + }, + "other_currents": { + "attr_id": 303, + "value": "I h" + }, + "other_implementer": { + "attr_id": 308, + "value": "Nowotny, Thomas " + }, + "other_neurons": { + "attr_id": 301, + "value": "STG Lateral Pyloric (LP)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "t.nowotny@sussex.ac.uk" + }, + "simPFid": { + "attr_id": 488, + "value": 60 + }, + "ver_date": "2012-06-19T22:58:53", + "ver_number": 3 + }, + "116981": { + "class_id": 19, + "created": "2009-01-28T14:30:41", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 116981, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33735, + "object_name": "Carnevale, Ted [Ted.Carnevale at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 64175, + "object_name": "Electrotonus" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 86943, + "object_name": "Rall W (1964)" + }, + { + "object_id": 101182, + "object_name": "Rall W (1964)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "116981_1", + "name": "Fig. 5--exc central vs. peripheral tree" + }, + { + "id": "116981_2", + "name": "Fig. 5 inset--brief excitation" + }, + { + "id": "116981_3", + "name": "Fig. 6--effect of location of excitatory input" + }, + { + "id": "116981_4", + "name": "Fig. 7--effect of activation sequence" + }, + { + "id": "116981_5", + "name": "Fig. 8--effect of inh location" + } + ], + "default": "116981_1" + }, + "name": "Effects of synaptic location and timing on synaptic integration (Rall 1964)", + "notes": { + "attr_id": 24, + "value": "Reproduces figures 5 - 8 from \r\nRall, W.\r\nTheoretical significance of dendritic trees for neuronal input-output relations.\r\nIn: Neural Theory and Modeling, ed. Reiss, R.F., Palo Alto: Stanford University Press (1964)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/116981", + "other_neurons": { + "attr_id": 301, + "value": "Simplified generalized neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ted.carnevale@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115962, + "object_name": "Unknown" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181222, + "object_name": "116981_5" + }, + { + "object_id": 181223, + "object_name": "116981_4" + }, + { + "object_id": 181224, + "object_name": "116981_1" + }, + { + "object_id": 181225, + "object_name": "116981_3" + }, + { + "object_id": 181226, + "object_name": "116981_2" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 230 + }, + "ver_date": "2015-01-02T22:35:19", + "ver_number": 6 + }, + "116983": { + "class_id": 19, + "created": "2009-01-28T17:33:17", + "currents": 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dendrites (Morita 2008)", + "notes": { + "attr_id": 24, + "value": "This is a rate-coding model of the neocortical spatial working memory circuit incorporating multiple dendritic branches of the individual pyramidal cell in order to examine how nonlinear dendritic integration, combined with the nonuniform distribution of the external input, affects the behavior of the whole circuit." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/117204", + "public_submitter_email": { + "attr_id": 309, + "value": "morita@brain.riken.jp" + }, + "ver_date": "2009-02-04T09:34:47", + "ver_number": 10 + }, + "117207": { + "class_id": 19, + "created": "2009-02-03T18:46:52", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + 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We\r\ncarried out multi-site voltage-sensitive dye imaging of membrane potential transients from thin basal\r\nbranches of prefrontal cortical pyramidal neurons before and after application of channel blockers. We\r\nfound that backpropagating action potentials (bAPs) are predominantly controlled by voltage-gated\r\nsodium and A-type potassium channels. In contrast, pharmacologically blocking the delayed rectifier\r\npotassium, voltage-gated calcium or Ih, conductance had little effect on dendritic action potential\r\npropagation. Optically recorded bAP waveforms were quantified and multicompartmental modeling\r\n(NEURON) was used to link the observed behavior with the underlying biophysical properties. The\r\nbest-fit model included a non-uniform sodium channel distribution with decreasing conductance with\r\ndistance from the soma, together with a non-uniform (increasing) A-type potassium conductance. AP\r\namplitudes decline with distance in this model, but to a lesser extent than previously thought. We used\r\nthis model to explore the mechanisms underlying two sets of published data involving high frequency\r\ntrains of action potentials, and the local generation of sodium spikelets. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/117207", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 240 + }, + "ver_date": "2014-05-28T14:56:00", + "ver_number": 13 + }, + "117330": { + "class_id": 19, + "created": "2009-02-05T20:58:16", + "gitrepo": false, + "id": 117330, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 117490, + "object_name": "Lundstrom BN, Famulare M, Sorensen LB, Spain WJ, Fairhall AL (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Hodgkin-Huxley simplifed 2D and 3D models (Lundstrom et al. 2009)", + "notes": { + "attr_id": 24, + "value": "\"Neuronal responses are often characterized by the\r\nfiring rate as a function of the stimulus mean, or the f\u2013I curve.\r\n\r\nWe introduce a novel classification of neurons into Types A,\r\nB−, and B+ according to how f\u2013I curves are modulated by\r\ninput fluctuations. ...\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/117330", + "public_submitter_email": { + "attr_id": 309, + "value": "lundbr@u.washington.edu" + }, + "simPFid": { + "attr_id": 488, + "value": 63 + }, + "ver_date": "2012-01-18T16:38:33", + "ver_number": 13 + }, + "117351": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 117359, + "object_name": "backup of web linked model" + } + ] + }, + "class_id": 19, + "created": "2009-02-12T18:35:17", + "gitrepo": false, + "id": 117351, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 117358, + "object_name": "Gilmanov IR, Samigullin DV, Vyskocil F, Nikolsky EE, Bukharaeva EA (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 22013, + "object_name": "Neuromuscular Junction" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 117352, + "object_name": "Pascal (web link to model)" + } + ] + }, + "name": "Quantal neurotransmitter release kinetics with fixed and mobile Ca2+ buffers (Gilmanov et al. 2008)", + "notes": { + "attr_id": 24, + "value": "\"... In this work, we used computer modeling of\r\nquanta release kinetics with various levels of calcium influx\r\nand in the presence of endogenous calcium buffers with\r\nvarying mobilities. \r\n\r\nThe results of this modeling revealed\r\nthe desynchronization of quanta release under low calcium\r\ninflux in the presence of an endogenous fixed calcium\r\nbuffer, with a diffusion coefficient much smaller than that\r\nof free Ca2+, and synchronization occurred upon adding a\r\nmobile buffer. This corresponds to changes in secretion\r\ntime course parameters found experimentally ...\"\r\n" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/117351", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2009-02-12T19:10:33", + "ver_number": 4 + }, + "117361": { + "citation": { + "attr_id": 391, + "value": "PMID: 19151930" + }, + "class_id": 19, + "created": "2009-02-13T13:30:02", + "gitrepo": false, + "id": 117361, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 117422, + "object_name": "Stewart, Robert [Robert.Stewart at pharm.ox.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 117421, + "object_name": "Stewart RD, Bair W (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Numerical Integration of Izhikevich and HH model neurons (Stewart and Bair 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The Parker-Sochacki method is a new technique for the numerical integration of differential equations applicable to many neuronal models.\r\nUsing this method, the solution order can be adapted according to the local conditions at each time step, enabling adaptive error control without changing the integration timestep. \r\nWe apply the Parker-Sochacki method to the Izhikevich \u2018simple\u2019 model and a Hodgkin-Huxley\r\ntype neuron, comparing the results with those obtained using the Runge-Kutta and Bulirsch-Stoer methods." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/117361", + "other_implementer": { + "attr_id": 308, + "value": "Stewart, Robert " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "Robert.Stewart@pharm.ox.ac.uk" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "ver_date": "2009-02-13T14:59:40", + "ver_number": 4 + }, + "117459": { + "class_id": 19, + "created": "2009-02-17T09:47:31", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 249, + "object_name": "I K,leak" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 117459, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 117488, + "object_name": "Cruz, Ginny [gcruz at monell.org]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 117341, + "object_name": "Cruz GE, Sahley CL, Muller KJ (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Competition for AP initiation sites in a circuit controlling simple learning (Cruz et al. 2007)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 102282, + "object_name": "Leech S cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 239, + "object_name": "Serotonin" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The spatial and temporal patterns of action potential initiations were studied in a behaving leech preparation to determine the basis of increased firing that accompanies sensitization, a form of non-associative learning requiring the\r\nS-interneurons.\r\n\r\n...\r\nThe S-interneurons, one in each ganglion and linked by electrical synapses with both neighbors to form a chain, are interposed between sensory\r\nand motor neurons.\r\n\r\n...\r\n the single site with the largest initiation rate, the S-cell in the\r\nstimulated segment, suppressed initiations in adjacent ganglia.\r\n\r\nExperiments showed this was both because (1) it received the earliest, greatest input and (2) the delayed synaptic\r\ninput to the adjacent S-cells coincided with the action potential refractory period.\r\n\r\nA compartmental model of the S-cell and its inputs showed that a simple, intrinsic mechanism of inexcitability after each action potential may account for suppression of impulse initiations.\r\n\r\nThus, a non-synaptic competition between neurons alters synaptic integration in the chain.\r\n\r\nIn one mode, inputs to different sites sum independently, whereas in another, synaptic input to a single site precisely specifies the overall pattern of activity.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/117459", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115958, + "object_name": "Leech" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181520, + "object_name": "117459" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 202 + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 114272, + "object_name": "Hirudinea (leech)" + } + ] + }, + "ver_date": "2019-02-26T10:22:01.033", + "ver_number": 10 + }, + "117508": { + "class_id": 19, + "created": "2009-02-20T10:04:58", + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122825, + "object_name": "nAChR alpha 7" + } + ] + }, + "gitrepo": false, + "id": 117508, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 117792, + "object_name": "Skorinkin, Andrei [askorink at yandex.ru]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 117513, + "object_name": "Gay EA, Giniatullin R, Skorinkin A, Yakel JL (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 117353, + "object_name": "Pascal/Delphi" + } + ] + }, + "name": "Rat alpha7 nAChR desensitization is modulated by W55 (Gay et al. 2008)", + "notes": { + "attr_id": 24, + "value": "\"The rat alpha7 nicotinic acetylcholine receptor (nAChR) can undergo rapid onset of desensitization; however, the mechanisms of desensitization are largely unknown. \r\n\r\nThe contribution of a tryptophan (W) residue at position 55 of the rat alpha7 nAChR subunit, which lies within the beta2 strand, was studied by mutating it to other hydrophobic and/or aromatic amino acids, followed by voltage-clamp experiments in Xenopus oocytes. \r\n\r\nWhen mutated to alanine, the alpha7-W55A nAChR desensitized more slowly, and recovered from desensitization more rapidly, than wildtype alpha7 nAChRs. \r\n\r\nThe contribution of desensitization was validated by kinetic modelling. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/117508", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 178, + "object_name": "Nicotinic" + } + ] + }, + "ver_date": "2015-07-26T13:51:48.513", + "ver_number": 9 + }, + "117514": { + "class_id": 19, + "created": "2009-02-23T11:10:36", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 117514, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 117579, + "object_name": "Kovacic, Gregor [kovacg at rpi.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 117456, + "object_name": "Kovacic G, Tao L, Cai D, Shelley MJ (2008)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Reverse-time correlation analysis for idealized orientation tuning dynamics (Kovacic et al. 2008)", + "notes": { + "attr_id": 24, + "value": "\"A theoretical analysis is presented of a\r\nreverse-time correlation method used in experimentally\r\ninvestigating orientation tuning dynamics of\r\nneurons in the primary visual cortex.\r\n\r\nAn exact mathematical\r\ncharacterization of the method is developed,\r\nand its connection with the Volterra\u2013Wiener nonlinear\r\nsystems theory is described.\r\n\r\nVarious mathematical\r\nconsequences and possible physiological implications\r\nof this analysis are illustrated using exactly solvable\r\nidealized models of orientation tuning.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/117514", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 134 + }, + "ver_date": "2018-10-17T16:51:06.383", + "ver_number": 7 + }, + "117691": { + "class_id": 19, + "created": "2009-02-25T10:24:25", + "gitrepo": false, + "id": 117691, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 117792, + "object_name": "Skorinkin, Andrei [askorink at yandex.ru]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 96767, + "object_name": "Nociception" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 117695, + "object_name": "Sokolova E, Skorinkin A, Moiseev I, Agrachev A, Nistri A, Giniatullin R (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 117353, + "object_name": "Pascal/Delphi" + } + ] + }, + "name": "Experimental and modeling studies of desensitization of P2X3 receptors (Sokolova et al. 2006)", + "notes": { + "attr_id": 24, + "value": "\"The function of ATP-activated P2X3 receptors involved in pain sensation is modulated by desensitization, a phenomenon poorly understood.\r\n\r\nThe present study used patch-clamp recording from cultured rat or mouse sensory neurons and kinetic\r\nmodeling to clarify the properties of P2X3 receptor desensitization.\r\n\r\n...\r\nDesensitization properties were well accounted for by a cyclic model in which\r\nreceptors could be desensitized from either open or closed\r\nstates.\r\n\r\nRecovery was assumed to be a multistate process with distinct kinetics dependent on the agonist-dependent dissociation rate from desensitized receptors.\r\n\r\n...\r\nBy using subthreshold concentrations of an HAD (high-affinity desensitization)-potent agonist, it might be possible\r\nto generate sustained inhibition of P2X3 receptors for controlling\r\nchronic pain.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/117691", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 220, + "object_name": "Sensory Receptors" + } + ] + }, + "ver_date": "2015-07-26T13:56:49.367", + "ver_number": 7 + }, + "117810": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 117811, + "object_name": "backup of weblink code remoto" + } + ] + }, + "class_id": 19, + "created": "2009-02-27T16:54:20", + "gitrepo": false, + "id": 117810, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 117809, + "object_name": "Cisi RR, Kohn AF (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113428, + "object_name": "Java (web link to model)" + } + ] + }, + "name": "Simulation system of spinal cord motor nuclei and assoc. nerves and muscles (Cisi and Kohn 2008)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 275, + "object_name": "Spinal cord lumbar Interneuron 1a GLY cell" + }, + { + "object_id": 276, + "object_name": "Spinal cord lumbar motor neuron alpha ACh cell" + }, + { + "object_id": 117813, + "object_name": "Spinal cord motor neuron slow twitch" + }, + { + "object_id": 117814, + "object_name": "Spinal cord motor neuron fatigue resistant" + }, + { + "object_id": 117815, + "object_name": "Spinal cord motor neuron fast fatiguing" + }, + { + "object_id": 117817, + "object_name": "Spinal cord Ib interneuron" + }, + { + "object_id": 117818, + "object_name": "Spinal cord renshaw cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A Web-based simulation system of the spinal\r\ncord circuitry responsible for muscle control is described.\r\nThe simulator employs two-compartment motoneuron\r\nmodels for S, FR and FF types, with synaptic inputs acting\r\nthrough conductance variations. \r\n\r\nFour motoneuron pools\r\nwith their associated interneurons are represented in the\r\nsimulator, with the possibility of inclusion of more than\r\n2,000 neurons and 2,000,000 synapses. \r\n\r\n... \r\nInputs to the motoneuron pool come from populations of\r\ninterneurons (Ia reciprocal inhibitory interneurons, Ib\r\ninterneurons, and Renshaw cells) and from stochastic point\r\nprocesses associated with descending tracts. \r\n\r\n...\r\nThe generation of the H-reflex\r\nby the Ia-motoneuron pool system and its modulation\r\nby spinal cord interneurons is included in the simulation\r\nsystem.\r\n" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/117810", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2009-03-02T11:38:45", + "ver_number": 7 + }, + "117966": { + "class_id": 19, + "created": "2009-03-04T06:03:28", + "gitrepo": false, + "id": 117966, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 117967, + "object_name": "Mensi, Skander [skander.mensi at epfl.ch]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 69692, + "object_name": "Jolivet R, Rauch A, L\u00fcscher HR, Gerstner W (2006)" + }, + { + "object_id": 69707, + "object_name": "Jolivet R, Lewis TJ, Gerstner W (2004)" + }, + { + "object_id": 115429, + "object_name": "Jolivet R, Kobayashi R, Rauch A, Naud R, Shinomoto S, Gerstner W (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Spike Response Model simulator (Jolivet et al. 2004, 2006, 2008)", + "notes": { + "attr_id": 24, + "value": "The Spike Response Model (SRM) optimized on the experimental data in the Single-Neuron modelling Competition ( www.incf.org/community/competitions ) for edition 2007 and edition 2008. The Spike Response Model is a simplified model of neuronal excitability where current linearly integrates to an artificial threshold. After the spike, the threshold is augmented and the voltage follows a voltage kernel that is the average voltage trace during and after a spike. The parameters were chosen to best fit the observed spike times with a method outlined in Jolivet et al. (2006)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/117966", + "other_implementer": { + "attr_id": 308, + "value": "Skander Mensi" + }, + "other_neurons": { + "attr_id": 301, + "value": "simplified neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "skander.mensi@epfl.ch" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115962, + "object_name": "Unknown" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 137 + }, + "ver_date": "2009-03-04T12:49:21", + "ver_number": 24 + }, + "118014": { + "class_id": 19, + "created": "2009-03-10T09:28:41", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 118014, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 117208, + "object_name": "Acker, Corey [acker at uchc.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 93421, + "object_name": "Acker CD, White JA (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Roles of I(A) and morphology in AP prop. in CA1 pyramidal cell dendrites (Acker and White 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ...Using conductance-based models of CA1 pyramidal cells, we show that underlying \u201ctraveling wave attractors\u201d control action potential propagation in the apical dendrites. \r\n\r\nBy computing these attractors, we dissect and quantify the effects of IA channels and dendritic morphology on bAP amplitudes. \r\n\r\nWe find that non-uniform activation properties of IA can lead to backpropagation failure similar to that observed experimentally in these cells. \r\n... \"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118014", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2009-03-10T15:36:20", + "ver_number": 5 + }, + "118020": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 118091, + "object_name": "backup of web-available model" + } + ] + }, + "class_id": 19, + "created": "2009-03-13T18:22:37", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 118020, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 118022, + "object_name": "Clewley, Robert [rclewley at gsu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + }, + { + "object_id": 225299, + "object_name": "Multiscale" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 118021, + "object_name": "Clewley R, Rotstein HG, Kopell N (2005)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + }, + { + "object_id": 91894, + "object_name": "XPP (web link to model)" + } + ] + }, + "name": "Reduction of nonlinear ODE systems possessing multiple scales (Clewley et al. 2005)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... We introduce a combined numerical and analytical technique that aids the identification of structure in a class of systems of nonlinear ordinary differential\r\nequations (ODEs) that are commonly applied in dynamical models of physical processes.\r\n... \r\nThese methods have been incorporated into a new software tool named Dssrt, which we demonstrate\r\non a limit cycle of a synaptically driven Hodgkin\u2013Huxley neuron model.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118020", + "ver_date": "2017-02-23T11:04:08.17", + "ver_number": 15 + }, + "118092": { + "citation": { + "attr_id": 391, + "value": "18425570" + }, + "class_id": 19, + "created": "2009-03-17T05:48:37", + "gitrepo": false, + "id": 118092, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 118093, + "object_name": "Mader, Wolfgang [wolfgang.mader at uni-ulm.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 118002, + "object_name": "Stein W, Straub O, Ausborn J, Mader W, Wolf H (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 118095, + "object_name": "MadSim" + } + ] + }, + "name": "A model of the femur-tibia control system in stick insects (Stein et al. 2008)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 118094, + "object_name": "Stick insect nonspiking interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We studied the femur-tibia joint control system of the insect leg, and its switch between resistance reflex in posture control and \"active reaction\" in walking. The \"active reaction\" is basically a reversal of the resistance reflex. Both responses are elicited by the same sensory input and the same neuronal network (the femur-tibia network). \r\nThe femur-tibia network was modeled by fitting the responses of model neurons to those obtained in animals. Each implemented neuron has a physiological counterpart. The strengths of 16 interneuronal pathways that integrate sensory input were then assigned three different values and varied independently, generating a database of more than 43 million network variants. The uploaded version contains the model that best represented the resistance reflex. Please see the README for more help.\r\nWe demonstrate that the combinatorial code of interneuronal pathways determines motor output. A switch between different behaviors such as standing to walking can thus be achieved by altering the strengths of selected sensory integration pathways.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118092", + "other_implementer": { + "attr_id": 308, + "value": "Mader, Wolfgang \r\nStein, Wolfgang " + }, + "other_neurons": { + "attr_id": 301, + "value": "stick insect nonspiking interneuron" + }, + "other_simulator": { + "attr_id": 307, + "value": "madSim" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "wstein@neurobiologie.de" + }, + "ver_date": "2009-03-19T15:03:54", + "ver_number": 11 + }, + "118098": { + "class_id": 19, + "created": "2009-03-18T02:59:43", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 118098, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 118099, + "object_name": "Hemond P, Migliore M, Ascoli GA, Jaffe DB (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "118098", + "name": "ModelView" + } + ], + "default": "118098" + }, + "name": "Ca3 pyramidal neuron: membrane response near rest (Hemond et al. 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In this paper, the model was used to show how the temporal summation of excitatory inputs in CA3 pyramidal neurons was affected by the presence of Ih in the dendrites in a frequency- and distance-dependent fashion." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118098", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181650, + "object_name": "118098" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 189 + }, + "ver_date": "2014-02-06T16:18:59", + "ver_number": 6 + }, + "118195": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 118196, + "object_name": "backup of web linked model" + } + ] + }, + "class_id": 19, + "created": "2009-03-18T17:06:13", + "gitrepo": false, + "id": 118195, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 118194, + "object_name": "Bruce IC (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Evaluation of stochastic diff. eq. approximation of ion channel gating models (Bruce 2009)", + "notes": { + "attr_id": 24, + "value": "Fox and Lu derived an algorithm based on stochastic differential equations for approximating the kinetics of ion channel gating that is simpler and faster than \"exact\" algorithms for simulating Markov process models of channel gating.\r\n\r\nHowever, the approximation may not be sufficiently accurate to predict statistics of action potential generation in some cases.\r\n\r\nThe objective of this study was to develop a framework for analyzing the inaccuracies and determining their origin.\r\n\r\nSimulations of a patch of membrane with voltage-gated sodium and potassium channels were performed using an exact algorithm for the kinetics of channel gating and the approximate algorithm of Fox & Lu.\r\n...\r\nThe results indicate that: (i) the source of the inaccuracy is that the Fox & Lu algorithm does not adequately describe the combined behavior of the multiple activation particles in each sodium and potassium channel, and (ii) the accuracy does not improve with increasing numbers of channels." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118195", + "ver_date": "2009-03-18T17:25:28", + "ver_number": 8 + }, + "118199": { + "citation": { + "attr_id": 391, + "value": "PMC2596310" + }, + "class_id": 19, + "created": "2009-03-20T11:39:05", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 118199, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 118201, + "object_name": "Ziegler, Lorric " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 118207, + "object_name": "Maintenance" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 118205, + "object_name": "Clopath C, Ziegler L, Vasilaki E, B\u00fcsing L, Gerstner W (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Tag Trigger Consolidation (Clopath and Ziegler et al. 2008)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model simulates different phases of LTP/D, i.e. the induction or early phase, the setting of synaptic tags, a trigger process for protein synthesis, and a slow transition leading to synaptic consolidation namely the late phase of synaptic plasticity. The model explains a large body of experimental data on synaptic tagging and capture, cross-tagging, and the late phases of LTP and LTD. Moreover, the model accounts for the dependence of LTP and LTD induction on voltage and presynaptic stimulation frequency." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118199", + "other_concept": { + "attr_id": 306, + "value": "Maintenance" + }, + "other_implementer": { + "attr_id": 308, + "value": "Ziegler, Lorric" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "lorric.ziegler@epfl.ch" + }, + "ver_date": "2014-06-02T10:31:07", + "ver_number": 7 + }, + "118261": { + "class_id": 19, + "created": "2009-03-23T13:31:32", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 118261, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 245691, + "object_name": "Vision" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 118017, + "object_name": "Ursino M, Cuppini C, Magosso E, Serino A, di Pellegrino G (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Multisensory integration in the superior colliculus: a neural network model (Ursino et al. 2009)", + "notes": { + "attr_id": 24, + "value": "\" ... The model includes three distinct neural areas:\r\ntwo unimodal areas (auditory and visual) are devoted to a\r\ntopological representation of external stimuli, and communicate\r\nvia synaptic connections with a third downstream\r\narea (in the SC) responsible for multisensory integration.\r\n\r\nThe present simulations show that the model, with a single\r\nset of parameters, can mimic various responses to different\r\ncombinations of external stimuli including the inverse\r\neffectiveness, both in terms of multisensory enhancement\r\nand contrast, the existence of within- and cross-modality\r\nsuppression between spatially disparate stimuli, a reduction\r\nof network settling time in response to cross-modal stimuli\r\ncompared with individual stimuli.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118261", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 118308, + "object_name": "Superior colliculus" + } + ] + }, + "ver_date": "2018-10-18T15:35:33.48", + "ver_number": 9 + }, + "118326": { + "class_id": 19, + "created": "2009-03-23T14:51:29", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 118326, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 118260, + "object_name": "Ray S, Bhalla US (2008)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 118327, + "object_name": "MOOSE/PyMOOSE (web link to method)" + } + ] + }, + "name": "Moose/PyMOOSE: interoperable scripting in Python for MOOSE (Ray and Bhalla 2008)", + "notes": { + "attr_id": 24, + "value": "\" ... We report the integration of Python scripting with the Multi-scale Object Oriented Simulation Environment (MOOSE). MOOSE is a general-purpose simulation system for compartmental neuronal models and for models of signaling pathways based on chemical kinetics. We show how the Python-scripting version of MOOSE, PyMOOSE, combines the power of a compiled simulator with the versatility and ease of use of Python. ... \"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118326", + "ver_date": "2017-06-22T17:13:59.57", + "ver_number": 11 + }, + "118389": { + "citation": { + "attr_id": 391, + "value": "J Neurosci, in press." + }, + "class_id": 19, + "created": "2009-03-27T19:06:07", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 116966, + "object_name": "I_K,Na" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "id": 118389, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 118393, + "object_name": "Hjorth, Johannes [hjorth at csc.kth.se]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 118390, + "object_name": "Hjorth J, Blackwell KT, Kotaleski JH (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Gap junction coupled network of striatal fast spiking interneurons (Hjorth et al. 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 118395, + "object_name": "Neostriatum fast spiking interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Gap junctions between striatal FS neurons has very weak ability to synchronise spiking. Input uncorrelated between neighbouring neurons is shunted, while correlated input is not." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118389", + "other_implementer": { + "attr_id": 308, + "value": "Hjorth, Johannes " + }, + "other_neurons": { + "attr_id": 301, + "value": "Striatal fast spiking interneurons" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "hjorth@kth.se" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2009-03-31T17:21:57", + "ver_number": 9 + }, + "118392": { + "citation": { + "attr_id": 391, + "value": "18266097" + }, + "class_id": 19, + "created": "2009-03-30T12:41:23", + "gitrepo": false, + "id": 118392, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 118698, + "object_name": "Varkonyi, Peter [vpeter at mit.bme.hu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 116962, + "object_name": "Parameter sensitivity" + }, + { + "object_id": 137847, + "object_name": "Phase Response Curves" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116106, + "object_name": "V\u00e1rkonyi PL, Kiemel T, Hoffman K, Cohen AH, Holmes P (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Phase oscillator models for lamprey central pattern generators (Varkonyi et al. 2008)", + "notes": { + "attr_id": 24, + "value": "In our paper, Varkonyi et al. 2008, we derive phase oscillator models for the lamprey central pattern generator from two biophysically based segmental models. We\r\nstudy intersegmental coordination and show how these models can provide stable intersegmental phase lags observed in real animals.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118392", + "other_implementer": { + "attr_id": 308, + "value": "Varkonyi, Peter " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "vpeter@mit.bme.hu" + }, + "simPFid": { + "attr_id": 488, + "value": 131 + }, + "ver_date": "2011-03-10T10:20:47", + "ver_number": 9 + }, + "118434": { + "class_id": 19, + "created": "2009-03-30T16:07:19", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 118434, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + }, + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + }, + { + "object_id": 98011, + "object_name": "Place cell/field" + }, + { + "object_id": 182690, + "object_name": "Spatial Navigation" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 117798, + "object_name": "Kulvicius T, Tamosiunaite M, Ainge J, Dudchenko P, W\u00f6rg\u00f6tter F (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Odor supported place cell model and goal navigation in rodents (Kulvicius et al. 2008)", + "notes": { + "attr_id": 24, + "value": "\" ...\r\nHere we model odor supported place cells by using a simple feed-forward network and analyze the impact of olfactory cues on place cell formation and spatial navigation. \r\n\r\nThe obtained place cells are used to solve a goal navigation task by a novel mechanism based on self-marking by odor patches combined with a Q-learning algorithm. \r\n\r\nWe also analyze the impact of place cell remapping on goal directed behavior when switching between two environments. \r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118434", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2018-10-17T16:06:46.153", + "ver_number": 4 + }, + "118524": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 118528, + "object_name": "backup of web link to model" + } + ] + }, + "class_id": 19, + "created": "2009-04-01T16:21:16", + "gitrepo": false, + "id": 118524, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 118527, + "object_name": "Wohrer A, Kornprobst P (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Virtual Retina: biological retina simulator, with contrast gain control (Wohrer and Kornprobst 2009)", + "notes": { + "attr_id": 24, + "value": "\"We propose a new retina simulation software, called Virtual Retina, which transforms a video into spike trains.\r\n\r\nOur goal is twofold: Allow large scale simulations (up to 100,000 neurons) in reasonable processing times and keep a strong biological plausibility, taking into account implementation constraints. \r\n\r\n...\r\nThis software will be an evolutionary tool for neuroscientists that need realistic large-scale input spike trains in subsequent treatments, and for educational purposes.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118524", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 118529, + "object_name": "Retina" + } + ] + }, + "ver_date": "2009-04-01T16:45:30", + "ver_number": 7 + }, + "118554": { + "citation": { + "attr_id": 391, + "value": "Yang, Z. et al (2009) Neural Computation 21(4):991-1018 and Hennig, M et al (2007) Neurocomputing, 70:1626-1629" + }, + "class_id": 19, + "created": "2009-04-02T10:35:12", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 118554, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 139659, + "object_name": "Hennig, Matthias H [mhhennig at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + }, + { + "object_id": 245693, + "object_name": "Vestibular" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 118605, + "object_name": "Yang Z, Hennig MH, Postlethwaite M, Forsythe ID, Graham BP (2009)" + }, + { + "object_id": 118606, + "object_name": "Hennig MH, Postlethwaite M, Forsythe ID, Graham BP (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Calyx of Held, short term plasticity (Yang Z et al. 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 85986, + "object_name": "Medial Nucleus of the Trapezoid Body (MNTB) neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model investigates mechanisms contributing to short term plasticity at the calyx of Held, a giant glutamatergic synapse in the mammalian brainstem auditory system. It is a stochastic version of the model described in:\r\n \r\nHennig, M., Postlethwaite, M., Forsythe, I.D. and Graham, B.P. (2007). A biophysical model of short-term plasticity at the calyx of Held. \r\nNeurocomputing, 70:1626-1629.\r\n\r\nThis version introduces stochastic vesicle recycling and release. It has been used to investigate the information transmission\r\nproperties of this synapse, as detailed in:\r\n\r\nYang, Z., Hennig, M., Postlethwaite, M., Forsythe, I.D. and Graham, B.P. (2008). \r\nWide-band information transmission at the calyx of Held. Neural Computation, 21(4):991-1018.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118554", + "public_submitter_email": { + "attr_id": 309, + "value": "b.graham@cs.stir.ac.uk" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 143145, + "object_name": "Auditory brainstem" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 80 + }, + "ver_date": "2018-10-18T15:36:00.393", + "ver_number": 7 + }, + "118631": { + "class_id": 19, + "created": "2009-04-03T05:55:57", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 118631, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 97968, + "object_name": "Giugliano, Michele [mgiugliano at gmail.com]" + }, + { + "object_id": 118634, + "object_name": "Delattre, Vincent" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 66938, + "object_name": "Koendgen H, Geisler C, Wang XJ, Fusi S, Luescher HR, Giugliano M (2004)" + }, + { + "object_id": 118633, + "object_name": "K\u00f6ndgen H, Geisler C, Fusi S, Wang XJ, L\u00fcscher HR, Giugliano M (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "118631", + "name": "ModelView" + } + ], + "default": "118631" + }, + "name": "Response properties of neocort. neurons to temporally modulated noisy inputs (Koendgen et al. 2008)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 146805, + "object_name": "Abstract Wang-Buzsaki neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Neocortical neurons are classified by current\u2013frequency relationship. This is a static description and it may be inadequate to interpret neuronal responses to time-varying stimuli. \r\n\r\nTheoretical studies (Brunel et al., 2001; Fourcaud-Trocm\u00e9 et al. 2003; Fourcaud-Trocm\u00e9 and Brunel 2005; Naundorf et al. 2005) suggested that single-cell dynamical response properties are necessary to interpret ensemble responses to fast input transients. Further, it was shown that input-noise linearizes and boosts the response bandwidth, and that the interplay between the barrage of noisy synaptic currents and the spike-initiation mechanisms determine the dynamical properties of the firing rate. \r\n\r\nIn order to allow a reader to explore such simulations, we prepared a simple NEURON implementation of the experiments performed in K\u00f6ndgen et al., 2008 (see also Fourcaud-Trocm\u00e9 al. 2003; Fourcaud-Trocm\u00e9 and Brunel 2005).\r\n\r\nIn addition, we provide sample MATLAB routines for exploring the sandwich model proposed in K\u00f6ndgen et al., 2008, employing a simple frequdency-domain filtering.\r\n\r\nThe simulations and the MATLAB routines are based on the linear response properties of layer 5 pyramidal cells estimated by injecting a superposition of a small-amplitude sinusoidal wave and a background noise, as in K\u00f6ndgen et al., 2008." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118631", + "other_implementer": { + "attr_id": 308, + "value": "Vincent Delattre" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181148, + "object_name": "118631" + } + ] + }, + "ver_date": "2023-03-03T11:26:17.507", + "ver_number": 25 + }, + "118662": { + "class_id": 19, + "created": "2009-04-06T13:20:06", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 118662, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 48424, + "object_name": "Gouwens, Nathan W [nathan_gouwens at hms.harvard.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 47316, + "object_name": "Invertebrate" + }, + { + "object_id": 64175, + "object_name": "Electrotonus" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 120524, + "object_name": "Gouwens NW, Wilson RI (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "118662_1", + "name": "Figure 4a cell 1" + }, + { + "id": "118662_2", + "name": "Figure 4a cell 2" + }, + { + "id": "118662_3", + "name": "Figure 4a cell 3" + }, + { + "id": "118662_4", + "name": "Figure 4b dendrite 1" + }, + { + "id": "118662_5", + "name": "Figure 4b dendrite 2" + }, + { + "id": "118662_6", + "name": "Figure 4b dendrite 3" + } + ], + "default": "118662_1" + }, + "name": "Drosophila projection neuron electrotonic structure (Gouwens and Wilson 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 120612, + "object_name": "Drosophila antennal lobe DM1 projection neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We address the issue of how electrical signals propagate in Drosophila neurons by modeling the electrotonic structure of the antennal lobe projection neurons innervating glomerulus DM1. The readme file contains instructions for running the model." + }, + "opensourcebrain": { + "value": "drosophila_projection_neuron" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118662", + "other_neurons": { + "attr_id": 301, + "value": "Drosophila antennal lobe DM1 projection neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "nathan_gouwens@hms.harvard.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 206363, + "object_name": "Drosophila" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 226 + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 120613, + "object_name": "Drosophila (fruit fly)" + } + ] + }, + "ver_date": "2019-02-26T10:10:01.623", + "ver_number": 22 + }, + "118759": { + "class_id": 19, + "created": "2009-04-09T15:44:17", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + } + ] + }, + "gitrepo": false, + "id": 118759, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 118716, + "object_name": "Babich O, Matveev V, Harris AL, Shirokov R (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 118760, + "object_name": "CalC Calcium Calculator (web link to model)" + } + ] + }, + "name": "Permeation and inactivation of CaV1.2 Ca2+ channels (Babich et al. 2007)", + "notes": { + "attr_id": 24, + "value": "The authors present data and a kinetics model of the CaV1.2 channel supporting the idea that Ca2+ block of the pore generates the U-shaped inactivation curve." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118759", + "ver_date": "2009-04-09T16:26:13", + "ver_number": 10 + }, + "118797": { + "class_id": 19, + "created": "2009-04-10T16:17:51", + "gitrepo": false, + "id": 118797, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 22010, + "object_name": "Facilitation" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 118765, + "object_name": "Matveev V, Bertram R, Sherman A (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 118760, + "object_name": "CalC Calcium Calculator (web link to model)" + } + ] + }, + "name": "Facilitation model based on bound Ca2+ (Matveev et al. 2006)", + "notes": { + "attr_id": 24, + "value": "\"Facilitation is a transient stimulation-induced increase in synaptic response, a\r\nubiquitous form of short-term synaptic plasticity that can regulate\r\nsynaptic transmission on fast time scales. \r\n\r\nIn their pioneering work, Katz and Miledi and Rahamimoff demonstrated the dependence of\r\nfacilitation on presynaptic Ca2+ influx and proposed that facilitation\r\nresults from the accumulation of residual Ca2+ bound to vesicle\r\nrelease triggers. \r\n\r\nHowever, this bound Ca2+ hypothesis appears to contradict the evidence that \r\nfacilitation is reduced by exogenous Ca2+ buffers. \r\n\r\nThis conclusion led to a widely held view that facilitation\r\nmust depend solely on the accumulation of Ca2+ in free form. \r\n\r\nHere we consider a more realistic implementation of the bound Ca2+\r\nmechanism, taking into account spatial diffusion of Ca2+, and show\r\nthat a model with slow Ca2+ unbinding steps can retain sensitivity to\r\nfree residual Ca2+. \r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118797", + "ver_date": "2015-07-26T13:58:50.357", + "ver_number": 5 + }, + "118799": { + "class_id": 19, + "created": "2009-04-13T09:56:01", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 118799, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 118701, + "object_name": "Oh M, Matveev V (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Loss of phase-locking in non-weakly coupled inhib. networks of type-I neurons (Oh and Matveev 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 150222, + "object_name": "Abstract Morris-Lecar neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "...\r\nHere we examine the loss of\r\nsynchrony caused by an increase in inhibitory coupling\r\nin networks of type-I Morris\u2013Lecar model oscillators,\r\nwhich is characterized by a period-doubling cascade\r\nand leads to mode-locked states with alternation in the\r\nfiring order of the two cells, as reported recently by\r\nMaran and Canavier (J Comput Nerosci, 2008) for a\r\nnetwork of Wang-Buzs\u00e1ki model neurons. \r\n\r\nAlthough\r\nalternating-order firing has been previously reported as\r\na near-synchronous state, we show that the stable phase\r\ndifference between the spikes of the two Morris\u2013Lecar\r\ncells can constitute as much as 70% of the unperturbed\r\noscillation period. \r\n\r\nFurther, we examine the generality\r\nof this phenomenon for a class of type-I oscillators that\r\nare close to their excitation thresholds, and provide\r\nan intuitive geometric description of such \u201cleap-frog\u201d\r\ndynamics. \r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118799", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2013-06-05T11:45:18", + "ver_number": 8 + }, + "118894": { + "class_id": 19, + "created": "2009-04-13T13:59:56", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 118894, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 118895, + "object_name": "Matveev, Victor V. [m a t v e e v at n j i t . e d u ]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 238957, + "object_name": "Markov-type model" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 118804, + "object_name": "Nguyen V, Mathias R, Smith GD (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 118760, + "object_name": "CalC Calcium Calculator (web link to model)" + } + ] + }, + "name": "Stochastic automata network Markov model descriptors of coupled Ca2+ channels (Nguyen et al. 2005)", + "notes": { + "attr_id": 24, + "value": "\"... Here we present a formalism by which mathematical models for Ca2+-regulated Ca2+ release sites\r\nare derived from stochastic models of single-channel gating that include Ca2+ activation, Ca2+\r\ninactivation, or both. \r\n\r\nSuch models are stochastic automata networks (SANs) that involve a large\r\nnumber of functional transitions, that is, the transition probabilities of the infinitesimal generator\r\nmatrix of one of the automata (i.e., an individual channel) may depend on the local [Ca2+] and\r\nthus the state of the other channels. Simulation and analysis of the SAN descriptors representing\r\nhomogeneous clusters of intracellular Ca2+ channels show that (1) release site density can modify\r\nboth the steady-state open probability and stochastic excitability of Ca2+ release sites, (2) Ca2+\r\ninactivation is not a requirement for Ca2+ puffs or sparks, and (3) a single-channel model with a\r\nbell-shaped open probability curve does not lead to release site activity that is a biphasic function of\r\nrelease site density.\r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118894", + "ver_date": "2018-02-26T11:15:59.293", + "ver_number": 10 + }, + "118986": { + "class_id": 19, + "created": "2009-04-14T10:41:36", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 118986, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 119004, + "object_name": "Miceli F, Soldovieri MV, Lugli L, Bellini G, Ambrosino P, Migliore M, del Giudice EM, Ferrari F, Pascotto A, Taglialatela M (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "118986", + "name": "ModelView" + } + ], + "default": "118986" + }, + "name": "CA1 pyramidal neurons: effects of a Kv7.2 mutation (Miceli et al. 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "NEURON mod files from the paper:\r\nMiceli et al, Neutralization of a unique, negatively-charged residue in the voltage sensor \r\nof K(V)7.2 subunits in a sporadic case of benign familial neonatal seizures, Neurobiol Dis., in press (2009).\r\nIn this paper, the model revealed that the gating changes introduced by a mutation in K(v)7.2 \r\ngenes encoding for the neuronal KM current in a case of benign familial neonatal seizures,\r\nincreased cell firing frequency, thereby triggering the neuronal hyperexcitability which underlies the observed neonatal epileptic condition.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/118986", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181601, + "object_name": "118986" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 179 + }, + "ver_date": "2014-02-06T16:14:47", + "ver_number": 6 + }, + "119153": { + "class_id": 19, + "created": "2009-04-15T13:50:33", + "gitrepo": false, + "id": 119153, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 118895, + "object_name": "Matveev, Victor V. [m a t v e e v at n j i t . e d u ]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 22010, + "object_name": "Facilitation" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 117391, + "object_name": "Matveev V, Zucker RS, Sherman A (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 118760, + "object_name": "CalC Calcium Calculator (web link to model)" + } + ] + }, + "name": "Facilitation through buffer saturation (Matveev et al. 2004)", + "notes": { + "attr_id": 24, + "value": "\"... Using computer simulations, we\r\nstudy the magnitude of synaptic facilitation (SF) that can be achieved by a buffer saturation mechanism (BSM), and explore its dependence on the\r\nendogenous buffering properties.\r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/119153", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2015-07-26T13:59:41.623", + "ver_number": 3 + }, + "119159": { + "class_id": 19, + "created": "2009-04-16T17:43:27", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 119159, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 136491, + "object_name": "Talathi Sachin [talathi at ufl.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 87578, + "object_name": "STDP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116109, + "object_name": "Talathi SS, Hwang DU, Ditto WL (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "STDP promotes synchrony of inhibitory networks in the presence of heterogeneity (Talathi et al 2008)", + "notes": { + "attr_id": 24, + "value": "\"Recently Haas et al. (J Neurophysiol 96:\r\n3305\u20133313, 2006), observed a novel form of spike timing\r\ndependent plasticity (iSTDP) in GABAergic synaptic\r\ncouplings in layer II of the entorhinal cortex. Depending\r\non the relative timings of the presynaptic input at\r\ntime tpre and the postsynaptic excitation at time tpost,\r\nthe synapse is strengthened (delta_t = t(post) - t(pre) > 0) or\r\nweakened (delta_t < 0). The temporal dynamic range of\r\nthe observed STDP rule was found to lie in the higher\r\ngamma frequency band (> or = 40 Hz), a frequency range\r\nimportant for several vital neuronal tasks. In this paper\r\nwe study the function of this novel form of iSTDP in\r\nthe synchronization of the inhibitory neuronal network.\r\nIn particular we consider a network of two unidirectionally\r\ncoupled interneurons (UCI) and two mutually\r\ncoupled interneurons (MCI), in the presence of\r\nheterogeneity in the intrinsic firing rates of each coupled\r\nneuron. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/119159", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 119191, + "object_name": "Entorhinal cortex" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 68 + }, + "ver_date": "2017-06-27T12:25:53.48", + "ver_number": 20 + }, + "119214": { + "class_id": 19, + "created": "2009-04-17T18:55:45", + "gitrepo": false, + "id": 119214, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 118895, + "object_name": "Matveev, Victor V. [m a t v e e v at n j i t . e d u ]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 22010, + "object_name": "Facilitation" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 117390, + "object_name": "Matveev V, Sherman A, Zucker RS (2002)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 118760, + "object_name": "CalC Calcium Calculator (web link to model)" + } + ] + }, + "name": "New and corrected simulations of synaptic facilitation (Matveev et al. 2002)", + "notes": { + "attr_id": 24, + "value": "\"A three-dimensional presynaptic calcium diffusion model developed to account for characteristics of transmitter release\r\nwas modified to provide for binding of calcium to a receptor and subsequent triggering of exocytosis. \r\n\r\nWhen low affinity (20 FLM) and\r\nrapid kinetics were assumed for the calcium receptor triggering exocytosis, and stimulus parameters were selected to match those\r\nof experiments, the simulations predicted a virtual invariance of the time course of transmitter release to paired stimulation,\r\nstimulation with pulses of different amplitude, and stimulation in different calcium solutions. \r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/119214", + "ver_date": "2015-07-26T14:00:37.377", + "ver_number": 4 + }, + "119266": { + "class_id": 19, + "created": "2009-04-21T11:03:22", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 112843, + "object_name": "I R" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 119266, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 112047, + "object_name": "Aging/Alzheimer`s" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 118967, + "object_name": "Markaki M, Orphanoudakis S, Poirazi P (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "119266", + "name": "ModelView" + } + ], + "default": "119266" + }, + "name": "Modelling reduced excitability in aged CA1 neurons as a Ca-dependent process (Markaki et al. 2005)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"We use a multi-compartmental model of a CA1 pyramidal cell to study changes in hippocampal excitability that result from aging-induced alterations in calcium-dependent membrane mechanisms.\r\n\r\nThe model incorporates N- and L-type calcium channels which are respectively coupled to fast and slow afterhyperpolarization potassium channels.\r\n\r\nModel parameters are calibrated using physiological data.\r\n\r\nComputer simulations reproduce the decreased excitability of aged CA1 cells, which results from increased internal calcium accumulation, subsequently larger postburst slow afterhyperpolarization, and enhanced spike frequency adaptation.\r\n\r\nWe find that aging-induced alterations in CA1 excitability can be modelled with simple coupling mechanisms that selectively link specific types of calcium channels to specific calcium-dependent potassium channels.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/119266", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181267, + "object_name": "119266" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 284 + }, + "ver_date": "2013-12-10T15:26:58", + "ver_number": 14 + }, + "119283": { + "class_id": 19, + "created": "2009-04-24T10:13:43", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 119283, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 119280, + "object_name": "Ferrante, Michele [mferr133 at bu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 112047, + "object_name": "Aging/Alzheimer`s" + }, + { + "object_id": 112853, + "object_name": "Schizophrenia" + }, + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 119284, + "object_name": "Ferrante M, Blackwell KT, Migliore M, Ascoli GA (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "119283_1", + "name": "(1-4) Gray - Control" + }, + { + "id": "119283_2", + "name": "(1) Black - Lamotrigine" + }, + { + "id": "119283_3", + "name": "(2) Black - Diazepam" + }, + { + "id": "119283_4", + "name": "(3) Black - Flindokalner" + }, + { + "id": "119283_5", + "name": "(4) Black - Lamotrigine+Flindokalner" + } + ], + "default": "119283_1" + }, + "name": "Computational neuropharmacology of CA1 pyramidal neuron (Ferrante et al. 2008)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In this paper, the model was used to show how neuroactive drugs targeting different neuronal mechanisms affect the signal integration in CA1 pyramidal neuron. Ferrante M, Blackwell KT, Migliore M, Ascoli GA (2008)" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/119283", + "other_concept": { + "attr_id": 306, + "value": "Comput. Neuropharmacology" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181584, + "object_name": "119283_1" + }, + { + "object_id": 181586, + "object_name": "119283_2" + }, + { + "object_id": 181587, + "object_name": "119283_5" + }, + { + "object_id": 181588, + "object_name": "119283_4" + }, + { + "object_id": 181902, + "object_name": "119283_3" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 204 + }, + "ver_date": "2015-01-02T21:54:38", + "ver_number": 9 + }, + "120115": { + "class_id": 19, + "created": "2009-04-24T16:45:23", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "id": 120115, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 118895, + "object_name": "Matveev, Victor V. [m a t v e e v at n j i t . e d u ]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 22010, + "object_name": "Facilitation" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 18315, + "object_name": "Yamada WM, Zucker RS (1992)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 118760, + "object_name": "CalC Calcium Calculator (web link to model)" + } + ] + }, + "name": "Transmitter release and Ca diffusion models (Yamada and Zucker 1992)", + "notes": { + "attr_id": 24, + "value": "\"A three-dimensional presynaptic calcium diffusion model developed to account for characteristics of transmitter release was modified to provide for binding of calcium to a receptor and subsequent triggering of exocytosis. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/120115", + "ver_date": "2015-07-26T14:01:17.05", + "ver_number": 4 + }, + "120117": { + "class_id": 19, + "created": "2009-04-27T17:21:54", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 120117, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 120118, + "object_name": "Andersson, Thomas [toma at math.su.se]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 96767, + "object_name": "Nociception" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 3906, + "object_name": "Herzog RI, Cummins TR, Waxman SG (2001)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "TTX-R Na+ current effect on cell response (Herzog et al 2001) (MATLAB)", + "notes": { + "attr_id": 24, + "value": "\"Small dorsal root ganglion (DRG) neurons, which include nociceptors, express multiple voltage-gated sodium currents. In addition to a classical fast inactivating tetrodotoxin-sensitive (TTX-S) sodium current, many of these cells express a TTX-resistant (TTX-R) sodium current that activates near -70 mV and is persistent at negative potentials. To investigate the possible contributions of this TTX-R persistent (TTX-RP) current to neuronal excitability, we carried out computer simulations using the Neuron program with TTX-S and -RP currents, fit by the Hodgkin-Huxley model, that closely matched the currents recorded from small DRG neurons. ...\" See paper for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/120117", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "simPFid": { + "attr_id": 488, + "value": 138 + }, + "ver_date": "2009-04-29T11:36:02", + "ver_number": 10 + }, + "120137": { + "class_id": 19, + "created": "2009-05-01T15:22:21", + "gitrepo": false, + "id": 120137, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 120136, + "object_name": "Marpeau F, Barua A, Josic K (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 120138, + "object_name": "FORTRAN (web link to a model)" + } + ] + }, + "name": "A finite volume method for stochastic integrate-and-fire models (Marpeau et al. 2009)", + "notes": { + "attr_id": 24, + "value": "\"The stochastic integrate and fire neuron is\r\none of the most commonly used stochastic models\r\nin neuroscience.\r\n\r\nAlthough some cases are analytically\r\ntractable, a full analysis typically calls for numerical\r\nsimulations.\r\n\r\nWe present a fast and accurate finite volume\r\nmethod to approximate the solution of the associated\r\nFokker-Planck equation. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/120137", + "ver_date": "2009-05-01T16:45:01", + "ver_number": 7 + }, + "120184": { + "citation": { + "attr_id": 391, + "value": "Lee, C.-C., Anton, M., Poon, C.-S., and McRae G. J. (2009) \u00a1\u00a7The Unified Theory of Homosynaptic Short Term Depression and Facilitation\u00a1\u00a8, J. Comut. Neurosci, 26(3):459-473." + }, + "class_id": 19, + "created": "2009-05-07T21:23:10", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "id": 120184, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 120235, + "object_name": "Lee, Chuang-Chung [chchlee at alum.mit.edu]" + }, + { + "object_id": 120236, + "object_name": "Anton, Mihai [mianton at mit.edu]" + }, + { + "object_id": 120237, + "object_name": "Poon, Chi-Sang [cpoon at mit.edu]" + }, + { + "object_id": 120238, + "object_name": "McRae, Gregory [mcrae at mit.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 22010, + "object_name": "Facilitation" + }, + { + "object_id": 22012, + "object_name": "Depression" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 120173, + "object_name": "Lee CC, Anton M, Poon CS, McRae GJ (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 83572, + "object_name": "Simulink" + } + ] + }, + "name": "A kinetic model unifying presynaptic short-term facilitation and depression (Lee et al. 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + }, + { + "object_id": 52419, + "object_name": "Auditory nerve" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"...\r\n\r\nHere, we propose a unified theory of synaptic short-term plasticity based on realistic yet tractable and testable model descriptions of the underlying intracellular biochemical processes. \r\n\r\nAnalysis of the model equations leads to a closed-form solution of the resonance frequency, a function of several critical biophysical parameters, as the single key indicator of the propensity for synaptic facilitation or depression under repetitive stimuli. \r\n\r\nThis integrative model is supported by a broad range of transient and frequency response experimental data including those from facilitating, depressing or mixed-mode synapses. \r\n... the model provides the reasons behind the switching behavior between facilitation and depression observed in experiments. ...\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/120184", + "other_implementer": { + "attr_id": 308, + "value": "Lee, Chuang-Chung \r\nAnton, Mihai \r\nPoon, Chi-Sang \r\nMcRae, Gregory " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "chchlee@alum.mit.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2009-05-08T16:20:37", + "ver_number": 9 + }, + "120227": { + "class_id": 19, + "created": "2009-05-08T15:00:54", + "gitrepo": false, + "id": 120227, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + }, + { + "object_id": 22012, + "object_name": "Depression" + }, + { + "object_id": 83539, + "object_name": "Attractor Neural Network" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 120226, + "object_name": "Chandrasekaran L, Matveev V, Bose A (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Multistability of clustered states in a globally inhibitory network (Chandrasekaran et al. 2009)", + "notes": { + "attr_id": 24, + "value": "\"We study a network of m identical excitatory cells projecting excitatory synaptic connections onto a single inhibitory interneuron, which is reciprocally coupled to all excitatory cells through inhibitory synapses possessing short-term synaptic depression.\r\n\r\nWe find that such a network with global inhibition possesses multiple stable activity patterns with distinct periods, characterized by the clustering of the excitatory cells into synchronized sub-populations.\r\n\r\nWe prove the existence and stability of n-cluster solutions in a m-cell network.\r\n\r\n... Implications for temporal coding and memory storage are discussed.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/120227", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2009-05-08T15:59:10", + "ver_number": 5 + }, + "120243": { + "citation": { + "attr_id": 391, + "value": "Huang CW, Cheng JT, Tsai JJ, Wu SN, Huang CC. Diabetic hyperglycemia aggravates seizures and status epilepticus-induced hippocampal damage. Neurotox Res (2009) 15:71-81." + }, + "class_id": 19, + "created": "2009-05-11T11:34:52", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 87769, + "object_name": "ATP-senstive potassium current" + } + ] + }, + "gitrepo": false, + "id": 120243, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 120244, + "object_name": "Huang CW, Cheng JT, Tsai JJ, Wu SN, Huang CC (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Contribution of ATP-sensitive potassium channels in the neuronal network (Huang et al. 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Epileptic seizures in diabetic hyperglycemia (DH) are not uncommon. \r\n\r\nThis study aimed to determine the acute behavioral, pathological, and electrophysiological effects of status epilepticus (SE) on diabetic animals. \r\n\r\n...\r\n\r\nWe also used a simulation model to evaluate intracellular adenosine triphosphate (ATP) and neuroexcitability.\r\n\r\n...\r\nIn the simulation, increased intracellular ATP concentration promoted action potential firing.\r\n\r\nThis finding that rats with DH had more brain damage after SE than rats without diabetes suggests the importance of intensively treating hyperglycemia and seizures in diabetic patients with epilepsy." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/120243", + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2009-05-11T13:38:41", + "ver_number": 3 + }, + "120246": { + "citation": { + "attr_id": 391, + "value": "Wang YJ, Chen BS, Lin MW, Lin AA, Peng H, Sung RJ, Wu SN. Time-dependent block of ultrarapid-delayed rectifier K(+) currents by aconitine, a potent cardiotoxin in heart-derived H9c2 myoblasts and in neonatal rat ventricular myocytes. Toxicol Sci (2008) 106:454-463." + }, + "class_id": 19, + "created": "2009-05-12T10:01:51", + "gitrepo": false, + "id": 120246, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 116229, + "object_name": "Wang YJ, Chen BS, Lin MW, Lin AA, Peng H, Sung RJ, Wu SN (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Action potential of adult rat ventricle (Wang et al. 2008)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88205, + "object_name": "Cardiac ventricular cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Aconitine (ACO), a highly toxic diterpenoid alkaloid, is recognized to have effects \r\non cardiac voltage-gated Na(+) channels. However, it remains unknown whether it has \r\nany effects on K(+) currents. The effects of ACO on ion currents in differentiated \r\nclonal cardiac (H9c2) cells and in cultured neonatal rat ventricular myocytes were \r\ninvestigated in this study. ...\" The rat action potential in this simulation was played back into the cell for experiments reported in this paper." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/120246", + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2009-05-12T10:51:34", + "ver_number": 3 + }, + "120320": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 120325, + "object_name": "local copy of web linked model" + } + ] + }, + "class_id": 19, + "created": "2009-05-13T16:29:25", + "gitrepo": false, + "id": 120320, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 120323, + "object_name": "Le Novere, Nicolas [lenov at ebi.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 120242, + "object_name": "Edelstein SJ, Schaad O, Henry E, Bertrand D, Changeux JP (1996)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 55276, + "object_name": "Virtual Cell (web link to model)" + }, + { + "object_id": 87474, + "object_name": "XML (web link to model)" + }, + { + "object_id": 91894, + "object_name": "XPP (web link to model)" + }, + { + "object_id": 114648, + "object_name": "Scilab (web link to model)" + }, + { + "object_id": 120321, + "object_name": "CellML (web link to model)" + }, + { + "object_id": 120322, + "object_name": "BioPAX (web link to model)" + }, + { + "object_id": 125746, + "object_name": "SBML (web link to model)" + } + ] + }, + "name": "A nicotinic acetylcholine receptor kinetic model (Edelstein et al. 1996)", + "notes": { + "attr_id": 24, + "value": "Nicotinic acetylcholine receptors are transmembrane\r\noligomeric proteins that mediate interconversions\r\nbetween open and closed channel states under the\r\ncontrol of neurotransmitters. \r\n\r\n..\r\n\r\nIn order to represent the functional properties of such\r\nreceptors, we have developed a kinetic model that links\r\nconformational interconversion rates to agonist binding\r\nand extends the general principles of the Monod-\r\nWyman-Changeux model of allosteric transitions. \r\n\r\n...\r\nApplication of the model to the peripheral nicotinic acetylcholine receptor\r\n(nAChR) accounts for the main properties of ligand-gating,\r\nincluding single-channel events, and several new\r\nrelationships are predicted.\r\n...\r\nIn terms of future developments, the analysis presented here provides\r\na physical basis for constructing more biologically realistic\r\nmodels of synaptic modulation that may be applied to\r\nartificial neural networks.\r\n" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/120320", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 178, + "object_name": "Nicotinic" + }, + { + "object_id": 218, + "object_name": "Cholinergic Receptors" + } + ] + }, + "ver_date": "2010-02-03T10:26:32", + "ver_number": 12 + }, + "120521": { + "class_id": 19, + "created": "2009-05-15T15:36:14", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 88209, + "object_name": "Na/Ca exchanger" + } + ] + }, + "gitrepo": false, + "id": 120521, + "implemented_by": { + 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These papers describe the sensitivity these models have to their parameters and the match of the models to experimental data." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/120521", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2009-05-15T15:49:10", + "ver_number": 2 + }, + "120692": { + "citation": { + "attr_id": 391, + "value": "6308260" + }, + "class_id": 19, + "created": "2009-05-27T19:36:54", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 120692, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33736, + "object_name": "Hines, Michael [Michael.Hines at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 120695, + "object_name": "Sodium pump" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 95372, + "object_name": "Chapman JB, Johnson EA, Kootsey JM (1983)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 120694, + "object_name": "Electrogenic pump" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "120692_1", + "name": "Figure 2-3" + }, + { + "id": "120692_2", + "name": "Figure 4-6" + }, + { + "id": "120692_3", + "name": "Figure 7" + }, + { + "id": "120692_4", + "name": "Figure 8-10" + }, + { + "id": "120692_5", + "name": "Figure 11-12" + } + ], + "default": "120692_1" + }, + "name": "Sodium potassium ATPase pump (Chapman et al. 1983)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The electrochemical properties of a widely accepted six-step reaction scheme for the Na,K-ATPase have been studied by computer simulation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/120692", + "other_concept": { + "attr_id": 306, + "value": "sodium pump" + }, + "other_currents": { + "attr_id": 303, + "value": "Na/K pump" + }, + "other_type": { + "attr_id": 305, + "value": "electrogenic pump" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "michael.hines@yale.edu" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181257, + "object_name": "120692_5" + }, + { + "object_id": 181258, + "object_name": "120692_4" + }, + { + "object_id": 181259, + "object_name": "120692_3" + }, + { + "object_id": 181260, + "object_name": "120692_2" + }, + { + "object_id": 181261, + "object_name": "120692_1" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 608 + }, + "ver_date": "2015-01-02T22:39:23", + "ver_number": 15 + }, + "120783": { + "citation": { + "attr_id": 391, + "value": "GENERATING OSCILLATORY BURSTS FROM A NETWORK OF REGULAR SPIKING NEURONS WITHOUT INHIBITION, Journal of Computational Neuroscience" + }, + "class_id": 19, + "created": "2009-06-01T14:09:10", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 120783, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 120784, + "object_name": "Lai, Dihui [dlai at artsci.wustl.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 120785, + "object_name": "Shao J, Lai D, Meyer U, Luksch H, Wessel R (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Generating oscillatory bursts from a network of regular spiking neurons (Shao et al. 2009)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Avian nucleus isthmi pars parvocellularis (Ipc) neurons are reciprocally connected with the tectal layer 10 (L10) neurons and respond with oscillatory bursts to visual stimulation. To elucidate mechanisms of oscillatory bursting in this network of regularly spiking neurons, we investigated an experimentally constrained model of coupled leaky integrate-and-fire neurons with spike-rate adaptation. The model reproduces the observed Ipc oscillatory bursting in response to simulated visual stimulation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/120783", + "other_implementer": { + "attr_id": 308, + "value": "Lai, Dihui " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "dlai@artsci.wustl.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 62 + }, + "ver_date": "2018-10-17T16:52:10.497", + "ver_number": 32 + }, + "120798": { + "class_id": 19, + "created": "2009-06-05T11:48:19", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 120798, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33772, + "object_name": "Morse, Tom [Tom.Morse at Yale.edu]" + }, + { + "object_id": 139419, + "object_name": "Stuart, Greg J. 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We developed and tested a kinetic Markov model to interpret physiologically relevant IKs properties, including pause-dependency and modulation by beta-adrenergic receptors (beta-AR). The model was developed from the Silva & Rudy formulation. Parameters were optimized on control and ISO experimental data, respectively." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/120835", + "other_implementer": { + "attr_id": 308, + "value": "Severi, Stefano " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "stefano.severi@unibo.it" + }, + "ver_date": "2018-02-26T11:20:46.567", + "ver_number": 7 + }, + "120907": { + "class_id": 19, + "created": "2009-06-10T16:57:30", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "id": 120907, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 120908, + "object_name": "Munro, Erin [ecmun at math.bu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 114307, + "object_name": "Conduction failure" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 126900, + "object_name": "Munro E, B\u00f6rgers C (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Mechanisms of very fast oscillations in axon networks coupled by gap junctions (Munro, Borgers 2010)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Axons connected by gap junctions can produce very fast oscillations (VFOs, > 80 Hz) when stimulated randomly at a low rate. The models here explore the mechanisms of VFOs that can be seen in an axonal plexus, (Munro & Borgers, 2009): a large network model of an axonal plexus, small network models of axons connected by gap junctions, and an implementation of the model underlying figure 12 in Traub et al. (1999) .\r\n\r\nThe large network model consists of 3,072 5-compartment axons connected in a random network. The 5-compartment axons are the 5 axonal compartments from the CA3 pyramidal cell model in Traub et al. (1994) with a fixed somatic voltage. The random network has the same parameters as the random network in Traub et al. 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"object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 97752, + "object_name": "Delay" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 121063, + "object_name": "Steephen JE, Manchanda R (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Effects of KIR current inactivation in NAc Medium Spiny Neurons (Steephen and Manchanda 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 112837, + "object_name": "Nucleus accumbens spiny projection neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Inward rectifying potassium (KIR) currents in medium spiny (MS) neurons of nucleus accumbens inactivate significantly in ~40% of the neurons but not in the rest, which may lead to differences in input processing by these two groups. \r\n\r\nUsing a 189-compartment computational model of the MS neuron, we investigate the influence of this property using injected current as well as spatiotemporally distributed synaptic inputs. \r\n\r\nOur study demonstrates that KIR current inactivation facilitates depolarization, firing frequency and firing onset in these neurons. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/121060", + "other_implementer": { + "attr_id": 308, + "value": "Steephen, John Eric" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "johneric@iitb.ac.in" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": 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many STN axons, although a subset of STN axons may also be activated at the stimulating frequency.\r\n\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/121253", + "other_concept": { + "attr_id": 306, + "value": "Depolarization block" + }, + "other_implementer": { + "attr_id": 308, + "value": "Bellinger, Steven" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "Peter.Steinmetz@chw.edu" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181242, + "object_name": "121253_1" + }, + { + "object_id": 181244, + "object_name": "121253_2" + } + ] + }, + "ver_date": "2015-01-02T22:30:50", + "ver_number": 9 + }, + "121259": { + "class_id": 19, + "created": "2009-06-20T13:06:43", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + 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Topics covered include passive electrical properties, composition of action potentials, trains of action potentials, multicompartment modeling, and research topics. The paper includes detailed instructions on how to run the simulations in the appendix." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/121600", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2017-09-22T17:08:41.74", + "ver_number": 12 + }, + "121628": { + "class_id": 19, + "created": "2009-06-24T17:47:00", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 121628, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3642, + "object_name": "Tutorial/Teaching" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 121613, + "object_name": "Grisham W, Schottler NA, Krasne FB (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "Software for teaching neurophysiology of neuronal circuits (Grisham et al. 2008)", + "notes": { + "attr_id": 24, + "value": "\"To circumvent the many problems in teaching neurophysiology as a \u201cwet lab,\u201d we developed SWIMMY, a virtual fish that swims by moving its virtual tail by means of a virtual neural circuit.\r\n...\r\nUsing SWIMMY, students (1) review the basics of neurophysiology, (2) identify the neurons in the circuit, (3) ascertain the neurons\u2019 synaptic interconnections, (4) discover which cells generate the motor pattern of swimming, (5) discover how the rhythm is generated, and finally (6) use an animation that corresponds to the activity of the motoneurons to discover the behavioral effects produced by various lesions and explain them in terms of their neural underpinnings.\r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/121628", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115962, + "object_name": "Unknown" + } + ] + }, + "ver_date": "2009-06-24T17:48:48", + "ver_number": 3 + }, + "122329": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 136023, + "object_name": "original authors version" + } + ] + }, + "class_id": 19, + "created": "2009-07-01T15:55:03", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 122329, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 122330, + "object_name": "Deister, Christopher [chris.deister at utsa.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 122331, + "object_name": "Noise Sensitivity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 122332, + "object_name": "Deister CA, Chan CS, Surmeier DJ, Wilson CJ (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "122329_1", + "name": "with SK soma" + }, + { + "id": "122329_2", + "name": "no SK soma" + } + ], + "default": "122329_1" + }, + "name": "Model of SK current`s influence on precision in Globus Pallidus Neurons (Deister et al. 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88217, + "object_name": "Globus pallidus neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... In numerical simulations, the availability of both Na+ and A-type K+ channels during autonomous firing were reduced when SK channels were removed, and a nearly equal reduction in Na+ and K+ subthreshold-activated ion channel availability produced a large decrease in the neuron's slope conductance near threshold. \r\n\r\nThis change made the neuron more sensitive to intrinsically generated noise.\r\n\r\nIn vivo, this change would also enhance the sensitivity of GP (Globus Pallidus) neurons to small synaptic inputs.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/122329", + "other_concept": { + "attr_id": 306, + "value": "Noise Sensitivity" + }, + "other_implementer": { + "attr_id": 308, + "value": "Deister, Christopher" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181393, + "object_name": "122329_2" + }, + { + "object_id": 181857, + "object_name": "122329_1" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 282 + }, + "ver_date": "2022-11-04T17:12:33.42", + "ver_number": 18 + }, + "122369": { + "class_id": 19, + "created": "2009-07-02T11:09:03", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "id": 122369, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 122446, + "object_name": "Pirini, Marco [marco.pirini at unibo.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 118365, + "object_name": "Pirini M, Rocchi L, Sensi M, Chiari L (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Investigation of different targets in deep brain stimulation for Parkinson`s (Pirini et al. 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + }, + { + "object_id": 74300, + "object_name": "Subthalamus nucleus projection neuron" + }, + { + "object_id": 88217, + "object_name": "Globus pallidus neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"We investigated by a computational model of the basal ganglia the different network effects of deep brain stimulation (DBS) for Parkinson\u2019s disease (PD) in different target sites in the subthalamic nucleus (STN), the globus pallidus pars interna (GPi), and the globus pallidus pars externa (GPe). \r\n\r\nA cellular-based model of the basal ganglia system (BGS), based on the model proposed by Rubin and Terman (J Comput Neurosci 16:211\u2013235, 2004), was developed.\r\n...\r\nOur results suggest that DBS in the STN could functionally restore the TC relay activity, while DBS in the GPe and in the GPi could functionally over-activate and inhibit it, respectively.\r\n\r\nOur results are consistent with the experimental and the clinical evidences on the network effects of DBS.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/122369", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 115951, + "object_name": "Thalamus" + }, 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simulate data from compound action potentials measured in mouse optic nerve from Claudin 11-null mice in Fig. 6 of:\r\nDevaux, J.J. & Gow, A. (2008) Tight Junctions Potentiate The Insulative Properties Of Small CNS Myelinated Axons. J Cell Biol 183, 909-921.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/122442", + "other_concept": { + "attr_id": 306, + "value": "Myelin properties" + }, + "other_implementer": { + "attr_id": 308, + "value": "Gow, Alexander\r\nDevaux, Jerome" + }, + "other_type": { + "attr_id": 305, + "value": "Oligodendrocyte" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "agow@med.wayne.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115962, + "object_name": "Unknown" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181319, + "object_name": "122442_1" + }, + { + "object_id": 181320, + "object_name": "122442_2" + } + ] + }, + "ver_date": "2015-01-02T22:07:35", + "ver_number": 14 + }, + "123086": { + "class_id": 19, + "created": "2009-07-19T21:47:05", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 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\u201cearly vision\u201d stage for long-range completion via\r\nfilling-in.\r\n\r\nThe reconstructed\r\nimage is then re-presented to the recognition system for global\r\nfunctions such as object\r\nrecognition.\r\n\r\n...\r\nMulti-scale simulations illustrate the\r\nvision/recognition system. \r\n\r\n...\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/123086", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2017-06-22T17:18:34.153", + "ver_number": 10 + }, + "123453": { + "class_id": 19, + "created": "2009-07-28T00:32:54", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122326, + "object_name": "Kv1.1 KCNA1" + }, + { + "object_id": 122803, + "object_name": "Kv4.3 KCND3" + }, + { + "object_id": 122808, + "object_name": "Kv3.3 KCNC3" + }, + { + "object_id": 123580, + "object_name": "Kv3.4 KCNC4" + }, + { + "object_id": 226057, + "object_name": "HCN1" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 123453, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 82363, + "object_name": "Akemann, Walther [akemann at brain.riken.jp]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 123270, + "object_name": "Akemann W, Lundby A, Mutoh H, Kn\u00f6pfel T (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "123453", + "name": "ModelView" + } + ], + "default": "123453" + }, + "name": "Effect of voltage sensitive fluorescent proteins on neuronal excitability (Akemann et al. 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Fluorescent protein voltage sensors are recombinant proteins that are designed as genetically encoded cellular\r\nprobes of membrane potential using mechanisms of voltage-dependent modulation of fluorescence. \r\n\r\nSeveral such proteins,\r\nincluding VSFP2.3 and VSFP3.1, were recently reported with reliable function in mammalian cells. \r\n\r\n...\r\n\r\nExpression of these proteins in cell membranes is accompanied by additional dynamic membrane capacitance, ...\r\n\r\nWe used recordings of\r\nsensing currents and fluorescence responses of VSFP2.3 and of VSFP3.1 to derive kinetic models of the voltage-dependent\r\nsignaling of these proteins. \r\n\r\nUsing computational neuron simulations, we quantitatively investigated the perturbing effects of\r\nsensing capacitance on the input/output relationship in two central neuron models, a cerebellar Purkinje and a layer 5 pyramidal\r\nneuron. \r\n... \". The Purkinje cell model is included in ModelDB." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/123453", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 228 + }, + "ver_date": "2017-03-18T10:04:05.28", + "ver_number": 13 + }, + "123623": { + "class_id": 19, + "created": "2009-07-30T09:40:51", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 123623, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33744, + "object_name": "Destexhe, Alain [Destexhe at iaf.cnrs-gif.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 122785, + "object_name": "Pospischil M, Toledo-Rodriguez M, Monier C, Piwkowska Z, Bal T, Fr\u00e9gnac Y, Markram H, Destexhe A (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "123623_1", + "name": "Regular-spiking pyramidal cell" + }, + { + "id": "123623_2", + "name": "Bursting pyramidal cell" + }, + { + "id": "123623_3", + "name": "Repetitive bursting pyramidal cell" + }, + { + "id": "123623_4", + "name": "LTS pyramidal cell" + }, + { + "id": "123623_5", + "name": "Fast-spiking interneuron" + } + ], + "default": "123623_1" + }, + "name": "Hodgkin-Huxley models of different classes of cortical neurons (Pospischil et al. 2008)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + }, + { + "object_id": 142232, + "object_name": "Neocortex spiking low threshold (LTS) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"We review here the development of Hodgkin-\r\nHuxley (HH) type models of cerebral cortex and thalamic\r\nneurons for network simulations.\r\n\r\nThe intrinsic electrophysiological\r\nproperties of cortical neurons were analyzed from\r\nseveral preparations, and we selected the four most prominent\r\nelectrophysiological classes of neurons.\r\n\r\nThese four classes\r\nare 'fast spiking', 'regular spiking', 'intrinsically bursting'\r\nand 'low-threshold spike' cells. For each class, we fit 'minimal'\r\nHH type models to experimental data.\r\n...\"" + }, + "opensourcebrain": { + "value": "pospischiletal2008" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/123623", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 262 + }, + "ver_date": "2016-05-26T15:44:10.18", + "ver_number": 18 + }, + "123815": { + "class_id": 19, + "created": "2009-08-18T11:40:42", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 123815, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33787, + "object_name": "Graham, Bruce [B.Graham at cs.stir.ac.uk]" + }, + { + "object_id": 93425, + "object_name": "Cutsuridis, Vassilis [vcutsuridis at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3542, + "object_name": "Pattern Recognition" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 114660, + "object_name": "Connectivity matrix" + }, + { + "object_id": 151331, + "object_name": "Storage/recall" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 123816, + "object_name": "Cutsuridis V, Cobb S, Graham BP (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "123815", + "name": "ModelView" + } + ], + "default": "123815" + }, + "name": "Encoding and retrieval in a model of the hippocampal CA1 microcircuit (Cutsuridis et al. 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 114079, + "object_name": "Hippocampus CA1 basket cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This NEURON code implements a small network model (100 pyramidal cells\r\nand 4 types of inhibitory interneuron) of storage and recall of patterns\r\nin the CA1 region of the mammalian hippocampus. Patterns of PC activity\r\nare stored either by a predefined weight matrix generated by Hebbian learning,\r\nor by STDP at CA3 Schaffer collateral AMPA synapses." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/123815", + "other_currents": { + "attr_id": 303, + "value": "many ionic currents" + }, + "other_neurons": { + "attr_id": 301, + "value": "CA1 axo-axonic cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "b.graham@cs.stir.ac.uk" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2022-05-27T10:21:39.987", + "ver_number": 15 + }, + "123848": { + "citation": { + "attr_id": 391, + "value": "19682031" + }, + "class_id": 19, + "created": "2009-08-25T20:28:03", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "id": 123848, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 123851, + "object_name": "Thomas, Evan [evan at evan-thomas.net]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 123849, + "object_name": "Thomas EA, Reid CA, Petrou S (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 123850, + "object_name": "parplex" + } + ] + }, + "name": "Na channel mutations in the dentate gyrus (Thomas et al. 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + }, + { + "object_id": 52416, + "object_name": "Dentate gyrus mossy cell" + }, + { + "object_id": 52417, + "object_name": "Dentate gyrus basket cell" + }, + { + "object_id": 88216, + "object_name": "Dentate gyrus hilar cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "These are source files to generate the data in Figure 6 from\r\n\"Mossy fiber sprouting interacts with sodium channel\r\nmutations to increase dentate gyrus excitability\" Thomas EA, Reid CA, Petrou S,\r\nEpilepsia (2009)" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/123848", + "other_implementer": { + "attr_id": 308, + "value": "Thomas, Evan " + }, + "other_simulator": { + "attr_id": 307, + "value": "parplex" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "evan@evan-thomas.net" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2009-08-26T10:38:11", + "ver_number": 3 + }, + "123897": { + "citation": { + "attr_id": 391, + "value": "Hu, W., C. Tian, et al. (2009). \"Distinct contributions of Na(v)1.6 and Na(v)1.2 in action potential initiation and backpropagation.\" Nat Neurosci 12(8): 996-1002." + }, + "class_id": 19, + "created": "2009-09-02T22:21:16", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92083, + "object_name": "Nav1.2 SCN2A" + }, + { + "object_id": 92087, + "object_name": "Nav1.6 SCN8A" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 123897, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 123910, + "object_name": "Hu, Wenqin [huwenqin at ion.ac.cn]" + }, + { + "object_id": 123911, + "object_name": "Hou, Han [hh at ion.ac.cn]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 123901, + "object_name": "Hu W, Tian C, Li T, Yang M, Hou H, Shu Y (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Action Potential initiation and backpropagation in Neocortical L5 Pyramidal Neuron (Hu et al. 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 167344, + "object_name": "Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell" + }, + { + "object_id": 167343, + "object_name": "Neocortex U1 L5B pyramidal pyramidal tract GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"...Previous computational studies have yielded conflicting conclusions\r\nabout the role of Na+ channel density and biophysical properties in\r\naction potential initiation as a result of inconsistent estimates of\r\nchannel density. Our modeling studies integrated the immunostaining\r\nand electrophysiological results and showed that the lowest\r\nthreshold for action potential initiation at the distal AIS was largely\r\ndetermined by the density of low-threshold Nav1.6 channels ... Distinct from the function of Nav1.6 channel, the Nav1.2 channel\r\nmay control action potential backpropagation because of its high\r\ndensity at the proximal AIS and high threshold. ... In conclusion, distal AIS accumulation of Nav1.6 channels determines\r\nthe low threshold for action potential initiation; whereas\r\nproximal AIS accumulation of Nav1.2 channels sets the threshold for\r\nthe generation of somatodendritic potentials and ensures action\r\npotential backpropagation to the soma and dendrites. Thus, Nav1.6\r\nand Nav1.2 channels serve distinct functions in action potential\r\ninitiation and backpropagation.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/123897", + "other_concept": { + "attr_id": 306, + "value": "AP" + }, + "other_currents": { + "attr_id": 303, + "value": "I Nav1.6; I Nav1.2" + }, + "other_implementer": { + "attr_id": 308, + "value": "Hu, Wenqin \r\nHou, Han " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "hh@ion.ac.cn" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181773, + "object_name": "123897_1" + }, + { + "object_id": 181775, + "object_name": "123897_3" + }, + { + "object_id": 181868, + "object_name": "123897_2" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 146 + }, + "ver_date": "2017-07-03T22:13:48.16", + "ver_number": 25 + }, + "123927": { + "class_id": 19, + "created": "2009-09-08T11:43:15", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 246, + "object_name": "I p,q" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 127284, + "object_name": "Nav SCN1B" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 123927, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 125153, + "object_name": "Horstmann, Marie-Therese [mhorstma at uni-bonn.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 142691, + "object_name": "Wimmer VC, Reid CA, Mitchell S, Richards KL, Scaf BB, Leaw BT, Hill EL, Royeck M, Horstmann MT, Cromer BA, Davies PJ, Xu R, Lerche H, Berkovic SF, Beck H, Petrou S (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "123927", + "name": "ModelView" + } + ], + "default": "123927" + }, + "name": "Complex CA1-neuron to study AP initiation (Wimmer et al. 2010)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Complex model of a pyramidal CA1-neuron, adapted from Royeck, M., et al. Role of axonal NaV1.6 sodium channels in action potential\r\ninitiation of CA1 pyramidal neurons. Journal of neurophysiology 100, 2361-2380\r\n(2008).\r\nIt contains a biophysically realistic morphology comprising 265 compartments (829 segments) and 15 different distributed Ca2+- and/or voltage-dependent conductances." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/123927", + "other_gene": { + "attr_id": 477, + "value": "Nav SCN1b" + }, + "other_implementer": { + "attr_id": 308, + "value": "Horstmann,Marie-Therese " + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181146, + "object_name": "123927" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 203 + }, + "ver_date": "2013-12-10T15:28:19", + "ver_number": 18 + }, + "123928": { + "citation": { + "attr_id": 391, + "value": "Oscillations, Phase-of-Firing Coding and STDP: an efficient learning scheme. J Neurosc (in press)" + }, + "class_id": 19, + "created": "2009-09-09T11:57:09", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 123928, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 123929, + "object_name": "Masquelier, Tim [timothee.masquelier at alum.mit.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3542, + "object_name": "Pattern Recognition" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 83538, + "object_name": "Unsupervised Learning" + }, + { + "object_id": 87578, + "object_name": "STDP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 123930, + "object_name": "Masquelier T, Hugues E, Deco G, Thorpe SJ (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Oscillations, phase-of-firing coding and STDP: an efficient learning scheme (Masquelier et al. 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model demonstrates how a common oscillatory drive for a group of neurons formats and reliabilizes their spike times - through an activation-to-phase conversion - so that repeating activation patterns can be easily detected and learned by a downstream neuron equipped with STDP, and then recognized in just one oscillation cycle." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/123928", + "other_implementer": { + "attr_id": 308, + "value": "Masquelier, Tim " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "timothee.masquelier@alum.mit.edu" + }, + "ver_date": "2017-07-28T13:56:12.797", + "ver_number": 10 + }, + "123986": { + "class_id": 19, + "created": "2009-09-18T09:49:00", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 123986, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3542, + "object_name": "Pattern Recognition" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 123606, + "object_name": "Horcholle-Bossavit G, Quenet B, Foucart O (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Oscillation and coding in a proposed NN model of insect olfaction (Horcholle-Bossavit et al. 2007)", + "notes": { + "attr_id": 24, + "value": "\"For the analysis of coding mechanisms in the insect olfactory system, a fully connected network of synchronously updated\r\nMcCulloch and Pitts neurons (MC-P type) was (previously) developed. ... Considering the update time as an intrinsic clock, this \u201cDynamic\r\nNeural Filter\u201d (DNF), which maps regions of input space into spatio-temporal sequences of neuronal activity, is able to produce\r\nexact binary codes extracted from the synchronized activities recorded at the level of projection neurons (PN) in the locust antennal\r\nlobe (AL) in response to different odors\r\n...\r\nWe find synaptic matrices which lead to both the emergence of robust oscillations and spatio-temporal patterns, using a\r\nformal criterion, based on a Normalized Euclidian Distance (NED), in order to measure the use of the temporal dimension as a\r\ncoding dimension by the DNF. Similarly to biological PN, the activity of excitatory neurons in the model can be both phase-locked\r\nto different cycles of oscillations which (is reminiscent of the) local field potential (LFP), and nevertheless exhibit dynamic behavior complex\r\nenough to be the basis of spatio-temporal codes.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/123986", + "ver_date": "2018-10-17T16:07:02.91", + "ver_number": 11 + }, + "123987": { + "class_id": 19, + "created": "2009-09-18T10:51:04", + "gitrepo": false, + "id": 123987, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 123988, + "object_name": "Horcholle-Bossavit G, Quenet B (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Neural model of frog ventilatory rhythmogenesis (Horcholle-Bossavit and Quenet 2009)", + "notes": { + "attr_id": 24, + "value": "\"In the adult frog respiratory system, periods of rhythmic movements of the buccal floor are interspersed\r\nby lung ventilation episodes. \r\n\r\nThe ventilatory activity results from the interaction of two hypothesized\r\noscillators in the brainstem. \r\n\r\nHere, we model these oscillators with two coupled neural networks, whose\r\nco-activation results in the emergence of new dynamics. \r\n\r\n..\r\nThe biological interest of this formal model is illustrated by\r\nthe persistence of the relevant dynamical features when perturbations are introduced in the model, i.e.\r\ndynamic noises and architecture modifications. \r\n\r\nThe implementation of the networks with clock-driven\r\ncontinuous time neurones provides simulations with physiological time scales.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/123987", + "simPFid": { + "attr_id": 488, + "value": 125 + }, + "ver_date": "2009-09-18T11:04:50", + "ver_number": 4 + }, + "124043": { + "citation": { + "attr_id": 391, + "value": "Synaptic integration in tuft dendrites of layer 5 pyramidal neurons: a new unifying principle. Larkum et al. 2009" + }, + "class_id": 19, + "created": "2009-09-20T09:14:43", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 246, + "object_name": "I p,q" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 124043, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 124046, + "object_name": "Polsky, Alon [alonpol at tx.technion.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 124045, + "object_name": "Larkum ME, Nevian T, Sandler M, Polsky A, Schiller J (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "124043", + "name": "ModelView" + } + ], + "default": "124043" + }, + "name": "Synaptic integration in tuft dendrites of layer 5 pyramidal neurons (Larkum et al. 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Simulations used in the paper. Voltage responses to current injections in different tuft locations; NMDA and calcium spike generation. Summation of multiple input distribution." + }, + "opensourcebrain": { + "value": "larkumetal2009" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/124043", + "other_implementer": { + "attr_id": 308, + "value": "Polsky, Alon\r\nalonpol@tx.technion.ac.il" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "alonpol@tx.technion.ac.il" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181869, + "object_name": "124043" + } + ] + }, + "ver_date": "2014-02-25T01:12:28", + "ver_number": 16 + }, + "124063": { + "citation": { + "attr_id": 391, + "value": "\"A Computational Study on the Role of Gap Junctions and Rod Ih Conductance in the Enhancement of the Dynamic Range of the Retina\". PLoS ONE 4(9): e6970. doi:10.1371/journal.pone.0006970" + }, + "class_id": 19, + "created": "2009-09-28T03:14:10", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 124063, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 124065, + "object_name": "Publio, Rodrigo [publio at oist.jp]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 140966, + "object_name": "Sensory processing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 124064, + "object_name": "Publio R, Oliveira RF, Roque AC (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A network model of the vertebrate retina (Publio et al. 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 270, + "object_name": "Retina ganglion GLU cell" + }, + { + "object_id": 1966, + "object_name": "Retina photoreceptor cone GLU cell" + }, + { + "object_id": 7020, + "object_name": "Retina bipolar GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In this work, we use a minimal conductance-based model of the ON rod pathways in the vertebrate retina to study the effects of electrical synaptic coupling via gap junctions among rods and among AII amacrine cells on the dynamic range of the retina. The model is also used to study the effects of the maximum conductance of rod hyperpolarization activated current Ih on the dynamic range of the retina, allowing a study of the interrelations between this intrinsic membrane parameter with those two retina connectivity characteristics." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/124063", + "other_concept": { + "attr_id": 306, + "value": "Dynamic Range" + }, + "other_implementer": { + "attr_id": 308, + "value": "Publio, Rodrigo\r\npublio@oist.jp" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "publio@oist.jp" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181394, + "object_name": "124063" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 142 + }, + "ver_date": "2015-01-02T22:52:22", + "ver_number": 12 + }, + "124111": { + "citation": { + "attr_id": 391, + "value": "Girard, B., Tabareau, N., Pham, Q.C., Berthoz, A. and Slotine, J.-J. (2008). Where neuroscience and dynamic system theory meet autonomous robotics: a contracting basal ganglia model for action selection. Neural Networks, 21(4):628-641. [doi:10.1016/j.neunet.2008.03.009]" + }, + "class_id": 19, + "created": "2009-09-29T06:57:05", + "gitrepo": false, + "id": 124111, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 124115, + "object_name": "Girard, Benoit [girard at isir.upmc.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 65222, + "object_name": "Gurney K, Prescott TJ, Redgrave P (2001)" + }, + { + "object_id": 116879, + "object_name": "Prescott TJ, Montes Gonz\u00e1lez FM, Gurney K, Humphries MD, Redgrave P (2006)" + }, + { + "object_id": 124114, + "object_name": "Girard B, Tabareau N, Pham QC, Berthoz A, Slotine JJ (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ 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Journal of Computational Neuroscience. \r\nSee http://dx.doi.org/10.1007/s10827-009-0191-1 for details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/124329", + "other_implementer": { + "attr_id": 308, + "value": "Rivest, Francois" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "francois.rivest@mail.mcgill.ca" + }, + "ver_date": "2015-03-09T11:51:22.833", + "ver_number": 6 + }, + "124392": { + "citation": { + "attr_id": 391, + "value": "Thomas EA, Reid CA, Berkovic SF, Petrou S (2009) Prediction by Modeling That Epilepsy May Be Caused by Very Small Functional Changes in Ion Channel Arch Neurol. 66(10):1225-1232" + }, + "class_id": 19, + "created": "2009-10-14T20:43:04", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "id": 124392, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 123851, + "object_name": "Thomas, Evan [evan at evan-thomas.net]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 124332, + "object_name": "Thomas EA, Reid CA, Berkovic SF, Petrou S (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 123850, + "object_name": "parplex" + } + ] + }, + "name": "Epilepsy may be caused by very small functional changes in ion channels (Thomas et al. 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + }, + { + "object_id": 52416, + "object_name": "Dentate gyrus mossy cell" + }, + { + "object_id": 52417, + "object_name": "Dentate gyrus basket cell" + }, + { + "object_id": 88216, + "object_name": "Dentate gyrus hilar cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We used a previously published model of the dentate gyrus with varying degrees of mossy fibre sprouting.We preformed a sensitivity analysis where we systematically varied individual properties of ion channels. The results predict that genetic variations in the properties of sodium channels are likely to have the biggest impact on network excitability. Furthermore, these changes may be as small as 1mV, which is currently undetectable using standard experimental practices." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/124392", + "public_submitter_email": { + "attr_id": 309, + "value": "evan@evan-thomas.net" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2009-10-15T09:54:23", + "ver_number": 5 + }, + "124394": { + "citation": { + "attr_id": 391, + "value": "Nevian T, Larkum ME, Polsky A, Schiller J. Properties of basal dendrites of layer 5 pyramidal neurons: a direct patch-clamp recording study. 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It is a scaleable model of the rat dentate gyrus including four cell types. 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Repetitive firing is modulated by maximal conductance and the\r\nvoltage dependence of the persistent Na+ current (INaP)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/125152", + "other_gene": { + "attr_id": 477, + "value": "Nav1.1SCN1B" + }, + "other_implementer": { + "attr_id": 308, + "value": "Horstmann, Marie-Therese" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181908, + "object_name": "125152" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 166 + }, + "ver_date": "2014-01-31T23:02:35", + "ver_number": 21 + }, + "125154": { + "biosimulations": { + "value": "modeldb-125154" + }, + "citation": { + "attr_id": 391, + "value": "Huang CW, Tsai JJ, Huang CC, Wu SN. Experimental and simulation studies on the mechanisms of levetiracetam-mediated inhibition of delayed-rectifier potassium current (Kv3.1): contribution to the firing of action potentials. 2009, J Physiol Pharmacol (in press)" + }, + "class_id": 19, + "created": "2009-11-25T09:31:32", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 88117, + "object_name": "I_KHT" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122817, + "object_name": "Kv3.1 KCNC1" + } + ] + }, + "gitrepo": false, + "id": 125154, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + }, + { + "object_id": 84610, + "object_name": "Huang, Chin-Wei" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 125155, + "object_name": "Huang CW, Tsai JJ, Huang CC, Wu SN (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Simulation studies on mechanisms of levetiracetam-mediated inhibition of IK(DR) (Huang et al. 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88207, + "object_name": "Neuroblastoma" + }, + { + "object_id": 114112, + "object_name": "NG108-15 neuronal cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Levetiracetam (LEV) is an S-enantiomer pyrrolidone derivative with established antiepileptic \r\nefficacy in generalized epilepsy and partial epilepsy. However, its effects on ion currents \r\nand membrane potential remain largely unclear. In this study, we investigated the effect of \r\nLEV on differentiated NG108-15 neurons. \r\n...\r\nSimulation studies in a modified \r\nHodgkin-Huxley neuron and network unraveled that the reduction of slowly inactivating IK(DR) resulted \r\nin membrane depolarization accompanied by termination of the firing of action potentials in a \r\nstochastic manner. Therefore, the inhibitory effects on slowly inactivating IK(DR) (Kv3.1-encoded \r\ncurrent) may constitute one of the underlying mechanisms through which LEV affects neuronal activity \r\nin vivo." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/125154", + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2009-11-27T14:00:29", + "ver_number": 7 + }, + "125290": { + "class_id": 19, + "created": "2009-11-27T08:38:11", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 125290, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83470, + "object_name": "El Boustani, Sami [elboustani at unic.cnrs-gif.fr]" + }, + { + "object_id": 125293, + "object_name": "Marre, Olivier [marre at unic.cnrs-gif.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 125294, + "object_name": "Maximum entropy models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 125291, + "object_name": "Marre O, El Boustani S, Fr\u00e9gnac Y, Destexhe A (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Maximum entropy model to predict spatiotemporal spike patterns (Marre et al. 2009)", + "notes": { + "attr_id": 24, + "value": "This MATLAB code implements a model-based analysis of spike trains.\r\nThe analysis predicts the occurrence of spatio-temporal patterns of\r\nspikes in the data, and is based on a maximum entropy principle by\r\nincluding both spatial and temporal correlations. The approach is\r\napplicable to unit recordings from any region of the brain.\r\n\r\nThe code is based on Marre, et al., 2009.\r\n\r\nThe MATLAB code was written by Sami El Boustani and Olivier Marre.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/125290", + "other_concept": { + "attr_id": 306, + "value": "maximum entropy models" + }, + "other_implementer": { + "attr_id": 308, + "value": "El Boustani, Sami ; Marre, Olivier " + }, + "other_neurons": { + "attr_id": 301, + "value": "cerebral cortex" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2016-06-14T15:40:24.057", + "ver_number": 10 + }, + "125378": { + "class_id": 19, + "created": "2009-12-06T20:59:10", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 245, + "object_name": "I T low threshold" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122810, + "object_name": "Cav3.1 CACNA1G" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 125378, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 125379, + "object_name": "Hayashida, Yuki [yukih at cs.kumamoto-u.ac.jp]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 46100, + "object_name": "Lee SC, Hayashida Y, Ishida AT (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Availability of low-threshold Ca2+ current in retinal ganglion cells (Lee SC et al. 2003)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 270, + "object_name": "Retina ganglion GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... we measured T-type current of isolated\r\ngoldfish retinal ganglion cells with perforated-patch voltageclamp\r\nmethods in solutions containing a normal extracellular Ca2+\r\nconcentration.\r\n\r\nThe voltage sensitivities and rates of current activation,\r\ninactivation, deactivation, and recovery from inactivation were similar\r\nto those of expressed +1G (CaV3.1) Ca2+ channel clones, except that\r\nthe rate of deactivation was significantly faster.\r\n\r\nWe reproduced the\r\namplitude and kinetics of measured T currents with a numerical\r\nsimulation based on a kinetic model developed for an +1G Ca2+\r\nchannel.\r\n\r\nFinally, we show that this model predicts the increase of\r\nT-type current made available between resting potential and spike\r\nthreshold by repetitive hyperpolarizations presented at rates that are\r\nwithin the bandwidth of signals processed in situ by these neurons.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/125378", + "other_concept": { + "attr_id": 306, + "value": "rebound excitation" + }, + "other_type": { + "attr_id": 305, + "value": "Markovian kinetic model" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "thomas.morse@snet.net" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 118529, + "object_name": "Retina" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181172, + "object_name": "125378" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 156 + }, + "ver_date": "2014-01-20T15:54:43", + "ver_number": 7 + }, + "125385": { + "class_id": 19, + "created": "2009-12-09T16:14:30", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 125385, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 125386, + "object_name": "Diba, Kamran [diba at andromeda.rutgers.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 68846, + "object_name": "Diba K, Koch C, Segev I (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Spike propagation in dendrites with stochastic ion channels (Diba et al. 2006)", + "notes": { + "attr_id": 24, + "value": "\"We investigate the effects of the stochastic nature\r\nof ion channels on the faithfulness, precision and reproducibility\r\nof electrical signal transmission in weakly active,\r\ndendritic membrane under in vitro conditions. \r\n\r\n...\r\n\r\nWe numerically simulate the effects of stochastic ion\r\nchannels on the forward and backward propagation of dendritic\r\nspikes in Monte-Carlo simulations on a reconstructed\r\nlayer 5 pyramidal neuron. \r\n\r\nWe report that in most instances\r\nthere is little variation in timing or amplitude for a single\r\nBPAP, while variable backpropagation can occur for trains\r\nof action potentials. \r\n\r\nAdditionally, we find that the generation\r\nand forward propagation of dendritic Ca2+ spikes are\r\nsusceptible to channel variability. This indicates limitations\r\non computations that depend on the precise timing of Ca2+\r\nspikes.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/125385", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2014-07-30T17:28:52", + "ver_number": 9 + }, + "125529": { + "biosimulations": { + "value": "modeldb-125529" + }, + "citation": { + "attr_id": 391, + "value": "Rubin JE, Shevtsova NA, Ermentrout GB, Smith JC, Rybak IA.\r\n\r\nJ Neurophysiol. 2009 Apr;101(4):2146-65." + }, + "class_id": 19, + "created": "2010-01-06T15:57:07", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + } + ] + }, + "gitrepo": false, + "id": 125529, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 125612, + "object_name": "Rubin, Jonathan E [jonrubin at pitt.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 125530, + "object_name": "Rubin JE, Shevtsova NA, Ermentrout GB, Smith JC, Rybak IA (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Respiratory central pattern generator network in mammalian brainstem (Rubin et al. 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 125611, + "object_name": "Respiratory column neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model is a reduced version of a spatially organized respiratory central pattern generation network consisting of four neuronal populations (pre-I, early-I, post-I, and aug-E). In this reduction, each population is represented by a single neuron, in an activity-based framework (which includes the persistent sodium current for the pre-I population). The model includes three sources of external drive and can produce several experimentally observed rhythms." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/125529", + "other_currents": { + "attr_id": 303, + "value": "generic adapting current" + }, + "other_neurons": { + "attr_id": 301, + "value": "Brainstem neuron, Respiratory column neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "rubin@math.pitt.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "ver_date": "2010-01-07T14:49:01", + "ver_number": 7 + }, + "125649": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 125652, + "object_name": "NeuronetExperimenter demo file" + }, + { + "object_id": 125653, + "object_name": "Neuronet Experimenter simulator file" + }, + { + "object_id": 125654, + "object_name": "Ca2+ activated etc Rubin et al. 2009 XPP file" + } + ] + }, + "class_id": 19, + "created": "2010-01-08T16:48:15", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 255, + "object_name": "I CAN" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 125649, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 125612, + "object_name": "Rubin, Jonathan E [jonrubin at pitt.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 125610, + "object_name": "Rubin JE, Hayes JA, Mendenhall JL, Del Negro CA (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + }, + { + "object_id": 91894, + "object_name": "XPP (web link to model)" + }, + { + "object_id": 125650, + "object_name": "NeuronetExperimenter (web link to model)" + } + ] + }, + "name": "Ca2+-activated I_CAN and synaptic depression promotes network-dependent oscil. (Rubin et al. 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 232103, + "object_name": "PreBotzinger complex neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... the preBotzinger complex...\r\nwe present and analyze a\r\nmathematical model demonstrating an unconventional mechanism\r\nof rhythm generation in which glutamatergic synapses and the\r\nshort-term depression of excitatory transmission play key rhythmogenic\r\nroles. \r\n\r\nRecurrent synaptic excitation triggers postsynaptic Ca2+-\r\nactivated nonspecific cation current (ICAN) to initiate a network-wide\r\nburst. \r\n\r\nRobust depolarization due to ICAN also causes voltage-dependent\r\nspike inactivation, which diminishes recurrent excitation and\r\nthus attenuates postsynaptic Ca2+ accumulation. \r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/125649", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 207, + "object_name": "mGluR" + } + ] + }, + "ver_date": "2017-09-13T17:34:14.057", + "ver_number": 8 + }, + "125676": { + "class_id": 19, + "created": "2010-01-11T11:39:13", + "gitrepo": false, + "id": 125676, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 118895, + "object_name": "Matveev, Victor V. [m a t v e e v at n j i t . e d u ]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 125655, + "object_name": "Matveev V, Bertram R, Sherman A (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 118760, + "object_name": "CalC Calcium Calculator (web link to model)" + } + ] + }, + "name": "Ca2+ current versus Ca2+ channel cooperativity of exocytosis (Matveev et al. 2009)", + "notes": { + "attr_id": 24, + "value": "\"... While varying extracellular or intracellular Ca2+ concentration assesses the intrinsic biochemical Ca2+ cooperativity of \r\nneurotransmitter release, varying the number of open Ca2+ channels using pharmacological channel block or the tail current \r\ntitration probes the cooperativity between individual Ca2+ channels in triggering exocytosis. \r\n...\r\n\r\nHere we provide a detailed analysis of the Ca2+ sensitivity measures probed by these experimental\r\nprotocols, present simple expressions for special cases, and demonstrate the distinction between the Ca2+ current cooperativity, defined\r\nby the relationship between exocytosis rate and the whole-terminal Ca2+ current magnitude, and the underlying Ca2+ channel cooperativity,\r\ndefined as the average number of channels involved in the release of a single vesicle. \r\n...\r\nFurther, we use three-dimensional computational modeling of buffered Ca2+\r\ndiffusion to analyze these distinct Ca2+ cooperativity measures, and demonstrate the role of endogenous Ca2+ buffers on such measures.\r\n\r\nWe show that buffers can either increase or decrease the Ca2+ current cooperativity of exocytosis, depending on their concentration and\r\nthe single-channel Ca2+ current.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/125676", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2010-01-11T13:33:32", + "ver_number": 2 + }, + "125683": { + "biosimulations": { + "value": "modeldb-125683" + }, + "class_id": 19, + "created": "2010-01-15T14:02:49", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 125683, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 34559, + "object_name": "Komendantov, Alexander O [akomenda at tulane.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 125682, + "object_name": "Komendantov AO, Kononenko NI (1996)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Deterministic chaos in a mathematical model of a snail neuron (Komendantov and Kononenko 1996)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 125684, + "object_name": "Helix pacemaker bursting neuron (RPa1)" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Chaotic regimes in a mathematical model of pacemaker activity in the bursting neurons of a snail Helix pomatia, have been investigated.\r\n\r\nThe model includes a slow-wave generating mechanism, a spike-generating mechanism, an inward Ca current, intracellular Ca ions, [Ca2+]in, their fast buffering and uptake by intracellular Ca stores, and a [Ca2+]in-inhibited Ca current.\r\n\r\nChemosensitive voltage-activated conductance, gB*, responsible for termination of the spike burst, and chemosensitive sodium conductance, gNa*, responsible for the depolarization phase of the slow-wave, were used as control parameters.\r\n...\r\nTime courses of the membrane potential and [Ca2+]in were employed to analyse different regimes in the model. \r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/125683", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 125736, + "object_name": "Helix pomatia (snail)" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 249924, + "object_name": "Helix pomatia (snail)" + } + ] + }, + "ver_date": "2019-02-26T10:19:05.737", + "ver_number": 10 + }, + "125689": { + "class_id": 19, + "created": "2010-01-19T15:53:29", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 125689, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 122104, + "object_name": "Carvalho TP, Buonomano DV (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Balance of excitation and inhibition (Carvalho and Buonomano 2009)", + "notes": { + "attr_id": 24, + "value": "\" ... \r\nHere, theoretical analyses reveal that excitatory synaptic\r\nstrength controls the threshold of the neuronal\r\ninput-output function, while inhibitory plasticity\r\nalters the threshold and gain.\r\n\r\nExperimentally, changes in the balance of excitation and inhibition\r\nin CA1 pyramidal neurons also altered their input-output\r\nfunction as predicted by the model.\r\n\r\nThese results support the existence of two functional\r\nmodes of plasticity that can be used to optimize\r\ninformation processing: threshold and gain plasticity.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/125689", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + 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"attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Hippocampus CA1: Temporal sensitivity of signaling pathways underlying LTP (Kim et al. 2010)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Temporal sensitivity of signaling pathways underlying L-LTP. Single compartment, deterministic model of calcium and dopamine activated pathways, leading to CaMKII and PKA activation. Experimental verification of model prediction." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/125733", + "public_submitter_email": { + "attr_id": 309, + "value": "kblackw1@gmu.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 223, + "object_name": "Dopaminergic Receptor" + } + ] + }, + "ver_date": "2010-02-26T10:48:08", + "ver_number": 7 + }, + "125745": { + "citation": { + "attr_id": 391, + "value": "10866945" + }, + "class_id": 19, + "created": "2010-02-02T10:11:59", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 88211, + "object_name": "I_SERCA" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 125745, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33735, + "object_name": "Carnevale, Ted [Ted.Carnevale at Yale.edu]" + }, + { + "object_id": 125786, + "object_name": "Fink, Charles C [cfink at uwm.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 150554, + "object_name": "Calcium waves" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 44438, + "object_name": "Fink CC, Slepchenko B, Moraru II, Watras J, Schaff JC, Loew LM (2000)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 55276, + "object_name": "Virtual Cell (web link to model)" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "125745", + "name": "ModelView" + } + ], + "default": "125745" + }, + "name": "Calcium waves in neuroblastoma cells (Fink et al. 2000)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88207, + "object_name": "Neuroblastoma" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Uses a model of IP3-mediated release of Ca from endoplasmic reticulum (ER) to study how initiation and propagation of Ca waves are affected by cell geometry, spatial distributions of ER and IP3 generation, and diffusion of Ca and mobile buffer.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/125745", + "other_concept": { + "attr_id": 306, + "value": "too many to list here" + }, + "other_currents": { + "attr_id": 303, + "value": "SERCA Ca channels (add nothing to charge bal eqn)" + }, + "other_implementer": { + "attr_id": 308, + "value": "Fink, C.C." + }, + "other_receptors": { + "attr_id": 302, + "value": "Bradykinin, IP3" + }, + "other_simulator": { + "attr_id": 307, + "value": "VirtualCell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ted.carnevale@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 153715, + "object_name": "IP3" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115962, + "object_name": "Unknown" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181453, + "object_name": "125745" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 191 + }, + "ver_date": "2015-01-02T23:04:13", + "ver_number": 19 + }, + "125748": { + "class_id": 19, + "created": "2010-02-04T07:53:41", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 125748, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 97968, + "object_name": "Giugliano, Michele [mgiugliano at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 261622, + "object_name": "Huntington's" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 128591, + "object_name": "Gambazzi L, Gokce O, Seredenina T, Katsyuba E, Runne H, Markram H, Giugliano M, Luthi-Carter R (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Huntington`s disease model (Gambazzi et al. 2010)", + "notes": { + "attr_id": 24, + "value": "\"Although previous studies of Huntington\u2019s disease (HD) have addressed many potential mechanisms of striatal neuron dysfunction and death, it is also known based on clinical findings that cortical function is dramatically disrupted in HD. With respect to disease etiology, however, the specific molecular and neuronal circuit bases for the cortical effects of mutant huntingtin (htt) have remained largely unknown. \r\n\r\nIn the present work we studied the relation between the molecular effects of mutant htt fragments in cortical cells and the corresponding behavior of cortical neuron microcircuits using a novel cellular model of HD. We observed that a transcript-selective diminution in activity-dependent BDNF expression preceded the onset of a synaptic connectivity deficit in ex vivo cortical networks, which manifested as decreased spontaneous collective burst-firing behavior measured by multi-electrode array substrates. Decreased BDNF expression was determined to be a significant contributor to network-level dysfunction, as shown by the ability of exogenous BDNF to ameliorate cortical microcircuit burst firing. \r\n\r\nThe molecular determinants of the dysregulation of activity-dependent BDNF expression by mutant htt appear to be distinct from previously elucidated mechanisms, as they do not involve known NRSF/REST-regulated promoter sequences, but instead result from dysregulation of BDNF exon IV and VI transcription. These data elucidate a novel HD-related deficit in BDNF gene regulation as a plausible mechanism of cortical neuron hypoconnectivity and cortical function deficits in HD. Moreover, the novel model paradigm established here is well-suited to further mechanistic and drug screening research applications.\r\n\r\nA simple mathematical model is proposed to interpret the observations and to explore the impact of specific synaptic dysfunctions on network activity. Interestingly, the model predicts a decrease in synaptic connectivity to be an early effect of mutant huntingtin in cortical neurons, supporting the hypothesis of decreased, rather than increased, synchronized cortical firing in HD.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/125748", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2020-01-22T11:56:31.757", + "ver_number": 10 + }, + "125855": { + "class_id": 19, + "created": "2010-02-12T19:31:09", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 125855, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 125854, + "object_name": "Zilany MS, Bruce IC, Nelson PC, Carney LH (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + }, + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Long-term adaptation with power-law dynamics (Zilany et al. 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 1965, + "object_name": "Cochlea hair outer GLU cell" + }, + { + "object_id": 52419, + "object_name": "Auditory nerve" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "...\r\nA model of rate adaptation at the synapse between inner hair cells and auditory-nerve\r\n(AN) fibers that includes both exponential and power-law dynamics is presented here.\r\n\r\nExponentially\r\nadapting components with rapid and short-term time constants, which are mainly responsible for\r\nshaping onset responses, are followed by two parallel paths with power-law adaptation that provide\r\nslowly and rapidly adapting responses.\r\n\r\n...\r\nThe proposed model is capable of accurately predicting several sets of AN data,\r\nincluding amplitude-modulation transfer functions, long-term adaptation, forward masking, and\r\nadaptation to increments and decrements in the amplitude of an ongoing stimulus." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/125855", + "ver_date": "2018-10-18T15:37:21.457", + "ver_number": 8 + }, + "125857": { + "citation": { + "attr_id": 391, + "value": "Lee, Jin (2007) \"Fast Rhythmic Bursting Cells: The Horizontal Fiber System in the Cat\u2019s Primary Visual Cortex,\" Penn McNair Research Journal: Vol. 1: Iss. 1, Article 1" + }, + "class_id": 19, + "created": "2010-02-14T10:16:08", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 88210, + "object_name": "I_Na,Ca" + }, + { + "object_id": 116966, + "object_name": "I_K,Na" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 125857, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 125862, + "object_name": "Lee, Jin [jin3 at sas.upenn.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 125858, + "object_name": "Lee, J (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A Fast Rhythmic Bursting Cell: in vivo cell modeling (Lee 2007)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "One of the cellular mechanisms underlying the generation of gamma oscillations is a type of cortical pyramidal neuron named fast rhythmic bursting (FRB) cells. After cells from cats' primary visual cortices were filled with Neurobiotin, the brains were cut, and the cells were photographed. One FRB cell was chosen to be confocaled, reconstructed with Neurolucida software, and generated a detailed multi-compartmental model in the NEURON program. We explore firing properties of FRB cells and the role of enhanced Na+ conductance." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/125857", + "other_implementer": { + "attr_id": 308, + "value": "Lee, Jin " + }, + "other_neurons": { + "attr_id": 301, + "value": "Fast Rhythmic Bursting (Chattering) Cell" + }, + "other_type": { + "attr_id": 305, + "value": "in vivo cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jin3@sas.upenn.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181178, + "object_name": "125857_frb_12_19" + }, + { + "object_id": 181212, + "object_name": "125857_frb_12_21" + }, + { + "object_id": 181297, + "object_name": "125857_2frb" + }, + { + "object_id": 181658, + "object_name": "125857_frb3" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 140 + }, + "ver_date": "2018-10-17T16:52:33.817", + "ver_number": 8 + }, + "126052": { + "class_id": 19, + "created": "2010-02-22T17:31:50", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 126052, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 36860, + "object_name": "Zhang, Xuedong " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 37111, + "object_name": "Heinz MG, Colburn HS, Carney LH (2001)" + }, + { + "object_id": 125949, + "object_name": "Carney LH, Heinz MG, Evilsizer ME, Gilkey RH, Colburn HS (2002)" + }, + { + "object_id": 126500, + "object_name": "Colburn HS, Carney LH, Heinz MG (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + }, + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Integrate and fire model code for spike-based coincidence-detection (Heinz et al. 2001, others)", + "notes": { + "attr_id": 24, + "value": "Model code relevant to three papers; two on level discrimination and one on masked detection at low frequencies." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/126052", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2018-10-18T15:37:47.883", + "ver_number": 3 + }, + "126096": { + "class_id": 19, + "created": "2010-02-24T15:20:32", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 126096, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 126055, + "object_name": "Nelson PC, Carney LH (2004)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + }, + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Model of neural responses to amplitude-modulated tones (Nelson and Carney 2004)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52419, + "object_name": "Auditory nerve" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"A phenomenological model with time-varying excitation and inhibition was developed to study\r\npossible neural mechanisms underlying changes in the representation of temporal envelopes along\r\nthe auditory pathway. A modified version of an existing auditory-nerve model (Zhang et al., J.\r\nAcoust. Soc. Am. 109, 648\u2013670 (2001) was used to provide inputs to higher hypothetical\r\nprocessing centers. \r\n\r\nModel responses were compared directly to published physiological data at three\r\nlevels: the auditory nerve, ventral cochlear nucleus, and inferior colliculus.\r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/126096", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 144513, + "object_name": "Inferior Colliculus" + }, + { + "object_id": 118308, + "object_name": "Superior colliculus" + } + ] + }, + "ver_date": "2018-10-18T16:56:19.647", + "ver_number": 6 + }, + "126098": { + "citation": { + "attr_id": 391, + "value": "PMID: 20169176" + }, + "class_id": 19, + "created": "2010-02-25T21:52:01", + "gitrepo": false, + "id": 126098, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 126348, + "object_name": "Nakano, Takashi [nakano.takashi at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 126100, + "object_name": "Nakano T, Doi T, Yoshimoto J, Doya K (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "A kinetic model of dopamine- and calcium-dependent striatal synaptic plasticity (Nakano et al. 2010)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 260, + "object_name": "Neostriatum medium spiny direct pathway GABA cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A signaling pathway model of spines that express D1-type dopamine receptors was constructed to analyze the dynamic mechanisms of dopamine- and calcium-dependent plasticity. \r\n\r\nThe model incorporated all major signaling molecules, including dopamine- and cyclic AMP-regulated phosphoprotein with a molecular weight of 32 kDa (DARPP32), as well as AMPA receptor trafficking in the post-synaptic membrane. Simulations with dopamine and calcium inputs reproduced dopamine- and calcium-dependent plasticity. \r\n\r\nFurther in silico experiments revealed that the positive feedback loop consisted of protein kinase A (PKA), protein phosphatase 2A (PP2A), and the phosphorylation site at threonine 75 of DARPP-32 (Thr75) served as the major switch for inducing LTD and LTP. \r\n\r\nThe present model elucidated the mechanisms involved in bidirectional regulation of corticostriatal synapses and will allow for further exploration into causes and therapies for dysfunctions such as drug addiction.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/126098", + "other_implementer": { + "attr_id": 308, + "value": "Nakano, Takashi" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "nakano.takashi@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 223, + "object_name": "Dopaminergic Receptor" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2012-09-05T13:37:30", + "ver_number": 6 + }, + "126371": { + "class_id": 19, + "created": "2010-03-05T16:55:42", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 126371, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 37130, + "object_name": "Tan, Qing " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 126353, + "object_name": "Tan Q, Carney LH (2005)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Encoding and discrimination of vowel-like sounds (Tan and Carney 2005)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52419, + "object_name": "Auditory nerve" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The sensitivity of listeners to changes in the center frequency of vowel-like harmonic complexes as\r\na function of the center frequency of the complex cannot be explained by changes in the level of the\r\nstimulus [Lyzenga and Horst, J. Acoust. Soc. Am. 98, 1943\u20131955 (1995)]. \r\n\r\nRather, a complex pattern\r\nof sensitivity is seen; for a spectrum with a triangular envelope, the greatest sensitivity occurs when\r\nthe center frequency falls between harmonics, whereas for a spectrum with a trapezoidal envelope,\r\ngreatest sensitivity occurs when the center frequency is aligned with a harmonic.\r\n\r\nIn this study, the\r\nthresholds of a population model of auditory-nerve (AN) fibers were quantitatively compared to\r\nthese trends in psychophysical thresholds.\r\n\r\nSingle-fiber and population model responses were\r\nevaluated in terms of both average discharge rate and the combination of rate and timing\r\ninformation.\r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/126371", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2018-10-18T16:56:44.61", + "ver_number": 2 + }, + "126389": { + "class_id": 19, + "created": "2010-03-05T17:56:08", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 126389, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 126390, + "object_name": "Jackson, B. Scott [bsj22 at cornell.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 126372, + "object_name": "Jackson BS, Carney LH (2005)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + }, + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Auditory nerve spontaneous rate histograms (Jackson and Carney 2005)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52419, + "object_name": "Auditory nerve" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Histograms of spontaneous rate estimates of auditory nerve are well reproduced by models with two or three spontaneous rates and long range dependence." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/126389", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2018-10-18T16:57:04.047", + "ver_number": 2 + }, + "126392": { + "class_id": 19, + "created": "2010-03-05T18:42:46", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 126392, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 36860, + "object_name": "Zhang, Xuedong " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 78928, + "object_name": "Zhang X, Carney LH (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + }, + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Response properties of an integrate and fire model (Zhang and Carney 2005)", + "notes": { + "attr_id": 24, + "value": "\"A computational technique is described for calculation of the interspike interval and poststimulus time histograms for the responses of an\r\nintegrate-and-fire model to arbitrary inputs.\r\n...\r\nFor stationary inputs, the regularity of the output was studied in detail for various model parameters.\r\n\r\nFor nonstationary inputs, the effects of the model parameters on the output synchronization index were explored.\r\n\r\n... these response properties have been reported for some cells in the ventral cochlear nucleus in the auditory brainstem.\r\n\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/126392", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 143145, + "object_name": "Auditory brainstem" + } + ] + }, + "ver_date": "2018-10-18T16:57:36.783", + "ver_number": 5 + }, + "126465": { + "class_id": 19, + "created": "2010-03-18T12:31:10", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 126465, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 231104, + "object_name": "Goodman, Dan F. M. " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 136537, + "object_name": "Goodman DF, Brette R (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Spike-Timing-Based Computation in Sound Localization (Goodman and Brette 2010)", + "notes": { + "attr_id": 24, + "value": "\" ...\r\nIn neuron models consisting of spectro-temporal filtering and spiking\r\nnonlinearity, we found that the binaural structure induced by spatialized sounds is mapped to synchrony patterns that\r\ndepend on source location rather than on source signal. \r\n\r\nLocation-specific synchrony patterns would then result in the\r\nactivation of location-specific assemblies of postsynaptic neurons. \r\n\r\nWe designed a spiking neuron model which exploited\r\nthis principle to locate a variety of sound sources in a virtual acoustic environment using measured human head-related\r\ntransfer functions.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/126465", + "other_concept": { + "attr_id": 306, + "value": "Spatial hearing" + }, + "other_implementer": { + "attr_id": 308, + "value": "Dan F. M. Goodman" + }, + "ver_date": "2017-07-28T14:01:32.82", + "ver_number": 10 + }, + "126466": { + "class_id": 19, + "created": "2010-03-19T11:33:48", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 126466, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 124829, + "object_name": "Destexhe A (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 116010, + "object_name": "PyNN" + } + ] + }, + "name": "Asynchronous irregular and up/down states in excitatory and inhibitory NNs (Destexhe 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Randomly-connected networks of integrate-and-fire (IF) neurons are known to display asynchronous irregular (AI) activity states, which resemble the discharge activity recorded in the cerebral cortex of awake animals. \r\n\r\n...\r\nHere, we investigate the occurrence of AI states in networks of nonlinear IF neurons, such as the adaptive exponential IF (Brette-Gerstner-Izhikevich) model. This model can display intrinsic properties such as low-threshold spike (LTS), regular spiking (RS) or fast-spiking (FS). We successively investigate the oscillatory and AI dynamics of thalamic, cortical and thalamocortical networks using such models.\r\n...\"" + }, + "opensourcebrain": { + "value": "destexhe_jcns_2009" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/126466", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2016-08-18T11:55:28.277", + "ver_number": 12 + }, + "126467": { + "class_id": 19, + "created": "2010-03-19T17:20:53", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 126467, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 97864, + "object_name": "Gannier, Francois [francois.gannier at univ-tours.fr]" + }, + { + "object_id": 126468, + "object_name": "Malecot, Claire " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 63689, + "object_name": "Negroni JA, Lascano EC (1996)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "126467", + "name": "ModelView" + } + ], + "default": "126467" + }, + "name": "Cardiac sarcomere dynamics (Negroni and Lascano 1996)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52423, + "object_name": "Heart cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"A muscle model establishing the link between cross-bridge dynamics and intracellular Ca2+ kinetics was assessed by simulation of experiments performed in isolated cardiac muscle.\r\n\r\nThe model is composed by the series arrangement of muscle units formed by inextensible thick and thin filaments in parallel with an elastic element.\r\n\r\nAttached cross-bridges act as independent force generators whose force is linearly related to the elongation of their elastic structure.\r\n\r\nCa2+ kinetics is described by a four-state system of sites on the thin filament associated with troponin C: sites with free troponin C (T), sites with Ca2+ bound to troponin C (TCa); sites with Ca2+ bound to troponin C and attached cross-bridges (TCa*); and sites with troponin C not associated with Ca2+ and attached cross-bridges (T*).\r\n\r\nThe intracellular Ca2+ concentration ([Ca2+]) is controlled solely by the sarcoplasmic reticulum through an inflow function and a saturated outflow pump function.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/126467", + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181794, + "object_name": "126467" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 194 + }, + "ver_date": "2014-02-06T16:20:36", + "ver_number": 6 + }, + "126471": { + "class_id": 19, + "created": "2010-03-23T13:30:11", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 126471, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 126472, + "object_name": "Miller, Aaron [aaron at phys.psu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 105527, + "object_name": "Jun JK, Jin DZ (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Formation of synfire chains (Jun and Jin 2007)", + "notes": { + "attr_id": 24, + "value": "\"Temporally precise sequences of neuronal spikes that span hundreds of milliseconds are observed in many brain areas,\r\nincluding songbird premotor nucleus, cat visual cortex, and primary motor cortex.\r\n\r\nSynfire chains\u2014networks in which groups of\r\nneurons are connected via excitatory synapses into a unidirectional chain\u2014are thought to underlie the generation of such\r\nsequences. \r\n\r\nIt is unknown, however, how synfire chains can form in local neural circuits, especially for long chains. \r\n\r\nHere, we\r\nshow through computer simulation that long synfire chains can develop through spike-time dependent synaptic plasticity and\r\naxon remodeling\u2014the pruning of prolific weak connections that follows the emergence of a finite number of strong\r\nconnections. \r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/126471", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2018-10-17T16:52:53.06", + "ver_number": 6 + }, + "126489": { + "class_id": 19, + "created": "2010-03-29T17:06:30", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 126489, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 36860, + "object_name": "Zhang, Xuedong " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 126477, + "object_name": "Zhang X, Carney LH (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Models analysis for auditory-nerve synapse (Zhang and Carney 2005)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52419, + "object_name": "Auditory nerve" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"A general mathematical approach was proposed to study phenomenological models of the\r\ninner-hair-cell and auditory-nerve (AN) synapse complex. Two models (Meddis, 1986; Westerman\r\nand Smith, 1988) were studied using this unified approach. The responses of both models to a\r\nconstant-intensity stimulus were described mathematically, and the relationship between model\r\nparameters and response characteristics was investigated. ...\". The paper then modifies these\r\nto make a more physiologically realistic model.\r\n" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/126489", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2018-10-18T16:57:55.67", + "ver_number": 8 + }, + "126598": { + "class_id": 19, + "created": "2010-03-30T11:13:26", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 126598, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 37130, + "object_name": "Tan, Qing " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 126583, + "object_name": "Tan Q, Carney LH (2006)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Predicting formant-frequency discrimination in noise (Tan and Carney 2006)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52419, + "object_name": "Auditory nerve" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"To better understand how the auditory system extracts speech signals in the presence of noise,\r\ndiscrimination thresholds for the second formant frequency were predicted with simulations of\r\nauditory-nerve responses.\r\n\r\nThese predictions employed either average-rate information or combined\r\nrate and timing information, and either populations of model fibers tuned across a wide range of\r\nfrequencies or a subset of fibers tuned to a restricted frequency range.\r\n\r\nIn general, combined temporal\r\nand rate information for a small population of model fibers tuned near the formant frequency was\r\nmost successful in replicating the trends reported in behavioral data for formant-frequency\r\ndiscrimination. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/126598", + "ver_date": "2018-10-18T16:58:23.317", + "ver_number": 5 + }, + "126636": { + "class_id": 19, + "created": "2010-03-30T17:24:35", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 126636, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 126600, + "object_name": "Davidson SA, Gilkey RH, Colburn HS, Carney LH (2009)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + }, + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Models for diotic and dichotic detection (Davidson et al. 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52419, + "object_name": "Auditory nerve" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Several psychophysical models for masked detection were evaluated using reproducible noises.\r\n\r\nThe data were hit and false-alarm rates from three psychophysical studies of detection of 500-Hz tones\r\nin reproducible noise under diotic (N0S0) and dichotic (N0Spi) conditions with four stimulus\r\nbandwidths (50, 100, 115, and 2900 Hz).\r\n\r\nDiotic data were best predicted by an energy-based multiple-detector model that linearly combined stimulus energies at the outputs of several\r\ncritical-band filters.\r\n\r\nThe tone-plus-noise trials in the dichotic data were best predicted by models that\r\nlinearly combined either the average values or the standard deviations of interaural time and level\r\ndifferences; however, these models offered no predictions for noise-alone responses.\r\n\r\n...\". The Breebart et al. 2001 and the Dau et al. 1996 models are supplied at the Carney lab web site." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/126636", + "ver_date": "2018-10-18T16:58:46.337", + "ver_number": 6 + }, + "126637": { + "class_id": 19, + "created": "2010-04-01T23:39:23", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 246, + "object_name": "I p,q" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 126637, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 126638, + "object_name": "Brown, Sherry-Ann [sabrown at student.uchc.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 41934, + "object_name": "Intrinsic plasticity" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 137675, + "object_name": "Brown SA, Moraru II, Schaff JC, Loew LM (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A simplified cerebellar Purkinje neuron (the PPR model) (Brown et al. 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "These models were implemented in NEURON by Sherry-Ann Brown in the laboratory of Leslie M. Loew.\r\n\r\nThe files reproduce Figures 2c-f from Brown et al, 2011 \"Virtual NEURON: a Strategy For Merged Biochemical and Electrophysiological Modeling\".\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/126637", + "public_submitter_email": { + "attr_id": 309, + "value": "les@volt.uchc.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181147, + "object_name": "126637_2c" + }, + { + "object_id": 181256, + "object_name": "126637_cvode" + }, + { + "object_id": 181823, + "object_name": "126637_2d" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 143 + }, + "ver_date": "2023-01-30T09:32:12.367", + "ver_number": 15 + }, + "126640": { + "class_id": 19, + "created": "2010-04-16T04:27:20", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + }, + { + "object_id": 112841, + "object_name": "I Krp" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 126640, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 112854, + "object_name": "Addiction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 126641, + "object_name": "Spiga S, Lintas A, Migliore M, Diana M (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "126640", + "name": "ModelView" + } + ], + "default": "126640" + }, + "name": "NAcc medium spiny neuron: effects of cannabinoid withdrawal (Spiga et al. 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 112837, + "object_name": "Nucleus accumbens spiny projection neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Cannabinoid withdrawal produces a hypofunction of dopaminergic neurons targeting medium spiny neurons (MSN) of the forebrain. Administration of a CB1 receptor antagonist to control rats provoked structural abnormalities, reminiscent of those observed in withdrawal conditions and support the regulatory role of cannabinoids in neurogenesis, axonal growth and synaptogenesis. Experimental observations were incorporated into a realistic computational model which predicts a strong reduction in the excitability of morphologically-altered MSN, yielding a significant reduction in action potential output. These paper provided direct morphological evidence for functional abnormalities associated with cannabinoid dependence at the level of dopaminergic neurons and their post synaptic counterpart, supporting a hypodopaminergic state as a distinctive feature of the \u201caddicted brain\u201d." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/126640", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181455, + "object_name": "126640" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 290 + }, + "ver_date": "2014-02-25T01:01:34", + "ver_number": 10 + }, + "126776": { + "class_id": 19, + "created": "2010-04-17T03:35:55", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 126776, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 41934, + "object_name": "Intrinsic plasticity" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 126777, + "object_name": "Ascoli GA, Gasparini S, Medinilla V, Migliore M (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "126776_1", + "name": "control" + }, + { + "id": "126776_2", + "name": "4-AP" + }, + { + "id": "126776_3", + "name": "4-AP+ZD" + } + ], + "default": "126776_1" + }, + "name": "CA1 pyramidal neuron: rebound spiking (Ascoli et al.2010)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model demonstrates that CA1 pyramidal neurons support rebound spikes mediated by hyperpolarization-activated inward current (Ih), and normally masked by A-type potassium channels (KA). Partial KA reduction confined to one or few branches of the apical tuft may be sufficient to elicit a local spike following a train of synaptic inhibition. These data suggest that the plastic regulation of KA can provide a dynamic switch to unmask post-inhibitory spiking in CA1 pyramidal neurons, further increasing the signal processing power of the CA1 synaptic microcircuitry." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/126776", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181812, + "object_name": "126776_2" + }, + { + "object_id": 181813, + "object_name": "126776_3" + }, + { + "object_id": 181814, + "object_name": "126776_1" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 177 + }, + "ver_date": "2015-01-02T22:01:01", + "ver_number": 6 + }, + "126814": { + "class_id": 19, + "created": "2010-04-19T11:23:45", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 126814, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 126815, + "object_name": "Safiulina VF, Caiati MD, Sivakumaran S, Bisson G, Migliore M, Cherubini E (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "126814", + "name": "ModelView" + } + ], + "default": "126814" + }, + "name": "CA3 pyramidal neuron (Safiulina et al. 2010)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In this review some of the recent work carried out in our laboratory concerning the functional\r\nrole of GABAergic signalling at immature mossy fibres (MF)-CA3 principal cell synapses has\r\nbeen highlighted. To compare the relative strength of CA3 pyramidal cell\r\noutput in relation to their MF glutamatergic or GABAergic inputs in postnatal\r\ndevelopment, a realistic model was constructed taking into account the different\r\nbiophysical properties of these synapses.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/126814", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181606, + "object_name": "126814" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 183 + }, + "ver_date": "2015-01-02T23:16:24", + "ver_number": 17 + }, + "126899": { + "citation": { + "attr_id": 391, + "value": "12783953" + }, + "class_id": 19, + "created": "2010-04-21T05:11:05", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 126899, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83453, + "object_name": "Brette R" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 37966, + "object_name": "Rothman JS, Manis PB (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "CN bushy, stellate neurons (Rothman, Manis 2003) (Brian)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 273, + "object_name": "Cochlear nucleus bushy GLU cell" + }, + { + "object_id": 88113, + "object_name": "CN stellate cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Cochlear neuron model of Rothman & Manis (2003). Adapted from the Neuron implementation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/126899", + "public_submitter_email": { + "attr_id": 309, + "value": "romain.brette@ens.fr" + }, + "ver_date": "2017-07-28T13:50:02.073", + "ver_number": 4 + }, + "127021": { + "class_id": 19, + "created": "2010-05-06T22:33:23", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 226057, + "object_name": "HCN1" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 127021, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 156119, + "object_name": "Simoes-de-Souza, Fabio [fabio.souza at ufabc.edu.br]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 238957, + "object_name": "Markov-type model" + }, + { + "object_id": 242863, + "object_name": "Alcohol Use Disorder" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 127734, + "object_name": "Botta P, de Souza FM, Sangrey T, De Schutter E, Valenzuela CF (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 120694, + "object_name": "Electrogenic pump" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Alcohol excites Cerebellar Golgi Cells by inhibiting the Na+/K+ ATPase (Botta et al.2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 112687, + "object_name": "Cerebellum golgi cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Patch-clamp in cerebellar slices and computer modeling show that ethanol excites Golgi cells by inhibiting the Na+/K+ ATPase. In particular, voltage-clamp recordings of Na+/K+ ATPase currents indicated that ethanol partially inhibits this pump and this effect could be mimicked by low concentrations of the Na+/K+ ATPase blocker ouabain. The partial inhibition of Na+/K+ ATPase in a computer model of the Golgi cell reproduced these experimental findings that established a novel mechanism of action of ethanol on neural excitability." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/127021", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 152 + }, + "ver_date": "2022-11-04T13:15:51.427", + "ver_number": 26 + }, + "127022": { + "citation": { + "attr_id": 391, + "value": "O. Harish and D. Golomb, Control of the firing patterns of vibrissa motoneurons by modulatory and phasic synaptic inputs: a modeling study. J. Neurophysiol. 103: 2684 \u20132699, 2010." + }, + "class_id": 19, + "created": "2010-05-12T04:18:24", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gitrepo": false, + "id": 127022, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57906, + "object_name": "Golomb, David [golomb at bgu.ac.il]" + }, + { + "object_id": 127023, + "object_name": "Harish, Omri [omriharish at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 127084, + "object_name": "Locking, mixed mode" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 127026, + "object_name": "Harish O, Golomb D (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Control of vibrissa motoneuron firing (Harish and Golomb 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 127024, + "object_name": "Vibrissa motoneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 239, + "object_name": "Serotonin" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We construct and analyze a single-compartment, conductance-based model of vibrissa motoneurons. Low firing rates are supported in extended regimes by adaptation currents and the minimal firing rate decreases with the persistent sodium conductance gNaP and increases with M-potassium \r\nand h-cation conductances. Suprathreshold resonance results from the locking properties of vMN firing to stimuli and from reduction of firing rates at low frequencies by slow M and afterhyperpolarization potassium conductances. h conductance only slightly affects the suprathreshold resonance. When a vMN is subjected to a small periodic CPG input, serotonergically induced gNaP elevation may transfer the system from quiescence to a firing state that is highly\r\nlocked to the CPG input.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/127022", + "other_concept": { + "attr_id": 306, + "value": "Locking, mixed-mode" + }, + "other_currents": { + "attr_id": 303, + "value": "I_Na, I_NaP, I_Kdr, I_AHP, I_M, I_h" + }, + "other_implementer": { + "attr_id": 308, + "value": "Harish, Omri " + }, + "other_neurons": { + "attr_id": 301, + "value": "Vibrissa motoneuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "golomb@bgu.ac.il" + }, + "ver_date": "2010-05-13T07:14:35", + "ver_number": 6 + }, + "127190": { + "class_id": 19, + "created": "2010-05-25T14:55:59", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 240, + "object_name": "I Chloride" + } + ] + }, + "gitrepo": false, + "id": 127190, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 97968, + "object_name": "Giugliano, Michele [mgiugliano at gmail.com]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 141060, + "object_name": "Moroni M, Biro I, Giugliano M, Vijayan R, Biggin PC, Beato M, Sivilotti LG (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Pipette and membrane patch geometry effects on GABAa currents patch-clamp exps (Moroni et al. 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Ion currents, mediated by GABAa-receptors in outside-out membrane patches, may alter the concentration of Chloride ions inside the pipette and the membrane patch.\r\n\r\nGABAa-receptors are in fact ionotropic synaptic receptors, selective to Chloride ions. Therefore, chloride fluxes across the membrane patch correlate to GABAa-receptor opening.\r\n\r\nChloride ions accumulation, depletion and diffusion, inside the pipette and the membrane patch, affect by definition the Chloride equilibrium (i.e. Nernst) electrical potential. This in turn changes the ionic driving force underlying GABAa-mediated currents.\r\n\r\nIt follows that, in case of very small volumes and confined geometries, voltage-clamp recordings of GABAa-receptor currents carry information on both i) Chloride diffusion and ii) receptor kinetics.\r\n\r\nThe relevance of (i) and (ii) have been studied numerically by defining a 1-dimensional biophysical model, released here to the interested user." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/127190", + "other_concept": { + "attr_id": 306, + "value": "Ion diffusion" + }, + "other_type": { + "attr_id": 305, + "value": "Pipette and patch" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2011-10-07T15:48:08", + "ver_number": 17 + }, + "127192": { + "citation": { + "attr_id": 391, + "value": "Optimal hierarchical modular topologies for producing limited sustained activation of neural networks. Marcus Kaiser and Claus C. Hilgetag. Frontiers in Neuroinformatics vol. 4 article 8, May 2010. doi: 10.3389/fninf.2010.00008" + }, + "class_id": 19, + "created": "2010-05-27T11:38:15", + "gitrepo": false, + "id": 127192, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 114660, + "object_name": "Connectivity matrix" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 127200, + "object_name": "Kaiser M, Hilgetag CC (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Network topologies for producing limited sustained activation (Kaiser and Hilgetag 2010)", + "notes": { + "attr_id": 24, + "value": "Uses networks of cellular automata to test hypotheses about network topologies that can produce limited, sustained activity. Inspired by empirically-based ideas about neocortical architecture, but conceived and implemented at a level of abstraction that is not closely linked to empirical observations." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/127192", + "other_implementer": { + "attr_id": 308, + "value": "Marcus Kaiser, Claus C. Hilgetag" + }, + "other_neurons": { + "attr_id": 301, + "value": "Artificial neuron (cellular automata)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ted.carnevale@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2010-05-27T16:25:26", + "ver_number": 7 + }, + "127305": { + "class_id": 19, + "created": "2010-06-09T10:10:12", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "id": 127305, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 127301, + "object_name": "Hao J, Wang XD, Dan Y, Poo MM, Zhang XH (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "Spatial summation of excitatory and inhibitory inputs in pyramidal neurons (Hao et al. 2010)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... Based on realistic modeling and experiments in rat hippocampal\r\nslices, we derived a simple arithmetic rule for spatial summation\r\nof concurrent excitatory glutamatergic inputs (E) and inhibitory\r\nGABAergic inputs (I). \r\n\r\nThe somatic response can be well approximated\r\nas the sum of the excitatory postsynaptic potential (EPSP), the inhibitory\r\npostsynaptic potential (IPSP), and a nonlinear term proportional\r\nto their product (k*EPSP*IPSP), where the coefficient k reflects the\r\nstrength of shunting effect.\r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/127305", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2010-06-09T10:59:38", + "ver_number": 4 + }, + "127321": { + "citation": { + "attr_id": 391, + "value": "20529297" + }, + "class_id": 19, + "created": "2010-06-11T10:22:39", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 127321, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 127720, + "object_name": "Breitling, Rainer" + }, + { + "object_id": 127721, + "object_name": "Zhang, Mi [mi.zhang at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 127323, + "object_name": "Zhang M, Schafer WR, Breitling R (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 127324, + "object_name": "R (web link to model)" + } + ] + }, + "name": "A model of the temporal pattern generator of C. elegans egg-laying behavior (Zhang et. al 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 127712, + "object_name": "C elegans Hermaphrodite-Specific neuron (HSN)" + }, + { + "object_id": 127713, + "object_name": "C elegans VC motor neuron" + }, + { + "object_id": 127714, + "object_name": "C elegans uterine-vulval cell (uv1)" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... We suggest that the HSN neuron is the executive neuron driving egg-laying events. We propose that the VC neurons act as \"single egg counters\" that inhibit HSN activity for short periods in response to individual egg-laying events. We further propose that the uv1 neuroendocrine cells are \"cluster counters\", which inhibit HSN activity for longer periods and are responsible for the time constant of the inactive phase. Together they form an integrated circuit that drives the clustered egg-laying pattern. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/127321", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 127711, + "object_name": "Egg-laying circuit" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 127710, + "object_name": "C. elegans" + } + ] + }, + "ver_date": "2019-02-26T10:16:42.307", + "ver_number": 5 + }, + "127351": { + "class_id": 19, + "created": "2010-06-14T14:10:16", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 127351, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 124176, + "object_name": "Katz Y, Menon V, Nicholson DA, Geinisman Y, Kath WL, Spruston N (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "A two-stage model of dendritic integration in CA1 pyramidal neurons (Katz et al. 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... In a two-stage integration model, inputs contribute directly to dendritic spikes, and outputs from multiple branches sum in the axon. ... We used serial-section electron microscopy to reconstruct individual apical oblique dendritic branches of CA1 pyramidal neurons and observe a synapse distribution consistent with the two-stage integration model. Computational modeling suggests that the observed synapse distribution enhances the contribution of each dendritic branch to neuronal output.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/127351", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2012-09-05T15:57:07", + "ver_number": 7 + }, + "127355": { + "biosimulations": { + "value": "modeldb-127355" + }, + "citation": { + "attr_id": 391, + "value": "Brown AM. A modeling study predicts the presence of voltage gated Ca(2+) channels on myelinated central axons. Comput Meth Prog Biomed 2003;71:25-31." + }, + "class_id": 19, + "created": "2010-06-17T15:04:37", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "id": 127355, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3642, + "object_name": "Tutorial/Teaching" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 127356, + "object_name": "Brown AM (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Prediction for the presence of voltage-gated Ca2+ channels in myelinated central axons (Brown 2003)", + "notes": { + "attr_id": 24, + "value": "\"The objective of this current study was to investigate whether voltage gated Ca(2+) channels are present \r\non axons of the adult rat optic nerve (RON).\r\n\r\nSimulations of axonal excitability using a Hodgkin-Huxley based \r\none-compartment model incorporating I(Na), I(K) and leak currents were used to predict conditions under \r\nwhich the potential contribution of a Ca(2+) current to an evoked action potential could be measured. \r\n\r\n... , as predicted by the simulation, reducing the repolarizing effect of I(K) by adding the \r\nK(+) channel blocker 4-AP revealed a Ca(2+) component on the repolarizing phase of the action potential that \r\nwas blocked by the Ca(2+) channel inhibitor nifedipine.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/127355", + "other_neurons": { + "attr_id": 301, + "value": "myelinated central axon" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "wshengnan@vcu.edu" + }, + "ver_date": "2010-06-18T11:19:14", + "ver_number": 4 + }, + "127388": { + "class_id": 19, + "created": "2010-06-23T10:22:27", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 127388, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 127443, + "object_name": "Hahn, Philip [hahnp at ccf.org]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 127389, + "object_name": "Hahn PJ, McIntyre CC (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Basal ganglia network model of subthalamic deep brain stimulation (Hahn and McIntyre 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 74300, + "object_name": "Subthalamus nucleus projection neuron" + }, + { + "object_id": 88217, + "object_name": "Globus pallidus neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Basal ganglia network model of parkinsonian activity and subthalamic deep brain stimulation in non-human primates from the article \r\n\r\nInstructions are provided in the README.txt file. Contact hahnp@ccf.org if you have any questions about the implementation of the model. Please include \"ModelDB - BGnet\" in the subject heading." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/127388", + "other_implementer": { + "attr_id": 308, + "value": "Hahn, Philip" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181203, + "object_name": "127388" + } + ] + }, + "ver_date": "2014-01-21T11:53:52", + "ver_number": 7 + }, + "127507": { + "class_id": 19, + "created": "2010-07-01T18:00:52", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 127507, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 127650, + "object_name": "Huertas, Marco [huertas.marco at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 126464, + "object_name": "Kuznetsova AY, Huertas MA, Kuznetsov AS, Paladini CA, Canavier CC (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "127507_1", + "name": "Fig. 2a2" + }, + { + "id": "127507_2", + "name": "Fig. 2b2" + }, + { + "id": "127507_3", + "name": "Fig. 2f2" + }, + { + "id": "127507_4", + "name": "Fig. 6a solid" + }, + { + "id": "127507_5", + "name": "Fig. 6a dashed" + } + ], + "default": "127507_1" + }, + "name": "Regulation of firing frequency in a midbrain dopaminergic neuron model (Kuznetsova et al. 2010)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 261, + "object_name": "Substantia nigra pars compacta DA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A dopaminergic (DA) neuron model with a morphologicaly realistic dendritic architecture. The model captures several salient features of DA neurons under different pharmacological manipulations and exhibits depolarization block for sufficiently high current pulses applied to the soma." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/127507", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181424, + "object_name": "127507_5" + }, + { + "object_id": 181425, + "object_name": "127507_4" + }, + { + "object_id": 181426, + "object_name": "127507_3" + }, + { + "object_id": 181427, + "object_name": "127507_2" + }, + { + "object_id": 181428, + "object_name": "127507_1" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 323 + }, + "ver_date": "2015-01-02T22:40:06", + "ver_number": 7 + }, + "127512": { + "class_id": 19, + "created": "2010-07-02T09:42:12", + "gitrepo": false, + "id": 127512, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57906, + "object_name": "Golomb, David [golomb at bgu.ac.il]" + }, + { + "object_id": 127513, + "object_name": "Simony, Erez [erez.simony at weizmann.ac.il]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 127511, + "object_name": "Simony E, Bagdasarian K, Herfst L, Brecht M, Ahissar E, Golomb D (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 127515, + "object_name": "Muscle model and vibrissa biomechanics" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Temporal and spatial characteristics of vibrissa responses to motor commands (Simony et al. 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 127024, + "object_name": "Vibrissa motoneuron" + }, + { + "object_id": 127514, + "object_name": "Vibrissa motor plant" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"A mechanistic description of the generation of whisker movements is essential for understanding the control of whisking and vibrissal\r\nactive touch. We explore how facial-motoneuron spikes are translated, via an intrinsic muscle, to whisker movements. This is achieved by\r\nconstructing, simulating, and analyzing a computational, biomechanical model of the motor plant, and by measuring spiking to movement\r\ntransformations at small and large angles using high-precision whisker tracking in vivo. \r\n...\r\nThe model provides a direct translation from motoneuron spikes to whisker movements\r\nand can serve as a building block in closed-loop motor\u2013sensory models of active touch.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/127512", + "ver_date": "2010-07-05T13:46:14", + "ver_number": 9 + }, + "127728": { + "citation": { + "attr_id": 391, + "value": "Hendrickson, E.B., Edgerton, J.R., & Jaeger, D. (2010) The capabilities and limitations of conductance-based compartmental neuron models with reduced branched or unbranched morphologies and active dendrites. In Press at J. Comp. Neurosci." + }, + "class_id": 19, + "created": "2010-07-15T18:52:39", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gitrepo": false, + "id": 127728, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 127731, + "object_name": "Hendrickson, Eric B [eric.b.hendrickson at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 127729, + "object_name": "Hendrickson EB, Edgerton JR, Jaeger D (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "Comparison of full and reduced globus pallidus models (Hendrickson 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88217, + "object_name": "Globus pallidus neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In this paper, we studied what features of realistic full model activity patterns can and cannot be preserved by morphologically reduced models. To this end, we reduced the morphological complexity of a full globus pallidus neuron model possessing active dendrites and compared its spontaneous and driven responses to those of the reduced models." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/127728", + "other_implementer": { + "attr_id": 308, + "value": "Hendrickson, Eric B " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "eric.b.hendrickson@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "ver_date": "2010-07-19T10:27:10", + "ver_number": 3 + }, + "127878": { + "class_id": 19, + "created": "2010-07-20T14:58:13", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "id": 127878, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 127879, + "object_name": "Goldman, Mark [msgoldman at ucdavis.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 137847, + "object_name": "Phase Response Curves" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 24618, + "object_name": "Goldman MS, Golowasch J, Marder E, Abbott LF (2001)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Global structure, robustness, and modulation of neuronal models (Goldman et al. 2001)", + "notes": { + "attr_id": 24, + "value": "\"The electrical characteristics of many neurons are remarkably\r\nrobust in the face of changing internal and external conditions.\r\n\r\nAt the same time, neurons can be highly sensitive to neuromodulators.\r\n\r\nWe find correlates of this dual robustness and\r\nsensitivity in a global analysis of the structure of a\r\nconductance-based model neuron.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/127878", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115959, + "object_name": "Stomatogastric ganglion" + } + ] + }, + "ver_date": "2014-07-18T15:50:02", + "ver_number": 7 + }, + "127887": { + "citation": { + "attr_id": 391, + "value": "doi:10.1162/neco.2010.09-09-1097" + }, + "class_id": 19, + "created": "2010-07-23T19:15:52", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 116966, + "object_name": "I_K,Na" + } + ] + }, + "gitrepo": false, + "id": 127887, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 127889, + "object_name": "Omurtag, Ahmet [aomurtag at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 127888, + "object_name": "Omurtag A, Lytton WW (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Spectral method and high-order finite differences for nonlinear cable (Omurtag and Lytton 2010)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We use high-order approximation schemes for the space derivatives in the nonlinear cable equation and investigate the behavior of numerical solution errors by using exact solutions, where available, and grid convergence. The space derivatives are numerically approximated by means of differentiation matrices. A flexible form for the injected current is used that can be adjusted smoothly from a very broad to a narrow peak, which leads, for the passive cable, to a simple, exact solution. We provide comparisons with exact solutions in an unbranched passive cable, the convergence of solutions with progressive refinement of the grid in an active cable, and the simulation of spike initiation in a biophysically realistic single-neuron model.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/127887", + "other_implementer": { + "attr_id": 308, + "value": "Omurtag, Ahmet" + }, + "other_neurons": { + "attr_id": 301, + "value": "Rat layer 5 pyramidal neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "aomurtag@gmail.com" + }, + "ver_date": "2013-12-17T16:13:43", + "ver_number": 6 + }, + "127967": { + "class_id": 19, + "created": "2010-07-30T16:07:28", + "gitrepo": false, + "id": 127967, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 58584, + 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a wide\r\nvariety of desired activity patterns.\r\n...\r\n Our results\r\nreproduce data on premovement activity in motor\r\nand premotor cortex, and suggest that synaptic plasticity\r\nmay be a more rapid and powerful modulator of\r\nnetwork activity than generally appreciated.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/127967", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2017-10-26T11:07:39.377", + "ver_number": 4 + }, + "127992": { + "class_id": 19, + "created": "2010-08-11T15:29:19", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 127992, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 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However, little is known about how such networks respond to excitatory synaptic input. In addition to detailed electrophysiological recordings and histological investigations of electrically coupled Golgi cells in the cerebellum, a detailed network model of these cells was created. The cell models are based on reconstructed Golgi cell morphologies and the active conductances are taken from an earlier abstract Golgi cell model (Solinas et al 2007, accession no. 112685). Our results show that gap junction coupling can sometimes be inhibitory and either promote network synchronization or trigger rapid network desynchronization depending on the synaptic input. The model is available as a neuroConstruct project and can executable scripts can be generated for the NEURON simulator." + }, + "opensourcebrain": { + "value": "vervaekeetalgolgicellnetwork" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/127996", + "other_concept": { + "attr_id": 306, + "value": "Electrical Synapse" + }, + "other_implementer": { + "attr_id": 308, + "value": "Vervaeke, Koen \r\nGleeson, Padraig \r\n" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "p.gleeson@ucl.ac.uk" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2015-01-02T12:05:27", + "ver_number": 5 + }, + "128043": { + "class_id": 19, + "created": "2010-08-18T15:13:09", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "id": 128043, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 128044, + "object_name": "Cannon RC, O'Donnell C, Nolan MF (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 128066, + "object_name": "PSICS" + } + ] + }, + "name": "Stochastic ion channels and neuronal morphology (Cannon et al. 2010)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + }, + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 261, + "object_name": "Substantia nigra pars compacta DA cell" + }, + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... We introduce and\r\nvalidate new computational tools that enable efficient generation and simulation of models containing stochastic ion\r\nchannels distributed across dendritic and axonal membranes.\r\n\r\nComparison of five morphologically distinct neuronal cell\r\ntypes reveals that when all simulated neurons contain identical densities of stochastic ion channels, the amplitude of\r\nstochastic membrane potential fluctuations differs between cell types and depends on sub-cellular location.\r\n\r\n...\" The code is downloadable and more information is available at http://www.psics.org/" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/128043", + "ver_date": "2010-09-17T14:12:09", + "ver_number": 8 + }, + "128068": { + "citation": { + "attr_id": 391, + "value": "Zrenner, C., Eytan, D., Wallach, A., Thier H.-P., Marom, S. (2010) A generic framework for real-time multi-channel neuronal signal analysis, telemetry control and sub-millisecond latency feedback generation. Front. Neurosci." + }, + "class_id": 19, + "created": "2010-08-22T05:16:16", + "gitrepo": false, + "id": 128068, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 128073, + "object_name": "Zrenner, Christoph [christoph.zrenner at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 128069, + "object_name": "Zrenner CD, Eytan D, Wallach A, Thier HP, Marom S (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 83572, + "object_name": "Simulink" + } + ] + }, + "name": "Generic Bi-directional Real-time Neural Interface (Zrenner et al. 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 128077, + "object_name": "Neocortex dissociated cultured nerve cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Matlab/Simulink toolkit for generic multi-channel short-latency bi-directional neural-computer interactions. High-bandwidth (> 10 megabit per second) neural recording data can be analyzed in real-time while simultaneously generating specific complex electrical stimulation feedback with deterministically timed responses at sub-millisecond resolution. The commercially available 60-channel extracellular multi-electrode recording and stimulation set-up (Multichannelsystems GmbH MEA60) is used as an example hardware implementation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/128068", + "other_implementer": { + "attr_id": 308, + "value": "Zrenner, Christoph " + }, + "other_neurons": { + "attr_id": 301, + "value": "cortical culture" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "christoph.zrenner@gmail.com" + }, + "ver_date": "2015-07-26T14:09:20.977", + "ver_number": 6 + }, + "128079": { + "class_id": 19, + "created": "2010-08-23T11:44:54", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 128079, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 127920, + "object_name": "Schmidt-Hieber C, Bischofberger J (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Fast sodium channel gating in mossy fiber axons (Schmidt-Hieber et al. 2010)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... To study the mechanisms underlying AP initiation in unmyelinated hippocampal mossy fibers of adult mice, we recorded sodium currents in axonal and somatic membrane patches. \r\n\r\nWe demonstrate that sodium channel density in the proximal axon is ~5 times higher than in the soma. \r\n\r\nFurthermore, sodium channel activation and inactivation are ~2 times faster. \r\n\r\nModeling revealed that the fast activation localized the initiation site to the proximal axon even upon strong synaptic stimulation, while fast inactivation contributed to energy-efficient membrane charging during APs. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/128079", + "ver_date": "2015-09-24T12:58:23.817", + "ver_number": 14 + }, + "128446": { + "citation": { + "attr_id": 391, + "value": "PMID: 16207782" + }, + "class_id": 19, + "created": "2010-09-09T08:30:46", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 128446, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 128447, + "object_name": "Nieus, Thierry [thierry.nieus at iit.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 80157, + "object_name": "Nieus T, Sola E, Mapelli J, Saftenku E, Rossi P, D'Angelo E (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "128446", + "name": "Init & Run" + } + ], + "default": "128446" + }, + "name": "Short term plasticity at the cerebellar granule cell (Nieus et al. 2006)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 7346, + "object_name": "Cerebellum interneuron granule GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model reproduces short term plasticity of the mossy fibre to granule cell synapse. To reproduce synaptic currents recorded in experiments, a model of presynaptic release was used to determine the concentration of glutamate in the synaptic cleft that ultimately determined a synaptic response. The parameters of facilitation and depression were determined deconvolving AMPA EPSCs." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/128446", + "other_implementer": { + "attr_id": 308, + "value": "Nieus Thierry" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "thierry.nieus@iit.it" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181863, + "object_name": "128446" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 336 + }, + "ver_date": "2014-02-25T01:07:49", + "ver_number": 6 + }, + "128449": { + "class_id": 19, + "created": "2010-09-10T18:19:54", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 128449, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33804, + "object_name": "Gabbiani, F" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 128498, + "object_name": "Gabbiani F, Krapp HG (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Leaky integrate-and-fire model of spike frequency adaptation in the LGMD (Gabbiani and Krapp 2006)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 116948, + "object_name": "Locust Lobula Giant Movement Detector (LGMD) neuron" + }, + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This will reproduce Figure 9 of Gabbiani and Krapp (2006) J Neurophysiol 96:2951-2962.\r\n\r\nThe figure simply shows that a leaky-integrate-and-fire model cannot reproduce spike frequency adaptation as it is seen experimentally in \r\nthe LGMD neuron.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/128449", + "other_neurons": { + "attr_id": 301, + "value": "LGMD" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "gabbiani@bcm.edu" + }, + "ver_date": "2014-05-21T11:15:01", + "ver_number": 6 + }, + "128502": { + "class_id": 19, + "created": "2010-09-13T13:02:32", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "id": 128502, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 128503, + "object_name": "Goldwyn, Joshua [jhgoldwyn at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 122331, + "object_name": "Noise Sensitivity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 128771, + "object_name": "Goldwyn JH, Imennov NS, Famulare M, Shea-Brown E (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 45540, + "object_name": "FORTRAN" + } + ] + }, + "name": "On stochastic diff. eq. models for ion channel noise in Hodgkin-Huxley neurons (Goldwyn et al. 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52426, + "object_name": "Squid axon" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... We analyze three SDE models that have been proposed as approximations to the Markov chain model: one that describes the states of the ion channels and two that describe the states of the ion channel subunits. We show that the former channel-based approach can capture the distribution of channel noise and its effect on spiking in a Hodgkin-Huxley neuron model to a degree not previously demonstrated, but the latter two subunit-based approaches cannot. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/128502", + "other_implementer": { + "attr_id": 308, + "value": "Goldwyn, Joshua " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jgoldwyn@uw.edu" + }, + "ver_date": "2010-09-23T11:25:42", + "ver_number": 13 + }, + "128559": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 136805, + "object_name": "Initial version" + } + ] + }, + "class_id": 19, + "created": "2010-09-17T15:36:17", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { 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+ ], + "default": "128559" + }, + "name": "Mathematical model for windup (Aguiar et al. 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 128565, + "object_name": "Wide dynamic range neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Windup is characterized as a frequency-dependent\r\nincrease in the number of evoked action potentials in dorsal\r\nhorn neurons in response to electrical stimulation of afferent C-fibers.\r\n... \r\nThe approach presented here relies on mathematical and\r\ncomputational analysis to study the mechanism(s) underlying windup.\r\n\r\nFrom experimentally obtained windup profiles, we extract the time\r\nscale of the facilitation mechanisms that may support the characteristics\r\nof windup.\r\n\r\nGuided by these values and using simulations of a\r\nbiologically realistic compartmental model of a wide dynamic range\r\n(WDR) neuron, we are able to assess the contribution of each\r\nmechanism for the generation of action potentials windup.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/128559", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181241, + "object_name": "128559" + } + ] + }, + "ver_date": "2014-02-20T17:11:23", + "ver_number": 11 + }, + "128812": { + "class_id": 19, + "created": "2010-09-23T14:42:51", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 128812, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 128813, + "object_name": "Zilli, Eric [zilli at bu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + }, + { + "object_id": 194888, + "object_name": "Grid cell" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 128814, + "object_name": "Zilli EA, Hasselmo ME (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Grid cell oscillatory interference with noisy network oscillators (Zilli and Hasselmo 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114635, + "object_name": "Neocortex spiny stellate cell" + }, + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "To examine whether an oscillatory interference model of grid cell activity could work if the oscillators were noisy neurons, we implemented these simulations. Here the oscillators are networks (either synaptically- or gap-junction--coupled) of one or more noisy neurons (either Izhikevich's simple model or a Hodgkin-Huxley--type biophysical model) which drive a postsynaptic cell (which may be integrate-and-fire, resonate-and-fire, or the simple model) which should fire spatially as a grid cell if the simulation is successful." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/128812", + "other_implementer": { + "attr_id": 308, + "value": "Zilli, Eric " + }, + "ver_date": "2016-07-19T17:26:01.903", + "ver_number": 11 + }, + "128816": { + "citation": { + "attr_id": 391, + "value": "16615219, 17428776" + }, + "class_id": 19, + "created": "2010-09-27T09:09:53", + "gitrepo": false, + "id": 128816, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83561, + "object_name": "Humphries, Mark D [m.d.humphries at shef.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + }, + { + "object_id": 114660, + "object_name": "Connectivity matrix" + }, + { + "object_id": 128875, + "object_name": "Development" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 128817, + "object_name": "Humphries MD, Gurney K, Prescott TJ (2006)" + }, + { + "object_id": 128873, + "object_name": "Humphries MD, Gurney K, Prescott TJ (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Medial reticular formation of the brainstem: anatomy and dynamics (Humphries et al. 2006, 2007)", + "notes": { + "attr_id": 24, + "value": "A set of models to study the medial reticular formation (mRF) of the brainstem. We developed a collection of algorithms to derive the adult-state wiring of the model: one set a stochastic model; the other set mimicking the developmental process. We found that the anatomical models had small-world properties, irrespective of the choice of algorithm; and that the cluster-like organisation of the mRF may have arisen to minimise wiring costs. (The model code includes options to be run as dynamic models; papers examining these dynamics are included in the .zip file)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/128816", + "other_concept": { + "attr_id": 306, + "value": "Development" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "m.d.humphries@sheffield.ac.uk" + }, + "ver_date": "2011-08-19T09:57:34", + "ver_number": 4 + }, + "128818": { + "citation": { + "attr_id": 391, + "value": "Humphries, M. D., Lepora, N., Wood, R. & Gurney, K. (2009) Capturing dopaminergic modulation and bimodal membrane behaviour of striatal medium spiny neurons in accurate, reduced models. Frontiers in Computational Neuroscience, 3, 26" + }, + "class_id": 19, + "created": "2010-09-27T09:57:25", + "gitrepo": false, + "id": 128818, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83561, + "object_name": "Humphries, Mark D [m.d.humphries at shef.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 128985, + "object_name": "Humphries MD, Lepora N, Wood R, Gurney K (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Dopamine-modulated medium spiny neuron, reduced model (Humphries et al. 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 260, + "object_name": "Neostriatum medium spiny direct pathway GABA cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We extended Izhikevich's reduced model of the striatal medium spiny neuron (MSN) to account for dopaminergic modulation of its intrinsic ion channels and synaptic inputs. We tuned our D1 and D2 receptor MSN models using data from a recent (Moyer et al, 2007) large-scale compartmental model. Our new models capture the input-output relationships for both current injection and spiking input with remarkable accuracy, despite the order of magnitude decrease in system size. They also capture the paired pulse facilitation shown by MSNs. Our dopamine models predict that synaptic effects dominate intrinsic effects for all levels of D1 and D2 receptor activation. Our analytical work on these models predicts that the MSN is never bistable. Nonetheless, these MSN models can produce a spontaneously bimodal membrane potential similar to that recently observed in vitro following application of NMDA agonists. We demonstrate that this bimodality is created by modelling the agonist effects as slow, irregular and massive jumps in NMDA conductance and, rather than a form of bistability, is due to the voltage-dependent blockade of NMDA receptors" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/128818", + "other_neurons": { + "attr_id": 301, + "value": "Striatal medium spiny neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "m.d.humphries@sheffield.ac.uk" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 196, + "object_name": "D1" + }, + { + "object_id": 197, + "object_name": "D2" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2012-01-18T16:39:09", + "ver_number": 10 + }, + "128874": { + "citation": { + "attr_id": 391, + "value": "Humphries, M. D.; Wood, R. & Gurney, K. (2009) Dopamine-modulated dynamic cell assemblies generated by the GABAergic striatal microcircuit Neural Networks, 22, 1174-1188" + }, + "class_id": 19, + "created": "2010-09-27T11:59:21", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "id": 128874, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83561, + "object_name": "Humphries, Mark D [m.d.humphries at shef.ac.uk]" + }, + { + "object_id": 129066, + "object_name": "Wood, Ric [ric.wood at shef.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + }, + { + "object_id": 114660, + "object_name": "Connectivity matrix" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 129002, + "object_name": "Humphries MD, Wood R, Gurney K (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Striatal GABAergic microcircuit, dopamine-modulated cell assemblies (Humphries et al. 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 118395, + "object_name": "Neostriatum fast spiking interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "To begin identifying potential dynamically-defined computational elements within the striatum, we constructed a new three-dimensional model of the striatal microcircuit's connectivity, and instantiated this with our dopamine-modulated neuron models of the MSNs and FSIs. A new model of gap junctions between the FSIs was introduced and tuned to experimental data. We introduced a novel multiple spike-train analysis method, and apply this to the outputs of the model to find groups of synchronised neurons at multiple time-scales. We found that, with realistic in vivo background input, small assemblies of synchronised MSNs spontaneously appeared, consistent with experimental observations, and that the number of assemblies and the time-scale of synchronisation was strongly dependent on the simulated concentration of dopamine. We also showed that feed-forward inhibition from the FSIs counter-intuitively increases the firing rate of the MSNs." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/128874", + "other_implementer": { + "attr_id": 308, + "value": "Wood, Ric " + }, + "other_neurons": { + "attr_id": 301, + "value": "Striatal medium spiny neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "m.d.humphries@sheffield.ac.uk" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 196, + "object_name": "D1" + }, + { + "object_id": 197, + "object_name": "D2" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 223, + "object_name": "Dopaminergic Receptor" + } + ] + }, + "ver_date": "2010-09-27T17:21:07", + "ver_number": 3 + }, + "129067": { + "class_id": 19, + "created": "2010-09-28T19:50:20", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 129067, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 129069, + "object_name": "Blair, Hugh T." + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 129070, + "object_name": "Envelope synthesis" + }, + { + "object_id": 194888, + "object_name": "Grid cell" + }, + { + "object_id": 98011, + "object_name": "Place cell/field" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 142654, + "object_name": "Welday AC, Shlifer IG, Bloom ML, Zhang K, Blair HT (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "129067_1", + "name": "Fig 9A" + }, + { + "id": "129067_2", + "name": "Fig 9B" + } + ], + "default": "129067_1" + }, + "name": "Synthesis of spatial tuning functions from theta cell spike trains (Welday et al., 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + }, + { + "object_id": 129068, + "object_name": "Entorhinal cortex stellate cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A single compartment model reproduces the firing rate maps of place, grid, and boundary cells by receiving inhibitory inputs from theta cells. The theta cell spike trains are modulated by the rat's movement velocity in such a way that phase interference among their burst pattern creates spatial envelope function which simulate the firing rate maps." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/129067", + "other_concept": { + "attr_id": 306, + "value": "Envelope synthesis" + }, + "other_implementer": { + "attr_id": 308, + "value": "Blair, Hugh T." + }, + "other_neurons": { + "attr_id": 301, + "value": "Entorhinal stellate neuron" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2016-07-19T17:26:50.437", + "ver_number": 17 + }, + "129071": { + "class_id": 19, + "created": "2010-09-30T06:37:31", + "gitrepo": false, + "id": 129071, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 97998, + "object_name": "van Elburg, Ronald A.J. 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The discrete time BES-model is underlying current attempts to simulate cortical development, but is difficult to analyze. The new continuous time formulation facilitates analytical treatment thus allowing us to examine the structure of the model more closely. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/129071", + "other_concept": { + "attr_id": 306, + "value": "Morphology" + }, + "ver_date": "2011-02-28T11:03:11", + "ver_number": 15 + }, + "129149": { + "class_id": 19, + "created": "2010-10-08T10:51:22", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 129149, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 67028, + "object_name": "Lindsay AE, Lindsay KA, Rosenberg JR (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Increased computational accuracy in multi-compartmental cable models (Lindsay et al. 2005)", + "notes": { + "attr_id": 24, + "value": "Compartmental models of dendrites are the most widely used tool for investigating their electrical\r\nbehaviour. \r\n\r\nTraditional models assign a single potential to a compartment. \r\n\r\nThis potential is associated with the membrane potential at the centre of the segment represented by the compartment.\r\n\r\nAll input to that segment, independent of its location on the segment, is assumed to act at the centre of the segment with the potential of the\r\ncompartment. \r\n\r\nBy contrast, the compartmental model introduced in this article assigns a potential to each end of a\r\nsegment, and takes into account the location of input to a segment on the model solution by partitioning the effect of\r\nthis input between the axial currents at the proximal and distal boundaries of segments.\r\n\r\nFor a given neuron, the new and traditional approaches to compartmental modelling use the same number of locations at which the membrane\r\npotential is to be determined, and lead to ordinary differential equations that are structurally identical. However, the\r\nsolution achieved by the new approach gives an order of magnitude better accuracy and precision than that achieved\r\nby the latter in the presence of point process input." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/129149", + "ver_date": "2014-02-17T12:17:53", + "ver_number": 5 + }, + "135787": { + "class_id": 19, + "created": "2010-10-13T16:24:12", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 143445, + "object_name": "I_KD" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 135787, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 96432, + "object_name": "Shu Y, Duque A, Yu Y, Haider B, McCormick DA (2007)" + }, + { + "object_id": 114903, + "object_name": "Shu Y, Hasenstaub A, Duque A, Yu Y, McCormick DA (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "135787_1", + "name": "spike initialization" + }, + { + "id": "135787_2", + "name": "axon potential decay" + } + ], + "default": "135787_1" + }, + "name": "Intracortical synaptic potential modulation by presynaptic somatic potential (Shu et al. 2006, 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... 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(2009).\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/135838", + "other_implementer": { + "attr_id": 308, + "value": "Alle, Henrik " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "arnd.roth@ucl.ac.uk" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181573, + "object_name": "135838" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 147 + }, + "ver_date": "2014-01-19T10:05:34", + "ver_number": 7 + }, + "135839": { + "class_id": 19, + "created": "2010-10-14T09:40:55", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + 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}, + "name": "AP back-prop. explains threshold variability and rapid rise (McCormick et al. 2007, Yu et al. 2008)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This simple axon-soma model explained how the rapid rising phase in the somatic spike is derived from the propagated axon initiated spike,\r\nand how the somatic spike threshold variance is affected by spike propagation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/135839", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + 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"value": "We discovered a low-threshold, slowly inactivating K+ current, containing Kv1.2 alpha subunits, in axon initial segment, playing a key role in the modulation of spike threshold and spike duration as well as\r\nthe spike timing in prefrontal cortex layer V pyramidal cell of ferrets and rats.\r\nA kd.mod file implements this D current and put it in the axonal model: Neuron_Dcurrent.hoc. Run the model to see the gradual modulation effect over seconds on spike shape." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/135898", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181343, + "object_name": "135898" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 335 + }, + "ver_date": "2014-02-25T01:07:22", + "ver_number": 2 + }, + "135902": { + "class_id": 19, + "created": "2010-10-15T17:53:59", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "id": 135902, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33748, + "object_name": "Lazarewicz, Maciej [mlazarew at gmu.edu]" + }, + { + "object_id": 136020, + "object_name": "Stacey, William [wstacey at med.umich.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 125094, + "object_name": "Stacey WC, Lazarewicz MT, Litt B (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "High frequency oscillations in a hippocampal computational model (Stacey et al. 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + }, + { + "object_id": 35373, + "object_name": "Hippocampus CA1 interneuron oriens alveus GABA cell" + }, + { + "object_id": 114079, + "object_name": "Hippocampus CA1 basket cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... Using a physiological computer model of hippocampus, we investigate random synaptic activity\r\n(noise) as a potential initiator of HFOs (high-frequency oscillations).\r\n\r\nWe explore parameters necessary to produce these oscillations and quantify the response\r\nusing the tools of stochastic resonance (SR) and coherence resonance\r\n(CR).\r\n... \r\n\r\nOur results show that, under normal coupling conditions, synaptic noise was able to produce\r\ngamma (30\u2013100 Hz) frequency oscillations.\r\n\r\n\r\nSynaptic noise generated HFOs in the ripple range (100\u2013200 Hz) when the network had\r\nparameters similar to pathological findings in epilepsy: increased gap\r\njunctions or recurrent synaptic connections, loss of inhibitory interneurons\r\nsuch as basket cells, and increased synaptic noise.\r\n\r\n...\r\nWe propose that increased synaptic noise and physiological coupling mechanisms are sufficient to generate gamma\r\noscillations and that pathologic changes in noise and coupling similar\r\nto those in epilepsy can produce abnormal ripples.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/135902", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2013-01-22T11:57:54", + "ver_number": 8 + }, + "135903": { + "class_id": 19, + "created": "2010-10-15T17:56:35", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "id": 135903, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33748, + "object_name": "Lazarewicz, Maciej [mlazarew at gmu.edu]" + }, + { + "object_id": 136020, + "object_name": "Stacey, William [wstacey at med.umich.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 136022, + "object_name": "Stacey WC, Krieger A, Litt B (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Network recruitment to coherent oscillations in a hippocampal model (Stacey et al. 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + }, + { + "object_id": 35373, + "object_name": "Hippocampus CA1 interneuron oriens alveus GABA cell" + }, + { + "object_id": 114079, + "object_name": "Hippocampus CA1 basket cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... Here we demonstrate, via a detailed computational model, a mechanism whereby physiological noise and coupling initiate oscillations and then recruit neighboring tissue, in a manner well described by a combination of Stochastic Resonance and Coherence Resonance.\r\n\r\n We develop a novel statistical method to quantify recruitment using several measures of network synchrony.\r\n\r\nThis measurement demonstrates that oscillations spread via preexisting network connections such as interneuronal connections, recurrent synapses, and gap junctions, provided that neighboring cells also receive sufficient inputs in the form of random synaptic noise.\r\n\r\n...\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/135903", + "other_concept": { + "attr_id": 306, + "value": "recruitment of hippocamp" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2011-04-15T16:47:41", + "ver_number": 8 + }, + "136024": { + "class_id": 19, + "created": "2010-10-27T01:07:41", + "gitrepo": false, + "id": 136024, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 136025, + "object_name": "Chambers, Jordan [jordandchambers at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 139155, + "object_name": "Chambers JD, Bornstein JC, Thomas EA (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 83572, + "object_name": "Simulink" + } + ] + }, + "name": "Modelling enteric neuron populations and muscle fed-state motor patterns (Chambers et al. 2011)", + "notes": { + "attr_id": 24, + "value": "\"After a meal, the gastrointestinal tract exhibits a set of behaviours known as the fed state.\r\n... Segmentation manifests as rhythmic local constrictions that do not propagate along the intestine.\r\n...\r\nWe investigated the enteric circuits that regulate segmentation focusing on a central feature of the ENS: a recurrent excitatory network of intrinsic sensory neurons (ISNs) which are characterized by prolonged after-hyperpolarizing potentials (AHPs) following their action potentials.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/136024", + "other_implementer": { + "attr_id": 308, + "value": "Chambers, Jordan " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jordandchambers@gmail.com" + }, + "ver_date": "2011-05-06T10:08:50", + "ver_number": 8 + }, + "136026": { + "class_id": 19, + "created": "2010-10-27T13:00:41", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 136026, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33735, + "object_name": "Carnevale, Ted [Ted.Carnevale at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 112750, + "object_name": "Djurisic M, Popovic M, Carnevale N, Zecevic D (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "136026_1", + "name": "Passive tuft, control" + }, + { + "id": "136026_2", + "name": "Passive tuft, cm = 2*control" + }, + { + "id": "136026_3", + "name": "Passive tuft, Ra = 2*control" + }, + { + "id": "136026_4", + "name": "Passive tuft, both 2*control" + }, + { + "id": "136026_5", + "name": "Active tuft" + } + ], + "default": "136026_1" + }, + "name": "Functional structure of mitral cell dendritic tuft (Djurisic et al. 2008)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The computational modeling component of Djurisic et al. 2008 addressed two primary questions: whether amplification by active currents is necessary to explain the relatively mild attenuation suffered by tuft EPSPs spreading along the primary dendrite to the soma; what accounts for the relatively uniform peak EPSP amplitude throughout the tuft. These simulations show that passive spread from tuft to soma is sufficient to yield the low attenuation of tuft EPSPs, and that random distribution of a biologically plausible number of excitatory synapses throughout the tuft can produce the experimentally observed uniformity of depolarization.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/136026", + "public_submitter_email": { + "attr_id": 309, + "value": "ted.carnevale@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181210, + "object_name": "136026_2" + }, + { + "object_id": 181870, + "object_name": "136026_3" + }, + { + "object_id": 181896, + "object_name": "136026_1" + }, + { + "object_id": 181898, + "object_name": "136026_5" + }, + { + "object_id": 181899, + "object_name": "136026_4" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 251 + }, + "ver_date": "2018-10-17T16:07:28.927", + "ver_number": 10 + }, + "136095": { + "class_id": 19, + "created": "2010-11-01T18:06:09", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 136095, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33738, + "object_name": "Lytton, William [bill.lytton at downstate.edu]" + }, + { + "object_id": 97869, + "object_name": "Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 136098, + "object_name": "Information transfer" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 136050, + "object_name": "Neymotin SA, Jacobs KM, Fenton AA, Lytton WW (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "136095", + "name": "Run neocortical column simulation & display output" + } + ], + "default": "136095" + }, + "name": "Synaptic information transfer in computer models of neocortical columns (Neymotin et al. 2010)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + }, + { + "object_id": 7387, + "object_name": "Neocortex V1 interneuron basket PV GABA cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 114635, + "object_name": "Neocortex spiny stellate cell" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + }, + { + "object_id": 142232, + "object_name": "Neocortex spiking low threshold (LTS) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"...\r\nWe sought to measure how the activity of the network alters information flow from inputs to output patterns. \r\nInformation handling by the network reflected the degree of internal connectivity. ...\r\nWith greater connectivity strength, the recurrent network translated activity and information due to contribution of activity from intrinsic network dynamics. \r\n...\r\nAt still higher internal synaptic strength, the network corrupted the external information, producing a state where little external information came through.\r\nThe association of increased information retrieved from the network with increased gamma power supports the notion of gamma oscillations playing a role in information processing.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/136095", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181456, + "object_name": "136095" + } + ] + }, + "ver_date": "2023-04-30T15:21:57.973", + "ver_number": 12 + }, + "136097": { + "class_id": 19, + "created": "2010-11-01T23:35:23", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 252, + "object_name": "I Cl,Ca" + }, + { + "object_id": 254, + "object_name": "I CNG" + }, 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Collection contains model configured for dual odorant pulse delivery and model configured for prolonged odorant delivery. For those interested more in transduction processes, each whole cell recording model comes with a counter part file configured to show just the slow transduction current for ease of use and convenience. These transduction-only models typically run faster than the full multi-scale models but do not demonstrate action potentials." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/136097", + "other_concept": { + "attr_id": 306, + "value": "G-protein coupled" + }, + "other_currents": { + "attr_id": 303, + "value": "I ANO2" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "doughe57@msu.edu" + }, + "ver_date": "2018-10-17T16:07:43.897", + "ver_number": 8 + }, + "136175": { + "citation": { + "attr_id": 391, + "value": "Volker Steuber, Nathan Schultheiss, R Angus Silver, Erik De Schutter and Dieter Jaeger (2010). The control of rebound spiking by voltage-gated currents and synaptic input in a detailed compartmental model of deep cerebellar nucleus neurons. Journal of Computational Neuroscience (epub ahead of print, PMID: 21052805)." + }, + "class_id": 19, + "created": "2010-11-11T08:52:01", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "id": 136175, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 136250, + "object_name": "Steuber, Volker [v.steuber at herts.ac.uk]" + }, + { + "object_id": 136251, + "object_name": "Jaeger, Dieter [djaeger at emory.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 41934, + "object_name": "Intrinsic plasticity" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 136252, + "object_name": "Rebound firing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 136181, + "object_name": "Steuber V, Schultheiss NW, Silver RA, De Schutter E, Jaeger D (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "Cerebellar Nucleus Neuron (Steuber, Schultheiss, Silver, De Schutter & Jaeger, 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 136179, + "object_name": "Cerebellum deep nucleus neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is the GENESIS 2.3 implementation of a multi-compartmental deep cerebellar nucleus (DCN) neuron model with a full dendritic morphology and appropriate active conductances. We generated a good match of our simulations with DCN current clamp data we recorded in acute slices, including the heterogeneity in the rebound responses. We then examined how inhibitory and excitatory synaptic input interacted with these intrinsic conductances to control DCN firing. We found that the output spiking of the model reflected the ongoing balance of excitatory and inhibitory input rates and that changing the level of inhibition performed an additive operation. Rebound firing following strong Purkinje cell input bursts was also possible, but only if the chloride reversal potential was more negative than -70 mV to allow de-inactivation of rebound currents. Fast rebound bursts due to T-type calcium current and slow rebounds due to persistent sodium current could be differentially regulated by synaptic input, and the pattern of these rebounds was further influenced by HCN current. Our findings suggest that active properties of DCN neurons could play a crucial role for signal processing in the cerebellum." + }, + "opensourcebrain": { + "value": "cerebellarnucleusneuron" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/136175", + "other_concept": { + "attr_id": 306, + "value": "Rebound firing" + }, + "other_currents": { + "attr_id": 303, + "value": "ICaT INaP IHCN" + }, + "other_implementer": { + "attr_id": 308, + "value": "Steuber, Volker (v.steuber@herts.ac.uk)\r\nJaeger, Dieter (djaeger@emory.edu)" + }, + "other_neurons": { + "attr_id": 301, + "value": "Cerebellar Nucleus Neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "v.steuber@herts.ac.uk" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2012-04-27T15:38:25", + "ver_number": 8 + }, + "136176": { + "class_id": 19, + "created": "2010-11-12T10:21:22", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 136176, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 136177, + "object_name": "Rozsa, Balazs " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 137556, + "object_name": "Katona G, Kasz\u00e1s A, Turi GF, H\u00e1jos N, Tam\u00e1s G, Vizi ES, R\u00f3zsa B (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "CA1 stratum radiatum interneuron multicompartmental model (Katona et al. 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 136178, + "object_name": "Hippocampus CA1 stratum radiatum interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model examines dendritic NMDA-spike generation and propagation\r\nin the dendrites of CA1 stratum radiatum interneurons. It contains\r\nNMDA-channels in a clustered pattern on a dendrite and K-channels. The\r\nsimulation shows the whole NMDA spike and the rising phase of the\r\ntraces in separate windows.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/136176", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181888, + "object_name": "136176" + } + ] + }, + "ver_date": "2014-02-06T16:15:06", + "ver_number": 6 + }, + "136296": { + "class_id": 19, + "created": "2010-11-22T17:53:43", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 88215, + "object_name": "I_Ks" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gitrepo": false, + "id": 136296, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 136301, + "object_name": "Brazhe, Alexey [brazhe at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 136303, + "object_name": "Intermittent block" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 136297, + "object_name": "Brazhe AR, Maksimov GV, Mosekilde E, Sosnovtseva O (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 136302, + "object_name": "Lua" + } + ] + }, + "name": "Myelinated nerve fibre myelin resistance dependent on extracellular K+ level (Brazhe et al. 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52425, + "object_name": "Myelinated neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Excitation leads to rise in paranodal [K]e under the myelin. This causes structural changes in myelin structure and resistance. Current model aims to simulate this aspect. This is a space-clamped model of a double-cable nerve fibre." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/136296", + "other_concept": { + "attr_id": 306, + "value": "Intermittent block" + }, + "other_implementer": { + "attr_id": 308, + "value": "Brazhe, Alexey brazhe@gmail.com" + }, + "other_simulator": { + "attr_id": 307, + "value": "lua program" + }, + "other_type": { + "attr_id": 305, + "value": "myelinated nerve" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "brazhe@gmail.com" + }, + "ver_date": "2015-07-26T14:10:18.337", + "ver_number": 6 + }, + "136308": { + "class_id": 19, + "created": "2010-11-30T19:17:34", + "gitrepo": false, + "id": 136308, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 136491, + "object_name": "Talathi Sachin [talathi at ufl.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3635, + "object_name": "Synchronization" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 136494, + "object_name": "Talathi SS, Carney PR, Khargonekar PP (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Wang-Buzsaki Interneuron (Talathi et al., 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 146805, + "object_name": "Abstract Wang-Buzsaki neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The submitted code provides the relevant C++ files, matlabfiles and the data files essential to reproduce the figures in the JCNS paper titled Control of neural synchrony using channelrhodopsin-2: A computational study." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/136308", + "other_implementer": { + "attr_id": 308, + "value": "Talathi Sachin " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "talathi@ufl.edu" + }, + "ver_date": "2012-11-20T15:30:34", + "ver_number": 5 + }, + "136309": { + "class_id": 19, + "created": "2010-12-03T11:40:22", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 136309, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 120908, + "object_name": "Munro, Erin [ecmun at math.bu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144389, + "object_name": "Munro E, Kopell N (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Neocort. pyramidal cells subthreshold somatic voltage controls spike propagation (Munro Kopell 2012)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 167344, + "object_name": "Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "There is suggestive evidence that pyramidal cell axons in neocortex may be coupled by gap junctions into an ``axonal plexus\" capable of generating Very Fast Oscillations (VFOs) with frequencies exceeding 80 Hz. It is not obvious, however, how a pyramidal cell in such a network could control its output when action potentials are free to propagate from the axons of other pyramidal cells into its own axon. We address this problem by means of simulations based on 3D reconstructions of pyramidal cells from rat somatosensory cortex. We show that somatic depolarization enables propagation via gap junctions into the initial segment and main axon, while somatic hyperpolarization disables it. We show further that somatic voltage cannot effectively control action potential propagation through gap junctions on minor collaterals; action potentials may therefore propagate freely from such collaterals regardless of somatic voltage. In previous work, VFOs are all but abolished during the hyperpolarization phase of slow-oscillations induced by anesthesia in vivo. This finding constrains the density of gap junctions on collaterals in our model and suggests that axonal sprouting due to cortical lesions may result in abnormally high gap junction density on collaterals, leading in turn to excessive VFO activity and hence to epilepsy via kindling." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/136309", + "other_concept": { + "attr_id": 306, + "value": "Axonal gap junctions" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2017-06-26T21:38:47.763", + "ver_number": 19 + }, + "136310": { + "class_id": 19, + "created": "2010-12-05T15:36:47", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 136310, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33744, + "object_name": "Destexhe, Alain [Destexhe at iaf.cnrs-gif.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 84598, + "object_name": "Extracellular Fields" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 45661, + "object_name": "B\u00e9dard C, Kr\u00f6ger H, Destexhe A (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "136310", + "name": "=> 1. Calculate impedances" + } + ], + "default": "136310" + }, + "name": "Modeling local field potentials (Bedard et al. 2004)", + "notes": { + "attr_id": 24, + "value": "This demo simulates a model of local field potentials (LFP) with\r\nvariable resistivity. This model reproduces the low-pass\r\nfrequency filtering properties of extracellular potentials. The\r\nmodel considers inhomogeneous spatial profiles of conductivity\r\nand permittivity, which result from the multiple media (fluids,\r\nmembranes, vessels, ...) composing the extracellular space around\r\nneurons. Including non-constant profiles of conductivity enables\r\nthe model to display frequency filtering properties, ie slow\r\nevents such as EPSPs/IPSPs are less attenuated than fast events\r\nsuch as action potentials. The demo simulates Fig 6 of the\r\npaper." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/136310", + "other_currents": { + "attr_id": 303, + "value": "Extracellular current" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "destexhe@iaf.cnrs-gif.fr" + }, + "simPFid": { + "attr_id": 488, + "value": 283 + }, + "ver_date": "2022-05-27T10:28:27.267", + "ver_number": 10 + }, + "136315": { + "citation": { + "attr_id": 391, + "value": "J Neurosci 30: 15146-59. http://www.ncbi.nlm.nih.gov/pubmed/21068320" + }, + "class_id": 19, + "created": "2010-12-08T13:08:17", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 136317, + "object_name": "Kv4.1 KCND1" + } + ] + }, + "gitrepo": false, + "id": 136315, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 114640, + "object_name": "Gunay, Cengiz [cgunay at emory.edu]" + }, + { + "object_id": 114641, + "object_name": "Edgerton, Jeremy R. 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Dendritic fast sodium channels were found to increase the importance of distal synapses (both excitatory AND inhibitory), increase spike timing variability with in vivo-like synaptic input, and make the model neurons highly sensitive to clustered synchronous excitation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/136315", + "other_currents": { + "attr_id": 303, + "value": "Ih or I_HCN" + }, + "other_gene": { + "attr_id": 477, + "value": "Kv4.1 KCND1" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jedgert@emory.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2011-09-21T14:55:53", + "ver_number": 9 + }, + "136380": { + "class_id": 19, + "created": "2010-12-09T16:37:01", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 136380, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 136800, + "object_name": "Tripp, Bryan [bryan.tripp at mail.mcgill.ca]" + }, + { + "object_id": 136801, + "object_name": "Eliasmith, Chris [celiasmith at uwaterloo.ca]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 136355, + "object_name": "Tripp B, Eliasmith C (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Neural transformations on spike timing information (Tripp and Eliasmith 2007)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Here we employ computational methods to show that an ensemble\r\nof neurons firing at a constant mean rate can induce arbitrarily\r\nchosen temporal current patterns in postsynaptic cells. ...\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/136380", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2014-05-21T11:16:45", + "ver_number": 6 + }, + "136715": { + "citation": { + "attr_id": 391, + "value": "20543843" + }, + "class_id": 19, + "created": "2011-01-05T09:49:38", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 136715, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 136716, + "object_name": 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"object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"In cortical pyramidal neurons, the axon initial segment (AIS) is pivotal in synaptic integration.\r\n\r\nIt has been asserted that this is because there is a high density of Na+ channels in the AIS. \r\n\r\nHowever, we found that action potential-associated Na+ flux, as measured by high-speed fluorescence Na+ imaging, was about threefold larger in the rat AIS than in the soma. \r\n\r\nSpike-evoked Na+ flux in the AIS and the first node of Ranvier was similar and was eightfold lower in basal dendrites.\r\n\r\n...\r\nIn computer simulations, these data were consistent with the known features of action potential generation in these neurons.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/136715", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + 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"object_name": "Activity Patterns" + }, + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 83538, + "object_name": "Unsupervised Learning" + }, + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 122331, + "object_name": "Noise Sensitivity" + }, + { + "object_id": 136098, + "object_name": "Information transfer" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 142233, + "object_name": "Gilson M, Masquelier T, Hugues E (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic 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"other_neurons": { + "attr_id": 301, + "value": "Poisson and LIF neurons" + }, + "ver_date": "2017-07-28T17:31:08.693", + "ver_number": 12 + }, + "136773": { + "biosimulations": { + "value": "modeldb-136773" + }, + "class_id": 19, + "created": "2011-01-11T14:11:11", + "gitrepo": false, + "id": 136773, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 136797, + "object_name": "Barreto, Ernest " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 118376, + "object_name": "Cressman JR, Ullah G, Ziburkus J, Schiff SJ, Barreto E (2009)" + }, + { + "object_id": 142653, + "object_name": "Cressman JR, Ullah G, Ziburkus J, Schiff SJ, Barreto E (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + 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"Tripp BP, Eliasmith C (2010) Population models of temporal differentiation. Neural Comput 22:621-59" + }, + "class_id": 19, + "created": "2011-01-14T14:02:17", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 136799, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 136800, + "object_name": "Tripp, Bryan [bryan.tripp at mail.mcgill.ca]" + }, + { + "object_id": 136801, + "object_name": "Eliasmith, Chris [celiasmith at uwaterloo.ca]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 136254, + "object_name": "Tripp BP, Eliasmith C (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 136173, + "object_name": "Nengo" + } + ] + }, + "name": "Population models of temporal differentiation (Tripp and Eliasmith 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Temporal derivatives are computed by a wide variety of neural circuits, but the problem of performing this computation accurately has received little theoretical study. \r\n\r\nHere we systematically compare the performance of diverse networks that calculate derivatives using cell-intrinsic adaptation and synaptic depression dynamics, feedforward network dynamics, and recurrent network dynamics. \r\n\r\nExamples of each type of network are compared by quantifying the errors they introduce into the calculation and their rejection of high-frequency input noise. \r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/136799", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "ver_date": "2014-05-21T11:18:29", + "ver_number": 4 + }, + "136803": { + "class_id": 19, + "created": "2011-01-15T08:38:49", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 136803, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 245798, + "object_name": "Touch" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 125381, + "object_name": "Jones SR, Pritchett DL, Sikora MA, Stufflebeam SM, H\u00e4m\u00e4l\u00e4inen M, Moore CI (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Biophysically realistic neural modeling of the MEG mu rhythm (Jones et al. 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Variations in cortical oscillations in the alpha (7\u201314 Hz) and beta (15\u201329 Hz) range have been correlated with attention, working memory, and stimulus detection. The mu rhythm recorded with magnetoencephalography (MEG) is a prominent oscillation generated by Rolandic cortex containing alpha and beta bands. Despite its prominence, the neural mechanisms regulating mu are unknown. We characterized the ongoing MEG mu rhythm from a localized source in the finger representation of primary somatosensory (SI) cortex. Subjects showed variation in the relative expression of mu-alpha or mu-beta, which were nonoverlapping for roughly 50% of their respective durations on single trials. To delineate the origins of this rhythm, a biophysically principled computational neural model of SI was developed, with distinct laminae, inhibitory and excitatory neurons, and feedforward (FF, representative of lemniscal thalamic drive) and feedback (FB, representative of higher-order cortical drive or input from nonlemniscal thalamic nuclei) inputs defined by the laminar location of their postsynaptic effects. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/136803", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181411, + "object_name": "136803" + } + ] + }, + "ver_date": "2018-10-18T17:25:03.297", + "ver_number": 10 + }, + "136807": { + "class_id": 19, + "created": "2011-01-21T10:40:11", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 136807, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 83540, + "object_name": "Winner-take-all" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 169263, + "object_name": "Richmond P, Buesing L, Giugliano M, Vasilaki E (2011)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Democratic population decisions result in robust policy-gradient learning (Richmond et al. 2011)", + "notes": { + "attr_id": 24, + "value": "This model demonstrates the use of GPU programming (with CUDA) to simulate a two-layer network of Integrate-and-Fire neurons with varying degrees of recurrent connectivity and to investigate its ability to learn a simplified navigation task using a learning rule stemming from Reinforcement Learning, a policy-gradient rule." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/136807", + "other_implementer": { + "attr_id": 308, + "value": "NVIDIA Corp." + }, + "other_simulator": { + "attr_id": 307, + "value": "CUDA" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "p.richmond@sheffield.ac.uk" + }, + "ver_date": "2015-01-26T17:06:04", + "ver_number": 4 + }, + "136808": { + "citation": { + "attr_id": 391, + "value": "Nathan F. Lepora, Paul G. Overton and K. Gurney. Efficient fitting of conductance-based model neurons from somatic current clamp. J. Comp Neuro (In press)." + }, + "class_id": 19, + "created": "2011-01-24T14:59:21", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": false, + "id": 136808, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 136809, + "object_name": "Lepora, Nathan [n.lepora at shef.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 116962, + "object_name": "Parameter sensitivity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 136810, + "object_name": "Lepora NF, Overton PG, Gurney K (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Parameter estimation for Hodgkin-Huxley based models of cortical neurons (Lepora et al. 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + }, + { + "object_id": 142232, + "object_name": "Neocortex spiking low threshold (LTS) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Simulation and fitting of two-compartment (active soma, passive dendrite) \r\nfor different classes of cortical neurons. The fitting technique \r\nindirectly matches neuronal currents derived from somatic membrane \r\npotential data rather than fitting the voltage traces directly. \r\nThe method uses an analytic solution for the somatic ion channel \r\nmaximal conductances given approximate models of the channel kinetics, \r\nmembrane dynamics and dendrite. This approach is tested on model-derived \r\ndata for various cortical neurons. \r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/136808", + "other_implementer": { + "attr_id": 308, + "value": "Lepora, Nathan " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "n.lepora@shef.ac.uk" + }, + "ver_date": "2011-10-28T23:36:55", + "ver_number": 7 + }, + "137259": { + "class_id": 19, + "created": "2011-01-31T14:53:42", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 137259, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 98006, + "object_name": "Baker, John L [jbakerb at gmu.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 137261, + "object_name": "Baker JL, Perez-Rosello T, Migliore M, Barrionuevo G, Ascoli GA (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A model of unitary responses from A/C and PP synapses in CA3 pyramidal cells (Baker et al. 2010)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model was used to reproduce experimentally determined mean synaptic response characteristics of unitary AMPA and NMDA synaptic stimulations in CA3 pyramidal cells with the objective of inferring the most likely response properties of the corresponding types of synapses. The model is primarily concerned with passive cells, but models of active dendrites are included." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/137259", + "other_concept": { + "attr_id": 306, + "value": "Synaptic properties" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181429, + "object_name": "137259" + } + ] + }, + "ver_date": "2014-01-18T22:30:35", + "ver_number": 10 + }, + "137263": { + "biosimulations": { + "value": "modeldb-137263" + }, + "citation": { + "attr_id": 391, + "value": "Chen BS, Lo YC, Liu YC, Wu SN. Effects of transient receptor potential-like current on the firing pattern of action potentials in the Hodgkin-Huxley neuron during exposure to sinusoidal external voltage. Chinese Journal of Physiology 2010; 53(6):423-429." + }, + "class_id": 19, + "created": "2011-02-08T01:20:34", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 137264, + "object_name": "I trp" + } + ] + }, + "gitrepo": false, + "id": 137263, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3642, + "object_name": "Tutorial/Teaching" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 137265, + "object_name": "Chen BS, Lo YC, Lius YC, Wu SN (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Effect of trp-like current on APs during exposure to sinusoidal voltage (Chen et al. 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52426, + "object_name": "Squid axon" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"...\r\nPrevious work showed that magnetic electrical field-induced antinoceptive action is mediated by activation of capsaicin-sensitive sensory afferents. In this study, a modified Hodgkin-Huxley model, in which TRP-like current (I-TRP) was incorporated, was implemented to predict the firing behavior of action potentials (APs), as the model neuron was exposed to sinusoidal changes in externally-applied voltage. \r\n\r\n...\r\nOur simulation results suggest that modulation of TRP-like channels functionally expressed in small-diameter peripheral sensory neurons should be an important mechanism through which it can contribute to the firing pattern of APs.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/137263", + "other_currents": { + "attr_id": 303, + "value": "I(trp)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2011-02-08T11:51:23", + "ver_number": 7 + }, + "137502": { + "citation": { + "attr_id": 391, + "value": "Humphries, M. D., Wood, R., Gurney, K. (2010) Reconstructing the three-dimensional GABAergic microcircuit of the striatum. PLoS Comput Biol, 6, e1001011" + }, + "class_id": 19, + "created": "2011-02-10T12:39:40", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "id": 137502, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83561, + "object_name": "Humphries, Mark D [m.d.humphries at shef.ac.uk]" + }, + { + "object_id": 129066, + "object_name": "Wood, Ric [ric.wood at shef.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 83540, + "object_name": "Winner-take-all" + }, + { + "object_id": 114660, + "object_name": "Connectivity matrix" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 137503, + "object_name": "Humphries MD, Wood R, Gurney K (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Striatal GABAergic microcircuit, spatial scales of dynamics (Humphries et al, 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 118395, + "object_name": "Neostriatum fast spiking interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The main thrust of this paper was the development of the 3D anatomical network of the striatum's GABAergic microcircuit. We grew dendrite and axon models for the MSNs and FSIs and extracted probabilities for the presence of these neurites as a function of distance from the soma. From these, we found the probabilities of intersection between the neurites of two neurons given their inter-somatic distance, and used these to construct three-dimensional striatal networks. These networks were examined for their predictions for the distributions of the numbers and distances of connections for all the connections in the microcircuit. We then combined the neuron models from a previous model (Humphries et al, 2009; ModelDB ID: 128874) with the new anatomical model. We used this new complete striatal model to examine the impact of the anatomical network on the firing properties of the MSN and FSI populations, and to study the influence of all the inputs to one MSN within the network." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/137502", + "other_neurons": { + "attr_id": 301, + "value": "Striatal medium spiny neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "m.d.humphries@sheffield.ac.uk" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 196, + "object_name": "D1" + }, + { + "object_id": 197, + "object_name": "D2" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 138633, + "object_name": "Striatum" + } + ] + }, + "ver_date": "2011-04-08T11:10:11", + "ver_number": 6 + }, + "137505": { + "class_id": 19, + "created": "2011-02-15T16:23:31", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 137505, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 137507, + "object_name": "Murray, Teresa A. [bioengineer1 at hotmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 120836, + "object_name": "Murray TA (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Model of long range transmission of gamma oscillation (Murray 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"...\r\nA minimal mathematical model was developed for a\r\npreliminary study of long-range neural transmission of gamma\r\noscillation from the CA3 to the entorhinal cortex via the CAI\r\nregion of the hippocampus, a subset within a larger complex set of\r\npathways. A module was created for each local population of\r\nneurons with common intrinsic properties and connectivity to\r\nsimplify the connection process and make the model more flexible.\r\nThree modules were created using MATLAB Simulink\u00ae and tested\r\nto confirm that they transmit gamma through the system. The\r\nmodel also revealed that a portion of the signal from CAI to the\r\nentorhinal cortex may be lost in transmission under certain\r\nconditions.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/137505", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2011-02-15T16:50:05", + "ver_number": 1 + }, + "137676": { + "class_id": 19, + "created": "2011-02-26T14:08:26", + "gitrepo": false, + "id": 137676, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 141124, + "object_name": "Monaco, Joseph D. 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However, internal motion signals and the neural activity that integrates them can be noisy, leading inevitably to inaccurate position estimates. The rat hippocampus and entorhinal cortex support a flexible system of spatial representation that is critical to spatial learning and memory. The position signal encoded by this system is thought to rely on path integration, but it must be recalibrated by familiar landmarks to maintain accuracy. To explore the interaction between path integration and external landmarks, we present a model of hippocampal activity based on the interference of theta-frequency oscillations that are modulated by realistic animal movements around a track. We show that spatial activity degrades with noise, but introducing external cues based on direct sensory feedback can prevent this degradation. When these cues are put into conflict with each other, their interaction produces a diverse array of response changes that resembles experimental observations. 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This suggests that calcium stochasticity and microdomains should be considered when modeling certain KCa currents near subthreshold conditions." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/137745", + "other_concept": { + "attr_id": 306, + "value": "Stochastic amplification" + }, + "other_implementer": { + "attr_id": 308, + "value": "Stanley, David A" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2018-02-26T11:25:36.783", + "ver_number": 69 + }, + "137845": { + "class_id": 19, + "created": "2011-03-08T13:27:02", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 137845, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33736, + "object_name": "Hines, Michael [Michael.Hines at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 142487, + "object_name": "Hines M, Kumar S, Sch\u00fcrmann F (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Spike exchange methods for a Blue Gene/P supercomputer (Hines et al., 2011)", + "notes": { + "attr_id": 24, + "value": "Tests several spike exchange methods on a Blue Gene/P supercomputer on up to 64K cores." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/137845", + "public_submitter_email": { + "attr_id": 309, + "value": "michael.hines@yale.edu" + }, + "ver_date": "2022-05-30T15:09:24.46", + "ver_number": 7 + }, + "137846": { + "class_id": 19, + "created": "2011-03-09T17:11:50", + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 226057, + "object_name": "HCN1" + }, + { + "object_id": 226058, + "object_name": "HCN2" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 137846, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 114641, + "object_name": "Edgerton, Jeremy R. 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We describe a set of algorithms to simulate large spiking neural networks\r\nefficiently with high-level languages using vector-based operations.\r\nThese algorithms constitute the core of Brian, a spiking neural network\r\nsimulator written in the Python language. Vectorized simulation makes\r\nit possible to combine the flexibility of high-level languages with the\r\ncomputational efficiency usually associated with compiled languages.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/137989", + "ver_date": "2017-07-28T11:52:54.223", + "ver_number": 8 + }, + "138082": { + "citation": { + "attr_id": 391, + "value": "Kinetic Modeling of Nav1.7 Provides Insight Into Erythromelalgia-associated F1449V Mutation (Gurkiewicz et al. 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Gurkiewicz, A. Korngreen, S. Waxman, and A. Lampert. J.Neurophysiol. (2011).\r\n\r\nThe parameters for the K65, K53 and K63 transitions were derived from microscopic reversibility relationships in the model." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/138082", + "other_concept": { + "attr_id": 306, + "value": "Erythromelalgia" + }, + "other_gene": { + "attr_id": 477, + "value": "Nav1.7 F1449V" + }, + "other_implementer": { + "attr_id": 308, + "value": "Gurkiewicz, Meron " + }, + "other_neurons": { + "attr_id": 301, + "value": "Dorsal Root Ganglion" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "gurkiewicz@gmail.com" + }, + "ver_date": "2011-03-24T16:37:14", + "ver_number": 9 + }, + "138205": { + "citation": { + "attr_id": 391, + "value": "Migliore M, De Blasi I, Tegolo D, Migliore R (2011) A modeling study suggesting how a reduction in the context-dependent input on CA1 pyramidal neurons could generate schizophrenic behavior. 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Migliore, I. De Blasi, D. Tegolo, R. Migliore, Neural Networks,(2011), doi:10.1016/j.neunet.2011.01.001. Starting from the experimentally supported assumption on hippocampal neurons we explore an experimentally testable prediction at the single neuron level. The model shows how and to what extent a pathological hypofunction of a contextdependent distal input on a CA1 neuron can generate hallucinations by altering the normal recall of objects on which the neuron has been previously tuned. The results suggest that a change in the context during the recall phase may cause an occasional but very significant change in the set of active dendrites used for features recognition, leading to a distorted perception of objects." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/138205", + "public_submitter_email": { + "attr_id": 309, + "value": "michele.migliore@pa.ibf.cnr.it" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181452, + "object_name": "138205" + } + ] + }, + "ver_date": "2022-11-04T12:37:33.57", + "ver_number": 16 + }, + "138273": { + "class_id": 19, + "created": "2011-03-25T16:20:16", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 138273, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 137854, + "object_name": "Ostroumov K (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Stochastic 3D model of neonatal rat spinal motoneuron (Ostroumov 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 276, + "object_name": "Spinal cord lumbar motor neuron alpha ACh cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Although existing models of motoneurons have indicated the distributed role of certain conductances in\r\nregulating firing, it is unclear how the spatial distribution of certain currents is ultimately shaping motoneuron output. \r\n\r\nThus, it would be helpful to\r\nbuild a bridge between histological and electrophysiological data. \r\n\r\nThe present report is based on the construction of a 3D motoneuron model based\r\non available parameters applicable to the neonatal spinal cord. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/138273", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2011-03-25T17:32:34", + "ver_number": 2 + }, + "138321": { + "citation": { + "attr_id": 391, + "value": "http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002022" + }, + "class_id": 19, + "created": "2011-03-27T13:36:24", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 138321, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 110661, + "object_name": "Korngreen, Alon [alon.korngreen at gmail.com]" + }, + { + "object_id": 138322, + "object_name": "Pashut, Tamar [tamar.pashut at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 138323, + "object_name": "Magnetic stimulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 138324, + "object_name": "Pashut T, Wolfus S, Friedman A, Lavidor M, Bar-Gad I, Yeshurun Y, Korngreen A (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "138321", + "name": "ModelView" + } + ], + "default": "138321" + }, + "name": "Mechanisms of magnetic stimulation of central nervous system neurons (Pashut et al. 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 52426, + "object_name": "Squid axon" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Transcranial magnetic stimulation (TMS) is a widely applied tool for probing cognitive function in humans and is one of the best tools for clinical treatments and interfering with cognitive tasks. Surprisingly, while TMS has been commercially available for decades, the cellular mechanisms underlying magnetic stimulation remain unclear. Here we investigate these mechanisms using compartmental modeling. We generated a numerical scheme allowing simulation of the physiological response to magnetic stimulation of neurons with arbitrary morphologies and active properties. Computational experiments using this scheme suggested that TMS affects neurons in the central nervous system (CNS) primarily by somatic stimulation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/138321", + "other_concept": { + "attr_id": 306, + "value": "magnetic stimulation" + }, + "other_implementer": { + "attr_id": 308, + "value": "Pashut, Tamar " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "alon.korngree@biu.ac.il" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181622, + "object_name": "138321" + } + ] + }, + "ver_date": "2017-02-16T09:34:33.747", + "ver_number": 6 + }, + "138379": { + "class_id": 19, + "created": "2011-03-29T14:48:02", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 138379, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33738, + "object_name": "Lytton, William [bill.lytton at downstate.edu]" + }, + { + "object_id": 97869, + "object_name": "Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 128811, + "object_name": "Laminar Connectivity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 139925, + "object_name": "Neymotin SA, Lee H, Park E, Fenton AA, Lytton WW (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Emergence of physiological oscillation frequencies in neocortex simulations (Neymotin et al. 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + }, + { + "object_id": 7387, + "object_name": "Neocortex V1 interneuron basket PV GABA cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 114635, + "object_name": "Neocortex spiny stellate cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Coordination of neocortical oscillations has been hypothesized to underlie the \"binding\" essential to cognitive function.\r\n\r\nHowever, the mechanisms that generate neocortical oscillations in physiological frequency bands remain unknown.\r\n\r\nWe hypothesized that interlaminar relations in neocortex would provide multiple intermediate loops that would play particular roles in generating oscillations, adding different dynamics to the network.\r\n\r\nWe simulated networks from sensory neocortex using 9 columns of event-driven rule-based neurons wired according to anatomical data and driven with random white-noise synaptic inputs.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/138379", + "other_concept": { + "attr_id": 306, + "value": "Hubs" + }, + "other_neurons": { + "attr_id": 301, + "value": "Low-threshold-spiking cortical interneuron" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181715, + "object_name": "138379" + } + ] + }, + "ver_date": "2023-04-30T15:23:25.667", + "ver_number": 36 + }, + "138382": { + "citation": { + "attr_id": 391, + "value": "20981513" + }, + "class_id": 19, + "created": "2011-03-29T23:32:42", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 138382, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 138165, + "object_name": "Anwar H, Hong S, De Schutter E (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "138382_1", + "name": "Detailed Calcium dynamics model" + }, + { + "id": "138382_2", + "name": "Calcium transients using different buffering models" + }, + { + "id": "138382_3", + "name": "Calcium spikes using single pool model" + }, + { + "id": "138382_4", + "name": "Calcium spikes using double pool model" + }, + { + "id": "138382_5", + "name": "Calcium spikes using detailed model" + }, + { + "id": "138382_6", + "name": "Calcium spikes using DCM" + } + ], + "default": "138382_1" + }, + "name": "Controlling KCa channels with different Ca2+ buffering models in Purkinje cell (Anwar et al. 2012)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In this work, we compare the dynamics of different buffering models during generation of a dendritic Ca2+ spike in a single compartment model of a Purkinje cell dendrite. The Ca2+ buffering models used are 1) a single Ca2+ pool, 2) two Ca2+ pools respectively for the fast and slow transients, 3) a detailed calcium model with buffers, pump (Schmidt et al., 2003), and diffusion and 4) a calcium model with buffers, pump and diffusion compensation. The parameters of single pool and double pool are tuned, using Neurofitter (Van Geit et al., 2007), to approximate the behavior of detailed calcium dynamics over range of 0.5 \u00c2\u00b5M to 8 \u00c2\u00b5M of intracellular calcium. The diffusion compensation is modeled using a buffer-like mechanism called DCM. To use DCM robustly for different diameter compartments, its parameters are estimated, using Neurofitter (Van Geit et al., 2007), as a function of compartment diameter (0.8 \u00c2\u00b5m-20 \u00c2\u00b5m)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/138382", + "other_implementer": { + "attr_id": 308, + "value": "Anwar, Haroon " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "anwar@oist.jp" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181350, + "object_name": "138382_4" + }, + { + "object_id": 181351, + "object_name": "138382_5" + }, + { + "object_id": 181352, + "object_name": "138382_6" + }, + { + "object_id": 181355, + "object_name": "138382_2" + }, + { + "object_id": 181356, + "object_name": "138382_3" + }, + { + "object_id": 181702, + "object_name": "138382_1" + } + ] + }, + "ver_date": "2023-01-30T14:36:33.143", + "ver_number": 18 + }, + "138421": { + "class_id": 19, + "created": "2011-03-30T11:27:58", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 138421, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 115975, + "object_name": "Tort AB, Rotstein HG, Dugladze T, Gloveli T, Kopell NJ (2007)" + }, + { + "object_id": 121619, + "object_name": "Gloveli T, Dugladze T, Rotstein HG, Traub RD, Monyer H, Heinemann U, Whittington MA, Kopell NJ (2005)" + }, + { + "object_id": 138380, + "object_name": "Kopell N, Borgers C, Pervouchine D, Malerba P, Tort ABL (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Gamma and theta rythms in biophysical models of hippocampus circuits (Kopell et al. 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + }, + { + "object_id": 35373, + "object_name": "Hippocampus CA1 interneuron oriens alveus GABA cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... the main rhythms displayed by the hippocampus, the gamma (30\u201390 Hz) and theta (4\u201312 Hz) rhythms. We concentrate on modeling\r\nin vitro experiments, but with an eye toward possible in vivo implications. ...\r\nWe use simpler biophysical models; all cells have a single compartment only, and the\r\ninterneurons are restricted to two types: fast-spiking (FS) basket cells and oriens\r\nlacunosum-moleculare (O-LM) cells. \r\n... , we aim not so much at reproducing dynamics in great detail, but at clarifying the essential mechanisms underlying the production of the rhythms and their interactions (Kopell, 2005). ...\"\r\n" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/138421", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2011-03-30T15:20:21", + "ver_number": 6 + }, + "138631": { + "class_id": 19, + "created": "2011-04-07T18:12:30", + "gitrepo": false, + "id": 138631, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 65417, + "object_name": "Parkinson's" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 138606, + "object_name": "Moustafa AA, Gluck MA (2011)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Prefrontal\u2013striatal Parkinsons comp. model of multicue category learning (Moustafa and Gluck 2011)", + "notes": { + "attr_id": 24, + "value": "\"...\r\nIn this\r\nmodel, PFC dopamine is key for attentional learning, whereas\r\nbasal ganglia dopamine, consistent with other models, is key for\r\nreinforcement and motor learning.\r\n\r\nThe model assumes that competitive\r\ndynamics among PFC neurons is the neural mechanism\r\nunderlying stimulus selection with limited attentional resources,\r\nwhereas competitive dynamics among striatal neurons is the\r\nneural mechanism underlying action selection. \r\n\r\nAccording to our\r\nmodel, PD is associated with decreased phasic and tonic dopamine\r\nlevels in both PFC and basal ganglia. \r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/138631", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 138633, + "object_name": "Striatum" + } + ] + }, + "ver_date": "2011-04-08T11:09:36", + "ver_number": 4 + }, + "138634": { + "class_id": 19, + "created": "2011-04-08T13:25:41", + "gitrepo": false, + "id": 138634, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 138588, + "object_name": "Moustafa AA, Myers CE, Gluck MA (2009)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "A neurocomputational model of classical conditioning phenomena (Moustafa et al. 2009)", + "notes": { + "attr_id": 24, + "value": "\"... Here, we show that the same information-processing function proposed\r\nfor the hippocampal region in the Gluck and Myers (1993) model can also be implemented in\r\na network without using the backpropagation algorithm. Instead, our newer instantiation of\r\nthe theory uses only (a) Hebbian learning methods which match more closely with synaptic\r\nand associative learning mechanisms ascribed to the hippocampal region and (b) a more\r\nplausible representation of input stimuli. \r\nWe demonstrate here that this new more\r\nbiologically plausible model is able to simulate various behavioral effects, including latent\r\ninhibition, acquired equivalence, sensory preconditioning, negative patterning, and context\r\nshift effects. \r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/138634", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2011-08-19T10:03:44", + "ver_number": 2 + }, + "138950": { + "class_id": 19, + "created": "2011-04-25T16:00:56", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 138950, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 128503, + "object_name": "Goldwyn, Joshua [jhgoldwyn at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 238957, + "object_name": "Markov-type model" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 138952, + "object_name": "Goldwyn JH, Shea-Brown E (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Stochastic versions of the Hodgkin-Huxley equations (Goldwyn, Shea-Brown 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A Matlab gui for simulating different channel noise models using the Hodgkin-Huxley equations. Methods provided and reviewed in Goldwyn and Shea-Brown (2011) are: current noise, subunit noise, conductance noise, and Markov chain, as well as the standard deterministic Hodgkin-Huxley model." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/138950", + "other_concept": { + "attr_id": 306, + "value": "Channel noise" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jgoldwyn@uw.edu" + }, + "ver_date": "2018-02-26T11:27:05.08", + "ver_number": 10 + }, + "138951": { + "class_id": 19, + "created": "2011-04-25T16:09:18", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 138951, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3542, + "object_name": "Pattern Recognition" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + }, + { + "object_id": 98011, + "object_name": "Place cell/field" + }, + { + "object_id": 114660, + "object_name": "Connectivity matrix" + }, + { + "object_id": 194888, + "object_name": "Grid cell" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 140050, + "object_name": "Monaco JD, Abbott LF, Abbott LF (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Modular grid cell responses as a basis for hippocampal remapping (Monaco and Abbott 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 129068, + "object_name": "Entorhinal cortex stellate cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Hippocampal place fields, the local regions of activity recorded from place cells in exploring rodents, can undergo large changes in relative\r\nlocation during remapping.\r\n\r\nThis process would appear to require some form of modulated global input.\r\n\r\nGrid-cell responses recorded from layer II of medial entorhinal cortex in rats have been observed to realign concurrently with hippocampal remapping, making them a candidate input source.\r\n\r\nHowever, this realignment occurs coherently across colocalized ensembles of grid cells (Fyhn et al., 2007).\r\n\r\nThe hypothesized entorhinal contribution to remapping depends on whether this coherence extends to all grid cells, which is currently\r\nunknown.\r\n\r\nWe study whether dividing grid cells into small numbers of independently realigning modules can both account for this\r\nlocalized coherence and allow for hippocampal remapping.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/138951", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + }, + { + "object_id": 115947, + "object_name": "Dentate gyrus" + }, + { + "object_id": 119191, + "object_name": "Entorhinal cortex" + } + ] + }, + "ver_date": "2016-07-19T17:27:39.313", + "ver_number": 8 + }, + "138970": { + "citation": { + "attr_id": 391, + "value": "B. Sen Bhattacharya, D. Coyle, L. Maguire, \"A thalamo-cortico-thalamic neural mass model to study alpha rhythms in Alzheimer's Disease\", (in press) Neural Networks Sp. issue on Brain Disorders, 2011. doi:10.1016/j.neunet.2011.02.009" + }, + "class_id": 19, + "created": "2011-05-04T07:14:00", + "gitrepo": false, + "id": 138970, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 139147, + "object_name": "Sen-Bhattacharya, Basabdatta [basab.sen.b at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 112047, + "object_name": "Aging/Alzheimer`s" + }, + { + "object_id": 139148, + "object_name": "Brain Rhythms" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 138938, + "object_name": "Bhattacharya BS, Coyle D, Maguire LP (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + }, + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 83572, + "object_name": "Simulink" + } + ] + }, + "name": "A Neural mass computational model of the Thalamocorticothalamic circuitry (Bhattacharya et al. 2011)", + "notes": { + "attr_id": 24, + "value": "The model presented here is a bio-physically plausible version of a simple thalamo-cortical neural mass computational model proposed by Lopes da Silva in 1974 to simulate brain EEG activity within the alpha band (8-13 Hz). The thalamic and cortical circuitry are presented as separate modules in this model with cell populations as in biology. The connectivity between cell populations are as reported by Sherman, S. in Scholarpedia, 2006. The values of the synaptic connectivity parameters are as reported by Van Horn et al, 2000. In our paper (doi:10.1016/j.neunet.2011.02.009), we study the model behaviour while varying the values of the synaptic connectivity parameters (Cyyy) in the model about their respective 'basal' (intial) values." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/138970", + "other_concept": { + "attr_id": 306, + "value": "Brain Rhythms" + }, + "other_implementer": { + "attr_id": 308, + "value": "Sen Bhattacharya, Basabdatta " + }, + "other_neurons": { + "attr_id": 301, + "value": "Thalmo-cortical cell population" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "bs.bhattacharya@ulster.ac.uk" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2017-10-24T08:30:01.117", + "ver_number": 13 + }, + "139150": { + "class_id": 19, + "created": "2011-05-05T16:31:02", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 139150, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 124046, + "object_name": "Polsky, Alon [alonpol at tx.technion.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 139271, + "object_name": "Poleg-Polsky A, Diamond JS (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "139150", + "name": "ModelView" + } + ], + "default": "139150" + }, + "name": "Interacting synaptic conductances during, distorting, voltage clamp (Poleg-Polsky and Diamond 2011)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This simulation examines the accuracy of the voltage clamp technique\r\nin detecting the excitatory and the inhibitory components of the\r\nsynaptic drive." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/139150", + "public_submitter_email": { + "attr_id": 309, + "value": "apmega@yahoo.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181522, + "object_name": "139150" + } + ] + }, + "ver_date": "2013-12-10T15:30:26", + "ver_number": 8 + }, + "139266": { + "citation": { + "attr_id": 391, + "value": "paper in preparation" + }, + "class_id": 19, + "created": "2011-05-09T13:16:15", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + } + ] + }, + "gitrepo": false, + "id": 139266, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 139269, + "object_name": "Zandt, Bas-Jan [Bas-Jan.Zandt at biomed.uib.no]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 139270, + "object_name": "Anoxic depolarization" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 140468, + "object_name": "Zandt BJ, ten Haken B, van Dijk JG, van Putten MJ (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Single neuron with ion concentrations to model anoxic depolarization (Zandt et al. 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A minimal single neuron model, including changing ion concentrations and homeostasis mechanisms. It shows the sudden depolarization that occurs after prolonged anoxia/ischemia." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/139266", + "other_concept": { + "attr_id": 306, + "value": "Anoxic depolarization" + }, + "other_currents": { + "attr_id": 303, + "value": "I Cl, leak; I Na, leak" + }, + "other_implementer": { + "attr_id": 308, + "value": "Zandt, Bas-Jan\r\n" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "b.zandt@utwente.nl" + }, + "ver_date": "2015-08-04T15:15:39.327", + "ver_number": 9 + }, + "139296": { + "citation": { + "attr_id": 391, + "value": "DOI: 10.1007/s10827-011-0332-1" + }, + "class_id": 19, + "created": "2011-05-11T09:12:25", + "gitrepo": false, + "id": 139296, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 139299, + "object_name": "Taylor, Peter Neal [peter.taylor at postgrad.manchester.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 139148, + "object_name": "Brain Rhythms" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 139298, + "object_name": "Taylor PN, Baier G (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A spatially extended model for macroscopic spike-wave discharges (Taylor and Baier 2011)", + "notes": { + "attr_id": 24, + "value": "A spatially extended neural field model for generating spike-wave based on the Amari (1977) model implemented in MATLAB." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/139296", + "other_implementer": { + "attr_id": 308, + "value": "Taylor, Peter Neal" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "peter.taylor@postgrad.manchester.ac.uk" + }, + "ver_date": "2011-05-11T11:12:04", + "ver_number": 4 + }, + "139418": { + "class_id": 19, + "created": "2011-05-19T09:51:44", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 139418, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 189948, + "object_name": "Fietkiewicz, Chris" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 232102, + "object_name": "Respiratory control" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 142658, + "object_name": "Fietkiewicz C, Loparo KA, Wilson CG (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Bursting respiratory net: clustered architecture gives large phase diff`s (Fietkiewicz et al 2011)", + "notes": { + "attr_id": 24, + "value": "Using a previous model of respiratory rhythm generation, we modified the network architecture such that cells can be segregated into two clusters. Cells within a given cluster burst with smaller phase differences than do cells from different clusters. This may explain the large phase differences seen experimentally, as reported in the paper." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/139418", + "other_concept": { + "attr_id": 306, + "value": "Respiratory rhythmicity" + }, + "other_implementer": { + "attr_id": 308, + "value": "Fietkiewicz, Chris" + }, + "other_neurons": { + "attr_id": 301, + "value": "Bursting pacemaker from pre-B\u00c3\u00b6tzinger complex" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181274, + "object_name": "139418_1" + }, + { + "object_id": 181770, + "object_name": "139418_2" + } + ] + }, + "ver_date": "2021-05-22T13:57:17.283", + "ver_number": 13 + }, + "139421": { + "class_id": 19, + "created": "2011-05-20T13:16:18", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { 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There are 232 computer code examples from the book available through the http://www.elsevierdirect.com/companions/9780123748829/pictures/code/index.html code link here and in the below page copied from the books companion web site." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/139457", + "ver_date": "2016-06-02T14:53:00.717", + "ver_number": 4 + }, + "139653": { + "class_id": 19, + "created": "2011-06-01T15:54:48", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 139653, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 140733, + "object_name": "Hay, Etay [etay.hay at mail.huji.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 145814, + "object_name": "Hay E, Hill S, Sch\u00fcrmann F, Markram H, Segev I (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "139653_1", + "name": "Fig 4A. 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Models fitted to intracellular spike and membrane potential recordings from frog (Rana temporaria). The models can be stimulated by intracellular step current, frequency current (ZAP) or synaptic stimulation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/139654", + "other_implementer": { + "attr_id": 308, + "value": "R\u00c3\u0192\u00c2\u00b6ssert, Christian " + }, + "other_neurons": { + "attr_id": 301, + "value": "Vestibular neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "christian.a@roessert.de" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181615, + "object_name": "139654_3" + }, + { + "object_id": 181616, + "object_name": "139654_2" + }, + { + "object_id": 181617, + "object_name": "139654_1" + }, + { + "object_id": 181618, + "object_name": "139654_6" + }, + { + "object_id": 181619, + "object_name": "139654_5" + }, + { + "object_id": 181620, + "object_name": "139654_4" + } + ] + }, + "ver_date": "2018-10-18T17:00:19.323", + "ver_number": 14 + }, + "139655": { + "class_id": 19, + "created": "2011-06-06T17:36:32", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 139655, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53429, + "object_name": "Blackwell, Avrama [avrama at gmu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 145179, + "object_name": "Kim M, Park AJ, Havekes R, Chay A, Guercio LA, Oliveira RF, Abel T, Blackwell KT (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 139757, + "object_name": "NeuroRD" + } + ] + }, + "name": "Hippocampus CA1: Simulations of LTP signaling pathways (Kim M et al. 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a multi-compartmental, stochastic version of the Kim et al. 2010 paper. There are a few additional reactions, and some of the rate constants have been updated. It addresses the role of molecule anchoring in PKA dependent hippocampal LTP.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/139655", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 223, + "object_name": "Dopaminergic Receptor" + } + ] + }, + "ver_date": "2017-08-01T17:25:28.637", + "ver_number": 8 + }, + "139656": { + "class_id": 19, + "created": "2011-06-07T02:26:28", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 226057, + "object_name": "HCN1" + }, + { + "object_id": 226058, + "object_name": "HCN2" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 139656, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 156119, + "object_name": "Simoes-de-Souza, Fabio [fabio.souza at ufabc.edu.br]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 139997, + "object_name": "Sim\u00f5es de Souza F, De Schutter E (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Cerebellar cortex oscil. robustness from Golgi cell gap jncs (Simoes de Souza and De Schutter 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 7346, + "object_name": "Cerebellum interneuron granule GLU cell" + }, + { + "object_id": 112687, + "object_name": "Cerebellum golgi cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Previous one-dimensional network modeling of the cerebellar granular layer has been successfully\r\nlinked with a range of cerebellar cortex oscillations observed in vivo. However, the recent discovery of gap\r\njunctions between Golgi cells (GoCs), which may cause oscillations by themselves, has raised the question of how\r\ngap-junction coupling affects GoC and granular-layer oscillations. To investigate this question, we developed a\r\nnovel two-dimensional computational model of the GoC-granule cell (GC) circuit with and without gap junctions\r\nbetween GoCs. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/139656", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2022-11-04T12:57:22.457", + "ver_number": 19 + }, + "139657": { + "citation": { + "attr_id": 391, + "value": "Cornelia Kopp-Scheinpflug, Adam JB Tozer, Susan W Robinson Bruce L Tempel, Matthias H Hennig and Ian D Forsythe (2011). The Sound of Silence: ionic mechanisms encoding sound termination. Neuron, in press." + }, + "class_id": 19, + "created": "2011-06-07T08:11:56", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122818, + "object_name": "HCN Cnga1" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 139657, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 139659, + "object_name": "Hennig, Matthias H [mhhennig at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 136252, + "object_name": "Rebound firing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 139660, + "object_name": "Kopp-Scheinpflug C, Tozer AJ, Robinson SW, Tempel BL, Hennig MH, Forsythe ID (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "139657_1", + "name": "50 hz" + }, + { + "id": "139657_2", + "name": "100 hz" + }, + { + "id": "139657_3", + "name": "200 hz" + }, + { + "id": "139657_4", + "name": "300 hz" + }, + { + "id": "139657_5", + "name": "400 hz" + } + ], + "default": "139657_1" + }, + "name": "Superior paraolivary nucleus neuron (Kopp-Scheinpflug et al. 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 139658, + "object_name": "Superior paraolivary nucleus neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 214, + "object_name": "Glycine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a model of neurons in the brainstem superior paraolivary nucleus (SPN), which produce very salient offset firing during sound stimulation. Rebound offset firing is triggered by IPSPs coming from the medial nucleus of the trapezoid body (MNTB). This model shows that AP firing can emerge from inhibition through integration of large IPSPs, driven by an\r\nextremely negative chloride reversal potential, combined with a large hyperpolarization-\r\nactivated non-specific cationic current (IH), with a secondary contribution from a T-type calcium conductance (ITCa). As a result, tiny gaps in sound stimuli of just 3-4ms can elicit reliable APs that signal such brief offsets." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/139657", + "public_submitter_email": { + "attr_id": 309, + "value": "mhhennig@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 1361, + "object_name": "Glycine" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181751, + "object_name": "139657_4" + }, + { + "object_id": 181752, + "object_name": "139657_5" + }, + { + "object_id": 181754, + "object_name": "139657_1" + }, + { + "object_id": 181755, + "object_name": "139657_2" + }, + { + "object_id": 181756, + "object_name": "139657_3" + } + ] + }, + "ver_date": "2022-11-04T12:48:44.087", + "ver_number": 9 + }, + "139760": { + "citation": { + "attr_id": 391, + "value": "Halfmann, K., & Crisp, K. (2011). A kinematic study of pulsation in the dorsal blood vessel of the blackworm, Lumbriculus variegatus. Impulse, " + }, + "class_id": 19, + "created": "2011-06-08T12:30:18", + "gitrepo": false, + "id": 139760, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 139836, + "object_name": "Halfmann K, Crisp K (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 22013, + "object_name": "Neuromuscular Junction" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Model of peristalsis in the dorsal blood vessel of Lumbriculus variegatus (Halfmann and Crisp 2011)", + "notes": { + "attr_id": 24, + "value": "The blackworm, Lumbriculus variegatus, has a segmented dorsal blood vessel that acts as a peristaltic pump to move blood through its closed circulatory system. Here, we conducted a kinematic study using videography and computational modeling as a first step toward understanding the control of DBV pulsation. A simple feed-forward system of distributed, coupled neuronal oscillators is a sufficient model was a sufficient model to explain the control of pulsation in the blackworm." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/139760", + "public_submitter_email": { + "attr_id": 309, + "value": "kameko-halfmann@uiowa.edu" + }, + "ver_date": "2011-06-10T10:06:04", + "ver_number": 7 + }, + "139883": { + "citation": { + "attr_id": 391, + "value": "Shyam Diwakar, Paola Lombardo, Sergio Solinas, Giovanni Naldi, Egidio D'Angelo. Local field potential modeling predicts dense activation in cerebellar granule cells clusters under LTP and LTD control, PLoS ONE, 2011." + }, + "class_id": 19, + "created": "2011-06-12T16:49:21", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 139883, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 116853, + "object_name": "Diwakar, Shyam [shyam at amrita.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 84598, + "object_name": "Extracellular Fields" + }, + { + "object_id": 139996, + "object_name": "Evoked LFP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 139993, + "object_name": "Diwakar S, Lombardo P, Solinas S, Naldi G, D'Angelo E (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 54156, + "object_name": "Octave" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "139883_1", + "name": "In vitro" + }, + { + "id": "139883_2", + "name": "In vivo" + } + ], + "default": "139883_1" + }, + "name": "Reconstructing cerebellar granule layer evoked LFP using convolution (ReConv) (Diwakar et al. 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 7346, + "object_name": "Cerebellum interneuron granule GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model allows reconstruction of evoked local field potentials as seen in the cerebellar granular layer. The approach uses a detailed model of cerebellar granule neuron to generate data traces and then uses a \"ReConv\" or jittered repetitive convolution technique to reproduce post-synaptic local field potentials in the granular layer. The algorithm was used to generate both in vitro and in vivo evoked LFP and reflected the changes seen during LTP and LTD, when such changes were induced in the underlying neurons by modulating release probability of synapses and sodium channel regulated intrinsic excitability of the cells." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/139883", + "other_concept": { + "attr_id": 306, + "value": "evoked LFP" + }, + "other_type": { + "attr_id": 305, + "value": "population signal" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "shyam@amrita.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2022-12-16T10:49:58.413", + "ver_number": 14 + }, + "140033": { + "class_id": 19, + "created": "2011-06-16T06:27:48", + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 266550, + "object_name": "FMR1 FMRP" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 140033, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 97968, + "object_name": "Giugliano, Michele [mgiugliano at gmail.com]" + }, + { + "object_id": 140045, + "object_name": "Loebel, Alex [alex.loebel at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + }, + { + "object_id": 83539, + "object_name": "Attractor Neural Network" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 140967, + "object_name": "Testa-Silva G, Loebel A, Giugliano M, de Kock CP, Mansvelder HD, Meredith RM (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Hyperconnectivity, slow synapses in PFC mental retardation and autism model (Testa-Silva et al 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The subdirectory 'matlab' contains MATLAB scripts (The Mathworks, USA) that can be used to reproduce the panels of Figures 4-5.\r\n\r\nThis directory contains files to reproduce sample computer simulations presented in the 2011 paper authored by\r\n\r\nMeredith, R., Testa-Silva, G., Loebel, A., Giugliano, M., de Kock, C.; Mansvelder, H. \r\n\"Hyperconnectivity and slow synapses in prefrontal cortex of a model for mental retardation and autism\".\r\n\r\n\r\nABSTRACT\r\n\"... We propose that these findings are tightly linked: using a network model, we show that slower synapses are essential to counterbalance hyperconnectivity in order to maintain a dynamic range of excitatory activity. However, the slow synaptic time constants induce decreased responsiveness to low frequency stimulation, which may explain deficits in integration and information processing in attentional neuronal networks in neurodevelopmental disorders.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/140033", + "other_implementer": { + "attr_id": 308, + "value": "Alex Loebel" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "ver_date": "2020-06-10T13:53:50.857", + "ver_number": 15 + }, + "140038": { + "citation": { + "attr_id": 391, + "value": "http://www.ncbi.nlm.nih.gov/pubmed/19571204" + }, + "class_id": 19, + "created": "2011-06-20T18:01:00", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 88208, + "object_name": "Late Na" + } + ] + }, + "gitrepo": false, + "id": 140038, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 140039, + "object_name": "Devor, Marshall [marshlu at vms.huji.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 124739, + "object_name": "Kovalsky Y, Amir R, Devor M (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Dorsal root ganglion (DRG) neuronal model (Kovalsky et al. 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 136307, + "object_name": "Dorsal Root Ganglion (DRG) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model, diverged from oscillatory parameters seen in live cells and failed to produce characteristic ectopic discharge patterns. Here we show that use of a more complete set of Na+ conductances--which includes several delayed components--enables simulation of the entire repertoire of oscillation-triggered electrogenic phenomena seen in live dorsal root ganglion (DRG) neurons. This includes a physiological window of induction and natural patterns of spike discharge. An INa+ component at 2-20 ms was particularly important, even though it represented only a tiny fraction of overall INa+ amplitude. With the addition of a delayed rectifier IK+ the singlet firing seen in some DRG neurons can also be simulated. The model reveals the key conductances that underlie afferent ectopia, conductances that are potentially attractive targets in the search for more effective treatments of neuropathic pain." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/140038", + "other_implementer": { + "attr_id": 308, + "value": "Devor, Marshall " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "lee.dongchul@gmail.com" + }, + "ver_date": "2011-06-21T10:34:23", + "ver_number": 3 + }, + "140246": { + "class_id": 19, + "created": "2011-06-29T16:03:06", + "gitrepo": false, + "id": 140246, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 140100, + "object_name": "Csercsik D, Farkas I, Hrabovszky E, Liposits Z (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "A simple integrative electrophysiological model of bursting GnRH neurons (Csercsik et al. 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 113950, + "object_name": "GnRH neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In this paper a modular model of the GnRH\r\nneuron is presented. For the aim of simplicity, the\r\ncurrents corresponding to fast time scales and action\r\npotential generation are described by an impulsive system,\r\nwhile the slower currents and calcium dynamics\r\nare described by usual ordinary differential equations\r\n(ODEs). The model is able to reproduce the depolarizing\r\nafterpotentials, afterhyperpolarization, periodic\r\nbursting behavior and the corresponding calcium transients\r\nobserved in the case of GnRH neurons." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/140246", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 212, + "object_name": "Glutamate" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "ver_date": "2017-07-19T16:40:50.98", + "ver_number": 8 + }, + "140249": { + "class_id": 19, + "created": "2011-07-01T09:53:52", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2397, + "object_name": "I Mixed" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 140249, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 140251, + "object_name": "Halnes, Geir [geir.halnes at nmbu.no]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 136252, + "object_name": "Rebound firing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 141509, + "object_name": "Halnes G, Augustinaite S, Heggelund P, Einevoll GT, Migliore M (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A multi-compartment model for interneurons in the dLGN (Halnes et al. 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 140252, + "object_name": "Thalamus lateral geniculate nucleus interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model for dLGN interneurons is presented in two parameterizations (P1 & P2), which were fitted to current-clamp data from two different interneurons (IN1 & IN2). The model qualitatively reproduces the responses in IN1 & IN2 under 8 different experimental condition, and quantitatively reproduces the I/O-relations (#spikes elicited as a function of injected current)." + }, + "opensourcebrain": { + "value": "dlgninterneuronhalnesetal2011" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/140249", + "other_implementer": { + "attr_id": 308, + "value": "Halnes, Geir" + }, + "other_neurons": { + "attr_id": 301, + "value": "Thalamic Interneuron in dorsal LGN" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "geir.halnes@umb.no" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181151, + "object_name": "140249_b1" + }, + { + "object_id": 181152, + "object_name": "140249_b2" + }, + { + "object_id": 181254, + "object_name": "140249_a1" + }, + { + "object_id": 181255, + "object_name": "140249_a2" + } + ] + }, + "ver_date": "2015-01-02T22:06:02", + "ver_number": 16 + }, + "140254": { + "citation": { + "attr_id": 391, + "value": "Klaus A, Planert H, Hjorth J, Berke JD, Silberberg G and Hellgren Kotaleski J (2011). Striatal fast-spiking interneurons: from firing patterns to postsynaptic impact. Front. Syst. Neurosci. 5:57. doi: 10.3389/fnsys.2011.00057" + }, + "class_id": 19, + "created": "2011-07-05T16:03:53", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "id": 140254, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 140256, + "object_name": "Klaus, Andreas " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 97751, + "object_name": "Stuttering" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 140255, + "object_name": "Klaus A, Planert H, Hjorth J, Berke JD, Silberberg G, Kotaleski JH (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + }, + { + "object_id": 140257, + "object_name": "PGENESIS" + } + ] + }, + "name": "Firing patterns in stuttering fast-spiking interneurons (Klaus et al. 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 118395, + "object_name": "Neostriatum fast spiking interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a morphologically extended version of the fast-spiking interneuron by Golomb et al. (2007).\r\n\r\nThe model captures the stuttering firing pattern and subthreshold oscillations in response to step current input as observed in many cortical and striatal fast-spiking cells." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/140254", + "other_implementer": { + "attr_id": 308, + "value": "Klaus, Andreas" + }, + "other_neurons": { + "attr_id": 301, + "value": "Fast-spiking interneuron" + }, + "other_simulator": { + "attr_id": 307, + "value": "Genesis and PGenesis" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "Andreas.Klaus@nih.gov" + }, + "ver_date": "2011-07-06T15:23:11", + "ver_number": 6 + }, + "140299": { + "class_id": 19, + "created": "2011-07-06T02:43:52", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 140299, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 141234, + "object_name": "Nomura, Masaki [nomura at rcai.riken.jp]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 141230, + "object_name": "Kubota Y, Karube F, Nomura M, Gulledge AT, Mochizuki A, Schertel A, Kawaguchi Y (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "140299_1", + "name": "BE17BNoActive" + }, + { + "id": "140299_2", + "name": "BE59DNoActive" + }, + { + "id": "140299_3", + "name": "BE77CNoActive" + }, + { + "id": "140299_4", + "name": "LV38ENoActive" + } + ], + "default": "140299_1" + }, + "name": "Four cortical interneuron subtypes (Kubota et al. 2011)", + "notes": { + "attr_id": 24, + "value": "\" ... Using electron microscopy and serial reconstructions, we analyzed the dendritic trees of\r\nfour morphologically distinct neocortical interneuron subtypes to reveal two underlying organizational\r\nprinciples common to all.\r\n\r\nFirst, cross-sectional areas at any given point within a dendrite were proportional\r\nto the summed length of all dendritic segments distal to that point.\r\n\r\n...\r\nSecond, dendritic cross-sections\r\nbecame progressively more elliptical at more proximal, larger diameter, dendritic locations.\r\n\r\nFinally,\r\ncomputer simulations revealed that these conserved morphological features limit distance dependent\r\nfiltering of somatic EPSPs and facilitate distribution of somatic depolarization into all dendritic\r\ncompartments.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/140299", + "other_concept": { + "attr_id": 306, + "value": "Precise Morphology" + }, + "other_implementer": { + "attr_id": 308, + "value": "Nomura, Masaki" + }, + "other_neurons": { + "attr_id": 301, + "value": "nonpyramidal neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "nomura@rcai.riken.jp" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181326, + "object_name": "140299_2" + }, + { + "object_id": 181332, + "object_name": "140299_4" + }, + { + "object_id": 181570, + "object_name": "140299_1" + }, + { + "object_id": 181647, + "object_name": "140299_3" + } + ] + }, + "ver_date": "2015-01-02T22:34:51", + "ver_number": 11 + }, + "140462": { + "class_id": 19, + "created": "2011-07-12T10:54:45", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 88117, + "object_name": "I_KHT" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 140462, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 140470, + "object_name": "Masurkar, Arjun [avmasurkar at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 140405, + "object_name": "Masurkar AV, Chen WR (2011)" + }, + { + "object_id": 140439, + "object_name": "Masurkar AV, Chen WR (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "140462", + "name": "ModelView" + } + ], + "default": "140462" + }, + "name": "Calcium and potassium currents of olfactory bulb juxtaglomerular cells (Masurkar and Chen 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 3978, + "object_name": "Olfactory bulb main interneuron periglomerular GABA cell" + }, + { + "object_id": 140469, + "object_name": "Olfactory bulb main juxtaglomerular cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Inward and outward currents of the olfactory bulb juxtaglomerular cells are characterized in the experiments and modeling in these two Masurkar and Chen 2011 papers." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/140462", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181542, + "object_name": "140462" + } + ] + }, + "ver_date": "2018-10-17T16:07:59.457", + "ver_number": 9 + }, + "140465": { + "class_id": 19, + "created": "2011-07-13T12:05:50", + "gitrepo": false, + "id": 140465, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 140833, + "object_name": "Bol, Kieran [kieran_bol at hotmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3643, + 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"MATLAB" + } + ] + }, + "name": "Cancelling redundant input in ELL pyramidal cells (Bol et al. 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88116, + "object_name": "ELL pyramidal cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The paper investigates the property of the electrosensory lateral line lobe (ELL) of the brain of weakly electric fish to cancel predictable stimuli. Electroreceptors on the skin encode all signals in their firing activity, but superficial pyramidal (SP) cells in the ELL that receive this feedforward input do not respond to constant sinusoidal signals. This cancellation putatively occurs using a network of feedback delay lines and burst-induced synaptic plasticity between the delay lines and the SP cell that learns to cancel the redundant input. Biologically, the delay lines are parallel fibres from cerebellar-like granule cells in the eminentia granularis posterior.\r\n\r\nA model of this network (e.g. electroreceptors, SP cells, delay lines and burst-induced plasticity) was constructed to test whether the current knowledge of how the network operates is sufficient to cancel redundant stimuli. \r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/140465", + "other_implementer": { + "attr_id": 308, + "value": "Bol, Kieran " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "kbol098@uottawa.ca" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2011-08-08T11:03:57", + "ver_number": 4 + }, + "140471": { + "citation": { + "attr_id": 391, + "value": "PMID: 21723921" + }, + "class_id": 19, + "created": "2011-07-15T11:36:49", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 140471, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 140473, + "object_name": "Jaffe, David B [david.jaffe at utsa.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 140472, + "object_name": "Jaffe DB, Wang B, Brenner R (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Shaping of action potentials by different types of BK channels (Jaffe et al., 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Dentate gyrus granule cells highly express the beta4 accessory subunit which confer BK channels with type II properties. The properties of heterologously-expressed BK channels (with and without the beta4 subunit) were used to construct channel models. These were then used to study how they affect single action potentials and trains of spikes in a model dentate gyrus granule cells (based on Aradi and Holmes, 1999)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/140471", + "other_implementer": { + "attr_id": 308, + "value": "Jaffe, David " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "david.jaffe@utsa.edu" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181307, + "object_name": "140471" + } + ] + }, + "ver_date": "2013-12-10T15:32:02", + "ver_number": 10 + }, + "140599": { + "class_id": 19, + "created": "2011-07-20T13:52:03", + "gitrepo": false, + "id": 140599, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 140600, + "object_name": "Mechler, Ferenc [fmechler at med.cornell.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 140550, + "object_name": "Mechler F, Victor JD (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Dipole Localization Kit (Mechler & Victor, 2012)", + "notes": { + "attr_id": 24, + "value": "We localize a single neuron from the spatial sample of its EAP\r\namplitudes recorded with a multisite probe (with 6 or more independent\r\nmeasurement sites or channels, e.g., a silicon polytrode, a stepped\r\ntetrode, etc.) This is an inverse problem and we solve it by fitting a\r\nmodel to the EAPs that consists of a volume conductor model of the\r\nneural tissue (known), a realistic model of the probe (known), and a\r\nsingle dipole current source of the model neuron (unknown). The dipole\r\nis free to change position, size, and orientation (a total of 6\r\nparameters) at each moment during the action potential." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/140599", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2015-07-26T13:09:27.877", + "ver_number": 16 + }, + "140732": { + "class_id": 19, + "created": "2011-07-28T23:32:31", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "id": 140732, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 140769, + "object_name": "Anderson WD, Galv\u00e1n EJ, Mauna JC, Thiels E, Barrionuevo G (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "CA3 Radiatum/Lacunosum-Moleculare interneuron, Ih (Anderson et al. 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 139885, + "object_name": "Hippocampus CA3 stratum oriens lacunosum-moleculare interneuron" + }, + { + "object_id": 140767, + "object_name": "Hippocampus CA3 stratum radiatum lacunosum-moleculare interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The present study examines the biophysical\r\nproperties and functional implications of Ih in hippocampal\r\narea CA3 interneurons with somata in strata radiatum and\r\nlacunosum-moleculare.... The functional\r\nconsequences of Ih were examined with regard to temporal\r\nsummation and impedance measurements. ...\r\nFrom impedance measurements, we\r\nfound that Ih did not confer theta-band resonance, but\r\nflattened the impedance\u2013frequency relations instead. ... Finally, a model of Ih was employed in\r\ncomputational analyses to confirm and elaborate upon the\r\ncontributions of Ih to impedance and temporal summation.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/140732", + "other_currents": { + "attr_id": 303, + "value": "Ih" + }, + "other_neurons": { + "attr_id": 301, + "value": "CA3 radiatum, lacunosom-moleculare interneuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "warrena@temple.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2015-07-26T14:23:40.323", + "ver_number": 6 + }, + "140788": { + "biosimulations": { + "value": "modeldb-140788" + }, + "class_id": 19, + "created": "2011-08-04T13:39:52", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 140826, + "object_name": "I Ca,p" + } + ] + }, + "gitrepo": false, + "id": 140788, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 140824, + "object_name": "Kurian, Mini " + }, + { + "object_id": 140825, + "object_name": "Crook, Sharon " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 41934, + "object_name": "Intrinsic plasticity" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 140794, + "object_name": "Kurian M, Crook SM, Jung R (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Motoneuron model of self-sustained firing after spinal cord injury (Kurian et al. 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 276, + "object_name": "Spinal cord lumbar motor neuron alpha ACh cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ...\r\nDuring the acute-stage of spinal cord injury (SCI), the endogenous ability to generate plateaus is lost; however, during the chronic-stage of SCI, plateau potentials reappear with prolonged self-sustained firing that has been implicated in the development of spasticity. In this work, we extend previous modeling studies to systematically investigate the mechanisms underlying the generation of plateau potentials in motoneurons, including the influences of specific ionic currents, the morphological characteristics of the soma and dendrite, and the interactions between persistent inward currents and synaptic input.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/140788", + "other_currents": { + "attr_id": 303, + "value": "INaP, ICaP" + }, + "other_implementer": { + "attr_id": 308, + "value": "Kurian, Mini; Crook, Sharon" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "minikurian@gmail.com" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115954, + "object_name": "Spinal motoneuron" + } + ] + }, + "ver_date": "2011-08-08T17:04:37", + "ver_number": 11 + }, + "140789": { + "class_id": 19, + "created": "2011-08-05T09:41:32", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 140789, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 147582, + "object_name": "Matthia (Norenberg), Anja [anja.matthiae at charite.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 147581, + "object_name": "N\u00f6renberg A, Hu H, Vida I, Bartos M, Jonas P (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "140789", + "name": "ModelView" + } + ], + "default": "140789" + }, + "name": "Detailed passive cable model of Dentate Gyrus Basket Cells (Norenberg et al. 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52417, + "object_name": "Dentate gyrus basket cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Fast-spiking, parvalbumin-expressing basket cells (BCs) play a key role in feedforward and feedback inhibition in the hippocampus. \r\n...\r\nTo quantitatively address this question, we developed detailed passive cable models of BCs in the dentate gyrus based on dual somatic or somatodendritic recordings and complete morphologic reconstructions. \r\n\r\nBoth specific membrane capacitance and axial resistivity were comparable to those of pyramidal neurons, but the average somatodendritic specific membrane resistance (R(m)) was substantially lower in BCs. \r\n\r\nFurthermore, R(m) was markedly nonuniform, being lowest in soma and proximal dendrites, intermediate in distal dendrites, and highest in the axon. \r\n...\r\nFurther computational analysis revealed that these unique cable properties accelerate the time course of synaptic potentials at the soma in response to fast inputs, while boosting the efficacy of slow distal inputs. \r\n\r\nThese properties will facilitate both rapid phasic and efficient tonic activation of BCs in hippocampal microcircuits.\r\n" + }, + "opensourcebrain": { + "value": "norenbergetal2010_dgbasketcell" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/140789", + "other_implementer": { + "attr_id": 308, + "value": "Matthi\u00e4 (N\u00f6renberg), Anja" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + }, + { + "object_id": 115947, + "object_name": "Dentate gyrus" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181535, + "object_name": "140789" + } + ] + }, + "ver_date": "2014-02-06T17:15:53", + "ver_number": 14 + }, + "140828": { + "class_id": 19, + "created": "2011-08-07T18:18:07", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 140828, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 116907, + "object_name": "Direction Selectivity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 129136, + "object_name": "Branco T, Clark BA, H\u00e4usser M (2010)" + }, + { + "object_id": 140857, + "object_name": "Branco T, H\u00e4usser M (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "140828", + "name": "ModelView" + } + ], + "default": "140828" + }, + "name": "Dendritic Discrimination of Temporal Input Sequences (Branco et al. 2010)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Compartmental model of a layer 2/3 pyramidal cell in the rat somatosensory cortex, exploring NMDA-dependent sensitivity to the temporal sequence of synaptic activation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/140828", + "other_implementer": { + "attr_id": 308, + "value": "Branco,Tiago" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "t.branco@ucl.ac.uk" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181657, + "object_name": "140828" + } + ] + }, + "ver_date": "2020-09-16T14:29:05.583", + "ver_number": 14 + }, + "140881": { + "class_id": 19, + "created": "2011-08-17T16:11:47", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 140881, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33738, + "object_name": "Lytton, William [bill.lytton at downstate.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 140882, + "object_name": "Lytton WW, Neymotin SA, Wester JC, Contreras D (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Computational Surgery (Lytton et al. 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + }, + { + "object_id": 142232, + "object_name": "Neocortex spiking low threshold (LTS) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Figure 2 in\r\n Neocortical simulation for epilepsy surgery guidance: Localization and intervention,\r\n by William W. Lytton, Samuel A. Neymotin, Jason C. Wester, and Diego Contreras\r\n in Computational Surgery and Dual Training, Springer, 2011\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/140881", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2023-04-30T15:26:14.297", + "ver_number": 7 + }, + "140964": { + "class_id": 19, + "created": "2011-08-18T15:28:48", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 140964, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 140863, + "object_name": "Mihalas S, Dong Y, von der Heydt R, Niebur E (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 87474, + "object_name": "XML (web link to model)" + }, + { + "object_id": 140884, + "object_name": "ERNST (Event Related Neuronal Simulation Tool) (web link to model)" + } + ] + }, + "name": "Event-related simulation of neural processing in complex visual scenes (Mihalas et al. 2011)", + "notes": { + "attr_id": 24, + "value": "\"... We here present an environment for the\r\nimplementation of large networks of generalized integrate-and-fire\r\nneurons which uses an asynchronous event-based algorithm.\r\n...\r\nThe neuronal network to be simulated\r\nand all parameters are defined in extendible markup language.\r\nA model of the primate early visual system is implemented. The\r\nuse of the tool is illustrated by simulating the processing of both\r\nsimple and complex visual scenes through retina, thalamus and\r\nprimary visual cortex.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/140964", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 115951, + "object_name": "Thalamus" + }, + { + "object_id": 118529, + "object_name": "Retina" + } + ] + }, + "ver_date": "2018-10-17T16:55:03.307", + "ver_number": 6 + }, + "141061": { + "class_id": 19, + "created": "2011-09-02T16:58:28", + "gitrepo": false, + "id": 141061, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 97869, + "object_name": "Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 84598, + "object_name": "Extracellular Fields" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 141228, + "object_name": "Neymotin SA, Lytton WW, Olypher AV, Fenton AA (2011)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Measuring neuronal identification quality in ensemble recordings (isoitools) (Neymotin et al. 2011)", + "notes": { + "attr_id": 24, + "value": "\"... Here we describe information theoretic\r\nmeasures of action potential waveform isolation applicable to any dataset, that have an intuitive,\r\nuniversal interpretation, and that are not dependent on the methods or choice of parameters for\r\nsingle unit isolation, and that have been validated using a dataset.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/141061", + "other_concept": { + "attr_id": 306, + "value": "Spike-sorting" + }, + "other_implementer": { + "attr_id": 308, + "value": "Andre Fenton" + }, + "ver_date": "2011-12-19T14:21:08", + "ver_number": 8 + }, + "141062": { + "class_id": 19, + "created": "2011-09-08T09:18:08", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 141062, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 123929, + "object_name": "Masquelier, Tim [timothee.masquelier at alum.mit.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3542, + "object_name": "Pattern Recognition" + }, + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 83538, + "object_name": "Unsupervised Learning" + }, + { + "object_id": 83540, + "object_name": "Winner-take-all" + }, + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 128875, + "object_name": "Development" + }, + { + "object_id": 136098, + "object_name": "Information transfer" + }, + { + "object_id": 147931, + "object_name": "Orientation selectivity" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 141131, + "object_name": "Masquelier T (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Relative spike time coding and STDP-based orientation selectivity in V1 (Masquelier 2012)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + }, + { + "object_id": 270, + "object_name": "Retina ganglion GLU cell" + }, + { + "object_id": 1966, + "object_name": "Retina photoreceptor cone GLU cell" + }, + { + "object_id": 7020, + "object_name": "Retina bipolar GLU cell" + }, + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Phenomenological spiking model of the cat early visual system. We show how natural vision can drive spike time correlations on sufficiently fast time scales to lead to the acquisition of orientation-selective V1 neurons through STDP. This is possible without reference times such as stimulus onsets, or saccade landing times. But even when such reference times are available, we demonstrate that the relative spike times encode the images more robustly than the absolute ones." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/141062", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2018-10-17T16:55:20.527", + "ver_number": 9 + }, + "141063": { + "class_id": 19, + "created": "2011-09-08T10:52:37", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 226057, + "object_name": "HCN1" + }, + { + "object_id": 226058, + "object_name": "HCN2" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 141063, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144508, + "object_name": "Orio, Patricio [patricio.orio at uv.cl]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 182362, + "object_name": "Sensory coding" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 146837, + "object_name": "Orio P, Parra A, Madrid R, Gonz\u00e1lez O, Belmonte C, Viana F (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Role of Ih in firing patterns of cold thermoreceptors (Orio et al., 2012)", + "notes": { + "attr_id": 24, + "value": "\" ... Here we investigated the role of Ih in cold-sensitive (CS) nerve endings, where cold sensory transduction actually takes place. Corneal CS nerve endings in mice show a rhythmic spiking activity at neutral skin temperature that switches to bursting mode when the temperature is lowered. \r\n...\r\nMathematical modeling shows that the firing phenotype of CS nerve endings from HCN1-/- mice can be reproduced by replacing HCN1 channels with the slower HCN2 channels rather than by abolishing Ih. We propose that Ih carried by HCN1 channels helps tune the frequency of the oscillation and the length of bursts underlying regular spiking in cold thermoreceptors, having important implications for neural coding of cold sensation. \r\n\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/141063", + "other_implementer": { + "attr_id": 308, + "value": "Orio, Patricio" + }, + "other_neurons": { + "attr_id": 301, + "value": "Cold sensitive nerve ending" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115960, + "object_name": "Generic" + } + ] + }, + "ver_date": "2017-03-18T10:15:51.553", + "ver_number": 10 + }, + "141192": { + "class_id": 19, + "created": "2011-09-11T11:19:20", + "gitrepo": false, + "id": 141192, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 141195, + "object_name": "Maran SK, Sieling FH, Demla K, Prinz AA, Canavier CC (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "A four compartmental model for ABPD complex in crustacean pyloric network (Maran et al. 2011)", + "notes": { + "attr_id": 24, + "value": "\"Central pattern generators (CPGs) frequently include bursting neurons that serve as pacemakers for rhythm generation.\r\n\r\nPhase resetting curves (PRCs) can provide insight into mechanisms underlying phase locking in such circuits.\r\n\r\nPRCs were constructed for a pacemaker bursting complex in the pyloric circuit in the stomatogastric ganglion of the lobster and crab.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/141192", + "other_implementer": { + "attr_id": 308, + "value": "Maran, Selva (selvamaran@gmail.com)" + }, + "other_neurons": { + "attr_id": 301, + "value": "STG pyloric pacemaker complex (ABPD)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "selvamaran@gmail.com" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115959, + "object_name": "Stomatogastric ganglion" + } + ] + }, + "ver_date": "2011-09-12T13:38:05", + "ver_number": 13 + }, + "141226": { + "class_id": 19, + "created": "2011-09-19T14:37:47", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 141226, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33795, + "object_name": "Bhalla, Upinder S [bhalla at ncbs.res.in]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 225299, + "object_name": "Multiscale" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 139642, + "object_name": "Bhalla US (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "Multiscale interactions between chemical and electric signaling in LTP (Bhalla 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Synaptic plasticity leads to long-term changes in excitability, whereas cellular homeostasis maintains\r\nexcitability. Both these processes involve interactions between molecular events, electrical events, and\r\nnetwork activity. Here I explore these intersections with a multilevel model that embeds molecular events\r\nfollowing synaptic calcium influx into a multicompartmental electrical model of a CA1 hippocampal\r\nneuron. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/141226", + "other_concept": { + "attr_id": 306, + "value": "Multiscale, Homeostasis" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2017-02-23T11:03:26.697", + "ver_number": 4 + }, + "141270": { + "citation": { + "attr_id": 391, + "value": "Stochastic signaling network mediates the probabilistic induction of long-term depression" + }, + "class_id": 19, + "created": "2011-09-21T19:23:16", + "gitrepo": false, + "id": 141270, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 112689, + "object_name": "De Schutter, Erik [erik at oist.jp]" + }, + { + "object_id": 144557, + "object_name": "Antunes, Gabriela [gabri_antunes at hotmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144497, + "object_name": "Antunes G, De Schutter E (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 144556, + "object_name": "STEPS" + } + ] + }, + "name": "Cerebellar long-term depression (LTD) (Antunes and De Schutter 2012)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Many cellular processes involve small number of molecules and undergo stochastic fluctuations in their levels of activity. Among these processes is cerebellar long-term depression (LTD), a form of synaptic plasticity expressed as a reduction in the number of synaptic AMPA receptors (AMPARs) in Purkinje cells. Using a stochastic model of the signaling network and mechanisms of AMPAR trafficking involved in LTD, we show that the network activity in single synapses switches between two discrete stable states (LTD and non-LTD). Stochastic fluctuations affecting more intensely the level of activity of a few components of the network lead to the probabilistic induction of LTD and threshold dithering. The non-uniformly distributed stochasticity of the network allows the stable occurrence of several different macroscopic levels of depression, determining the experimentally observed sigmoidal relationship between the magnitude of depression and the concentration of the triggering signal." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/141270", + "other_implementer": { + "attr_id": 308, + "value": "Antunes, Gabriela " + }, + "other_simulator": { + "attr_id": 307, + "value": "STEPS" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "gabri_antunes@hotmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2012-07-06T10:53:04", + "ver_number": 11 + }, + "141272": { + "class_id": 19, + "created": "2011-09-22T16:05:52", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 141272, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144508, + "object_name": "Orio, Patricio [patricio.orio at uv.cl]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 238957, + "object_name": "Markov-type model" + }, + { + "object_id": 238958, + "object_name": "Stochastic simulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144507, + "object_name": "Orio P, Soudry D (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 83452, + "object_name": "SciLab" + } + ] + }, + "name": "Simple and accurate Diffusion Approximation algor. for stochastic ion channels (Orio & Soudry 2012)", + "notes": { + "attr_id": 24, + "value": "\" ... We derived the (Stochastic Differential Equations) SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable \u2013 allowing an easy, transparent and efficient (Diffusion Approximation) DA implementation, avoiding unnecessary approximations. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as (Markov Chains) MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods, except when short time steps or low channel numbers were used.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/141272", + "other_concept": { + "attr_id": 306, + "value": "Stochastic simulation" + }, + "other_implementer": { + "attr_id": 308, + "value": "Orio, Patricio" + }, + "simPFid": { + "attr_id": 488, + "value": 614 + }, + "ver_date": "2018-02-26T11:31:19.97", + "ver_number": 17 + }, + "141273": { + "class_id": 19, + "created": "2011-09-23T11:56:55", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 141273, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 141508, + "object_name": "Vierling-Claassen, Dorea " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 139148, + "object_name": "Brain Rhythms" + }, + { + "object_id": 139996, + "object_name": "Evoked LFP" + }, + { + "object_id": 245798, + "object_name": "Touch" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 138955, + "object_name": "Vierling-Claassen D, Cardin JA, Moore CI, Jones SR (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Engaging distinct oscillatory neocortical circuits (Vierling-Claassen et al. 2010)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + }, + { + "object_id": 142232, + "object_name": "Neocortex spiking low threshold (LTS) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Selective optogenetic drive of fast-spiking (FS) interneurons (INs) leads to enhanced local field potential (LFP) power across the traditional \u201cgamma\u201d frequency band (20\u201380 Hz; Cardin et al., 2009).\r\n\r\nIn contrast, drive to regular-spiking (RS) pyramidal cells enhances power at lower frequencies, with a peak at 8 Hz.\r\n\r\nThe first result is consistent with previous computational studies emphasizing the role of FS and the time constant of GABAA synaptic inhibition in gamma rhythmicity.\r\n\r\nHowever, the same theoretical models do not typically predict low-frequency LFP enhancement with RS drive.\r\n\r\nTo develop hypotheses as to how the same network can support these contrasting behaviors, we constructed a biophysically principled network model of primary somatosensory neocortex containing FS, RS, and low-threshold spiking (LTS) INs. ...\"" + }, + "opensourcebrain": { + "value": "vierlingclaassenetal2010" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/141273", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2022-11-04T17:16:24.333", + "ver_number": 7 + }, + "141274": { + "citation": { + "attr_id": 391, + "value": "A mathematical model of action potentials of mouse sinoatrial node cells with molecular bases. Kharche S, Yu J, Lei M, Zhang H. Am J Physiol Heart Circ Physiol. 2011 Sep;301(3):H945-63." + }, + "class_id": 19, + "created": "2011-09-26T11:02:37", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 88206, + "object_name": "I_HERG" + }, + { + "object_id": 88209, + "object_name": "Na/Ca exchanger" + }, + { + "object_id": 88212, + "object_name": "KCNQ1" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92082, + "object_name": "Nav1.1 SCN1A" + }, + { + "object_id": 92083, + "object_name": "Nav1.2 SCN2A" + }, + { + "object_id": 122807, + "object_name": "HERG KCNH2" + }, + { + "object_id": 122810, + "object_name": "Cav3.1 CACNA1G" + }, + { + "object_id": 122812, + "object_name": "Cav1.3 CACNA1D" + }, + { + "object_id": 122814, + "object_name": "Cav1.2 CACNA1C" + } + ] + }, + "gitrepo": false, + "id": 141274, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 141449, + "object_name": "Kharche, Sanjay " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 141450, + "object_name": "Cardiac pacemaking" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 141276, + "object_name": "Kharche S, Yu J, Lei M, Zhang H (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Biophysically detailed model of the mouse sino-atrial node cell (Kharche et al. 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52422, + "object_name": "Cardiac atrial cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model is developed to study the role of various electrophysiological mechanisms in generating cardiac pacemaking action potentials (APs).The model incorporates membrane ionic currents and intracellular mechanisms contributing to spontaneous mouse SAN APs. The model was validated by testing the functional roles of individual membrane currents in one and multiple parameter analyses.The roles of intracellular Ca2+-handling mechanisms on cardiac pacemaking were also investigated in the model." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/141274", + "other_concept": { + "attr_id": 306, + "value": "cardiac pacemaking" + }, + "other_implementer": { + "attr_id": 308, + "value": "Kharche, Sanjay" + }, + "other_type": { + "attr_id": 305, + "value": "cardiac cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "Sanjay.Kharche@manchester.ac.uk" + }, + "ver_date": "2011-09-26T17:10:54", + "ver_number": 4 + }, + "141505": { + "citation": { + "attr_id": 391, + "value": "Kerr CC, Neymotin SA, Chadderdon GL, Fietkiewicz CT, Francis JT, Lytton WW (under second-round review). Electrostimulation as a prosthesis for repair of information flow in a computer model of neocortex. 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This model consists of 4700 event-driven, rule-based neurons, wired according to anatomical data, and driven by both white-noise synaptic inputs and a sensory signal recorded from a rat thalamus. Its purpose is to explore the effects of cortical damage, along with the repair of this damage via a neuroprosthesis." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/141505", + "other_implementer": { + "attr_id": 308, + "value": "Kerr, Cliff" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "cliffk@neurosim.downstate.edu\r\n" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2023-04-30T15:28:07.223", + "ver_number": 23 + }, + "141507": { + "class_id": 19, + "created": "2011-09-30T09:09:24", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 141507, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 97901, + "object_name": "Anderson WS, Kudela P, Cho J, Bergey GK, Franaszczuk PJ (2007)" + }, + { + "object_id": 185315, + "object_name": "Anderson WS, Kudela P, Weinberg S, Bergey GK, Franaszczuk PJ (2009)" + }, + { + "object_id": 185317, + "object_name": "Anderson WS, Azhar F, Kudela P, Bergey GK, Franaszczuk PJ (2012)" + }, + { + "object_id": 185319, + "object_name": "Azhar F, Anderson WS (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Network model with neocortical architecture (Anderson et al 2007,2012; Azhar et al 2012)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + }, + { + "object_id": 7387, + "object_name": "Neocortex V1 interneuron basket PV GABA cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Architecturally realistic neocortical model using seven classes of excitatory and inhibitory single compartment Hodgkin-Huxley cells. This is an addendum to ModelDB Accession # 98902, Studies of stimulus parameters for seizure disruption (Anderson et al. 2007). Wiring is adapted from the minicolumn hypothesis and incorporates visual and neocortical wiring data. Simulation demonstrates spontaneous bursting onset and cessation. This activity can be induced by random fluctuations in the surrounding background input." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/141507", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "ver_date": "2018-10-17T16:56:52.483", + "ver_number": 28 + }, + "141699": { + "citation": { + "attr_id": 391, + "value": "21984318" + }, + "class_id": 19, + "created": "2011-10-11T11:23:43", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 141699, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 141703, + "object_name": "So, Rosa " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 141702, + "object_name": "So RQ, Kent AR, Grill WM (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Basal ganglia-thalamic network model for deep brain stimulation (So et al. 2012)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + }, + { + "object_id": 74300, + "object_name": "Subthalamus nucleus projection neuron" + }, + { + "object_id": 88217, + "object_name": "Globus pallidus neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a model of the basal ganglia-thalamic network, modified from the Rubin and Terman model (High frequency stimulation of the Subthalamic Nucleus, Rubin and Terman 2004). We subsequently used this model to investigate the effectiveness of STN and GPi DBS as well as lesion when various proportions of local cells and fibers of passage were activated or silenced. The BG network exhibited characteristics consistent with published experimental data, both on the level of single cells and on the network level. Perhaps most notably, and in contrast to the original RT model, the changes in the thalamic error index with changes in the DBS frequency matched well the changes in clinical symptoms with changes in DBS frequency." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/141699", + "other_implementer": { + "attr_id": 308, + "value": "So, Rosa" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "yq11@duke.edu" + }, + "ver_date": "2019-11-22T17:27:09.35", + "ver_number": 6 + }, + "141835": { + "class_id": 19, + "created": "2011-10-13T11:33:12", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 141835, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3642, + "object_name": "Tutorial/Teaching" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 141834, + "object_name": "Ermentrout GB, Terman DH (2010)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 91894, + "object_name": "XPP (web link to model)" + } + ] + }, + "name": "Mathematical Foundations of Neuroscience (Ermentrout and Terman 2010)", + "notes": { + "attr_id": 24, + "value": "Ermentrout and Terman's book on dynamical systems and computational methods in neuroscience has associated computer code available at http://www.math.pitt.edu/~bard/bardware/neurobook/allodes.html. The main emphasis in the book is on single-neuron biophysics and there is also systems neuroscience theory and applications to networks. The electronic text of the book is freely available!" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/141835", + "ver_date": "2016-06-16T13:29:19.673", + "ver_number": 7 + }, + "141991": { + "class_id": 19, + "created": "2011-10-17T19:06:20", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 141991, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 143084, + "object_name": "Jercog, Pablo [pjercog at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 143085, + "object_name": "Synaptic-input statistic" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 141992, + "object_name": "Jercog PE, Svirskis G, Kotak VC, Sanes DH, Rinzel J (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Two Models for synaptic input statistics for the MSO neuron model (Jercog et al. 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88110, + "object_name": "Medial Superior Olive (MSO) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model is a point neuron model with ionic currents from Rothman & Mannis (2003) and with an update of the low threshold potassium current (IKLT) measured in-vitro by Mathews & Jercog et al (2010).\r\nThis model in conjunction with the synaptic input models presented here has been used to gain insight into mechanisms that account for experimentally observed asymmetries in ITD tuning (Brand et al, 2002).\r\nAsymmetry and displacement of the ITD response function is achieved in the model by the interplay between asymmetry of the excitatory inputs arriving from the two sides and the precise voltage dependent activation of IKLT.\r\nIn Jercog et al (2010) we propose two different mathematical ways, physiologically plausible scenarios, of generating the asymmetry in the bilateral synaptic input events. Here, we present two models for simulating the stochastic synaptic input trains." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/141991", + "other_concept": { + "attr_id": 306, + "value": "Synaptic-input Statistic" + }, + "other_implementer": { + "attr_id": 308, + "value": "jercog, pablo, " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "pjercog@gmail.com" + }, + "ver_date": "2018-10-18T17:00:45.587", + "ver_number": 30 + }, + "142062": { + "class_id": 19, + "created": "2011-10-19T13:42:52", + "gitrepo": false, + "id": 142062, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 87578, + "object_name": "STDP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 142012, + "object_name": "Richert M, Nageswaran JM, Dutt N, Krichmar JL (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Efficient simulation environment for modeling large-scale cortical processing (Richert et al. 2011)", + "notes": { + "attr_id": 24, + "value": "\"We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich neuron networks, having spike-timing dependent plasticity and short-term plasticity. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/142062", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2011-10-19T15:05:47", + "ver_number": 8 + }, + "142104": { + "class_id": 19, + "created": "2011-10-20T16:31:11", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 142104, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 137843, + "object_name": "Stanley, David A " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 139148, + "object_name": "Brain Rhythms" + }, + { + "object_id": 145886, + "object_name": "Circadian Rhythms" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 154582, + "object_name": "Stanley DA, Talathi SS, Parekh MB, Cordiner DJ, Zhou J, Mareci TH, Ditto WL, Carney PR (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Hippocampal CA3 network and circadian regulation (Stanley et al. 2013)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + }, + { + "object_id": 154093, + "object_name": "Hippocampus CA3 interneuron basket GABA cell" + }, + { + "object_id": 139885, + "object_name": "Hippocampus CA3 stratum oriens lacunosum-moleculare interneuron" + }, + { + "object_id": 154750, + "object_name": "Hippocampus septum medial GABAergic neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model produces the hippocampal CA3 neural network model used \r\nin the paper below. It has two modes of operation, a default mode and a circadian mode. In the circadian mode, parameters are swept through a range of values. \r\nThis model can be quite easily adapted to produce theta and gamma oscillations, as certain parameter sweeps will reveal (see Figures). BASH scripts interact with GENESIS\r\n2.3 to implement parameter sweeps.\r\nThe model contains four cell types derived from prior papers.\r\nCA3 pyramidal are derived from Traub et al (1991); Basket, \r\nstratum oriens (O-LM), and Medial Septal GABAergic\r\n(MSG) interneurons are taken from Hajos et al (2004)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/142104", + "other_neurons": { + "attr_id": 301, + "value": "medial septal GABAergic" + }, + "other_simulator": { + "attr_id": 307, + "value": "Bash" + }, + "other_type": { + "attr_id": 305, + "value": "Parameter Sweep" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + }, + { + "object_id": 154751, + "object_name": "Medial Septum" + } + ] + }, + "ver_date": "2015-02-17T23:50:14.793", + "ver_number": 27 + }, + "142199": { + "citation": { + "attr_id": 391, + "value": "I. Hayut, B.W. Connors, E.E. Fanselow and D. Golomb, LTS and FS inhibitory interneurons, short-term synaptic plasticity, and cortical circuit dynamics. PLoS Comput. Biol. 7(10): e1002248." + }, + "class_id": 19, + "created": "2011-10-28T10:52:05", + "gitrepo": false, + "id": 142199, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57906, + "object_name": "Golomb, David [golomb at bgu.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 142200, + "object_name": "Hayut I, Fanselow EE, Connors BW, Golomb D (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Rate model of a cortical RS-FS-LTS network (Hayut et al. 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + }, + { + "object_id": 142232, + "object_name": "Neocortex spiking low threshold (LTS) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A rate model of cortical networks composed of RS, FS and LTS neurons. Synaptic depression is modelled according to the Tsodyks-Markram scheme." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/142199", + "other_neurons": { + "attr_id": 301, + "value": "Neocortical RS, FS and LTS neurons" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "golomb@bgu.ac.il" + }, + "ver_date": "2011-10-28T23:45:55", + "ver_number": 6 + }, + "142273": { + "citation": { + "attr_id": 391, + "value": "Krasne FB, Fanselwo MS, Zelikowsy M. (2011). Design of a neurally pluasibel model of fear learning. Front. Behav. Neurosci., 5, Artile 41." + }, + "class_id": 19, + "created": "2011-10-30T15:20:07", + "gitrepo": false, + "id": 142273, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 142276, + "object_name": "Krasne, Frank [krasne at psych.ucla.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83535, + "object_name": "Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 142275, + "object_name": "Krasne FB, Fanselow MS, Zelikowsky M (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "FRAT: An amygdala-centered model of fear conditioning (Krasne et al. 2011)", + "notes": { + "attr_id": 24, + "value": "Model of Pavlovian fear conditioning and extinction (due to neuromodulator-controlled LTP on principal cells and inhibory interneurons)occur in amygdala and contextual representations are learned in hippocampus. Many properties of fear conditioning are accounted for." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/142273", + "other_implementer": { + "attr_id": 308, + "value": "Krasne, Frank " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "krasne@psych.ucla.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 142628, + "object_name": "Amygdala" + } + ] + }, + "ver_date": "2011-11-10T14:21:47", + "ver_number": 5 + }, + "142630": { + "biosimulations": { + "value": "modeldb-142630" + }, + "class_id": 19, + "created": "2011-11-08T14:05:53", + "gitrepo": false, + "id": 142630, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 136797, + "object_name": "Barreto, Ernest " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 121254, + "object_name": "Depolarization block" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 140546, + "object_name": "Barreto E, Cressman JR (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Ion concentration dynamics as a mechanism for neuronal bursting (Barreto & Cressman 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"We describe a simple conductance-based model neuron that includes intra and\r\nextracellular ion concentration dynamics and show that this model exhibits periodic\r\nbursting. The bursting arises as the fast-spiking behavior of the neuron is modulated\r\nby the slow oscillatory behavior in the ion concentration variables and vice versa. By\r\nseparating these time scales and studying the bifurcation structure of the neuron, we catalog\r\nseveral qualitatively different bursting profiles that are strikingly similar to those seen in\r\nexperimental preparations. Our work suggests that ion concentration dynamics may play an\r\nimportant role in modulating neuronal excitability in real biological systems.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/142630", + "other_neurons": { + "attr_id": 301, + "value": "Hodgkin-Huxley + ion concentration dynamics" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ebarreto@gmu.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115960, + "object_name": "Generic" + } + ] + }, + "ver_date": "2011-12-21T16:46:12", + "ver_number": 13 + }, + "142990": { + "class_id": 19, + "created": "2011-11-18T12:02:27", + "gitrepo": false, + "id": 142990, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144029, + "object_name": "Yarrow, Stuart [s.yarrow at ed.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144028, + "object_name": "Yarrow S, Challis E, Seri\u00e8s P (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Fisher and Shannon information in finite neural populations (Yarrow et al. 2012)", + "notes": { + "attr_id": 24, + "value": "Here we model populations of rate-coding neurons with bell-shaped tuning curves and multiplicative Gaussian noise. This Matlab code supports the calculation of information theoretic (mutual information, stimulus-specific information, stimulus-specific surprise) and Fisher-based measures (Fisher information, I_Fisher, SSI_Fisher) in these population models. The information theoretic measures are computed by Monte Carlo integration, which allows computationally-intensive decompositions of the mutual information to be computed for relatively large populations (hundreds of neurons)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/142990", + "other_implementer": { + "attr_id": 308, + "value": "Yarrow, Stuart " + }, + "other_neurons": { + "attr_id": 301, + "value": "Generic rate-coding neuron" + }, + "other_type": { + "attr_id": 305, + "value": "Coding population" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "s.yarrow@ed.ac.uk" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115962, + "object_name": "Unknown" + } + ] + }, + "ver_date": "2015-07-26T14:26:26.863", + "ver_number": 13 + }, + "142993": { + "class_id": 19, + "created": "2011-11-21T17:24:54", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 139267, + "object_name": "I Cl, leak" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 143068, + "object_name": "CLC-2 CLCN2" + } + ] + }, + "gitrepo": false, + "id": 142993, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53570, + "object_name": "Prescott, Steven [steve.prescott at sickkids.ca]]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 143004, + "object_name": "Chloride regulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 142626, + "object_name": "Ratt\u00e9 S, Prescott SA (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "ClC-2 channels regulate neuronal excitability, not intracellular Cl- levels (Ratte & Prescott 2011)", + "notes": { + "attr_id": 24, + "value": "\"The model is for a generic, single compartment neuron with multiple ion currents. The most notable mechanisms include ClC-2 (a rectifying chloride-leak channel) and KCC2 (potassium chloride co-transporter 2). A significant feature of the model is that it tracks intracellular chloride concentration. Moreover, the GABA-A receptor is modeled as passing both chloride and bicarbonate ions, which is important for proper calculation of the GABA reversal potential. Ornstein-Unlenbeck processes to simulate synaptic inhibition and excitation are also included.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/142993", + "ver_date": "2011-11-22T18:45:23", + "ver_number": 12 + }, + "143072": { + "biosimulations": { + "value": "modeldb-143072" + }, + "class_id": 19, + "created": "2011-11-22T16:45:30", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 143072, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53570, + "object_name": "Prescott, Steven [steve.prescott at sickkids.ca]]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 96767, + "object_name": "Nociception" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 142992, + "object_name": "Coggan JS, Ocker GK, Sejnowski TJ, Prescott SA (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Explaining pathological changes in axonal excitability by dynamical analysis (Coggan et al. 2011)", + "notes": { + "attr_id": 24, + "value": "\"... To help decipher the biophysical basis for \u2018paroxysmal\u2019\r\nspiking, we replicated afterdischarge (i.e. continued spiking after a brief stimulus) in a\r\nminimal conductance-based axon model.\r\n\r\n...\r\nA perturbation\r\ncould abruptly switch the system between two (quasi-)stable attractor states: rest and\r\nrepetitive spiking.\r\n...\r\n Initiation of afterdischarge was explained by activation of the\r\npersistent inward current forcing the system to cross a saddle point that separates the basins of\r\nattraction associated with each attractor.\r\n\r\nTermination of afterdischarge was explained by the\r\nattractor associated with repetitive spiking being destroyed.\r\n...\r\nThe model also explains other features of\r\nparoxysmal symptoms, including temporal summation and refractoriness.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/143072", + "ver_date": "2019-05-07T16:11:29.287", + "ver_number": 8 + }, + "143083": { + "citation": { + "attr_id": 391, + "value": "Muller L, Brette R and Gutkin B (2011) Spike-timing dependent plasticity and feed-forward input oscillations produce precise and invariant spike phase-locking. Front. Comput. Neurosci. 5:45. doi: 10.3389/fncom.2011.00045" + }, + "class_id": 19, + "created": "2011-11-28T10:17:35", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 143083, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83453, + "object_name": "Brette R" + }, + { + "object_id": 143086, + "object_name": "Muller, Lyle [muller at inaf.cnrs-gif.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 143087, + "object_name": "Muller L, Brette R, Gutkin B (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "STDP and oscillations produce phase-locking (Muller et al. 2011)", + "notes": { + "attr_id": 24, + "value": "\"... In this note, we investigate a simple mechanism for learning precise LFP-to-spike coupling in feed-forward networks \u2013 the reliable, periodic modulation of presynaptic firing rates during oscillations, coupled with spike-timing dependent plasticity. When oscillations are within the biological range (2\u2013150 Hz), firing rates of the inputs change on a timescale highly relevant to spike-timing dependent plasticity (STDP). Through analytic and computational methods, we find points of stable phase-locking for a neuron with plastic input synapses.\r\n\r\nThese points correspond to precise phase-locking behavior in the feed-forward network. The location of these points depends on the oscillation frequency of the inputs, the STDP time constants, and the balance of potentiation and de-potentiation in the STDP rule.\r\n...\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/143083", + "other_implementer": { + "attr_id": 308, + "value": "Muller L" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "lyle.e.muller@gmail.com" + }, + "ver_date": "2017-07-28T17:31:29.82", + "ver_number": 10 + }, + "143100": { + "citation": { + "attr_id": 391, + "value": "21993572" + }, + "class_id": 19, + "created": "2011-12-04T21:58:23", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + }, + { + "object_id": 88212, + "object_name": "KCNQ1" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 143100, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 143112, + "object_name": "Kitano, Katsunori [kkt23219 at is.ritsumei.ac.jp]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 137847, + "object_name": "Phase Response Curves" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 143101, + "object_name": "Fujita T, Fukai T, Kitano K (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "143100", + "name": "ModelView" + } + ], + "default": "143100" + }, + "name": "Phase response curve of a globus pallidal neuron (Fujita et al. 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88217, + "object_name": "Globus pallidus neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 236, + "object_name": "Ions" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We investigated how changes in ionic conductances alter the phase response curve (PRC) of a globus pallidal (GP) neuron and stability of a synchronous activity of a GP network, using a single-compartmental conductance-based neuron model. The results showed the PRC and the stability were influenced by changes in the persistent sodium current, the Kv3 potassium, the M-type potassium and the calcium-dependent potassium current." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/143100", + "other_implementer": { + "attr_id": 308, + "value": "Kitano, Katsunori" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "kitano@ci.ritsumei.ac.jp" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181771, + "object_name": "143100" + } + ] + }, + "ver_date": "2014-02-25T01:06:08", + "ver_number": 18 + }, + "143114": { + "citation": { + "attr_id": 391, + "value": "Effects of membrane afterhyperpolarization on discharge interval statistics: A model study of responses of lateral superior olive neurons to ipsilateral and bilateral acoustic stimuli. J. Neurophysiol. 103: 2355-2371." + }, + "class_id": 19, + "created": "2011-12-05T17:37:44", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 143114, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53903, + "object_name": "Zhou, Yi [yizhou at bu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + }, + { + "object_id": 121254, + "object_name": "Depolarization block" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 143115, + "object_name": "Zhou Y, Colburn HS (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Effects of the membrane AHP on the Lateral Superior Olive (LSO) (Zhou & Colburn 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 143146, + "object_name": "Lateral Superior Olive (LSO) cell" + }, + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This simulation study investigated how membrane afterhyperpolarization (AHP) influences spiking activity of neurons in the Lateral Superior Olive (LSO). The model incorporates a general integrate-and-fire spiking mechanism with a first-order adaptation channel. Simulations focus on differentiating the effects of GAHP, tauAHP, and input strength on (1) spike interval statistics, such as negative serial correlation and chopper onset, and (2) neural sensitivity to interaural level difference (ILD) of LSO neurons. The model simulated electrophysiological data collected in cat LSO (Tsuchitani and Johnson, 1985)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/143114", + "other_neurons": { + "attr_id": 301, + "value": "Lateral Superior Olive (LSO) cell" + }, + "other_type": { + "attr_id": 305, + "value": "Integrate-and-fire" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "zhouyi@jhu.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 143145, + "object_name": "Auditory brainstem" + } + ] + }, + "ver_date": "2018-10-18T17:01:40.853", + "ver_number": 15 + }, + "143148": { + "citation": { + "attr_id": 391, + "value": "22157113" + }, + "class_id": 19, + "created": "2011-12-14T12:20:50", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 143148, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 117967, + "object_name": "Mensi, Skander [skander.mensi at epfl.ch]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 143151, + "object_name": "Mensi S, Naud R, Pozzorini C, Avermann M, Petersen CC, Gerstner W (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Extraction and classification of three cortical neuron types (Mensi et al. 2012)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This script proposes a new convex fitting procedure that allows the parameters estimation of a large class of stochastic Integrate-and-Fire model upgraded with spike-triggered current and moving threshold from patch-clamp experiments (i.e. given the injected current and the recorded membrane potential).\r\nThis script applies the method described in the paper to estimate the parameters of a reference model from a single voltage trace and the corresponding input current and evaluate the performance of the fitted model on a separated test set." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/143148", + "other_concept": { + "attr_id": 306, + "value": "Somatic neuron model" + }, + "other_currents": { + "attr_id": 303, + "value": "spike-triggered current" + }, + "other_neurons": { + "attr_id": 301, + "value": "Cortical neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "skander.mensi@epfl.ch" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2017-05-15T09:55:11.677", + "ver_number": 16 + }, + "143248": { + "class_id": 19, + "created": "2011-12-21T10:39:14", + "gitrepo": false, + "id": 143248, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 98011, + "object_name": "Place cell/field" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 143249, + "object_name": "Castro L, Aguiar P (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Phase precession through acceleration of local theta rhythm (Castro & Aguiar 2011)", + "notes": { + "attr_id": 24, + "value": "\"... Here we\r\npresent a biophysical spiking model for phase precession in\r\nhippocampal CA1 which focuses on the interaction between\r\nplace cells and local inhibitory interneurons.\r\n\r\nThe model\u2019s\r\nfunctional block is composed of a place cell (PC) connected\r\nwith a local inhibitory cell (IC) which is modulated by the\r\npopulation theta rhythm.\r\n\r\nBoth cells receive excitatory inputs\r\nfrom the entorhinal cortex (EC).\r\n...\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/143248", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2012-01-10T13:47:06", + "ver_number": 6 + }, + "143253": { + "biosimulations": { + "value": "modeldb-143253" + }, + "citation": { + "attr_id": 391, + "value": "Huang CW, Chow JC, Tsai JJ, Wu SN. Characterizing the effects of eugenol on neuronal ionic currents and hyperexcitability. Psychopharmacology 2011; Dec 13; [Epub ahead of print]" + }, + "class_id": 19, + "created": "2011-12-27T08:30:31", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 87769, + "object_name": "ATP-senstive potassium current" + } + ] + }, + "gitrepo": false, + "id": 143253, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + }, + { + "object_id": 84610, + "object_name": "Huang, Chin-Wei" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 143255, + "object_name": "Huang CW, Chow JC, Tsai JJ, Wu SN (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Effects of eugenol on the firing of action potentials in NG108-15 neurons (Huang et al. 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88207, + "object_name": "Neuroblastoma" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Rationale: Eugenol (EUG, 4-allyl-2-methoxyphenol), the main component of essential oil extracted from cloves, has various uses in medicine because of its potential to modulate neuronal excitability. \r\n\r\nHowever, its effects on the ionic mechanisms remains incompletely understood. \r\n\r\nObjectives: We aimed to investigate EUG`s effects on neuronal ionic currents and excitability, especially on voltage-gated ion currents, and to verify the effects on a hyperexcitability-temporal lobe seizure model. \r\n\r\nMethods: With the aid of patch-clamp technology, we first investigated the effects of EUG on ionic currents in NG108-15 neuronal cells differentiated with cyclic AMP. We then used modified Pinsky-Rinzel simulation modeling to evaluate its effects on spontaneous action potentials (APs). \r\n\r\nFinally, we investigated its effects on pilocarpine-induced seizures in rats. \r\n\r\nResults: EUG depressed the transient and late components of INa in the neurons. \r\n\r\nIt not only increased the degree of INa inactivation, but specifically suppressed the non-inactivating INa (INa(NI)). \r\n\r\n... In addition, EUG diminished L-type Ca2+ current and delayed rectifier K+ current only at higher concentrations. EUG`s effects on APs frequency reduction was verified by the simulation modeling. \r\n\r\nIn pilocarpine-induced seizures, the EUG-treated rats showed no shorter seizure latency but a lower seizure severity and mortality than the control rats. \r\n\r\n...\r\nConclusion: The synergistic blocking effects of INa and INa(NI) contributes to the main mechanism through which EUG affects the firing of neuronal APs and modulate neuronal hyperexcitability such as pilocarpine-induced temporal lobe seizures.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/143253", + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2012-01-03T16:58:23", + "ver_number": 6 + }, + "143442": { + "class_id": 19, + "created": "2012-01-04T16:36:32", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 143445, + "object_name": "I_KD" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 143442, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 142175, + "object_name": "Foust AJ, Yu Y, Popovic M, Zecevic D, McCormick DA (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "143442", + "name": "ModelView" + } + ], + "default": "143442" + }, + "name": "Spike repolarization in axon collaterals (Foust et al. 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Voltage sensing dye experiments and simulations characterize the location and re-polarizing function of Kv1 channels in cortical neurons.\r\n\"... (the papers) results indicate that action potential-induced synaptic transmission may operate through a mix of analog\u2013digital transmission owing to the properties of Kv1 channels in axon collaterals and presynaptic boutons.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/143442", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181882, + "object_name": "143442" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 488 + }, + "ver_date": "2014-02-25T01:09:34", + "ver_number": 4 + }, + "143446": { + "class_id": 19, + "created": "2012-01-05T14:46:31", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 240, + "object_name": "I Chloride" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 143446, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 84598, + "object_name": "Extracellular Fields" + }, + { + "object_id": 139996, + "object_name": "Evoked LFP" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 7134, + "object_name": "Wilson M, Bower JM (1992)" + }, + { + "object_id": 110235, + "object_name": "Bower JM, Beeman D (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 18747, + "object_name": "GENESIS (web link to model)" + } + ] + }, + "name": "Simulations of oscillations in piriform cortex (Wilson & Bower 1992)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 167344, + "object_name": "Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"1. A large-scale computer model of the piriform cortex was\r\nconstructed on the basis of the known anatomic and physiological\r\norganization of this region.\r\n2. The oscillatory field potential and electroencephalographic\r\n(EEG) activity generated by the model was compared with actual\r\nphysiological results. The model was able to produce patterns of\r\nactivity similar to those recorded physiologically in response to\r\nboth weak and strong electrical shocks to the afferent input. The\r\nmodel also generated activity patterns similar to EEGs recorded in\r\nbehaving animals.\r\n3. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/143446", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115949, + "object_name": "Olfactory cortex" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 611 + }, + "ver_date": "2018-10-17T16:08:16.58", + "ver_number": 6 + }, + "143602": { + "class_id": 19, + "created": "2012-01-06T14:41:34", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "id": 143602, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33787, + "object_name": "Graham, Bruce [B.Graham at cs.stir.ac.uk]" + }, + { + "object_id": 74299, + "object_name": "Gillies, Andrew [andrew at anc.ed.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3642, + "object_name": "Tutorial/Teaching" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 142627, + "object_name": "Sterratt D, Graham B, Gillies A, Willshaw D (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "Principles of Computational Modelling in Neuroscience (Book) (Sterratt et al. 2011)", + "notes": { + "attr_id": 24, + "value": "\"... This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signaling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modeling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/143602", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2012-01-06T15:35:16", + "ver_number": 3 + }, + "143604": { + "class_id": 19, + "created": "2012-01-06T17:23:29", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 143604, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33735, + "object_name": "Carnevale, Ted [Ted.Carnevale at 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Thus the decreased effectiveness\r\nof temporally irregular DBS trains is due to long pauses in the\r\nstimulus trains, not the degree of temporal irregularity alone.\r\n\r\nWe also\r\nconducted computer simulations of neuronal responses to the experimental\r\nstimulus trains using a biophysical model of the thalamic\r\nnetwork.\r\n\r\nTrains that suppressed tremor in volunteers also suppressed\r\nfluctuations in thalamic transmembrane potential at the frequency\r\nassociated with cerebellar burst-driver inputs.\r\n\r\nClinical and computational\r\nfindings indicate that DBS suppresses tremor by masking burst-driver\r\ninputs to the thalamus and that pauses in stimulation prevent\r\nsuch masking. Although stimulation of other anatomic targets may\r\nprovide tremor suppression, we propose that the most relevant neuronal\r\ntargets for effective tremor suppression are the afferent cerebellar\r\nfibers that terminate in the thalamus.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/143633", + "public_submitter_email": { + "attr_id": 309, + "value": "merrill.birdno@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "ver_date": "2019-03-28T11:03:55.01", + "ver_number": 8 + }, + "143635": { + "class_id": 19, + "created": "2012-01-13T16:15:13", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 88117, + "object_name": "I_KHT" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 143635, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 143637, + "object_name": "Casale, Amanda [Amanda.Casale at yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 143636, + "object_name": "Conductance distributions" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 143634, + "object_name": "Casale AE, McCormick DA (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Active dendritic action potential propagation (Casale & McCormick 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 140252, + "object_name": "Thalamus lateral geniculate nucleus interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model explores the dendritic sodium and potassium conductances needed to recapitulate voltage-sensitive dye optical recordings of thalamic interneuron dendrites in the dorsal lateral geniculate nucleus. Model ion channels were selected based on pharmacological data." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/143635", + "other_concept": { + "attr_id": 306, + "value": "Conductance distributions" + }, + "other_implementer": { + "attr_id": 308, + "value": "Casale. Amanda [amanda.casale at yale.edu]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "amanda.casale@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181410, + "object_name": "143635" + } + ] + }, + "ver_date": "2014-01-19T10:06:22", + "ver_number": 11 + }, + "143671": { + "class_id": 19, + "created": "2012-01-17T17:07:25", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 143671, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83510, + "object_name": "Powers, Randy [rkpowers at u.washington.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + 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to different patterns of excitatory and inhibitory\r\ninputs.\r\n\r\nThe models were first tuned to approximate the current- and\r\nvoltage-clamp responses of low- and medium-threshold spinal motoneurons\r\nstudied in decerebrate cats and then driven with different patterns of\r\nexcitatory and inhibitory inputs.\r\n\r\nThe responses of the models to excitatory\r\ninputs reproduced a number of features of human motor unit\r\ndischarge.\r\n\r\nHowever, the pattern of rate modulation was strongly influenced\r\nby the temporal and spatial pattern of concurrent inhibitory inputs.\r\n\r\nThus, even though PIC activation is likely to exert a strong influence on\r\nfiring rate modulation, PIC activation in combination with different\r\npatterns of excitatory and inhibitory synaptic inputs can produce a wide\r\nvariety of motor unit discharge patterns.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/143671", + "public_submitter_email": { + 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Marasco, A.Limongiello, C.Marchetti, H.Marie,B.Tirozzi, M.Migliore (2012). J Comput. Neurosci. In press. DOI: 10.1007/s10827-012-0383-y.\r\nExperimental findings shown that under sustained input current of increasing strength neurons eventually stop firing, entering a depolarization block.\r\nWe analyze the spiking dynamics of CA1 pyramidal neuron models using the same set of ionic currents on both an accurate morphological reconstruction and on its reduction to a single-compartment.\r\n The results show the specic ion channel properties and kinetics that are needed to\r\nreproduce the experimental findings, and how their interplay can drastically modulate the neuronal dynamics and the input current range leading to depolarization block." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/143719", + "other_concept": { + "attr_id": 306, + "value": "Bifurcation" + }, + "other_implementer": { + "attr_id": 308, + "value": "Bianchi, Daniela\r\nLimongiello, Alessandro" + }, + "other_simulator": { + "attr_id": 307, + "value": "Mathematica" + }, + 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"object_id": 143822, + "object_name": "Vogels, Tim [tim.vogels at epfl.ch]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 87578, + "object_name": "STDP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 143729, + "object_name": "Vogels TP, Sprekeler H, Zenke F, Clopath C, Gerstner W (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Inhibitory plasticity balances excitation and inhibition (Vogels et al. 2011)", + "notes": { + "attr_id": 24, + "value": "\"Cortical neurons receive balanced excitatory and inhibitory synaptic currents.\r\n\r\nSuch a balance could be\r\nestablished and maintained in an experience-dependent manner by synaptic plasticity at inhibitory\r\nsynapses.\r\n\r\nWe show that this mechanism provides an explanation for the sparse firing patterns observed\r\nin response to natural stimuli and fits well with a recently observed interaction of excitatory and\r\ninhibitory receptive field plasticity.\r\n\r\n...\r\nOur results suggest an essential\r\nrole of inhibitory plasticity in the formation and maintenance of functional cortical circuitry.\"" + }, + "opensourcebrain": { + "value": "vogelsetal2011" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/143751", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2017-07-28T13:53:57.05", + "ver_number": 8 + }, + "143753": { + "citation": { + "attr_id": 391, + "value": "21373177" + }, + "class_id": 19, + "created": "2012-01-21T08:35:31", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 143753, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 116961, + "object_name": "Nowotny, Thomas [t.nowotny at sussex.ac.uk]" + }, + { + "object_id": 143754, + "object_name": "Zavada, Andrei [johnhommer at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 143757, + "object_name": "Zavada A, Buckley CL, Martinez D, Rospars JP, Nowotny T (2011)" + } + ] + }, + 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essential for mating success. We propose and test a minimalist competition-based feed-forward neuronal model capable of detecting a certain ratio of pheromone components independently of overall concentration. This model represents an elementary recognition unit for binary mixtures which we propose is entirely contained in the macroglomerular complex (MGC) of the male moth. A set of such units, along with projection neurons (PNs), can provide the input to higher brain centres. We found that (1) accuracy is mainly achieved by maintaining a certain ratio of connection strengths between olfactory receptor neurons (ORN) and local neurons (LN), much less by properties of the interconnections between the competing LNs proper. (2) successful ratio recognition is achieved using latency-to-first-spike in the LN populations which. (3) longer durations of the competition process between LNs did not result in higher recognition accuracy." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/143753", + "other_implementer": { + "attr_id": 308, + "value": "Zavada, Andrei " + }, + "other_simulator": { + "attr_id": 307, + "value": "CNrun" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "johnhommer@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "ver_date": "2018-10-17T16:09:27.817", + "ver_number": 6 + }, + "143760": { + "class_id": 19, + "created": "2012-01-21T23:20:17", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 143760, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 128503, + "object_name": "Goldwyn, Joshua [jhgoldwyn at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 143998, + "object_name": "Goldwyn JH, Rubinstein JT, Shea-Brown E (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Point process framework for modeling electrical stimulation of auditory nerve (Goldwyn et al. 2012)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52419, + "object_name": "Auditory nerve" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A point process model of the auditory nerve that provides a compact and accurate description of neural responses to electric stimulation. Inspired by the framework of generalized linear models, the model consists of a cascade of linear and nonlinear stages. A semi-analytical procedure uniquely determines each parameter in the model on the basis of fundamental statistics from recordings of single fiber responses to electric stimulation, including threshold, relative spread, jitter, and chronaxie. The model also accounts for refractory and summation effects that influence the responses of auditory nerve fibers to high pulse rate stimulation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/143760", + "other_concept": { + "attr_id": 306, + "value": "Point process" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jhg262@nyu.edu" + }, + "ver_date": "2018-10-18T17:02:04.26", + "ver_number": 7 + }, + "144006": { + "class_id": 19, + "created": "2012-01-31T09:39:35", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 144006, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 128813, + "object_name": "Zilli, Eric [zilli at bu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 83539, + "object_name": "Attractor Neural Network" + }, + { + "object_id": 182690, + "object_name": "Spatial Navigation" + }, + { + "object_id": 194888, + "object_name": "Grid cell" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 145888, + "object_name": "Zilli EA (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Grid cell spatial firing models (Zilli 2012)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 129068, + "object_name": "Entorhinal cortex stellate cell" + }, + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This package contains MATLAB implementations of most models (published from 2005 to 2011) of the hexagonal firing field arrangement of grid cells." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144006", + "other_concept": { + "attr_id": 306, + "value": "Path integration" + }, + "ver_date": "2016-07-19T17:28:08.36", + "ver_number": 5 + }, + "144007": { + "citation": { + "attr_id": 391, + "value": "Jones and Gabbiani (2011) Impact of Neural Noise on a Sensory-Motor Pathway Signaling Impending Collision. J Neurophysiol. Epub 2011 Nov 23." + }, + "class_id": 19, + "created": "2012-01-31T17:38:17", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 144007, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144282, + "object_name": "Jones, Peter W [pwjones at cnbc.cmu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144009, + "object_name": "Jones PW, Gabbiani F (2012)" + }, + { + "object_id": 241144, + "object_name": "Jones PW, Gabbiani F (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "LGMD Variability and logarithmic compression in dendrites (Jones and Gabbiani, 2012, 2012B)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 116948, + "object_name": "Locust Lobula Giant Movement Detector (LGMD) neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A compartmental model of the LGMD with a simplified, rake shaped, excitatory dendrite. It receives spontaneous input and excitatory and inhibitory synaptic inputs triggered by visual stimuli. It generates realistic responses to looming through the velocity dependent scaling and delay of individual excitatory synaptic inputs, with variability. We use the model to show that the key determinants of output variability are spontaneous input and temporal jitter of the excitatory inputs, rather than variability in magnitude of individual inputs (2012B, J Neurophysiol). We also use the model to analyze the transformation of the excitatory signals through the visual pathway; concluding that the representation of stimulus velocity is transformed from an expansive relationship at the level of the LGMD inputs to a logarithmic one at the level of its membrane potential (2012, J Neurosci).\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144007", + "other_implementer": { + "attr_id": 308, + "value": "Jones, Peter W" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "pwjones76@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 178, + "object_name": "Nicotinic" + }, + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "ver_date": "2018-10-17T17:02:41.21", + "ver_number": 8 + }, + "144010": { + "citation": { + "attr_id": 391, + "value": "22127761" + }, + "class_id": 19, + "created": "2012-01-31T18:53:11", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 144010, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144015, + "object_name": "Beverlin, Bryce " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144014, + "object_name": "Beverlin B, Kakalios J, Nykamp D, Netoff TI (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Epileptic seizure model with Morris-Lecar neurons (Beverlin and Netoff 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + }, + { + "object_id": 150222, + "object_name": "Abstract Morris-Lecar neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Here we use phase-response curves (PRC) from Morris-Lecar (M-L) model neurons with synaptic depression and gradually decrease input current to cells within a network simulation. This method effectively decreases firing rates resulting in a shift to greater network synchrony illustrating a possible mechanism of the transition phenomenon. PRCs are measured from the M-L conductance based model cell with a range of input currents within the limit cycle. A large network of 3000 excitatory neurons is simulated with a network topology generated from second-order statistics which allows a range of population synchrony. The population synchrony of the oscillating cells is measured with the Kuramoto order parameter, which reveals a transition from tonic to clonic phase exhibited by our model network." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144010", + "other_implementer": { + "attr_id": 308, + "value": "Beverlin, Bryce" + }, + "other_neurons": { + "attr_id": 301, + "value": "Morris-Lecar" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "beverlin@physics.umn.edu" + }, + "ver_date": "2013-06-05T11:46:11", + "ver_number": 6 + }, + "144027": { + "class_id": 19, + "created": "2012-02-02T18:10:52", + "gitrepo": false, + "id": 144027, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 140048, + "object_name": "London M, Roth A, Beeren L, H\u00e4usser M, Latham PE (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Perturbation sensitivity implies high noise and suggests rate coding in cortex (London et al. 2010)", + "notes": { + "attr_id": 24, + "value": "\"... The network simulations\r\nwere also based on a previously published model(Latham et al. 2000), but with modifications to\r\nallow the addition and detection of extra spikes (see Supplementary Information,\r\nsection 7).\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144027", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2012-02-25T00:07:38", + "ver_number": 8 + }, + "144054": { + "class_id": 19, + "created": "2012-02-03T14:52:46", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 144054, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144056, + "object_name": "McTavish, Thomas S [thomas.mctavish at yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144055, + "object_name": "McTavish TS, Migliore M, Shepherd GM, Hines ML (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Synchrony by synapse location (McTavish et al. 2012)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + }, + { + "object_id": 3979, + "object_name": "Olfactory bulb main interneuron granule MC GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model considers synchrony between mitral cells induced via shared\r\ngranule cell interneurons while taking into account the spatial\r\nconstraints of the system. In particular, since inhibitory inputs\r\ndecay passively along the lateral dendrites, this model demonstrates\r\nthat an optimal arrangement of the inhibitory synapses will be near\r\nthe cell bodies of the relevant mitral cells." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144054", + "other_concept": { + "attr_id": 306, + "value": "Lateral inhibition" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "ver_date": "2018-10-17T16:09:43.08", + "ver_number": 7 + }, + "144089": { + "class_id": 19, + "created": "2012-02-06T02:26:14", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 255, + "object_name": "I CAN" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 144089, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144090, + "object_name": "Sidiropoulou, Kyriaki [sidirop at imbb.forth.gr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144394, + "object_name": "Sidiropoulou K, Poirazi P (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "144089", + "name": "ModelView" + } + ], + "default": "144089" + }, + "name": "Layer V PFC pyramidal neuron used to study persistent activity (Sidiropoulou & Poirazi 2012)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... Here, we use a compartmental modeling approach to search for discriminatory features in the properties of incoming stimuli to a PFC pyramidal neuron and/or its response that signal which of these stimuli will result in persistent activity emergence. \r\n\r\nFurthermore, we use our modeling approach to study cell-type specific differences in persistent activity properties, via implementing a regular spiking (RS) and an intrinsic bursting (IB) model neuron. \r\n...\r\nCollectively, our results pinpoint to specific features of the neuronal response to a given stimulus that code for its ability to induce persistent activity and predict differential roles of RS and IB neurons in persistent activity expression.\r\n\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144089", + "other_concept": { + "attr_id": 306, + "value": "Firing patterns" + }, + "other_implementer": { + "attr_id": 308, + "value": "Sidiropoulou, Kyriaki " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "sidirop@imbb.forth.gr" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 153715, + "object_name": "IP3" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181904, + "object_name": "144089" + } + ] + }, + "ver_date": "2015-01-02T23:18:29", + "ver_number": 15 + }, + "144096": { + "citation": { + "attr_id": 391, + "value": "PMID: 22219292" + }, + "class_id": 19, + "created": "2012-02-07T14:31:49", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 144096, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144100, + "object_name": "Shushruth S, Mangapathy P, Ichida JM, Bressloff PC, Schwabe L, Angelucci A (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Surround Suppression in V1 via Withdraw of Balanced Local Excitation in V1 (Shushruth 2012)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + }, + { + "object_id": 7387, + "object_name": "Neocortex V1 interneuron basket PV GABA cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model is mean-field network models, which is set up as a so-called ring-model, i. e. it is a highly idealized model of an orientation hypercolumn in primary visual cortex. Long-range intra-areal and inter-areal feedback connections are modeled phenomenologically as an external input. In this model, there are recurrent interactions via short-range local connections between orientation columns, but not between hypercolumns." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144096", + "other_implementer": { + "attr_id": 308, + "value": "Lars Schwabe\r\nlars.schwabe@uni-rostock.de" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "lars.schwabe@uni-rostock.de" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2018-10-17T17:02:58.27", + "ver_number": 5 + }, + "144372": { + "citation": { + "attr_id": 391, + "value": "18784991" + }, + "class_id": 19, + "created": "2012-02-12T23:28:22", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 144372, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 93399, + "object_name": "Stiefel, Klaus [stiefel at salk.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 118664, + "object_name": "Stiefel KM, Gutkin BS, Sejnowski TJ (2009)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Cortical pyramidal neuron, phase response curve (Stiefel et al 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Three models of increasing complexity all showing a switch from type II (biphasic) to type I (monophasic) phase response curves with a cholinergic down-modulation of K+ conductances." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144372", + "public_submitter_email": { + "attr_id": 309, + "value": "k.stiefel@uws.edu.au" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 204, + "object_name": "Muscarinic" + } + ] + }, + "ver_date": "2019-11-27T14:18:16.99", + "ver_number": 6 + }, + "144376": { + "class_id": 19, + "created": "2012-02-14T14:19:38", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 144376, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144377, + "object_name": "Skolnick, Yosef [yskolnick at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 113999, + "object_name": "Winograd M, Destexhe A, Sanchez-Vives MV (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Hodgkin-Huxley model of persistent activity in PFC neurons (Winograd et al. 2008) (NEURON python)", + "notes": { + "attr_id": 24, + "value": "The paper demonstrate a form of graded persistent activity activated by hyperpolarization. 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The only difference is that the calcium signal is here provided by the high-threshold calcium current (instead of the low-threshold calcium current in thalamic neurons)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144376", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181766, + "object_name": "144376_2" + }, + { + "object_id": 181803, + "object_name": "144376_3" + }, + { + "object_id": 181806, + "object_name": "144376_1" + } + ] + }, + "ver_date": "2015-01-02T22:30:15", + "ver_number": 10 + }, + "144380": { + "citation": { + "attr_id": 391, + "value": "21659016" + }, + "class_id": 19, + "created": "2012-02-20T17:54:02", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 116966, + "object_name": "I_K,Na" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 144382, + "object_name": "Osmosis-driven water flux" + } + ] + }, + "gitrepo": false, + "id": 144380, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33780, + "object_name": "Antic, Srdjan [antic at neuron.uchc.edu]" + }, + { + "object_id": 144381, + "object_name": "Lee, Jonghwan [jonghwan at nmr.mgh.harvard.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 144383, + "object_name": "Cellular volume dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144025, + "object_name": "Lee J, Boas DA, Kim SJ (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A multiphysics neuron model for cellular volume dynamics (Lee et al. 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This paper introduces a novel neuron model, where the cell volume is a time-varying variable and multiple physical principles are combined to build governing equations. Using this model, we analyzed neuronal volume responses during excitation, which elucidated the waveforms of fast intrinsic optical signals observed experimentally across the literature. In addition, we analyzed volume responses on a longer time scale with repetitive stimulation to study the characteristics of slow cell swelling." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144380", + "other_concept": { + "attr_id": 306, + "value": "Cellular volume dynamics" + }, + "other_currents": { + "attr_id": 303, + "value": "Osmosis-driven water flux" + }, + "other_implementer": { + "attr_id": 308, + "value": "Lee, Jonghwan [jonghwan@nmr.mgh.harvard.edu]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jonghwan@nmr.mgh.harvard.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 2391, + "object_name": "Ion Receptors" + } + ] + }, + "ver_date": "2012-02-21T11:04:53", + "ver_number": 9 + }, + "144385": { + "class_id": 19, + "created": "2012-02-23T18:15:03", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 144385, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33772, + "object_name": "Morse, Tom [Tom.Morse at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 5042, + "object_name": "Shepherd GM, Brayton RK (1979)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "144385", + "name": "ModelView" + } + ], + "default": "144385" + }, + "name": "Dendro-dendritic synaptic circuit (Shepherd Brayton 1979)", + "notes": { + "attr_id": 24, + "value": "A NEURON simulation has been created to model the passive spread of an EPSP from a mitral cell synapse on a granule cell spine. The EPSP was shown to propagate subthreshold through the dendritic shaft into an adjacent spine with significant amplitude (figure 2B)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144385", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181460, + "object_name": "144385" + } + ] + }, + "ver_date": "2018-10-17T16:09:58.087", + "ver_number": 18 + }, + "144387": { + "citation": { + "attr_id": 391, + "value": "PMC2851246" + }, + "class_id": 19, + "created": "2012-02-24T17:24:13", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "id": 144387, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144404, + "object_name": "Prinz, Astrid [astrid.prinz at emory.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 81002, + "object_name": "Prinz AA, Bucher D, Marder E (2004)" + }, + { + "object_id": 144407, + "object_name": "G\u00fcnay C, Prinz AA (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Lobster STG pyloric network model with calcium sensor (Gunay & Prinz 2010) (Prinz et al. 2004)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 116963, + "object_name": "Stomatogastric Ganglion (STG) Lateral Pyloric (LP) cell" + }, + { + "object_id": 144405, + "object_name": "Stomatogastric ganglion (STG) pyloric dilator (PD) neuron" + }, + { + "object_id": 144406, + "object_name": "Stomatogastric ganglion (STG) pyloric neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This pyloric network model simulator is a C/C++ program that saves 384 different calcium sensor values that are candidates for activity sensors (Gunay and Prinz, 2010). The simulator was used to scan all of the 20 million pyloric network models that were previously collected in a database (Prinz et al, 2004)." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144387", + "other_concept": { + "attr_id": 306, + "value": "Homeostasis" + }, + "other_implementer": { + "attr_id": 308, + "value": "Prinz, Astrid\r\n" + }, + "other_neurons": { + "attr_id": 301, + "value": "STG Pyloric Dilator, STG Pyloric" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "cgunay@emory.edu" + }, + "ver_date": "2012-03-19T18:07:06", + "ver_number": 14 + }, + "144392": { + "class_id": 19, + "created": "2012-03-09T02:04:03", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 144392, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 143168, + "object_name": "Shah MM, Migliore M, Brown DA (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "144392_1", + "name": "soma=0" + }, + { + "id": "144392_2", + "name": "km in both" + }, + { + "id": "144392_3", + "name": "axon=0" + } + ], + "default": "144392_1" + }, + "name": "CA1 pyramidal neurons: effects of Kv7 (M-) channels on synaptic integration (Shah et al. 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "NEURON mod files from the paper:\r\nShah et al., 2011.\r\n\r\nIn this study, using a combination of electrophysiology\r\nand computational modelling, we show that these channels selectively influence peri-somatic but not dendritic post-synaptic excitatory synaptic potential (EPSP) integration in CA1 pyramidal cells. This may be important for their relative contributions to physiological processes such as synaptic plasticity as well as patho-physiological conditions such as epilepsy." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144392", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181158, + "object_name": "144392_1" + }, + { + "object_id": 181792, + "object_name": "144392_2" + }, + { + "object_id": 181793, + "object_name": "144392_3" + } + ] + }, + "ver_date": "2015-01-02T22:38:08", + "ver_number": 8 + }, + "144401": { + "class_id": 19, + "created": "2012-03-14T14:45:09", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 144401, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144402, + "object_name": "Vladimirov, Nikita " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 114307, + "object_name": "Conduction failure" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144413, + "object_name": "Vladimirov N, Tu Y, Traub RD (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "CA1 pyramidal cell: reconstructed axonal arbor and failures at weak gap junctions (Vladimirov 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Model of pyramidal CA1 cells connected by gap junctions in their axons.\r\nCell geometry is based on anatomical reconstruction of rat CA1 cell (NeuroMorpho.Org ID: NMO_00927) with long axonal arbor.\r\n\r\nModel init_2cells.hoc shows failures of second spike propagation in a spike doublet, depending on conductance of an axonal gap junction.\r\n\r\nModel init_ring.hoc shows that spike failure result in reentrant oscillations of a spike in a loop of axons connected by gap junctions, where one gap junction is weak.\r\n\r\nThe paper shows that in random networks of axons connected by gap junctions, oscillations are driven by single pacemaker loop of axons. The shortest loop, around which a spike can travel, is the most likely pacemaker.\r\nThis principle allows us to predict the frequency of oscillations from network connectivity and visa versa. We propose that this type of oscillations corresponds to so-called fast ripples in epileptic hippocampus." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144401", + "other_implementer": { + "attr_id": 308, + "value": "Vladimirov, Nikita" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "nikita.vladimirov@gmail.com" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181876, + "object_name": "144401" + } + ] + }, + "ver_date": "2014-01-31T23:01:34", + "ver_number": 23 + }, + "144403": { + "class_id": 19, + "created": "2012-03-15T01:17:49", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 112844, + "object_name": "I Q" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 144403, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144411, + "object_name": "Buckley, Christopher [chrisbuckley at brain.riken.jp]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + }, + { + "object_id": 225299, + "object_name": "Multiscale" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144410, + "object_name": "Buckley CL, Nowotny T (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "A Moth MGC Model-A HH network with quantitative rate reduction (Buckley & Nowotny 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We provide the model used in Buckley & Nowotny (2011). It consists of a network of Hodgkin Huxley neurons coupled by slow GABA_B synapses which is run alongside a quantitative reduction described in the associated paper." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144403", + "other_implementer": { + "attr_id": 308, + "value": "Buckley, Christopher chrisbuckley@brain.riken.jp" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "chrisbuckley@riken.brain.jp" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 203, + "object_name": "GabaB" + } + ] + }, + "ver_date": "2017-02-23T11:04:30.693", + "ver_number": 9 + }, + "144416": { + "citation": { + "attr_id": 391, + "value": "Yamazaki T, Nagao S (2012) A Computational Mechanism for Unified Gain and Timing Control in the Cerebellum. PLoS ONE 7(3): e33319. doi:10.1371/ journal.pone.0033319" + }, + "class_id": 19, + "created": "2012-03-17T20:45:12", + "gitrepo": false, + "id": 144416, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144418, + "object_name": "Yamazaki, Tadashi " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 115792, + "object_name": "Yamazaki T, Tanaka S (2007)" + }, + { + "object_id": 151232, + "object_name": "Yamazaki T, Nagao S (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Cerebellar gain and timing control model (Yamazaki & Tanaka 2007)(Yamazaki & Nagao 2012)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + }, + { + "object_id": 7346, + "object_name": "Cerebellum interneuron granule GLU cell" + }, + { + "object_id": 112687, + "object_name": "Cerebellum golgi cell" + }, + { + "object_id": 136179, + "object_name": "Cerebellum deep nucleus neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This paper proposes a hypothetical computational mechanism for unified gain and timing control in the cerebellum. The hypothesis is justified by computer simulations of a large-scale spiking network model of the cerebellum." + }, + "opensourcebrain": { + "value": "cerebellargainandtiming" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144416", + "other_implementer": { + "attr_id": 308, + "value": "Yamazaki, Tadashi" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "pone11@neuralgorithm.org" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2012-03-18T09:02:31", + "ver_number": 4 + }, + "144450": { + "citation": { + "attr_id": 391, + "value": "G\u00f3mez Gonz\u00e1lez JF, Mel BW, Poirazi P.\"Distinguishing linear vs. nonlinear integration in CA1 radial oblique dendrites: it?s about time.\" Frontiers in Computational Neuroscience. 2011;5:44. Epub 2011 Nov 14." + }, + "class_id": 19, + "created": "2012-03-29T05:00:31", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 144450, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144452, + "object_name": "G\u00f3mez Gonz\u00e1lez JF, Mel BW, Poirazi P (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Linear vs non-linear integration in CA1 oblique dendrites (G\u00f3mez Gonz\u00e1lez et al. 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The hippocampus in well known for its role in learning and memory processes. The CA1 region is the output of the hippocampal formation and pyramidal neurons in this region are the elementary units responsible for the processing and transfer of information to the cortex. Using this detailed single neuron model, it is investigated the conditions under which individual CA1 pyramidal neurons process incoming information in a complex (non-linear) as opposed to a passive (linear) manner.\r\nThis detailed compartmental model of a CA1 pyramidal neuron is based on one described previously (Poirazi, 2003). The model was adapted to five different reconstructed morphologies for this study, and slightly modified to fit the experimental data of (Losonczy, 2006), and to incorporate evidence in pyramidal neurons for the non-saturation of NMDA receptor-mediated conductances by single glutamate pulses. We first replicate the main findings of (Losonczy, 2006), including the very brief window for nonlinear integration using single-pulse stimuli. We then show that double-pulse stimuli increase a CA1 pyramidal neuron\u2019s tolerance for input asynchrony by at last an order of magnitude. Therefore, it is shown using this model, that the time window for nonlinear integration is extended by more than an order of magnitude when inputs are short bursts as opposed to single spikes.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144450", + "public_submitter_email": { + "attr_id": 309, + "value": "jfcgomez@ull.edu.es" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2014-02-25T12:47:50", + "ver_number": 6 + }, + "144455": { + "class_id": 19, + "created": "2012-04-10T11:40:31", + "gitrepo": false, + "id": 144455, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83464, + "object_name": "Djurfeldt M" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144454, + "object_name": "Djurfeldt M (2012)" + }, + { + "object_id": 144456, + "object_name": "Djurfeldt M, Lundqvist M, Johansson C, Rehn M, Ekeberg O, Lansner A (2008b)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Connection-set Algebra (CSA) for the representation of connectivity in NN models (Djurfeldt 2012)", + "notes": { + "attr_id": 24, + "value": "\"The connection-set algebra (CSA) is a novel\r\nand general formalism for the description of connectivity\r\nin neuronal network models, from small-scale to\r\nlarge-scale structure. ... The expressiveness of CSA\r\nmakes prototyping of network structure easy. A C++\r\nversion of the algebra has been implemented and used\r\nin a large-scale neuronal network simulation (Djurfeldt\r\net al., IBM J Res Dev 52(1/2):31\u201342, 2008b) and an\r\nimplementation in Python has been publicly released.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144455", + "ver_date": "2015-07-26T14:29:30.37", + "ver_number": 5 + }, + "144463": { + "citation": { + "attr_id": 391, + "value": "22072667" + }, + "class_id": 19, + "created": "2012-04-19T20:42:10", + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122810, + "object_name": "Cav3.1 CACNA1G" + }, + { + "object_id": 122812, + "object_name": "Cav1.3 CACNA1D" + }, + { + "object_id": 122814, + "object_name": "Cav1.2 CACNA1C" + }, + { + "object_id": 122819, + "object_name": "Cav2.1 CACNA1A" + } + ] + }, + "gitrepo": false, + "id": 144463, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144465, + "object_name": "O`Donnell, Cian [cian AT salk.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 143448, + "object_name": "O'Donnell C, Nolan MF, van Rossum MC (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "CA1 pyramidal neuron dendritic spine with plasticity (O`Donnell et al. 2011)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Biophysical model of a dendritic spine and adjacent dendrite with synapse. Model parameters adjusted to fit CA3-CA1 Shaffer collateral synapse data from literature. Model includes both electrical and Ca2+ dynamics, including AMPARs, NMDARs, 4 types of CaV channel, and leak conductance. Spine and synapse are plastic according to Ca2+ dependent rule.\r\n\r\nThe aim of the model is to explore the effects of dendritic spine structural plasticity on the rules of synaptic plasticity." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144463", + "other_implementer": { + "attr_id": 308, + "value": "O Donnell, Cian " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "cian@salk.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2012-04-20T14:03:45", + "ver_number": 6 + }, + "144468": { + "class_id": 19, + "created": "2012-04-23T11:45:18", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "id": 144468, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144474, + "object_name": "Schmandt, Nicolaus [nts21 at case.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144471, + "object_name": "Schmandt NT, Gal\u00e1n RF (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Markov Chain-based Stochastic Shielding Hodgkin Huxley Model (Schmandt, Galan 2012)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144468", + "other_concept": { + "attr_id": 306, + "value": "Intrinsic Channel Noise" + }, + "other_implementer": { + "attr_id": 308, + "value": "Schmandt, Nicolaus " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "nts21@case.edu" + }, + "ver_date": "2012-07-11T11:19:31", + "ver_number": 11 + }, + "144477": { + "class_id": 19, + "created": "2012-04-25T14:35:57", + "gitrepo": false, + "id": 144477, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144478, + "object_name": "Spencer, Kevin M. [kevin_spencer at hms.harvard.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 112853, + "object_name": "Schizophrenia" + }, + { + "object_id": 139148, + "object_name": "Brain Rhythms" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 138251, + "object_name": "Spencer KM (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 144479, + "object_name": "IDL" + } + ] + }, + "name": "Functional consequences of cortical circuit abnormalities on gamma in schizophrenia (Spencer 2009)", + "notes": { + "attr_id": 24, + "value": "\"Schizophrenia is characterized by cortical circuit abnormalities, which might be reflected in\r\ngamma-frequency (30\u2013100 Hz) oscillations in the electroencephalogram. Here we used a computational\r\nmodel of cortical circuitry to examine the effects that neural circuit abnormalities might have\r\non gamma generation and network excitability. The model network consisted of 1000 leaky integrateand-\r\nfi re neurons with realistic connectivity patterns and proportions of neuron types [pyramidal\r\ncells (PCs), regular-spiking inhibitory interneurons, and fast-spiking interneurons (FSIs)].\r\n...\r\nThe results of this study suggest that a multimodal\r\napproach, combining non-invasive neurophysiological and structural measures, might be\r\nable to distinguish between different neural circuit abnormalities in schizophrenia patients.\r\n...\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144477", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2012-04-25T15:02:55", + "ver_number": 3 + }, + "144481": { + "class_id": 19, + "created": "2012-04-25T19:16:41", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92087, + "object_name": "Nav1.6 SCN8A" + } + ] + }, + "gitrepo": false, + "id": 144481, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 114307, + "object_name": "Conduction failure" + }, + { + "object_id": 120695, + "object_name": "Sodium pump" + }, + { + "object_id": 121254, + "object_name": "Depolarization block" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144487, + "object_name": "Boucher PA, Jo\u00f3s B, Morris CE (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 45540, + "object_name": "FORTRAN" + } + ] + }, + "name": "Nodes of Ranvier with left-shifted Nav channels (Boucher et al. 2012)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52425, + "object_name": "Myelinated neuron" + }, + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The two programs CLSRanvier.f and propagation.f simulate the excitability of a myelinated axon with injured nodes of Ranvier.\r\nThe injury is simulated as the Coupled Left Shift (CLS) of the activation(V) and inactivation(V) (availability) of a fraction of Nav channels." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144481", + "other_currents": { + "attr_id": 303, + "value": "I Na, I_window" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "bjoos@uottawa.ca" + }, + "ver_date": "2012-04-26T18:10:14", + "ver_number": 14 + }, + "144482": { + "citation": { + "attr_id": 391, + "value": "Delgado JY, G\u00f3mez Gonz\u00e1lez JF, Desai NS. Pyramidal neuron conductance state gates spike-timing-dependent plasticity. J Neurosci. 2010 Nov 24;30 (47):15713-25. doi: 10.1523/JNEUROSCI.3068-10.2010," + }, + "class_id": 19, + "created": "2012-04-26T06:34:23", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 144482, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144483, + "object_name": "Gomez-Gonzalez, JF [jfcgomez at ull.edu.es]" + }, + { + "object_id": 144491, + "object_name": "Delgado JY, [jyamir at ucla.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 143636, + "object_name": "Conductance distributions" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144484, + "object_name": "Delgado JY, G\u00f3mez-Gonz\u00e1lez JF, Desai NS (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "144482", + "name": "Run" + } + ], + "default": "144482" + }, + "name": "Pyramidal neuron conductances state and STDP (Delgado et al. 2010)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Neocortical neurons in vivo process each of their individual inputs in the context of ongoing synaptic background activity, produced by the thousands of presynaptic partners a typical neuron has. That background activity affects multiple aspects of neuronal and network function. However, its effect on the induction of spike-timing dependent plasticity (STDP) is not clear.\r\nUsing the present biophysically-detailed computational model, it is not only able to replicate the conductance-dependent shunting of dendritic potentials (Delgado et al,2010), but show that synaptic background can truncate calcium dynamics within dendritic spines, in a way that affects potentiation more strongly than depression.\r\nThis program uses a simplified layer 2/3 pyramidal neuron constructed in NEURON.\r\nIt was similar to the model of Traub et al., J Neurophysiol. (2003), and consisted of a soma, an apical shaft, distal dendrites, five basal dendrites, an axon, and a single spine. The spine\u2019s location was variable along the apical shaft (initial 50 μm) and apical. The axon contained an axon hillock region, an initial segment, segments with myelin, and nodes of Ranvier, in order to have realistic action potential generation. For more information about the model see supplemental material, Delgado et al 2010." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144482", + "other_implementer": { + "attr_id": 308, + "value": "Gomez-Gonzalez, JF" + }, + "other_neurons": { + "attr_id": 301, + "value": "Layer 2/3 pyramidal neuron in auditory cortex" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jfcgomez@ull.edu.es" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 144485, + "object_name": "Auditory cortex" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181306, + "object_name": "144482" + } + ] + }, + "ver_date": "2022-11-04T13:19:25.307", + "ver_number": 18 + }, + "144489": { + "class_id": 19, + "created": "2012-04-27T04:36:46", + "gitrepo": false, + "id": 144489, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 147643, + "object_name": "Dangerfield, Ciara E. [ciara.dangerfiel at dtc.ox.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144506, + "object_name": "Dangerfield CE, Kay D, Burrage K (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Reflected SDE Hodgkin-Huxley Model (Dangerfield et al. 2012)", + "notes": { + "attr_id": 24, + "value": "Matlab code for simulating channel noise using the original Hodgkin-Huxley equations and a variant of the Hodkgin-Huxley model from (Bruce, Annals Bio Eng, Vol 36, pp 824-838, 2009). Methods used in simulation are SSA, SDE method and RSDE method." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144489", + "other_concept": { + "attr_id": 306, + "value": "Ion Channel Kinetics" + }, + "other_implementer": { + "attr_id": 308, + "value": "Dangerfield, Ciara E. [ciara.dangerfiel@dtc.ox.ac.uk]" + }, + "ver_date": "2013-12-18T11:23:33", + "ver_number": 14 + }, + "144490": { + "class_id": 19, + "created": "2012-04-27T10:06:31", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 2397, + "object_name": "I Mixed" + }, + { + "object_id": 112843, + "object_name": "I R" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 144490, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144545, + "object_name": "Sterratt, David " + }, + { + "object_id": 144554, + "object_name": "Groen, Martine R [martine.groen at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144544, + "object_name": "Sterratt DC, Groen MR, Meredith RM, van Ooyen A (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "CA1 pyramidal neuron: synaptically-induced bAP predicts synapse location (Sterratt et al. 2012)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is an adaptation of Poirazi et al.'s (2003) CA1 model that is used to measure BAP-induced voltage and calcium signals in spines after simulated Schaffer\r\ncollateral synapse stimulation. In the model, the peak calcium concentration is highly\r\ncorrelated with soma-synapse distance under a number of physiologically-realistic\r\nsuprathreshold stimulation regimes and for a range of dendritic morphologies. There are also simulations demonstrating that peak calcium can be used to set up a synaptic democracy\r\nin a homeostatic manner, whereby synapses regulate their synaptic strength on the\r\nbasis of the difference between peak calcium and a uniform target value." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144490", + "other_implementer": { + "attr_id": 308, + "value": "Groen, Martine R\r\n" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "david.c.sterratt@ed.ac.uk" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181685, + "object_name": "144490" + } + ] + }, + "ver_date": "2014-02-06T16:14:16", + "ver_number": 12 + }, + "144499": { + "class_id": 19, + "created": "2012-05-14T11:14:06", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 144499, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144500, + "object_name": "Leggitt, Alan [alan.leggitt at ucsf.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 238957, + "object_name": "Markov-type model" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 138952, + "object_name": "Goldwyn JH, Shea-Brown E (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Stochastic versions of the Hodgkin-Huxley equations (Goldwyn, Shea-Brown 2011) (pylab)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A pylab version from Alan Leggitt for simulating different channel noise models using the Hodgkin-Huxley equations. Methods provided and reviewed in Goldwyn and Shea-Brown (2011) are: current noise, subunit noise, conductance noise, and Markov chain, as well as the standard deterministic Hodgkin-Huxley model." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144499", + "ver_date": "2018-02-26T11:39:57.793", + "ver_number": 7 + }, + "144502": { + "class_id": 19, + "created": "2012-05-16T11:27:13", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 245, + "object_name": "I T low threshold" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 144502, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 116982, + "object_name": "Torben-Nielsen, Ben [btorbennielsen at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 148588, + "object_name": "Torben-Nielsen B, Segev I, Yarom Y (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Inferior Olive, subthreshold oscillations (Torben-Nielsen, Segev, Yarom 2012)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 144503, + "object_name": "Inferior olive neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The Inferior Olive is a brain structure in which neurons are solely connected to each other through gap-junctions. Its behavior is characterized by spontaneous subthreshold oscillation, frequency changes in the subthreshold oscillation, stable phase differences between neurons, and propagating waves of activity.\r\n\r\nOur model based on actual IO topology can reproduce these behaviors and provides a mechanistic explanation thereof." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144502", + "other_currents": { + "attr_id": 303, + "value": "I_leak" + }, + "other_implementer": { + "attr_id": 308, + "value": "Torben-Nielsen, Benjamin " + }, + "other_neurons": { + "attr_id": 301, + "value": "Inferior Olive neuron" + }, + "other_simulator": { + "attr_id": 307, + "value": "PyNEURON" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "btorbennielsen@gmail.com" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181463, + "object_name": "144502" + } + ] + }, + "ver_date": "2014-02-15T14:30:24", + "ver_number": 6 + }, + "144509": { + "citation": { + "attr_id": 391, + "value": "PMID: 19625516" + }, + "class_id": 19, + "created": "2012-06-04T09:35:31", + "gitrepo": false, + "id": 144509, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144512, + "object_name": "Aubie, Brandon [aubiebn at mcmaster.ca]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 97752, + "object_name": "Delay" + }, + { + "object_id": 136252, + "object_name": "Rebound firing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 141065, + "object_name": "Aubie B, Becker S, Faure PA (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Duration-tuned neurons from the inferior colliculus of the big brown bat (Aubie et al. 2009)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "dtnet is a generalized neural network simulator written in C++ with an easy to use XML description language to generate arbitrary neural networks and then run simulations covering many different parameter values. For example, you can specify ranges of parameter values for several different connection weights and then automatically run simulations over all possible parameters. Graphing ability is built in as long as the free, open-source, graphing application GLE (http://glx.sourceforge.net/) is installed.\r\n\r\nIncluded in the examples folder are simulation descriptions that were used to generate the results in Aubie et al. (2009). Refer to the README file for instructions on compiling and running these examples.\r\n\r\nThe most recent source code can be obtained from GitHub: https://github.com/baubie/dtnet\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/144509", + "other_implementer": { + "attr_id": 308, + "value": "Aubie, Brandon " + }, + "other_neurons": { + "attr_id": 301, + "value": "Locus Coeruleus neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "rhuerta@ucsd.edu" + }, + "ver_date": "2012-09-26T14:00:56", + "ver_number": 5 + }, + "145672": { + "class_id": 19, + "created": "2012-09-13T16:03:09", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 112842, + "object_name": "Kv4.2 KCND2" + }, + { + "object_id": 123580, + "object_name": "Kv3.4 KCNC4" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 145672, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 145678, + "object_name": "Ritter, David [david.ritter at jefferson.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 238957, + "object_name": "Markov-type model" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 146109, + "object_name": "Fineberg JD, Ritter DM, Covarrubias M (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 125744, + "object_name": "IonChannelLab" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "145672_1", + "name": "Figure11C CSI" + }, + { + "id": "145672_2", + "name": "Figure11D" + }, + { + "id": "145672_3", + "name": "Figure11E" + }, + { + "id": "145672_4", + "name": "Figure11C CSI+OSI" + } + ], + "default": "145672_1" + }, + "name": "Neurophysiological impact of inactivation pathways in A-type K+ channels (Fineberg et al 2012)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "These models predict the differential effects of varying pathways of inactivation (closed state inactivation, CSI, or open state inactivation, OSI). Specifically, Markov models of Kv4 potassium channels with CSI or CSI+OSI were inserted into the CA1 pyramidal neuron model from Migliore et al (1999; ModelDB accession #2796) to determine the neurophysiological impact of inactivation pathways. Furthermore, Markov models of Kv4.2 and Kv3.4 channels are used to illustrate a method by which to test what pathway of inactivation a channel uses." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/145672", + "other_implementer": { + "attr_id": 308, + "value": "Ritter, David " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "david.ritter@jefferson.edu" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181717, + "object_name": "145672_1" + }, + { + "object_id": 181718, + "object_name": "145672_2" + }, + { + "object_id": 181719, + "object_name": "145672_3" + }, + { + "object_id": 181720, + "object_name": "145672_4" + } + ] + }, + "ver_date": "2018-02-26T11:40:29.51", + "ver_number": 23 + }, + "145800": { + "biosimulations": { + "value": "modeldb-145800" + }, + "class_id": 19, + "created": "2012-09-13T23:10:16", + "gitrepo": false, + "id": 145800, + "implemented_by": { + "attr_id": 299, + "value": [ + { + 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circadian clock. \r\n\r\nOur model can accurately predict diverse experimental data including the phenotypes of mutations or knockdown of clock genes as well as the time courses and relative expression of clock transcripts and proteins. \r\n\r\nUsing this model, we show how a universal motif of circadian timekeeping, where repressors tightly bind activators rather than directly binding to DNA, can generate oscillations when activators and repressors are in stoichiometric balance. \r\n\u2026\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/145801", + "other_implementer": { + "attr_id": 308, + "value": "Kim, Jae Kyoung [jaekkim at umich.edu]" + }, + "other_neurons": { + "attr_id": 301, + "value": "SCN" + }, + "ver_date": "2016-05-26T11:31:00.403", + "ver_number": 15 + }, + "145803": { + "class_id": 19, + "created": "2012-09-14T10:47:36", + "gitrepo": false, + "id": 145803, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 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conductances from single-trial\r\nVm recordings.\r\n\r\nThis method is based on oversampling of the Vm . We test the method numerically using\r\nmodels of increasing complexity.\r\n\r\nFinally, the method is evaluated using controlled conductance injection\r\nin cortical neurons in vitro using the dynamic-clamp technique.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/145803", + "public_submitter_email": { + "attr_id": 309, + "value": "tom/morse@yale.edu" + }, + "ver_date": "2012-09-14T11:00:06", + "ver_number": 4 + }, + "145836": { + "class_id": 19, + "created": "2012-09-16T11:21:20", + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122827, + "object_name": "NR2B GRIN2B" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 145836, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 145837, + "object_name": "Moradi, Keivan [k.moradi at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 147468, + "object_name": "Moradi K, Moradi K, Ganjkhani M, Hajihasani M, Gharibzadeh S, Kaka G (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A fast model of voltage-dependent NMDA Receptors (Moradi et al. 2013)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "These are two or triple-exponential models of the voltage-dependent NMDA receptors. Conductance of these receptors increase voltage-dependently with a \"Hodgkin and Huxley-type\" gating style that is also depending on glutamate-binding. Time course of the gating of these receptors in response to glutamate are also changing voltage-dependently. Temperature sensitivity and desensitization of these receptor are also taken into account.\r\nThree previous kinetic models that are able to simulate the voltage-dependence of the NMDARs are also imported to the NMODL. These models are not temperature sensitive. \r\n\r\nThese models are compatible with the \"event delivery system\" of NEURON. 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The mapping of arbitrary sets of synaptic inputs, without additional fine tuning, would also allow the convenient and efficient implementation of a new generation of large-scale simulations of brain regions reproducing the biological variability observed in real neurons, with unprecedented advances to understand higher brain functions.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/146376", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2017-02-15T13:58:50.113", + "ver_number": 20 + }, + "146499": { + "class_id": 19, + "created": "2012-10-25T16:49:30", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 146499, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 146501, + "object_name": "Nicola, Wilten [wnicola at uwaterloo.ca]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 148235, + "object_name": "Nicola W, Campbell SA (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Mean Field Equations for Two-Dimensional Integrate and Fire Models (Nicola and Campbell, 2013)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + }, + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The zip file contains the files used to perform numerical simulation and bifurcation studies of large networks of two-dimensional integrate and fire neurons and of the corresponding mean field models derived in our paper. 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Surprisingly, we found that although the voltage profile of the cell was uniform, the calcium influx was restricted to \u2018hot spots\u2019 which correspond to synaptic clusters or large conductance synapses" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/146565", + "public_submitter_email": { + "attr_id": 309, + "value": "apmega@yahoo.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181457, + "object_name": "146565" + } + ] + }, + "ver_date": "2018-10-18T17:35:04.007", + "ver_number": 12 + }, + "146583": { + "citation": { + "attr_id": 391, + "value": "PMID: 22442645 ; PLoS Comput Biol. 2012;8(3):e1002398. 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Using a computational network\r\nmodel we show that fundamental aspects of the adult neurogenesis\r\nobserved in the olfactory bulb -- the persistent addition of new\r\ninhibitory granule cells to the network, their activity-dependent\r\nsurvival, and the reciprocal character of their synapses with the\r\nprincipal mitral cells -- are sufficient to restructure the network\r\nand to alter its encoding of odor stimuli adaptively so as to reduce\r\nthe correlations between the bulbar representations of similar\r\nstimuli. The model captures the experimentally observed\r\nrole of neurogenesis in perceptual learning and the enhanced response\r\nof young granule cells to novel stimuli. Moreover, it makes specific\r\npredictions for the type of odor enrichment that should be effective\r\nin enhancing the ability of animals to discriminate similar odor\r\nmixtures. NSF grant DMS-0719944.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/146583", + "other_implementer": { + "attr_id": 308, + "value": "Siu Fai Chow" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "h-riecke@northwestern.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "ver_date": "2018-10-17T16:11:08.903", + "ver_number": 6 + }, + "146734": { + "citation": { + "attr_id": 391, + "value": "22805060" + }, + "class_id": 19, + "created": "2012-11-19T15:48:59", + "gitrepo": false, + "id": 146734, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 76884, + "object_name": "Ermentrout, Bard [bard_at_pitt.edu]" + }, + { + "object_id": 146804, + "object_name": "Komek, Kubra [kkomek at andrew.cmu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3635, + "object_name": "Synchronization" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 146735, + "object_name": "K\u00f6mek K, Bard Ermentrout G, Walker CP, Cho RY (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Modeling the effects of dopamine on network synchronization (Komek et al. 2012)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 146805, + "object_name": "Abstract Wang-Buzsaki neuron" + }, + { + "object_id": 146806, + "object_name": "Abstract theta neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Dopamine modulates cortical circuit activity in part through its actions on GABAergic interneurons, including increasing the excitability of fast-spiking interneurons. 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Together, our results indicate that dopamine can modulate cortical gamma band synchrony in an inverted-U fashion and that the physiologic effects of dopamine on single fast-spiking interneurons can give rise to such non-monotonic effects at the network level." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/146734", + "other_implementer": { + "attr_id": 308, + "value": "Komek, Kubra " + }, + "other_neurons": { + "attr_id": 301, + "value": "Wang-Buzsaki neuron & theta neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "kkomek@andrew.cmu.edu" + }, + "ver_date": "2012-11-21T10:45:17", + "ver_number": 8 + }, + "146813": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 156786, + "object_name": "original 2013 paper model" + } + ] + }, + "citation": { + "attr_id": 391, + "value": "de Almeida, Idiart and Linster, 2013." + }, + "class_id": 19, + "created": "2012-11-26T17:37:51", + "default_file_notes": 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analysis, our results show how static brain maps can be\r\nrelated to the results of brain activity mapping.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/146949", + "other_implementer": { + "attr_id": 308, + "value": "Suter, Benjamin [ben.suter at gmail.com]" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2023-04-30T15:30:59.217", + "ver_number": 12 + }, + "146950": { + "class_id": 19, + "created": "2012-12-04T14:06:59", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 146950, + "implemented_by": { + "attr_id": 299, + "value": [ + { + 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In particular, these stimuli can be made closer to naturalistic textures compared to usual stimuli such as gratings and random-dot kinetograms. We simplified the definition to parametrically define these \"Motion Clouds\" around the most prevalent feature axis (mean and bandwith): direction, spatial frequency, orientation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/146953", + "public_submitter_email": { + "attr_id": 309, + "value": "laurent.perrinet@univ-amu.fr" + }, + "ver_date": "2015-01-04T10:50:51", + "ver_number": 10 + }, + "147103": { + "class_id": 19, + "created": "2012-12-17T14:57:36", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 147103, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 136801, + "object_name": "Eliasmith, Chris [celiasmith at uwaterloo.ca]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 147066, + "object_name": "Eliasmith C, Stewart TC, Choo X, Bekolay T, DeWolf T, Tang Y, Tang C, Rasmussen D (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 136173, + "object_name": "Nengo" + } + ] + }, + "name": "A large-scale model of the functioning brain (spaun) (Eliasmith et al. 2012)", + "notes": { + "attr_id": 24, + "value": "\" ... In this work, we present a\r\n2.5-million-neuron model of the brain (called \u201cSpaun\u201d) that bridges this gap (between neural activity and biological function) by exhibiting many different\r\nbehaviors. The model is presented only with visual image sequences, and it draws all of its responses with\r\na physically modeled arm. Although simplified, the model captures many aspects of neuroanatomy,\r\nneurophysiology, and psychological behavior, which we demonstrate via eight diverse tasks.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/147103", + "other_concept": { + "attr_id": 306, + "value": "vision,inference,neural c" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2018-10-17T17:16:58.527", + "ver_number": 5 + }, + "147117": { + "class_id": 19, + "created": "2013-01-08T09:20:13", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 250, + "object_name": "I M" + } 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"osbv2_gh_repo": "https://github.com/OpenSourceBrain/147117", + "other_implementer": { + "attr_id": 308, + "value": "M\u00e4ki-Marttunen, Tuomo [tuomo.maki-marttunen (\u00e4t) tut.fi]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tuomo.maki-marttunen@tut.fi" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2014-05-21T11:22:51", + "ver_number": 15 + }, + "147141": { + "class_id": 19, + "created": "2013-01-11T10:00:15", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 147141, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33738, + "object_name": "Lytton, William [bill.lytton at downstate.edu]" + }, + { + "object_id": 97869, + "object_name": "Neymotin, 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We introduce synaptic scaling to a biologically-realistic spiking model of neocortex which can learn changes in oscillatory rhythms using STDP, and show that scaling is necessary to balance both positive and negative changes in input from potentiation and atrophy. We discuss some of the issues that arise when considering synaptic scaling in such a model, and show that scaling regulates activity whilst allowing learning to remain unaltered." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/147141", + "other_implementer": { + "attr_id": 308, + "value": "Rowan, Mark [m.s.rowan at cs.bham.ac.uk]" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2017-02-15T13:58:35.047", + "ver_number": 13 + }, + "147172": { + "class_id": 19, + "created": "2013-01-14T14:46:38", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + 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deficiencies through a set of well designed python APIs and provides an improved UI, allowing users to explore and interact with the model. \r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/147185", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2014-05-14T12:10:07", + "ver_number": 4 + }, + "147218": { + "citation": { + "attr_id": 391, + "value": "20643060" + }, + "class_id": 19, + "created": "2013-01-20T13:58:01", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 140826, + "object_name": "I Ca,p" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 147218, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 147219, + "object_name": "Genet, St\u00e9phane [stephane.genet@upmc.fr]" + } + ] + }, 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The aim of this model is\r\nto explore how the different dynamical patterns in each each of these\r\nfield models affects the activity in the network model." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/147366", + "public_submitter_email": { + "attr_id": 309, + "value": "cliffk@neurosim.downstate.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2017-07-03T22:27:58.417", + "ver_number": 15 + }, + "147367": { + "class_id": 19, + "created": "2013-01-22T06:43:04", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 147367, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 149863, + "object_name": "Hituri, Katri [katri.hituri at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 150554, + "object_name": "Calcium waves" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 149753, + "object_name": "Hituri K, Linne ML (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 144556, + "object_name": "STEPS" + } + ] + }, + "name": "IP3R model comparison (Hituri and Linne 2013)", + "notes": { + "attr_id": 24, + "value": "In this study, four models of IP3R (Othmer and Tang, 1993; Dawson et al., 2003; Fraiman and Dawson, 2004; Doi et al., 2005) were selected among many to examine their behavior and compare them with experimental data available in literature.\r\n\r\nThe provided MATLAB script (run_IP3R_P0.m) will run the simulations and plot Figure 2A in \r\nthe paper.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/147367", + "other_implementer": { + "attr_id": 308, + "value": "Hituri, Katri [katri.hituri at gmail.com]" + }, + "other_receptors": { + "attr_id": 302, + "value": "IP3R" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 153715, + "object_name": "IP3" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115962, + "object_name": "Unknown" + } + ] + }, + "ver_date": "2017-02-15T13:58:17.273", + "ver_number": 21 + }, + "147460": { + "class_id": 19, + "created": "2013-01-26T17:17:26", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 88215, + "object_name": "I_Ks" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 147460, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 147462, + "object_name": "Overstreet, Cynthia [cynthiakoverstreet at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 147753, + "object_name": "Intracortical Microstimulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 147750, + "object_name": "Overstreet CK, Klein JD, Helms Tillery SI (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Direct recruitment of S1 pyramidal cells and interneurons via ICMS (Overstreet et al., 2013)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 167345, + "object_name": "Neocortex U1 L6 pyramidal corticalthalamic GLU cell" + }, + { + "object_id": 167344, + "object_name": "Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell" + }, + { + "object_id": 93384, + "object_name": "Neocortex bitufted interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Study of the pyramidal cells and interneurons recruited by intracortical microstimulation in primary somatosensory cortex. Code includes morphological models for seven types of pyramidal cells and eight types of interneurons, NEURON code to simulate ICMS, and an artificial reconstruction of a 3D slab of cortex implemented in MATLAB." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/147460", + "other_concept": { + "attr_id": 306, + "value": "Intracortical Microstim." + }, + "other_implementer": { + "attr_id": 308, + "value": "Overstreet, Cynthia\r\n" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "cynthiakoverstreet@gmail.com" + }, + "ver_date": "2017-07-03T22:37:04.197", + "ver_number": 14 + }, + "147461": { + "class_id": 19, + "created": "2013-01-28T10:18:41", + "gitrepo": false, + "id": 147461, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 82787, + "object_name": "Working memory" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 148590, + "object_name": "Conde-Sousa E, Aguiar P (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Storing serial order in intrinsic excitability: a working memory model (Conde-Sousa & Aguiar 2013)", + "notes": { + "attr_id": 24, + "value": "\" \u2026 Here we present a model for working\r\nmemory which relies on the modulation of the intrinsic excitability properties of neurons, instead of synaptic plasticity, to retain novel information for periods of seconds to minutes. \r\n\r\nWe show that it is possible to effectively use this mechanism to store the serial order in a sequence of patterns of activity.\r\n\u2026\r\nThe presented model exhibits properties which\r\nare in close agreement with experimental results in working\r\nmemory. ...\r\n\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/147461", + "public_submitter_email": { + "attr_id": 309, + "value": "pauloaguiar@fc.up.pt" + }, + "ver_date": "2013-04-02T11:21:47", + "ver_number": 15 + }, + "147487": { + "class_id": 19, + "created": "2013-01-30T11:27:45", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 147487, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 149697, + "object_name": "Camu\u00f1as-Mesa, Luis Alejandro [lacm1 at le.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 84598, + "object_name": "Extracellular Fields" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 149421, + "object_name": "Camu\u00f1as-Mesa LA, Quiroga RQ (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "A detailed and fast model of extracellular recordings (Camunas-Mesa & Qurioga 2013)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 52425, + "object_name": "Myelinated neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"We present a novel method to generate realistic simulations of extracellular recordings. The simulations were obtained by superimposing the activity of neurons placed randomly in a cube of brain tissue. Detailed models of individual neurons were used to reproduce the extracellular action potentials of close-by neurons. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/147487", + "other_implementer": { + "attr_id": 308, + "value": "Camu\u00f1as-Mesa, Luis Alejandro [lacm1@le.ac.uk]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "lacm1@leicester.ac.uk" + }, + "ver_date": "2015-07-26T13:07:24.29", + "ver_number": 15 + }, + "147514": { + "citation": { + "attr_id": 391, + "value": "22696545" + }, + "class_id": 19, + "created": "2013-01-31T14:03:22", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 146507, + "object_name": "Ca pump" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 147514, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 147515, + "object_name": "van der Velden, Luuk [l.j.j.vandervelden at uva.nl]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144533, + "object_name": "van der Velden L, van Hooft JA, Chameau P (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Altered complexity in layer 2/3 pyramidal neurons (Luuk van der Velden et al. 2012)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 239, + "object_name": "Serotonin" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Our experimental results show that hypercomplexity of the apical dendritic tuft of layer 2/3 pyramidal neurons affects neuronal excitability by reducing the amount of spike frequency adaptation. \r\n\r\nThis difference in firing pattern, related to a higher dendritic complexity, was accompanied by an altered development of the afterhyperpolarization slope with successive action potentials. \r\n\r\nOur abstract and realistic neuronal models, which allowed manipulation of the dendritic complexity, showed similar effects on neuronal excitability and confirmed the impact of apical dendritic complexity.\r\n\r\nAlterations of dendritic complexity, as observed in several pathological conditions such as neurodegenerative diseases or neurodevelopmental disorders, may thus not only affect the input to layer 2/3 pyramidal neurons but also shape their firing pattern and consequently alter the information processing in the cortex.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/147514", + "other_implementer": { + "attr_id": 308, + "value": "van der Velden, Luuk [l.j.j.vandervelden at uva.nl]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "l.j.j.vandervelden@uva.nl" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 208, + "object_name": "5-HT3" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181514, + "object_name": "147514" + } + ] + }, + "ver_date": "2014-01-20T15:53:47", + "ver_number": 6 + }, + "147538": { + "class_id": 19, + "created": "2013-02-01T10:56:21", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 147538, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 147758, + "object_name": "Narayanan, Rishikesh [rishi at iisc.ac.in]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 147540, + "object_name": "Narayanan R, Johnston D (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "BCM-like synaptic plasticity with conductance-based models (Narayanan Johnston, 2010)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ...\r\nAlthough the BCM-like plasticity framework\r\nhas been a useful formulation to understand synaptic plasticity\r\nand metaplasticity, a mechanism for the activity-dependent regulation\r\nof this modification threshold has remained an open question. In this\r\nsimulation study based on CA1 pyramidal cells, we use a modification\r\nof the calcium-dependent hypothesis proposed elsewhere and show\r\nthat a change in the hyperpolarization-activated, nonspecific-cation h\r\ncurrent is capable of shifting the modification threshold. \r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/147538", + "other_implementer": { + "attr_id": 308, + "value": "Narayanan, Rishikesh" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181446, + "object_name": "147538_1" + }, + { + "object_id": 181859, + "object_name": "147538_2" + } + ] + }, + "ver_date": "2015-01-02T21:55:42", + "ver_number": 12 + }, + "147539": { + "class_id": 19, + "created": "2013-02-01T14:38:06", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 147539, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 147758, + "object_name": "Narayanan, Rishikesh [rishi at iisc.ac.in]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 143636, + "object_name": "Conductance distributions" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 111869, + "object_name": "Narayanan R, Johnston D (2007)" + }, + { + "object_id": 115650, + "object_name": "Narayanan R, Johnston D (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "147539_1", + "name": "Membrane voltage sensitivity" + }, + { + "id": "147539_2", + "name": "Rm sensitivity" + }, + { + "id": "147539_3", + "name": "Cm sensitivity" + }, + { + "id": "147539_4", + "name": "h conductance sensitivity" + }, + { + "id": "147539_5", + "name": "h activation vhalf sensitivity" + }, + { + "id": "147539_6", + "name": "h activation time constant sensitivity" + } + ], + "default": "147539_1" + }, + "name": "Resonance properties through Chirp stimulus responses (Narayanan Johnston 2007, 2008)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "...we constructed a simple, single-compartment\r\nmodel with Ih as the only active current... we found that both resonance frequency and resonance strength increased monotonically with the increase in the h conductance, supporting the notion of a direct, graded relationship between h conductance and resonance properties... (Narayanan and Johnston, 2007). ...we show that the h channels introduce an apparent negative delay in the local voltage response of these neurons with respect to the injected current within the theta frequency range... we found that the total inductive phase increased monotonically with the h conductance, whereas it had a bell-shaped dependence on both the membrane voltage and the half-maximal activation voltage for the h conductance. (Narayanan and Johnston, 2008)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/147539", + "other_concept": { + "attr_id": 306, + "value": "Membrane Resonance" + }, + "other_implementer": { + "attr_id": 308, + "value": "Rishikesh Narayanan" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2015-11-23T10:46:41.13", + "ver_number": 20 + }, + "147578": { + "class_id": 19, + "created": "2013-02-02T07:27:33", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 147578, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 147758, + "object_name": "Narayanan, Rishikesh [rishi at iisc.ac.in]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 111869, + "object_name": "Narayanan R, Johnston D (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "147578_1", + "name": "Compute Input Resistances Along Trunk" + }, + { + "id": "147578_2", + "name": "Save Local Chirp Responses for Locations Along Trunk" + } + ], + "default": "147578_1" + }, + "name": "Chirp stimulus responses in a morphologically realistic model (Narayanan and Johnston, 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "...we built a multicompartmental model with a morphologically realistic three-dimensional reconstruction of a CA1 pyramidal neuron. The only active conductance we added to the model was the h conductance. ... We conclude that experimentally observed gradient in density of h channels could theoretically account for experimentally observed gradient in resonance properties (Narayanan and Johnston, 2007)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/147578", + "other_concept": { + "attr_id": 306, + "value": "Membrane resonance" + }, + "other_implementer": { + "attr_id": 308, + "value": "Rishikesh Narayanan" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2016-03-31T10:18:45.223", + "ver_number": 11 + }, + "147740": { + "citation": { + "attr_id": 391, + "value": "Victor P. Andreev. Cytoplasmic electric fields and electroosmosis: possible solution for the paradoxes of the intracellular transport of biomolecules. PLoS One, submitted." + }, + "class_id": 19, + "created": "2013-02-07T16:02:31", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 147740, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 147742, + "object_name": "Andreev, Victor [vandreev at med.miami.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 147743, + "object_name": "Andreev VP (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 147741, + "object_name": "COMSOL (web link to model)" + } + ] + }, + "name": "Cytoplasmic electric fields and electroosmosis (Andreev 2013)", + "notes": { + "attr_id": 24, + "value": "The paper presents two mathematical models describing the role of electroosmosis in the transport of the negatively charged messenger proteins to the negatively charged nucleus and in the recovery of the fluorescence after photobleaching. The parameters of the models were derived from the extensive review of the literature data. Computer simulations were performed within the COMSOL 4.2a software environment. The first model demonstrated that the presence of electroosmosis might intensify the flux of messenger proteins to the nucleus and allow the efficient transport of the negatively charged phosphorylated messenger proteins against the electrostatic repulsion of the negatively charged nucleus. The second model revealed that the presence of the electroosmotic flow made the time of fluorescence recovery dependent on the position of the bleaching spot relative to cellular membrane." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/147740", + "other_implementer": { + "attr_id": 308, + "value": "Andreev, Victor [vandreev@med.miami.edu]" + }, + "other_type": { + "attr_id": 305, + "value": "any polarized cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "VAndreev@med.miami.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115960, + "object_name": "Generic" + } + ] + }, + "ver_date": "2013-04-24T17:09:56", + "ver_number": 6 + }, + "147748": { + "biosimulations": { + "value": "modeldb-147748" + }, + "citation": { + "attr_id": 391, + "value": "PMID: 19557415" + }, + "class_id": 19, + "created": "2013-02-08T17:57:31", + "gitrepo": false, + "id": 147748, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 147755, + "object_name": "Rempe, Michael [mrempe at whitworth.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 93420, + "object_name": "Sleep" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 147252, + "object_name": "Rempe MJ, Best J, Terman D (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Human sleep/wake cycle (Rempe et al. 2010)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model simulates sleep in the human brain and is consistent with both the flip/flop concept and the two-process model of sleep regulation. The model also gives a possible mechanism for the changes in sleep timing seen in narcolepsy." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/147748", + "other_implementer": { + "attr_id": 308, + "value": "Rempe, Michael [mrempe at whitworth.edu]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "mrempe@whitworth.edu" + }, + "ver_date": "2013-02-11T14:28:18", + "ver_number": 3 + }, + "147756": { + "class_id": 19, + "created": "2013-02-11T08:56:16", + "gitrepo": false, + "id": 147756, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 147758, + "object_name": "Narayanan, Rishikesh [rishi at iisc.ac.in]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 74230, + "object_name": "Narayanan R, Narayan A, Chattarji S (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Region-specific atrophy in dendrites (Narayanan, Narayan, Chattarji, 2005)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "...in this study, we develop an algorithm that uses statistics from precise morphometric analyses to systematically remodel neuronal reconstructions. We use the distribution function of the ratio of two normal distributed random variables to specify the probabilities of remodeling along various regions of the dendritic arborization. We then use these probabilities to drive an iterative algorithm for manipulating the dendritic tree in a region-specific manner. As a test, we apply this framework to a well characterized example of dendritic remodeling: stress-induced dendritic atrophy in hippocampal CA3 pyramidal cells. We show that our pruning algorithm is capable of eliciting atrophy that matches biological data from rodent models of chronic stress.\r\n
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The model predics how this alteration can affect synaptic plasticity and signal integration. 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Genotype\u2013phenotype correlations in neonatal epilepsies caused by mutations in the voltage sensor of Kv7.2 potassium channel subunits, PNAS 2013 Feb 25. [Epub ahead of print]\r\n\r\nIn this paper, functional studies revealed that in homomeric or heteromeric configuration with KV7.2 and/or KV7.3 subunits, R213W and \r\nR213Q mutations markedly destabilized the open state, causing a dramatic decrease in channel voltage sensitivity.\r\nModeling these channels in CA1 hippocampal pyramidal cells revealed that both mutations increased cell firing frequency,\r\nwith the R213Q mutation prompting more dramatic functional changes compared with the R213W mutation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/148094", + "other_gene": { + "attr_id": 477, + "value": "Kv7.2" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2015-05-26T17:36:11.953", + "ver_number": 9 + }, + "148253": { + "citation": { + "attr_id": 391, + "value": "20575006" + }, + "class_id": 19, + "created": "2013-03-08T07:24:56", + "currents": { + 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depolarization. \r\n\r\nA potential mechanism underlying the depolarization is an acute chloride (Cl(-)) accumulation resulting in a shift of the GABA(A) reversal potential (E(GABA)).\r\n\r\nThe amount of GABA-evoked Cl(-) accumulation and accompanying depolarization depends on presynaptic and postsynaptic properties of GABAergic transmission, as well as on cellular morphology and regulation of Cl(-) intracellular concentration ([Cl(-)](i)).\r\n\r\nTo analyze the influence of these factors on the Cl(-) and voltage behavior, we studied spatiotemporal dynamics of activity-dependent [Cl(-)](i) changes in multicompartmental models of hippocampal cells based on realistic morphological data.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/148253", + "other_currents": { + "attr_id": 303, + "value": "I Bicarbonate" + }, + "other_implementer": { + "attr_id": 308, + "value": "Jedlicka, Peter Mohapatra, Namrata " + }, + 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"object_name": "Therapeutics" + }, + { + "object_id": 137847, + "object_name": "Phase Response Curves" + }, + { + "object_id": 145886, + "object_name": "Circadian Rhythms" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 148321, + "object_name": "Kim JK, Forger DB, Marconi M, Wood D, Doran A, Wager T, Chang C, Walton KM (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 143722, + "object_name": "Mathematica" + } + ] + }, + "name": "Circadian clock model in mammals (PK/PD model) (Kim & Forger 2013)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 145884, + "object_name": "Suprachiasmatic nucleus (SCN) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A systems pharmacology model of the mammalian circadian clock including PF-670462 (CK1d/e inhibitor)." + 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"value": "\"\u2026 Recently, a lot of interest has been devoted to desynchronizing delayed feedback deep brain stimulation (DBS).\r\n...\r\nThis study explores the action of delayed feedback stimulation on partially synchronized oscillatory dynamics, similar to what one observes experimentally in parkinsonian patients.\r\n\u2026\"\r\n\r\nImplemented by Andrey Dovzhenok, to whom questions should be addressed." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/148637", + "other_implementer": { + "attr_id": 308, + "value": "Dovzhenok, Andrey" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "andrey.dovzhenok@ucmail.uc.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2013-03-29T14:47:49", + "ver_number": 10 + }, + "148644": { + "class_id": 19, + "created": "2013-04-01T17:34:12", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 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We show that, without any fitting or tuning procedures, it is\r\npossible to map any morphologically and biophysically accurate neuron model\r\ninto an equivalent reduced version. Using this method for Purkinje cells, we\r\ndemonstrate how run times can be reduced up to 200-fold, while accurately taking into account the effects of arbitrarily located and activated\r\nsynaptic inputs.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/149000", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + }, + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2022-05-16T16:05:22.053", + "ver_number": 18 + }, + "149100": { + "class_id": 19, + "created": "2013-04-17T05:19:42", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 149100, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 140733, + "object_name": "Hay, Etay [etay.hay at mail.huji.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 116962, + "object_name": "Parameter sensitivity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 150228, + "object_name": "Hay E, Sch\u00fcrmann F, Markram H, Segev I (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "149100", + "name": "ModelView" + } + ], + "default": "149100" + }, + "name": "Preserving axosomatic spiking features despite diverse dendritic morphology (Hay et al., 2013)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The authors found that linearly scaling the ion channel conductance densities of a reference model with the conductance load in 28 3D reconstructed layer 5 thick-tufted pyramidal cells was necessary to match the experimental statistics of these cells electrical firing properties." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/149100", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2017-02-15T13:57:56.427", + "ver_number": 17 + }, + "149162": { + "class_id": 19, + "created": "2013-04-18T16:58:11", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 149162, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 149163, + "object_name": "Zhang, Yili [Yili.Zhang at uth.tmc.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 149101, + "object_name": "Liu RY, Zhang Y, Baxter DA, Smolen P, Cleary LJ, Byrne JH (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Rescue of plasticity by a computationally predicted protocol (Liu et al. 2013)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52413, + "object_name": "Aplysia sensory neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... A computational model,\r\nwhich simulated molecular processes underlying long-term\r\nsynaptic facilitation (LTF) induction, predicted a rescue protocol of five pulses of 5-HT at non-uniform interstimulus\r\nintervals that overcame the consequences of reduced CREB-binding protein (CBP) and restored LTF. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/149162", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115956, + "object_name": "Aplysia" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 249921, + "object_name": "Aplysia" + } + ] + }, + "ver_date": "2019-02-26T10:08:39.3", + "ver_number": 8 + }, + "149174": { + "citation": { + "attr_id": 391, + "value": "23566163" + }, + "class_id": 19, + "created": "2013-04-21T02:08:31", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 149174, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 123851, + "object_name": "Thomas, Evan [evan at evan-thomas.net]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 149383, + "object_name": "Drug binding" + }, + { + "object_id": 238957, + "object_name": "Markov-type model" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 149381, + "object_name": "Thomas EA, Petrou S (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "State dependent drug binding to sodium channels in the dentate gyrus (Thomas & Petrou 2013)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + }, + { + "object_id": 52416, + "object_name": "Dentate gyrus mossy cell" + }, + { + "object_id": 52417, + "object_name": "Dentate gyrus basket cell" + }, + { + "object_id": 88216, + "object_name": "Dentate gyrus hilar cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A Markov model of sodium channels was developed that includes drug binding to fast inactivated states. This was incorporated into a model of the dentate gyrus to investigate the effects of anti-epileptic drugs on neuron and network properties." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/149174", + "other_concept": { + "attr_id": 306, + "value": "drug binding" + }, + "other_implementer": { + "attr_id": 308, + "value": "Thomas, Evan " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "evan@evan-thomas.net" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2018-02-26T11:42:00.963", + "ver_number": 7 + }, + "149175": { + "citation": { + "attr_id": 391, + "value": "Smolen P, Baxter DA, Byrne JH (2006). A model of the roles of essential kinases in the induction and expression of late long-term potentiation. Biophys. 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The model simulates tetanic, -burst, pairing-induced, and chemical L-LTP, as well as L-LTP due to synaptic tagging. The model also simulates inhibition of L-LTP by inhibition of MAPK, CAMKII, PKA, or CAMKIV. 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Biophysical models at both physical scales have been formalized\r\nunder assumptions that impede the creation of such links. In this\r\nwork, we address this issue by proposing amulticompartment model that\r\nallows the introduction of complex extracellular and intracellular resistivity\r\nprofiles. This model accounts for the geometrical and electrotonic\r\nproperties of any type of neuron through the combination of four devices:\r\nthe integrator, the propagator, the 3D connector, and the collector. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/149737", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2013-05-02T13:57:14", + "ver_number": 1 + }, + "149739": { + "class_id": 19, + "created": "2013-05-03T16:23:41", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 88215, + "object_name": "I_Ks" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 140826, + "object_name": "I Ca,p" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 149739, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 149751, + "object_name": "Li, Guoshi [guoshi_li at med.unc.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 182362, + "object_name": "Sensory coding" + }, + { + "object_id": 222956, + "object_name": "Neuromodulation" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 149750, + "object_name": "Li G, Cleland TA (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A two-layer biophysical olfactory bulb model of cholinergic neuromodulation (Li and Cleland 2013)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + }, + { + "object_id": 3978, + "object_name": "Olfactory bulb main interneuron periglomerular GABA cell" + }, + { + "object_id": 3979, + "object_name": "Olfactory bulb main interneuron granule MC GABA cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a two-layer biophysical olfactory bulb (OB) network model to study cholinergic neuromodulation. Simulations show that nicotinic receptor activation sharpens mitral cell receptive field, while muscarinic receptor activation enhances network synchrony and gamma oscillations. This general model suggests that the roles of nicotinic and muscarinic receptors in OB are both distinct and complementary to one another, together regulating the effects of ascending cholinergic inputs on olfactory bulb transformations." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/149739", + "public_submitter_email": { + "attr_id": 309, + "value": "gl275@cornell.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 178, + "object_name": "Nicotinic" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 204, + "object_name": "Muscarinic" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2022-05-27T18:28:00.443", + "ver_number": 9 + }, + "149910": { + "citation": { + "attr_id": 391, + "value": "PMID: 23473315" + }, + "class_id": 19, + "created": "2013-05-07T11:45:03", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 149910, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 149911, + "object_name": "Gutierrez, GJ [gg99 at brandeis.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 149418, + "object_name": "Gutierrez GJ, O'Leary T, Marder E (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Competing oscillator 5-cell circuit and Parameterscape plotting (Gutierrez et al. 2013)", + "notes": { + "attr_id": 24, + "value": "Our 5-cell model consists of competing fast and slow oscillators connected to a hub neuron with electrical and inhibitory synapses. Motivated by the Stomatogastric Ganglion (STG) circuit in the crab, we explored the patterns of coordination in the network as a function of the electrical coupling and inhibitory synapse strengths with the help of a novel visualization method that we call the \"Parameterscape.\" The code submitted here will allow you to run circuit simulations and to produce a Parameterscape with the results." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/149910", + "other_currents": { + "attr_id": 303, + "value": "I leak" + }, + "other_implementer": { + "attr_id": 308, + "value": "Gutierrez, GJ " + }, + "other_neurons": { + "attr_id": 301, + "value": "Morris-Lecar" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "gg99@brandeis.edu" + }, + "ver_date": "2013-05-10T20:16:00", + "ver_number": 12 + }, + "149913": { + "citation": { + "attr_id": 391, + "value": "@ARTICLE{10.3389/fncom.2013.00064, AUTHOR={Garrido, Jesus A and Ros, Eduardo and D\u2018Angelo, Egidio}, TITLE={Spike timing regulation on the millisecond scale by distributed synaptic plasticity at the cerebellum input stage: a simulation study}, JOURNAL={Frontiers in Computational Neuroscience}, VOLUME={7}, YEAR={2013}, NUMBER={64}, URL={http://www.frontiersin.org/computational_neuroscience/10.3389/fncom.2013.00064/abstract}, DOI={10.3389/fncom.2013.00064}, ISSN={1662-5188}, ABSTRACT={ The way long-term synaptic plasticity regulates neuronal spike patterns is not completely understood. This issue is especially relevant for the cerebellum, which is endowed with several forms of long-term synaptic plasticity and has been predicted to operate as a timing and a learning machine. Here we have used a computational model to simulate the impact of multiple distributed synaptic weights in the cerebellar granular layer network. In response to mossy fiber bursts, synaptic weights at multiple connections played a crucial role to regulate spike number and positioning in granule cells. The weight at mossy fiber to granule cell synapses regulated the delay of the first spike and the weight at mossy fiber and parallel fiber to Golgi cell synapses regulated the duration of the time-window during which the first-spike could be emitted. Moreover, the weights of synapses controlling Golgi cell activation regulated the intensity of granule cell inhibition and therefore the number of spikes that could be emitted. First spike timing was regulated with millisecond precision and the number of spikes ranged from 0 to 3. Interestingly, different combinations of synaptic weights optimized either first-spike timing precision or spike number, efficiently controlling transmission and filtering properties. These results predict that distributed synaptic plasticity regulates the emission of quasi-digital spike patterns on the millisecond time scale and allows the cerebellar granular layer to flexibly control burst transmission along the mossy fiber pathway.}}" + }, + "class_id": 19, + "created": "2013-05-09T07:16:31", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 149913, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 150204, + "object_name": "Garrido, Jesus A [jesus.garrido at unipv.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 150129, + "object_name": "Garrido JA, Ros E, D'Angelo E (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 150203, + "object_name": "EDLUT" + } + ] + }, + "name": "Distributed synaptic plasticity and spike timing (Garrido et al. 2013)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 7346, + "object_name": "Cerebellum interneuron granule GLU cell" + }, + { + "object_id": 112687, + "object_name": "Cerebellum golgi cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Here we have used a computational model to simulate the impact of multiple distributed synaptic weights in the cerebellar granular layer network. In response to mossy fiber bursts, synaptic weights at multiple connections played a crucial role to regulate spike number and positioning in granule cells. Interestingly, different combinations of synaptic weights optimized either first-spike timing precision or spike number, efficiently controlling transmission and filtering properties. These results predict that distributed synaptic plasticity regulates the emission of quasi-digital spike patterns on the millisecond time scale and allows the cerebellar granular layer to flexibly control burst transmission along the mossy fiber pathway." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/149913", + "other_concept": { + "attr_id": 306, + "value": "Spike timing regulation" + }, + "other_implementer": { + "attr_id": 308, + "value": "Garrido, Jesus A." + }, + "other_simulator": { + "attr_id": 307, + "value": "EDLUT" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jesus.garrido@unipv.it" + }, + "ver_date": "2013-05-28T15:20:54", + "ver_number": 10 + }, + "149914": { + "citation": { + "attr_id": 391, + "value": "Probabilistic Inference of Short-Term Synaptic Plasticity in Neocortical Microcircuits, Frontiers in Computational Neuroscience (under review)" + }, + "class_id": 19, + "created": "2013-05-09T11:18:26", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 149914, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 149959, + "object_name": "Costa, Rui Ponte [ruipontecosta at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 150082, + "object_name": "Costa RP, Sj\u00f6str\u00f6m PJ, van Rossum MC (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Prob. Inference of Short-Term Synaptic Plasticity in Neocort. Microcircuits (Costa et al. 2013)", + "notes": { + "attr_id": 24, + "value": "\" ... As a solution (for Short Term Plasticity (STP) inference), we introduce a Bayesian formulation, which yields the posterior distribution over the model parameters given the data. First, we show that common STP protocols yield broad distributions over some model parameters. Using our result we propose a experimental protocol to more accurately determine synaptic dynamics parameters. Next, we infer the model parameters using experimental data from three different neocortical excitatory connection types. This reveals connection-specific distributions, which we use to classify synaptic dynamics. Our approach to demarcate connection-specific synaptic dynamics is an important improvement on the state of the art and reveals novel features from existing data.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/149914", + "other_implementer": { + "attr_id": 308, + "value": "Costa, Rui P. 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Being\r\nconstitutively active, the K+ current (IKV1) mediated by these channels stabilizes the rate and\r\nregulates the temporal precision of self-sustained firing of these neurons. \r\n...\r\nThrough the use of multi-compartmental modelling and ... the physiological significance of the described functions for processing\r\nand communication of information from the lateral DCN to thalamic relay nuclei is established." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150024", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181154, + "object_name": "150024_1" + }, + { + "object_id": 181177, + "object_name": "150024_2" + }, + { + "object_id": 181199, + "object_name": "150024_4" + }, + { + "object_id": 181624, + "object_name": "150024_3" + } + ] + }, + "ver_date": "2022-11-04T13:26:38.643", + "ver_number": 9 + }, + "150031": { + "class_id": 19, + "created": "2013-05-19T12:31:37", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 150031, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 150034, + "object_name": "Solanka, Lukas [l.solanka at sms.ed.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 83539, + "object_name": "Attractor Neural Network" + }, + { + "object_id": 194888, + "object_name": "Grid cell" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 150033, + "object_name": "Pastoll H, Solanka L, van Rossum MC, Nolan MF (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "An attractor network model of grid cells and theta-nested gamma oscillations (Pastoll et al 2013)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 129068, + "object_name": "Entorhinal cortex stellate cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A two population spiking continuous attractor model of grid cells. This model combines the attractor dynamics with theta-nested gamma oscillatory activity. It reproduces the behavioural response of grid cells (grid fields) in medial entorhinal cortex, while at the same time allowing for nested gamma oscillations of post-synaptic currents." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150031", + "other_implementer": { + "attr_id": 308, + "value": "Solanka, Lukas " + }, + "other_neurons": { + "attr_id": 301, + "value": "Fast spiking interneuron" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 119191, + "object_name": "Entorhinal cortex" + } + ] + }, + "ver_date": "2020-03-23T13:11:21.183", + "ver_number": 20 + }, + "150067": { + "class_id": 19, + "created": "2013-05-27T05:37:41", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 150067, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 150204, + "object_name": "Garrido, Jesus A [jesus.garrido at unipv.it]" + }, + { + "object_id": 150226, + "object_name": "Luque, Niceto R. 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The robot used a three-joint arm and performed repetitive fast manipulations with different masses along an 8-shape trajectory. In accordance with biological evidence, the cerebellum model was endowed with both LTD and LTP at the PF-PC, MF-DCN and PC-DCN synapses. This resulted in a network scheme whose effectiveness was extended considerably compared to one including just PF-PC synaptic plasticity. Indeed, the system including distributed plasticity reliably self-adapted to manipulate different masses and to learn the arm-object dynamics over a time course that included fast learning and consolidation, along the lines of what has been observed in behavioral tests. In particular, PF-PC plasticity operated as a time correlator between the actual input state and the system error, while MF-DCN and PC-DCN plasticity played a key role in generating the gain controller. This model suggests that distributed synaptic plasticity allows generation of the complex learning properties of the cerebellum." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150067", + "other_implementer": { + "attr_id": 308, + "value": "Garrido, Jesus A." + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jesus.garrido@unipv.it" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2017-11-03T10:57:59.717", + "ver_number": 15 + }, + "150206": { + "class_id": 19, + "created": "2013-05-29T13:51:22", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 150206, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 124115, + "object_name": "Girard, Benoit [girard at isir.upmc.fr]" + }, + { + "object_id": 185886, + "object_name": "Li\u00e9nard, Jean [lienard at isir.upmc.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 83540, + "object_name": "Winner-take-all" + }, + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 185885, + "object_name": "Li\u00e9nard J, Girard B (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Biologically Constrained Basal Ganglia model (BCBG model) (Lienard, Girard 2014)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 260, + "object_name": "Neostriatum medium spiny direct pathway GABA cell" + }, + { + "object_id": 153360, + "object_name": "Neostriatum medium spiny indirect pathway GABA cell" + }, + { + "object_id": 167343, + "object_name": "Neocortex U1 L5B pyramidal pyramidal tract GLU cell" + }, + { + "object_id": 167344, + "object_name": "Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell" + }, + { + "object_id": 182544, + "object_name": "Substantia nigra pars reticulata principal GABA cell" + }, + { + "object_id": 74300, + "object_name": "Subthalamus nucleus projection neuron" + }, + { + "object_id": 88217, + "object_name": "Globus pallidus neuron" + }, + { + "object_id": 118395, + "object_name": "Neostriatum fast spiking interneuron" + }, + { + "object_id": 150619, + "object_name": "Neostriatum spiny neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We studied the physiology and function of the basal ganglia through the design of mean-field models of the whole basal ganglia. The parameterizations are optimized with multi-objective evolutionary algorithm to respect best a collection of numerous anatomical data and electrophysiological data. The main outcomes of our study are: \u2022 The strength of the GPe to GPi/SNr connection does not support opposed activities in the GPe and GPi/SNr. \u2022 STN and MSN target more the GPe than the GPi/SNr. \u2022 Selection arises from the structure of the basal ganglia, without properly segregated direct and indirect pathways and without specific inputs from pyramidal tract neurons of the cortex. Selection is enhanced when the projection from GPe to GPi/SNr has a diffuse pattern." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150206", + "other_implementer": { + "attr_id": 308, + "value": "Li\u00e9nard, Jean " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "lienard@isir.upmc.fr" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2015-12-02T11:04:24.18", + "ver_number": 10 + }, + "150207": { + "class_id": 19, + "created": "2013-05-30T05:51:56", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 150207, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 150208, + "object_name": "Montes, Jesus [jmontes at cesvima.upm.es]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 151730, + "object_name": "Montes J, Gomez E, Merch\u00e1n-P\u00e9rez A, Defelipe J, Pe\u00f1a JM (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53895, + "object_name": "Java" + }, + { + "object_id": 150209, + "object_name": "R" + } + ] + }, + "name": "A Method for Prediction of Receptor Activation in the Simulation of Synapses (Montes et al. 2013)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the activation of synaptic receptors, at very low computational cost. The method is designed to learn patterns and general principles from previous Monte Carlo simulations and to predict synapse behavior from them. The resulting procedure is accurate, automatic and can predict synapse behavior under experimental conditions that are different to the ones used during the learning phase. Since our method efficiently reduces the computational costs, it is suitable for the simulation of the vast number of synapses that occur in the mammalian brain." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150207", + "other_implementer": { + "attr_id": 308, + "value": "Montes, Jesus " + }, + "other_simulator": { + "attr_id": 307, + "value": "R" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jmontes@cesvima.upm.es" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2013-12-19T23:29:32", + "ver_number": 12 + }, + "150211": { + "class_id": 19, + "created": "2013-05-30T18:34:20", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 150211, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 97968, + "object_name": "Giugliano, Michele [mgiugliano at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 114660, + "object_name": "Connectivity matrix" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 169257, + "object_name": "Vasilaki E, Giugliano M (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Emergence of Connectivity Motifs in Networks of Model Neurons (Vasilaki, Giugliano 2014)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Recent evidence suggests that short-term dynamics of excitatory synaptic transmission is correlated to stereotypical connectivity motifs. \r\nWe show that these connectivity motifs emerge in networks of model neurons, from the interactions between short-term synaptic dynamics (SD) and long-term spike-timing dependent plasticity (STDP)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150211", + "other_implementer": { + "attr_id": 308, + "value": "Vasilaki, Eleni" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 115950, + "object_name": "Olfactory bulb" + }, + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "ver_date": "2018-10-17T16:13:07.497", + "ver_number": 9 + }, + "150217": { + "citation": { + "attr_id": 391, + "value": "PMID: 23708878" + }, + "class_id": 19, + "created": "2013-06-05T09:16:53", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 150217, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 150218, + "object_name": "Zeldenrust, Fleur [fleurzeldenrust at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 150219, + "object_name": "Reliability" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 150221, + "object_name": "Zeldenrust F, Chameau PJ, Wadman WJ (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Reliability of Morris-Lecar neurons with added T, h, and AHP currents (Zeldenrust et al. 2013)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 150222, + "object_name": "Abstract Morris-Lecar neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We investigated the reliability of the timing of spikes in a spike train in a Morris-Lecar model with several extensions. A frozen Gaussian noise current, superimposed on a DC current, was injected. The neuron responded with spike trains that showed trial-to-trial variability. The reliability depends on the shape (steepness) of the current input versus spike frequency output curve. The model also allowed to study the contribution of three relevant ionic membrane currents to reliability: a T-type calcium current, a cation selective h-current and a calcium dependent potassium current in order to allow bursting, investigate the consequences of a more complex current-frequency relation and produce realistic firing rates." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150217", + "other_concept": { + "attr_id": 306, + "value": "Reliability" + }, + "other_implementer": { + "attr_id": 308, + "value": "Zeldenrust, Fleur " + }, + "other_neurons": { + "attr_id": 301, + "value": "Morris-Lecar neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "fleurzeldenrust@gmail.com" + }, + "ver_date": "2013-06-05T10:58:58", + "ver_number": 7 + }, + "150225": { + "class_id": 19, + "created": "2013-06-11T11:31:07", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 150225, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 150204, + "object_name": "Garrido, Jesus A [jesus.garrido at unipv.it]" + }, + { + "object_id": 150226, + "object_name": "Luque, Niceto R. 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The classical long-term synaptic plasticity between parallel fibers (PFs) and Purkinje cells (PCs), which is driven by the inferior olive (IO), can only account for limited aspects of learning. Recently, the role of additional forms of plasticity in the granular layer, molecular layer and deep cerebellar nuclei (DCN) has been considered. In particular, learning at DCN synapses allows for generalization, but convergence to a stable state requires hundreds of repetitions. In this paper we have explored the putative role of the IO-DCN connection by endowing it with adaptable weights and exploring its implications in a closed-loop robotic manipulation task. Our results show that IO-DCN plasticity accelerates convergence of learning by up to two orders of magnitude without conflicting with the generalization properties conferred by DCN plasticity. Thus, this model suggests that multiple distributed learning mechanisms provide a key for explaining the complex properties of procedural learning and open up new experimental questions for synaptic plasticity in the cerebellar network." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150225", + "other_implementer": { + "attr_id": 308, + "value": "Luque Niceto R. & Garrido Jesus A." + }, + "other_neurons": { + "attr_id": 301, + "value": "CEREBELLAR MODEL" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "nluque@ugr.es" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2015-07-08T17:22:18.323", + "ver_number": 8 + }, + "150239": { + "class_id": 19, + "created": "2013-06-17T14:20:06", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + 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from the collective resonance of all thalamic relay neurons. \r\nWe show here that the transfer efficiency\r\nof sensory input transmission depends on three key features: i) the number of thalamocortical cells involved in the many-to-one\r\nconvergence from thalamus to cortex, ii) the statistics of the corticothalamic synaptic bombardment and iii) the level of\r\ncorrelation imposed between converging thalamic relay cells. \r\nIn particular, our results demonstrate counterintuitively that\r\nthe retinocortical signal transfer efficiency increases when the level of correlation across thalamic cells decreases. \r\n\u2026\u201d\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150240", + "public_submitter_email": { + "attr_id": 309, + "value": "sbehuret@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + 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Sensory and motor populations included excitatory cells\r\nand two types of interneurons. Neurons were interconnected with AMPA/NMDA, and\r\nGABAA synapses. We trained our model using spike-timing-dependent reinforcement\r\nlearning to control a 2-joint virtual arm to reach to a fixed target. \r\n...\r\n\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150245", + "other_concept": { + "attr_id": 306, + "value": "Motor control" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2023-04-30T15:58:02.81", + "ver_number": 10 + }, + "150284": { + "class_id": 19, + "created": "2013-07-15T15:48:48", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + 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"value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A biophysical single compartment model of the dorsal lateral striatum medium spiny neuron is presented here. The model is an implementation then adaptation of a previously described model (Mahon et al. 2002). The model has been adapted to include NMDA and AMPA receptor models that have been fit to dorsal lateral striatal neurons. The receptor models allow for excitation by other neuron models." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150556", + "other_currents": { + "attr_id": 303, + "value": "I K slow, I K fast" + }, + "other_implementer": { + "attr_id": 308, + "value": "Biddell,Kevin [kevin.biddell@gmail.com]" + }, + "other_neurons": { + "attr_id": 301, + "value": "Dorsal Lateral Striatum Medium Spiny Neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "kevin.biddell@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181175, + "object_name": "150556" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 610 + }, + "ver_date": "2022-05-27T11:01:55.563", + "ver_number": 13 + }, + "150621": { + "citation": { + "attr_id": 391, + "value": "PMID: 11040268" + }, + "class_id": 19, + "created": "2013-08-09T15:42:23", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 88215, + "object_name": "I_Ks" + }, + { + "object_id": 112841, + "object_name": "I Krp" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 150621, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 150557, + "object_name": "Biddell, Kevin [kevin.biddell at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 121080, + "object_name": "Mahon S, Deniau JM, Charpier S, Delord B (2000)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "150621_1", + "name": "Figure 2A" + }, + { + "id": "150621_2", + "name": "Figure 3A" + } + ], + "default": "150621_1" + }, + "name": "Striatal Output Neuron (Mahon, Deniau, Charpier, Delord 2000)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 260, + "object_name": "Neostriatum medium spiny direct pathway GABA cell" + }, + { + "object_id": 146805, + "object_name": "Abstract Wang-Buzsaki neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Striatal output neurons (SONs) integrate glutamatergic synaptic inputs originating from the cerebral cortex. In vivo electrophysiological data have shown that a prior depolarization of SONs induced a short-term (1 sec)increase in their membrane excitability, which facilitated the ability of corticostriatal synaptic potentials to induce firing. Here we propose, using a computational model of SONs, that the use-dependent, short-term increase in the responsiveness of SONs mainly results from the slow kinetics of a voltage-dependent, slowly inactivating potassium A-current. This mechanism confers on SONs a form of intrinsic short-term memory that optimizes the synaptic input\u00e2\u20ac\u201coutput relationship as a function of their past activation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150621", + "public_submitter_email": { + "attr_id": 309, + "value": "kevin.biddell@gmail.com" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181652, + "object_name": "150621_2" + }, + { + "object_id": 181655, + "object_name": "150621_1" + } + ] + }, + "ver_date": "2015-01-02T22:34:35", + "ver_number": 5 + }, + "150622": { + "citation": { + "attr_id": 391, + "value": "Briley PM & Summerfield AQ. Age-related deterioration of the representation of space in human auditory cortex. Neurobiology of Aging." + }, + "class_id": 19, + "created": "2013-08-19T14:34:04", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 150622, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 146106, + "object_name": "Briley, Paul M [paul dot briley at york dot ac dot uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 150623, + "object_name": "Briley PM, Summerfield AQ (2014)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Revised opponent-channel model of auditory space cortical representation (Briley & Summerfield 2013)", + "notes": { + "attr_id": 24, + "value": "This is the computational opponent-channel model used by Briley et al. (2013) to model electroencephalographic (EEG) responses from the auditory cortices of young, younger-old and older-old adults to abrupt shifts in sound-source location, and to predict each groups' psychophysical measures of spatial acuity." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150622", + "public_submitter_email": { + "attr_id": 309, + "value": "paul.briley@york.ac.uk" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 144485, + "object_name": "Auditory cortex" + } + ] + }, + "ver_date": "2018-10-18T17:06:20.397", + "ver_number": 6 + }, + "150635": { + "class_id": 19, + "created": "2013-08-27T11:54:34", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 246, + "object_name": "I p,q" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I 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thoroughly.\r\n\r\nIn this study, a detailed computational model employing novel simulation routines is\r\napplied to identify the roles that stochastic ion channels, spatial arrangements of ion\r\nchannels and stochastic intracellular calcium have towards producing calcium burst\r\nvariability.\r\n\u2026\r\nOur findings suggest that stochastic intracellular calcium\r\nmechanisms play a crucial role in dendritic calcium spike generation and are, therefore, an\r\nessential consideration in studies of neuronal excitability and plasticity.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150635", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2017-06-27T17:13:37.067", + 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(Beeman 2013)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 7387, + "object_name": "Neocortex V1 interneuron basket PV GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... The primary objective of this modeling study was to determine the effects of axonal conduction velocity (often neglected, but significant), as well as synaptic time constants, on the ability of such a network to create and propagate cortical waves. ... The model is also being used to study the interaction between single and two-tone input and normal background activity, and the effects of synaptic depression from thalamic inputs. The simulation scripts have the additional purpose of serving as tutorial examples for the construction of cortical networks with GENESIS. The present model has fostered the development of the G-3 Python network analysis and visualization tools used in this study... It is my hope that this short tutorial and the example\r\nsimulation scripts can provide a head start for a graduate student or\r\npostdoc who is beginning a cortical modeling project.\r\n\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150678", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 144485, + "object_name": "Auditory cortex" + } + ] + }, + "ver_date": "2016-07-07T12:39:42.843", + "ver_number": 5 + }, + "150691": { + "class_id": 19, + "created": "2013-09-19T09:31:27", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 150691, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33736, + "object_name": "Hines, Michael [Michael.Hines at Yale.edu]" + }, + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52407, + "object_name": "Heart disease" + }, + { + "object_id": 143636, + "object_name": "Conductance distributions" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 155458, + "object_name": "Casaleggio A, Hines ML, Migliore M (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Model of arrhythmias in a cardiac cells network (Casaleggio et al. 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88205, + "object_name": "Cardiac ventricular cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Here we explore the possible processes leading to the occasional onset and termination of the (usually) non-fatal arrhythmias widely observed in the heart.\r\n\r\nUsing a computational model of a two-dimensional network of cardiac cells, we tested the hypothesis that an ischemia alters the properties of the gap junctions inside the ischemic area.\r\n...\r\n In conclusion, our model strongly supports the hypothesis that non-fatal arrhythmias can develop from post-ischemic alteration of the electrical connectivity in a relatively small area of the cardiac cell network, and suggests experimentally testable predictions on their possible treatments.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150691", + "ver_date": "2022-05-27T11:03:58.187", + "ver_number": 13 + }, + "150697": { + "class_id": 19, + "created": "2013-09-21T14:37:53", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 150697, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 150699, + "object_name": "Cofer, David [dcofer at neurorobotictech.com]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 170088, + "object_name": "Chung B, Bacqu\u00e9-Cazenave J, Cofer DW, Cattaert D, Edwards DH (2015)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 150700, + "object_name": "AnimatLab v1" + } + ] + }, + "name": "Crayfish hybrid experimental model (Chung et al. 2015)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The Crayfish hybrid experimental model is an AnimatLab v1 neuromechanical model of the crayfish thorax and 5th walking leg that provides a virtual periphery to the live crayfish central nervous system. Run in real time, levator and depressor muscles are excited by motor nerve discharges of the CNS. Up and down movement of the leg shortens and stretches a model stretch receptor that controls movement of a real stretch receptor that provides sensory feedback to the CNS. Real-time sensory feedback provided by the model increases the locomotor cycle frequency by three-fold." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150697", + "other_implementer": { + "attr_id": 308, + "value": "David Cofer (dcofer@neurorobotictech.com)" + }, + "other_neurons": { + "attr_id": 301, + "value": "Integrate and fire model" + }, + "other_simulator": { + "attr_id": 307, + "value": "AnimatLab v1" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "dedwards@gsu.edu" + }, + "ver_date": "2016-03-31T17:38:02.4", + "ver_number": 12 + }, + "150698": { + "class_id": 19, + "created": "2013-09-21T15:00:47", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 150698, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 150699, + "object_name": "Cofer, David [dcofer at neurorobotictech.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 150701, + "object_name": "Posture and locomotion" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 170039, + "object_name": "Bacqu\u00e9-Cazenave J, Chung B, Cofer DW, Cattaert D, Edwards DH (2015)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 150700, + "object_name": "AnimatLab v1" + } + ] + }, + "name": "Crayfish hybrid simulation model (Bacque-Cazenave et al. 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 150702, + "object_name": "Crayfish motor neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A neuromechanical model of the crayfish leg and thorax and the postural and locomotor circuitry built and run in AnimatLab v1. The model simulates experiments run with the BCI preparation model in which the model was linked in real time to the in vivo crayfish thoracic nerve cord. The model shows that current understanding of the neural circuitry can account for the increase in locomotor frequency when the sensori-motor feedback loop is intact." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150698", + "other_concept": { + "attr_id": 306, + "value": "posture and locomotion" + }, + "other_implementer": { + "attr_id": 308, + "value": "David Cofer (dcofer@neurorobotictech.com)" + }, + "other_neurons": { + "attr_id": 301, + "value": "crayfish motor neuron" + }, + "other_simulator": { + "attr_id": 307, + "value": "AnimatLab v1" + }, + "other_type": { + "attr_id": 305, + "value": "Neuromechanical" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "dedwards@gsu.edu" + }, + "ver_date": "2015-02-05T12:28:48.903", + "ver_number": 7 + }, + "150804": { + "citation": { + "attr_id": 391, + "value": "PMID: 24026336" + }, + "class_id": 19, + "created": "2013-09-23T16:44:41", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 150804, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 150841, + "object_name": "Stefanescu, Roxana [roxanast75 at gmail.com]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 150813, + "object_name": "Stefanescu RA, Shivakeshavan RG, Khargonekar PP, Talathi SS (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Computational modelling of channelrhodopsin-2 photocurrent characteristics (Stefanescu et al. 2013)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 35373, + "object_name": "Hippocampus CA1 interneuron oriens alveus GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The codes are directly related with the results presented in the manuscript; in brief, it is a computational investigation on the effects of optogenetic actuation on excitatory and inhibitory neurons when 3- and 4- state model is used to implement the ChR2 kinetics. Different parameters of optostimulation are investigated and the results compared with experimental data previously published by other research groups." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150804", + "other_implementer": { + "attr_id": 308, + "value": "Stefanescu Roxana[roxanast75@gmail.com]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "roxanast75@gmail.com" + }, + "ver_date": "2015-07-26T14:36:23.403", + "ver_number": 8 + }, + "150806": { + "class_id": 19, + "created": "2013-09-24T11:59:00", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 150806, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33800, + "object_name": "Traub, Roger D [rtraub at us.ibm.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 93420, + "object_name": "Sleep" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 150690, + "object_name": "Carracedo LM, Kjeldsen H, Cunnington L, Jenkins A, Schofield I, Cunningham MO, Davies CH, Traub RD, Whittington MA (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 45540, + "object_name": "FORTRAN" + } + ] + }, + "name": "Dynamic cortical interlaminar interactions (Carracedo et al. 2013)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + }, + { + "object_id": 227971, + "object_name": "Neocortex V1 L5B pyramidal pyramidal tract GLU cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + }, + { + "object_id": 142232, + "object_name": "Neocortex spiking low threshold (LTS) neuron" + }, + { + "object_id": 150807, + "object_name": "Neocortex deep neurogliaform interneuron" + }, + { + "object_id": 150808, + "object_name": "Neocortex superficial neurogliaform interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... Here we demonstrate the mechanism underlying a purely neocortical delta rhythm generator and show a remarkable laminar, cell subtype and local subcircuit delineation between delta\r\nand nested theta rhythms. We show that spike timing during delta-nested theta rhythms controls an iterative, reciprocal interaction between deep and superficial cortical layers resembling the unsupervised learning processes proposed for laminar neural networks by Hinton and colleagues ... and mimicking the alternating cortical dynamics of sensory and memory processing during wakefulness.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150806", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2017-07-03T22:38:47.747", + "ver_number": 2 + }, + "150846": { + "class_id": 19, + "created": "2013-09-29T11:47:30", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 150846, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 151950, + "object_name": "Aguiar, Paulo [pauloaguiar at fc.up.pt]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + }, + { + "object_id": 98011, + "object_name": "Place cell/field" + }, + { + "object_id": 194888, + "object_name": "Grid cell" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 189779, + "object_name": "Castro L, Aguiar P (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Grid cells from place cells (Castro & Aguiar, 2014)", + "notes": { + "attr_id": 24, + "value": "\" ...Here we present a novel model for the emergence of gridlike firing patterns that stands on two key hypotheses: (1) spatial information in GCs is provided from PC activity and (2) grid fields result from a combined synaptic plasticity mechanism involving inhibitory and excitatory neurons mediating the connections between PCs and GCs. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150846", + "other_implementer": { + "attr_id": 308, + "value": "Aguiar, Paulo [pauloaguiar at fc.up.pt]" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + }, + { + "object_id": 119191, + "object_name": "Entorhinal cortex" + } + ] + }, + "ver_date": "2017-06-21T20:44:48.487", + "ver_number": 9 + }, + "150912": { + "class_id": 19, + "created": "2013-10-02T14:48:49", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + }, + { + "object_id": 112841, + "object_name": "I Krp" + }, + { + "object_id": 112843, + "object_name": "I R" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122812, + "object_name": "Cav1.3 CACNA1D" + }, + { + "object_id": 122814, + "object_name": "Cav1.2 CACNA1C" + }, + { + "object_id": 122820, + "object_name": "Cav2.2 CACNA1B" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 150912, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 145924, + "object_name": "Evans, Rebekah [Rebekah.Evans at nih.gov]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 150857, + "object_name": "Evans RC, Maniar YM, Blackwell KT (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "Calcium influx during striatal upstates (Evans et al. 2013)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 260, + "object_name": "Neostriatum medium spiny direct pathway GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"...\r\nTo\r\ninvestigate the mechanisms that underlie the relationship between\r\ncalcium and AP timing, we have developed a realistic biophysical\r\nmodel of a medium spiny neuron (MSN).\r\n...\r\nUsing this model, we found that either the slow inactivation of\r\ndendritic sodium channels (NaSI) or the calcium inactivation of\r\nvoltage-gated calcium channels (CDI) can cause high calcium corresponding\r\nto early APs and lower calcium corresponding to later APs.\r\n\r\nWe found that only CDI can account for the experimental observation\r\nthat sensitivity to AP timing is dependent on NMDA receptors.\r\n\r\nAdditional simulations demonstrated a mechanism by which MSNs\r\ncan dynamically modulate their sensitivity to AP timing and show that\r\nsensitivity to specifically timed pre- and postsynaptic pairings (as in\r\nspike timing-dependent plasticity protocols) is altered by the timing of\r\nthe pairing within the upstate.\r\n\u2026\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/150912", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 138633, + "object_name": "Striatum" + } + ] + }, + "ver_date": "2013-10-02T14:52:10", + "ver_number": 2 + }, + "151126": { + "class_id": 19, + "created": "2013-10-09T14:37:51", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + 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However, not much is known about how CREB-dependent neuronal alterations in synaptic strength, excitability and\r\nLTP can boost memory formation in the complex architecture of a neuronal network. Using a model of a CA1 microcircuit, we investigate whether\r\nhippocampal CA1 pyramidal neuron properties altered by increasing CREB activity may contribute to improve memory storage and recall. With a set of patterns presented to a network, we find that the pattern recall quality under AD-like conditions is significantly better when boosting CREB function with respect to control. The results are robust and consistent upon increasing the synaptic damage expected by AD progression, supporting the idea that the use of CREB-based therapies could provide a new approach\r\nto treat AD." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/151126", + "other_concept": { + "attr_id": 306, + "value": "Storage/Recall; CREB;" + }, + "other_neurons": { + "attr_id": 301, + "value": "CA1 Bistratified cell; CA1 Axo axonic cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "danielabianchi12@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2022-05-27T11:09:32.113", + "ver_number": 21 + }, + "151282": { + "class_id": 19, + "created": "2013-10-20T14:48:17", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 226057, + "object_name": "HCN1" + }, + { + "object_id": 226058, + "object_name": "HCN2" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 151282, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33748, + "object_name": "Lazarewicz, Maciej [mlazarew at gmu.edu]" + }, + { + "object_id": 97869, + "object_name": "Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 139148, + "object_name": "Brain Rhythms" + }, + { + "object_id": 143636, + "object_name": "Conductance distributions" + }, + { + "object_id": 225299, + "object_name": "Multiscale" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 151286, + "object_name": "Neymotin SA, Hilscher MM, Moulin TC, Skolnick Y, Lazarewicz MT, Lytton WW (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Ih tunes oscillations in an In Silico CA3 model (Neymotin et al. 2013)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + }, + { + "object_id": 154093, + "object_name": "Hippocampus CA3 interneuron basket GABA cell" + }, + { + "object_id": 139885, + "object_name": "Hippocampus CA3 stratum oriens lacunosum-moleculare interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... We investigated oscillatory control using a multiscale computer model of hippocampal CA3, where each cell class\r\n(pyramidal, basket, and oriens-lacunosum moleculare cells), contained type-appropriate isoforms of Ih.\r\n\r\nOur model\r\ndemonstrated that modulation of pyramidal and basket Ih allows tuning theta and gamma oscillation frequency and\r\namplitude. Pyramidal Ih also controlled cross-frequency coupling (CFC) and allowed shifting gamma generation towards\r\nparticular phases of the theta cycle, effected via Ih\u2019s ability to set pyramidal excitability. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/151282", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2023-02-06T11:48:27.37", + "ver_number": 17 + }, + "151338": { + "class_id": 19, + "created": "2013-10-28T14:58:24", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 88215, + "object_name": "I_Ks" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 151338, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 151436, + "object_name": "Kozlov, Alexander [akozlov at nada.kth.se]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 152733, + "object_name": "Kozlov AK, Kardamakis AA, Hellgren Kotaleski J, Grillner S (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "Gating of steering signals through phasic modulation of reticulospinal neurons (Kozlov et al. 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114428, + "object_name": "Spinal lamprey neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... 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Supporting this possibility, we found both in compartmental models and electrophysiological recordings in brain slices that the responses of basal dendrites to spatially separated inputs are indeed strongly asymmetric.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/151404", + "other_implementer": { + "attr_id": 308, + "value": "Behabadi, Bardia [bardiafb+mdb at gmail.com]" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2016-06-21T12:15:32.53", + "ver_number": 13 + }, + "151443": { + "citation": { + "attr_id": 391, + "value": "Balbi P., Martinoia S., Colombo R., Massobrio P. Modelling the spinal α-motoneuron recurrent discharge: Reappraisal of the F wave. Clin Neurophysiol, published online 25 October 2013, DOI: http://dx.doi.org/10.1016/j.clinph.2013.09.025" + }, + "class_id": 19, + "created": "2013-11-02T12:55:07", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 151443, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 151444, + "object_name": "Balbi, Pietro [piero.balbi at fsm.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 151445, + "object_name": "Recurrent Discharge" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 151446, + "object_name": "Balbi P, Martinoia S, Colombo R, Massobrio P (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "151443", + "name": "ModelView" + } + ], + "default": "151443" + }, + "name": "Recurrent discharge in a reduced model of cat spinal motoneuron (Balbi et al, 2013)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 276, + "object_name": "Spinal cord lumbar motor neuron alpha ACh cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Following a distal stimulation of a motor fibre, only a fraction of spinal motoneurons are able to produce a re-excitation of the initial segment leading to an orthodromically conducted action potential, known as recurrent discharge. In order to show the reciprocal interplay of the axonal initial segment and the soma leading to recurrent discharge in detail, a reduced model of a cat spinal motoneuron was developed." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/151443", + "other_concept": { + "attr_id": 306, + "value": "Recurrent discharge" + }, + "other_implementer": { + "attr_id": 308, + "value": "Balbi, Pietro (piero.balbi@fsm.it)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "piero.balbi@fsm.it" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181621, + "object_name": "151443" + } + ] + }, + "ver_date": "2014-02-25T01:16:09", + "ver_number": 15 + }, + "151458": { + "citation": { + "attr_id": 391, + "value": "24062681" + }, + "class_id": 19, + "created": "2013-11-06T21:40:05", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I 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"value": [ + { + "object_id": 87589, + "object_name": "Astrocyte" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "An electrodiffusive formalism was developed for computing the dynamics of the membrane potential\r\nand ion concentrations in the intra- and extracellular space in a one-dimensional geometry (cable). This (general) formalism was implemented in a model of astrocytes exchanging K+, Na+ and Cl- ions with the extracellular space (ECS). \r\nA limited region (0< x" + }, + "other_neurons": { + "attr_id": 301, + "value": "Drosophila aCC motoneuron, 3rd instar larva" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "cgunay@emory.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 218, + "object_name": "Cholinergic Receptors" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 206363, + "object_name": "Drosophila" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 120613, + "object_name": "Drosophila (fruit fly)" + } + ] + }, + "ver_date": "2019-02-26T10:10:18.14", + "ver_number": 12 + }, + "152111": { + "class_id": 19, + "created": "2014-01-13T14:50:57", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 152111, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 180682, + "object_name": "Carey RM, Sherwood WE, Shipley MT, Borisyuk A, Wachowiak M (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Olfactory bulb juxtaglomerular models (Carey et al., 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + }, + { + "object_id": 268, + "object_name": "Olfactory receptor GLU cell" + }, + { + "object_id": 3978, + "object_name": "Olfactory bulb main interneuron periglomerular GABA cell" + }, + { + "object_id": 3979, + "object_name": "Olfactory bulb main interneuron granule MC GABA cell" + }, + { + "object_id": 153283, + "object_name": "Olfactory bulb main interneuron granule TC GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ...We investigated how OB circuits shape inhalation-driven\r\ndynamics in MCs using a modeling approach that was highly constrained by experimental\r\nresults. First, we constructed models of canonical OB circuits that included mono- and disynaptic\r\nfeedforward excitation, recurrent inhibition and feedforward inhibition of the MC. We then used\r\nexperimental data to drive inputs to the models and to tune parameters; inputs were derived from\r\nsensory neuron responses during natural odorant sampling (sniffing) in awake rats, and model\r\noutput was compared to recordings of MC responses to odorants sampled with the same sniff\r\nwaveforms. This approach allowed us to identify OB circuit features underlying the temporal\r\ntransformation of sensory inputs into inhalation-linked patterns of MC spike output.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/152111", + "other_neurons": { + "attr_id": 301, + "value": "Olfactory bulb external tufted cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ryanmcarey@gmail.com" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "ver_date": "2018-10-17T16:14:28.917", + "ver_number": 10 + }, + "152112": { + "citation": { + "attr_id": 391, + "value": "JH Goldwyn, M Mc Laughlin, E Verschooten, PX Joris, J Rinzel. \"A model of the medial superior olive explains spatiotemporal features of local field potentials\". Submitted to J Neuroscience January 14, 2014." + }, + "class_id": 19, + "created": "2014-01-14T14:41:47", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 88118, + "object_name": "I_KLT" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 152112, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 128503, + "object_name": "Goldwyn, Joshua [jhgoldwyn at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 139996, + "object_name": "Evoked LFP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 152271, + "object_name": "Goldwyn JH, Mc Laughlin M, Verschooten E, Joris PX, Rinzel J (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A model of local field potentials generated by medial superior olive neurons (Goldwyn et al 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88110, + "object_name": "Medial Superior Olive (MSO) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A computational model of local field potentials generated by medial superior olive neurons. These field potentials are known as the \"auditory neurophonic\". MSO neuron is modeled as a soma and two dendrites (following Mathews et al, Nature Neurosci, 2010). Intracellular and a 1D extracellular domain are dynamically coupled and solved to simulate spatial-temporal patterns of membrane voltage and extracellular voltage in response to trains of synaptic inputs (monolateral or bilateral, excitation and/or inhibition). The model produces spatio-temporal patterns similar to neurophonic responses recorded in vivo, as discussed in the accompanying manuscript." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/152112", + "public_submitter_email": { + "attr_id": 309, + "value": "jgoldwyn@nyu.edu" + }, + "ver_date": "2014-07-21T10:00:57", + "ver_number": 4 + }, + "152113": { + "biosimulations": { + "value": "modeldb-152113" + }, + "citation": { + "attr_id": 391, + "value": "Bidirectional Control of Absence Seizures by the Basal Ganglia: A Computational Evidence (PLoS Computational Biology, 2014)" + }, + "class_id": 19, + "created": "2014-01-15T06:33:59", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 152113, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 152114, + "object_name": "Guo, Daqing [dqguo at uestc.edu.cn]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 152115, + "object_name": "Chen M, Guo D, Wang T, Jing W, Xia Y, Xu P, Luo C, Valdes-Sosa PA, Yao D (2014)" + }, + { + "object_id": 185091, + "object_name": "Chen M, Guo D, Li M, Ma T, Wu S, Ma J, Cui Y, Xia Y, Xu P, Yao D (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Basal ganglia-corticothalamic (BGCT) network (Chen et al., 2014)", + "notes": { + "attr_id": 24, + "value": "We developed a biophysical model of the basal ganglia-corticothalamic\r\nnetwork in this work.\r\n\r\n\"... We demonstrate that the typical absence seizure activities can be\r\ncontrolled and modulated by the direct GABAergic projections from the\r\nsubstantia nigra pars reticulata (SNr) to either the thalamic\r\nreticular nucleus (TRN) or the specific relay nuclei (SRN) of\r\nthalamus, through different biophysical mechanisms.\r\n... results highlight the bidirectional functional roles of\r\nbasal ganglia in controlling and modulating absence seizures, and\r\nmight provide novel insights into the therapeutic treatments of this\r\nbrain disorder.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/152113", + "other_implementer": { + "attr_id": 308, + "value": "Guo Daqing(dqguo@uestc.edu.cn)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "dqguo@uestc.edu.cn" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 115951, + "object_name": "Thalamus" + }, + { + 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"https://github.com/OpenSourceBrain/152197", + "other_concept": { + "attr_id": 306, + "value": "Color selectivity, Vision" + }, + "other_implementer": { + "attr_id": 308, + "value": "akihiro.eguchi@psy.ox.ac.uk" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 115951, + "object_name": "Thalamus" + }, + { + "object_id": 118529, + "object_name": "Retina" + } + ] + }, + "ver_date": "2018-10-17T17:18:52.67", + "ver_number": 14 + }, + "152200": { + "class_id": 19, + "created": "2014-01-21T08:37:37", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": 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A computational model of transmitter release demonstrated that a PKC-dependent mobilization process was sufficient to explain the maintenance of STF at nondepressed synapses and the facilitation of depressed synapses." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/152200", + "other_implementer": { + "attr_id": 308, + "value": "zhou,lian" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "lian.zhou@uth.tmc.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115956, + "object_name": "Aplysia" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 249921, + "object_name": "Aplysia" + } + ] + }, + "ver_date": "2019-02-26T10:08:53.613", + "ver_number": 16 + }, + "152292": { + "biosimulations": { + "value": "modeldb-152292" + }, + "citation": { + "attr_id": 391, + "value": "C. Park and J.E. Rubin, J. Comp. Neurosci., 34:345-366, 2013." + }, + "class_id": 19, + "created": "2014-01-31T10:19:31", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 152292, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 125612, + "object_name": "Rubin, Jonathan E [jonrubin at pitt.edu]" + }, + { + "object_id": 152294, + "object_name": "Park, Choongseok [cpark at ncat.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 152293, + "object_name": "Park C, Rubin JE (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "PreBotzinger Complex inspiratory neuron with NaP and CAN currents (Park and Rubin 2013)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 125611, + "object_name": "Respiratory column neuron" + }, + { + "object_id": 232103, + "object_name": "PreBotzinger complex neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We have built on earlier models to develop a single-compartment Hodgkin-Huxley type model incorporating NaP and CAN currents, both of which can play important roles in bursting of inspiratory neurons in the PreBotzinger Complex of the mammalian respiratory brain stem. The model tracks the evolution of membrane potential, related (in)activation variables, calcium concentration, and available fraction of IP3 channels. The model can produce several types of bursting, presented and analyzed from a dynamical systems perspective in our paper." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/152292", + "other_implementer": { + "attr_id": 308, + "value": "Park, Choongseok " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jonrubin@pitt.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 153715, + "object_name": "IP3" + } + ] + }, + "ver_date": "2017-09-13T17:34:49.86", + "ver_number": 10 + }, + "152539": { + "class_id": 19, + "created": "2014-02-06T17:15:07", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 152539, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 152540, + "object_name": "Cavallari, Stefano [stefano.cavallari at iit.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 152537, + "object_name": "Cavallari S, Panzeri S, Mazzoni A (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "I&F recurrent networks with current- or conductance-based synapses (Cavallari et al. 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Recurrent networks of two populations (excitatory and inhibitory) of randomly connected Leaky Integrate-and-Fire (LIF) neurons with either current- or conductance-based synapses from the paper S. Cavallari, S. Panzeri and A. Mazzoni (2014)" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/152539", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2015-07-22T09:53:45.1", + "ver_number": 11 + }, + "152625": { + "class_id": 19, + "created": "2014-02-10T15:04:48", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 152625, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 152626, + "object_name": "Hernandez, Oscar E [ohernandezb at uninorte.edu.co]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3642, + "object_name": "Tutorial/Teaching" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 152290, + "object_name": "Hern\u00e1ndez OE, Zurek EE (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "Software for teaching the Hodgkin-Huxley model (Hernandez & Zurek 2013) (SENB written in NEURON hoc)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52426, + "object_name": "Squid axon" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... The SENB software offers several advantages for teaching and learning electrophysiology. First, SENB offers\r\nease and flexibility in determining the number of stimuli. Second, SENB allows immediate and simultaneous\r\nvisualization, in the same window and time frame, of the evolution of the electrophysiological variables. Third, SENB\r\ncalculates parameters such as time and space constants, stimuli frequency, cellular area and volume, sodium and\r\npotassium equilibrium potentials, and propagation velocity of the action potentials. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/152625", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "simPFid": { + "attr_id": 488, + "value": 607 + }, + "ver_date": "2014-02-10T15:27:54", + "ver_number": 2 + }, + "152636": { + "citation": { + "attr_id": 391, + "value": "Caplan JS, Williams AH, Marder E (submitted). Many parameter sets in a multicompartment model oscillator are robust to temperature perturbations." + }, + "class_id": 19, + "created": "2014-02-14T15:01:44", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 152636, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 152638, + "object_name": "Caplan, Jonathan S [joncaplan at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 116962, + "object_name": "Parameter sensitivity" + }, + { + "object_id": 152639, + "object_name": "Temperature" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 152637, + "object_name": "Caplan JS, Williams AH, Marder E (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Temperature-Dependent Pyloric Pacemaker Kernel (Caplan JS et al., 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 144405, + "object_name": "Stomatogastric ganglion (STG) pyloric dilator (PD) neuron" + }, + { + "object_id": 144406, + "object_name": "Stomatogastric ganglion (STG) pyloric neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... Here we demonstrate\r\nthat biophysical models of channel noise can give rise to two kinds of\r\nrecently discovered stochastic facilitation effects in a\r\nHodgkin-Huxley-like model of auditory brainstem neurons. The first,\r\nknown as slope-based stochastic resonance (SBSR), enables phasic\r\nneurons to emit action potentials that can encode the slope of inputs\r\nthat vary slowly relative to key time constants in the model. The\r\nsecond, known as inverse stochastic resonance (ISR), occurs in\r\ntonically firing neurons when small levels of noise inhibit tonic\r\nfiring and replace it with burstlike dynamics.\r\n\r\n... our results show that possible\r\nassociated computational benefits may occur due to channel noise in\r\nneurons of the auditory brainstem.\r\n... \"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/152636", + "other_concept": { + "attr_id": 306, + "value": "Temperature" + }, + "other_implementer": { + "attr_id": 308, + "value": "Caplan, Jonathan S " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ahwillia@brandeis.edu" + }, + "ver_date": "2018-10-18T17:07:33.647", + "ver_number": 6 + }, + "152788": { + "citation": { + "attr_id": 391, + "value": "Hjorth JJJ, van Pelt J, Mansvelder HD, van Ooyen A. Competitive Dynamics during Resource-Driven Neurite Outgrowth. PLoS One. 2014 Feb 3;9(2):e86741. doi: 10.1371/journal.pone.0086741" + }, + "class_id": 19, + "created": "2014-02-25T04:00:20", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 152788, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 118393, + "object_name": "Hjorth, Johannes [hjorth at csc.kth.se]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 128875, + "object_name": "Development" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 152789, + "object_name": "Hjorth JJ, van Pelt J, Mansvelder HD, van Ooyen A (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Resource competition in growing neurites (Hjorth et al 2014)", + "notes": { + "attr_id": 24, + "value": "Computer model of neurite outgrowth in a simplified neuron. A growth limiting resource is produced in the soma, transported through the neurites and consumed at the growth cones." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/152788", + "other_neurons": { + "attr_id": 301, + "value": "Simplified neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "hjorth@csc.kth.se" + }, + "ver_date": "2015-07-26T14:38:02.813", + "ver_number": 12 + }, + "152897": { + "citation": { + "attr_id": 391, + "value": "1. Hopgkin AL, Huxley AF. A quantitative description of membrane current and its application to conduction and excitation in nverve. J Physiol 1952;117:500-44. 2. Chen BS, Lo YC, Liu YC, Wu SN. Effects of transient receptor-like current on firing pattern of actions potentials in the Hodgkin-Huxley neuron during exposure to sinusoidal external voltage. Chin J Physiol 2010;53:423-9." + }, + "class_id": 19, + "created": "2014-02-26T01:55:33", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 152897, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + }, + { + "object_id": 84610, + "object_name": "Huang, Chin-Wei" + }, + { + "object_id": 114111, + "object_name": "Chen, Bing-Shuo" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 29126, + "object_name": "HODGKIN AL, HUXLEY AF (1952)" + }, + { + "object_id": 137265, + "object_name": "Chen BS, Lo YC, Lius YC, Wu SN (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 150209, + "object_name": "R" + } + ] + }, + "name": "Squid axon (Hodgkin, Huxley 1952) used in (Chen et al 2010) (R language)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52426, + "object_name": "Squid axon" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... Previous work showed that magnetic electrical field-induced antinoceptive action is mediated by activation of capsaicin-sensitive sensory afferents. In this study, a modified Hodgkin-Huxley model, in which TRP-like current (I-TRP) was incorporated, was implemented to predict the firing behavior of action potentials (APs), as the model neuron was exposed to sinusoidal changes in externally-applied voltage. ... Our simulation results suggest that modulation of TRP-like channels functionally expressed in small-diameter peripheral sensory neurons should be an important mechanism through which it can contribute to the firing pattern of APs.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/152897", + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2014-02-26T14:42:19", + "ver_number": 5 + }, + "152913": { + "class_id": 19, + "created": "2014-02-28T17:55:16", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 152913, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 84598, + "object_name": "Extracellular Fields" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 152912, + "object_name": "Tahayori B, Meffin H, Dokos S, Burkitt AN, Grayden DB (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 152965, + "object_name": "COMSOL" + } + ] + }, + "name": "Modeling extracellular electrical stimulation (Tahayori et al. 2012)", + "notes": { + "attr_id": 24, + "value": "\"The validity of approximate equations describing the membrane potential under extracellular electrical stimulation (Meffin et al 2012 J. Neural Eng. 9 065005) is investigated through finite element analysis in this paper. To this end, the finite element method is used to simulate a cylindrical neurite under extracellular stimulation. Laplace's equations with appropriate boundary conditions are solved numerically in three dimensions and the results are compared to the approximate analytic solutions. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/152913", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2015-07-26T13:05:48.24", + "ver_number": 4 + }, + "152966": { + "biosimulations": { + "value": "modeldb-152966" + }, + "citation": { + "attr_id": 391, + "value": "(1) Wang YJ, Sung RJ, Lin MW, Wu SN. Contribution of BK(Ca)-channel activity in human cardiac fibroblasts to electrical coupling of cardiomyocytes-fibroblasts. J Membr Biol 2006;213:175-185. (2) Luo CH, Rudy Y. A dynamic model of the cardiac ventricular action potential. II. Afterdepolarizations, triggered activity, and potentiation. Circ Res 1994;74:1097-1113." + }, + "class_id": 19, + "created": "2014-03-01T08:10:28", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 87769, + "object_name": "ATP-senstive potassium current" + }, + { + "object_id": 88206, + "object_name": "I_HERG" + }, + { + "object_id": 88209, + "object_name": "Na/Ca exchanger" + }, + { + "object_id": 88210, + "object_name": "I_Na,Ca" + }, + { + "object_id": 88211, + "object_name": "I_SERCA" + }, + { + "object_id": 88212, + "object_name": "KCNQ1" + }, + { + "object_id": 88215, + "object_name": "I_Ks" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 152966, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 152968, + "object_name": "Triggered activity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 56013, + "object_name": "Luo CH, Rudy Y (1994)" + }, + { + "object_id": 112806, + "object_name": "Wang YJ, Sung RJ, Lin MW, Wu SN (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Ventricular cell model (Luo Rudy dynamic model) (Luo Rudy 1994) used in (Wang et al 2006) (XPP)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88205, + "object_name": "Cardiac ventricular cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A mathematical model of the membrane action potential of the mammalian ventricular cell introduced in Luo, Rudy 1991 and used in Wang et al 2006 is made available here in XPP. The model is based, whenever possible, on recent single-cell and single-channel data and incorporates the possibility of changing extracellular potassium concentration [K]o. ... The results are consistent with recent experimental observations, and the model simulations relate these phenomena to the underlying ionic channel kinetics. See papers for more and details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/152966", + "other_concept": { + "attr_id": 306, + "value": "Triggered activity" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "ver_date": "2015-07-26T14:38:59.55", + "ver_number": 13 + }, + "153030": { + "class_id": 19, + "created": "2014-03-05T03:17:05", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 153030, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 153038, + "object_name": "Dar , Gaurav [gdar at goa.bits-pilani.ac.in]" + }, + { + "object_id": 153090, + "object_name": "Gangal, Himanshu [himanshugangal at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 222737, + "object_name": "Gangal H, Dar G (2014)" + }, + { + "object_id": 153031, + "object_name": "Lee SG, Kim S (2006)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Squid axon: Bifurcation analysis of mode-locking (Lee & Kim 2006) (Gangal & Dar 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 236, + "object_name": "Ions" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model was built with the purpose of finding mode lockings between the input sinusoidal current frequency and the output frequency. Phase plase plane analysis, spike statistics, mode locking formulation etc. can be done with the help of the model. Any additional functionality can be added as the base code return the correct action potential values." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/153030", + "other_currents": { + "attr_id": 303, + "value": "I Na , I Leakage" + }, + "other_implementer": { + "attr_id": 308, + "value": "Dar , Gaurav " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "himanshugangalcoursera@gmail.com" + }, + "ver_date": "2017-01-10T17:51:38.123", + "ver_number": 9 + }, + "153092": { + "citation": { + "attr_id": 391, + "value": "Eivind S. Norheim, John Wyller, Eilen Nordlie, and Gaute T. Einevoll: A minimal mechanistic model for temporal signal processing in the lateral geniculate nucleus, Cognitive Neurodynamics 6: 259-281 (2012)" + }, + "class_id": 19, + "created": "2014-03-11T11:44:40", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 153092, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 153095, + "object_name": "Norheim, Eivind [norheim.eivind at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3642, + "object_name": "Tutorial/Teaching" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 153097, + "object_name": "Norheim ES, Wyller J, Nordlie E, Einevoll GT (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "LGNcircuit: Minimal LGN network model of temporal processing of visual input (Norheim et al. 2012)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The responses of relay cells in the lateral geniculate nucleus (LGN) are shaped by their diverse set of impinging inputs: feedforward synaptic inputs stemming from retina, and feedback inputs stemming from the visual cortex and the thalamic reticular nucleus. This MATLAB model, with an easy-to-use graphical user interface (GUI), explores possible roles of these feedforward and feedback inputs in shaping the temporal part of the receptive fields of LGN relay cells with, so called, ON symmetry. A minimal mechanistic firing-rate model tailored to elucidate salient feedforward and feedback effects is considered including, in particular, feedforward excitation and inhibition (via interneurons) from retinal ON cells and excitatory and inhibitory (via thalamic reticular nucleus cells and interneurons) feedback\r\nfrom cortical ON and OFF cells. Various types of visual stimuli can be explored: flashing spots, impulses, sinusoidal gratings." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/153092", + "other_implementer": { + "attr_id": 308, + "value": "Norheim, Eivind [norheim.eivind@gmail.com]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "Gaute.Einevoll@nmbu.no" + }, + "ver_date": "2018-10-17T17:19:12.08", + "ver_number": 12 + }, + "153196": { + "class_id": 19, + "created": "2014-03-16T02:06:53", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 151550, + "object_name": "Channelrhodopsin (ChR)" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 153196, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33766, + "object_name": "MacIntyre, CC [ccm4 at case.edu]" + }, + { + "object_id": 153200, + "object_name": "Foutz, Thomas J [tfoutz at uw.edu]" + }, + { + "object_id": 153201, + "object_name": "Arlow, Richard L [richard.arlow at case.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 116962, + "object_name": "Parameter sensitivity" + }, + { + "object_id": 147753, + "object_name": "Intracortical Microstimulation" + }, + { + "object_id": 222956, + "object_name": "Neuromodulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144540, + "object_name": "Foutz TJ, Arlow RL, McIntyre CC (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Optical stimulation of a channelrhodopsin-2 positive pyramidal neuron model (Foutz et al 2012)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A computational tool to explore the underlying principles of optogenetic neural stimulation. This \"light-neuron\" model consists of theoretical representations of the light dynamics generated by a fiber optic in brain tissue, coupled to a multicompartment cable model of a cortical pyramidal neuron (Hu et al. 2009, ModelDB #123897) embedded with channelrhodopsin-2 (ChR2) membrane dynamics. Simulations predict that the activation threshold is sensitive to many of the properties of ChR2 (density, conductivity, and kinetics), tissue medium (scattering and absorbance), and the fiber-optic light source (diameter and numerical aperture). This model system represents a scientific instrument to characterize the effects of optogenetic neuromodulation, as well as an engineering design tool to help guide future development of optogenetic technology." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/153196", + "other_implementer": { + "attr_id": 308, + "value": "Foutz, Thomas J ; McIntyre, Cameron C ; Arlow, Richard L " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.foutz@gmail.com" + }, + "ver_date": "2017-01-13T12:18:51.027", + "ver_number": 10 + }, + "153280": { + "citation": { + "attr_id": 391, + "value": "Lee et al., under review/in press" + }, + "class_id": 19, + "created": "2014-03-20T16:14:20", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 153280, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 153194, + "object_name": "Bezaire, Marianne [mariannejcase at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 114660, + "object_name": "Connectivity matrix" + }, + { + "object_id": 128811, + "object_name": "Laminar Connectivity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 154374, + "object_name": "Lee SH, Marchionni I, Bezaire M, Varga C, Danielson N, Lovett-Barron M, Losonczy A, Soltesz I (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Parvalbumin-positive basket cells differentiate among hippocampal pyramidal cells (Lee et al. 2014)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 114079, + "object_name": "Hippocampus CA1 basket cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This detailed microcircuit model explores the network level effects of sublayer specific connectivity in the mouse CA1. The differences in strengths and numbers of synapses between PV+ basket cells and either superficial sublayer or deep sublayer pyramidal cells enables a routing of inhibition from superficial to deep pyramidal cells. At the network level of this model, the effects become quite prominent when one compares the effect on firing rates when either the deep or superficial pyramidal cells receive a selective increase in excitation.\r\n" + }, + "opensourcebrain": { + "value": "nc_superdeep" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/153280", + "other_concept": { + "attr_id": 306, + "value": "Circuit Analysis" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "mariannejcase@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2022-05-27T11:13:24.393", + "ver_number": 9 + }, + "153351": { + "class_id": 19, + "created": "2014-04-01T06:25:57", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 153351, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 153725, + "object_name": "Chen, Weiliang [w.chen at oist.jp]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 153699, + "object_name": "Chen W, De Schutter E (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 153713, + "object_name": "STEPS (web link to model)" + } + ] + }, + "name": "Python-based toolkits for STEPS (Chen and De Schutter 2014)", + "notes": { + "attr_id": 24, + "value": "We provide two examples to demonstrate the use of the geometry preparation toolkit and the visualization tool for STEPS, described in Chen W and De Schutter E (2014).\r\n\r\nThe package contains two folders, each for an individual example. The ip3r folder contains mesh data and simulation scripts for the IP3 receptor model described in the paper, and the AD folder contains data and scripts for Anomalous Diffusion simulation.\r\n \r\nPlease read the instruction.pdf file within the package for detail instructions." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/153351", + "other_implementer": { + "attr_id": 308, + "value": "\r\n" + }, + "other_receptors": { + "attr_id": 302, + "value": "IP3" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "w.chen@oist.jp" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 153715, + "object_name": "IP3" + } + ] + }, + "ver_date": "2014-04-21T11:40:27", + "ver_number": 16 + }, + "153355": { + "class_id": 19, + "created": "2014-04-04T10:19:06", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 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153358, + "object_name": "Leech heart motor neuron (HE)" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"...\r\n To explore the relationship between conductances,\r\nand in particular how they influence the activity of motor neurons in\r\nthe well characterized leech heartbeat system, we developed a new\r\nmulti-compartmental Hodgkin-Huxley style leech heart motor neuron\r\nmodel.\r\n\r\nTo do so, we evolved a population of model instances, which\r\ndiffered in the density of specific conductances, capable of achieving\r\nspecific output activity targets given an associated input pattern.\r\n\r\n...\r\nWe found that the strengths of many conductances,\r\nincluding those with differing dynamics, had strong partial\r\ncorrelations and that these relationships appeared to be linked by\r\ntheir influence on heart motor neuron activity.\r\n\r\n Conductances that had\r\npositive correlations opposed one another and had the opposite effects\r\non activity metrics when perturbed whereas conductances that had\r\nnegative correlations could compensate for one another and had similar\r\neffects on activity metrics.\r\n\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/153355", + "other_neurons": { + "attr_id": 301, + "value": "Leech heart motor neuron (HE)" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115958, + "object_name": "Leech" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 114272, + "object_name": "Hirudinea (leech)" + } + ] + }, + "ver_date": "2019-02-26T10:23:01.95", + "ver_number": 8 + }, + "153452": { + "class_id": 19, + "created": "2014-04-09T22:36:44", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 153452, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 153454, + "object_name": "Hong, Sungho [shhong at oist.jp]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + 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"attr_id": 24, + "value": "\u201c\r\n\u2026\r\nUsing simulations and experiments in rat hippocampal neurons, we show here that pairs of neurons receiving correlated input also exhibit correlations arising from precise spike-time synchronization.\r\n\r\nContrary to rate comodulation, spike-time synchronization is unaffected by firing rate, thus enabling synchrony- and rate-based coding to operate independently.\r\n\r\nThe type of output correlation depends on whether intrinsic neuron properties promote integration or coincidence detection: \u201cideal\u201d integrators (with spike generation sensitive to stimulus mean) exhibit rate comodulation, whereas ideal coincidence detectors (with spike generation sensitive to stimulus variance) exhibit precise spike-time synchronization.\r\n\r\n\u2026\r\n\r\nOur results explain how different types of correlations arise based on how individual neurons generate spikes, and why spike-time synchronization and rate comodulation can encode different stimulus properties.\r\n\r\nOur results also highlight the importance of neuronal properties for population-level coding insofar as neural networks can employ different coding schemes depending on the dominant operating mode of their constituent neurons. \u201c" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/153452", + "other_implementer": { + "attr_id": 308, + "value": "Hong, Sungho [shhong at oist.jp]" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115960, + "object_name": "Generic" + } + ] + }, + "ver_date": "2017-02-15T13:55:56.763", + "ver_number": 10 + }, + "153573": { + "citation": { + "attr_id": 391, + "value": "Kenji Morita & Ayaka Kato. Striatal dopamine ramping may indicate flexible reinforcement learning with forgetting in the cortico-basal ganglia circuits. Frontiers in Neural Circuits 8:36. doi: 10.3389/fncir.2014.00036 (2014)." + }, + "class_id": 19, + "created": "2014-04-13T14:30:23", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 153573, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 117206, + "object_name": "Morita, Kenji [morita at p.u-tokyo.ac.jp]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 153576, + "object_name": "Morita K, Kato A (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Striatal dopamine ramping: an explanation by reinforcement learning with decay (Morita & Kato, 2014)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Incorporation of decay of learned values into temporal-difference (TD) learning (Sutton & Barto, 1998, Reinforcement Learning (MIT Press)) causes ramping of TD reward prediction error (RPE), which could explain, given the hypothesis that dopamine represents TD RPE (Montague et al., 1996, J Neurosci 16:1936; Schultz et al., 1997, Science 275:1593), the reported ramping of the dopamine concentration in the striatum in a reward-associated spatial navigation task (Howe et al., 2013, Nature 500:575)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/153573", + "other_implementer": { + "attr_id": 308, + "value": "Morita, Kenji " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "morita@p.u-tokyo.ac.jp" + }, + "ver_date": "2014-04-14T15:28:52", + "ver_number": 3 + }, + "153574": { + "class_id": 19, + "created": "2014-04-14T01:15:09", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 153574, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33795, + "object_name": "Bhalla, Upinder S [bhalla at ncbs.res.in]" + }, + { + "object_id": 182361, + "object_name": "Gilra, Aditya [aditya_gilra -at- yahoo -period- com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 182362, + "object_name": "Sensory coding" + }, + { + "object_id": 238957, + "object_name": "Markov-type model" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 182291, + "object_name": "Gilra A, Bhalla US (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 186718, + "object_name": "MOOSE/PyMOOSE" + } + ] + }, + "name": "Olfactory bulb microcircuits model with dual-layer inhibition (Gilra & Bhalla 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + }, + { + "object_id": 3978, + "object_name": "Olfactory bulb main interneuron periglomerular GABA cell" + }, + { + "object_id": 3979, + "object_name": "Olfactory bulb main interneuron granule MC GABA cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A detailed network model of the dual-layer dendro-dendritic inhibitory microcircuits in the rat olfactory bulb comprising compartmental mitral, granule and PG cells developed by Aditya Gilra, Upinder S. Bhalla (2015). \r\nAll cell morphologies and network connections are in NeuroML v1.8.0. PG and granule cell channels and synapses are also in NeuroML v1.8.0. Mitral cell channels and synapses are in native python." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/153574", + "other_concept": { + "attr_id": 306, + "value": "Sensory coding" + }, + "other_implementer": { + "attr_id": 308, + "value": "Gilra, Aditya [aditya_gilra -at- yahoo -period- com]" + }, + "other_neurons": { + "attr_id": 301, + "value": "Olfactory bulb granule cell" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "ver_date": "2018-10-17T16:14:55.177", + "ver_number": 19 + }, + "153633": { + "class_id": 19, + "created": "2014-04-14T18:04:35", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 153633, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 25097, + "object_name": "Brunel N, Hakim V (1999)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 137990, + "object_name": "Brian (web link to method)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Fast global oscillations in networks of I&F neurons with low firing rates (Brunel and Hakim 1999)", + "notes": { + "attr_id": 24, + "value": "Dynamics of a network of sparsely connected inhibitory current-based integrate-and-fire neurons. Individual neurons fire irregularly at low rate but the network is in an oscillatory global activity regime where neurons are weakly synchronized." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/153633", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2017-07-28T17:32:44.817", + "ver_number": 5 + }, + "153635": { + "class_id": 19, + "created": "2014-04-15T10:31:56", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 153635, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83453, + "object_name": "Brette R" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 69696, + "object_name": "Brette R, Gerstner W (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 137990, + "object_name": "Brian (web link to method)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Adaptive exponential integrate-and-fire model (Brette & Gerstner 2005)", + "notes": { + "attr_id": 24, + "value": "\"We introduce a two-dimensional integrate-and-fire model that combines an exponential spike mechanism with an adaptation equation, based on recent theoretical findings. \r\n...\r\nThe model is especially reliable in high-conductance states, typical of cortical activity in vivo, in which intrinsic conductances were found to have a reduced role in shaping spike trains. These results are promising because this simple model has enough expressive power to reproduce qualitatively several electrophysiological classes described in vitro.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/153635", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2017-07-28T17:32:27.97", + "ver_number": 5 + }, + "153660": { + "class_id": 19, + "created": "2014-04-16T11:58:19", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 153660, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83453, + "object_name": "Brette R" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 153638, + "object_name": "Platkiewicz J, Brette R (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 137990, + "object_name": "Brian (web link to method)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "A threshold equation for action potential initiation (Platkiewicz & Brette 2010)", + "notes": { + "attr_id": 24, + "value": "\"We examined in models the influence of Na channel activation, inactivation, slow voltage-gated channels and synaptic conductances on spike threshold. \r\n\r\nWe propose a threshold equation which quantifies the contribution of all these mechanisms. \r\n\r\nIt provides an instantaneous time-varying value of the threshold, which applies to neurons with fluctuating inputs. \r\n...\r\nWe find that spike threshold depends logarithmically on Na channel density, and that Na channel inactivation and K channels can dynamically modulate it in an adaptive way: the threshold increases with membrane potential and after every action potential. \r\n\" See paper for more." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/153660", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2017-07-28T17:32:10.93", + "ver_number": 4 + }, + "153697": { + "class_id": 19, + "created": "2014-04-17T10:41:42", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 153697, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83453, + "object_name": "Brette R" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 127084, + "object_name": "Locking, mixed mode" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 145602, + "object_name": "Brette R (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 137990, + "object_name": "Brian (web link to method)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Phase locking in leaky integrate-and-fire model (Brette 2004)", + "notes": { + "attr_id": 24, + "value": "\"This shows the phase-locking structure of a LIF driven by a sinusoidal current. When the current crosses the threshold (a<3), the model almost always phase locks (in a measure-theoretical sense).\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/153697", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2017-07-28T17:36:18.867", + "ver_number": 3 + }, + "153740": { + "class_id": 19, + "created": "2014-04-21T21:59:03", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 153740, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 153712, + "object_name": "Koon YL, Koh CG, Chiam KH (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Strategy for kinase transport by microtubules to nerve terminals (Koon et al. 2014)", + "notes": { + "attr_id": 24, + "value": "This model was used in the computational study of the strategies of protein transport in the context of JNK (c-JUN NH2-terminal kinase) transport along microtubules to the terminals of neuronal cells. Diffusion governs the first strategy. In the second strategy, proteins of the JNK signaling cascade bind to scaffolds and the whole protein-scaffold cargo is transported by kinesin motors along microtubules. Using the results from the simulations, the two distinct strategies for transport were compared." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/153740", + "other_implementer": { + "attr_id": 308, + "value": "Koon, Yen Ling [koonyl at bii.a-star.edu.sg]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "koonyl@bii.a-star.edu.sg" + }, + "ver_date": "2014-05-08T10:56:48", + "ver_number": 5 + }, + "153985": { + "class_id": 19, + "created": "2014-04-22T13:53:33", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 153985, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83453, + "object_name": "Brette R" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 136098, + "object_name": "Information transfer" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 145590, + "object_name": "Rossant C, Leijon S, Magnusson AK, Brette R (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 137990, + "object_name": "Brian (web link to method)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Sensitivity of noisy neurons to coincident inputs (Rossant et al. 2011)", + "notes": { + "attr_id": 24, + "value": "\"Two distant or coincident spikes are injected into a noisy balanced leaky integrate-and-fire neuron. The PSTH of the neuron in response to these inputs is calculated along with the extra number of spikes in the two cases. This number is higher for the coincident spikes, showing the sensitivity of a noisy neuron to coincident inputs.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/153985", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2017-07-28T17:36:54.17", + "ver_number": 3 + }, + "153988": { + "class_id": 19, + "created": "2014-04-24T10:15:57", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 153988, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83539, + "object_name": "Attractor Neural Network" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 14141, + "object_name": "Diesmann M, Gewaltig MO, Aertsen A (1999)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 137990, + "object_name": "Brian (web link to method)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Stable propagation of synchronous spiking in cortical neural networks (Diesmann et al 1999)", + "notes": { + "attr_id": 24, + "value": "\"...\r\nHere we show that precisely synchronized action\r\npotentials can propagate within a model of cortical network activity\r\nthat recapitulates many of the features of biological systems. An\r\nattractor, yielding a stable spiking precision in the (sub)millisecond\r\nrange, governs the dynamics of synchronization. Our results indicate\r\nthat a combinatorial neural code, based on rapid associations of\r\ngroups of neurons co-ordinating their activity at the single spike\r\nlevel, is possible within a cortical-like network.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/153988", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2017-07-28T17:37:11.917", + "ver_number": 3 + }, + "153998": { + "class_id": 19, + "created": "2014-04-28T11:15:02", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 153998, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ 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"2014-05-03T12:10:27", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 154096, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33738, + "object_name": "Lytton, William [bill.lytton at downstate.edu]" + }, + { + "object_id": 97869, + "object_name": "Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org]" + }, + { + "object_id": 147189, + "object_name": "Rowan, Mark [m.s.rowan at cs.bham.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 112047, + "object_name": "Aging/Alzheimer`s" + }, + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 154063, + "object_name": "Rowan MS, Neymotin SA, Lytton WW (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Electrostimulation to reduce synaptic scaling driven progression of Alzheimers (Rowan et al. 2014)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + }, + { + "object_id": 7387, + "object_name": "Neocortex V1 interneuron basket PV GABA cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 114635, + "object_name": "Neocortex spiny stellate cell" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + }, + { + "object_id": 142232, + "object_name": "Neocortex spiking low threshold (LTS) neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"...\r\n\r\nAs cells die and synapses lose their drive, remaining cells suffer an initial decrease in activity.\r\n\r\nNeuronal homeostatic synaptic scaling then provides a feedback mechanism to restore activity.\r\n...\r\nThe scaling mechanism increases the firing rates of remaining cells in the network to compensate for decreases in network activity.\r\n\r\nHowever, this effect can itself become a pathology, ...\r\n\r\nHere, we present a mechanistic explanation of how directed brain stimulation might be expected to slow AD progression based on computational simulations in a 470-neuron biomimetic model of a neocortical column.\r\n...\r\n\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/154096", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2017-02-15T13:55:20.47", + "ver_number": 10 + }, + "154192": { + "class_id": 19, + "created": "2014-05-07T10:59:00", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 154192, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 154779, + "object_name": "McFarland, James M [jmmcfarl at umd.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 154097, + "object_name": "McFarland JM, Cui Y, Butts DA (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Nonlinear neuronal computation based on physiologically plausible inputs (McFarland et al. 2013)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 270, + "object_name": "Retina ganglion GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"...\r\nHere we present an approach for modeling sensory processing, termed the Nonlinear Input\r\nModel (NIM), which is based on the hypothesis that the dominant nonlinearities imposed by physiological mechanisms arise\r\nfrom rectification of a neuron\u2019s inputs. Incorporating such \u2018upstream nonlinearities\u2019 within the standard linear-nonlinear (LN)\r\ncascade modeling structure implicitly allows for the identification of multiple stimulus features driving a neuron\u2019s response,\r\nwhich become directly interpretable as either excitatory or inhibitory.\r\n\r\nBecause its form is analogous to an integrate-and-fire\r\nneuron receiving excitatory and inhibitory inputs, model fitting can be guided by prior knowledge about the inputs to a\r\ngiven neuron, and elements of the resulting model can often result in specific physiological predictions.\r\n\r\nFurthermore, by\r\nproviding an explicit probabilistic model with a relatively simple nonlinear structure, its parameters can be efficiently\r\noptimized and appropriately regularized. ... \u201d" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/154192", + "ver_date": "2014-06-02T11:53:37", + "ver_number": 5 + }, + "154198": { + "class_id": 19, + "created": "2014-05-09T14:13:23", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 154198, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83453, + "object_name": "Brette R" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3542, + "object_name": "Pattern Recognition" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 116907, + "object_name": "Direction Selectivity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 154043, + "object_name": "St\u00fcrzl W, Kempter R, van Hemmen JL (2000)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 137990, + "object_name": "Brian (web link to method)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Theory of arachnid prey localization (Sturzl et al. 2000)", + "notes": { + "attr_id": 24, + "value": "\"Sand scorpions and many other arachnids locate their prey through highly sensitive slit sensilla at the tips (tarsi) of their eight legs. This sensor array responds to vibrations with stimulus-locked action potentials encoding the target direction. We present a neuronal model to account for stimulus angle determination using a population of second-order neurons, each receiving excitatory input from one tarsus and inhibition from a triad opposite to it. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/154198", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2017-07-28T17:37:31.2", + "ver_number": 3 + }, + "154288": { + "class_id": 19, + "created": "2014-05-09T15:25:11", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 154288, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83453, + "object_name": "Brette R" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3542, + "object_name": "Pattern Recognition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 154201, + "object_name": "G\u00fctig R, Sompolinsky H (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 137990, + "object_name": "Brian (web link to method)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Time-warp-invariant neuronal processing (Gutig & Sompolinsky 2009)", + "notes": { + "attr_id": 24, + "value": "\" ... Here, we report that time-warp-invariant neuronal processing can be subserved by the shunting action of synaptic conductances that automatically rescales the effective integration time of postsynaptic neurons.\r\n\r\nWe propose a novel spike-based learning rule for synaptic conductances that adjusts the degree of synaptic shunting to the temporal processing requirements of a given task.\r\n\r\nApplying this general biophysical mechanism to the example of speech processing, we propose a neuronal network model for time-warp-invariant word discrimination and demonstrate its excellent performance on a standard benchmark speech-recognition task.\r\n\r\n...\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/154288", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2017-07-28T17:37:49.54", + "ver_number": 3 + }, + "154293": { + "class_id": 19, + "created": "2014-05-09T16:13:22", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 154293, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83453, + "object_name": "Brette R" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 150219, + "object_name": "Reliability" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 80242, + "object_name": "Brette R, Guigon E (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 137990, + "object_name": "Brian (web link to method)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Reliability of spike timing is a general property of spiking model neurons (Brette & Guigon 2003)", + "notes": { + "attr_id": 24, + "value": "\"... Here we show, through simulations and theoretical\r\nconsiderations, that for a general class of spiking neuron models,\r\nwhich includes, in particular, the leaky integrate-and-fire model as\r\nwell as nonlinear spiking models, aperiodic currents, contrary to\r\nperiodic currents, induce reproducible responses, which are stable\r\nunder noise, change in initial conditions and deterministic\r\nperturbations of the input. We provide a theoretical explanation for\r\naperiodic currents that cross the threshold.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/154293", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2017-07-28T17:36:38.22", + "ver_number": 3 + }, + "154348": { + "class_id": 19, + "created": "2014-05-09T17:30:32", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 154348, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83453, + "object_name": "Brette R" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 128875, + "object_name": "Development" + }, + { + "object_id": 245799, + "object_name": "Whisking" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 146579, + "object_name": "Kremer Y, L\u00e9ger JF, Goodman D, Brette R, Bourdieu L (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 137990, + "object_name": "Brian (web link to method)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Late emergence of the whisker direction selectivity map in rat barrel cortex (Kremer et al. 2011)", + "notes": { + "attr_id": 24, + "value": "\"... We discovered that the emergence of a direction map in rat barrel cortex occurs long after all known critical periods in the somatosensory system.\r\n\r\nThis map is remarkably specific, taking a pinwheel-like form centered near the barrel center and aligned to the barrel cortex somatotopy.\r\n\r\nWe suggest that this map may arise from intracortical mechanisms and demonstrate by simulation that the combination of spike-timing-dependent plasticity at synapses between layer 4 and layer 2/3 and realistic pad stimulation is sufficient to produce such a map.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/154348", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2018-10-18T17:35:31.803", + "ver_number": 4 + }, + "154732": { + "class_id": 19, + "created": "2014-05-19T11:08:09", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 154732, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33787, + "object_name": "Graham, Bruce [B.Graham at cs.stir.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 154733, + "object_name": "Graham BP, Saudargiene A, Cobb S (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Spine head calcium in a CA1 pyramidal cell model (Graham et al. 2014)", + "notes": { + "attr_id": 24, + "value": "\"We use a computational model of a hippocampal CA1 pyramidal cell to demonstrate that spine\r\nhead calcium provides an instantaneous readout at each synapse of the postsynaptic weighted\r\nsum of all presynaptic activity impinging on the cell. The form of the readout is equivalent\r\nto the functions of weighted, summed inputs used in neural network learning rules. Within a\r\ndendritic layer, peak spine head calcium levels are either a linear or sigmoidal function of\r\nthe number of coactive synapses, with nonlinearity depending on the ability of voltage spread\r\nin the dendrites to reach calcium spike threshold. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/154732", + "other_neurons": { + "attr_id": 301, + "value": "CA1 pyramidal cell" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "simPFid": { + "attr_id": 488, + "value": 493 + }, + "ver_date": "2022-05-27T11:16:53.72", + "ver_number": 11 + }, + "154739": { + "class_id": 19, + "created": "2014-05-21T05:25:53", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 154739, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 154743, + "object_name": "Visser, Sid [sid.visser at nottingham.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3642, + "object_name": "Tutorial/Teaching" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 97752, + "object_name": "Delay" + }, + { + "object_id": 136252, + "object_name": "Rebound firing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 154744, + "object_name": "Visser S, Van Gils SA (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 154742, + "object_name": "Norns - Neural Net Studio" + } + ] + }, + "name": "Norns - Neural Network Studio (Visser & Van Gils 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + }, + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The Norns - Neural Network Studio is a software package for designing, simulation and analyzing networks of spiking neurons. It consists of three parts:\r\n\r\n1. \"Urd\": a Matlab frontend with high-level functions for quickly defining networks\r\n2. \"Verdandi\": an optimized C++ simulation environment which runs the simulation defined by Urd\r\n3. \"Skuld\": an advanced Matlab graphical user interface (GUI) for visual inspection of simulated data." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/154739", + "other_implementer": { + "attr_id": 308, + "value": "Visser, Sid [sid.visser@nottingham.ac.uk]" + }, + "other_neurons": { + "attr_id": 301, + "value": "Leaky Integrate-and-Fire neuron; Izhikevich neuron" + }, + "other_simulator": { + "attr_id": 307, + "value": "Norns - Neural Net Studio" + }, + "ver_date": "2016-07-20T10:59:17.19", + "ver_number": 8 + }, + "154769": { + "class_id": 19, + "created": "2014-05-30T16:35:16", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 154769, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 154353, + "object_name": "Vo T, Bertram R, Tabak J, Wechselberger M (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 91894, + "object_name": "XPP (web link to model)" + } + ] + }, + "name": "Mixed mode oscillations as a mechanism for pseudo-plateau bursting (Vo et al. 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 62277, + "object_name": "Pituitary cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"We combine bifurcation analysis with the theory of canard-induced mixed mode oscillations to investigate the dynamics of a novel form of bursting. This bursting oscillation, which arises from a model of the electrical activity of a pituitary cell, is characterized by small impulses or spikes riding on top of an elevated voltage plateau. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/154769", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2014-06-06T17:43:56", + "ver_number": 3 + }, + "154770": { + "class_id": 19, + "created": "2014-05-31T03:10:14", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 154770, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 154777, + "object_name": "Cohen, Michael X [mikexcohen at gmail.com]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 154776, + "object_name": "Cohen MX (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Input strength and time-varying oscillation peak frequency (Cohen MX 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + }, + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The purpose of this paper is to argue that a single neural functional principle\u2014temporal fluctuations in oscillation peak frequency (\u201cfrequency sliding\u201d)\u2014can be used as a common analysis approach to bridge multiple scales within neuroscience. The code provided here recreates the network models used to demonstrate changes in peak oscillation frequency as a function of static and time-varying input strength, and also shows how correlated frequency sliding can be used to identify functional connectivity between two networks." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/154770", + "other_implementer": { + "attr_id": 308, + "value": "Cohen, Michael X [mikexcohen at gmail.com]" + }, + "other_neurons": { + "attr_id": 301, + "value": "adaptive exponential" + }, + "ver_date": "2017-07-28T13:54:19.363", + "ver_number": 14 + }, + "154871": { + "class_id": 19, + "created": "2014-06-06T17:26:18", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 154871, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 154849, + "object_name": "Teka W, Tabak J, Bertram R (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 91894, + "object_name": "XPP (web link to model)" + } + ] + }, + "name": "The relationship between two fast/slow analysis techniques for bursting oscill. (Teka et al. 2012)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 62277, + "object_name": "Pituitary cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Bursting oscillations in excitable systems reflect multi-timescale dynamics. \r\n\r\nThese oscillations have\r\noften been studied in mathematical models by splitting the equations into fast and slow\r\nsubsystems.\r\n\r\nTypically, one treats the slow variables as parameters of the fast subsystem and studies\r\nthe bifurcation structure of this subsystem.\r\n\r\nThis has key features such as a z-curve (stationary\r\nbranch) and a Hopf bifurcation that gives rise to a branch of periodic spiking solutions.\r\n\r\nIn models\r\nof bursting in pituitary cells, we have recently used a different approach that focuses on the\r\ndynamics of the slow subsystem.\r\n\r\nCharacteristic features of this approach are folded node\r\nsingularities and a critical manifold.\r\n\u2026\r\n We find that the z-curve and Hopf bifurcation of the twofast/\r\none-slow decomposition are closely related to the voltage nullcline and folded node singularity\r\nof the one-fast/two-slow decomposition, respectively. They become identical in the double singular\r\nlimit in which voltage is infinitely fast and calcium is infinitely slow.\"\r\n" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/154871", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2014-06-06T17:46:16", + "ver_number": 6 + }, + "154872": { + "class_id": 19, + "created": "2014-06-07T15:12:27", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 154872, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 146951, + "object_name": "McDougal, Robert [robert.mcdougal at yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 155858, + "object_name": "McDougal RA, Morse TM, Hines ML, Shepherd GM (2015)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "ModelView: online structural analysis of computational models (McDougal et al. 2015)", + "notes": { + "attr_id": 24, + "value": "\" ...\r\nTo aid users, we have developed ModelView, a\r\nweb application for NEURON models in ModelDB that presents a graphical view of\r\nmodel structure augmented with contextual information. Web presentation provides\r\na rich, simulator-independent environment for interacting with graphs. The necessary\r\ndata is generated by combining manual curation, text-mining the source code, querying\r\nModelDB, and simulator introspection. ... With this\r\ntool, researchers can examine the structure of hundreds of models in ModelDB in a\r\nstandardized presentation without installing any software, downloading the model, or\r\nreading model source code.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/154872", + "ver_date": "2016-06-03T13:36:41.547", + "ver_number": 9 + }, + "154927": { + "class_id": 19, + "created": "2014-06-09T11:41:45", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 154927, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 154777, + "object_name": "Cohen, Michael X [mikexcohen at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3642, + "object_name": "Tutorial/Teaching" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 84598, + "object_name": "Extracellular Fields" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 154835, + "object_name": "Cohen MX (2014)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Analyzing neural time series data theory and practice (Cohen 2014)", + "notes": { + "attr_id": 24, + "value": "\"This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/154927", + "ver_date": "2017-07-25T17:08:19.73", + "ver_number": 5 + }, + "154955": { + "class_id": 19, + "created": "2014-06-10T14:28:30", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 154955, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 154930, + "object_name": "Vo T, Tabak J, Bertram R, Wechselberger M (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 91894, + "object_name": "XPP (web link to model)" + } + ] + }, + "name": "Understanding how fast activating K+ channels promote bursting in pituitary cells (Vo et al 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 62277, + "object_name": "Pituitary cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"...\r\nExperimental\r\nobservations have shown ... that fast-activating\r\nvoltage- and calcium-dependent potassium (BK) current\r\ntends to promote bursting in pituitary cells.\r\n\r\nThis burst promoting\r\neffect requires fast activation of the BK current,\r\notherwise it is inhibitory to bursting. In this work, we analyze\r\na pituitary cell model in order to answer the question\r\nof why the BK activation must be fast to promote bursting.\r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/154955", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2014-06-10T14:41:53", + "ver_number": 1 + }, + "154967": { + "citation": { + "attr_id": 391, + "value": "23516346" + }, + "class_id": 19, + "created": "2014-06-12T14:25:29", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 154967, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53429, + "object_name": "Blackwell, Avrama [avrama at gmu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 154972, + "object_name": "Kim B, Hawes SL, Gillani F, Wallace LJ, Blackwell KT (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 139757, + "object_name": "NeuroRD" + } + ] + }, + "name": "Gq coupled signaling pathways involved in striatal synaptic plasticity (Kim et al. 2013)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 260, + "object_name": "Neostriatum medium spiny direct pathway GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Model of Gq coupled signaling pathways underlying synaptic plasticity in striatal medium spiny projection neurons. Reactions and diffusion are implemented stochastically in a dendrite with one or more diffusionally coupled spines. Simulations demonstrate that theta burst stimulation, which produces LTP, increases the ratio of PKC:2AG as compared to 20 Hz stimulation, which produces LTD." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/154967", + "public_submitter_email": { + "attr_id": 309, + "value": "avrama@gmu.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 184, + "object_name": "mGluR1" + } + ] + }, + "ver_date": "2014-06-13T14:58:18", + "ver_number": 6 + }, + "154968": { + "citation": { + "attr_id": 391, + "value": "22346744" + }, + "class_id": 19, + "created": "2014-06-12T15:22:13", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 154968, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53429, + "object_name": "Blackwell, Avrama [avrama at gmu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 150896, + "object_name": "Oliveira RF, Kim M, Blackwell KT (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 139757, + "object_name": "NeuroRD" + } + ] + }, + "name": "Dopamine activation of signaling pathways in a medium spiny projection neuron (Oliveira et al. 2012)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 260, + "object_name": "Neostriatum medium spiny direct pathway GABA cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Large scale stochastic reaction-diffusion model of signaling pathways in a medium spiny projection neuron dendrite with spines to investigate whether the critical function of anchoring is to locate PKA near the cAMP that activates it or near its targets, such as AMPA receptors located in the post-synaptic density. Simulations, implemented in NeuroRD, show that PKA colocalization with adenylate cyclase, either in the spine head or in the dendrite, leads to greater phosphorylation of DARPP-32 Thr34 and AMPA receptor GluA1 Ser845 than when PKA is anchored away from adenylate cyclase." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/154968", + "public_submitter_email": { + "attr_id": 309, + "value": "avrama@gmu.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 196, + "object_name": "D1" + } + ] + }, + "ver_date": "2014-07-08T10:31:17", + "ver_number": 12 + }, + "155057": { + "class_id": 19, + "created": "2014-06-16T14:45:05", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 255, + 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Mean and standard deviation of the firing rates in the populations are calculated. The sigmoid is derived from the single cell FI-curve, allowing for easy implementation of pathological conditions.\r\n\r\nNMM is compared with a detailed spiking network model consisting of HH neurons.\r\n\r\nNMM code in Matlab. The network model is simulated using Norns (ModelDB # 154739)" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/155130", + "other_concept": { + "attr_id": 306, + "value": "Neural mass model" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "b.zandt@utwente.nl" + }, + "ver_date": "2014-06-24T12:04:32", + "ver_number": 7 + }, + "155131": { + "citation": { + "attr_id": 391, + "value": "doi: 10.12688/f1000research.2-88.v2" + }, + "class_id": 19, + "created": "2014-06-21T03:59:19", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 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"object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "Self-organized olfactory pattern recognition (Kaplan & Lansner 2014)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + }, + { + "object_id": 268, + "object_name": "Olfactory receptor GLU cell" + }, + { + "object_id": 269, + "object_name": "Piriform cortex anterior pyramidal layer II GLU cell" + }, + { + "object_id": 3978, + "object_name": "Olfactory bulb main interneuron periglomerular GABA cell" + }, + { + "object_id": 3979, + "object_name": "Olfactory bulb main interneuron granule MC GABA cell" + }, + { + "object_id": 153283, + "object_name": "Olfactory bulb main interneuron granule TC GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ...\r\nWe present a large-scale network model with single and multi-compartmental Hodgkin\u2013Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC).\r\n\r\nOdor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants.\r\n...\r\nThe PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian\u2013Bayesian learning.\r\n\r\nWe demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants.\r\n\r\nFurthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/155157", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115949, + "object_name": "Olfactory cortex" + }, + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "ver_date": "2018-10-17T16:15:13.94", + "ver_number": 7 + }, + "155565": { + "citation": { + "attr_id": 391, + "value": "Dynamic mechanisms of neocortical focal seizure onset. Y. Wang, M. Goodfellow, P.N. Taylor, G. Baier. PLOS Computational Biology (2014)" + }, + "class_id": 19, + "created": "2014-06-26T10:26:56", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 155565, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 155567, + "object_name": "Wang, Yujiang [yujiang.wang at newcastle.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 97752, + "object_name": "Delay" + }, + { + "object_id": 139148, + "object_name": "Brain Rhythms" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 155566, + "object_name": "Wang Y, Goodfellow M, Taylor PN, Baier G (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A cortical sheet mesoscopic model for investigating focal seizure onset dynamics (Wang et al. 2014)", + "notes": { + "attr_id": 24, + "value": "The model uses realistically coupled, discretised, Wilson-Cowan units to describe the spatio-temporal activity of a cortical sheet. This model has been used the investigate the dynamic onset mechanisms of focal seizures." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/155565", + "other_implementer": { + "attr_id": 308, + "value": "Yujiang Wang " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "yujiang.wang@newcastle.ac.uk" + }, + "ver_date": "2014-06-27T13:05:01", + "ver_number": 9 + }, + "155568": { + "citation": { + "attr_id": 391, + "value": "http://www.ncbi.nlm.nih.gov/pubmed/24811867" + }, + "class_id": 19, + "created": "2014-06-26T12:48:25", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 155568, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 155598, + "object_name": "Tejada, Julian [julian.tejada at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 155599, + "object_name": "Neurogenesis" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 154476, + "object_name": "Tejada J, Garcia-Cairasco N, Roque AC (2014)" + }, + { + "object_id": 154558, + "object_name": "Tejada J, Arisi GM, Garc\u00eda-Cairasco N, Roque AC (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Dentate gyrus network model (Tejada et al 2014)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + }, + { + "object_id": 52416, + "object_name": "Dentate gyrus mossy cell" + }, + { + "object_id": 52417, + "object_name": "Dentate gyrus basket cell" + }, + { + "object_id": 88216, + "object_name": "Dentate gyrus hilar cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Here we adapted an existing computational model of the dentate gyrus (J Neurophysiol 93: 437-453, 2005) by replacing the reduced granule cell models with morphologically detailed models coming from (3D) reconstructions of mature cells.\r\n...\r\nDifferent fractions of the mature granule cell models were replaced by morphologically reconstructed models of newborn dentate granule cells from animals with PILO-induced Status Epilepticus, which have apical dendritic alterations and spine loss, and control animals, which do not have these alterations. \r\n\r\nThis complex arrangement of cells and processes allowed us to study the combined effect of mossy fiber sprouting, altered apical dendritic tree and dendritic spine loss in newborn granule cells on the excitability of the dentate gyrus model. \r\n\r\nOur simulations suggest that alterations in the apical dendritic tree and dendritic spine loss in newborn granule cells have opposing effects on the excitability of the dentate gyrus after Status Epilepticus. Apical dendritic alterations potentiate the increase of excitability provoked by mossy fiber sprouting while spine loss curtails this increase.\r\n\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/155568", + "other_concept": { + "attr_id": 306, + "value": "Neurogenesis" + }, + "other_implementer": { + "attr_id": 308, + "value": "Tejada, Julian " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "julian.tejada@gmail.com" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115947, + "object_name": "Dentate gyrus" + } + ] + }, + "ver_date": "2022-12-16T10:53:51.667", + "ver_number": 15 + }, + "155601": { + "citation": { + "attr_id": 391, + "value": "24387588" + }, + "class_id": 19, + "created": "2014-06-27T17:35:12", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 155601, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 155669, + "object_name": "Proddutur A, Yu J, Elgammal FS, Santhakumar V (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Basket cell extrasynaptic inhibition modulates network oscillations (Proddutur et al., 2013)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52417, + "object_name": "Dentate gyrus basket cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Among the rhythmic firing patterns observed in brain, gamma oscillations, which are involved in memory formation and retrieval, are generated by networks of fast-spiking basket cells (FS-BCs) with robust interconnectivity through fast GABA synapses. Recently, we identified presence of extrasynaptic tonic GABA currents in FS-BCs and showed that experimentally-induced seizures enhance extrasynaptic tonic GABA currents and render GABA reversal potential (EGABA) depolarizing (Yu et al., 2013). Extrasynaptic GABA currents are mediated by extra- and peri-synaptically located GABAARs and can contribute to synaptic decay kinetics. Additionally, shunting rather than hyperpolarizing EGABA has been shown to increase the frequency and reduce coherence of network oscillations. Using homogeneous networks of biophysically-based, multi-compartmental model FS-BCs, we examined how the presence of extrasynaptic GABA currents and the experimentally identified seizure-induced alterations in GABA currents and EGABA modify the frequency and coherence of network firing." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/155601", + "other_receptors": { + "attr_id": 302, + "value": "ExtrasynapticGABAa" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ap269@njit.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "ver_date": "2022-11-04T12:42:38.03", + "ver_number": 13 + }, + "155602": { + "citation": { + "attr_id": 391, + "value": "23324316" + }, + "class_id": 19, + "created": "2014-06-29T12:20:42", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 155602, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 155603, + "object_name": "Yu J, Proddutur A, Elgammal FS, Ito T, Santhakumar V (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Status epilepticus alters dentate basket cell tonic inhibition (Yu J et al 2013)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52417, + "object_name": "Dentate gyrus basket cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Status epilepticus (SE) leads to changes in dentate inhibitory neuronal networks and alters synaptic and tonic inhibition in granule cells. Recently, we identified that one week after pilocarpine-induced status epilepticus, dentate fast-spiking basket cells (FS-BCs), which underlie fast perisomatic inhibition, show two distinct changes in inhibition: (1) enhanced tonic currents (IGABA) and (2)depolarizing shift in GABA reversal (EGABA) following SE. These two changes can have opposing effects on neuronal inhibition with increases in tonic GABA conductance (gGABA) reducing excitability when the GABA currents are shunting (or hyperpolarizing) and potentially enhancing excitability when GABA currents are depolarizing. The following model is used to examine the post-SE changes in tonic GABA conductance, together with the depolarized GABA reversal potential modify FS-BC excitability and dentate network activity." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/155602", + "other_receptors": { + "attr_id": 302, + "value": "Extrasynaptic GABAa" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ap269@njit.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "ver_date": "2022-11-04T13:13:26.687", + "ver_number": 7 + }, + "155705": { + "citation": { + "attr_id": 391, + "value": "Avella Gonzalez, O. J., Van Aerde, K. I., Mansvelder, H. D., Van Pelt, J., and Van Ooyen, A. (2014). Inter-network interactions: impact of connections between oscillatory neuronal networks on oscillation frequency and pattern. PloS ONE, in press." + }, + "class_id": 19, + "created": "2014-06-30T14:52:10", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 155705, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 155712, + "object_name": "Avella G. Oscar Javier [oscarjavella at gmail dot com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 139148, + "object_name": "Brain Rhythms" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 155711, + "object_name": "Avella Gonzalez OJ, van Aerde KI, Mansvelder HD, van Pelt J, van Ooyen A (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A two networks model of connectivity-dependent oscillatory activity (Avella OJ et al. 2014)", + "notes": { + "attr_id": 24, + "value": "Activity in a cortical network may express a single oscillation frequency, alternate between two or more distinct frequencies, or continually express multiple frequencies. In addition, oscillation amplitude may fluctuate over time. Interactions between oscillatory networks may contribute, but their effects are poorly known. Here, we created a two model networks, one generating on its own a relatively slow frequency (slow network) and one generating a fast frequency (fast network). We chose the slow or the fast network as source network projecting feed-forward connections to the other, or target network, and systematically investigated how type and strength of inter-network connections affected target network activity. Our results strongly depended on three factors: the type of the relevant (main) connection, its strength and the amount of source synapses. For high inter-network connection strengths, we found that the source network could completely impose its rhythm on the target network. Interestingly, the slow network was more effective at imposing its rhythm on the fast network than the other way around. The strongest entrainment occurred when excitatory cells of the slow network projected to excitatory or inhibitory cells of the fast network. Just as observed in rat activity at the prefrontal cortex satisfies the behavior described above, such that together, our results suggest that input from other oscillating networks may markedly alter a network\u00e2\u20ac\u2122s frequency spectrum and may partly be responsible for the rich repertoire of temporal oscillation patterns observed in the brain." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/155705", + "other_implementer": { + "attr_id": 308, + "value": "Avella G. Oscar Javier [oscarjavella at gmail dot com]" + }, + "other_neurons": { + "attr_id": 301, + "value": "Excitatory/inhibitory single-compartments" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "oscarjavella@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2015-01-04T21:40:44", + "ver_number": 16 + }, + "155727": { + "class_id": 19, + "created": "2014-07-08T11:27:31", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 155727, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33804, + "object_name": "Gabbiani, F" + }, + { + "object_id": 155728, + "object_name": "Trousdale, James [jamest212 at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + }, + { + "object_id": 114660, + "object_name": "Connectivity matrix" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 157162, + "object_name": "Trousdale J, Carroll SR, Gabbiani F, Josic K (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Vertical System (VS) tangential cells network model (Trousdale et al. 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 157238, + "object_name": "Fly vertical system tangential cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Network model of the VS tangential cell system, with 10 cells per hemisphere. Each cell is a two compartment model with one compartment for dendrites and one for the axon. The cells are coupled through axonal gap junctions. The code allows to simulate responses of the VS network to a variety of visual stimuli to investigate coding as a function of gap junction strength." + }, + "other_implementer": { + "attr_id": 308, + "value": "Trousdale, James [jamest212 at gmail.com]" + }, + "other_neurons": { + "attr_id": 301, + "value": "VS tangential cells of the fly" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 178, + "object_name": "Nicotinic" + }, + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "ver_date": "2017-02-15T14:01:03.603", + "ver_number": 12 + }, + "155731": { + "class_id": 19, + "created": "2014-07-09T17:24:16", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122810, + "object_name": "Cav3.1 CACNA1G" + }, + { + "object_id": 122819, + "object_name": "Cav2.1 CACNA1A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 155731, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 150636, + "object_name": "Anwar, Haroon [haroon.anwar at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 156261, + "object_name": "Anwar H, Roome CJ, Nedelescu H, Chen W, Kuhn B, De Schutter E (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 144556, + "object_name": "STEPS" + } + ] + }, + "name": "Calcium dynamics depend on dendritic diameters (Anwar et al. 2014)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... in dendrites there is a strong contribution of morphology because the peak calcium levels are strongly determined by the surface to volume ratio (SVR) of each branch, which is inversely related to branch diameter. In this study we explore the predicted variance of dendritic calcium concentrations due to local changes in dendrite diameter and how this is affected by the modeling approach used. We investigate this in a model of dendritic calcium spiking in different reconstructions of cerebellar Purkinje cells and in morphological analysis of neocortical and hippocampal pyramidal neurons. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/155731", + "other_gene": { + "attr_id": 477, + "value": "mslo, SK2" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "haroon.anwar@gmail.com" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2014-08-07T11:05:31", + "ver_number": 10 + }, + "155735": { + "class_id": 19, + "created": "2014-07-12T13:02:03", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 155735, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 169343, + "object_name": "Couto, Joao [jpcouto at 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(Couto et al 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "NEURON implementation of stochastic gating in the Khaliq-Raman Purkinje cell model.\r\n\r\nNEURON implementation of the De Schutter and Bower model of a Purkinje Cell.\r\n\r\nMatlab scripts to compute the Phase Response Curve (PRC).\r\n\r\nLCG configuration files to experimentally determine the PRC. \r\n\r\nIntegrate and Fire models (leaky and non-leaky) implemented in BRIAN to see the influence of the PRC in a network of unconnected neurons receiving sparse common input." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/155735", + "other_implementer": { + "attr_id": 308, + "value": "Couto, Joao [jpcouto at gmail.com]" + }, + "other_simulator": { + "attr_id": 307, + "value": "LCG" + }, + "ver_date": "2017-07-28T13:57:28.47", + "ver_number": 15 + }, + "155796": { + "citation": { + "attr_id": 391, + "value": "25012843" + }, + "class_id": 19, + "created": "2014-07-16T03:16:51", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 2397, + "object_name": "I Mixed" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92084, + "object_name": "Nav1.3 SCN3A" + }, + { + "object_id": 92088, + "object_name": "Nav1.7 SCN9A" + }, + { + "object_id": 92091, + "object_name": "Nav1.9 SCN11A SCN12A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + 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The model contains all the major voltage-gated and calcium-gated currents observed in these neurons. This model can reproduce physiological observations such as the response to multiple brief depolarizing currents, prolonged depolarizing currents and hyperpolarizing currents. This model can be used to predict how different currents influence the excitability of intrinsic sensory neurons in the gut." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/155796", + "other_neurons": { + "attr_id": 301, + "value": "Intrinsic sensory neuron in gastrointestinal tract" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jordandchambers@gmail.com" + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181568, + "object_name": "155796_2" + }, + { + "object_id": 181571, + "object_name": "155796_3" + }, + { + "object_id": 181576, + "object_name": "155796_1" + } + ] + }, + "ver_date": "2015-01-04T21:41:11", + "ver_number": 7 + }, + "155856": { + "citation": { + "attr_id": 391, + "value": "Teka W, Marinov TM, Santamaria F (2014) Neuronal Spike Timing Adaptation Described with a Fractional Leaky Integrate-and-Fire Model. PLoS Comput Biol 10(3): e1003526. doi:10.1371/journal.pcbi.1003526" + }, + "class_id": 19, + "created": "2014-07-17T16:28:25", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 139268, + "object_name": "I Na, leak" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 155856, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 155860, + "object_name": "Teka W, Marinov TM, Santamaria F (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Fractional leaky integrate-and-fire model (Teka et al. 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 155861, + "object_name": "Abstract integrate-and-fire fractional leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We developed the Fractional Leaky Integrate-and-Fire model that can produce downward and upward spike time adaptions observed on pyramidal cells.The adaptation emerges from the fractional exponent of the voltage dynamics." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/155856", + "other_currents": { + "attr_id": 303, + "value": "I leak" + }, + "other_implementer": { + "attr_id": 308, + "value": "Teka, Wondimu" + }, + "other_neurons": { + "attr_id": 301, + "value": "Fractional Leaky Integrate-and-Fire" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "wondimu.teka@utsa.edu" + }, + "ver_date": "2014-07-18T11:55:00", + "ver_number": 4 + }, + "156027": { + "class_id": 19, + "created": "2014-07-24T17:07:14", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 156027, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 84598, + "object_name": "Extracellular Fields" + }, + { + "object_id": 139996, + "object_name": "Evoked LFP" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 5054, + "object_name": "Rall W, Shepherd GM (1968)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "name": "Theoretical reconstrucion of field potentials and dendrodendritic synaptic...(Rall & Shepherd 1968)", + "notes": { + "attr_id": 24, + "value": "This\r\nwas the first application of compartmental modeling using the Rall\r\napproach to brain neurons. It combined multicompartmental representation\r\nof a mitral cell and a granule cell with the first Hodgkin-Huxley-like\r\naction potential\r\nto model antidromic activation of the mitral cell, followed by synaptic\r\nexcitation of the granule cell and synaptic inhibition of the mitral cell.\r\nCombined with reconstruction of the field\r\npotentials generated around these neurons, and detailed comparisons with\r\nsingle\r\ncell recordings, it led to prediction of dendrodendritic interactions\r\nmediating\r\nself and lateral inhibition of the mitral cells by the granule cells. It\r\nhas been regarded as\r\nthe first computational model of a brain microcircuit (see also Shepherd\r\nand Brayton, 1979). Recreation of the model is pending." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/156027", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "ver_date": "2018-10-17T16:15:31.83", + "ver_number": 11 + }, + "156039": { + "class_id": 19, + "created": "2014-07-29T07:21:21", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 156039, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 156071, + "object_name": "Allken, Vaneeda [vaneeda at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 143636, + "object_name": "Conductance distributions" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 156042, + "object_name": "Allken V, Chepkoech JL, Einevoll GT, Halnes G (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "The subcellular distribution of T-type Ca2+ channels in LGN interneurons (Allken et al. 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 140252, + "object_name": "Thalamus lateral geniculate nucleus interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ...To study\r\nthe relationship between the (Ca2+ channel) T-distribution and several (LGN interneuron) IN response properties, we here\r\nrun a series of simulations where we vary the T-distribution in a multicompartmental IN\r\nmodel with a realistic morphology. We find that the somatic response to somatic\r\ncurrent injection is facilitated by a high T-channel density in the soma-region.\r\nConversely, a high T-channel density in the distal dendritic region is found to facilitate\r\ndendritic signalling in both the outward direction (increases the response in\r\ndistal dendrites to somatic input) and the inward direction (the soma responds stronger\r\nto distal synaptic input). ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/156039", + "other_implementer": { + "attr_id": 308, + "value": "Allken Vaneeda" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "vaneeda@gmail.com" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181308, + "object_name": "156039" + } + ] + }, + "ver_date": "2023-01-30T14:25:10.39", + "ver_number": 16 + }, + "156072": { + "citation": { + "attr_id": 391, + "value": "24863422" + }, + "class_id": 19, + "created": "2014-07-30T07:43:12", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 156072, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 156073, + "object_name": "Tomsett, Richard [r at autap.se]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 139148, + "object_name": "Brain Rhythms" + }, + { + "object_id": 156115, + "object_name": "Gamma oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 156077, + "object_name": "Tomsett RJ, Ainsworth M, Thiele A, Sanayei M, Chen X, Gieselmann MA, Whittington MA, Cunningham MO, Kaiser M (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 156116, + "object_name": "VERTEX" + } + ] + }, + "name": "Large-scale model of neocortical slice in vitro exhibiting persistent gamma (Tomsett et al. 2014)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + }, + { + "object_id": 7387, + "object_name": "Neocortex V1 interneuron basket PV GABA cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 114635, + "object_name": "Neocortex spiny stellate cell" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + }, + { + "object_id": 142232, + "object_name": "Neocortex spiking low threshold (LTS) neuron" + }, + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model contains 15 neuron populations (8 excitatory, 7 inhibitory) arranged into 4 cortical layers (layer 1 empty, layers 2/3 combined). It produces a persistent gamma oscillation driven by layer 2/3. It runs using the VERTEX simulator, which is written in Matlab and is available from http://www.vertexsimulator.org" + }, + "opensourcebrain": { + "value": "large-scale-model-of-neocortical-slice-in-vitro-exhibiting-persistent-gamma" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/156072", + "other_concept": { + "attr_id": 306, + "value": "Gamma oscillations" + }, + "other_implementer": { + "attr_id": 308, + "value": "Tomsett, Richard " + }, + "other_simulator": { + "attr_id": 307, + "value": "VERTEX" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "indigentmartian@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2015-07-26T13:03:36.127", + "ver_number": 9 + }, + "156120": { + "citation": { + "attr_id": 391, + "value": "Avella Gonzalez OJ, van Aerde KI, van Elburg RAJ, Poil S-S, Mansvelder HD, et al. (2012) External Drive to Inhibitory Cells Induces Alternating Episodes of High- and Low-Amplitude Oscillations. PLoS Comput Biol 8(8): e1002666. doi:10.1371/journal.pcbi.1002666" + }, + "class_id": 19, + "created": "2014-08-01T13:46:32", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 156120, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 155712, + "object_name": "Avella G. Oscar Javier [oscarjavella at gmail dot com]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 155757, + "object_name": "Avella Gonzalez OJ, van Aerde KI, van Elburg RA, Poil SS, Mansvelder HD, Linkenkaer-Hansen K, van Pelt J, van Ooyen A (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "156120", + "name": "ModelView" + } + ], + "default": "156120" + }, + "name": "Single E-I oscillating network with amplitude modulation (Avella Gonzalez et al. 2012)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 156124, + "object_name": "Abstract single compartment conductance based cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... Intriguingly, the amplitude of ongoing oscillations, such as measured in EEG recordings, fluctuates irregularly, with episodes of high amplitude (HAE) alternating with episodes of low amplitude (LAE).\r\n...\r\nHere, we show that transitions between HAE and LAE in the alpha/beta frequency band occur in a generic neuronal network model consisting of interconnected inhibitory (I) and excitatory (E) cells that are externally driven by sustained depolarizing currents(cholinergic input) and trains of action potentials that activate excitatory synapses. \r\n\r\nIn the model, action potentials onto inhibitory cells represent input from other brain areas and desynchronize network activity, being crucial for the emergence of amplitude fluctuations.\r\n...\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/156120", + "other_neurons": { + "attr_id": 301, + "value": "single compartment -conductance based cell-" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "oscarjavella@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181561, + "object_name": "156120" + } + ] + }, + "ver_date": "2015-01-04T21:42:58", + "ver_number": 8 + }, + "156162": { + "class_id": 19, + "created": "2014-08-02T12:20:32", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 156162, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 156161, + "object_name": "Friedrich P, Vella M, Guly\u00e1s AI, Freund TF, K\u00e1li S (2014)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Software (called Optimizer) for fitting neuronal models (Friedrich et al. 2014)", + "notes": { + "attr_id": 24, + "value": "\" ... Our software, called Optimizer, implements a modular and extensible framework for\r\nthe optimization of neuronal models, and also features a graphical interface which\r\nmakes it easy for even non-expert users to handle many commonly occurring scenarios. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/156162", + "ver_date": "2014-08-07T10:35:46", + "ver_number": 6 + }, + "156260": { + "citation": { + "attr_id": 391, + "value": "24304860" + }, + "class_id": 19, + "created": "2014-08-06T17:04:08", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 156260, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53429, + "object_name": "Blackwell, Avrama [avrama at gmu.edu]" + }, + { + "object_id": 156778, + "object_name": "Damodaran, Sriraman [dsriraman at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 156735, + "object_name": "Damodaran S, Evans RC, Blackwell KT (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "Synchronicity of fast-spiking interneurons balances medium-spiny neurons (Damodaran et al. 2014)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 260, + "object_name": "Neostriatum medium spiny direct pathway GABA cell" + }, + { + "object_id": 153360, + "object_name": "Neostriatum medium spiny indirect pathway GABA cell" + }, + { + "object_id": 118395, + "object_name": "Neostriatum fast spiking interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This study investigates the role of feedforward and feedback inhibition in maintaining the balance between D1 and D2 MSNs of the striatum. The synchronized firing of FSIs are found to be critical in this mechanism and specifically the gap junction connections between FSIs." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/156260", + "other_implementer": { + "attr_id": 308, + "value": "Damodaran, Sriraman \r\ndsriraman@gmail.com" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "dsriraman@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "ver_date": "2014-08-20T10:19:43", + "ver_number": 6 + }, + "156470": { + "class_id": 19, + "created": "2014-08-14T13:32:21", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 156470, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 154858, + "object_name": "Teka W, Tabak J, Vo T, Wechselberger M, Bertram R (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + }, + { + "object_id": 91894, + "object_name": "XPP (web link to model)" + } + ] + }, + "name": "The dynamics underlying pseudo-plateau bursting in a pituitary cell model (Teka et al. 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 62277, + "object_name": "Pituitary cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... pseudo-plateau bursts, are unlike bursts studied mathematically in neurons (plateau bursting) and the standard fast-slow analysis used for plateau bursting is of limited use. Using an alternative fast-slow analysis, with one fast and two slow variables, we show that pseudo-plateau bursting is a canard-induced mixed mode oscillation. ...\" See paper for other results." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/156470", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2014-08-14T16:50:58", + "ver_number": 5 + }, + "156733": { + "class_id": 19, + "created": "2014-08-18T14:34:55", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 156733, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 116577, + "object_name": "Solinas, Sergio [solinas at unipv.it]" + }, + { + "object_id": 139758, + "object_name": "Roessert, Christian [christian.a at roessert.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 238957, + "object_name": "Markov-type model" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 167811, + "object_name": "R\u00f6ssert C, Solinas S, D'Angelo E, Dean P, Porrill J (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Information transmission in cerebellar granule cell models (Rossert et al. 2014)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 7346, + "object_name": "Cerebellum interneuron granule GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... In this modeling study we analyse how electrophysiological\r\ngranule cell properties and spike sampling influence information coded\r\nby firing rate modulation, assuming no signal-related, i.e.,\r\nuncorrelated inhibitory feedback (open-loop mode). A detailed\r\none-compartment granule cell model was excited in simulation by either\r\ndirect current or mossy-fiber synaptic inputs. Vestibular signals were\r\nrepresented as tonic inputs to the flocculus modulated at frequencies\r\nup to 20 Hz (approximate upper frequency limit of vestibular-ocular\r\nreflex, VOR). Model outputs were assessed using estimates of both the\r\ntransfer function, and the fidelity of input-signal reconstruction\r\nmeasured as variance-accounted-for. The detailed granule cell model\r\nwith realistic mossy-fiber synaptic inputs could transmit infoarmation\r\nfaithfully and linearly in the frequency range of the\r\nvestibular-ocular reflex. ... \"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/156733", + "public_submitter_email": { + "attr_id": 309, + "value": "christian.a@roessert.de" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2018-02-26T11:48:23.003", + "ver_number": 14 + }, + "156780": { + "class_id": 19, + "created": "2014-08-20T11:11:42", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 156780, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 140733, + "object_name": "Hay, Etay [etay.hay at mail.huji.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 128811, + "object_name": "Laminar Connectivity" + }, + { + "object_id": 147931, + "object_name": "Orientation selectivity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 167346, + "object_name": "Hay E, Segev I (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Microcircuits of L5 thick tufted pyramidal cells (Hay & Segev 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"...\r\nWe simulated detailed conductance-based models of\r\nTTCs (Layer 5 thick tufted pyramidal cells) forming recurrent microcircuits that were interconnected as\r\nfound experimentally; the network was embedded in a realistic background\r\nsynaptic activity. \r\n\r\n...\r\nOur findings indicate that dendritic nonlinearities are pivotal in\r\ncontrolling the gain and the computational functions of TTCs microcircuits,\r\nwhich serve as a dominant output source for the neocortex.\r\n\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/156780", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2022-05-27T11:35:35.987", + "ver_number": 21 + }, + "156781": { + "class_id": 19, + "created": "2014-08-21T10:22:41", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "id": 156781, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 156785, + "object_name": "Choi H, Zhang L, Cembrowski MS, Sabottke CF, Markowitz AL, Butts DA, Kath WL, Singer JH, Riecke H (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Bursting and oscillations in RD1 Retina driven by AII Amacrine Neuron (Choi et al. 2014)", + "notes": { + "attr_id": 24, + "value": "\"In many forms of retinal degeneration, photoreceptors die but inner retinal circuits remain intact. In the rd1 mouse, an established model for blinding retinal diseases, spontaneous activity in the coupled network of AII amacrine and ON cone bipolar cells leads to rhythmic bursting of ganglion cells. Since such activity could impair retinal and/or cortical responses to restored photoreceptor function, understanding its nature is important for developing treatments of retinal pathologies. Here we analyzed a compartmental model of the wild-type mouse AII amacrine cell to predict that the cell's intrinsic membrane properties, specifically, interacting fast Na and slow, M-type K conductances, would allow its membrane potential to oscillate when light-evoked excitatory synaptic inputs were withdrawn following photoreceptor degeneration. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/156781", + "other_implementer": { + "attr_id": 308, + "value": "Cembrowski, Mark [mcembrowski at gmail.com]\r\nChoi, Hannah [hannahchoi2014 at u.northwestern.edu]\r\nRiecke, Hermann [h-riecke at northwestern.edu]\r\nKath, William L [kath at northwestern.edu]" + }, + "other_neurons": { + "attr_id": 301, + "value": "Retina AII amacrine neuron" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 118529, + "object_name": "Retina" + } + ] + }, + "ver_date": "2016-03-22T15:10:24.647", + "ver_number": 14 + }, + "156828": { + "class_id": 19, + "created": "2014-08-24T16:34:49", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 156828, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 156119, + "object_name": "Simoes-de-Souza, Fabio [fabio.souza at ufabc.edu.br]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 4675, + "object_name": "Bhalla US, Bower JM (1993)" + }, + { + "object_id": 7136, + "object_name": "Woolf TB, Shepherd GM, Greer CA (1991)" + }, + { + "object_id": 167668, + "object_name": "Sim\u00f5es-de-Souza FM, Antunes G, Roque AC (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "Granule Cells of the Olfactory Bulb (Simoes_De_Souza et al. 2014)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 3979, + "object_name": "Olfactory bulb main interneuron granule MC GABA cell" + }, + { + "object_id": 153283, + "object_name": "Olfactory bulb main interneuron granule TC GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Electrical responses of three classes of granule cells of the olfactory bulb to synaptic activation in different dendritic locations. The constructed models were based on morphological detailed compartmental reconstructions of three granule cell classes of the olfactory bulb with active dendrites described by Bhalla and Bower (J. Neurophysiol. 69: 1948-1965, 1993) and dendritic spine distributions described by Woolf et al. (J. Neurosci. 11: 1837-1854, 1991). The computational studies with the model neurons showed that different quantities of spines have to be activated in each dendritic region to induce an action potential, which always was originated in the active terminal dendrites, independently of the location of the stimuli and the morphology of the dendritic tree." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/156828", + "other_neurons": { + "attr_id": 301, + "value": "Olfactory bulb granule cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "fabio.souza@ufabc.edu.br" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2018-10-17T16:15:46.73", + "ver_number": 8 + }, + "156830": { + "class_id": 19, + "created": "2014-08-26T19:49:31", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 156830, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 156833, + "object_name": "Makadia, Hirenkumar K [hiren.makadia at gmail.com]" + }, + { + "object_id": 156834, + "object_name": "Anderson, Warren D [warren.anderson at jefferson.edu]" + }, + { + "object_id": 156835, + "object_name": "Fey, Dirk [dirk.fey at ucd.ie]" + }, + { + "object_id": 169243, + "object_name": "Vadigepalli, Rajanikanth [Rajanikanth.Vadigepalli at jefferson.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 222956, + "object_name": "Neuromodulation" + }, + { + "object_id": 225299, + "object_name": "Multiscale" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 156831, + "object_name": "Makadia HK, Anderson WD, Fey D, Sauter T, Schwaber JS, Vadigepalli R (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Model of AngII signaling and membrane electrophysiology (Makadia, Anderson, Fey et al., 2015)", + "notes": { + "attr_id": 24, + "value": "We developed a novel multiscale model to bridge neuropeptide receptor-activated signaling pathway with membrane electrophysiology. \r\nThe model studies the effects of Angiotensin II (AngII) on neuronal excitability changes mediated by signaling dynamics and downstream\r\nphosphorylation of ion channels. The multiscale model was implemented as a set of ordinary differential equations solved using the ode15s\r\nsolver in Matlab (Mathworks, USA). The signaling reactions were modeled with either mass-action or Michaelis--Menten kinetics and ion\r\nchannel electrophysiology was modeled according to the Hodgkin-Huxley formalism. These models were initially validated against their\r\nrespective data domains independently and were integrated to develop a multiscale model of signaling and electrophysiology." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/156830", + "public_submitter_email": { + "attr_id": 309, + "value": "hiren.makadia@gmail.com" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 183076, + "object_name": "Brainstem" + } + ] + }, + "ver_date": "2017-02-23T11:06:21.433", + "ver_number": 21 + }, + "157157": { + "class_id": 19, + "created": "2014-09-02T02:08:45", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 157157, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 157159, + "object_name": "Saudargiene, Ausra [ausra.saudargiene at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 87578, + "object_name": "STDP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 188652, + "object_name": "Saudargiene A, Cobb S, Graham BP (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "CA1 pyramidal neuron: synaptic plasticity during theta cycles (Saudargiene et al. 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 114079, + "object_name": "Hippocampus CA1 basket cell" + }, + { + "object_id": 151329, + "object_name": "Hippocampus CA1 bistratified cell" + }, + { + "object_id": 151330, + "object_name": "Hippocampus CA1 axo-axonic cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This NEURON code implements a microcircuit of CA1 pyramidal neuron and consists of a detailed model of CA1 pyramidal cell and four types of inhibitory interneurons (basket, bistratified, axoaxonic and oriens lacunosum-moleculare cells). 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Migliore, et al. (2015). \r\n\r\nIn this paper, we investigate the possibility that the experimental protocols on synaptic plasticity may result in different consequences (e.g., LTD instead of LTP), according to the initial conditions of the stimulated synapses, and can generate confusing results. Using biophysical models of synaptic plasticity and hippocampal CA1 pyramidal neurons, we study how, why, and to what extent EPSPs observed at the soma after induction of LTP/LTD reflects the actual (local) synaptic state. The model and the results suggest a physiologically plausible explanation of why LTD induction is experimentally difficult, and they offer experimentally testable predictions on the stimulation protocols that may be more effective." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/157339", + "other_implementer": { + "attr_id": 308, + "value": "Migliore, Rosanna [rosanna.migliore at cnr.it]" + }, + "other_type": { + "attr_id": 305, + "value": "Dendrite" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2015-03-10T11:34:02.61", + "ver_number": 34 + }, + "167414": { + "class_id": 19, + "created": "2014-09-16T11:22:16", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 167414, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 46840, + "object_name": "D'Angelo, Egidio [dangelo at unipv.it]" + }, + { + "object_id": 150204, + "object_name": "Garrido, Jesus A [jesus.garrido at unipv.it]" + }, + { + "object_id": 150226, + "object_name": "Luque, Niceto R. 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We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/167414", + "other_implementer": { + "attr_id": 308, + "value": "Antonietti, Alberto [alberto.antonietti at polimi.it]\r\n" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2017-02-15T14:01:37.053", + "ver_number": 10 + }, + "167499": { + "citation": { + "attr_id": 391, + "value": "Egger, Schmitt et al.: Robustness of sensory-evoked excitation is increased by layer 1 inhibitory inputs to distal apical dendrites (submitted)" + }, + "class_id": 19, + "created": "2014-09-20T11:46:39", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 167499, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 167501, + "object_name": "Egger, Robert [robert.egger at nyumc.org]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 245799, + "object_name": "Whisking" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 185322, + "object_name": "Egger R, Schmitt AC, Wallace DJ, Sakmann B, Oberlaender M, Kerr JN (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Distal inhibitory control of sensory-evoked excitation (Egger, Schmitt et al. 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Model of a cortical layer (L) 2 pyramidal neuron embedded in an anatomically realistic network of two barrel columns in rat vibrissal cortex. This model is used to investigate the effects of spatially and temporally specific inhibition from L1 inhibitory interneurons on the sensory-evoked subthreshold responses of the L2 pyramidal neuron, and can be used to create simulation results underlying Figures 3D, 4B, 4C and 4E from (Egger, Schmitt et al. 2015)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/167499", + "other_implementer": { + "attr_id": 308, + "value": "Egger, Robert " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "robert.egger@tuebingen.mpg.de" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2018-10-18T17:35:53.08", + "ver_number": 12 + }, + "167638": { + "class_id": 19, + "created": "2014-09-24T17:29:51", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 167638, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 168095, + "object_name": "Schneider CJ, Cuntz H, Soltesz I (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Generation of granule cell dendritic morphology (Schneider et al. 2014)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The following code was used to generate a complete population of 1.2 million granule cell dendritic morphologies within a realistic three-dimensional context. These generated dendritic morphologies match the known biological variability and context-dependence of morphological features." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/167638", + "other_implementer": { + "attr_id": 308, + "value": "Schneider, Calvin " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "calvinjs@uci.edu" + }, + "ver_date": "2014-10-24T15:05:58", + "ver_number": 4 + }, + "167694": { + "citation": { + "attr_id": 391, + "value": "24357611" + }, + "class_id": 19, + "created": "2014-10-01T17:16:47", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 167694, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 151406, 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intensity and spatial configuration of its own synaptic inputs, and is nearly invariant to the rate of bAP-mediated 'cross-talk' from other dendrites over a 100-fold range...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/167694", + "public_submitter_email": { + "attr_id": 309, + "value": "bardiafb+mdb@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2014-10-03T16:15:56", + "ver_number": 2 + }, + "167714": { + "biosimulations": { + "value": "modeldb-167714" + }, + "class_id": 19, + "created": "2014-10-08T13:46:21", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 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24, + "value": "The classical Hodgkin--Huxley (HH) model neglects the time-dependence of ion concentrations in spiking dynamics. The dynamics is therefore limited to a time scale of milliseconds, which is determined by the membrane capacitance multiplied by the resistance of the ion channels, and by the gating time constants. This model includes slow dynamics in an extended HH framework that simulates time-dependent ion concentrations, pumps, and buffers. Fluxes across the neuronal membrane change intra- and extracellular ion concentrations, whereby the latter can also change through contact to reservoirs in the surroundings. The dynamics on three distinct slow times scales is determined by the cell volume-to-surface-area ratio and the membrane permeability (seconds), the buffer time constants (tens of seconds), and the slower backward buffering (minutes to hours). The modulatory dynamics and the newly emerging excitable dynamics corresponds to pathological conditions observed in epileptiform burst activity, and spreading depression in migraine aura and stroke, respectively." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/167714", + "other_concept": { + "attr_id": 306, + "value": "Spreading depression" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "markus.dahlem@gmail.com" + }, + "ver_date": "2014-10-09T10:31:02", + "ver_number": 6 + }, + "167715": { + "citation": { + "attr_id": 391, + "value": "24860703" + }, + "class_id": 19, + "created": "2014-10-08T15:31:52", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92082, + "object_name": "Nav1.1 SCN1A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 167715, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 167720, + "object_name": "Dahlem MA, Schumacher J, H\u00fcbel N (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Spreading depression model for FHM3 with Nav1.1 mutation (Dahlem et al. 2014)", + "notes": { + "attr_id": 24, + "value": "Familial hemiplegic migraine (FHM) is a rare subtype of migraine with aura. A mutation causing FHM type 3 (FHM3) has been identified in SCN1A encoding the Nav1.1 Na+ channel. This genetic defect affects the inactivation gate. The code describes an extended Hodgkin-Huxley framework with dynamic ion concentrations in a wilde-type and mutant form.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/167715", + "other_concept": { + "attr_id": 306, + "value": "Spreading depression" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "markus.dahlem@gmail.com" + }, + "ver_date": "2014-10-08T22:12:54", + "ver_number": 3 + }, + "167772": { + "class_id": 19, + "created": "2014-10-09T10:30:44", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 167772, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144508, + "object_name": "Orio, Patricio [patricio.orio at uv.cl]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 167809, + "object_name": "Pezo D, Soudry D, Orio P (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Comparison of DA-based Stochastic Algorithms (Pezo et al. 2014)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + }, + { + "object_id": 52426, + "object_name": "Squid axon" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ...\r\nHere we review and test a set of the most recently published DA (Langevin-based Diffusion Approximation) implementations (Goldwyn et al., 2011; Linaro et al., 2011; Dangerfield et al., 2012; Orio and Soudry, 2012; Schmandt and Gal\u00e1n, 2012; G\u00fcler, 2013; Huang et al., 2013a), comparing all of them in a set of numerical simulations that asses numerical accuracy and computational efficiency on three different models: the original Hodgkin and Huxley model, a model with faster sodium channels, and a multi-compartmental model inspired in granular cells.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/167772", + "other_concept": { + "attr_id": 306, + "value": "Stochastic simulation" + }, + "ver_date": "2017-02-15T14:02:09.39", + "ver_number": 12 + }, + "167875": { + "citation": { + "attr_id": 391, + "value": "Clopath C*, Badura A*, de Zeuuw CI, Brunel N. A cerebellar learning model of vestibulo-ocular reflex adaptation in wild-type and mutant mice. J. of Neurosci. 34(21): 7203-7215, 2014" + }, + "class_id": 19, + "created": "2014-10-16T12:36:16", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 167875, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144567, + "object_name": "Clopath, Claudia [c.clopath at imperial.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 167876, + "object_name": "Clopath C, Badura A, De Zeeuw CI, Brunel N (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Vestibulo-Ocular Reflex model in Matlab (Clopath at al. 2014)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ...\r\nWe then\r\nintroduce a minimal model that consists of learning at the parallel\r\nfibers to Purkinje cells with the help of the climbing\r\nfibers. Although the minimal model reproduces the behavior of the\r\nwild-type animals and is analytically tractable, it fails at\r\nreproducing the behavior of mutant mice and the electrophysiology\r\ndata. Therefore, we build a detailed model involving plasticity at the\r\nparallel fibers to Purkinje cells' synapse guided by climbing fibers,\r\nfeedforward inhibition of Purkinje cells, and plasticity at the mossy\r\nfiber to vestibular nuclei neuron synapse. The detailed model\r\nreproduces both the behavioral and electrophysiological data of both\r\nthe wild-type and mutant mice and allows for experimentally testable\r\npredictions.\r\n\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/167875", + "other_type": { + "attr_id": 305, + "value": "Vestibulo-Ocular Reflex" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "c.clopath@imperial.ac.uk" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2014-10-17T10:50:34", + "ver_number": 6 + }, + "168143": { + "class_id": 19, + "created": "2014-10-27T06:59:20", + "gitrepo": false, + "id": 168143, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 126348, + "object_name": "Nakano, Takashi [nakano.takashi at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 180792, + "object_name": "Nakano T, Otsuka M, Yoshimoto J, Doya K (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 2832, + "object_name": "NEST" + } + ] + }, + "name": "A spiking neural network model of model-free reinforcement learning (Nakano et al 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Spiking neural\r\nnetworks provide a theoretically grounded means to test computational\r\nhypotheses on neurally plausible algorithms of reinforcement learning\r\nthrough numerical simulation.\r\n\r\n...\r\nIn this work, we use a spiking neural network model to approximate the\r\nfree energy of a restricted Boltzmann machine and apply it to the\r\nsolution of PORL (partially observable reinforcement learning)\r\nproblems with high-dimensional observations.\r\n... The way spiking neural networks handle PORL problems may provide a glimpse into the underlying laws of neural information processing which can only be discovered through such a top-down approach. \"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/168143", + "public_submitter_email": { + "attr_id": 309, + "value": "nakano.takashi@gmail.com" + }, + "ver_date": "2015-03-09T10:51:41.953", + "ver_number": 5 + }, + "168148": { + "class_id": 19, + "created": "2014-10-29T13:14:58", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2397, + "object_name": "I Mixed" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 112844, + "object_name": "I Q" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 226057, + "object_name": "HCN1" + }, + { + "object_id": 226058, + "object_name": "HCN2" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 168148, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 168308, + "object_name": "Stadler, Konstantin [konstantin.stadler at ntnu.no]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 168170, + "object_name": "Stadler K, Bierwirth C, Stoenica L, Battefeld A, Reetz O, Mix E, Schuchmann S, Velmans T, Rosenberger K, Br\u00e4uer AU, Lehnardt S, Nitsch R, Budt M, Wolff T, Kole MH, Strauss U (2014)" + }, + { + "object_id": 168171, + "object_name": "Reetz O, Stadler K, Strauss U (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "168148", + "name": "ModelView" + } + ], + "default": "168148" + }, + "name": "Firing neocortical layer V pyramidal neuron (Reetz et al. 2014; Stadler et al. 2014)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Neocortical Layer V model with firing behaviour adjusted to in vitro\r\nobservations. The model was used to investigate the effects of IFN and\r\nPKC on the excitability of neurons (Stadler et al 2014, Reetz et al.\r\n2014). The model contains new channel simulations for HCN1, HCN2 and the\r\nbig calcium dependent potassium channel BK." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/168148", + "other_implementer": { + "attr_id": 308, + "value": "Stadler, Konstantin (konstantin.stadler at ntnu.no)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "konstantin.stadler@ntnu.no" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2017-03-18T10:11:35.57", + "ver_number": 22 + }, + "168310": { + "citation": { + "attr_id": 391, + "value": "http://www.ncbi.nlm.nih.gov/pubmed/24550786" + }, + "class_id": 19, + "created": "2014-10-31T06:53:19", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 88215, + "object_name": "I_Ks" + }, + { + "object_id": 143445, + "object_name": "I_KD" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 168310, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144090, + "object_name": "Sidiropoulou, Kyriaki [sidirop at imbb.forth.gr]" + }, + { + "object_id": 168311, + "object_name": "Konstantoudaki, Xanthippi [xeniakons at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 167379, + "object_name": "Konstantoudaki X, Papoutsi A, Chalkiadaki K, Poirazi P, Sidiropoulou K (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "168310", + "name": "ModelView" + } + ], + "default": "168310" + }, + "name": "Pyramidal neuron, fast, regular, and irregular spiking interneurons (Konstantoudaki et al 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + }, + { + "object_id": 142232, + "object_name": "Neocortex spiking low threshold (LTS) neuron" + }, + { + "object_id": 168312, + "object_name": "Neocortex spiking irregular interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a model network of prefrontal cortical microcircuit based primarily on rodent data. It includes 16 pyramidal model neurons, 2 fast spiking interneuron models, 1 regular spiking interneuron model and 1 irregular spiking interneuron model. The goal of the paper was to use this model network to determine the role of specific interneuron subtypes in persistent activity" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/168310", + "other_implementer": { + "attr_id": 308, + "value": "Konstantoudaki, Xanthippi " + }, + "other_neurons": { + "attr_id": 301, + "value": "Neocortical irregular spiking" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "sidirop@imbb.forth.gr" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181340, + "object_name": "168310" + } + ] + }, + "ver_date": "2015-01-04T21:43:56", + "ver_number": 9 + }, + "168314": { + "class_id": 19, + "created": "2014-11-01T15:19:53", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 168314, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 151331, + "object_name": "Storage/recall" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 168316, + "object_name": "Hummos A, Franklin CC, Nair SS (2014)" + }, + { + "object_id": 231819, + "object_name": "Hummos A, Nair SS (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Role for short term plasticity and OLM cells in containing spread of excitation (Hummos et al 2014)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + }, + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + }, + { + "object_id": 154093, + "object_name": "Hippocampus CA3 interneuron basket GABA cell" + }, + { + "object_id": 139885, + "object_name": "Hippocampus CA3 stratum oriens lacunosum-moleculare interneuron" + }, + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + }, + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This hippocampus model was developed by matching experimental data, including neuronal behavior, synaptic current dynamics, network spatial connectivity patterns, and short-term synaptic plasticity. Furthermore, it was constrained to perform pattern completion and separation under the effects of acetylcholine. The model was then used to investigate the role of short-term synaptic depression at the recurrent synapses in CA3, and inhibition by basket cell (BC) interneurons and oriens lacunosum-moleculare (OLM) interneurons in containing the unstable spread of excitatory activity in the network." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/168314", + "other_implementer": { + "attr_id": 308, + "value": "Hummos, Ali [Hummosa@gmail.com]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "hummosa@gmail.com" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2015-02-17T23:51:42.217", + "ver_number": 12 + }, + "168407": { + "class_id": 19, + "created": "2014-11-05T19:17:53", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 168407, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 168408, + "object_name": "Skorheim S, Razak K, Bazhenov M (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Network models of frequency modulated sweep detection (Skorheim et al. 2014)", + "notes": { + "attr_id": 24, + "value": "\"Frequency modulated (FM) sweeps are common in species-specific vocalizations, including human speech. Auditory neurons selective for the direction and rate of frequency change in FM sweeps are present across species, but the synaptic mechanisms underlying such selectivity are only beginning to be understood. Even less is known about mechanisms of experience-dependent changes in FM sweep selectivity. We present three network models of synaptic mechanisms of FM sweep direction and rate selectivity that explains experimental data ... \"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/168407", + "public_submitter_email": { + "attr_id": 309, + "value": "Innerspaceapollo@gmail.com" + }, + "ver_date": "2018-10-18T17:07:52.337", + "ver_number": 6 + }, + "168414": { + "class_id": 19, + "created": "2014-11-10T11:05:37", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 168414, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 168415, + "object_name": "Zhang, Tianhe [tz5@duke.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 168412, + "object_name": "Zhang TC, Janik JJ, Grill WM (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "modelviews": { + "all": [ + { + "id": "168414_1", + "name": "WindUp" + }, + { + "id": "168414_2", + "name": "Fiber, No EP" + }, + { + "id": "168414_3", + "name": "Foreman SCS" + } + ], + "default": "168414_1" + }, + "name": "Effects of spinal cord stimulation on WDR dorsal horn network (Zhang et al 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 128565, + "object_name": "Wide dynamic range neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... To study the mechanisms underlying SCS (Spinal cord stimulation), we constructed a biophysically-based network model of the dorsal horn circuit consisting of interconnected dorsal horn interneurons and a wide dynamic range (WDR) projection neuron and representations of both local and surround receptive field inhibition.\r\n\r\nWe validated the network model by reproducing cellular and network responses relevant to pain processing including wind-up, A-fiber mediated inhibition, and surround receptive field inhibition. ...\" See paper for more." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/168414", + "other_concept": { + "attr_id": 306, + "value": "Spinal Cord Stimulation" + }, + "other_implementer": { + "attr_id": 308, + "value": "Zhang, Tianhe [tz5@duke.edu]" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + }, + { + "object_id": 1361, + "object_name": "Glycine" + } + ] + }, + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181530, + "object_name": "168414_3" + }, + { + "object_id": 181532, + "object_name": "168414_2" + }, + { + "object_id": 181705, + "object_name": "168414_1" + } + ] + }, + "ver_date": "2023-02-16T10:20:58.397", + "ver_number": 22 + }, + "168418": { + "citation": { + "attr_id": 391, + "value": "Polese D, Martinelli E, Marco S, Di Natale C, Gutierrez-Galvez A (2014) Understanding Odor Information Segregation in the Olfactory Bulb by Means of Mitral and Tufted Cells. PLoS ONE 9(10): e109716. doi:10.1371/journal.pone.0109716" + }, + "class_id": 19, + "created": "2014-11-11T05:34:09", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 168418, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 168402, + "object_name": "Polese D, Martinelli E, Marco S, Di Natale C, Gutierrez-Galvez A (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Understanding odor information segregation in the olfactory bulb by MC/TCs (Polese et al. 2014)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + }, + { + "object_id": 3978, + "object_name": "Olfactory bulb main interneuron periglomerular GABA cell" + }, + { + "object_id": 153282, + "object_name": "Olfactory bulb main tufted middle GLU cell" + }, + { + "object_id": 168419, + "object_name": "Olfactory bulb short axon cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Odor identification is one of the main tasks of the olfactory system. It is performed almost independently from the concentration of the odor providing a robust recognition. This capacity to ignore concentration information does not preclude the olfactory system from estimating concentration itself. Significant experimental evidence has indicated that the olfactory system is able to infer simultaneously odor identity and intensity. However, it is still unclear at what level or levels of the olfactory pathway this segregation of information occurs. In this work, we study whether this odor information segregation is performed at the input stage of the olfactory bulb: the glomerular layer." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/168418", + "other_neurons": { + "attr_id": 301, + "value": "Olfactory bulb Short Axon cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "davide.polese@artov.imm.cnr.it" + }, + "ver_date": "2018-10-17T16:16:02.283", + "ver_number": 7 + }, + "168590": { + "citation": { + "attr_id": 391, + "value": "Simmonds and Chacron, PLoS Comp Biol, doi:10.1371/journal.pcbi.1004034" + }, + "class_id": 19, + "created": "2014-11-14T09:03:01", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 249, + "object_name": "I K,leak" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 168590, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 168597, + "object_name": "Simmonds B, Chacron MJ (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "ELL pyramidal neuron (Simmonds and Chacron 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88116, + "object_name": "ELL pyramidal cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "network model of ELL pyramidal neurons receiving both feedforward and feedback inputs" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/168590", + "public_submitter_email": { + "attr_id": 309, + "value": "maurice.chacron@mcgill.ca" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2015-07-29T11:10:07.103", + "ver_number": 7 + }, + "168591": { + "class_id": 19, + "created": "2014-11-14T15:24:47", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 168591, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + }, + { + "object_id": 153637, + "object_name": "Cavarretta, Francesco [francescocavarretta at hotmail.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 83538, + "object_name": "Unsupervised Learning" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 169458, + "object_name": "Migliore M, Cavarretta F, Marasco A, Tulumello E, Hines ML, Shepherd GM (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "3D olfactory bulb: operators (Migliore et al, 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + }, + { + "object_id": 3979, + "object_name": "Olfactory bulb main interneuron granule MC GABA cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... Using a 3D model of mitral and granule cell interactions supported by experimental findings, combined with a matrix-based representation of glomerular operations, we identify the mechanisms for forming one or more glomerular units in response to a given odor, how and to what extent the glomerular units interfere or interact with each other during learning, their computational role within the olfactory bulb microcircuit, and how their actions can be formalized into a theoretical framework in which the olfactory bulb can be considered to contain \"odor operators\" unique to each individual. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/168591", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "ver_date": "2018-10-17T16:16:55.007", + "ver_number": 17 + }, + "168599": { + "citation": { + "attr_id": 391, + "value": "Oscillations emerging from noise-driven steady state in networks with electrical synapses and subthreshold resonance (Tchumatchenko T* and Clopath C*, Nature Communications, 2014)" + }, + "class_id": 19, + "created": "2014-11-18T05:35:39", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 168599, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144567, + "object_name": "Clopath, Claudia [c.clopath at imperial.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 168600, + "object_name": "Tchumatchenko T, Clopath C (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Oscillations emerging from noise-driven NNs (Tchumatchenko & Clopath 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Here we show how the oscillation frequency is shaped by single neuron resonance, electrical and chemical synapses.The presence of both gap junctions and subthreshold resonance are necessary for the emergence of oscillations. Our results are in agreement with several experimental observations such as network responses to oscillatory inputs and offer a much-needed conceptual link connecting a collection of disparate effects observed in networks.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/168599", + "other_neurotransmitter": { + "attr_id": 304, + "value": "Glutamate" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "c.clopath@imperial.ac.uk" + }, + "ver_date": "2014-11-21T17:48:53", + "ver_number": 6 + }, + "168856": { + "citation": { + "attr_id": 391, + "value": "A computational study of stimulus driven epileptic seizure abatement. PN Taylor, Y Wang, M Goodfellow, J Dauwels, F Moeller, U Stephani, G Baier. PLoS One (To Appear)" + }, + "class_id": 19, + "created": "2014-11-28T07:19:35", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 168856, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 155567, + "object_name": "Wang, Yujiang [yujiang.wang at newcastle.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 138323, + "object_name": "Magnetic stimulation" + }, + { + "object_id": 139148, + "object_name": "Brain Rhythms" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 226221, + "object_name": "Taylor PN, Wang Y, Goodfellow M, Dauwels J, Moeller F, Stephani U, Baier G (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A bistable model of Spike-Wave seizure and background activity (Taylor et al. 2014)", + "notes": { + "attr_id": 24, + "value": "This is a four-variable model (in the Amari formalism) of bistable Spike-Wave seizure dynamics and background activity (fixed point). The published code is the deterministic version of the model in the related publication.\r\nThis model can be used to investigate seizure abatement using stimulation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/168856", + "other_concept": { + "attr_id": 306, + "value": "Single pulse stimulation" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "yujiang.wang@newcastle.ac.uk" + }, + "ver_date": "2015-01-13T13:29:48", + "ver_number": 5 + }, + "168858": { + "citation": { + "attr_id": 391, + "value": "Coskren PJ, Luebke JI, Kabaso BD, Wearne SL, Yadav A, Rumbell T, Hof PR, Weaver CM. Functional consequences of age-related morphologic changes to pyramidal neurons of the rhesus monkey prefrontal cortex. Submitted to the Journal of Computational Neuroscience (2014)." + }, + "class_id": 19, + "created": "2014-12-01T17:05:07", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 168858, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 87515, + "object_name": "Weaver, Christina [christina.weaver at fandm.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 112047, + "object_name": "Aging/Alzheimer`s" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 183997, + "object_name": "Coskren PJ, Luebke JI, Kabaso D, Wearne SL, Yadav A, Rumbell T, Hof PR, Weaver CM (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Rhesus Monkey Layer 3 Pyramidal Neurons: Young vs aged PFC (Coskren et al. 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Layer 3 (L3) pyramidal neurons in the lateral prefrontal cortex (LPFC) of rhesus monkeys exhibit dendritic regression, spine loss and increased action potential (AP) firing rates during normal aging. The relationship between these structural and functional alterations, if any, is unknown. Computational models using the digital reconstructions with Hodgkin-Huxley and AMPA channels allowed us to assess relationships between demonstrated age-related changes and to predict physiological changes that have not yet been tested empirically. Tuning passive parameters for each model predicted significantly higher membrane resistance (Rm) in aged versus young neurons. This Rm increase alone did not account for the empirically observed fI-curves, but coupling these Rm values with subtle differences in morphology and membrane capacitance Cm did. The predicted differences in passive parameters (or other parameters with similar effects) are mathematically plausible, but\r\nmust be tested empirically." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/168858", + "public_submitter_email": { + "attr_id": 309, + "value": "christina.weaver@fandm.edu" + }, + "ver_date": "2015-08-12T16:28:21.503", + "ver_number": 7 + }, + "168861": { + "citation": { + "attr_id": 391, + "value": "J.A. Garc\u00eda-\u00adGrajales, G. Rucabado, A. Garc\u00eda-\u00adDopico, J.M. Pe\u00f1a and A. J\u00e9rusalem. Neurite, a finite difference large scale parallel program for the simulation of the electrical signal propagation in neurites under mechanical loading. PLoS ONE, (Under review), 2014" + }, + "class_id": 19, + "created": "2014-12-02T07:41:51", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 168861, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 180548, + "object_name": "Garcia-Grajales, Julian Andres " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 180554, + "object_name": "Garc\u00eda-Grajales JA, Rucabado G, Garc\u00eda-Dopico A, Pe\u00f1a JM, J\u00e9rusalem A (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Neurite: electrophysiological-mechanical coupling simulation framework (Garcia-Grajales et al 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52425, + "object_name": "Myelinated neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Neurite simulates the electrical signal propagation in myelinated and unmyelinated axons, and in dendritic trees under mechanical loading. Two different solvers are available (explicit and implicit) with sequential (CPU) and parallel (GPUs) versions" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/168861", + "other_implementer": { + "attr_id": 308, + "value": "Garcia-Grajales Julian Andres" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "julian.garciagrajales@maths.ox.ac.uk" + }, + "ver_date": "2016-02-05T10:24:42.003", + "ver_number": 21 + }, + "168866": { + "citation": { + "attr_id": 391, + "value": "PMID: 23431402" + }, + "class_id": 19, + "created": "2014-12-09T21:40:50", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 168866, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 180476, + "object_name": "Mor\u00e9n J, Shibata T, Doya K (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 2832, + "object_name": "NEST" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "A spatial model of the intermediate superior colliculus (Moren et. al. 2013)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A spatial model of the intermediate superior colliculus. It reproduces the collicular saccade-generating output profile from NMDA receptor-driven burst neurons, shaped by integrative inhibitory feedback from spreading buildup neuron activity. The model is consistent with the view that collicular activity directly shapes the temporal profile of saccadic eye movements.\r\n\r\nWe use the Adaptive exponential integrate and fire neuron model, augmented with an NMDA-like membrane potential-dependent receptor. In addition, we use a synthetic spike integrator model as a stand-in for a spike-integrator circuit in the reticular formation.\r\n\r\nNOTE: We use a couple of custom neuron models, so the supplied model file includes an entire version of NEST. I also include a patch that applies to a clean version of the simulator (see the doc/README)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/168866", + "public_submitter_email": { + "attr_id": 309, + "value": "jan.moren@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 118308, + "object_name": "Superior colliculus" + } + ] + }, + "ver_date": "2015-02-24T09:11:12.27", + "ver_number": 5 + }, + "168867": { + "citation": { + "attr_id": 391, + "value": "The dynamics of sensory integration in a hierarchical network explains choice probabilities in cortical area MT" + }, + "class_id": 19, + "created": "2014-12-15T08:22:38", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 168867, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 168868, + "object_name": "Wimmer, Klaus [wimmer.klaus at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83539, + "object_name": "Attractor Neural Network" + }, + { + "object_id": 83540, + "object_name": "Winner-take-all" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 170127, + "object_name": "Wimmer K, Compte A, Roxin A, Peixoto D, Renart A, de la Rocha J (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Hierarchical network model of perceptual decision making (Wimmer et al 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Neuronal variability in sensory cortex predicts perceptual decisions. 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We have used this model to simulate a standard two-alternative forced-choice motion discrimination task." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/168867", + "other_concept": { + "attr_id": 306, + "value": "Balanced network" + }, + "other_implementer": { + "attr_id": 308, + "value": "Wimmer, Klaus " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "wimmer.klaus@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2017-07-28T13:53:13.247", + "ver_number": 11 + }, + "168874": { + "class_id": 19, + "created": "2014-12-16T18:07:50", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 88211, + "object_name": "I_SERCA" + }, + { + "object_id": 143445, + "object_name": "I_KD" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 168874, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 97869, + "object_name": "Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org]" + }, + { + "object_id": 146951, + "object_name": "McDougal, Robert [robert.mcdougal at yale.edu]" + }, + { + "object_id": 168951, + "object_name": "Sherif, Mohamed [mohamed.sherif.md at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 150554, + "object_name": "Calcium waves" + }, + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 168952, + "object_name": "Neymotin SA, McDougal RA, Sherif MA, Fall CP, Hines ML, Lytton WW (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Neuronal dendrite calcium wave model (Neymotin et al, 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + }, + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"...\r\nWe developed a reaction-diffusion model of an apical dendrite with diffusible inositol triphosphate (IP3 ), diffusible Ca2+, IP3 receptors (IP3 Rs), endoplasmic reticulum (ER) Ca2+ leak, and ER pump (SERCA) on ER.\r\n...\r\nAt least two modes of Ca2+ wave spread have been suggested: a continuous mode based on presumed relative homogeneity of ER within the cell; and a pseudo-saltatory model where Ca2+ regeneration occurs at discrete points with diffusion between them. \r\n\r\nWe compared the effects of three patterns of hypothesized IP3 R distribution: 1. continuous homogeneous ER, 2. hotspots with increased IP3R density (IP3 R hotspots), 3. areas of increased ER density (ER stacks). All three modes produced Ca2+ waves with velocities similar to those measured in vitro (~50 - 90\u00b5m /sec). ...\r\n\r\nThe measures were sensitive to changes in density and spacing of IP3 R hotspots and stacks.\r\n...\r\nAn extended electrochemical model, including voltage gated calcium channels and AMPA synapses, demonstrated that membrane priming via AMPA stimulation enhances subsequent Ca2+ wave amplitude and duration. Our modeling suggests that pharmacological targeting of IP3 Rs and SERCA could allow modulation of Ca2+ wave propagation in diseases where Ca2+ dysregulation has been implicated. \r\n\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/168874", + "other_implementer": { + "attr_id": 308, + "value": "mohamed.sherif.md@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2022-05-27T11:40:32.947", + "ver_number": 12 + }, + "168950": { + "class_id": 19, + "created": "2014-12-18T01:30:55", + "gitrepo": false, + "id": 168950, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 139758, + "object_name": "Roessert, Christian [christian.a at roessert.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + }, + { + "object_id": 185098, + "object_name": "Reservoir Computing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 185099, + "object_name": "R\u00f6ssert C, Dean P, Porrill J (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Basis for temporal filters in the cerebellar granular layer (Roessert et al. 2015)", + "notes": { + "attr_id": 24, + "value": "This contains the models, functions and resulting data as used in: Roessert C, Dean P, Porrill J. At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters. It is based on code used for Yamazaki T, Tanaka S (2005) Neural modeling of an internal clock. Neural Comput 17:1032-58" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/168950", + "other_concept": { + "attr_id": 306, + "value": "Reservoir Computing" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "christian.a@roessert.de" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2015-09-23T09:49:43.357", + "ver_number": 19 + }, + "168957": { + "citation": { + "attr_id": 391, + "value": "other implementers Tuthill, John Reiser, Michael" + }, + "class_id": 19, + "created": "2015-01-04T08:05:14", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 168957, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 168961, + "object_name": "Tuthill, John [john.c.tuthill at gmail.com]" + }, + { + "object_id": 168962, + "object_name": "Reiser, Michael " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 168963, + "object_name": "Motion Detection" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 168960, + "object_name": "Tuthill JC, Chiappe ME, Reiser MB (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Reichardt Model for Motion Detection in the Fly Visual System (Tuthill et al, 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 168964, + "object_name": "Fly lamina neuron" + }, + { + "object_id": 168965, + "object_name": "Fly medulla neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This simulation implements a correlation-type model for visual motion detection, as originally described by Hassenstein and Reichardt (1956), and analyzes the response of the model to standard and reverse-phi motion stimuli. \r\n\r\nDetails are provided in: Tuthill JC, et al. (2011)" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/168957", + "other_concept": { + "attr_id": 306, + "value": "Motion Detection" + }, + "other_neurons": { + "attr_id": 301, + "value": "Fly Lamina, Fly Medulla" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "john.c.tuthill@gmail.com" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 118529, + "object_name": "Retina" + }, + { + "object_id": 206363, + "object_name": "Drosophila" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 120613, + "object_name": "Drosophila (fruit fly)" + } + ] + }, + "ver_date": "2019-02-26T10:11:27.34", + "ver_number": 10 + }, + "169023": { + "citation": { + "attr_id": 391, + "value": "Electrogenic properties of the Na+/K+ ATPase control\u2019s transitions between normal and pathological brain states. Under review in J Neurophysiology." + }, + "class_id": 19, + "created": "2015-01-06T03:11:47", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 169023, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33765, + "object_name": "Bazhenov, Maxim [Bazhenov at Salk.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 169340, + "object_name": "Krishnan GP, Filatov G, Shilnikov A, Bazhenov M (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 125368, + "object_name": "CONTENT" + } + ] + }, + "name": "Single cell model with variable ion concentrations and Na+/K+ ATPase (Krishnan et al. 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a single cell model with variable intra and extra cellular ion concentrations for Na+, K+ and Cl- ions. This model also incorporates both the electrogenic and ionic concentration effects of the Na+/K+ pump. \r\n\r\nThe program in this archive will run under CONTENT software to generate various bifurcation plots." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/169023", + "other_concept": { + "attr_id": 306, + "value": "Ion concentrations" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "girik@ucr.edu" + }, + "ver_date": "2015-01-15T17:22:04", + "ver_number": 8 + }, + "169208": { + "class_id": 19, + "created": "2015-01-09T22:18:40", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 169208, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 128875, + "object_name": "Development" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 169210, + "object_name": "Young RG, Castelfranco AM, Hartline DK (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Lillie Transition: onset of saltatory conduction in myelinating axons (Young et al. 2013)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52425, + "object_name": "Myelinated neuron" + }, + { + "object_id": 52426, + "object_name": "Squid axon" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Included are the NEURON (.hoc) files needed to generate the data used in our Young, Castelfranco, Hartline (2013) paper. The resulting .dat files are in the same folder as the MATLAB (.m) files that are used to sort the data." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/169208", + "other_concept": { + "attr_id": 306, + "value": "Myelination" + }, + "ver_date": "2015-01-28T17:10:18", + "ver_number": 22 + }, + "169240": { + "citation": { + "attr_id": 391, + "value": "Pedro Mateos-Aparicio, Ricardo Murphy and Johan F. Storm (2014). Complementary functions of SK and Kv7/M potassium channels in excitability control and synaptic integration in rat hippocampal dentate granule cells. Journal of Physiology 592, 669-693." + }, + "class_id": 19, + "created": "2015-01-13T12:51:37", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 169240, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 169381, + "object_name": "Murphy, Ricardo [ricardo.murphy at medisin.uio.no]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + }, + { + "object_id": 143636, + "object_name": "Conductance distributions" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 169383, + "object_name": "Mateos-Aparicio P, Murphy R, Storm JF (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Dentate granule cell: mAHP & sAHP; SK & Kv7/M channels (Mateos-Aparicio et al., 2014)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model is based on that of Aradi & Holmes (1999; Journal of Computational Neuroscience 6, 215-235). It was used to help understand the contribution of M and SK channels to the medium afterhyperpolarization (mAHP) following one or seven spikes, as well as the contribution of M channels to the slow afterhyperpolarization (sAHP). We found that SK channels are the main determinants of the mAHP, in contrast to CA1 pyramidal cells where the mAHP is primarily caused by the opening of M channels. The model reproduced these experimental results, but we were unable to reproduce the effects of the M-channel blocker XE991 on the sAHP. It is suggested that either the XE991-sensitive component of the sAHP is not due to M channels, or that when contributing to the sAHP, these channels operate in a mode different from that associated with the mAHP." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/169240", + "other_implementer": { + "attr_id": 308, + "value": "Murphy, Ricardo " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ricardo.murphy@medisin.uio.no" + }, + "ver_date": "2015-01-21T15:15:46", + "ver_number": 4 + }, + "169242": { + "class_id": 19, + "created": "2015-01-13T14:13:37", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 169242, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + }, + { + "object_id": 22010, + "object_name": "Facilitation" + }, + { + "object_id": 22012, + "object_name": "Depression" + }, + { + "object_id": 83535, + "object_name": "Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 185546, + "object_name": "Esposito U, Giugliano M, Vasilaki E (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Adaptation of Short-Term Plasticity parameters (Esposito et al. 2015)", + "notes": { + "attr_id": 24, + "value": "\"The anatomical connectivity among neurons has been experimentally found to be largely non-random across brain areas. This means that certain connectivity motifs occur at a higher frequency than would be expected by chance. Of particular interest, short-term synaptic plasticity properties were found to colocalize with specific motifs: an over-expression of bidirectional motifs has been found in neuronal pairs where short-term facilitation dominates synaptic transmission among the neurons, whereas an over-expression of unidirectional motifs has been observed in neuronal pairs where short-term depression dominates. In previous work we found that, given a network with fixed short-term properties, the interaction between short- and long-term plasticity of synaptic transmission is sufficient for the emergence of specific motifs. Here, we introduce an error-driven learning mechanism for short-term plasticity that may explain how such observed correspondences develop from randomly initialized dynamic synapses. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/169242", + "ver_date": "2015-11-13T15:39:23.66", + "ver_number": 9 + }, + "169278": { + "class_id": 19, + "created": "2015-01-15T09:55:15", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 169278, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 169290, + "object_name": "Rudnicki, Marek [marek.rudnicki at tum.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 125854, + "object_name": "Zilany MS, Bruce IC, Nelson PC, Carney LH (2009)" + }, + { + "object_id": 169280, + "object_name": "Zilany MS, Bruce IC, Carney LH (2014)" + }, + { + "object_id": 169289, + "object_name": "Holmberg M (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Cochlea: inner ear models in Python (Zilany et al 2009, 2014; Holmberg M 2007)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 1965, + "object_name": "Cochlea hair outer GLU cell" + }, + { + "object_id": 52419, + "object_name": "Auditory nerve" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Collection of inner ear models in Python." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/169278", + "public_submitter_email": { + "attr_id": 309, + "value": "marekrud@gmail.com" + }, + "ver_date": "2018-10-18T17:08:16.587", + "ver_number": 14 + }, + "169457": { + "class_id": 19, + "created": "2015-01-23T05:14:10", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92082, + "object_name": "Nav1.1 SCN1A" + }, + { + "object_id": 92088, + "object_name": "Nav1.7 SCN9A" + }, + { + "object_id": 122811, + "object_name": "Cav3.3 CACNA1I" + }, + { + "object_id": 122812, + "object_name": "Cav1.3 CACNA1D" + }, + { + "object_id": 122814, + "object_name": "Cav1.2 CACNA1C" + }, + { + "object_id": 122816, + "object_name": "Kv2.1 KCNB1" + }, + { + "object_id": 226057, + "object_name": "HCN1" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 169457, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 147118, + "object_name": "Maki-Marttunen, Tuomo [tuomomm at uio.no]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 112853, + "object_name": "Schizophrenia" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 187894, + "object_name": "M\u00e4ki-Marttunen T, Halnes G, Devor A, Witoelar A, Bettella F, Djurovic S, Wang Y, Einevoll GT, Andreassen OA, Dale AM (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Schiz.-linked gene effects on intrinsic single-neuron excitability (Maki-Marttunen et al. 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Python scripts for running NEURON simulations that model a layer V pyramidal cell with certain genetic variants implemented. The genes included are obtained from genome-wide association studies of schizophrenia." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/169457", + "public_submitter_email": { + "attr_id": 309, + "value": "tuomomm@uio.no" + }, + "ver_date": "2017-03-18T10:12:32.617", + "ver_number": 16 + }, + "169775": { + "citation": { + "attr_id": 391, + "value": "25149693" + }, + "class_id": 19, + "created": "2015-01-27T23:50:08", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 169775, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 140966, + "object_name": "Sensory processing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 169776, + "object_name": "Matsui H, Zheng M, Hoshino O (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Default mode network model (Matsui et al 2014)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Default mode network (DMN) shows intrinsic, high-level activity at rest. We tested a hypothesis proposed for its role in sensory information processing: Intrinsic DMN activity facilitates neural responses to sensory input. A neural network model, consisting of a sensory network (Nsen) and a DMN, was simulated. The Nsen contained cell assemblies. Each cell assembly comprised principal cells, GABAergic interneurons (Ia, Ib), and glial cells. We let the Nsen carry out a perceptual task: detection of sensory stimuli. \u2026\r\nThis enabled the Nsen to reliably detect the stimulus. We suggest that intrinsic default model network activity may accelerate the reaction speed of the sensory network by modulating its ongoing-spontaneous activity in a subthreshold manner. Ambient GABA contributes to achieve an optimal ongoing spontaneous subthreshold neuronal state, in which GABAergic gliotransmission triggered by the intrinsic default model network activity may play an important role." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/169775", + "other_implementer": { + "attr_id": 308, + "value": "Matsui, H" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "rinnakyarotto@hotmail.co.jp" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "ver_date": "2015-01-30T17:26:25", + "ver_number": 6 + }, + "169983": { + "citation": { + "attr_id": 391, + "value": "Unsupervised learning of an efficient short-term memory network, Vertechi*, P and Brendel*, W and Machens C. K., Advances in Neural Information Processing Systems 27, 2014" + }, + "class_id": 19, + "created": "2015-01-30T15:07:57", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 169983, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 170014, + "object_name": "Brendel, Wieland [wieland.brendel at bethgelab.org]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 170013, + "object_name": "Vertechi P, Brendel W, Machens CK (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Unsupervised learning of an efficient short-term memory network (Vertechi, Brendel & Machens 2014)", + "notes": { + "attr_id": 24, + "value": "Learning in recurrent neural networks has been a topic fraught with\r\ndifficulties and problems. We here report substantial progress in the\r\nunsupervised learning of recurrent networks that can keep track\r\nof an input signal. Specifically, we show how these networks can \r\nlearn to efficiently represent their present and past inputs, based \r\non local learning rules only." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/169983", + "other_implementer": { + "attr_id": 308, + "value": "Brendel, Wieland " + }, + "other_neurons": { + "attr_id": 301, + "value": "rate neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "wieland.brendel@bethgelab.org" + }, + "ver_date": "2015-07-26T14:43:55.047", + "ver_number": 8 + }, + "169984": { + "citation": { + "attr_id": 391, + "value": "25609629" + }, + "class_id": 19, + "created": "2015-02-02T12:36:42", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 167500, + "object_name": "Kir" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + 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The results of that simulation are used to fit a phenomenological model, which is also included in this file." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/180373", + "public_submitter_email": { + "attr_id": 309, + "value": "adamimos@gmail.com" + }, + "ver_date": "2015-04-30T13:34:23.373", + "ver_number": 8 + }, + "180789": { + "class_id": 19, + "created": "2015-03-05T09:28:41.64", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + }, + { + "object_id": 88206, + "object_name": "I_HERG" + }, + { + "object_id": 88209, + "object_name": "Na/Ca exchanger" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 140826, + "object_name": "I Ca,p" + }, + { + "object_id": 143445, + "object_name": "I_KD" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 180789, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 180866, + "object_name": "Forrest, Michael [mikeforrest at hotmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 120695, + "object_name": "Sodium pump" + }, + { + "object_id": 121254, + "object_name": "Depolarization block" + }, + { + "object_id": 169987, + "object_name": "Dendritic Bistability" + }, + { + "object_id": 238957, + "object_name": "Markov-type model" + }, + { + "object_id": 242863, + "object_name": "Alcohol Use Disorder" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 180865, + "object_name": "Forrest MD (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Alcohol action in a detailed Purkinje neuron model and an efficient simplified model (Forrest 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... we employ a\r\nnovel reduction algorithm to produce a 2 compartment model of the cerebellar Purkinje\r\nneuron from a previously published, 1089 compartment model. It runs more than 400 times\r\nfaster and retains the electrical behavior of the full model. So, it is more suitable for inclusion\r\nin large network models, where computational power is a limiting issue. We show the utility\r\nof this reduced model by demonstrating that it can replicate the full model\u2019s response to\r\nalcohol, which can in turn reproduce experimental recordings from Purkinje neurons\r\nfollowing alcohol application.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/180789", + "other_implementer": { + "attr_id": 308, + "value": "Forrest, Michael [mikeforrest@hotmail.com]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "mikeforrest@hotmail.com" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2018-06-07T16:02:56.39", + "ver_number": 12 + }, + "180791": { + "citation": { + "attr_id": 391, + "value": "Toyoizumi T, Kaneko M, Stryker MP, Miller KD (2014)\r\nModeling the dynamic interaction of hebbian and homeostatic plasticity.\r\nNeuron 84:497-510" + }, + "class_id": 19, + "created": "2015-03-09T01:08:51.763", + 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synaptic strength is the product of\r\na synapse-specific Hebbian factor and a postsynaptic-\r\ncell-specific homeostatic factor, with each factor\r\nseparately arriving at a stable inactive state.\r\n\r\nThis\r\nmodel captures ODP dynamics and has plausible\r\nbiophysical substrates.\r\n\r\nWe confirm model predictions\r\nexperimentally that plasticity is inactive at\r\nstable states and that synaptic strength overshoots\r\nduring recovery from visual deprivation.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/180791", + "other_concept": { + "attr_id": 306, + "value": "Hebb&homeostatic scaling" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "taro.toyoizumi@brain.riken.jp" + }, + "ver_date": "2015-03-14T18:02:26.06", + "ver_number": 11 + }, + "180823": { + "citation": { + "attr_id": 391, + "value": "25737547" + }, + "class_id": 19, + "created": "2015-03-11T00:58:46.237", + "gitrepo": false, + "has_modelview": { + 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synapses. \r\n\r\nWith this model, we conducted a\r\nsimulation study of the gain adaptation of optokinetic response (OKR)\r\neye movement.\r\n\r\nOur model reproduced several important aspects of\r\npreviously reported experimental results in wild-type and\r\ncerebellum-related gene-manipulated mice. \r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/180823", + "public_submitter_email": { + "attr_id": 309, + "value": "pnas14@neuralgorithm.org" + }, + "ver_date": "2016-05-24T11:14:54.257", + "ver_number": 5 + }, + "181010": { + "citation": { + "attr_id": 391, + "value": "Lim S, Goldman MS (2013) Balanced cortical microcircuitry for maintaining information in working memory, Nature Neuroscience 16:1360-1314" + }, + "class_id": 19, + "created": "2015-03-19T00:50:55.663", + "gitrepo": false, + "id": 181010, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 181030, + "object_name": "Lim, Sukbin " + } + ] + }, + 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When recurrent excitatory and inhibitory inputs to memory neurons were balanced in strength and offset in time, drifts in activity triggered a corrective signal that counteracted memory decay. Circuits containing this mechanism temporally integrated their inputs, generated the irregular neural firing observed during persistent activity and were robust against common perturbations that severely disrupted previous models of short-term memory storage." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/181010", + "public_submitter_email": { + "attr_id": 309, + "value": "sukbin80@gmail.com" + }, + "ver_date": "2015-03-19T15:35:31.803", + "ver_number": 8 + }, + "181032": { + "class_id": 19, + "created": "2015-03-24T16:15:42.41", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 181032, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 181044, + "object_name": "Cox, Conor [cdcox1 at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 181038, + "object_name": "Trieu BH, Kram\u00e1r EA, Cox CD, Jia Y, Wang W, Gall CM, Lynch G (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Effect of polysynaptic facilitaiton between piriform-hippocampal network stages (Trieu et al 2015)", + "notes": { + "attr_id": 24, + "value": "This is a model of a multistage network with stages representing regions and synaptic contacts from the olfactory cortex to region CA1 of the hippocampus in Brian2 spiking neural network simulator (Trieu et al 2015).\r\n\r\nIt is primarily designed to assess how synaptic facilitation at multiple stages in response to theta firing changes the output of the network. Further developments will be posted at: \r\n\r\ngithub.com/cdcox/multistage_network\r\n\r\nThis model was prepared by Conor D Cox, University of California, Irvine\r\n\r\nFor questions please contact Conor at cdcox1@gmail.com" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/181032", + "other_implementer": { + "attr_id": 308, + "value": "Conor D Cox cdcox1@gmail.com" + }, + "other_simulator": { + "attr_id": 307, + "value": "Brian2" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "cdcox1@gmail.com" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + }, + { + "object_id": 115947, + "object_name": "Dentate gyrus" + }, + { + "object_id": 115949, + "object_name": "Olfactory cortex" + }, + { + "object_id": 115960, + "object_name": "Generic" + }, + { + "object_id": 119191, + "object_name": "Entorhinal cortex" + } + ] + }, + "ver_date": "2018-10-17T16:17:09.383", + "ver_number": 20 + }, + "181035": { + "citation": { + "attr_id": 391, + "value": "F. Solari, M. Chessa, N. Medathati, and P. Kornprobst. What can we expect from a V1-MT feedforward architecture for optical flow estimation? Submitted to Signal Processing: Image Communication, 2015.\r\n\r\nM. Chessa, S.P. Sabatini, F. Solari. A systematic analysis of a V1-MT neural model for motion estimation. Submitted to Neurocomputing, 2015." + }, + "class_id": 19, + "created": "2015-03-30T03:57:28.357", + "gitrepo": false, + "id": 181035, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 181036, + "object_name": "Chessa, Manuela [manuela.chessa at unige.it]" + }, + { + "object_id": 181037, + "object_name": "Solari, Fabio [fabio.solari at unige.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 168963, + "object_name": "Motion Detection" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 182497, + "object_name": "Solari F, Chessa M, Medathati NVK, Kornprobst P (2015)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "FFV1MT: A V1-MT feedforward architecture for optical flow estimation (Solari et a., 2015)", + "notes": { + "attr_id": 24, + "value": "A neural feed-forward model composed of two layers that mimic the V1-MT primary motion\r\npathway, derived from previous works by Heeger and Simoncelli." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/181035", + "other_implementer": { + "attr_id": 308, + "value": "Chessa, Manuela [manuela.chessa@unige.it]\r\nSolari, Fabio [fabio.solari@unige.it]" + }, + "other_neurons": { + "attr_id": 301, + "value": "Visual Cortex" + }, + "other_type": { + "attr_id": 305, + "value": "Functional model" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "manuela.chessa@unige.it" + }, + "ver_date": "2015-05-20T11:09:52.933", + "ver_number": 8 + }, + "181962": { + "citation": { + "attr_id": 391, + "value": "Toporikova, N., Chevalier, M., & Thoby-Brisson, M. (2015).Sigh and Eupnea Rhythmogenesis Involve Distinct Interconnected Subpopulations: A Combined\r\nComputational and Experimental Study. E. Journal of Neuroscience.\r\nDOI:http://dx.doi.org/10.1523/ENEURO.0074-14.2015" + }, + "class_id": 19, + "created": "2015-04-13T16:55:27.223", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "id": 181962, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 181963, + "object_name": "Toporikova N, Chevalier M, Thoby-Brisson M (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Model of eupnea and sigh generation in respiratory network (Toporikova et al 2015)", + "notes": { + "attr_id": 24, + "value": "Based on recent in vitro data obtained in the mouse embryo, we have built a computational model consisting of two compartments, interconnected through appropriate synapses. One compartment generates sighs and the other produces eupneic bursts. The model reproduces basic features of simultaneous sigh and eupnea generation (two types of bursts differing in terms of\r\nshape, amplitude, and frequency of occurrence) and mimics the effect of blocking glycinergic synapses" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/181962", + "public_submitter_email": { + "attr_id": 309, + "value": "toporikovan@wlu.edu" + }, + "ver_date": "2015-04-15T10:38:10.193", + "ver_number": 6 + }, + "181967": { + "class_id": 19, + "created": "2015-04-21T16:02:08.72", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 181967, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 93425, + "object_name": "Cutsuridis, Vassilis [vcutsuridis at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3542, + "object_name": "Pattern Recognition" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 139148, + "object_name": "Brain Rhythms" + }, + { + "object_id": 151331, + "object_name": "Storage/recall" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 181914, + "object_name": "Cutsuridis V, Poirazi P (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Long time windows from theta modulated inhib. in entorhinal\u2013hippo. loop (Cutsuridis & Poirazi 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + }, + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + }, + { + "object_id": 154093, + "object_name": "Hippocampus CA3 interneuron basket GABA cell" + }, + { + "object_id": 52416, + "object_name": "Dentate gyrus mossy cell" + }, + { + "object_id": 52417, + "object_name": "Dentate gyrus basket cell" + }, + { + "object_id": 88216, + "object_name": "Dentate gyrus hilar cell" + }, + { + "object_id": 114079, + "object_name": "Hippocampus CA1 basket cell" + }, + { + "object_id": 139885, + "object_name": "Hippocampus CA3 stratum oriens lacunosum-moleculare interneuron" + }, + { + "object_id": 151329, + "object_name": "Hippocampus CA1 bistratified cell" + }, + { + "object_id": 151330, + "object_name": "Hippocampus CA1 axo-axonic cell" + }, + { + "object_id": 181968, + "object_name": "Hippocampus CA3 axo-axonic cells" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"A recent experimental study (Mizuseki et al., 2009) has shown that the temporal\r\ndelays between population activities in successive entorhinal and hippocampal anatomical stages are\r\nlonger (about 70\u201380 ms) than expected from axon conduction velocities and passive synaptic integration\r\nof feed-forward excitatory inputs. We investigate via computer simulations the mechanisms that give\r\nrise to such long temporal delays in the hippocampus structures. \r\n...\r\nThe model shows that the experimentally\r\nreported long temporal delays in the DG, CA3 and CA1 hippocampal regions are due to theta\r\nmodulated somatic and axonic inhibition...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/181967", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2022-05-27T11:42:27.393", + "ver_number": 9 + }, + "182129": { + "class_id": 19, + "created": "2015-05-05T02:46:32.153", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 182129, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + }, + { + "object_id": 266547, + "object_name": "Lupascu, Carmen Alina " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266546, + "object_name": "Lupascu CA, Morabito A, Ruggeri F, Parisi C, Pimpinella D, Pizzarelli R, Meli G, Marinelli S, Cherubini E, Cattaneo A, Migliore M (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Computational modeling of gephyrin-dependent inhibitory transsynaptic signaling (Lupascu et al 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 156124, + "object_name": "Abstract single compartment conductance based cell" + } + ] + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/182129", + "other_implementer": { + "attr_id": 308, + "value": "Carmen Alina Lupascu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2022-05-27T11:46:39.56", + "ver_number": 11 + }, + "182134": { + "class_id": 19, + "created": "2015-05-07T11:15:53.127", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 182134, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 226051, + "object_name": "Fink, Christian G. [cgfink at owu.edu]" + }, + { + "object_id": 226052, + "object_name": "Gliske, Stephen [sgliske at umich.edu]" + }, + { + "object_id": 136020, + "object_name": "Stacey, William [wstacey at med.umich.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 182907, + "object_name": "Fink CG, Gliske S, Catoni N, Stacey WC (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Normal ripples, abnormal ripples, and fast ripples in a hippocampal model (Fink et al. 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 114079, + "object_name": "Hippocampus CA1 basket cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"...We use a computational model of hippocampus to investigate possible network mechanisms underpinning normal ripples, pathological ripples, and fast ripples. Our results unify several prior findings regarding HFO mechanisms, and also make several new predictions regarding abnormal HFOs. We show that HFOs are generic, emergent phenomena whose characteristics reflect a wide range of connectivity and network input. Although produced by different mechanisms, both normal and abnormal HFOs generate similar ripple frequencies, underscoring that peak frequency is unable to distinguish the two. Abnormal ripples are generic phenomena that arise when input to pyramidal cells overcomes network inhibition, resulting in high-frequency, uncoordinated firing. In addition, fast ripples transiently and sporadically arise from the precise conditions that produce abnormal ripples. Lastly, we show that such abnormal conditions do not require any specific network structure to produce coherent HFOs, as even completely asynchronous activity is capable of producing abnormal ripples and fast ripples in this manner. These results provide a generic, network-based explanation for the link between pathological ripples and fast ripples, and a unifying description for the entire spectrum from normal ripples to pathological fast ripples.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/182134", + "public_submitter_email": { + "attr_id": 309, + "value": "cgfink@owu.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2017-03-20T18:16:03.36", + "ver_number": 22 + }, + "182135": { + "class_id": 19, + "created": "2015-05-07T12:05:38.69", + "gitrepo": false, + "id": 182135, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 144264, + "object_name": "Koene RA, Tijms B, van Hees P, Postma F, de Ridder A, Ramakers GJ, van Pelt J, van Ooyen A (2009)" + }, + { + "object_id": 182141, + "object_name": "van Ooyen A, Carnell A, de Ridder S, Tarigan B, Mansvelder HD, Bijma F, de Gunst M, van Pelt J (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "NETMORPH: creates NNs with realistic neuron morphologies (Koene et al. 2009, van Ooyen et al. 2014)", + "notes": { + "attr_id": 24, + "value": "NETMORPH is a simulation tool for building synaptically\r\nconnected networks with realistic neuron morphologies. Axonal and\r\ndendritic morphologies are created by using stochastic rules for the behavior of individual growth cones, the structures at the tip of outgrowing axons and dendrites that mediate elongation and branching. Axons and dendrites are not guided by any extracellular cues. Synapses are\r\nformed when crossing axonal and dendritic segments come sufficiently close to each other. See the README in the archive for more information." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/182135", + "other_implementer": { + "attr_id": 308, + "value": "van Ooyen, Arjen [arjen.van.ooyen at gmail.com]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "arjen.van.ooyen@gmail.com" + }, + "ver_date": "2015-07-26T14:48:43.463", + "ver_number": 37 + }, + "182142": { + "citation": { + "attr_id": 391, + "value": "Keller D, Babai N, Kochubey O, Han Y, Markram H, Sch\u00fcrmann F, et al. (2015) An Exclusion Zone for Ca2+ Channels around Docked Vesicles Explains Release Control by Multiple Channels at a CNS Synapse. PLoS Comput Biol 11(5): e1004253. doi:10.1371/journal.pcbi.1004253" + }, + "class_id": 19, + "created": "2015-05-08T08:23:40.323", + "gitrepo": false, + "id": 182142, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 182147, + "object_name": "Keller D, Babai N, Kochubey O, Han Y, Markram H, Sch\u00fcrmann F, Schneggenburger R (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 2835, + "object_name": "MCell" + } + ] + }, + "name": "Active zone model of Ca2+ secretion coupling (Keller et al. 2015)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... At a large model synapse, the calyx of Held, transmitter release is controlled by several Ca2+ channels in a \"domain overlap\" mode, at least in young animals. To study the geometrical constraints of Ca2+ channel placement in domain overlap control of release, we used stochastic MCell modelling, at active zones for which the position of docked vesicles was derived from electron microscopy (EM). \r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/182142", + "other_concept": { + "attr_id": 306, + "value": "Vesicle Release" + }, + "other_neurons": { + "attr_id": 301, + "value": "Calyx of Held" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "dkeller@eskimo.com" + }, + "ver_date": "2015-05-08T10:15:20.997", + "ver_number": 4 + }, + "182373": { + "class_id": 19, + "created": "2015-05-14T16:16:24.447", + "gitrepo": false, + "id": 182373, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3542, + "object_name": "Pattern Recognition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 182371, + "object_name": "King PD, Zylberberg J, DeWeese MR (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Inhibitory cells enable sparse coding in V1 model (King et al. 2013)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Here we show that adding a separate population\r\nof inhibitory neurons to a spiking model of V1 provides conformance to Dale\u2019s Law, proposes a computational role for at least one class\r\nof interneurons, and accounts for certain observed physiological properties in V1. ... \"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/182373", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2015-05-14T17:15:16.67", + "ver_number": 4 + }, + "182515": { + "citation": { + "attr_id": 391, + "value": "Ferguson KA, Huh CYL, Amilhon B et al. Simple, biologically-constrained CA1 pyramidal cell models using an intact, whole hippocampus context [v1; ref status: indexed, http://f1000r.es/37u] F1000Research 2014, 3:104 (doi: 10.12688/f1000research.3894.1)" + }, + "class_id": 19, + "created": "2015-05-21T08:29:12.43", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 182515, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 136252, + "object_name": "Rebound firing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 182521, + "object_name": "Ferguson KA, Huh CY, Amilhon B, Williams S, Skinner FK (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "CA1 pyramidal neuron (Ferguson et al. 2014)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Izhikevich-based models of CA1 pyramidal cells, with parameters constrained based on a whole hippocampus preparation.\r\n\r\nStrongly and weakly adapting models based on the experimental data have been developed.\r\n\r\nCode produces example model output.\r\n\r\nThe code will also be made available on OSB." + }, + "opensourcebrain": { + "value": "ca1-pyr-cell-ferguson-et-al-2014" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/182515", + "public_submitter_email": { + "attr_id": 309, + "value": "frances.skinner@gmail.com" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2017-07-28T11:57:49.77", + "ver_number": 8 + }, + "182685": { + "class_id": 19, + "created": "2015-05-29T13:27:40.133", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 182685, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 183714, + "object_name": "Bush, Daniel [drdanielbush @ gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 182690, + "object_name": "Spatial Navigation" + }, + { + "object_id": 194888, + "object_name": "Grid cell" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 182686, + "object_name": "Bush D, Barry C, Manson D, Burgess N (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Models of Vector Navigation with Grid Cells (Bush et al., 2015)", + "notes": { + "attr_id": 24, + "value": "Four models of vector navigation in large scale 2D space using grid cell representations of location are included: \r\n(1) The 'Distance Cell' model, which directly decodes absolute start and goal locations in allocentric space from rate-coded grid cell representations before computing the displacement between them; \r\n(2) The 'Rate-coded Vector Cell' model, which directly decodes the displacement between start and goal locations from rate-coded grid cell representations; \r\n(3) The 'Phase-coded Vector Cell' model, which directly decodes the displacement between start and goal locations from the temporally-coded grid cell representations provided by phase precession; \r\n(4) The 'Linear Look-ahead' model, which uses a directed search through grid cell representations, initiated at the start location and then moving along a specific axis at a constant speed, to compute the displacement between start and goal locations." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/182685", + "other_concept": { + "attr_id": 306, + "value": "Spatial Navigation" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 119191, + "object_name": "Entorhinal cortex" + } + ] + }, + "ver_date": "2016-07-19T17:29:59.207", + "ver_number": 11 + }, + "182758": { + "class_id": 19, + "created": "2015-06-01T13:44:27.397", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 182758, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 234452, + "object_name": "Terman, David [terman at math.ohio-state.edu]" + }, + { + "object_id": 234453, + "object_name": "Malone, Taylor " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 39981, + "object_name": "Terman D, Rubin JE, Yew AC, Wilson CJ (2002)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + }, + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Activity patterns in a subthalamopallidal network of the basal ganglia model (Terman et al 2002)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 182735, + "object_name": "Subthalamic nucleus principal GABA cell" + }, + { + "object_id": 182591, + "object_name": "Globus pallidus principal GABA cell" + }, + { + "object_id": 74300, + "object_name": "Subthalamus nucleus projection neuron" + }, + { + "object_id": 88217, + "object_name": "Globus pallidus neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Based on recent experimental data, we have developed a\r\nconductance-based computational network model of the subthalamic\r\nnucleus and the external segment of the globus pallidus\r\nin the indirect pathway of the basal ganglia. Computer\r\nsimulations and analysis of this model illuminate the roles of the\r\ncoupling architecture of the network, and associated synaptic\r\nconductances, in modulating the activity patterns displayed by\r\nthis network. Depending on the relationships of these coupling\r\nparameters, the network can support three general classes of\r\nsustained firing patterns: clustering, propagating waves, and\r\nrepetitive spiking that may show little regularity or correlation. ...\". Terman's XPP code and a partial implementation by Taylor Malone in NEURON and python are included." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/182758", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2017-11-12T09:52:31.013", + "ver_number": 18 + }, + "182759": { + "citation": { + "attr_id": 391, + "value": "PMID: 25569445\r\nhttp://www.ncbi.nlm.nih.gov/pubmed/25569445" + }, + "class_id": 19, + "created": "2015-06-02T08:09:00", + "gitrepo": false, + "id": 182759, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 182782, + "object_name": "Sadeh, Sadra [s.sadeh at ucl.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + 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"object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Emergence of contrast-invariant orientation selectivity in large-scale networks of excitatory and inhibitory neurons using integrate-and-fire neuron models." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/182759", + "other_implementer": { + "attr_id": 308, + "value": "Sadeh, Sadra [s.sadeh at ucl.ac.uk]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "s.sadeh@gmail.com" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2015-06-04T16:19:10.34", + "ver_number": 18 + }, + "182784": { + "class_id": 19, + "created": "2015-06-04T09:22:11.483", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 182784, + 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"value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Functional balanced networks with synaptic plasticity (Sadeh et al, 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model investigates the impact of learning on functional sensory networks. \r\nIt uses large-scale recurrent networks of excitatory and inhibitory spiking neurons equipped with synaptic plasticity. \r\nIt explains enhancement of orientation selectivity and emergence of feature-specific connectivity in visual cortex of rodents during development, as reported in experiments." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/182784", + "public_submitter_email": { + "attr_id": 309, + "value": "s.sadeh@gmail.com" + }, + "ver_date": "2018-10-17T17:20:02.253", + "ver_number": 7 + }, + "182785": { + "class_id": 19, + "created": "2015-06-07T15:06:33.937", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 182785, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 146951, + "object_name": "McDougal, Robert [robert.mcdougal at yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 189310, + "object_name": "McDougal RA, Shepherd GM (2015)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + 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We introduce the use of 3D printing as a technique for visualizing traced morphologies. Our method for generating printable versions of a cell or group of cells is to expand dendrite and axon diameters and then to transform the tracing into a 3D object with a neuronal surface generating algorithm like Constructive Tessellated Neuronal Geometry (CTNG). ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/182785", + "ver_date": "2016-05-13T12:36:22.053", + "ver_number": 10 + }, + "182797": { + "class_id": 19, + "created": "2015-06-11T23:45:59.237", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 182797, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 143636, + "object_name": "Conductance distributions" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 182799, + "object_name": "Sekulic V, Chen TC, Lawrence JJ, Skinner FK (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Multi-comp. 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Skinner (2013). \u201cExperimentally constrained CA1 fast-firing parvalbumin-positive interneuron network models exhibit sharp transitions into coherent high frequency rhythms\u201d. Frontiers in Computational Neuroscience. 7:144 doi: 10.3389/fncom.2013.00144. http://www.frontiersin.org/journal/10.3389/fncom.2013.00144/abstract" + }, + "class_id": 19, + "created": "2015-06-16T13:27:07.37", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 182843, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 182527, + "object_name": "Ferguson KA, Huh CY, Amilhon B, Williams S, Skinner FK (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "CA1 PV+ fast-firing hippocampal interneuron (Ferguson et al. 2013)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114079, + "object_name": "Hippocampus CA1 basket cell" + }, + { + "object_id": 151329, + "object_name": "Hippocampus CA1 bistratified cell" + }, + { + "object_id": 151330, + "object_name": "Hippocampus CA1 axo-axonic cell" + }, + { + "object_id": 182866, + "object_name": "Hippocampus CA1 PV+ fast-firing interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This two-variable simple model is derived based on patch-clamp recordings from the CA1 region of a whole hippocampus preparation of PV+ fast-firing cells. \r\nSince basket cells, axo-axonic cells and bistratified cells can be PV+ and fast-firing, this model could be representative of these cell types. The model code will also be made available on OSB." + }, + "opensourcebrain": { + "value": "ca1-pv-fast-firing-cell-ferguson-et-al-2013" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/182843", + "other_neurons": { + "attr_id": 301, + "value": "CA1 PV+ fast-firing" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "frances.skinner@gmail.com" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2017-07-28T11:57:33.567", + "ver_number": 11 + }, + "182906": { + "class_id": 19, + "created": "2015-06-30T16:49:19.973", + "gitrepo": false, + "id": 182906, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 184498, + "object_name": "Zhao X, Robinson PA (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Neural-field model of generalized seizures (Zhao et al., 2015)", + "notes": { + "attr_id": 24, + "value": "The mechanisms underlying generalized seizures are explored with neural field theory. A corticothalamic neural field model is used to explore changes leading to pathological seizure states. It is found that absence seizures arise from instabilities in the system\r\nand replicate experimental studies in numerous animal models and clinical studies." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/182906", + "public_submitter_email": { + "attr_id": 309, + "value": "xzha6112@uni.sydney.edu.au" + }, + "ver_date": "2015-07-02T13:30:13.347", + "ver_number": 5 + }, + "182988": { + "citation": { + "attr_id": 391, + "value": "TRPM8-DEPENDENT DYNAMIC RESPONSE IN A MATHEMATICAL MODEL OF COLD THERMORECEPTOR\r\nOlivares E, Salgado S, Maidana JP, Herrera G, Campos M, Madrid R, Orio P\r\nSubmitted to PLoS One, July 2015" + }, + "class_id": 19, + "created": "2015-07-01T13:36:10.56", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 137264, + "object_name": "I trp" + }, + { + "object_id": 185112, + "object_name": "I TRPM8" + } + ] + }, + "gitrepo": false, + "id": 182988, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144508, + "object_name": "Orio, Patricio [patricio.orio at uv.cl]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + }, + { + "object_id": 152639, + "object_name": "Temperature" + }, + { + "object_id": 182362, + "object_name": "Sensory coding" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 185108, + "object_name": "Olivares E, Salgado S, Maidana JP, Herrera G, Campos M, Madrid R, Orio P (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "TRPM8-dependent dynamic response in cold thermoreceptors (Olivares et al. 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 136307, + "object_name": "Dorsal Root Ganglion (DRG) cell" + }, + { + "object_id": 185113, + "object_name": "Dorsal Root Ganglion cell: cold sensing" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model reproduces the dynamic response of cold thermoreceptors, transiently changing the firing rate upon heating or cooling. 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Neurons were interconnected with AMPA/NMDA, and GABAA synapses. We trained our model using spike-timing-dependent reinforcement learning to control a virtual musculoskeletal human arm, with realistic anatomical and biomechanical properties, to reach a target. Virtual arm position was used to simultaneously control a robot arm via a network interface." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/183014", + "other_concept": { + "attr_id": 306, + "value": "Motor control" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2018-10-18T17:29:32.497", + "ver_number": 30 + }, + "183015": { + "class_id": 19, + "created": "2015-07-06T13:18:20.533", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 183015, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 146951, + "object_name": "McDougal, Robert [robert.mcdougal at yale.edu]" + }, + { + "object_id": 183020, + "object_name": "Seidenstein, Alexandra [ahs342 at nyu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 156682, + "object_name": "McDougal RA, Hines ML, Lytton WW (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Reaction-diffusion in the NEURON simulator (McDougal et al 2013)", + "notes": { + "attr_id": 24, + "value": "\"In order to support research on the role of cell biological principles (genomics, proteomics, signaling cascades and reaction dynamics) on the dynamics of neuronal response in health and disease, NEURON's Reaction-Diffusion (rxd) module in Python provides specification and simulation for these dynamics, coupled with the electrophysiological dynamics of the cell membrane. Arithmetic operations on species and parameters are overloaded, allowing arbitrary reaction formulas to be specified using Python syntax. These expressions are then transparently compiled into bytecode that uses NumPy for fast vectorized calculations. At each time step, rxd combines NEURON's integrators with SciPy's sparse linear algebra library.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/183015", + "other_implementer": { + "attr_id": 308, + "value": "Alexandra Seidenstein" + }, + "ver_date": "2015-07-26T14:50:06.547", + "ver_number": 11 + }, + "183017": { + "class_id": 19, + "created": "2015-07-06T14:18:19.607", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 183017, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 150034, + "object_name": "Solanka, Lukas [l.solanka at sms.ed.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83539, + "object_name": "Attractor Neural Network" + }, + { + "object_id": 98011, + "object_name": "Place cell/field" + }, + { + "object_id": 139148, + "object_name": "Brain Rhythms" + }, + { + "object_id": 194888, + "object_name": "Grid cell" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 183018, + "object_name": "Solanka L, van Rossum MC, Nolan MF (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + }, + { + "object_id": 225554, + "object_name": "NEST (web link to model)" + } + ] + }, + "name": "Noise promotes independent control of gamma oscillations and grid firing (Solanka et al 2015)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Neural computations underlying cognitive functions require calibration of the strength of excitatory and inhibitory synaptic connections and are associated with modulation of gamma frequency oscillations in network activity. However, principles relating gamma oscillations, synaptic strength and circuit computations are unclear. We address this in attractor network models that account for grid firing and theta-nested gamma oscillations in the medial entorhinal cortex. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/183017", + "other_concept": { + "attr_id": 306, + "value": "Grid cell" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 119191, + "object_name": "Entorhinal cortex" + } + ] + }, + "ver_date": "2019-02-21T09:40:14.893", + "ver_number": 8 + }, + "183077": { + "citation": { + "attr_id": 391, + "value": "Aumentado-Armstrong et al. PLoS Comp Biol 10.1371/journal.pcbi.1004430" + }, + "class_id": 19, + "created": "2015-07-09T09:51:38.433", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 183077, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 183078, + "object_name": "Aumentado-Armstrong T, Metzen MG, Sproule MK, Chacron MJ (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Midbrain torus semicircularis neuron model (Aumentado-Armstrong et al. 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 156124, + "object_name": "Abstract single compartment conductance based cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This paper investigates how midbrain electrosensory neurons give invariant responses to natural communication stimuli. A model explains that such invariance can be achieved by combining afferent input from ON and OFF cells." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/183077", + "public_submitter_email": { + "attr_id": 309, + "value": "maurice.chacron@mcgill.ca" + }, + "ver_date": "2015-07-10T08:41:01.857", + "ver_number": 13 + }, + "183251": { + "class_id": 19, + "created": "2015-07-13T16:34:03.933", + "gitrepo": false, + "id": 183251, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 118393, + "object_name": "Hjorth, Johannes [hjorth at csc.kth.se]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 128875, + "object_name": "Development" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 183089, + "object_name": "Hjorth JJ, Sterratt DC, Cutts CS, Willshaw DJ, Eglen SJ (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + }, + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + }, + { + "object_id": 127324, + "object_name": "R (web link to model)" + } + ] + }, + "name": "Quantitative assessment of computational models for retinotopic map formation (Hjorth et al. 2015)", + "notes": { + "attr_id": 24, + "value": "\"Molecular and activity-based cues acting together are thought to guide retinal axons to their terminal sites in vertebrate optic tectum or superior colliculus (SC) to form an ordered map of connections. The details of mechanisms involved, and the degree to which they might interact, are still not well understood. We have developed a framework within which existing computational models can be assessed in an unbiased and quantitative manner against a set of experimental data curated from the mouse retinocollicular system. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/183251", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 118308, + "object_name": "Superior colliculus" + } + ] + }, + "ver_date": "2015-07-26T14:53:44.477", + "ver_number": 7 + }, + "183300": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 222353, + "object_name": "event generator for table of prob to spikes supporting S2 fig" + }, + { + "object_id": 222354, + "object_name": "gui trace creation supporting information figure S3 B, C, D" + }, + { + "object_id": 222355, + "object_name": "matlab archive and diagnostic related" + }, + { + "object_id": 222356, + "object_name": "manual simulated breath writer" + }, + { + "object_id": 222357, + "object_name": "model analysis matlab code" + }, + { + "object_id": 222358, + "object_name": "matlab code created test data sets for experiment analysis code testing" + } + ] + }, + "class_id": 19, + "created": "2015-07-14T12:32:07.383", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 183300, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33772, + "object_name": "Morse, Tom [Tom.Morse at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 182362, + "object_name": "Sensory coding" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 218089, + "object_name": "Short SM, Morse TM, McTavish TS, Shepherd GM, Verhagen JV (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Mitral cell activity gating by respiration and inhibition in an olfactory bulb NN (Short et al 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + }, + { + "object_id": 3978, + "object_name": "Olfactory bulb main interneuron periglomerular GABA cell" + }, + { + "object_id": 3979, + "object_name": "Olfactory bulb main interneuron granule MC GABA cell" + }, + { + "object_id": 207694, + "object_name": "Olfactory bulb main tufted cell external" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "To explore interactions between respiration, inhibition, and olfaction, \r\nexperiments using light to active channel rhodopsin in sensory neurons expressing Olfactory Marker Protein were performed in mice and modeled in silico.\r\nThis archive contains NEURON models that were run on parallel computers to explore the interactions between varying strengths of respiratory activity and olfactory sensory neuron input and the roles of periglomerular, granule, and external tufted cells in shaping mitral cell responses." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/183300", + "public_submitter_email": { + "attr_id": 309, + "value": "shaina.short@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "ver_date": "2022-05-27T18:31:32.337", + "ver_number": 25 + }, + "183371": { + "citation": { + "attr_id": 391, + "value": "Sweeney Y, Hellgren Kotaleski J, Hennig MH (2015) A Diffusive Homeostatic Signal Maintains Neural Heterogeneity and Responsiveness in Cortical Networks. 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Our model simulates the activity-dependent synthesis and diffusion of nitric oxide in a recurrent network model of integrate-and-fire neurons. The concentration of nitric oxide is then used as homeostatic readout which modulates the firing threshold of each neuron." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/183371", + "other_concept": { + "attr_id": 306, + "value": "Volume transmission" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "yann.sweeney@ed.ac.uk" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 2375, + "object_name": "NO" + } + ] + }, + "ver_date": "2017-07-28T13:52:16.123", + "ver_number": 12 + }, + "183424": { + "citation": { + "attr_id": 391, + "value": "PMID: 26142457" + }, + "class_id": 19, + "created": "2015-07-18T04:26:16.887", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 183424, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 189098, + "object_name": "Kubota, Yoshi [yoshiy at nips.ac.jp]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 189096, + "object_name": "Kubota Y, Kondo S, Nomura M, Hatada S, Yamaguchi N, Mohamed AA, Karube F, L\u00fcbke J, Kawaguchi Y (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A Layer V CCS type pyramidal cell, inhibitory synapse current conduction (Kubota Y et al., 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 167343, + "object_name": "Neocortex U1 L5B pyramidal pyramidal tract GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A layer V crossed-corticostriatal (CCS) \u2018slender untufted\u2019 pyramidal cell model of rat frontal cortex was built using Neurolucida tracing as well as 3D reconstructed dendrites of serial electron micrographs to give the model as authentic morphology as possible." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/183424", + "public_submitter_email": { + "attr_id": 309, + "value": "yoshiy@nips.ac.jp" + }, + "ver_date": "2017-07-03T22:02:59.183", + "ver_number": 6 + }, + "183718": { + "class_id": 19, + "created": "2015-07-23T18:05:06.387", + "gitrepo": false, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 183718, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 186894, + "object_name": "Tikidji-Hamburyan, Ruben [ruben.tikidji.hamburyan at gmail.com] " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 156115, + "object_name": "Gamma oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 186893, + "object_name": "Tikidji-Hamburyan RA, Mart\u00ednez JJ, White JA, Canavier CC (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "PIR gamma oscillations in network of resonators (Tikidji-Hamburyan et al. 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 128565, + "object_name": "Wide dynamic range neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... The coupled oscillator model implemented with Wang\u2013Buzsaki model neurons is not\r\nsufficiently robust to heterogeneity in excitatory drive, and therefore intrinsic frequency, to account for in vitro models of ING. Similarly, in a\r\ntightly synchronized regime, the stochastic population oscillator model is often characterized by sparse firing, whereas interneurons both in vivo\r\nand in vitro do not fire sparsely during gamma,but rather on average every other cycle. We substituted so-called resonator neural models, which\r\nexhibit class 2 excitability and postinhibitory rebound (PIR), for the integrators that are typically used. This results in much greater robustness\r\nto heterogeneity that actually increases as the average participation in spikes per cycle approximates physiological levels. Moreover, dynamic\r\nclamp experiments that show autapse-induced firing in entorhinal cortical interneurons support the idea that PIR can serve as a network gamma\r\nmechanism. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/183718", + "other_implementer": { + "attr_id": 308, + "value": "Tikidji-Hamburyan, Ruben [rath@gwu.edu]" + }, + "other_simulator": { + "attr_id": 307, + "value": "NEURON+Python" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "rtikid@lsuhsc.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 119191, + "object_name": "Entorhinal cortex" + } + ] + }, + "ver_date": "2015-12-17T17:48:58.303", + "ver_number": 12 + }, + "183722": { + "class_id": 19, + "created": "2015-07-31T19:20:37.617", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122807, + "object_name": "HERG KCNH2" + } + ] + }, + "gitrepo": false, + "id": 183722, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 183721, + "object_name": "Yu N, Canavier CC (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Phase plane reveals two slow variables in midbrain dopamine neuron bursts (Yu and Canavier, 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 261, + "object_name": "Substantia nigra pars compacta DA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Midbrain dopamine neurons exhibit a novel type of bursting that we call \u201cinverted square wave bursting\u201d when exposed to Ca2+-activated small conductance (SK) K+ channel blockers in vitro. This type of bursting has three phases: hyperpolarized silence, spiking, and depolarization block. We find that two slow variables are required for this type of bursting, and we show that the three-dimensional bifurcation diagram for inverted square wave bursting is a folded surface with upper (depolarized) and lower (hyperpolarized) branches. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/183722", + "ver_date": "2015-07-31T19:58:00.81", + "ver_number": 6 + }, + "183948": { + "class_id": 19, + "created": "2015-08-06T21:47:05.183", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 183948, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 128503, + "object_name": "Goldwyn, Joshua [jhgoldwyn at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 84598, + "object_name": "Extracellular Fields" + }, + { + "object_id": 139996, + "object_name": "Evoked LFP" + }, + { + "object_id": 183995, + "object_name": "Ephaptic coupling" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 187475, + "object_name": "Goldwyn JH, Rinzel J (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Ephaptic coupling in passive cable and MSO neuron models (Goldwyn & Rinzel 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88110, + "object_name": "Medial Superior Olive (MSO) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Simulation code to explore how the synchronous activity of a bundle of neurons generates extracellular voltage, and how this extracellular voltage influences the membrane potential of \"nearby\" neurons. A non-synaptic mechanism known as ephaptic coupling. A model of a passive cable population (including user-friendly matlab GUI) and a model of medial superior olive neurons are included." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/183948", + "other_concept": { + "attr_id": 306, + "value": "Ephaptic coupling" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jhgoldwyn@gmail.com" + }, + "ver_date": "2016-02-01T15:01:47.963", + "ver_number": 8 + }, + "183949": { + "class_id": 19, + "created": "2015-08-07T14:29:27.56", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122327, + "object_name": "Kv1.2 KCNA2" + }, + { + "object_id": 122822, + "object_name": "Kv1.9 Kv7.1 KCNQ1" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 183949, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83510, + "object_name": "Powers, Randy [rkpowers at u.washington.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 183084, + "object_name": "Powers RK, Heckman CJ (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Discharge hysteresis in motoneurons (Powers & Heckman 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 276, + "object_name": "Spinal cord lumbar motor neuron alpha ACh cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Motoneuron\r\nactivity is strongly influenced by the activation of persistent\r\ninward currents (PICs) mediated by voltage-gated sodium and calcium\r\nchannels. ... It has recently been suggested that a number\r\nof factors other than PIC can contribute to delta F (firing rate differences between motoneurons) values, including\r\nmechanisms underlying spike frequency adaptation and spike threshold\r\naccommodation. In the present study, we used a set of compartmental\r\nmodels representing a sample of 20 motoneurons with a range\r\nof thresholds to investigate how several different intrinsic motoneuron\r\nproperties can potentially contribute to variations in F values. ... Our results indicate that, although other\r\nfactors can contribute, variations in discharge hysteresis and delta F\r\nvalues primarily reflect the contribution of dendritic PICs to motoneuron\r\nactivation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/183949", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2022-11-04T13:12:03.773", + "ver_number": 13 + }, + "184054": { + "citation": { + "attr_id": 391, + "value": "Dendritic sodium spikes are required for long-term potentiation at distal synapses on hippocampal pyramidal neurons\r\nYujin Kim*, Ching-Lung Hsu*, Mark Cembrowski, Brett Mensh, Nelson Spruston\r\neLife 2015;10.7554/eLife.06414\r\nDOI: http://dx.doi.org/10.7554/eLife.06414" + }, + "class_id": 19, + "created": "2015-08-13T10:24:57.287", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 184054, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 184057, + "object_name": "Cembrowski, Mark S [cembrowskim at janelia.hhmi.org]" + }, + { + "object_id": 184058, + "object_name": "Hsu, Ching-Lung [hsuc at janelia.hhmi.org]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 143636, + "object_name": "Conductance distributions" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 184056, + "object_name": "Kim Y, Hsu CL, Cembrowski MS, Mensh BD, Spruston N (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "CA1 pyramidal neuron: Dendritic Na+ spikes are required for LTP at distal synapses (Kim et al 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model simulates the effects of dendritic sodium spikes initiated in distal apical dendrites on the voltage and the calcium dynamics revealed by calcium imaging. It shows that dendritic sodium spike promotes large and transient calcium influxes via NMDA receptor and L-type voltage-gated calcium channels, which contribute to the induction of LTP at distal synapses." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/184054", + "public_submitter_email": { + "attr_id": 309, + "value": "hsuc@janelia.hhmi.org" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2018-06-12T11:14:18.773", + "ver_number": 13 + }, + "184139": { + "citation": { + "attr_id": 391, + "value": "Jung Ho Hyun, Kisang Eom, Kyu-Hee Lee, Jin Young Bae, Yong Chul Bae, Myoung-Hwan Kim, Sooyun Kim, Won-Kyung Ho and Suk-Ho Lee (2015) Kv1.2 mediates heterosynaptic modulation of direct cortical synaptic inputs in CA3 pyramidal cells. J. Physiol. 593(16): 3617-3643." + }, + "class_id": 19, + "created": "2015-08-15T01:42:19.723", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 143445, + "object_name": "I_KD" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122327, + "object_name": "Kv1.2 KCNA2" + } + ] + }, + "gitrepo": false, + "id": 184139, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 187083, + "object_name": "Hyun JH, Eom K, Lee KH, Bae JY, Bae YC, Kim MH, Kim S, Ho WK, Lee SH (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "CA3 pyramidal neurons: Kv1.2 mediates modulation of cortical inputs (Hyun et al., 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model simulates the contribution of dendritic Na+ and D-type K+ channels to EPSPs at three different locations of apical dendrites, which mimicking innervation sites of mossy fibers (MF), recurrent fibers (AC), and perforant pathway (PP)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/184139", + "public_submitter_email": { + "attr_id": 309, + "value": "leesukho@snu.ac.kr" + }, + "ver_date": "2016-01-07T16:18:49.893", + "ver_number": 6 + }, + "184140": { + "class_id": 19, + "created": "2015-08-17T00:18:55.237", + "gitrepo": false, + "id": 184140, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 146501, + "object_name": "Nicola, Wilten [wnicola at uwaterloo.ca]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 185336, + "object_name": "Ferguson KA, Njap F, Nicola W, Skinner FK, Campbell SA (2015)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Mean-field systems and small scale neural networks (Ferguson et al. 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We explore adaptation induced bursting as a mechanism for theta oscillations in hippocampal area CA1. To do this, we have developed a mean-field system for a network of fitted Izhikevich neurons with sparse coupling and heterogeneity. The code contained here runs the mean-field system pointwise or on a two-parameter mesh, in addition to networks of neurons that are smaller then those considered in the paper. The file README.pdf contains instructions on use. \r\n\r\nNote that the following file (peakfinder): \r\n\r\nhttp://www.mathworks.com/matlabcentral/fileexchange/25500-peakfinder-x0--sel--thresh--extrema--includeendpoints--interpolate-\r\n\r\nis required to compute burst frequencies in the mean-field system and must be downloaded and placed in the same root folder as MFSIMULATOR.mat" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/184140", + "public_submitter_email": { + "attr_id": 309, + "value": "wnicola@uwaterloo.ca" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2015-12-21T15:27:33.037", + "ver_number": 8 + }, + "184141": { + "citation": { + "attr_id": 391, + "value": "C.Vich and A.Guillamon (2015), preprint accepted to Journal of Computational Neuroscience" + }, + "class_id": 19, + "created": "2015-08-17T05:28:22.683", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 184141, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 184150, + "object_name": "Vich, Catalina [catalina.vich at uib.es]" + }, + { + "object_id": 184151, + "object_name": "Guillamon, Antoni " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 243515, + "object_name": "Conductances estimation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 185758, + "object_name": "Vich C, Guillamon A (2015)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Estimation of conductance in a conductance-based model of quadratic type (Vich & Guillamon 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 129068, + "object_name": "Entorhinal cortex stellate cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We assume to have a quadratic approximation of a conductance-based neuron model,\u00a0as in H.Rotstein (2015). Given the resulting membrane potential (v) and\u00a0the course of the gating variable (w), this program estimates the synaptic\u00a0current that the neuron is receiving at each time.\u00a0\r\n\u00a0 Moreover, given the voltage traces for two different applied (steady) currents and the excitatory and inhibitory reversal potentials, the program estimates the excitatory and\u00a0inhibitory conductances separately.\r\n\u00a0 Finally, the program gives the option of estimating the synaptic conductance. This conductance can be estimated in two different ways: (1) if only one voltage trace is given, the synaptic conductance is estimated using the synaptic reversal potential; (2) however, if two voltage traces are given (for two different applied currents), then the synaptic conductance can be either estimated using the synaptic reversal potential or the leak conductance." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/184141", + "other_concept": { + "attr_id": 306, + "value": "estimation of conductance" + }, + "other_implementer": { + "attr_id": 308, + "value": "Vich, Catalina and Guillamon, Antoni " + }, + "other_type": { + "attr_id": 305, + "value": "conductance-based model" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "catalina.vich@uib.es" + }, + "ver_date": "2018-06-25T15:51:52.947", + "ver_number": 22 + }, + "184142": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 184144, + "object_name": "Standalone NEURON" + } + ] + }, + "class_id": 19, + "created": "2015-08-17T08:56:17.353", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "default_file_notes": { + "attr_id": 392, + "value": "Allen SDK" + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 184142, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 182684, + "object_name": "Allen Institute (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Allen Institute: Scnn1a-Tg3-Cre VISp layer 4 329321704", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 184128, + "object_name": "Neocortex layer 4 pyramidal cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is an Allen Cell Types Database model of a Scnn1a-Tg3-Cre neuron from layer 4 of the mouse primary visual cortex. 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Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/184142", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2018-10-17T17:20:16.447", + "ver_number": 9 + }, + "184145": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 184146, + "object_name": "Standalone NEURON" + } + ] + }, + "class_id": 19, + "created": "2015-08-17T09:07:21.117", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "default_file_notes": { + "attr_id": 392, + "value": "Allen SDK" + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 184145, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 182684, + "object_name": "Allen Institute (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Allen Institute: Pvalb-IRES-Cre VISp layer 6a 471081668", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 184136, + "object_name": "Neocortex layer 6a interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is an Allen Cell Types Database model of a Pvalb-IRES-Cre neuron from layer 6a of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. 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Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/184352", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2018-10-17T17:30:19.15", + "ver_number": 4 + }, + "184404": { + "class_id": 19, + "created": "2015-08-20T03:01:49.447", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 184409, + "object_name": "I MI" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 184404, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + }, + { + "object_id": 222956, + "object_name": "Neuromodulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 118002, + "object_name": "Stein W, Straub O, Ausborn J, Mader W, Wolf H (2008)" + }, + { + "object_id": 118123, + "object_name": "Ausborn J, Stein W, Wolf H (2007)" + }, + { + "object_id": 184405, + "object_name": "St\u00e4dele C, Heigele S, Stein W (2015)" + }, + { + "object_id": 184583, + "object_name": "Daur N, Diehl F, Mader W, Stein W (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 118095, + "object_name": "MadSim" + } + ] + }, + "name": "Interaction of leak and IMI conductance on the STG over broad temperature range (Stadele et al 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114303, + "object_name": "Stomatogastric Ganglion (STG) Lateral Gastric (LG) cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 184582, + "object_name": "CabTRP 1a" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The ZIP file contains a Hodgkin-Huxley based circuit model and the simulation environment MadSim used to study the interaction of leak and IMI on the gastric mill network of the crab (Cancer borealis) as represented in (C. 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Stein, 2015)\r\n\r\nMadSim, the simulation environment used for this study, is freeware and included in the package." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/184404", + "other_currents": { + "attr_id": 303, + "value": "I MI" + }, + "other_neurons": { + "attr_id": 301, + "value": "Stomatogastric ganglion gastric mill CPG" + }, + "other_neurotransmitter": { + "attr_id": 304, + "value": "CabTRP 1a" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "carola@nruobiologie.de" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115959, + "object_name": "Stomatogastric ganglion" + } + ] + }, + "ver_date": "2017-01-13T12:19:58.507", + "ver_number": 20 + }, + "184410": { + "citation": { + "attr_id": 391, + "value": "Synaptic Efficacy as a Function of Ionotropic Receptor Distribution: A Computational Study\r\nPLOS One 2015" + }, + "class_id": 19, + "created": "2015-08-20T20:11:10.41", + "gitrepo": false, + "id": 184410, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 184411, + "object_name": "Allam, Sushmita [sushmita.allam at gmail.com]" + }, + { + "object_id": 184412, + "object_name": "Bouteiller, Jean-Marie C [jbouteil at usc.edu]" + }, + { + "object_id": 184413, + "object_name": "Hu, Eric [ehu at usc.edu]" + }, + { + "object_id": 184423, + "object_name": "Ambert, Nicolas [nicolas.ambert at rhenovia.com]" + }, + { + "object_id": 184424, + "object_name": "Greget, Renaud [renaud.greget at rhenovia.com]" + }, + { + "object_id": 184425, + "object_name": "Bischoff, Serge [serge.bischoff at rhenovia.com" + }, + { + "object_id": 184426, + "object_name": "Baudry, Michel [michel.baudry at rhenovia.com]" + }, + { + "object_id": 184427, + "object_name": "Berger, Theodore W. 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International Conference on Neural Field Theory, Reading, U.K., June 2014.\r\n\r\nNichols, E., Hutt, A., 2013. Neural field simulator: fast computation and 3D-visualization. BMC Neuroscience 14(1), p.179." + }, + "class_id": 19, + "created": "2015-08-26T07:18:18.45", + "gitrepo": false, + "id": 184479, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 187111, + "object_name": "Nichols, Eric J [ericjnichols at gmail.com]" + }, + { + "object_id": 187112, + "object_name": "Hutt, Axel [axel.hutt @ inria.fr]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 184480, + "object_name": "Nichols E, Green K, Hutt A (2015)" + }, + { + "object_id": 184483, + "object_name": "Nichols E, Green K, Hutt A, van Veen L (2014)" + }, + { + "object_id": 184485, + "object_name": "Nichols E, Hutt A (2014)" + }, + { + "object_id": 184486, + "object_name": "Nichols E, Hutt A (2013)" + }, + { + "object_id": 187109, + "object_name": "Nichols EJ, Hutt A (2015)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 187110, + "object_name": "Neural Field Simulator" + } + ] + }, + "name": "Neural Field Simulator (Nichols & Hutt 2012-2015)", + "notes": { + "attr_id": 24, + "value": "The NeuralFieldSimulator considers scalar spatially homogeneous neural fields taking into account finite axonal transmission speed and synaptic temporal derivatives of first and second order. A text-based interface offers complete control of field parameters and several approaches are used to accelerate simulations. A graphical output utilizes video hardware acceleration to display running output with reduced computational cost compared to exclusively software-based simulators. Diverse applications of simulations include breather oscillations, static and dynamic Turing patterns and activity spreading with finite propagation speed." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/184479", + "other_type": { + "attr_id": 305, + "value": "Neural Field" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ericjnichols@gmail.com" + }, + "ver_date": "2016-01-12T10:52:24.21", + "ver_number": 7 + }, + "184487": { + "citation": { + "attr_id": 391, + "value": "Rui Ponte Costa, Robert C Froemke, Per Jesper Sj\u00f6str\u00f6m, Mark C W van Rossum - Unified pre- and postsynaptic long-term plasticity enables reliable and flexible learning, eLife 2015;10.7554/eLife.09457" + }, + "class_id": 19, + "created": "2015-08-26T14:35:46.22", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 184487, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 149959, + "object_name": "Costa, Rui Ponte [ruipontecosta at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 87578, + "object_name": "STDP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 237563, + "object_name": "Costa RP, Froemke RC, Sj\u00f6str\u00f6m PJ, van Rossum MC (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Memory savings through unified pre- and postsynaptic STDP (Costa et al 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 226, + "object_name": "NO" + }, + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 184489, + "object_name": "Endocannabinoid" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Although it is well known that long-term synaptic plasticity can be expressed both pre- and postsynaptically, the functional consequences of this arrangement have remained elusive. We show that spike-timing-dependent plasticity with both pre- and postsynaptic expression develops receptive fields with reduced variability and improved discriminability compared to postsynaptic plasticity alone. These long-term modifications in receptive field statistics match recent sensory perception experiments. In these simulations we demonstrate that learning with this form of plasticity leaves a hidden postsynaptic memory trace that enables fast relearning of previously stored information, providing a cellular substrate for memory savings. Our results reveal essential roles for presynaptic plasticity that are missed when only postsynaptic expression of long-term plasticity is considered, and suggest an experience-dependent distribution of pre- and postsynaptic strength changes." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/184487", + "other_neurotransmitter": { + "attr_id": 304, + "value": "Endocannabinoid" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ruipontecosta@gmail.com" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2017-07-28T13:54:42.04", + "ver_number": 14 + }, + "184491": { + "class_id": 19, + "created": "2015-09-01T13:46:12.727", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 184491, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 230781, + "object_name": "Pavlides, Alex " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 185022, + "object_name": "Beta oscillations" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 225773, + "object_name": "Pavlides A, Hogan SJ, Bogacz R (2015)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Excessive beta oscillations in Parkinson's disease (Pavlides et al. 2015)", + "notes": { + "attr_id": 24, + "value": "\" ... Understanding the generation of beta oscillations is important to improve treatments for Parkinson\u2019s disease. Competing theories exist for how these oscillations are generated in the affected brain circuits, which include the motor cortex and a set of subcortical nuclei called the basal ganglia. This paper suggests two hypotheses for the generation of beta oscillations. The first hypothesis is that beta oscillations are generated in the motor cortex, and the basal ganglia resonate to the cortical input. The second hypothesis additionally proposes that feedback from the basal ganglia to cortex is critically important for the presence of the oscillations. We show that the models can successfully account for a wide range of experimental data concerning the presence of beta oscillations in Parkinson\u2019s disease.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/184491", + "public_submitter_email": { + "attr_id": 309, + "value": "alex.pavlides@hotmail.com" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 116868, + "object_name": "Subthalamic Nucleus" + }, + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2017-07-14T17:36:02.823", + "ver_number": 6 + }, + "184497": { + "class_id": 19, + "created": "2015-09-02T17:38:24.377", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 184497, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 112047, + "object_name": "Aging/Alzheimer`s" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 206978, + "object_name": "Rumbell TH, Draguljic D, Yadav A, Hof PR, Luebke JI, Weaver CM (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Rhesus Monkey Young and Aged L3 PFC Pyramidal Neurons (Rumbell et al. 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A stereotypical pyramidal neuron morphology with ion channel parameter combinations that reproduce firing patterns of one young and one aged rhesus monkey L3 PFC pyramidal neurons. Parameters were found through an automated optimization method." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/184497", + "public_submitter_email": { + "attr_id": 309, + "value": "timrumbell@gmail.com" + }, + "ver_date": "2018-01-06T13:35:35.133", + "ver_number": 7 + }, + "184545": { + "citation": { + "attr_id": 391, + "value": "Front Syst Neurosci. 2015 Aug 7;9:110. doi: 10.3389/fnsys.2015.00110. eCollection 2015.\r\n\r\nNetwork models provide insights into how oriens-lacunosum-moleculare and bistratified cell interactions influence the power of local hippocampal CA1 theta oscillations.\r\nFerguson KA, Huh CY, Amilhon B, Manseau F, Williams S, Skinner FK." + }, + "class_id": 19, + "created": "2015-09-04T15:37:25.157", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 184545, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 184546, + "object_name": "Ferguson KA, Huh CY, Amilhon B, Manseau F, Williams S, Skinner FK (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "CA1 SOM+ (OLM) hippocampal interneuron (Ferguson et al. 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This two-variable simple model is derived based on patch-clamp recordings from the CA1 region of a whole hippocampus preparation of SOM+ inhibitory cells. \r\n\r\nThe model code will also be made available on OSB." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/184545", + "public_submitter_email": { + "attr_id": 309, + "value": "frances.skinner@gmail.com" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2017-07-28T11:58:29.267", + "ver_number": 7 + }, + "184725": { + "class_id": 19, + "created": "2015-09-09T18:00:44.153", + "gitrepo": false, + "id": 184725, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 124046, + "object_name": "Polsky, Alon [alonpol at tx.technion.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 140740, + "object_name": "Polsky A, Mel B, Schiller J (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "NMDA spikes in basal dendrites of L5 pyramidal neurons (Polsky et al. 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"...\r\nIn apical\r\ndendrites of neocortical pyramidal neurons, calcium spikes are known\r\nto contribute to burst generation, but a comparable understanding of\r\nbasal dendritic mechanisms is lacking. Here we show that NMDA spikes\r\nin basal dendrites mediate both detection and generation of bursts\r\nthrough a postsynaptic mechanism. \r\n\r\nHigh-frequency inputs to basal\r\ndendrites markedly facilitated NMDA spike initiation compared with\r\nlow-frequency activation or single inputs.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/184725", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "ver_date": "2015-09-09T18:02:08.89", + "ver_number": 2 + }, + "184731": { + "citation": { + "attr_id": 391, + "value": "In final revisions for PLoS Comp Biol" + }, + "class_id": 19, + "created": "2015-09-11T16:11:29.38", + "gitrepo": false, + "id": 184731, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53429, + "object_name": "Blackwell, Avrama [avrama at gmu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 136106, + "object_name": "G-protein coupled" + }, + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 187057, + "object_name": "Chay A, Zamparo I, Koschinski A, Zaccolo M, Blackwell KT (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 53895, + "object_name": "Java" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "CA1 pyramidal cell receptor dependent cAMP dynamics (Chay et al. 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 230, + "object_name": "Norephinephrine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We use a combination of live cell imaging and stochastic modeling of signaling pathways to investigate how noradrenergic receptor stimulation interacts with calcium to control cAMP, required for synaptic plasticity and memory in the hippocampus. Our simulation results explain the mechanism whereby prior noradrenergic receptor stimulation does not enhance the subsequent NMDA stimulated cAMP elevation. Specifically, our results demonstrate the the negative feedback loop from cAMP, through PKA, to PDE4 cannot explain the results, and that switching of the noradrenergic receptor from Gs to Gi is required." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/184731", + "public_submitter_email": { + "attr_id": 309, + "value": "kblackw1@gmu.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 215, + "object_name": "Adrenergic" + } + ] + }, + "ver_date": "2016-03-14T14:50:52.88", + "ver_number": 6 + }, + "184732": { + "citation": { + "attr_id": 391, + "value": "\"Variability in respiratory rhythm generation: in vitro and in silico models\", C Fietkiewicz, GO Shafer, EA Platt, CG Wilson, Communications in Nonlinear Science and Numerical Simulation, Volume 32, March 2016, Pages 158\u2013168 (doi: 10.1016/j.cnsns.2015.08.018)" + }, + "class_id": 19, + "created": "2015-09-11T18:08:20.683", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 184732, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 122331, + "object_name": "Noise Sensitivity" + }, + { + "object_id": 128875, + "object_name": "Development" + }, + { + "object_id": 232102, + "object_name": "Respiratory control" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 185010, + "object_name": "Fietkiewicz C, Shafer GO, Platt EA, Wilson CG (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "pre-B\u00f6tzinger complex variability (Fietkiewicz et al. 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 125611, + "object_name": "Respiratory column neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Based on\r\nexperimental observations, we developed a computational model that can\r\nbe embedded in more comprehensive models of respiratory and\r\ncardiovascular autonomic control. Our simulation results successfully\r\nreproduce the variability we observed experimentally. The in silico\r\nmodel suggests that age-dependent variability may be due to a\r\ndevelopmental increase in mean synaptic conductance between preB\u00f6tC\r\nneurons. We also used simulations to explore the effects of stochastic\r\nspiking in sensory relay neurons. Our results suggest that stochastic\r\nspiking may actually stabilize modulation of both respiratory rate and\r\nits variability when the rate changes due to physiological demand.\r\n\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/184732", + "public_submitter_email": { + "attr_id": 309, + "value": "cxf47@case.edu" + }, + "ver_date": "2021-05-22T13:58:04.617", + "ver_number": 6 + }, + "185014": { + "class_id": 19, + "created": "2015-09-14T04:19:02.81", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 88215, + "object_name": "I_Ks" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 185014, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + }, + { + "object_id": 156115, + "object_name": "Gamma oscillations" + }, + { + "object_id": 185022, + "object_name": "Beta oscillations" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 226071, + "object_name": "David F, Courtiol E, Buonviso N, Fourcaud-Trocm\u00e9 N (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Gamma-beta alternation in the olfactory bulb (David, Fourcaud-Trocm\u00e9 et al., 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + }, + { + "object_id": 3979, + "object_name": "Olfactory bulb main interneuron granule MC GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model, a simplified olfactory bulb network with mitral and granule cells, proposes a framework for two regimes of oscillation in the olfactory bulb: \r\n1 - a weak inhibition regime (with no granule spike) where the network oscillates in the gamma (40-90Hz) band\r\n2 - a strong inhibition regime (with granule spikes) where the network oscillates in the beta (15-30Hz) band.\r\nSlow modulations of sensory and centrifugal inputs, phase shifted by a quarter of cycle, possibly combined with short term depression of the mitral to granule AMPA synapse, allows the network to alternate between the two regimes as observed in anesthetized animals." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/185014", + "other_concept": { + "attr_id": 306, + "value": "Beta oscillations" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "nicolas.fourcaud-trocme@cnrs.fr" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "ver_date": "2021-03-10T11:15:42.71", + "ver_number": 16 + }, + "185021": { + "citation": { + "attr_id": 391, + "value": "K.A. Ferguson*, F. Njap*, W. Nicola*, F.K. Skinner#, S.A. Campbell# (2015) \"Examining the limits of cellular adaptation bursting mechanisms in biologically-based excitatory networks of the hippocampus\". Journal of Computational Neuroscience (accepted)\r\n(* shared first authors, #shared senior authors)" + }, + "class_id": 19, + "created": "2015-09-14T17:52:58.907", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 185021, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 185336, + "object_name": "Ferguson KA, Njap F, Nicola W, Skinner FK, Campbell SA (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "CA1 pyramidal neuron network model (Ferguson et al 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "From the paper:\r\nFigure 4 (1000 cell network) is reproduced by running this brian code.\r\nThe raster plot and one of the excitatory cell voltage is produced." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/185021", + "public_submitter_email": { + "attr_id": 309, + "value": "frances.skinner@gmail.com" + }, + "ver_date": "2017-07-28T11:58:09.947", + "ver_number": 5 + }, + "185090": { + "class_id": 19, + "created": "2015-09-15T15:43:06.857", + "gitrepo": false, + "id": 185090, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 185093, + "object_name": "Gehring TV, Luksys G, Sandi C, Vasilaki E (2015)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Detailed analysis of trajectories in the Morris water maze (Gehring et al. 2015)", + "notes": { + "attr_id": 24, + "value": "MATLAB code that can be used for detailed behavioural analyzes of the trajectories of animals be means of a semi-supervised clustering algorithm. The method is applied here to trajectories in the Morris Water Maze (see Gehring, T. V. et al., Scientific Reports, 2015) but the code can easily be adapted to other types experiments. For more information and the latest version of the code please refer to https://bitbucket.org/tiagogehring/mwm_trajectories" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/185090", + "public_submitter_email": { + "attr_id": 309, + "value": "tiagogehring@gmail.com" + }, + "ver_date": "2015-09-15T17:54:45.733", + "ver_number": 6 + }, + "185122": { + "citation": { + "attr_id": 391, + "value": "1. Miller GM, Ogunnaike BA, Schwaber JS, Vadigepalli R (2010) Robust dynamic balance of AP-1 transcription factors in a neuronal gene regulatory network. BMC Systems Biology 4: 171.\r\n2. Makadia, H.K., Schwaber, J.S., and Vadigepalli, R. (2015). Intracellular information processing through encoding and decoding of dynamic signaling features. PLOS Computational Biology (accepted)." + }, + "class_id": 19, + "created": "2015-09-25T12:35:09.257", + "gitrepo": false, + "id": 185122, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 156833, + "object_name": "Makadia, Hirenkumar K [hiren.makadia at gmail.com]" + }, + { + "object_id": 169243, + "object_name": "Vadigepalli, Rajanikanth [Rajanikanth.Vadigepalli at jefferson.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 169158, + "object_name": "Miller GM, Ogunnaike BA, Schwaber JS, Vadigepalli R (2010)" + }, + { + "object_id": 185889, + "object_name": "Makadia HK, Schwaber JS, Vadigepalli R (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Activator protein 1(AP-1) transcriptional regulatory model in brainstem neurons (Makadia et al 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 185139, + "object_name": "Brainstem neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We have developed a mathematical model of AT1R-activated signaling kinases and a downstream transcriptional regulatory network controlling the family of activator protein 1 (AP-1) transcription factors. The signaling interactions of the transcriptional model were modeled with either mass-action or Michaelis--Menten kinetics, whereas the phenomenological model of the kinases used exponentials. These models were validated against their respective data domains independently and were integrated into one. The model was implemented as a set of ordinary differential equations solved using the ode15s solver in Matlab (Mathworks, USA)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/185122", + "other_gene": { + "attr_id": 477, + "value": "c-Fos, c-Jun" + }, + "other_neurons": { + "attr_id": 301, + "value": "brainstem neuron" + }, + "other_receptors": { + "attr_id": 302, + "value": "AT1R" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "hiren.makadia@gmail.com" + }, + "ver_date": "2015-09-30T17:19:57.38", + "ver_number": 10 + }, + "185123": { + "class_id": 19, + "created": "2015-09-25T14:36:45.583", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + } + ] + }, + "gitrepo": false, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 185123, + "implemented_by": 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We evaluated the impact of molecular variability in the expression of cell signaling components and ion channels\r\n on electrophysiological excitability and neuromodulation. We employed a computational approach that integrated neuropeptide receptor-mediated signaling with electrophysiology. We simulated a population of neurons in which expression levels of a neuropeptide receptor and multiple ion channels were simultaneously\r\n varied within a physiological range. We analyzed the effects of variation on the electrophysiological response to a neuropeptide stimulus. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/185300", + "other_neurotransmitter": { + "attr_id": 304, + "value": "Angiotensin" + }, + "other_receptors": { + "attr_id": 302, + "value": "AT1R" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "warren.anderson@jefferson.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 186227, + "object_name": "AT1R" + } + ] + }, + "ver_date": "2017-01-13T12:20:40.1", + "ver_number": 7 + }, + "185328": { + "class_id": 19, + "created": "2015-10-14T14:01:13.643", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 185328, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33736, + "object_name": "Hines, Michael [Michael.Hines at Yale.edu]" + }, + { + "object_id": 150452, + "object_name": "Moore, John W [moore at neuro.duke.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 185327, + "object_name": "Moore JW (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Enhancing the HH eqs: simulations based on the first publication in Biophys J (Moore 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52426, + "object_name": "Squid axon" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The experiments in the Cole and Moore article in the first issue of the Biophysical Journal provided the first independent experimental confirmation of the Hodgkin-Huxley (HH) equations. A log-log plot of the K current versus time showed that raising the HH variable n to the sixth power provided the best fit to the data. Subsequent simulations using n6 and setting the resting potential at the in vivo value simplifies the HH equations by eliminating the leakage term. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/185328", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "ver_date": "2015-10-15T10:16:22.583", + "ver_number": 5 + }, + "185330": { + "class_id": 19, + "created": "2015-10-16T11:33:47.167", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 185330, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 189151, + "object_name": "Enrico, Paolo [enrico at uniss.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 112854, + "object_name": "Addiction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 189150, + "object_name": "Enrico P, Migliore M, Spiga S, Mulas G, Caboni F, Diana M (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "VTA neurons: Morphofunctional alterations in acute opiates withdrawal (Enrico et al. 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 189152, + "object_name": "Ventral tegmental area dopamine neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Here we present a biophysical model of a DA VTA neuron based on 3D morphological reconstruction and electrophysiological data, showing how opiates withdrawal-driven morphological and electrophysiological changes could affect the firing rate and discharge pattern....\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/185330", + "ver_date": "2019-04-17T16:53:57.11", + "ver_number": 12 + }, + "185332": { + "class_id": 19, + "created": "2015-10-19T16:26:01.12", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 88209, + "object_name": "Na/Ca exchanger" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 185332, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 185339, + "object_name": "Zylbertal, Asaph [asaph.zylbertal at mail.huji.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 82787, + "object_name": "Working memory" + }, + { + "object_id": 185343, + "object_name": "Persistent activity" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 185341, + "object_name": "Zylbertal A, Kahan A, Ben-Shaul Y, Yarom Y, Wagner S (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "AOB mitral cell: persistent activity without feedback (Zylbertal et al., 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 185340, + "object_name": "Olfactory bulb (accessory) mitral cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Persistent activity has been reported in many brain areas and is\r\nhypothesized to mediate working memory and emotional brain states and\r\nto rely upon network or biophysical feedback. Here we demonstrate a\r\nnovel mechanism by which persistent neuronal activity can be generated\r\nwithout feedback, relying instead on the slow removal of Na+ from\r\nneurons following bursts of activity. This is a realistic\r\nconductance-based model that was constructed using the detailed\r\nmorphology of a single typical accessory olfactory bulb (AOB) mitral\r\ncell for which the electrophysiological properties were\r\ncharacterized." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/185332", + "other_concept": { + "attr_id": 306, + "value": "Persistent activity" + }, + "other_implementer": { + "attr_id": 308, + "value": "Zylbertal, Asaph [asaph.zylbertal at mail.huji.ac.il]" + }, + "other_neurons": { + "attr_id": 301, + "value": "Accessory olfactory bulb mitral cell" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "ver_date": "2018-10-17T16:19:02.88", + "ver_number": 14 + }, + "185334": { + "citation": { + "attr_id": 391, + "value": "Bose, A., & Rubin, J. E. (2015). Strategies to Maximize Burst Lengths in Rhythmic Anti-Phase Activity of Networks with Reciprocal Inhibition. International Journal of Bifurcation and Chaos, 25(07), 1540004." + }, + "class_id": 19, + "created": "2015-10-19T22:44:04.63", + "gitrepo": false, + "id": 185334, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 125612, + "object_name": "Rubin, Jonathan E [jonrubin at pitt.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 185337, + "object_name": "Bose A, Rubin JE (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Double boundary value problem (A. Bose and J.E. Rubin, 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "For two neurons coupled with mutual inhibition, we investigate the strategies that each neuron should utilize in order to maximize the number of spikes it can fire (or equivalently the amount of time it is active) before the other neuron takes over. We derive a one-dimensional map whose fixed points correspond to periodic anti-phase bursting solutions. The model here solves a novel double boundary value problem that can be used to obtain the graph of this map.\r\n\r\nRead More: http://www.worldscientific.com/doi/abs/10.1142/S0218127415400040" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/185334", + "other_concept": { + "attr_id": 306, + "value": "Mutual inhibition" + }, + "other_neurons": { + "attr_id": 301, + "value": "half-center oscillator" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jonrubin@pitt.edu" + }, + "ver_date": "2015-10-20T10:04:30.877", + "ver_number": 3 + }, + "185338": { + "class_id": 19, + "created": "2015-10-23T07:37:57.707", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 185338, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 230416, + "object_name": "Ueyama, Yuki [yuki.ueyama at st.seikei.ac.jp]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 186770, + "object_name": "Motor control" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 230330, + "object_name": "Ueyama Y (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 127515, + "object_name": "Muscle model and vibrissa biomechanics" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Model predictive control model for an isometric motor task (Ueyama 2017)", + "notes": { + "attr_id": 24, + "value": "A model predictive control model for an isometric motor task." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/185338", + "other_neurons": { + "attr_id": 301, + "value": "Motor cortex neuron" + }, + "other_type": { + "attr_id": 305, + "value": "Motor control" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ueyama-yuki@rehab.go.jp" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Yuki Ueyama" + }, + "ver_date": "2017-07-10T16:51:27.51", + "ver_number": 8 + }, + "185350": { + "citation": { + "attr_id": 391, + "value": "Jedlicka P, Benuskova L, Abraham WC (2015) A Voltage-Based STDP Rule Combined with Fast BCM-Like Metaplasticity Accounts for LTP and Concurrent \u201cHeterosynaptic\u201d LTD in the Dentate Gyrus In Vivo. PLoS Computational Biology" + }, + "class_id": 19, + "created": "2015-10-25T16:24:35.153", + "gitrepo": false, + "id": 185350, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 148255, + "object_name": "Jedlicka, Peter [jedlicka at em.uni-frankfurt.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 185352, + "object_name": "Jedlicka P, Benuskova L, Abraham WC (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Modeling dentate granule cells heterosynaptic plasticity using STDP-BCM rule (Jedlicka et al. 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "... Here we study how key components of learning mechanisms in the brain, namely spike timing-dependent plasticity and metaplasticity, interact with spontaneous activity in the input pathways of the neuron. Using biologically realistic simulations we show that ongoing background activity is a key determinant of the degree of long-term potentiation and long-term depression of synaptic transmission between nerve cells in the hippocampus of freely moving animals. This work helps better understand the computational rules which drive synaptic plasticity in vivo.\r\n..." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/185350", + "public_submitter_email": { + "attr_id": 309, + "value": "jedlicka@em.uni-frankfurt" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2016-02-26T01:22:05.503", + "ver_number": 9 + }, + "185355": { + "citation": { + "attr_id": 391, + "value": "Intrinsic rescaling of granule cells restores pattern separation ability of a dentate gyrus network model during epileptic hyperexcitability.\r\nYim MY, Hanuschkin A, Wolfart J.\r\nHippocampus. 2015 Mar;25(3):297-308. doi: 10.1002/hipo.22373.\r\nPMID: 25269417" + }, + "class_id": 19, + "created": "2015-10-27T04:28:40.093", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 240, + "object_name": "I Chloride" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 167500, + "object_name": "Kir" + }, + { + "object_id": 185501, + "object_name": "Kir2 leak" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122824, + "object_name": "IRK" + }, + { + "object_id": 185462, + "object_name": "Kir2.1 KCNJ2" + }, + { + "object_id": 185502, + "object_name": "Kir2.2 KCNJ12" + }, + { + "object_id": 185503, + "object_name": "Kir2.3 KCNJ4" + }, + { + "object_id": 185504, + "object_name": "Kir2.4 KCNJ14" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 185355, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 185460, + "object_name": "Yim, Man Yi [manyi.yim at googlemail.com]" + }, + { + "object_id": 185461, + "object_name": "Hanuschkin, Alexander " + }, + { + "object_id": 185500, + "object_name": "Wolfart, Jakob " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 41934, + "object_name": "Intrinsic plasticity" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + }, + { + "object_id": 185463, + "object_name": "Pattern Separation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 185356, + "object_name": "Yim MY, Hanuschkin A, Wolfart J (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Dentate gyrus network model pattern separation and granule cell scaling in epilepsy (Yim et al 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + }, + { + "object_id": 52416, + "object_name": "Dentate gyrus mossy cell" + }, + { + "object_id": 52417, + "object_name": "Dentate gyrus basket cell" + }, + { + "object_id": 88216, + "object_name": "Dentate gyrus hilar cell" + }, + { + "object_id": 124389, + "object_name": "Dentate gyrus MOPP cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The dentate gyrus (DG) is thought to enable efficient hippocampal memory acquisition via pattern separation. With patterns defined as spatiotemporally distributed action potential sequences, the principal DG output neurons (granule cells, GCs), presumably sparsen and separate similar input patterns from the perforant path (PP). In electrophysiological experiments, we have demonstrated that during temporal lobe epilepsy (TLE), GCs downscale their excitability by transcriptional upregulation of \u2018leak\u2019 channels. Here we studied whether this cell type-specific intrinsic plasticity is in a position to homeostatically adjust DG network function. We modified an established conductance-based computer model of the DG network such that it realizes a spatiotemporal pattern separation task, and quantified its performance with and without the experimentally constrained leaky GC phenotype. ..." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/185355", + "other_concept": { + "attr_id": 306, + "value": "Pattern Separation" + }, + "other_currents": { + "attr_id": 303, + "value": "Kir2 leak" + }, + "other_implementer": { + "attr_id": 308, + "value": "Wolfart, Jakob" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jakob.wolfart@uni-rostock.de" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115947, + "object_name": "Dentate gyrus" + } + ] + }, + "ver_date": "2022-05-27T11:50:37.643", + "ver_number": 24 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"Olfactory bulb" + } + ] + }, + "ver_date": "2018-10-17T16:19:21.037", + "ver_number": 17 + }, + "185512": { + "citation": { + "attr_id": 391, + "value": "Journal of Computational Neuroscience, Article 582 (JCNS-D-15-00036R2)\r\nEffect of Polarization Induced by Non-Weak Electric Fields on the Excitability \r\n\r\nof Elongated Neurons With Active Dendrite" + }, + "class_id": 19, + "created": "2015-11-07T16:10:50.807", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "id": 185512, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 136797, + "object_name": "Barreto, Ernest " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 84598, + "object_name": "Extracellular Fields" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 185597, + "object_name": "Reznik RI, Barreto E, 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of Polarization Induced by Non-Weak Electric Fields on the Excitability of Elongated Neurons With Active Dendrite.\r\n\r\nIn response to polarization, the active currents in the dendrites of pyramidal neurons play a pivotal role in the excitability of elongated neurons. Depending on a number of parameters either hyperpolarizing or depolarizing currents in the dendrite dominate as polarization is increased. Furthermore, the impact that these active dendrite channels (Ca, KAHP, etc) occur when only a small fraction of their channels are open." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/185512", + "public_submitter_email": { + "attr_id": 309, + "value": "robert.reznik@icloud.com" + }, + "ver_date": "2015-11-16T16:55:05.253", + "ver_number": 7 + }, + "185513": { + "citation": { + "attr_id": 391, + "value": "S. K. Sudhakar, B. Torben-Nielsen, E. De Schutter: Cerebellar nuclear neurons use time and rate coding to transmit Purkinje neuron pauses. PLoS Computational Biology in press" + }, + "class_id": 19, + "created": "2015-11-09T00:49:11.94", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 185513, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + }, + { + "object_id": 136252, + "object_name": "Rebound firing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 185541, + "object_name": "Sudhakar SK, Torben-Nielsen B, De Schutter E (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Cerebellar nuclear neuron (Sudhakar et al., 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 136179, + "object_name": "Cerebellum deep nucleus neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... In this modeling study, we investigate different forms of Purkinje neuron simple spike pause synchrony and its influence on candidate coding strategies in the cerebellar nuclei. That is, we investigate how different alignments of synchronous pauses in synthetic Purkinje neuron spike trains affect either time-locking or rate-changes in the downstream nuclei. We find that Purkinje neuron synchrony is mainly represented by changes in the firing rate of cerebellar nuclei neurons. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/185513", + "public_submitter_email": { + "attr_id": 309, + "value": "shyam_u2@yahoo.co.in" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2023-01-27T11:58:16.043", + "ver_number": 25 + }, + "185858": { + "class_id": 19, + "created": "2015-11-20T09:44:57.707", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T 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232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Neuronal persistent activity has been primarily assessed in terms of electrical mechanisms, without attention to the complex array of molecular events that also control cell excitability. We developed a multiscale neocortical model proceeding from the molecular to the network level to assess the contributions of calcium regulation of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels in providing additional and complementary support of continuing activation in the network. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/185858", + "other_implementer": { + "attr_id": 308, + "value": "Anna Bulanova" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 184, + "object_name": "mGluR1" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 207, + "object_name": "mGluR" + }, + { + "object_id": 212, + "object_name": "Glutamate" + }, + { + "object_id": 213, + "object_name": "Gaba" + }, + { + "object_id": 153715, + "object_name": "IP3" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2022-05-27T11:53:00.113", + "ver_number": 21 + }, + "185864": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 156264, + "object_name": "modified version of Hu et al. 2009" + }, + { + "object_id": 156265, + "object_name": "axon prject with Hu et al 2009" + }, + { + "object_id": 185862, + "object_name": "" + }, + { + "object_id": 187612, + "object_name": "manual version" + }, + { + "object_id": 232072, + "object_name": "GUI with Morse 2010 base" + }, + { + "object_id": 247967, + "object_name": "50 Hz 500 ms version" + } + ] + }, + "class_id": 19, + "created": "2015-11-21T11:03:17.007", + "default_file_notes": { + "attr_id": 392, + "value": "current" + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 185864, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33772, + "object_name": "Morse, Tom [Tom.Morse at Yale.edu]" + }, + { + "object_id": 146951, + "object_name": "McDougal, Robert [robert.mcdougal at yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 112047, + "object_name": "Aging/Alzheimer`s" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267665, + "object_name": "Yuan P, Zhang M, Tong L, Morse TM, McDougal RA, Ding H, Chan D, Cai Y, Grutzendler J" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Axonal spheroids and conduction defects in Alzheimer\u2019s disease (Yuan, Zhang, Tong, et al 2022)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "PAAS (plaque-associated axonal spheroid(s)) are dystrophic structures that sprout from axons that pass through amyloid beta plaques. If small enough they do not interfere with action potential (AP) propagation. Medium sized ones delay APs, and large, or many spheroids can block single APs and block or delay trains of APs." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/185864", + "other_implementer": { + "attr_id": 308, + "value": "Yuan, Peng (Paul)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2023-02-21T09:56:26.85", + "ver_number": 8 + }, + "185875": { + "citation": { + "attr_id": 391, + "value": "Smolen 2015" + }, + "class_id": 19, + "created": "2015-11-24T15:02:41.457", + "gitrepo": false, + "id": 185875, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 149176, + "object_name": "Smolen, Paul [paul.d.smolen at uth.tmc.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 185514, + "object_name": "Smolen P (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113428, + "object_name": "Java (web link to model)" + } + ] + }, + "name": "Modeling maintenance of Long-Term Potentiation in clustered synapses (Smolen 2015)", + "notes": { + "attr_id": 24, + "value": "\" ... Here a model is developed that connects ongoing, competing processes of synaptic\r\ngrowth and weakening to stochastic processes of receptor insertion and removal in dendritic spines. The model simulates long-term\r\n(>1 yr) persistence of groups of strong synapses. A unimodal weight distribution results. For stability of this distribution it proved\r\nessential to incorporate resource competition between synapses organized into small clusters. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/185875", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "ver_date": "2015-11-24T15:08:17.52", + "ver_number": 6 + }, + "186768": { + "class_id": 19, + "created": "2015-12-15T05:30:33.34", + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 226057, + "object_name": "HCN1" + }, + { + "object_id": 226058, + "object_name": "HCN2" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 186768, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 97869, + "object_name": "Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org]" + }, + { + "object_id": 189085, + "object_name": "Sanjay, M [msanjaycmc at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 139148, + "object_name": "Brain Rhythms" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 186840, + "object_name": "Sanjay M, Neymotin SA, Krothapalli SB (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "CA3 Network Model of Epileptic Activity (Sanjay et. al, 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + }, + { + "object_id": 154093, + "object_name": "Hippocampus CA3 interneuron basket GABA cell" + }, + { + "object_id": 139885, + "object_name": "Hippocampus CA3 stratum oriens lacunosum-moleculare interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This computational study investigates how a CA3 neuronal network consisting of pyramidal cells, basket cells and OLM interneurons becomes epileptic when dendritic inhibition to pyramidal cells is impaired due to the dysfunction of OLM interneurons. After standardizing the baseline activity (theta-modulated gamma oscillations), systematic changes are made in the connectivities between the neurons, as a result of step-wise impairment of dendritic inhibition." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/186768", + "public_submitter_email": { + "attr_id": 309, + "value": "msanjaycmc@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "M Sanjay" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2023-01-30T14:51:22.183", + "ver_number": 12 + }, + "186977": { + "citation": { + "attr_id": 391, + "value": "PMID: 26635594" + }, + "class_id": 19, + "created": "2015-12-21T16:59:07.25", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "id": 186977, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 155712, + "object_name": "Avella G. Oscar Javier [oscarjavella at gmail dot com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 186978, + "object_name": "Avella Gonzalez OJ, Mansvelder HD, van Pelt J, van Ooyen A (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "H-currents effect on the fluctuation of gamma/beta oscillations (Avella-Gonzalez et al., 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 156124, + "object_name": "Abstract single compartment conductance based cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model was designed to study the impact of H-currents on\r\nthe dynamics of cortical oscillations, and in paticular on\r\nthe occurrence of high and low amplitude episodes (HAE, LAE) in network oscillations.\r\nThe H-current is a slow, hyperpolarization-activated, depolarizing current\r\nthat contributes to neuronal resonance and membrane potential.\r\n\r\nWe characterized amplitude fluctuations in network oscillations by measuring \r\nthe average durations of HAEs and LAEs, and explored\r\nhow these were modulated by trains of external spikes, both in \r\nthe presence and absence of H-channels. \r\n\r\nWe looked at HAE duration, the frequency\r\nand power of network oscillations, and the effect\r\nof H-channels on the temporal voltage profile in single cells.\r\n\r\nWe found that H-currents increased the oscillation frequency and, in combination with external spikes, representing input from areas outside the network, strongly decreased the synchrony of firing. As a consequence, the oscillation power and the duration of episodes during which the network exhibited high-amplitude oscillations were greatly reduced in the presence of H-channels." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/186977", + "other_currents": { + "attr_id": 303, + "value": "H-currents, Na current, K current, leak current" + }, + "other_neurons": { + "attr_id": 301, + "value": "prefrontal cortex" + }, + "other_neurotransmitter": { + "attr_id": 304, + "value": "glutamate" + }, + "other_receptors": { + "attr_id": 302, + "value": "AMPA" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "oscarjavella@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Oscar Javier Avella Gonzalez" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "ver_date": "2018-07-11T17:11:28.477", + "ver_number": 8 + }, + "187213": { + "biosimulations": { + "value": "modeldb-187213" + }, + "class_id": 19, + "created": "2016-01-13T16:00:35.787", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 240, + "object_name": "I Chloride" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 116966, + "object_name": "I_K,Na" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + } + ] + }, + "gitrepo": false, + "id": 187213, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 120695, + "object_name": "Sodium pump" + }, + { + "object_id": 121254, + "object_name": "Depolarization block" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + }, + { + "object_id": 151948, + "object_name": "Potassium buffering" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 188279, + "object_name": "H\u00fcbel N, Andrew RD, Ullah G (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Anoxic depolarization, recovery: effect of brain regions and extracellular space (Hubel et al. 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 156124, + "object_name": "Abstract single compartment conductance based cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The extent of anoxic depolarization (AD), the initial electrophysiological event during ischemia, determines the degree of brain region-specific neuronal damage. Neurons in higher brain regions have stronger ADs and are more easily injured than neurons in lower brain region. The mechanism leading to such differences is not clear. We use a computational model based on a Hodgkin-Huxley framework which includes neural spiking dynamics, processes of ion accumulation, and homeostatic mechanisms like vascular coupling and Na/K-exchange pumps. We show that a large extracellular space (ECS) explains the recovery failure in high brain regions. A phase-space analysis shows that with a large ECS recovery from AD through potassium regulation is impossible. The code 'time_series.ode' can be used to simulate AD for a large and a small ECS and show the different behaviors. The code \u2018continuations.ode\u2019 can be used to show the fixed point structure. Depending on our choice of large or small ECS the fixed point curve implies the presence/absence of a recovery threshold that defines the potassium clearance demand." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/187213", + "public_submitter_email": { + "attr_id": 309, + "value": "niklas.huebel@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Niklas H\u00fcbel" + }, + "ver_date": "2016-01-19T10:38:28.11", + "ver_number": 9 + }, + "187473": { + "citation": { + "attr_id": 391, + "value": "J Neurophysiol. 2015 Dec 1;114(6):3140-53. doi: 10.1152/jn.00226.2015. Epub 2015 Sep 2.\r\nSpike propagation through the dorsal root ganglia in an unmyelinated sensory neuron: a modeling study.\r\nSundt D1, Gamper N2, Jaffe DB3.\r\nPMID: 26334005" + }, + "class_id": 19, + "created": "2016-02-01T11:31:41.087", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gitrepo": false, + "id": 187473, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 140473, + "object_name": "Jaffe, David B [david.jaffe at utsa.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 114307, + "object_name": "Conduction failure" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 187477, + "object_name": "Sundt D, Gamper N, Jaffe DB (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A model of the T-junction of a C-fiber sensory neuron (Sundt et al. 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 136307, + "object_name": "Dorsal Root Ganglion (DRG) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The effect of geometry and ionic mechanisms on spike propagation through the T-junction of an unmyelinated sensory neuron." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/187473", + "public_submitter_email": { + "attr_id": 309, + "value": "david.jaffe@utsa.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "David B. Jaffe" + }, + "ver_date": "2016-02-05T16:28:18.633", + "ver_number": 3 + }, + "187474": { + "class_id": 19, + "created": "2016-02-01T11:49:03.87", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 187474, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 147118, + "object_name": "Maki-Marttunen, Tuomo [tuomomm at uio.no]" + }, + { + "object_id": 187609, + "object_name": "Metzner, Christoph [c.metzner at herts.ac.uk]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 145814, + "object_name": "Hay E, Hill S, Sch\u00fcrmann F, Markram H, Segev I (2011)" + }, + { + "object_id": 167346, + "object_name": "Hay E, Segev I (2015)" + }, + { + "object_id": 233515, + "object_name": "M\u00e4ki-Marttunen T, Halnes G, Devor A, Metzner C, Dale AM, Andreassen OA, Einevoll GT (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 154351, + "object_name": "NeuroML" + } + ] + }, + "name": "Layer V pyramidal cell model with reduced morphology (M\u00e4ki-Marttunen et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... In this work, we develop and apply an automated, stepwise method for fitting a neuron model to data with fine spatial resolution, such as that achievable with voltage sensitive dyes (VSDs) and Ca2+ imaging.\r\n...\r\nWe apply our method to simulated data from layer 5 pyramidal cells (L5PCs) and construct a model with reduced neuronal morphology. We connect the reduced-morphology neurons into a network and validate against simulated data from a high-resolution L5PC network model. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/187474", + "other_implementer": { + "attr_id": 308, + "value": "Metzner, Christoph [c.metzner@herts.ac.uk]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tuomo.maki-marttunen@tut.fi" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2019-03-28T15:09:10.95", + "ver_number": 38 + }, + "187599": { + "citation": { + "attr_id": 391, + "value": "H\u00fcbel, N. and Ullah, G., \"Anions Govern Cell Volume: a Case Study of Relative Astrocytic and Neuronal Swelling in Spreading Depolarization\", PLoS ONE, 10.1371/journal.pone.0147060 (2016)" + }, + "class_id": 19, + "created": "2016-02-02T13:27:05.42", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 240, + "object_name": "I Chloride" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 246, + "object_name": "I p,q" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 116966, + "object_name": "I_K,Na" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 144382, + "object_name": "Osmosis-driven water flux" + } + ] + }, + "gitrepo": false, + "id": 187599, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 121254, + "object_name": "Depolarization block" + }, + { + "object_id": 139270, + "object_name": "Anoxic depolarization" + }, + { + "object_id": 167716, + "object_name": "Spreading depression" + }, + { + "object_id": 183421, + "object_name": "Volume transmission" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 188546, + "object_name": "H\u00fcbel N, Ullah G (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 120799, + "object_name": "Glia" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "An ion-based model for swelling of neurons and astrocytes (Hubel & Ullah 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 87589, + "object_name": "Astrocyte" + }, + { + "object_id": 156124, + "object_name": "Abstract single compartment conductance based cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 236, + "object_name": "Ions" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The programs describe ion dynamics and osmosis-driven cellular swelling. \u201ccode_fig3.ode\u201d shows a \r\nscenario of permanent cessation of energy supply / Na/K-pump activity, and the induced transition from \r\nnormal conditions to the Donnan equilibrium for an isolated neuron and its extracellular space. \r\n\r\n\u201ccode_Fig7.ode\u201d shows spreading depolarization induced by an interruption of energy supply in a model \r\nconsisting of a neuron, a glia cell and the extracellular space. The simulations show the evolution of ion\r\nconcentrations, Nernst potentials, the membrane potential, gating variables and cellular volumes." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/187599", + "public_submitter_email": { + "attr_id": 309, + "value": "niklas.huebel@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Niklas H\u00fcbel" + }, + "ver_date": "2016-02-03T15:19:45.54", + "ver_number": 5 + }, + "187600": { + "citation": { + "attr_id": 391, + "value": "Teka W, Stockton D, Santamaria F. \"Power-law dynamics of membrane\r\nconductances increase spiking diversity in a Hdgkin-Huxley model\" PLoS\r\nComputational Biology, in press, 2016." + }, + "class_id": 19, + "created": "2016-02-02T16:18:39.833", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "id": 187600, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 187684, + "object_name": "Teka W, Stockton D, Santamaria F (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Hodgkin\u2013Huxley model with fractional gating (Teka et al. 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + }, + { + "object_id": 128565, + "object_name": "Wide dynamic range neuron" + }, + { + "object_id": 155861, + "object_name": "Abstract integrate-and-fire fractional leaky neuron" + }, + { + "object_id": 156124, + "object_name": "Abstract single compartment conductance based cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 236, + "object_name": "Ions" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We use fractional order derivatives to model the kinetic dynamics of the gate variables for the potassium and sodium conductances of the Hodgkin-Huxley model. Our results show that power-law dynamics of the different gate variables result\r\nin a wide range of action potential shapes and spiking patterns, even in the case where the model was stimulated with constant current. As a consequence, power-law behaving conductances result in an increase in the number of spiking patterns a neuron can generate and, we propose, expand the computational capacity of the neuron." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/187600", + "other_neurons": { + "attr_id": 301, + "value": "Fractional differentiator neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "wondimuwub@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Wondimu Teka" + }, + "ver_date": "2016-03-03T13:19:58.503", + "ver_number": 3 + }, + "187603": { + "class_id": 19, + "created": "2016-02-04T07:36:20.677", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 187603, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 234245, + "object_name": "Wilmes, Katharina A. 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To ensure the stability of these traces in the presence of further learning, however, a regulation of plasticity appears beneficial. Here, we take up the recent suggestion that dendritic inhibition can switch plasticity of excitatory synapses on and off by gating backpropagating action potentials (bAPs) and calcium spikes, i.e., by gating the coincidence signals required for Hebbian forms of plasticity. We analyze temporal and spatial constraints of such a gating and investigate whether it is possible to suppress bAPs without a simultaneous annihilation of the forward-directed information flow via excitatory postsynaptic potentials (EPSPs). In a computational analysis of conductance-based multi-compartmental models, we demonstrate that a robust control of bAPs and calcium spikes is possible in an all-or-none manner, enabling a binary switch of coincidence signals and plasticity. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/187603", + "public_submitter_email": { + "attr_id": 309, + "value": "katharina.wilmes@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Katharina Anna Wilmes" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2017-10-29T23:19:59.15", + "ver_number": 20 + }, + "187604": { + "class_id": 19, + "created": "2016-02-04T22:10:38.58", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": true + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 187604, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 153194, + "object_name": "Bezaire, Marianne [mariannejcase at gmail.com]" + }, + { + "object_id": 217983, + "object_name": "Raikov, Ivan [ivan.g.raikov at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 114660, + "object_name": "Connectivity matrix" + }, + { + "object_id": 128811, + "object_name": "Laminar Connectivity" + }, + { + "object_id": 156115, + "object_name": "Gamma oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 218127, + "object_name": "Bezaire MJ, Raikov I, Burk K, Vyas D, Soltesz I (2016)" + }, + { + "object_id": 206350, + "object_name": "Bezaire M, Raikov I, Burk K, Armstrong C, Soltesz I (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "Hippocampal CA1 NN with spontaneous theta, gamma: full scale & network clamp (Bezaire et al 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 35373, + "object_name": "Hippocampus CA1 interneuron oriens alveus GABA cell" + }, + { + "object_id": 114079, + "object_name": "Hippocampus CA1 basket cell" + }, + { + "object_id": 136178, + "object_name": "Hippocampus CA1 stratum radiatum interneuron" + }, + { + "object_id": 151329, + "object_name": "Hippocampus CA1 bistratified cell" + }, + { + "object_id": 151330, + "object_name": "Hippocampus CA1 axo-axonic cell" + }, + { + "object_id": 182866, + "object_name": "Hippocampus CA1 PV+ fast-firing interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model is a full-scale, biologically constrained rodent hippocampal CA1 network model that includes 9 cells types (pyramidal cells and 8 interneurons) with realistic proportions of each and realistic connectivity between the cells. In addition, the model receives realistic numbers of afferents from artificial cells representing hippocampal CA3 and entorhinal cortical layer III. The model is fully scaleable and parallelized so that it can be run at small scale on a personal computer or large scale on a supercomputer. The model network exhibits spontaneous theta and gamma rhythms without any rhythmic input. The model network can be perturbed in a variety of ways to better study the mechanisms of CA1 network dynamics. Also see online code at http://bitbucket.org/mbezaire/ca1 and further information at http://mariannebezaire.com/models/ca1" + }, + "opensourcebrain": { + "value": "nc_ca1" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/187604", + "other_implementer": { + "attr_id": 308, + "value": "Raikov, Ivan [ivan.g.raikov@gmail.com]" + }, + "other_neurons": { + "attr_id": 301, + "value": "Hippocampus CA1 neurogliaform cell" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 212, + "object_name": "Glutamate" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2022-05-27T12:12:03.73", + "ver_number": 15 + }, + "187605": { + "class_id": 19, + "created": "2016-02-09T14:43:00.417", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 88211, + "object_name": "I_SERCA" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 187605, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 116962, + "object_name": "Parameter sensitivity" + }, + { + "object_id": 136106, + "object_name": "G-protein coupled" + }, + { + "object_id": 222956, + "object_name": "Neuromodulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 187682, + "object_name": "Cui Y, Prokin I, Xu H, Delord B, Genet S, Venance L, Berry H (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 45540, + "object_name": "FORTRAN" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Endocannabinoid dynamics gate spike-timing dependent depression and potentiation (Cui et al 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 260, + "object_name": "Neostriatum medium spiny direct pathway GABA cell" + }, + { + "object_id": 153360, + "object_name": "Neostriatum medium spiny indirect pathway GABA cell" + }, + { + "object_id": 150619, + "object_name": "Neostriatum spiny neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The endocannabinoid (eCB) system is considered involved in synaptic depression.\r\nRecent reports have also linked eCBs to synaptic potentiation. However it is not known how eCB signaling may support such bidirectionality. To question the mechanisms of this phenomena in spike-timing dependent plasticity (STDP) at corticostriatal synapses, we combined electrophysiology experiments with biophysical modeling. We demonstrate that STDP is controlled by eCB levels and dynamics: prolonged and moderate levels of eCB lead to eCB-mediated long-term depression (eCB-tLTD) while short and large eCB transients produce eCB-mediated long-term potentiation (eCB-tLTP). Therefore, just like neurotransmitters glutamate or GABA, eCB form a bidirectional system." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/187605", + "other_currents": { + "attr_id": 303, + "value": "I_TRPV1" + }, + "other_receptors": { + "attr_id": 302, + "value": "CB1R" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "isprokin@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ilya Prokin" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 207, + "object_name": "mGluR" + }, + { + "object_id": 212, + "object_name": "Glutamate" + }, + { + "object_id": 153715, + "object_name": "IP3" + } + ] + }, + "ver_date": "2017-01-13T12:21:27.967", + "ver_number": 13 + }, + "187610": { + "class_id": 19, + "created": "2016-02-11T15:41:11.133", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "id": 187610, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 184057, + "object_name": "Cembrowski, Mark S [cembrowskim at janelia.hhmi.org]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 187619, + "object_name": "Bloss EB, Cembrowski MS, Karsh B, Colonell J, Fetter RD, Spruston N (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "CA1 pyramidal neuron synaptic integration (Bloss et al. 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... We examined synaptic connectivity\r\nbetween molecularly defined inhibitory interneurons\r\nand CA1 pyramidal cell dendrites using\r\ncorrelative light-electron microscopy and large-volume\r\narray tomography. We show that interneurons\r\ncan be highly selective in their connectivity to specific\r\ndendritic branch types and, furthermore,\r\nexhibit precisely targeted connectivity to the origin\r\nor end of individual branches. Computational simulations\r\nindicate that the observed subcellular\r\ntargeting enables control over the nonlinear integration\r\nof synaptic input or the initiation and\r\nbackpropagation of action potentials in a branchselective\r\nmanner. Our results demonstrate that\r\nconnectivity between interneurons and pyramidal\r\ncell dendrites is more precise and spatially segregated\r\nthan previously appreciated, which may be\r\na critical determinant of how inhibition shapes dendritic\r\ncomputation.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/187610", + "public_submitter_email": { + "attr_id": 309, + "value": "cembrowskim@janelia.hhmi.org" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Mark Cembrowski" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "ver_date": "2018-07-11T17:14:47.833", + "ver_number": 6 + }, + "187615": { + "class_id": 19, + "created": "2016-02-23T05:33:51.087", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + }, + { + "object_id": 88209, + "object_name": "Na/Ca exchanger" + }, + { + "object_id": 88211, + "object_name": "I_SERCA" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 167500, + "object_name": "Kir" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92082, + "object_name": "Nav1.1 SCN1A" + }, + { + "object_id": 122811, + "object_name": "Cav3.3 CACNA1I" + }, + { + "object_id": 122812, + "object_name": "Cav1.3 CACNA1D" + }, + { + "object_id": 122814, + "object_name": "Cav1.2 CACNA1C" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 187615, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 147118, + "object_name": "Maki-Marttunen, Tuomo [tuomomm at uio.no]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 112853, + "object_name": "Schizophrenia" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 233562, + "object_name": "M\u00e4ki-Marttunen T, Lines GT, Edwards AG, Tveito A, Dale AM, Einevoll GT, Andreassen OA (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Pleiotropic effects of SCZ-associated genes (M\u00e4ki-Marttunen et al. 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 52422, + "object_name": "Cardiac atrial cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Python and MATLAB scripts for studying the dual effects of SCZ-related genes on layer 5 pyramidal cell firing and sinoatrial node cell pacemaking properties. The study is based on two L5PC models (Hay et al. 2011, Almog & Korngreen 2014) and SANC models (Kharche et al. 2011, Severi et al. 2012)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/187615", + "other_gene": { + "attr_id": 477, + "value": "CACNB2, ATP2A2, HCN1" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tuomomm@uio.no; tuomo.maki-marttunen@tut.fi" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tuomo M\u00e4ki-Marttunen" + }, + "ver_date": "2023-01-27T13:08:40.367", + "ver_number": 12 + }, + "187687": { + "class_id": 19, + "created": "2016-03-08T03:10:59.55", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 187687, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 189164, + "object_name": "Henningson, Erik [erikh at maths.lth.se]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 116962, + "object_name": "Parameter sensitivity" + }, + { + "object_id": 128875, + "object_name": "Development" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 189163, + "object_name": "Diehl S, Henningsson E, Heyden A (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Axon growth model (Diehl et al. 2016)", + "notes": { + "attr_id": 24, + "value": "The model describes the elongation over time of an axon from a small neurite to its steady-state length. The elongation depends on the availability of tubulin dimers in the growth cone. The dimers are produced in the soma and then transported along the axon to the growth cone.\r\n\r\nMathematically the model consists of a partial differential equation coupled with two nonlinear ordinary differential equations. \r\n\r\nThe code implements a spatial scaling to deal with the growing (and shrinking) domain and a temporal scaling to deal with evolutions on different time scales. Further, the numerical scheme is chosen to fully utilize the structure of the problems. To summarize, this results in fast and reliable axon growth simulations." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/187687", + "other_implementer": { + "attr_id": 308, + "value": "Henningson, Erik [erikh@maths.lth.se]" + }, + "ver_date": "2016-05-10T11:17:00.557", + "ver_number": 15 + }, + "188423": { + "citation": { + "attr_id": 391, + "value": "Masquelier T, Portelli G and Kornprobst P (2016). Microsaccades enable efficient synchrony-based coding in the retina: a simulation study. Scientific Reports." + }, + "class_id": 19, + "created": "2016-03-16T08:08:43.977", + "gitrepo": false, + "id": 188423, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 123929, + "object_name": "Masquelier, Tim [timothee.masquelier at alum.mit.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3542, + "object_name": "Pattern Recognition" + }, + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 136098, + "object_name": "Information transfer" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 182362, + "object_name": "Sensory coding" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 188424, + "object_name": "Masquelier T, Portelli G, Kornprobst P (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Microsaccades and synchrony coding in the retina (Masquelier et al. 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 270, + "object_name": "Retina ganglion GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We show that microsaccades (MS) enable efficient synchrony-based coding among the primate retinal ganglion cells (RGC). We find that each MS causes certain RGCs to fire synchronously, namely those whose receptive fields contain contrast edges after the MS. The emitted synchronous spike volley thus rapidly transmits the most salient edges of the stimulus. We demonstrate that the readout could be done rapidly by simple coincidence-detector neurons, and that the required connectivity could emerge spontaneously with spike timing-dependent plasticity." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/188423", + "public_submitter_email": { + "attr_id": 309, + "value": "timothee.masquelier@alum.mit.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Timoth\u00e9e Masquelier" + }, + "ver_date": "2016-03-16T10:49:50.407", + "ver_number": 3 + }, + "188543": { + "class_id": 19, + "created": "2016-03-18T17:11:15.54", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 188543, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 188387, + "object_name": "Markram H, Muller E, Ramaswamy S, Reimann MW, Abdellah M, Sanchez CA, Ailamaki A, Alonso-Nanclares L, Antille N, Arsever S, Kahou GA, Berger TK, Bilgili A, Buncic N, Chalimourda A, Chindemi G, Courcol JD, Delalondre F, Delattre V, Druckmann S, Dumusc R, Dynes J, Eilemann S, Gal E, Gevaert ME, Ghobril JP, Gidon A, Graham JW, Gupta A, Haenel V, Hay E, Heinis T, Hernando JB, Hines M, Kanari L, Keller D, Kenyon J, Khazen G, Kim Y, King JG, Kisvarday Z, Kumbhar P, Lasserre S, Le B\u00e9 JV, Magalh\u00e3es BR, Merch\u00e1n-P\u00e9rez A, Meystre J, Morrice BR, Muller J, Mu\u00f1oz-C\u00e9spedes A, Muralidhar S, Muthurasa K, Nachbaur D, Newton TH, Nolte M, Ovcharenko A, Palacios J, Pastor L, Perin R, Ranjan R, Riachi I, Rodr\u00edguez JR, Riquelme JL, R\u00f6ssert C, Sfyrakis K, Shi Y, Shillcock JC, Silberberg G, Silva R, Tauheed F, Telefont M, Toledo-Rodriguez M, Tr\u00e4nkler T, Van Geit W, D\u00edaz JV, Walker R, Wang Y, Zaninetta SM (2015)" + }, + { + "object_id": 188478, + "object_name": "Ramaswamy S, Courcol JD, Abdellah M, Adaszewski SR, Antille N, Arsever S, Atenekeng G, Bilgili A, Brukau Y, Chalimourda A, Chindemi G, Delalondre F, Dumusc R, Eilemann S, Gevaert ME, Gleeson P, Graham JW, Hernando JB, Kanari L, Katkov Y, Keller D, King JG, Ranjan R, Reimann MW, R\u00f6ssert C, Shi Y, Shillcock JC, Telefont M, Van Geit W, Diaz JV, Walker R, Wang Y, Zaninetta SM, DeFelipe J, Hill SL, Muller J, Segev I, Sch\u00fcrmann F, Muller EB, Markram H (2015)" + }, + { + "object_id": 188479, + "object_name": "Reimann MW, King JG, Muller EB, Ramaswamy S, Markram H (2015)" + }, + { + "object_id": 232955, + "object_name": "Abdellah M, Hernando J, Antille N, Eilemann S, Markram H, Sch\u00fcrmann F (2017)" + }, + { + "object_id": 233054, + "object_name": "Reimann MW, Nolte M, Scolamiero M, Turner K, Perin R, Chindemi G, Dlotko P, Levi R, Hess K, Markram H (2017)" + }, + { + "object_id": 230838, + "object_name": "Gal E, London M, Globerson A, Ramaswamy S, Reimann MW, Muller E, Markram H, Segev I (2017)" + }, + { + "object_id": 233323, + "object_name": "Reimann MW, Horlemann AL, Ramaswamy S, Muller EB, Markram H (2017)" + }, + { + "object_id": 260056, + "object_name": "Barros-Zulaica N, Rahmon J, Chindemi G, Perin R, Markram H, Muller E, Ramaswamy S (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 154350, + "object_name": "NeuroML (web link to model)" + } + ] + }, + "name": "The neocortical microcircuit collaboration portal (Markram et al. 2015)", + "notes": { + "attr_id": 24, + "value": "\"This portal provides an online public resource of the Blue Brain Project's first release of a digital reconstruction of the microcircuitry of juvenile Rat somatosensory cortex, access to experimental data sets used in the reconstruction, and the resulting models.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/188543", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Markram group" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228597, + "object_name": "Barrel cortex" + } + ] + }, + "ver_date": "2017-09-28T15:33:48.533", + "ver_number": 7 + }, + "188544": { + "class_id": 19, + "created": "2016-03-20T15:43:21.913", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 188544, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 182905, + "object_name": "Dura-Bernal, Salvador [salvadordura at gmail.com]" + }, + { + "object_id": 33738, + "object_name": "Lytton, William [bill.lytton at downstate.edu]" + }, + { + "object_id": 183020, + "object_name": "Seidenstein, Alexandra [ahs342 at nyu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 225299, + "object_name": "Multiscale" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 195862, + "object_name": "Lytton WW, Seidenstein AH, Dura-Bernal S, McDougal RA, Sch\u00fcrmann F, Hines ML (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 195884, + "object_name": "NetPyNE" + } + ] + }, + "name": "Parallelizing large networks in NEURON (Lytton et al. 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + }, + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Large multiscale neuronal network simulations and\r\ninnovative neurotechnologies are required for development of these models requires\r\ndevelopment of new simulation technologies.\r\n\r\nWe describe here the current use of\r\nthe NEURON simulator with MPI (message passing interface) for simulation in\r\nthe domain of moderately large networks on commonly available High\r\nPerformance Computers (HPCs).\r\n\r\nWe discuss the\r\nbasic layout of such simulations, including the methods of simulation setup, the\r\nrun-time spike passing paradigm and post-simulation data storage and data\r\nmanagement approaches.\r\n\r\nWe also compare three types of\r\nnetworks, ...\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/188544", + "other_simulator": { + "attr_id": 307, + "value": "NetPyNE" + }, + "ver_date": "2017-02-23T11:07:45.137", + "ver_number": 11 + }, + "188552": { + "class_id": 19, + "created": "2016-03-24T10:31:47.107", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 188552, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 188554, + "object_name": "Naze, Sebastien [sebastien.naze at univ-amu.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 225299, + "object_name": "Multiscale" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 188551, + "object_name": "Naze S, Bernard C, Jirsa V (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Multiscale modeling of epileptic seizures (Naze et al. 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 150222, + "object_name": "Abstract Morris-Lecar neuron" + }, + { + "object_id": 188553, + "object_name": "Abstract Hindmarsh-Rose neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... In the context of epilepsy, the functional properties of the network at the source of a seizure are disrupted by a possibly large set of factors at the cellular and molecular levels. It is therefore needed to sacrifice some biological accuracy to model seizure dynamics in favor of macroscopic realizations. Here, we present a neuronal network model that convenes both neuronal and network representations with the goal to describe brain dynamics involved in the development of epilepsy. We compare our modeling results with animal in vivo recordings to validate our approach in the context of seizures. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/188552", + "ver_date": "2017-02-23T11:08:18.783", + "ver_number": 7 + }, + "188977": { + "class_id": 19, + "created": "2016-03-30T18:00:18.85", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 188977, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 93420, + "object_name": "Sleep" + }, + { + "object_id": 139148, + "object_name": "Brain Rhythms" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 189469, + "object_name": "Malerba P, Krishnan GP, Fellous JM, Bazhenov M (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "CA1 pyramidal cells, basket cells, ripples (Malerba et al 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 182866, + "object_name": "Hippocampus CA1 PV+ fast-firing interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Model of CA1 pyramidal layer Ripple activity, triggered when receiving current input (to represent CA3 sharp-waves). \r\nCells are Adaptive-Exponential Integrate and Fire neurons, receiving independent OU noise." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/188977", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2017-03-07T18:09:33.137", + "ver_number": 7 + }, + "189088": { + "class_id": 19, + "created": "2016-04-17T05:25:10.047", + "gitrepo": false, + "id": 189088, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 189092, + "object_name": "Atherton, Laura " + }, + { + "object_id": 189093, + "object_name": "Prince, Luke " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 249382, + "object_name": "Atherton LA, Prince LY, Tsaneva-Atanasova K (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + }, + { + "object_id": 91894, + "object_name": "XPP (web link to model)" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Fully continuous Pinsky-Rinzel model for bifurcation analysis (Atherton et al. 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + }, + { + "object_id": 189094, + "object_name": "Pinsky-Rinzel CA1/3 pyramidal cell " + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The original, 2-compartment, CA3 cell, Pinsky-Rinzel model (Pinsky, Rinzel 1994) has several discontinuous functions that prevent the use of standard bifurcation analysis tools to study the model. Here we present a modified, fully continuous system that captures the behaviour of the original model, while permitting the use of available numerical continuation software to perform full-system bifurcation and fast-slow analysis in XPPAUT." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/189088", + "other_implementer": { + "attr_id": 308, + "value": "Laura Atherton and Luke Prince" + }, + "other_neurons": { + "attr_id": 301, + "value": "Pinsky-Rinzel Ca1/3 pyramidal cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "k.tsaneva-atanasova@exeter.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Krasimira Tsaneva-Atanasova" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2016-04-27T15:01:40.833", + "ver_number": 17 + }, + "189153": { + "class_id": 19, + "created": "2016-04-20T10:34:03.723", + "gitrepo": true, + 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+ "object_id": 195105, + "object_name": "Neymotin SA, Dura-Bernal S, Lakatos P, Sanger TD, Lytton WW (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Multitarget pharmacology for Dystonia in M1 (Neymotin et al 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 167344, + "object_name": "Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell" + }, + { + "object_id": 7387, + "object_name": "Neocortex V1 interneuron basket PV GABA cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + }, + { + "object_id": 142232, + "object_name": "Neocortex spiking low threshold (LTS) neuron" + }, + { + "object_id": 182505, + "object_name": "Neocortex layer 4 neuron" + }, + { + "object_id": 184130, + "object_name": "Neocortex layer 2-3 interneuron" + }, + { + "object_id": 184132, + "object_name": "Neocortex layer 4 interneuron" + }, + { + "object_id": 184134, + "object_name": "Neocortex layer 5 interneuron" + }, + { + "object_id": 184136, + "object_name": "Neocortex layer 6a interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... We developed a multiscale model of primary motor cortex, ranging from molecular, up to cellular, and network levels, containing 1715 compartmental model neurons with multiple ion channels and intracellular molecular dynamics. We wired the model based on electrophysiological data obtained from mouse motor cortex circuit mapping experiments. We used the model to reproduce patterns of heightened activity seen in dystonia by applying independent random variations in parameters to identify pathological parameter sets. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/189154", + "other_concept": { + "attr_id": 306, + "value": "Drug Discovery, Dystonia" + }, + "other_currents": { + "attr_id": 303, + "value": "I_KDr" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 207, + "object_name": "mGluR" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2022-05-27T12:23:42.287", + "ver_number": 15 + }, + "189155": { + "class_id": 19, + "created": "2016-04-25T11:34:44.593", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 116966, + "object_name": "I_K,Na" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 189155, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 194894, + "object_name": "Wang, Jicheng [jiw23 at pitt.edu]" + }, + { + "object_id": 194895, + "object_name": "Tai, Changfeng " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 190562, + "object_name": "Fribance S, Wang J, Roppolo JR, de Groat WC, Tai C (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Axonal HH-model for temperature stimulation (Fribance et al 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52426, + "object_name": "Squid axon" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... To analyze the temperature effect, our study modified\r\nthe classical HH axonal model by incorporating a membrane\r\ncapacitance-temperature relationship. The modified model\r\nsuccessfully simulated the generation and propagation of action\r\npotentials induced by a rapid increase in local temperature\r\nwhen the Curie temperature of membrane capacitance is below\r\n40 \u00b0C, while the classical model failed to simulate the\r\naxonal excitation by temperature stimulation. The new model\r\npredicts that a rapid increase in local temperature produces a\r\nrapid increase in membrane capacitance, which causes an inward\r\nmembrane current across the membrane capacitor strong\r\nenough to depolarize the membrane and generate an action\r\npotential. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/189155", + "ver_date": "2016-07-20T13:43:36.963", + "ver_number": 6 + }, + "189160": { + "class_id": 19, + "created": "2016-04-27T12:22:22.833", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 189160, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 189181, + "object_name": "Hass, Joachim [joachim.hass at zi-mannheim.de]" + }, + { + "object_id": 189182, + "object_name": "Hert\u00e4g, Loreen [loreen.hertaeg at tu-berlin.de]" + }, + { + "object_id": 82786, + "object_name": "Durstewitz, Daniel [daniel.durstewitz at plymouth.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 128811, + "object_name": "Laminar Connectivity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 189183, + "object_name": "Hass J, Hert\u00e4g L, Durstewitz D (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A detailed data-driven network model of prefrontal cortex (Hass et al 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Data-based PFC-like circuit with layer 2/3 and 5, synaptic clustering, four types of interneurons and cell-type specific short-term synaptic plasticity; neuron parameters fitted to in vitro data, all other parameters constrained by experimental literature. Reproduces key features of in vivo resting state activity without specific tuning." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/189160", + "other_implementer": { + "attr_id": 308, + "value": "Hass, Joachim \r\nHert\u00e4g, Loreen " + }, + "other_neurons": { + "attr_id": 301, + "value": "Prefrontal cortex pyramidal cell and interneuron" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "ver_date": "2016-05-31T14:41:37.96", + "ver_number": 7 + }, + "189186": { + "class_id": 19, + "created": "2016-05-06T02:59:12.76", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 189186, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 189301, + "object_name": "Szoboszlay, M [szoboszlay.miklos at koki.mta.hu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 189188, + "object_name": "Szoboszlay M, Lorincz A, Lanore F, Vervaeke K, Silver RA, Nusser Z (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 143823, + "object_name": "neuroConstruct" + } + ] + }, + "name": "Functional properties of dendritic gap junctions in Cerebellar Golgi cells (Szoboszlay et al. 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 112687, + "object_name": "Cerebellum golgi cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... We investigated the properties of gap junctions\r\nin cerebellar interneurons by combining paired\r\nsomato-somatic and somato-dendritic recordings,\r\nanatomical reconstructions, immunohistochemistry,\r\nelectron microscopy, and modeling. By fitting\r\ndetailed compartmental models of Golgi cells to\r\ntheir somato-dendritic voltage responses, we determined\r\ntheir passive electrical properties and the\r\nmean gap junction conductance (0.9 nS). ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/189186", + "ver_date": "2016-05-07T18:21:08.213", + "ver_number": 13 + }, + "189308": { + "class_id": 19, + "created": "2016-05-11T03:43:11.433", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 189308, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 147118, + "object_name": "Maki-Marttunen, Tuomo [tuomomm at uio.no]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 140966, + "object_name": "Sensory processing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 234655, + "object_name": "Medan V, M\u00e4ki-Marttunen T, Sztarker J, Preuss T (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Goldfish Mauthner cell (Medan et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 189309, + "object_name": "Mauthner cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ...In fish, evasion of a diving bird that breaks the water surface depends on integrating visual and auditory stimuli with very different characteristics. How do neurons process such differential sensory inputs at the dendritic level? For that we studied the Mauthner-cells (M-cells) in the goldfish startle circuit, which receive visual and auditory inputs via two separate dendrites, both accessible for in vivo recordings. We asked if electrophysiological membrane properties and dendrite morphology, studied in vivo, play a role in selective sensory processing in the M-cell. Our results show that anatomical and electrophysiological differences between the dendrites combine to produce stronger attenuation of visually evoked post synaptic potentials (PSPs) than to auditory evoked PSPs. Interestingly, our recordings showed also cross-modal dendritic interaction, as auditory evoked PSPs invade the ventral dendrite (VD) as well as the opposite, visual PSPs invade the lateral dendrite (LD). However, these interactions were asymmetrical with auditory PSPs being more prominent in the VD than visual PSPs in the LD. Modelling experiments imply that this asymmetry is caused by active conductances expressed in the proximal segments of the VD. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/189308", + "other_neurons": { + "attr_id": 301, + "value": "Mauthner cell" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 234657, + "object_name": "Goldfish" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 249922, + "object_name": "Goldfish" + } + ] + }, + "ver_date": "2019-02-26T10:17:21.75", + "ver_number": 14 + }, + "189344": { + "biosimulations": { + "value": "modeldb-189344" + }, + "class_id": 19, + "created": "2016-05-17T11:56:53.667", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 88211, + "object_name": "I_SERCA" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 189344, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 226599, + "object_name": "Handy, Gregory [handy at math.utah.edu]" + }, + { + "object_id": 226600, + "object_name": "Taheri, Marsa " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 226598, + "object_name": "Handy G, Taheri M, White JA, Borisyuk A (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 120799, + "object_name": "Glia" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "A mathematical model of evoked calcium dynamics in astrocytes (Handy et al 2017)", + "notes": { + "attr_id": 24, + "value": "\" ...Here we present a qualitative analysis of a recent mathematical model of astrocyte calcium responses. We show how the major response types are generated in the model as a result of the underlying bifurcation structure. By varying key channel parameters, mimicking blockers used by experimentalists, we manipulate this underlying bifurcation structure and predict how the distributions of responses can change. We find that store-operated calcium channels, plasma membrane bound channels with little activity during calcium transients, have a surprisingly strong effect, underscoring the importance of considering these channels in both experiments and mathematical settings. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/189344", + "other_currents": { + "attr_id": 303, + "value": "I_SOC" + }, + "other_implementer": { + "attr_id": 308, + "value": "Gregory Handy, Marsa Taheri" + }, + "other_simulator": { + "attr_id": 307, + "value": "XPP" + }, + "other_type": { + "attr_id": 305, + "value": "Astrocytes" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 153715, + "object_name": "IP3" + } + ] + }, + "ver_date": "2017-03-31T12:26:31.747", + "ver_number": 17 + }, + "189346": { + "class_id": 19, + "created": "2016-05-18T11:38:09.393", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 189346, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 238463, + "object_name": "Kim, Christopher M [christopher.kim3 at nih.gov]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 121254, + "object_name": "Depolarization block" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 230688, + "object_name": "Kim CM, Nykamp DQ (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113733, + "object_name": "Brian" + } + ] + }, + "name": "Modeling epileptic seizure induced by depolarization block (Kim & Dykamp 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 150222, + "object_name": "Abstract Morris-Lecar neuron" + }, + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The inhibitory restraint necessary to suppress\r\naberrant activity can fail when inhibitory neurons cease to\r\ngenerate action potentials as they enter depolarization block.\r\nWe investigate possible bifurcation structures that arise at\r\nthe onset of seizure-like activity resulting from depolarization\r\nblock in inhibitory neurons. Networks of conductance based\r\nexcitatory and inhibitory neurons are simulated to\r\ncharacterize different types of transitions to the seizure\r\nstate, and a mean field model is developed to verify the generality\r\nof the observed phenomena of excitatory-inhibitory\r\ndynamics. ...\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/189346", + "public_submitter_email": { + "attr_id": 309, + "value": "c.kim@bcf.uni-freiburg.de" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Christopher Kim" + }, + "ver_date": "2018-02-14T18:14:32.97", + "ver_number": 7 + }, + "189347": { + "class_id": 19, + "created": "2016-05-20T14:58:48.11", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 189347, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 124046, + "object_name": "Polsky, Alon [alonpol at tx.technion.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 116907, + "object_name": "Direction Selectivity" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 189348, + "object_name": "Poleg-Polsky A, Diamond JS (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Multiplication by NMDA receptors in Direction Selective Ganglion cells (Poleg-Polsky & Diamond 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 270, + "object_name": "Retina ganglion GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 209, + "object_name": "Acetylcholine" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model demonstrates how signal amplification with NMDARs depends on the synaptic environment. When direction selectivity (DS) detection is mediated by DS inhibition, NMDARs multiply other synaptic conductances. In the case of DS tuned excitation, NMDARs contribute additively." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/189347", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 178, + "object_name": "Nicotinic" + } + ] + }, + "ver_date": "2018-10-17T17:31:32.11", + "ver_number": 7 + }, + "189786": { + "class_id": 19, + "created": "2016-05-30T05:23:57.507", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 189786, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 189919, + "object_name": "Capogrosso, Marco " + }, + { + "object_id": 189920, + "object_name": "Formento, Emanuele " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 222956, + "object_name": "Neuromodulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 189794, + "object_name": "Moraud EM, Capogrosso M, Formento E, Wenger N, DiGiovanna J, Courtine G, Micera S (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Muscle spindle feedback circuit (Moraud et al, 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 117813, + "object_name": "Spinal cord motor neuron slow twitch" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Here, we developed a computational model of the muscle spindle feedback circuits of the rat ankle that predicts the interactions between Epidural Stimulation and spinal circuit dynamics during gait." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/189786", + "other_concept": { + "attr_id": 306, + "value": "Epidural Stimulation" + }, + "other_implementer": { + "attr_id": 308, + "value": "Marco Capogrosso; Emanuele Formento" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115954, + "object_name": "Spinal motoneuron" + } + ] + }, + "ver_date": "2017-01-13T12:21:55.887", + "ver_number": 9 + }, + "189922": { + "class_id": 19, + "created": "2016-05-31T18:58:57.753", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 189922, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 189924, + "object_name": "Ganguly, Mohit [mohit.ganguly at vanderbilt.edu]" + }, + { + "object_id": 189925, + "object_name": "Clay, John R " + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 189923, + "object_name": "Clay JR, Paydarfar D, Forger DB (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Model of Type 3 firing in neurons (Clay et al 2008)", + "notes": { + "attr_id": 24, + "value": "An ionic model for Type 3 firing in neurons (Clay et al 2008)\r\n\r\nSome neurons fire only once in response to a sustained depolarizing current pulse, type 3 behavior. One example, surprisingly, is the squid giant axon. The Hodgkin-Huxley (HH) model of this preparation fires repetitively for these conditions \u2013 type 2, a result that is not observed experimentally as shown in the above paper. Changing one parameter of their model of IK is sufficient to mimic the result." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/189922", + "other_implementer": { + "attr_id": 308, + "value": "Mohit Ganguly\r\nJohn R Clay" + }, + "ver_date": "2023-01-30T14:07:28.68", + "ver_number": 10 + }, + "189946": { + "class_id": 19, + "created": "2016-06-01T22:41:28.81", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 189946, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 189948, + "object_name": "Fietkiewicz, Chris" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 189947, + "object_name": "Fietkiewicz C, Loparo KA (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Macroscopic model of epilepsy (Fietkiewicz & Loparo 2016)", + "notes": { + "attr_id": 24, + "value": "Simulates epileptiform EEG. Original model used for Figure 2 in Fietkiewicz and Loparo 2016. The MATLAB program uses Euler integration to create the basic plot in Figure 2. The model is based on the original model specified in Wendling F, Bartolomei F, Bellanger JJ, Chauvel P. Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition. Eur J Neurosci, 2002;15(9):1499-1508." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/189946", + "ver_date": "2016-06-02T13:01:20.033", + "ver_number": 6 + }, + "190140": { + "class_id": 19, + "created": "2016-06-13T14:42:51.873", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 190140, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 190160, + "object_name": "Parasuram, Harilal [harilalp@am.amrita.edu]" + }, + { + "object_id": 116853, + "object_name": "Diwakar, Shyam [shyam at amrita.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 84598, + "object_name": "Extracellular Fields" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 190161, + "object_name": "Parasuram H, Nair B, D'Angelo E, Hines M, Naldi G, Diwakar S (2016)" + }, + { + "object_id": 225418, + "object_name": "Diwakar S, Medini C, Nair M, Parasuram H, Vijayan A, Nair B (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Modeling single neuron LFPs and extracellular potentials with LFPsim (Parasuram et al. 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 7346, + "object_name": "Cerebellum interneuron granule GLU cell" + }, + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "LFPsim - Simulation scripts to compute Local Field Potentials (LFP) from cable compartmental models of neurons and networks implemented in the NEURON simulation environment." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/190140", + "other_concept": { + "attr_id": 306, + "value": "Local Field Potentials" + }, + "other_implementer": { + "attr_id": 308, + "value": "Parasuram, Harilal [harilalp@am.amrita.edu]" + }, + "ver_date": "2017-02-01T10:23:48.687", + "ver_number": 12 + }, + "190261": { + "biosimulations": { + "value": "modeldb-190261" + }, + "class_id": 19, + "created": "2016-06-15T13:14:18.403", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 190261, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53570, + "object_name": "Prescott, Steven [steve.prescott at sickkids.ca]]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + }, + { + "object_id": 96767, + "object_name": "Nociception" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 190264, + "object_name": "Rho YA, Prescott SA (2012)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Dorsal root ganglion (primary somatosensory) neurons (Rho & Prescott 2012)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 150222, + "object_name": "Abstract Morris-Lecar neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In this paper, we demonstrate how dorsal root ganglion (DRG) neuron excitability can become pathologically altered, as occurs in neuropathic pain. Specifically, we reproduce pathological changes in spiking pattern (from transient to repetitive spiking) and the development of membrane potential oscillations and bursting." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/190261", + "other_implementer": { + "attr_id": 308, + "value": "Prescott, Steven [steve.prescott@sickkids.ca]" + }, + "other_simulator": { + "attr_id": 307, + "value": "XPP" + }, + "ver_date": "2016-06-16T11:31:14.96", + "ver_number": 5 + }, + "190304": { + "class_id": 19, + "created": "2016-06-15T16:26:12.483", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 190304, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53429, + "object_name": "Blackwell, Avrama [avrama at gmu.edu]" + }, + { + "object_id": 189720, + "object_name": "Jedrzejewska-Szmek, Joanna " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 230560, + "object_name": "J?drzejewska-Szmek J, Luczak V, Abel T, Blackwell KT (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 139757, + "object_name": "NeuroRD" + } + ] + }, + "name": "Signaling pathways underlying LTP in hippocampal CA1 pyramidal cells (Jedrzejewska-Szmek et al 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 229, + "object_name": "Ephinephrine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ...We investigated whether the diverse experimental evidence can be unified by creating a spatial, mechanistic model of multiple signaling pathways in hippocampal CA1 neurons. Our results show that the combination of activity of several key kinases can predict the occurrence of long-lasting forms of LTP for multiple experimental protocols. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/190304", + "public_submitter_email": { + "attr_id": 309, + "value": "jjedrzej@gmu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Joanna Jedrzejewska-Szmek" + }, + "ver_date": "2018-01-10T16:20:45.47", + "ver_number": 11 + }, + "190306": { + "class_id": 19, + "created": "2016-06-15T18:56:21.633", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 190306, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 195474, + "object_name": "Ho, E [ernest_ho at brown.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 97752, + "object_name": "Delay" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 151948, + "object_name": "Potassium buffering" + }, + { + "object_id": 156115, + "object_name": "Gamma oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 195475, + "object_name": "Ho ECY, Truccolo W (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Inhibition and glial-K+ interaction leads to diverse seizure transition modes (Ho & Truccolo 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 156124, + "object_name": "Abstract single compartment conductance based cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"How focal seizures initiate and evolve in human neocortex remains a fundamental problem in neuroscience. 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The effects of this diffusive plasticity are implemented in networks of rate-based neurons, and lead to the emergence of spatial structure in the synaptic connectivity of the network." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/190311", + "ver_date": "2016-06-18T09:40:28.683", + "ver_number": 3 + }, + "190559": { + "class_id": 19, + "created": "2016-07-08T06:24:34.23", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 190559, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + 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Clopath, C. (2016). Supervised Learning in Spiking Neural Networks with FORCE Training. arXiv preprint arXiv:1609.02545.\r\nfor further details. \r\n " + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/190565", + "ver_date": "2017-12-20T22:17:54.22", + "ver_number": 22 + }, + "190610": { + "class_id": 19, + "created": "2016-07-18T16:37:45.48", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 190610, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 194886, + "object_name": "Ahmad, Subutai [sahmad at numenta.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 190491, + "object_name": "Hawkins J, Ahmad S (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Theory of sequence memory in neocortex (Hawkins & Ahmad 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + }, + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 184128, + "object_name": "Neocortex layer 4 pyramidal cell" + }, + { + "object_id": 114635, + "object_name": "Neocortex spiny stellate cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... First we show that a neuron with several thousand synapses segregated on active dendrites can recognize hundreds of independent patterns of cellular activity even in the presence of large amounts of noise and pattern variation. We then propose a neuron model where patterns detected on proximal dendrites lead to action potentials, defining the classic receptive field of the neuron, and patterns detected on basal and apical dendrites act as predictions by slightly depolarizing the neuron without generating an action potential. By this mechanism, a neuron can predict its activation in hundreds of independent contexts. We then present a network model based on neurons with these properties that learns time-based sequences. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/190610", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2016-07-20T11:42:30.15", + "ver_number": 7 + }, + "194881": { + "class_id": 19, + "created": "2016-07-19T00:47:36.263", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 194881, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 194887, + "object_name": "Raudies, Florian [florian.raudies at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 194888, + "object_name": "Grid cell" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 194885, + "object_name": "Raudies F, Hasselmo ME (2015)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Grid cell model with compression effects (Raudies & Hasselmo, 2015)", + "notes": { + "attr_id": 24, + "value": "We present a model for compression of grid cell firing in modules to changes in barrier location." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/194881", + "other_implementer": { + "attr_id": 308, + "value": "Florian Raudies" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 119191, + "object_name": "Entorhinal cortex" + } + ] + }, + "ver_date": "2016-11-06T08:22:53.06", + "ver_number": 8 + }, + "194882": { + "class_id": 19, + "created": "2016-07-19T01:03:40.327", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 194882, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 194887, + "object_name": "Raudies, Florian [florian.raudies at gmail.com]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 194891, + "object_name": "Raudies F, Hasselmo ME (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Hippocampal spiking model for context dependent behavior (Raudies & Hasselmo 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Our model simulates the effect of context dependent behavior using discrete inputs to drive spiking activity representing place and item followed sequentially by a discrete representation of the motor actions involving a response to an item (digging for food) or the movement to a different item (movement to a different pot for food). This simple network was able to consistently learn the context-dependent responses." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/194882", + "other_implementer": { + "attr_id": 308, + "value": "Florian Raudies" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2016-07-20T11:27:51.84", + "ver_number": 7 + }, + "194883": { + "class_id": 19, + "created": "2016-07-19T01:08:16.023", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 194883, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 194887, + "object_name": "Raudies, Florian [florian.raudies at gmail.com]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 194893, + "object_name": "Raudies F, Zilli EA, Hasselmo ME (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Deep belief network learns context dependent behavior (Raudies, Zilli, Hasselmo 2014)", + "notes": { + "attr_id": 24, + "value": "We tested a rule generalization capability with a Deep Belief Network (DBN), Multi-Layer Perceptron network, and the combination of a DBN with a linear perceptron (LP). Overall, the combination of the DBN and LP had the highest success rate for generalization." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/194883", + "other_implementer": { + "attr_id": 308, + "value": "Florian Raudies" + }, + "ver_date": "2016-07-22T11:50:51.45", + "ver_number": 6 + }, + "194897": { + "citation": { + "attr_id": 391, + "value": "Dura-Bernal S, Neymotin SA, Kerr CC, Sivagnanam S, Majumdar A, Francis JT, Lyt-\r\nton WW. Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis. IBM Journal of Research and Development (Computational Neuroscience special issue)" + }, + "class_id": 19, + "created": "2016-07-22T15:20:26.62", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 194897, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 182905, + "object_name": "Dura-Bernal, Salvador [salvadordura at gmail.com]" + }, + { + "object_id": 146031, + "object_name": "Kerr, Cliff [cliffk at neurosim.downstate.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + }, + { + "object_id": 116858, + "object_name": "Reward-modulated STDP" + }, + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 186770, + "object_name": "Motor control" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 194898, + "object_name": "Dura-Bernal S, Neymotin SA, Kerr CC, Sivagnanam S, Majumdar A, Francis JT, Lytton WW (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Motor system model with reinforcement learning drives virtual arm (Dura-Bernal et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"We implemented a model of the motor system with the following components: dorsal premotor cortex (PMd), primary motor cortex (M1), spinal cord and musculoskeletal arm (Figure 1). PMd modulated M1 to select the target to reach, M1 excited the descending spinal cord neurons that drove the arm muscles, and received arm proprioceptive feedback (information about the arm position) via the ascending spinal cord neurons. \r\nThe large-scale model of M1 consisted of 6,208 spiking Izhikevich model neurons [37] of four types: regular-firing and bursting pyramidal neurons, and fast-spiking and low-threshold-spiking interneurons. These were distributed across cortical layers 2/3, 5A, 5B and 6, with cell properties, proportions, locations, connectivity, weights and delays drawn primarily from mammalian experimental data [38], [39], and described in detail in previous work [29]. The network included 486,491 connections, with synapses modeling properties of four different receptors ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/194897", + "public_submitter_email": { + "attr_id": 309, + "value": "salvadordura@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Salvador Dura-Bernal" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2022-07-23T12:38:58.35", + "ver_number": 15 + }, + "195206": { + "class_id": 19, + "created": "2016-08-03T09:23:05.243", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 195206, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 249322, + "object_name": "Hutt A, Mierau A, Lefebvre J (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Stochastic and periodic inputs tune ongoing oscillations (Hutt et al. 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 245940, + "object_name": "Abstract rate-based neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... We here analyze a network of recurrently connected spiking neurons with time delay displaying stable synchronous dynamics. Using mean-field and stability analyses, we investigate the influence of dynamic inputs on the frequency of firing rate oscillations. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/195206", + "ver_date": "2019-01-09T16:50:08.883", + "ver_number": 10 + }, + "195555": { + "class_id": 19, + "created": "2016-08-22T11:01:47.843", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 195555, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 146951, + "object_name": "McDougal, Robert [robert.mcdougal at yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 246764, + "object_name": "McDougal RA, Dalal I, Morse TM, Shepherd GM (2019)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Automated metadata suggester (McDougal et al 2018)", + "notes": { + "attr_id": 24, + "value": "This code provides an abstract processing method that predicts keywords for model entries in ModelDB." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/195555", + "ver_date": "2018-11-09T14:08:06.313", + "ver_number": 12 + }, + "195569": { + "class_id": 19, + "created": "2016-08-29T04:05:50.373", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 195569, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 195572, + "object_name": "Huang, Shiwei [shiweihuang at outlook.com]" + }, + { + "object_id": 153454, + "object_name": "Hong, Sungho [shhong at oist.jp]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 195578, + "object_name": "Huang S, Hong S, De Schutter E (2015)" + }, + { + "object_id": 195570, + "object_name": "Huang S, Hong S, De Schutter E (2015)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Concentration dependent nonlinear K+ and Cl- leak current (Huang et al. 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + }, + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"In their seminal works on squid giant axons, Hodgkin, and Huxley\r\napproximated the membrane leak current as Ohmic, i.e., linear, since\r\nin their preparation, sub-threshold current rectification due to the\r\ninfluence of ionic concentration is negligible.\r\n\r\nMost studies on\r\nmammalian neurons have made the same, largely untested,\r\nassumption. \r\n\r\nHere we show that the membrane time constant and input\r\nresistance of mammalian neurons (when other major voltage-sensitive\r\nand ligand-gated ionic currents are discounted) varies non-linearly\r\nwith membrane voltage, following the prediction of a\r\nGoldman-Hodgkin-Katz-based passive membrane model.\r\n...\" (see paper for details and more).\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/195569", + "ver_date": "2023-01-30T13:49:14.623", + "ver_number": 14 + }, + "195615": { + "class_id": 19, + "created": "2016-08-31T16:46:20.567", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 251, + 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"model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 217748, + "object_name": "Neymotin SA, Suter BA, Dura-Bernal S, Shepherd GM, Migliore M, Lytton WW (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Computer models of corticospinal neurons replicate in vitro dynamics (Neymotin et al. 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 227970, + "object_name": "Neocortex M1 L5B pyramidal pyramidal tract GLU cell" + }, + { + "object_id": 217749, + "object_name": "Neocortex primary motor area pyramidal layer 5 corticospinal cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Corticospinal neurons (SPI), thick-tufted pyramidal neurons in motor\r\ncortex layer 5B that project caudally via the medullary pyramids,\r\ndisplay distinct class-specific electrophysiological properties in\r\nvitro: strong sag with hyperpolarization, lack of adaptation, and a\r\nnearly linear frequency-current (FI) relationship. We used our\r\nelectrophysiological data to produce a pair of large archives of SPI\r\nneuron computer models in two model classes: 1. Detailed models with\r\nfull reconstruction; 2. Simplified models with 6 compartments. We\r\nused a PRAXIS and an evolutionary multiobjective optimization (EMO) in\r\nsequence to determine ion channel conductances. \r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/195615", + "other_implementer": { + "attr_id": 308, + "value": "Benjamin Suter, Ernie Forzano" + }, + "other_neurons": { + "attr_id": 301, + "value": "Corticospinal neuron" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2023-01-30T13:35:58.193", + "ver_number": 23 + }, + "195626": { + "class_id": 19, + "created": "2016-09-02T15:58:05.477", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 195626, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 195620, + "object_name": "Ferneyhough GB, Thibealut CM, Dascalu SM, Harris FC (2016)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "ModFossa: a library for modeling ion channels using Python (Ferneyhough et al 2016)", + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/195626", + "ver_date": "2016-09-02T16:25:43.373", + "ver_number": 5 + }, + "195658": { + "class_id": 19, + "created": "2016-09-08T16:11:21.303", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 195658, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 186894, + "object_name": "Tikidji-Hamburyan, Ruben [ruben.tikidji.hamburyan at gmail.com] " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 114660, + "object_name": "Connectivity matrix" + }, + { + "object_id": 128875, + "object_name": "Development" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 218016, + "object_name": "Tikidji-Hamburyan RA, El-Ghazawi TA, Triplett JW (2016)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 146899, + "object_name": "Cython" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Models of visual topographic map alignment in the Superior Colliculus (Tikidji-Hamburyan et al 2016)", + "notes": { + "attr_id": 24, + "value": "We develop two novel computational models of\r\nvisual map alignment in the SC that incorporate distinct activity-dependent\r\ncomponents. First, a Correlational Model assumes that V1 inputs achieve alignment\r\nwith established retinal inputs through simple correlative firing mechanisms. A second\r\nIntegrational Model assumes that V1 inputs contribute to the firing of SC neurons\r\nduring alignment. Both models accurately replicate in vivo findings in wild type,\r\ntransgenic and combination mutant mouse models, suggesting either activity-dependent\r\nmechanism is plausible." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/195658", + "public_submitter_email": { + "attr_id": 309, + "value": "ruben.tikidji.hamburyan@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ruben Tikidji-Hamburyan" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 118308, + "object_name": "Superior colliculus" + } + ] + }, + "ver_date": "2018-10-17T17:31:47.377", + "ver_number": 6 + }, + "195659": { + "class_id": 19, + "created": "2016-09-08T16:47:50.127", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 195659, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 195660, + "object_name": "Detorakis, Georgios [gdetorak at uci.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 93420, + "object_name": "Sleep" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 226617, + "object_name": "Spindles" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 25654, + "object_name": "Wang XJ (1994)" + }, + { + "object_id": 195655, + "object_name": "Detorakis G (2016)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Multiple dynamical modes of thalamic relay neurons (Wang XJ 1994)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The (Wang 1994) papers model was replicated in python by (Detorakis 2016). \"The model is conductance-based and takes advantage of the\r\ninterplay between a T-type calcium current and a non-specific cation sag current and\r\nthus, it is able to generate spindle and delta rhythms.\" The model also generates intermittent phase locking, non periodic firing, bursts, and tonic spike patterns." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/195659", + "other_implementer": { + "attr_id": 308, + "value": "Detorakis, Georgios [gdetorak at uci.edu]" + }, + "other_neurons": { + "attr_id": 301, + "value": "Thalamocoritcal relay neuron" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2017-04-04T13:54:44.587", + "ver_number": 6 + }, + "195666": { + "class_id": 19, + "created": "2016-09-09T17:39:23.09", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 143445, + "object_name": "I_KD" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 195666, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 240367, + "object_name": "Dewell, Richard Burkett [dewell at bcm.edu]" + }, + { + "object_id": 33804, + "object_name": "Gabbiani, F" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 240366, + "object_name": "Dewell RB, Gabbiani F (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "LGMD with 3D morphology and active dendrites (Dewell & Gabbiani 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 116948, + "object_name": "Locust Lobula Giant Movement Detector (LGMD) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a model of the locust LGMD looming sensitive neuron from Dewell & Gabbiani 2018. The morphology was constructed based on 2-photon imaging, and active conductances throughout the neuron were based on sharp electrode recordings in vivo." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/195666", + "other_implementer": { + "attr_id": 308, + "value": "Dewell, Richard" + }, + "ver_date": "2019-08-08T17:35:45.687", + "ver_number": 22 + }, + "195667": { + "class_id": 19, + "created": "2016-09-11T08:39:13.817", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 195667, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206286, + "object_name": "Eyal, Guy [guy.eyal at mail.huji.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 206291, + "object_name": "Membrane Properties" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 206290, + "object_name": "Eyal G, Verhoog MB, Testa-Silva G, Deitcher Y, Lodder JC, Benavides-Piccione R, Morales J, DeFelipe J, de Kock CP, Mansvelder HD, Segev I (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Human L2/3 pyramidal cells with low Cm values (Eyal et al. 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The advanced cognitive capabilities of the human brain are often attributed to our recently evolved neocortex. However, it is not known whether the basic building blocks of human neocortex, the pyramidal neurons, possess unique biophysical properties that might impact on cortical computations. Here we show that layer 2/3 pyramidal neurons from human temporal cortex (HL2/3 PCs) have a specific membrane capacitance (Cm) of ~0.5 \u00b5F/cm2, half of the commonly accepted \u201cuniversal\u201d value (~1 \u00b5F/cm2) for biological membranes. This finding was predicted by fitting in vitro voltage transients to theoretical transients then validated by direct measurement of Cm in nucleated patch experiments. Models of 3D reconstructed HL2/3 PCs demonstrated that such low Cm value significantly enhances both synaptic charge-transfer from dendrites to soma and spike propagation along the axon. This is the first demonstration that human cortical neurons have distinctive membrane properties, suggesting important implications for signal processing in human neocortex." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/195667", + "other_concept": { + "attr_id": 306, + "value": "Membrane Properties" + }, + "other_implementer": { + "attr_id": 308, + "value": "Guy Eyal" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2016-10-11T10:42:13.19", + "ver_number": 7 + }, + "195731": { + "class_id": 19, + "created": "2016-09-13T17:12:54.347", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 195731, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 195733, + "object_name": "Topalidou, Meropi " + }, + { + "object_id": 195732, + "object_name": "Rougier, Nicolas P. [Nicolas.Rougier at inria.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 195680, + "object_name": "Guthrie M, Leblois A, Garenne A, Boraud T (2013)" + }, + { + "object_id": 195730, + "object_name": "Topalidou M, Rougier NP (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Cognitive and motor cortico-basal ganglia interactions during decision making (Guthrie et al 2013)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a re-implementation of Guthrie et al 2013 by Topalidou and Rougier 2015. The original study investigated how multiple level action selection\r\ncould be performed by the basal ganglia." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/195731", + "other_implementer": { + "attr_id": 308, + "value": "Meropi Topalidou and Nicolas P. Rougier [Nicolas.Rougier@inria.fr]" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 138633, + "object_name": "Striatum" + }, + { + "object_id": 116868, + "object_name": "Subthalamic Nucleus" + }, + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2016-09-13T17:22:32.867", + "ver_number": 4 + }, + "195856": { + "class_id": 19, + "created": "2016-09-15T18:19:44.867", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 195856, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 195857, + "object_name": "Viejo, Guillaume [guillaume.viejo at isir.upmc.fr]" + }, + { + "object_id": 124115, + "object_name": "Girard, Benoit [girard at isir.upmc.fr]" + }, + { + "object_id": 195859, + "object_name": "Khamassi, Mehdi " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + }, + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + }, + { + "object_id": 83535, + "object_name": "Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 195738, + "object_name": "Keramati M, Dezfouli A, Piray P (2011)" + }, + { + "object_id": 195853, + "object_name": "Viejo G, Girard B, Khamassi M (2016)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Speed/accuracy trade-off between the habitual and the goal-directed processes (Kermati et al. 2011)", + "notes": { + "attr_id": 24, + "value": "\"This study is a reference implementation of Keramati, Dezfouli, and Piray 2011 that\r\nproposed an arbitration mechanism between a goal-directed strategy and a habitual\r\nstrategy, used to model the behavior of rats in instrumental conditionning tasks. The\r\nhabitual strategy is the Kalman Q-Learning from Geist, Pietquin, and Fricout 2009. We\r\nreplicate the results of the first task, i.e. the devaluation experiment with two states\r\nand two actions. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/195856", + "other_implementer": { + "attr_id": 308, + "value": "Guillaume Viejo [guillaume.viejo at isir.upmc.fr], Beno\u00eet Girard, and Mehdi Khamassi" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2016-09-15T18:30:13.67", + "ver_number": 4 + }, + "195886": { + "class_id": 19, + "created": "2016-09-16T19:23:01.843", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 195886, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 195887, + "object_name": "Berdahl, Carl H [Carl.Berdahl at verizon.net]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 196196, + "object_name": "Borderline Personality Disorder (BPD)" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 195889, + "object_name": "Berdahl CH (2010)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Borderline Personality Disorder (Berdahl, 2010)", + "notes": { + "attr_id": 24, + "value": "This research developed a neural network simulation constrained by known neuroanatomy and neurophysiology to generate ideas about the etiology of Borderline Personality Disorder. The simulations suggest an important role for the amygdala-ventromedial prefrontal cortex-amygdala circuit." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/195886", + "other_gene": { + "attr_id": 477, + "value": "SHH" + }, + "other_implementer": { + "attr_id": 308, + "value": "Berdahl, Carl H" + }, + "other_type": { + "attr_id": 305, + "value": "Shunting neuron" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 142628, + "object_name": "Amygdala" + }, + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + }, + { + "object_id": 183076, + "object_name": "Brainstem" + } + ] + }, + "ver_date": "2016-09-20T16:20:24.763", + "ver_number": 9 + }, + "195890": { + "class_id": 19, + "created": "2016-09-19T07:14:02.33", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 195890, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 225803, + "object_name": "Kato, Ayaka " + }, + { + "object_id": 117206, + "object_name": "Morita, Kenji [morita at p.u-tokyo.ac.jp]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 225630, + "object_name": "Kato A, Morita K (2016)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Reinforcement Learning with Forgetting: Linking Sustained Dopamine to Motivation (Kato Morita 2016)", + "notes": { + "attr_id": 24, + "value": "\"It has been suggested that dopamine (DA) represents\r\nreward-prediction-error (RPE) defined in reinforcement learning and\r\ntherefore DA responds to unpredicted but not predicted\r\nreward.\r\n\r\nHowever, recent studies have found DA response sustained\r\ntowards predictable reward in tasks involving self-paced behavior, and\r\nsuggested that this response represents a motivational signal.\r\n\r\nWe have previously shown that RPE can sustain if there is\r\ndecay/forgetting of learned-values, which can be implemented as decay\r\nof synaptic strengths storing learned-values. This account, however,\r\ndid not explain the suggested link between tonic/sustained DA and\r\nmotivation. In the present work, we explored the motivational effects\r\nof the value-decay in self-paced approach behavior, modeled as a\r\nseries of \u2018Go\u2019 or \u2018No-Go\u2019 selections towards a goal. Through\r\nsimulations, we found that the value-decay can enhance motivation,\r\nspecifically, facilitate fast goal-reaching, albeit\r\ncounterintuitively.\r\n...\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/195890", + "ver_date": "2017-03-07T16:43:41.127", + "ver_number": 10 + }, + "196197": { + "class_id": 19, + "created": "2016-09-20T20:18:38.55", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 196197, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 196198, + "object_name": "DeWoskin, Daniel [dadewoskin at gmail.com] " + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 246552, + "object_name": "Paul JR, DeWoskin D, McMeekin LJ, Cowell RM, Forger DB, Gamble KL (2016)" + }, + { + "object_id": 246612, + "object_name": "DeWoskin D, Myung J, Belle MD, Piggins HD, Takumi T, Forger DB (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "HH model neuron of the Suprachiasmatic Nucleus including a persistent Na+ channel (Paul et al 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 145884, + "object_name": "Suprachiasmatic nucleus (SCN) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Hodgkin-Huxley style model for a neuron of the Suprachiasmatic Nucleus (SCN). Modified from DeWoskin et al, PNAS, 2015 to include a persistent sodium current. The model is used to study the role of the kinase GSK3 in regulating the electrical activity of SCN neurons through a persistent sodium current." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/196197", + "other_implementer": { + "attr_id": 308, + "value": "DeWoskin, Daniel [dadewoskin at gmail.com]" + }, + "ver_date": "2018-11-08T18:04:00.77", + "ver_number": 5 + }, + "206227": { + "class_id": 19, + "created": "2016-09-21T16:48:07.757", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 206227, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206228, + "object_name": "Yang, Guangyu Robert " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 136098, + "object_name": "Information transfer" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + }, + { + "object_id": 245691, + "object_name": "Vision" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 196200, + "object_name": "Yang GR, Murray JD, Wang XJ (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 83551, + "object_name": "Connectionist Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "A dendritic disinhibitory circuit mechanism for pathway-specific gating (Yang et al. 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"While reading a book in a noisy caf\u00e9, how does your brain \u2018gate in\u2019 visual information while filtering out auditory stimuli? Here we propose a mechanism for such flexible routing of information flow in a complex brain network (pathway-specific gating), tested using a network model of pyramidal neurons and three classes of interneurons with connection probabilities constrained by data. We find that if inputs from different pathways cluster on a pyramidal neuron dendrite, a pathway can be gated-on by a disinhibitory circuit motif. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206227", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 213, + "object_name": "Gaba" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2018-10-18T17:08:40.123", + "ver_number": 6 + }, + "206232": { + "class_id": 19, + "created": "2016-09-27T17:17:18.923", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 206232, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206236, + "object_name": "Kumaravelu, Karthik [kk192 at duke.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 206235, + "object_name": "Kumaravelu K, Brocker DT, Grill WM (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Cortex-Basal Ganglia-Thalamus network model (Kumaravelu et al. 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + }, + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... We developed a biophysical network model comprising of the closed loop cortical-basal ganglia-thalamus circuit representing the healthy and parkinsonian rat brain. The network properties of the model were validated by comparing responses evoked in basal ganglia (BG) nuclei by cortical (CTX) stimulation to published experimental results. A key emergent property of the model was generation of low-frequency network oscillations. Consistent with their putative pathological role, low-frequency oscillations in model BG neurons were exaggerated in the parkinsonian state compared to the healthy condition. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206232", + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + }, + { + "object_id": 115951, + "object_name": "Thalamus" + }, + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 116868, + "object_name": "Subthalamic Nucleus" + } + ] + }, + "ver_date": "2016-12-12T15:02:23.417", + "ver_number": 8 + }, + "206238": { + "class_id": 19, + "created": "2016-09-28T15:15:33.843", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 206238, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206242, + "object_name": "Anderson, WS " + }, + { + "object_id": 206243, + "object_name": "Kudela, Pawel " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 206240, + "object_name": "Basu I, Kudela P, Korzeniewska A, Franaszczuk PJ, Anderson WS (2015)" + }, + { + "object_id": 206241, + "object_name": "Basu I, Kudela P, Anderson WS (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "A multilayer cortical model to study seizure propagation across microdomains (Basu et al. 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 167344, + "object_name": "Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell" + }, + { + "object_id": 167343, + "object_name": "Neocortex U1 L5B pyramidal pyramidal tract GLU cell" + }, + { + "object_id": 263, + "object_name": "Thalamus reticular nucleus GABA cell" + }, + { + "object_id": 142232, + "object_name": "Neocortex spiking low threshold (LTS) neuron" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + }, + { + "object_id": 184130, + "object_name": "Neocortex layer 2-3 interneuron" + }, + { + "object_id": 184134, + "object_name": "Neocortex layer 5 interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 231, + "object_name": "Amino Acids" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A realistic neural network was used to simulate a region of neocortex to obtain extracellular LFPs from \u2018virtual micro-electrodes\u2019 and produce test data for comparison with multisite microelectrode recordings. A model was implemented in the GENESIS neurosimulator. A simulated region of cortex was represented by layers 2/3, 5/6 (interneurons and pyramidal cells) and layer 4 stelate cells, spaced at 25 \u00b5m in each horizontal direction. Pyramidal cells received AMPA and NMDA inputs from neighboring cells at the basal and apical dendrites.\r\nThe LFP data was generated by simulating 16-site electrode array with the help of \u2018efield\u2019 objects arranged at the predetermined positions with respect to the surface of the simulated network. The LFP for the model is derived from a weighted average of the current sources summed over all cellular compartments. Cell models were taken from from Traub et al. (2005) J Neurophysiol 93(4):2194-232." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206238", + "other_implementer": { + "attr_id": 308, + "value": "Anderson WS, Kudela P" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2018-01-25T14:10:42.273", + "ver_number": 6 + }, + "206244": { + "class_id": 19, + "created": "2016-09-28T17:29:35.743", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 206244, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206245, + "object_name": "Muellner, Fiona E [fiona.muellner at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 187642, + "object_name": "M\u00fcllner FE, Wierenga CJ, Bonhoeffer T (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "CA1 pyramidal neuron: dendritic Ca2+ inhibition (Muellner et al. 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In our experimental study, we combined\r\npaired patch-clamp recordings and two-photon\r\nCa2+ imaging to quantify inhibition exerted by individual GABAergic contacts on hippocampal pyramidal cell dendrites. We observed that Ca2+ transients from back-propagating action potentials were significantly reduced during simultaneous activation of individual nearby GABAergic synapses. To simulate dendritic Ca2+ inhibition by individual GABAergic synapses, we employed a multi-compartmental CA1 pyramidal cell model with\r\ndetailed morphology, voltage-gated channel distributions, and calcium dynamics, based with modifications on the model of Poirazi et al.,\r\n2003, modelDB accession # 20212." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206244", + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2017-06-27T10:19:10.883", + "ver_number": 7 + }, + "206249": { + "class_id": 19, + "created": "2016-09-29T15:43:04.1", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 206249, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206278, + "object_name": "Kastellakis, George [gkastel at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 206391, + "object_name": "Kastellakis G, Silva AJ, Poirazi P (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Model of memory linking through memory allocation (Kastellakis et al. 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 206251, + "object_name": "Abstract integrate-and-fire leaky neuron with dendritic subunits" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Here, we present a simplified, biophysically inspired network model that incorporates multiple plasticity processes and explains linking of information at three different levels: (a) learning of a single associative memory (b) rescuing of a weak memory when paired with a strong one and (c) linking of multiple memories across time. By dissecting synaptic from intrinsic plasticity and neuron-wide from dendritically restricted protein capture, the model reveals a simple, unifying principle: Linked memories share synaptic clusters within the dendrites of overlapping populations of neurons" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206249", + "other_implementer": { + "attr_id": 308, + "value": "Kastellakis, George [gkastel@gmail.com]" + }, + "other_neurons": { + "attr_id": 301, + "value": "Integrate and fire neuron with dendritic subunits" + }, + "ver_date": "2016-10-07T14:22:05.637", + "ver_number": 14 + }, + "206252": { + "class_id": 19, + "created": "2016-09-29T22:23:23.767", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 167500, + "object_name": "Kir" + }, + { + "object_id": 143445, + "object_name": "I_KD" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 206252, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 144409, + "object_name": "Homeostasis" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 206322, + "object_name": "Ceballos CC, Li S, Roque AC, Tzounopoulos T, Le\u00e3o RM (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "DCN fusiform cell (Ceballos et al. 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 274, + "object_name": "Cochlear nucleus pyramidal/fusiform GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Dorsal cochlear nucleus principal neurons, fusiform neurons, display heterogeneous spontaneous action potential activity and thus represent an appropriate model to study the role of different conductances in establishing firing heterogeneity. Particularly, fusiform neurons are divided into quiet, with no spontaneous firing, or active neurons, presenting spontaneous, regular firing. These modes are determined by the expression levels of an intrinsic membrane conductance, an inwardly rectifying potassium current (IKir). We used a computational model to test whether other subthreshold conductances vary homeostatically to maintain membrane excitability constant across the two subtypes. We found that Ih expression covaries specifically with IKir in order to maintain membrane resistance constant. The impact of Ih on membrane resistance is dependent on the level of IKir expression, being much smaller in quiet neurons with bigger IKir, but Ih variations are not relevant for creating the quiet and active phenotypes. We conclude that in fusiform neurons the variations of their different subthreshold conductances are limited to specific conductances in order to create firing heterogeneity and maintain membrane homeostasis." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206252", + "public_submitter_email": { + "attr_id": 309, + "value": "cesarcelis@usp.br" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Cesar Ceballos" + }, + "ver_date": "2016-10-13T10:01:41.31", + "ver_number": 22 + }, + "206256": { + "class_id": 19, + "created": "2016-09-30T16:52:52.187", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 206256, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206257, + "object_name": "Galati, Domenico F " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 128875, + "object_name": "Development" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 206255, + "object_name": "Galati DF, Hiester BG, Jones KR (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "BDNF morphological contributions to AP enhancement (Galati et al. 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 128077, + "object_name": "Neocortex dissociated cultured nerve cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... We quantified BDNF\u2019s effect on\r\ncultured cortical neuron morphological parameters and found that BDNF stimulates\r\ndendrite growth and addition of dendrites while increasing both excitatory and inhibitory\r\npresynaptic inputs in a spatially restricted manner. To gain insight into how these\r\ncombined changes in neuron structure and synaptic input impact AP generation, we\r\nused the morphological parameters we gathered to generate computational models.\r\nSimulations suggest that BDNF-induced neuron morphologies generate more APs\r\nunder a wide variety of conditions. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206256", + "public_submitter_email": { + "attr_id": 309, + "value": "thomas.morse@snet.net" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "tom" + }, + "ver_date": "2016-09-30T17:01:13.607", + "ver_number": 4 + }, + "206267": { + "class_id": 19, + "created": "2016-10-03T13:06:07.063", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 167500, + "object_name": "Kir" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 206267, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 116577, + "object_name": "Solinas, Sergio [solinas at unipv.it]" + }, + { + "object_id": 206274, + "object_name": "Subramaniyam, Sathyaa [sakthisathyaa at gmail.com]" + }, + { + "object_id": 46840, + "object_name": "D'Angelo, Egidio [dangelo at unipv.it]" + }, + { + "object_id": 206273, + "object_name": "Goldfarb, Mitchell goldfarb at genectr.hunter.cuny.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 206271, + "object_name": "Dover K, Marra C, Solinas S, Popovic M, Subramaniyam S, Zecevic D, D'Angelo E, Goldfarb M (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Cerebellum granule cell FHF (Dover et al. 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 7346, + "object_name": "Cerebellum interneuron granule GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Neurons in vertebrate central nervous systems initiate and conduct sodium action potentials in distinct subcellular compartments that differ architecturally and electrically. Here, we report several unanticipated passive and active properties of the cerebellar granule cell's unmyelinated axon. Whereas spike initiation at the axon initial segment relies on sodium channel (Nav)-associated fibroblast growth factor homologous factor (FHF) proteins to delay Nav inactivation, distal axonal Navs show little FHF association or FHF requirement for high-frequency transmission, velocity and waveforms of conducting action potentials. ...'" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206267", + "other_implementer": { + "attr_id": 308, + "value": "Goldfarb, Mitchell\r\nSubramaniyam, Sathyaa" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 213, + "object_name": "Gaba" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2023-01-27T13:13:25.553", + "ver_number": 16 + }, + "206310": { + "class_id": 19, + "created": "2016-10-11T11:24:07.74", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 206310, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206330, + "object_name": "Creamer, Matthew [Matthew.Creamer at yale.edu]" + }, + { + "object_id": 206329, + "object_name": "Mano, Omer [Omer.Mano at yale.edu]" + }, + { + "object_id": 206331, + "object_name": "Clark, Damon [Damon.Clark at yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 168963, + "object_name": "Motion Detection" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 206318, + "object_name": "Salazar-Gatzimas E, Chen J, Creamer MS, Mano O, Mandel HB, Matulis CA, Pottackal J, Clark DA (2016)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Comparing correlation responses to motion estimation models (Salazar-Gatzimas et al. 2016)", + "notes": { + "attr_id": 24, + "value": "Code to generate responses of HRC-like and BL-like model elementary motion detectors to correlated noise stimuli, including two models with more realistic temporal filtering." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206310", + "other_implementer": { + "attr_id": 308, + "value": "Omer Mano, Matthew Creamer, Damon Clark" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "damon.clark@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Damon Clark" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 206363, + "object_name": "Drosophila" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 120613, + "object_name": "Drosophila (fruit fly)" + } + ] + }, + "ver_date": "2019-02-26T10:14:47.563", + "ver_number": 14 + }, + "206328": { + "class_id": 19, + "created": "2016-10-15T09:17:53.023", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 206328, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 207819, + "object_name": "Gupta, Rahul [gupta.sbt at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 207751, + "object_name": "Gupta R, Reneaux M, Karmeshu (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Spatially-varying glutamate diffusion coefficient at CA1 synaptic cleft space (Gupta et al. 2016)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Due to the heterogeneous macromolecular crowding and geometrical irregularity at central excitatory synapses, the diffusion coefficient of glutamate may exhibit spatial variation across the cleft space. To take into account the effect of emergent cleft heterogeneity on the generation of excitatory postsynaptic currents (EPSCs), a gamma statistical distribution of the glutamate diffusion coefficient is considered and, using the principle of superstatistics, the glutamate transients are computed as well as the activation of AMPA receptors is performed. This model demonstrates the numerical simulation of the Brownian diffusion of glutamate under distributed diffusion coefficient, the subsequent stochastic activation of AMPA receptors using Milstein-Nicoll scheme and modified Gillespie algorithm with minimum time-step correction, and the eventual stochastic profile of EPSC generation. The study is based on the CA1 synapses located at the dendrites of CA1 pyramidal neurons in the mammalian hippocampal region." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206328", + "public_submitter_email": { + "attr_id": 309, + "value": "gupta.sbt@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Rahul Gupta" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2016-12-06T15:31:10.563", + "ver_number": 8 + }, + "206337": { + "class_id": 19, + "created": "2016-10-22T17:03:51.443", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 206337, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206342, + "object_name": "Ulloa, Antonio [antonio.ulloa at alum.bu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 82787, + "object_name": "Working memory" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 86332, + "object_name": "Tagamets MA, Horwitz B (1998)" + }, + { + "object_id": 206344, + "object_name": "Ulloa A, Horwitz B (2016)" + }, + { + "object_id": 206348, + "object_name": "Horwitz B, Warner B, Fitzer J, Tagamets MA, Husain FT, Long TW (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Large-scale neural model of visual short-term memory (Ulloa, Horwitz 2016; Horwitz, et al. 2005,...)", + "notes": { + "attr_id": 24, + "value": "Large-scale neural model of visual short term memory embedded into a 998-node connectome. The model simulates electrical activity across neuronal populations of a number of brain regions and converts that activity into fMRI and MEG time-series. The model uses a neural simulator developed at the Brain Imaging and Modeling Section of the National Institutes of Health." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206337", + "other_implementer": { + "attr_id": 308, + "value": "Antonio Ulloa" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "antonio.ulloa@alum.bu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Antonio Ulloa" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "ver_date": "2016-10-24T23:20:02.343", + "ver_number": 6 + }, + "206356": { + "class_id": 19, + "created": "2016-10-25T17:09:58.997", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 206356, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206357, + "object_name": "Wystrach, Antoine " + }, + { + "object_id": 206358, + "object_name": "Lagogiannis, Konstantinos " + }, + { + "object_id": 206359, + "object_name": "Webb, Barbara " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 206354, + "object_name": "Wystrach A, Lagogiannis K, Webb B (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + }, + { + "object_id": 206355, + "object_name": "Mathematica (web link to model)" + } + ] + }, + "name": "Continuous lateral oscillations as a mechanism for taxis in Drosophila larvae (Wystrach et al 2016)", + "notes": { + "attr_id": 24, + "value": "\" ...Our analysis of larvae motion reveals a rhythmic, continuous lateral oscillation of the anterior body, encompassing all head-sweeps, small or large, without breaking the oscillatory rhythm. Further, we show that an agent-model that embeds this hypothesis reproduces a surprising number of taxis signatures observed in larvae. Also, by coupling the sensory input to a neural oscillator in continuous time, we show that the mechanism is robust and biologically plausible. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206356", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 206363, + "object_name": "Drosophila" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 120613, + "object_name": "Drosophila (fruit fly)" + } + ] + }, + "ver_date": "2019-02-26T10:15:08.893", + "ver_number": 7 + }, + "206364": { + "biosimulations": { + "value": "modeldb-206364" + }, + "class_id": 19, + "created": "2016-10-27T21:21:38.797", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 206364, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33797, + "object_name": "Roth, Arnd " + }, + { + "object_id": 206369, + "object_name": "Buchin, Anatoly [anat.buchin at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 136098, + "object_name": "Information transfer" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 183719, + "object_name": "Synaptic noise" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 206368, + "object_name": "Buchin A, Rieubland S, H\u00e4usser M, Gutkin BS, Roth A (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Inverse stochastic resonance of cerebellar Purkinje cell (Buchin et al. 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + }, + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + }, + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This code shows the simulations of the adaptive exponential integrate-and-fire model (http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model) at different stimulus conditions. The parameters of the model were tuned to the Purkinje cell of cerebellum to reproduce the inhibiion of these cells by noisy current injections. Similar experimental protocols were also applied to the detailed biophysical model of Purkinje cells, de Shutter & Bower (1994) model. The repository also includes the XPPaut version of the model with the corresponding bifurcation analysis." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206364", + "other_implementer": { + "attr_id": 308, + "value": "Anatoly Buchin\r\nArnd Roth" + }, + "other_simulator": { + "attr_id": 307, + "value": "Xppaut" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "anat.buchin@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Anatoly Buchin" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 213, + "object_name": "Gaba" + }, + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2019-03-23T16:36:09.007", + "ver_number": 10 + }, + "206365": { + "biosimulations": { + "value": "modeldb-206365" + }, + "class_id": 19, + "created": "2016-10-27T21:46:48.517", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 146505, + "object_name": "KCC2" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 240, + "object_name": "I Chloride" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 148257, + "object_name": "I_HCO3" + }, + { + "object_id": 241, + "object_name": "I Na,p" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 206365, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206369, + "object_name": "Buchin, Anatoly [anat.buchin at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 143004, + "object_name": "Chloride regulation" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + }, + { + "object_id": 151948, + "object_name": "Potassium buffering" + }, + { + "object_id": 139148, + "object_name": "Brain Rhythms" + }, + { + "object_id": 84598, + "object_name": "Extracellular Fields" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 222821, + "object_name": "Buchin A, Chizhov A, Huberfeld G, Miles R, Gutkin BS (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 120799, + "object_name": "Glia" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Subiculum network model with dynamic chloride/potassium homeostasis (Buchin et al 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + }, + { + "object_id": 182866, + "object_name": "Hippocampus CA1 PV+ fast-firing interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 236, + "object_name": "Ions" + }, + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is the code implementing the single neuron and spiking neural network dynamics. The network has the dynamic ion concentrations of extracellular potassium and intracellular chloride. The code contains multiple parameter variations to study various mechanisms of the neural excitability in the context of chloride homeostasis." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206365", + "other_implementer": { + "attr_id": 308, + "value": "Anatoly Buchin" + }, + "other_simulator": { + "attr_id": 307, + "value": "Xppaut" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "anat.buchin@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Anatoly Buchin" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2019-03-22T16:02:44.157", + "ver_number": 6 + }, + "206372": { + "class_id": 19, + "created": "2016-11-03T09:25:02.787", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 206372, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206377, + "object_name": "Chavlis, Spyridon [schavlis at imbb.forth.gr]" + }, + { + "object_id": 206376, + "object_name": "Petrantonakis, Panagiotis C. 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The model consists of point neurons (Integrate and fire) and in principal neurons, the granule cells, we have incorporated various number of dendrites." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206372", + "other_implementer": { + "attr_id": 308, + "value": "Spyridon Chavlis, Panagiotis C. 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Despite the cortical ubiquity of FFI,\r\nits specific function is not completely understood. FFI in CA1PCs is mediated by\r\ntwo physiologically and morphologically distinct GABAergic interneurons: fast-spiking,\r\nperisomatic-targeting basket cells and regular-spiking, dendritic-targeting bistratified\r\ncells. These two FFI pathways might create layer-specific computational sub-domains\r\nwithin the same CA1PC, but teasing apart their specific contributions remains\r\nexperimentally challenging. We implemented a biophysically realistic model of CA1PCs\r\nusing 40 digitally reconstructed morphologies and constraining synaptic numbers,\r\nlocations, amplitude, and kinetics with available experimental data. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206378", + "public_submitter_email": { + "attr_id": 309, + "value": "michele.ferrante@nih.gov" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Michele Ferrante" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2017-01-19T18:06:47.523", + "ver_number": 15 + }, + "206380": { + "biosimulations": { + "value": "modeldb-206380" + }, + "class_id": 19, + "created": "2016-11-05T23:20:21.657", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 206380, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206387, + "object_name": "Morozova, Ekaterina O [emorozov at indiana.edu]" + }, + { + "object_id": 206388, + "object_name": "Kuznetsov, Alexey " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 242018, + "object_name": "Synaptic Convergence" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 206382, + "object_name": "Morozova EO, Myroshnychenko M, Zakharov D, di Volo M, Gutkin B, Lapish CC, Kuznetsov A (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Excitability of DA neurons and their regulation by synaptic input (Morozova et al. 2016a, 2016b)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 261, + "object_name": "Substantia nigra pars compacta DA cell" + }, + { + "object_id": 189152, + "object_name": "Ventral tegmental area dopamine neuron" + }, + { + "object_id": 206389, + "object_name": "Ventral tegmental area GABA neuron " + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This code contains conductance-based models of Dopaminergic (DA) and GABAergic neurons, used in Morozova et al 2016 PLOS Computational Biology paper in order to study the type of excitability of the DA neurons and how it is influenced by the intrinsic and synaptic currents. We identified the type of excitability by calculating bifurcation diagrams and F-I curves using XPP file. This model was also used in Morozova et al 2016 J. Neurophysiology paper in order to study the effect of synchronization in GABAergic inputs on the firing dynamics of the DA neuron." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206380", + "other_implementer": { + "attr_id": 308, + "value": "Ekaterina O Morozova,\r\nAlexey Kuznetsov" + }, + "other_neurons": { + "attr_id": 301, + "value": "Dopamine neuron, GABA neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "emorozov@indiana.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ekaterina O Morozova" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2018-06-06T17:26:07.06", + "ver_number": 17 + }, + "206397": { + "class_id": 19, + "created": "2016-11-16T03:37:19.693", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 206397, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206377, + "object_name": "Chavlis, Spyridon [schavlis at imbb.forth.gr]" + }, + { + "object_id": 206376, + "object_name": "Petrantonakis, Panagiotis C. 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Here, we have used two-photon Ca2+ imaging to monitor the activity of mossy cells in awake, behaving mice. We find that mossy cells are significantly more active than dentate granule cells in vivo, exhibit significant spatial tuning during head-fixed spatial navigation, and undergo robust remapping of their spatial representations in response to contextual manipulation. Our results provide the first characterization of mossy cells in the behaving animal and demonstrate their active participation in spatial coding and contextual representation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206397", + "other_implementer": { + "attr_id": 308, + "value": "Panagiotis Petrantonakis\r\nPanayiota Poirazi" + }, + "other_type": { + "attr_id": 305, + "value": "Point Neuronal Network" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "schavlis@imbb.forth.gr" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Spyridon Chavlis" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115947, + "object_name": "Dentate gyrus" + } + ] + }, + "ver_date": "2017-07-28T13:53:32.9", + "ver_number": 19 + }, + "206398": { + "class_id": 19, + "created": "2016-11-16T12:10:34.35", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 206398, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206403, + "object_name": "Michalikova, Martina [tinka.michalikova at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 64175, + "object_name": "Electrotonus" + }, + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 243949, + "object_name": "Michalikova M, Remme MW, Kempter R (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Spikelet generation and AP initiation in a L5 neocortical pyr neuron (Michalikova et al. 2017) Fig 1", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The article by Michalikova et al. (2017) explores the generation of spikelets in cortical pyramidal neurons. The model cell, adapted from Hu et al. (2009), is a layer V pyramidal neuron. The cell is stimulated by fluctuating synaptic inputs and generates somatic APs and spikelets in response. The spikelets are initiated as APs at the AIS that do not activate the soma." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206398", + "other_concept": { + "attr_id": 306, + "value": "Spikelet" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tinka.michalikova@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Martina Michalikova" + }, + "ver_date": "2023-01-27T13:18:29.07", + "ver_number": 10 + }, + "206400": { + "class_id": 19, + "created": "2016-11-18T13:12:13.26", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 206400, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206403, + "object_name": "Michalikova, Martina [tinka.michalikova at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 64175, + "object_name": "Electrotonus" + }, + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 243949, + "object_name": "Michalikova M, Remme MW, Kempter R (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Spikelet generation and AP initiation in a simplified pyr neuron (Michalikova et al. 2017) Fig 3", + "notes": { + "attr_id": 24, + "value": "The article by Michalikova et al. (2017) explores the generation of spikelets in cortical pyramidal neurons.\r\nThis package contains code for simulating the model with simplified morphology shown in Figs 3 and S2." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206400", + "other_implementer": { + "attr_id": 308, + "value": "Michalikova, Martina" + }, + "other_simulator": { + "attr_id": 307, + "value": "NEURON with Python" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tinka.michalikova@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Martina Michalikova" + }, + "ver_date": "2023-01-30T13:52:29.947", + "ver_number": 10 + }, + "206405": { + "class_id": 19, + "created": "2016-11-19T13:17:56.537", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 206405, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 102284, + "object_name": "Crisp, Kevin [crisp at stolaf.edu]" + }, + { + "object_id": 206409, + "object_name": "Heller, Charles [hellerc at ohsu.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 206406, + "object_name": "Heller C, Crisp K (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Earthworm medial giant fiber conduction velocity across electrical synapses (Heller, Crisp 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + }, + { + "object_id": 206408, + "object_name": "Earthworm medial giant fiber" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The earthworm medial giant fiber (MGF) is composed of many neurons electrically coupled by high fidelity gap junctions. In addition, the MGF exhibits a distinct taper in diameter from anterior to posterior. The role of these gap junctions and their interaction with axonal taper in predicting conduction velocity has not been studied closely in the annelid. A model of an electrical synapse in the MGF was created to investigate the influence of, and interaction between, these two parameters." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/206405", + "other_implementer": { + "attr_id": 308, + "value": "Charles Heller" + }, + "other_neurons": { + "attr_id": 301, + "value": "Earthworm medial giant fiber" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "hellerc@ohsu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Charles Heller" + }, + "ver_date": "2018-11-09T14:41:49.873", + "ver_number": 7 + }, + "207695": { + "class_id": 19, + "created": "2016-11-30T07:26:58.203", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 88209, + "object_name": "Na/Ca exchanger" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 207695, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 185339, + "object_name": "Zylbertal, Asaph [asaph.zylbertal at mail.huji.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 185343, + "object_name": "Persistent activity" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 224892, + "object_name": "Zylbertal A, Yarom Y, Wagner S (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "A network of AOB mitral cells that produces infra-slow bursting (Zylbertal et al. 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 185340, + "object_name": "Olfactory bulb (accessory) mitral cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Infra-slow rhythmic neuronal activity with very long (> 10 s) period duration was described in many brain areas but little is known about the role of this activity and the mechanisms that produce it. Here we combine experimental and computational methods to show that synchronous infra-slow bursting activity in mitral cells of the mouse accessory olfactory bulb (AOB) emerges from interplay between intracellular dynamics and network connectivity. In this novel mechanism, slow intracellular Na+ dynamics endow AOB mitral cells with a weak tendency to burst, which is further enhanced and stabilized by chemical and electrical synapses between them. Combined with the unique topology of the AOB network, infra-slow bursting enables integration and binding of multiple chemosensory stimuli over prolonged time scale.\r\nThe example protocol simulates a two-glomeruli network with a single shared cell. Although each glomerulus is stimulated at a different time point, the activity of the entire population becomes synchronous (see paper Fig. 8)" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/207695", + "public_submitter_email": { + "attr_id": 309, + "value": "asaph.zylbertal@mail.huji.ac.il" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Asaph Zylbertal" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "ver_date": "2018-10-17T16:19:34.05", + "ver_number": 11 + }, + "217783": { + "class_id": 19, + "created": "2016-12-12T07:44:44.433", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 112843, + "object_name": "I R" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 217783, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 217881, + "object_name": "O'Connor, Simon [simon.oconnor at btinternet.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 217790, + "object_name": "Gorin M, Tsitoura C, Kahan A, Watznauer K, Drose DR, Arts M, Mathar R, O'Connor S, Hanganu-Opatz IL, Ben-Shaul Y, Spehr M (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Infraslow intrinsic rhythmogenesis in a subset of AOB projection neurons (Gorin et al 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 185340, + "object_name": "Olfactory bulb (accessory) mitral cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We investigated patterns of spontaneous neuronal activity in mouse accessory olfactory bulb mitral cells, the direct neural link between vomeronasal sensory input and limbic output. Both in vitro and in vivo, we identify a subpopulation of mitral cells that exhibit slow stereotypical rhythmic discharge. In intrinsically rhythmogenic neurons, these periodic activity patterns are maintained in absence of fast synaptic drive. The physiological mechanism underlying mitral cell autorhythmicity involves cyclic activation of three interdependent ionic conductances: subthreshold persistent Na(+) current, R-type Ca(2+) current, and Ca(2+)-activated big conductance K(+) current. Together, the interplay of these distinct conductances triggers infraslow intrinsic oscillations with remarkable periodicity, a default output state likely to affect sensory processing in limbic circuits. The model reproduces the intrinsic firing in a reconstructed single AOB mitral cell with ion channels kinetics fitted to experimental measurements of their steady state and time course." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/217783", + "other_currents": { + "attr_id": 303, + "value": "Potassium = Kdr + KA, K,Ca = BK Calcium = R type" + }, + "other_implementer": { + "attr_id": 308, + "value": "O'Connor, Simon" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "simon.oconnor@btinternet.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Simon O'Connor" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "ver_date": "2018-10-17T16:19:52.197", + "ver_number": 11 + }, + "217882": { + "class_id": 19, + "created": "2016-12-13T11:08:38.347", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 217882, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 217889, + "object_name": "Lombardo, Joseph [josslomb at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 196124, + "object_name": "Lombardo J, Harrington MA (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Regulation of motoneuron excitability by KCNQ/Kv7 modulators (Lombardo & Harrington 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 276, + "object_name": "Spinal cord lumbar motor neuron alpha ACh cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Computer simulations confirmed that pharmacological enhancement\r\nof KCNQ/Kv7 channel (M current) activity decreases excitability and\r\nalso suggested that the effects of inhibition of KCNQ/Kv7 channels on\r\nthe excitability of spinal MNs do not depend on a direct effect in\r\nthese neurons but likely on spinal cord synaptic partners. These\r\nresults indicate that KCNQ/Kv7 channels have a fundamental role in the\r\nmodulation of the excitability of spinal MNs acting both in these\r\nneurons and in their local presynaptic partners. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/217882", + "public_submitter_email": { + "attr_id": 309, + "value": "josslomb@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Joseph Lombardo" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115954, + "object_name": "Spinal motoneuron" + } + ] + }, + "ver_date": "2022-11-04T13:07:20.423", + "ver_number": 14 + }, + "217958": { + "class_id": 19, + "created": "2016-12-15T11:50:44.347", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 217958, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 217959, + "object_name": "Lindsay, Grace " + }, + { + "object_id": 217960, + "object_name": "Miller, Kenneth " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 217888, + "object_name": "Kuchibhotla KV, Gill JV, Lindsay GW, Papadoyannis ES, Field RE, Sten TA, Miller KD, Froemke RC (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Parallel cortical inhibition processing enables context-dependent behavior (Kuchibhotla et al. 2016)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Physical features of sensory stimuli are fixed, but sensory perception is context dependent. The precise mechanisms that\r\ngovern contextual modulation remain unknown. Here, we trained mice to switch between two contexts: passively listening to pure tones and performing a recognition task for the same stimuli. Two-photon imaging showed that many excitatory neurons in auditory cortex were suppressed during behavior, while some cells became more active. Whole-cell recordings showed that excitatory inputs were affected only modestly by context, but inhibition was more sensitive, with PV+, SOM+, and VIP+ interneurons balancing inhibition and disinhibition within the network. Cholinergic modulation was involved in context switching, with cholinergic axons increasing activity during behavior and directly depolarizing inhibitory cells. Network modeling captured these findings, but only when modulation coincidently drove all three interneuron subtypes, ruling out either inhibition or disinhibition alone as sole mechanism for active engagement. Parallel processing of cholinergic modulation by cortical interneurons therefore enables context-dependent behavior." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/217958", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Rob Fromeke" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 144485, + "object_name": "Auditory cortex" + } + ] + }, + "ver_date": "2018-10-18T17:11:54.17", + "ver_number": 4 + }, + "218015": { + "class_id": 19, + "created": "2016-12-19T15:42:39.113", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 218015, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 218014, + "object_name": "Chen, Chun-Chung [cjj at u.washington.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 217964, + "object_name": "Huang CH, Huang YT, Chen CC, Chan CK (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Reverberatory bursts propagation and synchronization in developing cultured NNs (Huang et al 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 150222, + "object_name": "Abstract Morris-Lecar neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Developing networks of neural systems can exhibit spontaneous,\r\nsynchronous activities called neural bursts, which can be important in\r\nthe organization of functional neural circuits.\r\n\r\n...\r\n\r\nUsing a propagation model we infer the spreading\r\nspeed of the spiking activity, which increases as the culture ages.\r\n\r\nWe\r\nperform computer simulations of the system using a physiological model\r\nof spiking networks in two spatial dimensions and find the parameters\r\nthat reproduce the observed resynchronization of spiking in the\r\nbursts.\r\n\r\nAn analysis of the simulated dynamics suggests that the\r\ndepletion of synaptic resources causes the resynchronization.\r\n\r\nThe spatial propagation dynamics of the simulations match well with\r\nobservations over the course of a burst and point to an interplay of\r\nthe synaptic efficacy and the noisy neural self-activation in\r\nproducing the morphology of the bursts.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/218015", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "ver_date": "2016-12-19T15:49:21.553", + "ver_number": 4 + }, + "218084": { + "class_id": 19, + "created": "2016-12-20T15:35:12.217", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 218084, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 223030, + "object_name": "Whittington, James C.R. [jcrwhittington at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 180821, + "object_name": "Hebbian plasticity" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 222958, + "object_name": "Whittington JCR, Bogacz R (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 223029, + "object_name": "Predictive Coding Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Supervised learning with predictive coding (Whittington & Bogacz 2017)", + "notes": { + "attr_id": 24, + "value": "\"To effciently learn from feedback, cortical networks need to update synaptic weights\r\non multiple levels of cortical hierarchy. An effective and well-known algorithm for\r\ncomputing such changes in synaptic weights is the error back-propagation algorithm. However, in the back-propagation algorithm, the change in synaptic weights\r\nis a complex function of weights and activities of neurons not directly connected\r\nwith the synapse being modified, whereas the changes in biological synapses are\r\ndetermined only by the activity of pre-synaptic and post-synaptic neurons. Several\r\nmodels have been proposed that approximate the back-propagation algorithm with\r\nlocal synaptic plasticity, but these models require complex external control over the\r\nnetwork or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning\r\nfully autonomously, employing only simple local Hebbian plasticity. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/218084", + "public_submitter_email": { + "attr_id": 309, + "value": "jcrwhittington@googlemail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "James Whittington" + }, + "ver_date": "2017-01-16T14:37:57.877", + "ver_number": 9 + }, + "218085": { + "class_id": 19, + "created": "2016-12-21T06:38:07.267", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 218085, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 183714, + "object_name": "Bush, Daniel [drdanielbush @ gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 194888, + "object_name": "Grid cell" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 194944, + "object_name": "Bush D, Burgess N (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Hybrid oscillatory interference / continuous attractor NN of grid cell firing (Bush & Burgess 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 155861, + "object_name": "Abstract integrate-and-fire fractional leaky neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Matlab code to simulate a hybrid oscillatory interference - continuous attractor network model of grid cell firing in pyramidal and stellate cells of rodent medial entorhinal cortex" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/218085", + "public_submitter_email": { + "attr_id": 309, + "value": "drdanielbush@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Daniel Bush" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 119191, + "object_name": "Entorhinal cortex" + } + ] + }, + "ver_date": "2016-12-22T09:46:16.69", + "ver_number": 7 + }, + "222321": { + "class_id": 19, + "created": "2016-12-29T11:23:32.54", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 146505, + "object_name": "KCC2" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 222321, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 222320, + "object_name": "Gentiletti, Damiano [gentiletti.damiano at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 218087, + "object_name": "Gentiletti D, Suffczynski P, Gnatkovsky V, de Curtis M (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Changes of ionic concentrations during seizure transitions (Gentiletti et al. 2016)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 236, + "object_name": "Ions" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... In order to\r\ninvestigate the respective roles of synaptic interactions and\r\nnonsynaptic mechanisms in seizure transitions, we developed a\r\ncomputational model of hippocampal cells, involving the extracellular\r\nspace, realistic dynamics of Na+, K+, Ca2+ and Cl - ions, glial uptake\r\nand extracellular diffusion mechanisms. We show that the network\r\nbehavior with fixed ionic concentrations may be quite different from\r\nthe neurons\u2019 behavior when more detailed modeling of ionic dynamics is\r\nincluded. In particular, we show that in the extended model strong\r\ndischarge of inhibitory interneurons may result in long lasting\r\naccumulation of extracellular K+, which sustains the depolarization of\r\nthe principal cells and causes their pathological discharges.\r\n...\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/222321", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 119191, + "object_name": "Entorhinal cortex" + } + ] + }, + "ver_date": "2016-12-29T13:48:52.81", + "ver_number": 3 + }, + "222359": { + "class_id": 19, + "created": "2017-01-03T10:21:24.75", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 222359, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 222436, + "object_name": "Justus, Daniel [daniel.justus at dzne.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 222365, + "object_name": "Justus D, Dal\u00fcgge D, Bothe S, Fuhrmann F, Hannes C, Kaneko H, Friedrichs D, Sosulina L, Schwarz I, Elliott DA, Schoch S, Bradke F, Schwarz MK, Remy S (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Synaptic integration by MEC neurons (Justus et al. 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 222437, + "object_name": "Entorhinal cortex pyramidal cell" + }, + { + "object_id": 129068, + "object_name": "Entorhinal cortex stellate cell" + }, + { + "object_id": 222438, + "object_name": "Entorhinal cortex fast-spiking interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Pyramidal cells, stellate cells and fast-spiking interneurons receive running speed dependent glutamatergic input from septo-entorhinal projections. These models simulate the integration of this input by the different MEC celltypes." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/222359", + "other_implementer": { + "attr_id": 308, + "value": "Justus, Daniel [daniel.justus@dzne.de]" + }, + "other_neurons": { + "attr_id": 301, + "value": "Entorhinal cortex cells" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "daniel.justus@dzne.de" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Daniel Justus" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 119191, + "object_name": "Entorhinal cortex" + } + ] + }, + "ver_date": "2017-01-11T09:27:35.41", + "ver_number": 8 + }, + "222715": { + "class_id": 19, + "created": "2017-01-04T15:08:22.097", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 222715, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 149163, + "object_name": "Zhang, Yili [Yili.Zhang at uth.tmc.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 222488, + "object_name": "Zhang Y, Smolen P, Alberini CM, Baxter DA, Byrne JH (2016)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Model of a BDNF feedback loop (Zhang et al 2016)", + "notes": { + "attr_id": 24, + "value": "\"Inhibitory avoidance (IA) training in rodents initiates a molecular\r\ncascade within hippocampal neurons. This cascade contributes to the\r\ntransition of short- to long-term memory (i.e., consolidation). Here,\r\na differential equation-based model was developed to describe a\r\npositive feedback loop within this molecular cascade. The feedback\r\nloop begins with an IA-induced release of brain-derived neurotrophic\r\nfactor (BDNF), which in turn leads to rapid phosphorylation of the\r\ncAMP response element-binding protein (pCREB), and a subsequent\r\nincrease in the level of the beta isoform of the CCAAT/enhancer binding\r\nprotein (C/EBPbeta). \r\n ... \" See paper for more." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/222715", + "public_submitter_email": { + "attr_id": 309, + "value": "Yili.zhang@uth.tmc.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Yili Zhang" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2017-01-05T15:55:50.39", + "ver_number": 11 + }, + "222716": { + "class_id": 19, + "created": "2017-01-04T17:49:18.63", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 222730, + "object_name": "I Na, slow inactivation" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 222716, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 222718, + "object_name": "Menon, Vilas [vilasmenon2008 at u dot northwestern dot edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 238957, + "object_name": "Markov-type model" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 124155, + "object_name": "Menon V, Spruston N, Kath WL (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Hippocampus CA1 pyramidal model with Na channel exhibiting slow inactivation (Menon et al. 2009)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "These NEURON simulations show the effect of prolonged inactivation of sodium channels on attenuation of trains of backpropagating action potentials (bAPs). The new sodium channel model is a Markov model derived using a state-mutating genetic algorithm, as described in the paper.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/222716", + "other_currents": { + "attr_id": 303, + "value": "I Na, slow inactivation" + }, + "other_implementer": { + "attr_id": 308, + "value": "Menon, Vilas [vilasmenon2008 at u dot northwestern dot edu]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "vilasmenon2008@u.northwestern.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Vilas Menon" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2018-02-26T11:50:06.003", + "ver_number": 13 + }, + "222725": { + "class_id": 19, + "created": "2017-01-05T17:31:50.187", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 222725, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 186894, + "object_name": "Tikidji-Hamburyan, Ruben [ruben.tikidji.hamburyan at gmail.com] " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 230892, + "object_name": "Tikidji-Hamburyan RA, Narayana V, Bozkus Z, El-Ghazawi TA (2017)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 2832, + "object_name": "NEST" + }, + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 1883, + "object_name": "GENESIS" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Brain networks simulators - a comparative study (Tikidji-Hamburyan et al 2017)", + "notes": { + "attr_id": 24, + "value": "\" ... In this article, we select the three most popular simulators, as determined by the number of models in the ModelDB database, such as NEURON, GENESIS, and BRIAN, and perform an independent evaluation of these simulators. In addition, we study NEST, one of the lead simulators of the Human Brain Project. First, we study them based on one of the most important characteristics, the range of supported models. Our investigation reveals that brain network simulators may be biased toward supporting a specific set of models. ... we carry out an evaluation using two case studies: a large network with simplified neural and synaptic models and a small network with detailed models. These two case studies allow us to avoid any bias toward a particular software package ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/222725", + "public_submitter_email": { + "attr_id": 309, + "value": "ruben.tikidji.hamburyan@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ruben Tikidji-Hamburyan" + }, + "ver_date": "2018-02-07T13:43:43.657", + "ver_number": 21 + }, + "222726": { + "class_id": 19, + "created": "2017-01-06T14:37:54.537", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 222730, + "object_name": "I Na, slow inactivation" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 222726, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 222718, + "object_name": "Menon, Vilas [vilasmenon2008 at u dot northwestern dot edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 222728, + "object_name": "Menon V, Musial TF, Liu A, Katz Y, Kath WL, Spruston N, Nicholson DA (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Distance-dependent synaptic strength in CA1 pyramidal neurons (Menon et al. 2013)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Menon et al. (2013) describes the experimentally-observed variation in synaptic AMPA and NMDA conductance as a function of distance from the soma. This model explores the effect of this variation on somatic EPSPs and dendritic spike initiation, as compared to the case of uniform AMPA and NMDA conductance." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/222726", + "other_currents": { + "attr_id": 303, + "value": "I Na, slow inactivating" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "vilasmenon2008@u.northwestern.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Vilas Menon" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2017-01-06T17:00:00.93", + "ver_number": 5 + }, + "222732": { + "class_id": 19, + "created": "2017-01-10T02:00:08.143", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 222732, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 150637, + "object_name": "Hepburn, Iain [ihepburn at oist.jp]" + }, + { + "object_id": 222733, + "object_name": "Jain, Anant [anantdavinci at gmail.com]" + }, + { + "object_id": 153090, + "object_name": "Gangal, Himanshu [himanshugangal at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 222735, + "object_name": "Hepburn I, Jain A, Gangal H, Yamamoto Y, Tanaka-Yamamoto K, De Schutter E (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 144556, + "object_name": "STEPS" + } + ] + }, + "name": "A model of cerebellar LTD including RKIP inactivation of Raf and MEK (Hepburn et al 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "An updated stochastic model of cerebellar Long Term Depression (LTD) with improved realism. 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The model is tuned to reproduce experimental calcium peak vs LTD amplitude curves accurately at 4 different calcium pulse durations." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/222732", + "public_submitter_email": { + "attr_id": 309, + "value": "ihepburn@oist.jp" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Iain Hepburn" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2017-01-26T22:32:30.717", + "ver_number": 10 + }, + "222932": { + "class_id": 19, + "created": "2017-01-13T10:48:25.333", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 222932, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 222955, + "object_name": "Pedrosa, Victor [v.pedrosa15 at imperial.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 222956, + "object_name": "Neuromodulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 222933, + "object_name": "Pedrosa V, Clopath C (2017)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "A simple model of neuromodulatory state-dependent synaptic plasticity (Pedrosa and Clopath, 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 155861, + "object_name": "Abstract integrate-and-fire fractional leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model is used to illustrate the role of neuromodulators in cortical plasticity. 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We then compare the neuromodulatory effects of upregulating learning rate versus the effects of upregulating neuronal activity. \"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/222932", + "other_concept": { + "attr_id": 306, + "value": "Neuromodulation" + }, + "other_implementer": { + "attr_id": 308, + "value": "Pedrosa, Victor [v.pedrosa15@imperial.ac.uk]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "victorpedrosabc@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Victor Pedrosa" + }, + "ver_date": "2017-01-13T13:18:26.613", + "ver_number": 4 + }, + "223031": { + "class_id": 19, + "created": "2017-01-16T16:26:53.493", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 247, + "object_name": "I A" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122817, + "object_name": "Kv3.1 KCNC1" + }, + { + "object_id": 122816, + "object_name": "Kv2.1 KCNB1" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "icg_channel_details": { + "attr_id": 640, + "value": [ + { + "object_id": 223034, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223046, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223047, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223049, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223055, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223035, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223038, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223039, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223042, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223045, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223050, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223051, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223056, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223037, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223053, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223040, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223057, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223043, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223036, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223052, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223033, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223044, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223054, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223041, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223032, + "object_name": "ICG Detail_223031" + }, + { + "object_id": 223048, + "object_name": "ICG Detail_223031" + } + ] + }, + "id": 223031, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 223060, + "object_name": "Guet-McCreight, Alexandre [alexandre.guet.mccreight at mail.utoronto.ca]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 121254, + "object_name": "Depolarization block" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 223059, + "object_name": "Guet-McCreight A, Camir\u00e9 O, Topolnik L, Skinner FK (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Interneuron Specific 3 Interneuron Model (Guet-McCreight et al, 2016)", + "notes": { + "attr_id": 24, + "value": "In this paper we develop morphologically detailed multi-compartment models of Hippocampal CA1 interneuron specific 3 interneurons using cell current-clamp recordings and dendritic calcium imaging data. In doing so, we developed several variant models, as outlined in the associated README.html file." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/223031", + "other_currents": { + "attr_id": 303, + "value": "I Kdrs, I Kdrf" + }, + "other_gene": { + "attr_id": 477, + "value": "Kv3.2 KCNC2" + }, + "other_neurons": { + "attr_id": 301, + "value": "Hippocampus CA1 Interneuron Specific 3 Interneuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "alexandre.guet.mccreight@mail.utoronto.ca" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Alexandre Guet-McCreight" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2022-05-27T12:28:50.047", + "ver_number": 21 + }, + "223144": { + "class_id": 19, + "created": "2017-01-18T16:40:42.4", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 223144, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 223270, + "object_name": "Manninen, Tiina [tiina.h.manninen at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 112550, + "object_name": "Lavrentovich M, Hemkin S (2008)" + }, + { + "object_id": 223271, + "object_name": "Lavrentovich M, Hemkin S (2009)" + }, + { + "object_id": 223272, + "object_name": "Manninen T, Havela R, Linne ML (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 120799, + "object_name": "Glia" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Ca2+ oscillations in single astrocytes (Lavrentovich and Hemkin 2008) (python) (Manninen et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 87589, + "object_name": "Astrocyte" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We tested the reproducibility and comparability of four astrocyte models (Manninen, Havela, Linne, 2017). Model by Lavrentovich and Hemkin (2008) was one of them. We implemented and ran the model by Lavrentovich and Hemkin (2008) using Jupyter Notebook. Model code produces results of Figure 1 in Manninen, Havela, Linne (2017)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/223144", + "other_implementer": { + "attr_id": 308, + "value": "Manninen, Tiina [tiina.h.manninen at gmail.com]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tiina.h.manninen@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tiina Manninen" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 153715, + "object_name": "IP3" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115960, + "object_name": "Generic" + } + ] + }, + "ver_date": "2017-01-25T11:28:47.927", + "ver_number": 13 + }, + "223269": { + "class_id": 19, + "created": "2017-01-18T18:30:14.947", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 223269, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 223270, + "object_name": "Manninen, Tiina [tiina.h.manninen at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 223096, + "object_name": "De Pitt\u00e0 M, Goldberg M, Volman V, Berry H, Ben-Jacob E (2009)" + }, + { + "object_id": 223272, + "object_name": "Manninen T, Havela R, Linne ML (2017)" + }, + { + "object_id": 223400, + "object_name": "De Pitt\u00e0 M, Goldberg M, Volman V, Berry H, Ben-Jacob E (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 120799, + "object_name": "Glia" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Glutamate-evoked Ca2+ oscillations in single astrocytes (De Pitta et al. 2009) (Manninen et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 87589, + "object_name": "Astrocyte" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We tested the reproducibility and comparability of four astrocyte models (Manninen, Havela, Linne, 2017). Model by De Pitta et al. (2009) was one of them. We implemented and ran the model by De Pitta et al. (2009) using Jupyter Notebook. Model code produces results of Figure 1 and Figures 3-5 in Manninen, Havela, Linne (2017)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/223269", + "other_implementer": { + "attr_id": 308, + "value": "Manninen, Tiina [tiina.h.manninen at gmail.com]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tiina.h.manninen@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tiina Manninen" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 153715, + "object_name": "IP3" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115960, + "object_name": "Generic" + } + ] + }, + "ver_date": "2017-01-25T11:26:45.393", + "ver_number": 10 + }, + "223273": { + "class_id": 19, + "created": "2017-01-19T12:01:31.83", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 223273, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 223270, + "object_name": "Manninen, Tiina [tiina.h.manninen at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 223272, + "object_name": "Manninen T, Havela R, Linne ML (2017)" + }, + { + "object_id": 223401, + "object_name": "Riera J, Hatanaka R, Ozaki T, Kawashima R (2011)" + }, + { + "object_id": 223402, + "object_name": "Riera J, Hatanaka R, Uchida T, Ozaki T, Kawashima R (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 120799, + "object_name": "Glia" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Spontaneous calcium oscillations in single astrocytes (Riera et al. 2011) (Manninen et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 87589, + "object_name": "Astrocyte" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We tested the reproducibility and comparability of four astrocyte models (Manninen, Havela, Linne, 2017). Model by Riera et al. (2011) was one of them. We implemented and ran the model by Riera et al. (2011) using Jupyter Notebook. Model codes produce results of Figures 1 and 2 in Manninen, Havela, Linne (2017)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/223273", + "public_submitter_email": { + "attr_id": 309, + "value": "tiina.h.manninen@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tiina Manninen" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 153715, + "object_name": "IP3" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2017-01-25T11:16:24.747", + "ver_number": 9 + }, + "223274": { + "class_id": 19, + "created": "2017-01-19T12:44:24.31", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 223274, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 223270, + "object_name": "Manninen, Tiina [tiina.h.manninen at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 223272, + "object_name": "Manninen T, Havela R, Linne ML (2017)" + }, + { + "object_id": 223485, + "object_name": "Dupont G, Lokenye EF, Challiss RA (2011)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 120799, + "object_name": "Glia" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Glutamate-evoked Ca2+ oscillations in single astrocytes (Modified from Dupont et al. 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 87589, + "object_name": "Astrocyte" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We tested the reproducibility and comparability of four astrocyte models (Manninen, Havela, Linne, 2017). Model by Dupont et al. (2011) was one of them, but we had to modify the model to get more similar results as in the original publication. We implemented and ran the modified model using Jupyter Notebook. Model code produces results of Figure 1 and Figures 3-5 in Manninen, Havela, Linne (2017)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/223274", + "public_submitter_email": { + "attr_id": 309, + "value": "tiina.h.manninen@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tiina Manninen" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 153715, + "object_name": "IP3" + }, + { + "object_id": 188, + "object_name": "mGluR5" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115960, + "object_name": "Generic" + } + ] + }, + "ver_date": "2017-01-25T11:45:06.227", + "ver_number": 11 + }, + "223648": { + "class_id": 19, + "created": "2017-01-20T18:16:01.533", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 223648, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 223270, + "object_name": "Manninen, Tiina [tiina.h.manninen at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 223272, + "object_name": "Manninen T, Havela R, Linne ML (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 120799, + "object_name": "Glia" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Reproducibility and comparability of models for astrocyte Ca2+ excitability (Manninen et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 87589, + "object_name": "Astrocyte" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We tested the reproducibility and comparability of four astrocyte models (Manninen, Havela, Linne, 2017). We implemented and ran the python models using Jupyter Notebook. Model code produces results of Figure 1 and Figures 3-5 and partly Figure 2 in Manninen, Havela, Linne (2017)." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/223648", + "public_submitter_email": { + "attr_id": 309, + "value": "tiina.h.manninen@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tiina Manninen" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 188, + "object_name": "mGluR5" + }, + { + "object_id": 153715, + "object_name": "IP3" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115960, + "object_name": "Generic" + } + ] + }, + "ver_date": "2017-01-25T13:41:30.88", + "ver_number": 10 + }, + "223649": { + "class_id": 19, + "created": "2017-01-24T12:13:31.987", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 223649, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 223247, + "object_name": "Alturki A, Feng F, Nair A, Guntu V, Nair SS (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Distinct current modules shape cellular dynamics in model neurons (Alturki et al 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 156124, + "object_name": "Abstract single compartment conductance based cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... We hypothesized that currents are grouped into distinct\r\nmodules that shape specific neuronal characteristics or signatures,\r\nsuch as resting potential, sub-threshold oscillations,\r\nand spiking waveforms, for several classes of\r\nneurons. For such a grouping to occur, the currents within\r\none module should have minimal functional interference\r\nwith currents belonging to other modules. This condition\r\nis satisfied if the gating functions of currents in the same\r\nmodule are grouped together on the voltage axis; in contrast,\r\nsuch functions are segregated along the voltage axis\r\nfor currents belonging to different modules. We tested this\r\nhypothesis using four published example case models and\r\nfound it to be valid for these classes of neurons. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/223649", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + }, + { + "object_id": 142628, + "object_name": "Amygdala" + } + ] + }, + "ver_date": "2018-10-17T16:20:09.34", + "ver_number": 9 + }, + "223890": { + "class_id": 19, + "created": "2017-01-26T18:27:23.15", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 223890, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 116907, + "object_name": "Direction Selectivity" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 223826, + "object_name": "Ding H, Smith RG, Poleg-Polsky A, Diamond JS, Briggman KL (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 223952, + "object_name": "NeuronC (web link to model)" + } + ] + }, + "name": "Species-specific wiring for direction selectivity in the mammalian retina (Ding et al 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 223953, + "object_name": "Retina amacrine cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Here we present a detailed connectomic reconstruction\r\nof SAC circuitry in mouse retina and describe two previously unknown features of synapse distributions along SAC\r\ndendrites: input and output synapses are segregated, with inputs restricted to proximal dendrites; and the distribution\r\nof inhibitory inputs is fundamentally different from that observed in rabbit retina. An anatomically constrained SAC\r\nnetwork model suggests that SAC\u2013SAC wiring differences between mouse and rabbit retina underlie distinct contributions\r\nof synaptic inhibition to velocity and contrast tuning and receptive field structure. In particular, the model indicates\r\nthat mouse connectivity enables SACs to encode lower linear velocities that account for smaller eye diameter, thereby\r\nconserving angular velocity tuning. These predictions are confirmed with calcium imaging of mouse SAC dendrites\r\nresponding to directional stimuli. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/223890", + "other_neurons": { + "attr_id": 301, + "value": "starburst amacrine" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 118529, + "object_name": "Retina" + } + ] + }, + "ver_date": "2017-02-09T14:05:57.243", + "ver_number": 4 + }, + "223891": { + "class_id": 19, + "created": "2017-01-26T21:57:48.733", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 223891, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 223950, + "object_name": "Connelly, William [connelly.bill at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 223892, + "object_name": "Connelly WM, Crunelli V, Errington AC (2016)" + }, + { + "object_id": 223898, + "object_name": "Connelly WM, Crunelli V, Errington AC (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Rat LGN Thalamocortical Neuron (Connelly et al 2015, 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Here, combining data from\r\nfluorescence-targeted dendritic recordings and Ca2+ imaging from\r\nlow-threshold spiking cells in rat brain slices with computational\r\nmodeling, the cellular mechanism responsible for LTS (Low Threshold Spike) generation is\r\nestablished. ...\" \" ... Using dendritic recording, 2-photon glutamate uncaging, and\r\ncomputational modeling, we investigated how rat dorsal lateral\r\ngeniculate nucleus thalamocortical neurons integrate excitatory\r\ncorticothalamic feedback. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/223891", + "other_implementer": { + "attr_id": 308, + "value": "Connelly, William" + }, + "other_neurons": { + "attr_id": 301, + "value": "Thalamocortical Neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "connelly.bill@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "William Connelly" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2020-10-07T12:44:46.583", + "ver_number": 8 + }, + "223955": { + "class_id": 19, + "created": "2017-01-28T18:51:15.71", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92083, + "object_name": "Nav1.2 SCN2A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 223955, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 224842, + "object_name": "Ben-Shalom, Roy [rbenshalom at ucdavis.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 239754, + "object_name": "Autism spectrum disorder" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 223956, + "object_name": "Ben-Shalom R, Keeshen CM, Berrios KN, An JY, Sanders SJ, Bender KJ (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Pyramidal neurons with mutated SCN2A gene (Nav1.2) (Ben-Shalom et al 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Model of pyramidal neurons that either hyper or hypo excitable due to SCN2A mutations. Mutations are taken from patients with ASD or Epilepsy " + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/223955", + "public_submitter_email": { + "attr_id": 309, + "value": "bens.roy@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Roy Ben-Shalom" + }, + "ver_date": "2018-04-19T15:22:52.72", + "ver_number": 11 + }, + "223962": { + "class_id": 19, + "created": "2017-01-29T15:57:56.42", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 223962, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 82787, + "object_name": "Working memory" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 224503, + "object_name": "Spera E, Migliore M, Unsworth N, Tegolo D (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Model of CA1 activity during working memory task (Spera et al. 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The cellular processes underlying individual differences in the Woring Memory Capacity (WMC) of humans are essentially unknown. Psychological experiments suggest that subjects with lower working memory capacity (LWMC), with respect to subjects with higher capacity (HWMC), take more time to recall items from a list because they search through a larger set of items and are much more susceptible to interference during retrieval. ... In this paper, we investigate the possible underlying mechanisms at the single neuron level by using a computational model of hippocampal CA1 pyramidal neurons, which have been suggested to be deeply involved in the recognition of specific items. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/223962", + "public_submitter_email": { + "attr_id": 309, + "value": "spera@dmi.unict.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Emiliano Spera" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2017-02-06T09:22:12.1", + "ver_number": 6 + }, + "224843": { + "class_id": 19, + "created": "2017-02-03T17:12:51.83", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 224843, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 224844, + "object_name": "Yuan, Yi [yuanyi513 at ysu.edu.cn]" + }, + { + "object_id": 224845, + "object_name": "Pang, Na" + }, + { + "object_id": 224846, + "object_name": "Chen, Yudong " + }, + { + "object_id": 224847, + "object_name": "Wang, Yi " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 84598, + "object_name": "Extracellular Fields" + }, + { + "object_id": 127084, + "object_name": "Locking, mixed mode" + }, + { + "object_id": 138323, + "object_name": "Magnetic stimulation" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 224839, + "object_name": "Yuan Y, Pang N, Chen Y, Wang Y, Li X (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Phase-locking analysis with transcranial magneto-acoustical stimulation (Yuan et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Transcranial magneto-acoustical stimulation (TMAS) uses ultrasonic waves and a static magnetic field to generate electric current in nerve tissues for the purpose of modulating neuronal activities. It has the advantage of high spatial resolution and penetration depth. Neuronal firing rhythms carry and transmit nerve information in neural systems. In this study, we investigated the phase-locking characteristics of neuronal firing rhythms with TMAS based on the Hodgkin-Huxley neuron model. The simulation results indicate that the modulation frequency of ultrasound can affect the phase-locking behaviors. The results of this study may help us to explain the potential firing mechanism of TMAS.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/224843", + "other_implementer": { + "attr_id": 308, + "value": "YW carried out numerical implementation of the TMAS, YY, NP, YC and YW done the simulation" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "tom morse" + }, + "ver_date": "2017-02-03T17:23:23.637", + "ver_number": 3 + }, + "224923": { + "class_id": 19, + "created": "2017-02-10T17:23:19.307", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 224923, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 84598, + "object_name": "Extracellular Fields" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 224924, + "object_name": "Miceli S, Ness TV, Einevoll GT, Schubert D (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Impedance spectrum in cortical tissue: implications for LFP signal propagation (Miceli et al. 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Here, we performed a detailed investigation of the frequency dependence of the conductivity within cortical tissue at microscopic distances using small current amplitudes within the typical (neuro)physiological micrometer and sub-nanoampere range. We investigated the propagation of LFPs, induced by extracellular electrical current injections via patch-pipettes, in acute rat brain slice preparations containing the somatosensory cortex in vitro using multielectrode arrays. Based on our data, we determined the cortical tissue conductivity over a 100-fold increase in signal frequency (5-500\r\nHz). Our results imply at most very weak\r\n frequency-dependent effects within the frequency range of physiological LFPs. Using biophysical modeling, we estimated the impact of different putative impedance spectra. Our results indicate that frequency dependencies of the order measured here and in most other studies have negligible impact on the typical analysis and modeling of LFP signals from extracellular brain recordings.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/224923", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2017-02-11T08:37:16.65", + "ver_number": 5 + }, + "224998": { + "class_id": 19, + "created": "2017-02-15T11:23:16.46", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 224998, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 224997, + "object_name": "Otopalik AG, Goeritz ML, Sutton AC, Brookings T, Guerini C, Marder E (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Sloppy morphological tuning in identified neurons of the crustacean STG (Otopalik et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114303, + "object_name": "Stomatogastric Ganglion (STG) Lateral Gastric (LG) cell" + }, + { + "object_id": 116963, + "object_name": "Stomatogastric Ganglion (STG) Lateral Pyloric (LP) cell" + }, + { + "object_id": 144405, + "object_name": "Stomatogastric ganglion (STG) pyloric dilator (PD) neuron" + }, + { + "object_id": 224999, + "object_name": "Stomatogastric Ganglion (STG) Gastric Mill (GM) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ...Theoretical studies suggest that morphology is tightly tuned to minimize wiring and conduction delay of synaptic events. We utilize high-resolution confocal microscopy and custom computational tools to characterize the morphologies of four neuron types in the stomatogastric ganglion (STG) of the crab Cancer borealis. Macroscopic branching patterns and fine cable properties are variable within and across neuron types. We compare these neuronal structures to synthetic minimal spanning neurite trees constrained by a wiring cost equation and find that STG neurons do not adhere to prevailing hypotheses regarding wiring optimization principles. In this highly-modulated and oscillating circuit, neuronal structures appear to be governed by a space-filling mechanism that outweighs the cost of inefficient wiring.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/224998", + "other_neurons": { + "attr_id": 301, + "value": "pyloric dilator (PD, gastric mill GM neurons" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "tom.morse@yale.edu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115959, + "object_name": "Stomatogastric ganglion" + } + ] + }, + "ver_date": "2017-02-15T11:38:56.057", + "ver_number": 3 + }, + "225075": { + "class_id": 19, + "created": "2017-02-16T06:12:13.56", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 225075, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 237554, + "object_name": "Hiratani,Naoki [N.Hiratani at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 243975, + "object_name": "Hiratani N, Fukai T (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "A model of optimal learning with redundant synaptic connections (Hiratani & Fukai 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a detailed neuron model of non-parametric near-optimal latent model acquisition using multisynaptic connections between pre- and postsynaptic neurons." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/225075", + "public_submitter_email": { + "attr_id": 309, + "value": "N.Hiratani@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Naoki Hiratani" + }, + "ver_date": "2018-07-30T11:24:23.283", + "ver_number": 15 + }, + "225080": { + "class_id": 19, + "created": "2017-02-17T17:59:57.513", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 225080, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 225082, + "object_name": "Milstein, Aaron D. 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We then demonstrate\r\nits application to real-world research by simulating the reaction-diffusion components\r\nof a published calcium burst model in both Purkinje neuron sub-branch and full dendrite\r\nmorphologies...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/225089", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "tom morse" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2017-02-20T11:39:33.24", + "ver_number": 4 + }, + "225095": { + "class_id": 19, + "created": "2017-02-20T14:45:45.153", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 225095, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 225168, + "object_name": "Mart\u00ednez-Ca\u00f1ada, Pablo [pablomc at ugr.es]" + }, + { + "object_id": 225169, + "object_name": "Carrillo, Richard R. 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The software platform can be interfaced with neural simulators (e.g., NEST) to connect with models of higher visual areas and with the Neurorobotics Platform of the HBP. The code is implemented in C++ and computations of spatiotemporal equations are optimized by means of recursive filtering techniques and multithreading.\r\n\r\nMost retina simulators are more focused on fitting specific retina functions. By contrast, the versatility of COREM allows the configuration of different retina models using a set of basic retina computational primitives. We implemented a series of retina models by combining these primitives to characterize some of the best-known phenomena observed in the retina: adaptation to the mean light intensity and temporal contrast, and differential motion sensitivity.\r\n\r\nThe code has been extensively tested in Linux. The software can be also adapted to Mac OS. Installation instructions as well as the user manual can be found in the Github repository: https://github.com/pablomc88/COREM" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/225095", + "other_neurons": { + "attr_id": 301, + "value": "retina horizontal cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "pablomc@ugr.es" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pablo Mart\u00ednez-Ca\u00f1ada" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 118529, + "object_name": "Retina" + } + ] + }, + "ver_date": "2018-10-17T17:33:22.41", + "ver_number": 11 + }, + "225301": { + "class_id": 19, + "created": "2017-02-23T22:31:41.467", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 225301, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 182782, + "object_name": "Sadeh, Sadra [s.sadeh at ucl.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 147931, + "object_name": "Orientation selectivity" + }, + { + "object_id": 225083, + "object_name": "Feature selectivity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 225303, + "object_name": "Sadeh S, Clopath C, Rotter S (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 2832, + "object_name": "NEST" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "A spiking NN for amplification of feature-selectivity with specific connectivity (Sadeh et al 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model simulates large-scale inhibition-dominated spiking networks with different degrees of recurrent specific connectivity. It shows how feature-specific connectivity leads to a linear amplification of feedforward tuning, as reported in recent electrophysiological single-neuron recordings in rodent neocortex. Moreover, feature-specific connectivity leads to the emergence of feature-selective reverberating activity, and entails pattern completion in network responses." + }, + "opensourcebrain": { + "value": "specnet-sade-et-al-2015" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/225301", + "public_submitter_email": { + "attr_id": 309, + "value": "s.sadeh@ucl.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Sadra Sadeh" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2017-04-03T11:31:15.75", + "ver_number": 9 + }, + "225311": { + "class_id": 19, + "created": "2017-02-24T11:05:36.273", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 225311, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 140251, + "object_name": "Halnes, Geir [geir.halnes at nmbu.no]" + }, + { + "object_id": 147118, + "object_name": "Maki-Marttunen, Tuomo [tuomomm at uio.no]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 84598, + "object_name": "Extracellular Fields" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 224939, + "object_name": "Halnes G, M\u00e4ki-Marttunen T, Keller D, Pettersen KH, Andreassen OA, Einevoll GT (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Effect of ionic diffusion on extracellular potentials (Halnes et al 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 167345, + "object_name": "Neocortex U1 L6 pyramidal corticalthalamic GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Recorded potentials in the extracellular space (ECS) of the brain is a\r\nstandard measure of population activity in neural\r\ntissue. Computational models that simulate the relationship between\r\nthe ECS potential and its underlying neurophysiological processes are\r\ncommonly used in the interpretation of such measurements. Standard\r\nmethods, such as volume-conductor theory and current-source density\r\ntheory, assume that diffusion has a negligible effect on the ECS\r\npotential, at least in the range of frequencies picked up by most\r\nrecording systems. This assumption remains to be verified. We here\r\npresent a hybrid simulation framework that accounts for diffusive\r\neffects on the ECS potential. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/225311", + "public_submitter_email": { + "attr_id": 309, + "value": "geih@nmbu.no" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Geir Halnes" + }, + "ver_date": "2017-07-03T22:39:12.25", + "ver_number": 8 + }, + "225428": { + "class_id": 19, + "created": "2017-03-01T16:52:00.793", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 225428, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 225426, + "object_name": "Cochran, Amy L. 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Each model makes different assumptions about mood dynamics. Our objective was to differentiate between models using only\r\ntime courses of mood. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/225428", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom" + }, + "ver_date": "2017-03-01T17:57:45.087", + "ver_number": 6 + }, + "225552": { + "class_id": 19, + "created": "2017-03-03T16:14:23.947", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 225552, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 225553, + "object_name": "Singh, Nishant [ns1144 at live.mdx.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 225550, + "object_name": "Singh N, Huyck C, Gandhi V, Jones A (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 225554, + "object_name": "NEST (web link to model)" + } + ] + }, + "name": "Neuron-based control mechanisms for a robotic arm and hand (Singh et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"A robotic arm and hand controlled by simulated neurons is\r\npresented. The robot makes use of a biological neuron simulator using\r\na point neural model. ... The robot performs a simple pick-and-place\r\ntask.\r\n...\r\nAs another benefit, it is hoped that further work\r\nwill also lead to a better understanding of human and other animal\r\nneural processing, particularly for physical motion. This is a\r\nmultidisciplinary approach combining cognitive neuroscience, robotics,\r\nand psychology.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/225552", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2017-03-03T16:49:59.903", + "ver_number": 3 + }, + "225583": { + "class_id": 19, + "created": "2017-03-06T16:58:38.69", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + 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"value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Collection of simulated data from a thalamocortical network model (Glabska, Chintaluri, Wojcik 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + }, + { + "object_id": 263, + "object_name": "Thalamus reticular nucleus GABA cell" + }, + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + }, + { + "object_id": 227971, + "object_name": "Neocortex V1 L5B pyramidal pyramidal tract GLU cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + }, + { + "object_id": 142232, + "object_name": "Neocortex spiking low threshold (LTS) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"A major challenge in experimental data analysis\r\nis the validation of analytical methods in a fully controlled\r\nscenario where the justification of the interpretation can\r\nbe made directly and not just by plausibility.\r\n\r\n...\r\nOne solution is to use simulations of realistic\r\nmodels to generate ground truth data.\r\n\r\nIn neuroscience, creating such data requires plausible models of\r\nneural activity, access to high performance computers, expertise and\r\ntime to prepare and run the simulations, and to process the output.\r\n\r\nTo facilitate such validation tests of analytical methods we provide\r\nrich data sets including intracellular voltage traces, transmembrane\r\ncurrents, morphologies, and spike times.\r\n\r\n...\r\nThe data were generated using the\r\nlargest publicly available multicompartmental model of thalamocortical\r\nnetwork (Traub et al. 2005), with activity evoked by different thalamic stimuli.\"\r\n" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/225583", + "other_concept": { + "attr_id": 306, + "value": "Epilepsy, Sleep spindles" + }, + "other_neurons": { + "attr_id": 301, + "value": "deep cortical interneuron" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2017-07-03T22:26:13.503", + "ver_number": 16 + }, + "225818": { + "class_id": 19, + "created": "2017-03-08T02:36:33.04", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 225818, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 231371, + "object_name": "Kobayashi T, Shimada Y, Fujiwara K, Ikeguchi T (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Reproducing infra-slow oscillations with dopaminergic modulation (Kobayashi et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... In this paper, to reproduce ISO (Infra-Slow Oscillations) in neural networks, we show that dopaminergic modulation of STDP is essential. More specifically, we discovered a close relationship between two dopaminergic effects: modulation of the STDP function and generation of ISO. We therefore, numerically investigated the relationship in detail and proposed a possible mechanism by which ISO is generated.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/225818", + "other_receptors": { + "attr_id": 302, + "value": "D5" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tkobayashi@hisenkei.net" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Toshihiro Kobayashi" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 196, + "object_name": "D1" + }, + { + "object_id": 225820, + "object_name": "D5" + }, + { + "object_id": 223, + "object_name": "Dopaminergic Receptor" + } + ] + }, + "ver_date": "2017-08-04T16:24:20.08", + "ver_number": 11 + }, + "225904": { + "class_id": 19, + "created": "2017-03-09T11:37:26.163", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 225904, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 169290, + "object_name": "Rudnicki, Marek [marek.rudnicki at tum.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 22012, + "object_name": "Depression" + }, + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + }, + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 225858, + "object_name": "Rudnicki M, Hemmert W (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 137990, + "object_name": "Brian (web link to method)" + }, + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "High entrainment constrains synaptic depression in a globular bushy cell (Rudnicki & Hemmert 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 273, + "object_name": "Cochlear nucleus bushy GLU cell" + }, + { + "object_id": 52419, + "object_name": "Auditory nerve" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Here we show how different levels of synaptic depression shape firing\r\nproperties of GBCs in in vivo-like conditions using computer simulations.\r\nWe analyzed how an interplay of synaptic depression (0 % to 70 %) and the\r\nnumber of auditory nerve fiber inputs (10 to 70) contributes to the\r\nvariability of the experimental data from previous studies. ... Overall, this study helps to understand how synaptic\r\nproperties shape temporal processing in the auditory system. It also integrates,\r\ncompares, and reconciles results of various experimental studies.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/225904", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 143145, + "object_name": "Auditory brainstem" + } + ] + }, + "ver_date": "2018-10-18T17:12:13.84", + "ver_number": 3 + }, + "225906": { + "class_id": 19, + "created": "2017-03-09T23:33:38.413", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 225906, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 181044, + "object_name": "Cox, Conor [cdcox1 at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 139148, + "object_name": "Brain Rhythms" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 154709, + "object_name": "Taxidis J, Coombes S, Mason R, Owen MR (2012)" + }, + { + "object_id": 227392, + "object_name": "Gunn BG, Cox CD, Chen Y, Frotscher M, Gall CM, Baram TZ, Lynch G (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "CRH modulates excitatory transmission and network physiology in hippocampus (Gunn et al. 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 146805, + "object_name": "Abstract Wang-Buzsaki neuron" + }, + { + "object_id": 189094, + "object_name": "Pinsky-Rinzel CA1/3 pyramidal cell " + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model simulates the effects of CRH on sharp waves in a rat CA1/CA3 model. It uses the frequency of the sharp waves as an output of the network." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/225906", + "public_submitter_email": { + "attr_id": 309, + "value": "cdcox1@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Conor D Cox" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2017-07-28T13:50:56.62", + "ver_number": 6 + }, + "226010": { + "class_id": 19, + "created": "2017-03-14T15:51:30.167", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 226010, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 226011, + "object_name": "Dougalis, Antonios [antoniosdougalis at hotmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 121254, + "object_name": "Depolarization block" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 227129, + "object_name": "Dougalis AG, Matthews GAC, Liss B, Ungless MA (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Dopamine neuron of the vent. periaqu. gray and dors. raphe nucleus (vlPAG/DRN) (Dougalis et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 227130, + "object_name": "Dopamine neuron of vlPAG/DRN" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The following computer model describes the electrophysiological properties of dopamine (DA) neurons of the ventrolateral periaquaductal gray and dorsal raphe nucleus (vlPAG/DRN). \r\nthe model and how to replicate Figures 7-10 of the manuscript (Dougalis et al., 2017 J Comput Neurosci).\r\n\r\nSUMMARY:\r\nWe have conducted a voltage-clamp study to provide\r\na kinetic description of major sodium, potassium and\r\ncalcium ionic currents operant on adult DA vlPAG/DRN neurons in brain slices obtained from pitx3-GFP mice. Based on experimentally derived voltage-clamp data, we then constructed a simplified, conductance-based,\r\nHodgkin and Huxley-type, computer model and validated its behaviour against in vitro neurophysiological data. Using simulations in the computational DA model, we explored the contribution of individual ionic currents in vlPAG/DRN DA neuron\u2019s spontaneous firing, pacemaker frequency and threshold for spike frequency adaptation in silico.\r\nThe data presented here extend our previous physiological characterization (Dougalis et al. 2012) and argue that DA neurons of the vlPAG/DRN express autorhythmicity in the absence of synaptic transmission via the interplay of potassium and sodium currents without the absolute need of calcium currents. The properties of the ionic currents recorded here (IH current, IA current), the lack of small oscillating potentials in the presence of sodium channel blockers taken together with the mechanisms for autorhythmicity (reliance more on sodium rather than calcium currents) also support further the idea that vlPAG/DRN DA neurons are operationally similar to VTA, rather than SNc, DA neurons. In particular, the properties of a slowly inactivating IA current in conjunction with the small and slowly activating IH current described herein pinpoint that vlPAG/DRN DA neurons are most similar to prefrontal cortex or medial shell of nucleus accumbens projecting DA neurons (see Lammel et al. 2008, 2011)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/226010", + "other_concept": { + "attr_id": 306, + "value": "pacemaking mechanism" + }, + "other_implementer": { + "attr_id": 308, + "value": "Dr. Antonios Dougalis (antoniosdougalis@hotmail.com)" + }, + "other_neurons": { + "attr_id": 301, + "value": "Dopamine neuron of vlPAG/DRN" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "antoniosdougalis@hotmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Antonios Dougalis" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 183076, + "object_name": "Brainstem" + } + ] + }, + "ver_date": "2017-04-18T17:26:41.65", + "ver_number": 64 + }, + "226074": { + "class_id": 19, + "created": "2017-03-21T11:07:38.053", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 226074, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 155567, + "object_name": "Wang, Yujiang [yujiang.wang at newcastle.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 185022, + "object_name": "Beta oscillations" + }, + { + "object_id": 156115, + "object_name": "Gamma oscillations" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 226132, + "object_name": "Wang Y, Trevelyan AJ, Valentin A, Alarcon G, Taylor PN, Kaiser M (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Mechanisms underlying different onset patterns of focal seizures (Wang Y et al 2017)", + "notes": { + "attr_id": 24, + "value": "\"Focal seizures are episodes of pathological brain activity that appear to arise from a \r\nlocalised area of the brain. The onset patterns of focal seizure activity have been studied \r\nintensively, and they have largely been distinguished into two types { low amplitude \r\nfast oscillations (LAF), or high amplitude spikes (HAS). Here we explore whether these \r\ntwo patterns arise from fundamentally different mechanisms. Here, we use a previously \r\nestablished computational model of neocortical tissue, and validate it as an adequate \r\nmodel using clinical recordings of focal seizures. We then reproduce the two onset \r\npatterns in their most defining properties and investigate the possible mechanisms \r\nunderlying the different focal seizure onset patterns in the model. ...\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/226074", + "public_submitter_email": { + "attr_id": 309, + "value": "yujiang.wang@ncl.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Yujiang Wang" + }, + "ver_date": "2017-05-04T17:23:56.323", + "ver_number": 10 + }, + "226254": { + "class_id": 19, + "created": "2017-03-22T16:08:10.92", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 112843, + "object_name": "I R" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 226254, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 145924, + "object_name": "Evans, Rebekah [Rebekah.Evans at nih.gov]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 226256, + "object_name": "Tarfa RA, Evans RC, Khaliq ZM (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "VTA dopamine neuron (Tarfa, Evans, and Khaliq 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 189152, + "object_name": "Ventral tegmental area dopamine neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In our model of a midbrain VTA dopamine neuron, we show that the decay kinetics of the A-type potassium current can control the timing of rebound action potentials." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/226254", + "other_currents": { + "attr_id": 303, + "value": "Ka, Kdr, BK, SK," + }, + "public_submitter_email": { + "attr_id": 309, + "value": "Rebekah.Evans@nih.gov" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Rebekah Evans" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2017-03-23T20:39:10.26", + "ver_number": 4 + }, + "226364": { + "class_id": 19, + "created": "2017-03-26T11:27:13.853", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 226364, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 145885, + "object_name": "Kim, Jae Kyoung [kimjack0 at kaist.ac.kr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 144409, + "object_name": "Homeostasis" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 226013, + "object_name": "Kim JK, Fiorillo CD (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Optimal balance predicts/explains amplitude and decay time of iPSGs (Kim & Fiorillo 2017)", + "notes": { + "attr_id": 24, + "value": "\"Synaptic inhibition counterbalances excitation, but it is not known what constitutes optimal\r\ninhibition. We previously proposed that perfect balance is achieved when the peak of an\r\nexcitatory postsynaptic potential (EPSP) is exactly at spike threshold, so that the slightest\r\nvariation in excitation determines whether a spike is generated. Using simulations, we show\r\nthat the optimal inhibitory postsynaptic conductance (IPSG) increases in amplitude and\r\ndecay rate as synaptic excitation increases from 1 to 800 Hz. As further proposed by theory,\r\nwe show that optimal IPSG parameters can be learned through anti-Hebbian rules. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/226364", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "ver_date": "2017-03-26T12:56:32.917", + "ver_number": 4 + }, + "226401": { + "class_id": 19, + "created": "2017-03-28T17:51:58.307", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 226401, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33772, + "object_name": "Morse, Tom [Tom.Morse at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 167352, + "object_name": "Gidon A, Segev I (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Principles governing the operation of synaptic inhibition in dendrites (Gidon & Segev 2012)", + "notes": { + "attr_id": 24, + "value": "A simple result of Gidon & Segev 2012 was provided where distal (off-path) inhibition is demonstrated to be more effective than proximal (on-path) inhibition in a ball and stick neuron." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/226401", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2017-03-28T17:53:02.967", + "ver_number": 3 + }, + "226422": { + "class_id": 19, + "created": "2017-03-30T11:05:14.393", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 226422, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 226424, + "object_name": "Appukuttan, Shailesh [shailesh.appukuttan at unic.cnrs-gif.fr;\r\nappukuttan.shailesh at gmail.com;]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 143152, + "object_name": "Kobayashi R, Tsubo Y, Shinomoto S (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Multi-timescale adaptive threshold model (Kobayashi et al 2009) (NEURON)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 226423, + "object_name": "Multi-timescale adaptive threshold non-resetting leaky integrate and fire" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... In this study,\r\nwe devised a simple, fast computational model that can be tailored to\r\nany cortical neuron not only for reproducing but also for predicting a\r\nvariety of spike responses to greatly fluctuating currents. The key\r\nfeatures of this model are a multi-timescale adaptive threshold\r\npredictor and a nonresetting leaky integrator. This model is capable\r\nof reproducing a rich variety of neuronal spike responses, including\r\nregular spiking, intrinsic bursting, fast spiking, and chattering, by\r\nadjusting only three adaptive threshold parameters.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/226422", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "ver_date": "2023-01-30T13:28:19.563", + "ver_number": 7 + }, + "226425": { + "class_id": 19, + "created": "2017-03-30T11:26:17.427", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 226425, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 226470, + "object_name": "Komarov, Maxim [mkomarov at ucsd.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 206231, + "object_name": "Komarov M, Bazhenov M (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Linking dynamics of the inhibitory network to the input structure (Komarov & Bazhenov 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Code to model 10 all-to-all coupled inhibitory neurons." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/226425", + "public_submitter_email": { + "attr_id": 309, + "value": "mkomarov@ucsd.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Maxim Komarov" + }, + "ver_date": "2017-03-30T18:42:55.06", + "ver_number": 8 + }, + "226432": { + "class_id": 19, + "created": "2017-03-30T11:52:50.927", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 226432, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 143152, + "object_name": "Kobayashi R, Tsubo Y, Shinomoto S (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Multi-timescale adaptive threshold model (Kobayashi et al 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 226423, + "object_name": "Multi-timescale adaptive threshold non-resetting leaky integrate and fire" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... In this study,\r\nwe devised a simple, fast computational model that can be tailored to\r\nany cortical neuron not only for reproducing but also for predicting a\r\nvariety of spike responses to greatly fluctuating currents. The key\r\nfeatures of this model are a multi-timescale adaptive threshold\r\npredictor and a nonresetting leaky integrator. This model is capable\r\nof reproducing a rich variety of neuronal spike responses, including\r\nregular spiking, intrinsic bursting, fast spiking, and chattering, by\r\nadjusting only three adaptive threshold parameters.\r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/226432", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "ver_date": "2017-03-30T12:32:08.373", + "ver_number": 5 + }, + "226471": { + "class_id": 19, + "created": "2017-03-30T23:32:21.537", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 226471, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 226613, + "object_name": "Chen, Jen-Yung [chen.jenyung at gmail.com]" + }, + { + "object_id": 226614, + "object_name": "Assisi, Collins " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 226477, + "object_name": "Chen JY, Marachlian E, Assisi C, Huerta R, Smith BH, Locatelli F, Bazhenov M (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "A model of antennal lobe of bee (Chen JY et al. 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Here we use calcium imaging to reveal how responses across antennal lobe projection neurons change after association of an input odor with appetitive reinforcement. After appetitive conditioning to 1-hexanol, the representation of an odor mixture containing 1-hexanol becomes more similar to this odor and less similar to the background odor acetophenone. We then apply computational modeling to investigate how changes in synaptic connectivity can account for the observed plasticity. Our study suggests that experience-dependent modulation of inhibitory interactions in the antennal lobe aids perception of salient odor components mixed with behaviorally irrelevant background odors.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/226471", + "public_submitter_email": { + "attr_id": 309, + "value": "chenjenyung@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Jen-Yung Chen" + }, + "ver_date": "2018-10-17T16:20:55.857", + "ver_number": 8 + }, + "226472": { + "class_id": 19, + "created": "2017-03-31T03:48:46.677", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 116966, + "object_name": "I_K,Na" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 226472, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 226606, + "object_name": "Schellenberger Costa, Michael [mschellenbergercosta at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 143724, + "object_name": "Bifurcation" + }, + { + "object_id": 93420, + "object_name": "Sleep" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 226605, + "object_name": "Weigenand A, Schellenberger Costa M, Ngo HV, Claussen JC, Martinetz T (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Neural mass model of the sleeping cortex (Weigenand et al 2014)", + "neurons": { + "attr_id": 25, + 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Neuronify allows students with no programming experience to easily build\r\nand explore networks in a plug-and-play manner picking network elements (neurons, stimulators, recording\r\ndevices) from a menu. The app is based on the commonly used integrate-and-fire type model neuron and\r\nhas adjustable neuronal and synaptic parameters. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/227114", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2017-04-13T15:09:23.993", + "ver_number": 5 + }, + "227318": { + "class_id": 19, + "created": "2017-04-21T19:11:14.987", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92088, + "object_name": "Nav1.7 SCN9A" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 227318, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33795, + "object_name": "Bhalla, Upinder S [bhalla at ncbs.res.in]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3542, + "object_name": "Pattern Recognition" + }, + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 227249, + "object_name": "Bhalla US (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 186718, + "object_name": "MOOSE/PyMOOSE" + } + ] + }, + "name": "Discrimination on behavioral time-scales mediated by reaction-diffusion in dendrites (Bhalla 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Sequences of events are ubiquitous in sensory, motor, and cognitive function. Key computational\r\n operations, including pattern recognition, event prediction, and plasticity, involve neural\r\n discrimination of spatio-temporal sequences. Here we show that synaptically-driven reaction\r\ndiffusion pathways on dendrites can perform sequence discrimination on behaviorally relevant\r\n time-scales. We used abstract signaling models to show that selectivity arises when inputs at\r\n successive locations are aligned with, and amplified by, propagating chemical waves triggered by\r\n previous inputs. We incorporated biological detail using sequential synaptic input onto spines in\r\n morphologically, electrically, and chemically detailed pyramidal neuronal models based on rat data." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/227318", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2019-02-19T17:18:15.98", + "ver_number": 13 + }, + "227363": { + "citation": { + "attr_id": 391, + "value": "Maex R, De Schutter E (1998) Synchronization of Golgi and granule cell firing in a detailed network model of the cerebellar granule cell layer. J. Neurophysiol. 80, 2521-2537. https://www.ncbi.nlm.nih.gov/pubmed/9819260" + }, + "class_id": 19, + "created": "2017-04-28T04:02:52.11", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 143445, + "object_name": "I_KD" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 227363, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 50400, + "object_name": "Maex, Reinoud [reinoud at bbf.uia.ac.be]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 12079, + "object_name": "Maex R, De Schutter E (1998)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "Cerebellar granular layer (Maex and De Schutter 1998)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 7346, + "object_name": "Cerebellum interneuron granule GLU cell" + }, + { + "object_id": 112687, + "object_name": "Cerebellum golgi cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Circuit model of the granular layer representing a one-dimensional array of single-compartmental granule cells (grcs) and Golgi cells (Gocs). This paper examines the effects of feedback inhibition (grc -> Goc -> grc) versus feedforward inhibition (mossy fibre -> Goc -> grc) on synchronization and oscillatory behaviour." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/227363", + "other_neurons": { + "attr_id": 301, + "value": "Cerebellum granule cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "reinoud.maex@uantwerpen.be" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Reinoud MAEX" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2017-05-01T11:29:25.327", + "ver_number": 4 + }, + "227577": { + "biosimulations": { + "value": "modeldb-227577" + }, + "citation": { + "attr_id": 391, + "value": "PMID: 27194335, PMCID: PMC4871989, DOI: 10.1523/JNEUROSCI.0339-16.2016" + }, + "class_id": 19, + "created": "2017-05-05T20:23:20.96", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 227577, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 125612, + "object_name": "Rubin, Jonathan E [jonrubin at pitt.edu]" + }, + { + "object_id": 227583, + "object_name": "Whalen, Timothy C [timcwhalen at gmail.com]" + }, + { + "object_id": 227584, + "object_name": "Corbit, Victoria L [vic22 at pitt.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 227582, + "object_name": "Corbit VL, Whalen TC, Zitelli KT, Crilly SY, Rubin JE, Gittis AH (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Pallidostriatal projections promote beta oscillations (Corbit, Whalen, et al 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88217, + "object_name": "Globus pallidus neuron" + }, + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + }, + { + "object_id": 118395, + "object_name": "Neostriatum fast spiking interneuron" + }, + { + "object_id": 150619, + "object_name": "Neostriatum spiny neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model consists of an inhibitory loop combining the projections from GPe neurons back to the striatum (shown experimentally to predominantly affect fast spiking interneurons, FSIs), together with the coupling from FSIs to medium spiny neurons (MSNs) in the striatum, along with the projections from MSNs to GPe. All models are in the Hodgkin-Huxley formalism, adapted from previously published models for each cell type. The connected circuit produces irregular activity under control conditions, but increasing FSI-to-MSN connectivity as observed experimentally under dopamine depletion yields exaggerated beta oscillations and synchrony. Additional mechanistic aspects are also explored." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/227577", + "other_implementer": { + "attr_id": 308, + "value": "Whalen, Timothy C (timcwhalen@gmail.com)\r\nCorbit, Victoria L (vic22@pitt.edu)" + }, + "other_neurons": { + "attr_id": 301, + "value": "Striatal FSIs, striatal MSNs" + }, + "other_type": { + "attr_id": 305, + "value": "Neuronal circuit" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jonrubin@pitt.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Jonathan Rubin" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2017-05-12T18:07:08.87", + "ver_number": 7 + }, + "227677": { + "class_id": 19, + "created": "2017-05-10T11:59:41.82", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 227677, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 227974, + "object_name": "Chehelcheraghi, Mojtaba " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 156115, + "object_name": "Gamma oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 227520, + "object_name": "Chehelcheraghi M, van Leeuwen C, Steur E, Nakatani C (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "A neural mass model of cross frequency coupling (Chehelcheraghi et al 2017)", + "notes": { + "attr_id": 24, + "value": "\"Electrophysiological signals of cortical activity show a range of possible frequency and\r\namplitude modulations, both within and across regions, collectively known as cross-frequency\r\ncoupling. To investigate whether these modulations could be considered as manifestations\r\nof the same underlying mechanism, we developed a neural mass model. The\r\nmodel provides five out of the theoretically proposed six different coupling types. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/227677", + "other_implementer": { + "attr_id": 308, + "value": "Chehelcheraghi, Mojtaba" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 227987, + "object_name": "Human" + } + ] + }, + "ver_date": "2017-05-19T13:50:48.72", + "ver_number": 7 + }, + "227678": { + "class_id": 19, + "created": "2017-05-10T16:19:58.033", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 87769, + "object_name": "ATP-senstive potassium current" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 227678, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33801, + "object_name": "Canavier, CC" + }, + { + "object_id": 234091, + "object_name": "Knowlton, Christopher [cknow1 at lsuhsc.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 234095, + "object_name": "Knowlton C, Kutterer S, Roeper J, Canavier CC (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Model for K-ATP mediated bursting in mSNc DA neurons (Knowlton et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 261, + "object_name": "Substantia nigra pars compacta DA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Burst firing in medial substantia nigra dopamine (mSN DA) neurons has been selectively linked to novelty-induced exploration behavior in mice. Burst firing in mSN DA neurons, in contrast to lateral SN DA neurons, requires functional ATP-sensitive potassium channels (K-ATP) both in vitro and in vivo. However, the precise role of K-ATP channels in promoting burst firing is un-known. We show experimentally that L-type calcium channel activity in mSN DA neurons en-hances open probability of K-ATP channels. We then generated a mathematical model to study the role of Ca2+ dynamics driving K-ATP channel function in mSN DA neurons during bursting. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/227678", + "other_implementer": { + "attr_id": 308, + "value": "Christopher Knowlton\r\nCarmen Canavier" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "cknow1@lsuhsc.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Christopher Knowlton" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2018-01-02T17:43:15.327", + "ver_number": 19 + }, + "227972": { + "class_id": 19, + "created": "2017-05-12T14:09:45.24", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 227972, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 227973, + "object_name": "Jercog, Daniel [daniel dot jercog at inserm dot fr] " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 249214, + "object_name": "Jercog D, Roxin A, Barth\u00f3 P, Luczak A, Compte A, de la Rocha J (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Models for cortical UP-DOWN states in a bistable inhibitory-stabilized network (Jercog et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In the idling brain, neuronal circuits transition between periods of sustained firing (UP state) and quiescence (DOWN state), a pattern the mechanisms of which remain unclear. We analyzed spontaneous cortical population activity from anesthetized rats and found that UP and DOWN durations were highly variable and that population rates showed no significant decay during UP periods. \r\nWe built a network rate model with excitatory (E) and inhibitory (I) populations exhibiting a novel bistable regime between a quiescent and an inhibition-stabilized state of arbitrarily low rate, where fluctuations triggered state transitions. In addition, we implemented these mechanisms in a more biophysically realistic spiking network, where DOWN-to-UP transitions are caused by synchronous high-amplitude events impinging onto the network.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/227972", + "other_implementer": { + "attr_id": 308, + "value": "Jercog, Daniel [daniel dot jercog at inserm dot fr] " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "daniel.jercog@inserm.fr" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Daniel Jercog" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2017-08-01T11:20:45.653", + "ver_number": 24 + }, + "227978": { + "citation": { + "attr_id": 391, + "value": "PMID: 26818524" + }, + "class_id": 19, + "created": "2017-05-15T14:10:31.283", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 227978, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 228308, + "object_name": "Kruse, Martin [mkruse at uw.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 227981, + "object_name": "Kruse M, Vivas O, Traynor-Kaplan A, Hille B (2016)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 55276, + "object_name": "Virtual Cell (web link to model)" + } + ] + }, + "name": "Phosphoinositide-Dependent Signaling in Sympathetic Neurons (SCG) (Kruse et al. 2016)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Phosphatidylinositol 4,5-bisphosphate [PI(4,5)P2] is a minor phospholipid in the cytoplasmic leaflet of the plasma membrane. Depletion of PI(4,5)P2 via phospholipase C-mediated hydrolysis leads to a decrease in exocytosis and alters electrical excitability in neurons. Restoration of PI(4,5)P2 is essential for a return to basal neuronal activity. However, the dynamics of phosphoinositide metabolism have not been analyzed in neurons. We studied the dynamics of phosphoinositide metabolism in sympathetic neu- rons upon muscarinic stimulation and used the kinetic information to develop a quantitative description of neuronal phospho- inositide metabolism. The measurements and analysis show a several-fold faster synthesis of PI(4,5)P2 in sympathetic neurons than in an electrically nonexcitable cell line, and provide a framework for future studies of PI(4,5)P2-dependent processes in neurons." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/227978", + "public_submitter_email": { + "attr_id": 309, + "value": "mkruse@uw.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Martin Kruse" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 179, + "object_name": "M1" + } + ] + }, + "ver_date": "2017-05-19T17:38:34.823", + "ver_number": 6 + }, + "228337": { + "biosimulations": { + "value": "modeldb-228337" + }, + "citation": { + "attr_id": 391, + "value": "Maex R, Grinevich VP, Grinevich V, Budygin E, Bencherif M, Gutkin B (2014) Understanding the role a7 nicotinic receptors play in dopamine efflux in nucleus accumbens. ACS Chem. Neurosci. 5, 1032-1340. doi: 10.1021/cn500126t. pubmed/25147933" + }, + "class_id": 19, + "created": "2017-05-20T08:45:52.54", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 228337, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 50400, + "object_name": "Maex, Reinoud [reinoud at bbf.uia.ac.be]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 112853, + "object_name": "Schizophrenia" + }, + { + "object_id": 112854, + "object_name": "Addiction" + }, + { + "object_id": 183421, + "object_name": "Volume transmission" + }, + { + "object_id": 222956, + "object_name": "Neuromodulation" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 228376, + "object_name": "Maex R, Grinevich VP, Grinevich V, Budygin E, Bencherif M, Gutkin B (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Nicotinic control of dopamine release in nucleus accumbens (Maex et al. 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 189152, + "object_name": "Ventral tegmental area dopamine neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + }, + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Minimal model of the VTA (ventral segmental area) representing two (GABA versus dopamine) neuron populations and two subtypes of nicotinic receptors (alpha4beta2 versus alpha7). The model is used to tell apart circuit from receptor mechanisms in the nicotinic control of dopamine release and its pharmacological manipulation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/228337", + "public_submitter_email": { + "attr_id": 309, + "value": "reinoud.maex@uantwerpen.be" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Reinoud Maex" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 178, + "object_name": "Nicotinic" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2017-05-22T09:15:14.027", + "ver_number": 3 + }, + "228373": { + "class_id": 19, + "created": "2017-05-21T09:34:59.59", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 228373, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 228371, + "object_name": "Martinet, L-E " + }, + { + "object_id": 228372, + "object_name": "Kramer, M.A [mak at bu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 227675, + "object_name": "Martinet LE, Fiddyment G, Madsen JR, Eskandar EN, Truccolo W, Eden UT, Cash SS, Kramer MA (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Human seizures couple across spatial scales through travelling wave dynamics (Martinet et al 2017)", + "notes": { + "attr_id": 24, + "value": "\" ... We show that during seizure large-scale neural populations spanning centimetres of cortex coordinate with small neural groups spanning cortical columns, and provide evidence that rapidly propagating waves of activity underlie this increased inter-scale coupling. We develop a corresponding computational model to propose specific mechanisms\u2014namely, the effects of an increased extracellular potassium concentration diffusing in space\u2014that support the observed spatiotemporal dynamics. Understanding the multi-scale, spatiotemporal dynamics of human seizures\u2014and connecting these dynamics to specific biological mechanisms\u2014promises new insights to treat this devastating disease." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/228373", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2017-05-21T09:38:22.613", + "ver_number": 4 + }, + "228596": { + "class_id": 19, + "created": "2017-05-22T11:02:08.947", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 228596, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57906, + "object_name": "Golomb, David [golomb at bgu.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 245798, + "object_name": "Touch" + }, + { + "object_id": 245799, + "object_name": "Whisking" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 229283, + "object_name": "Gutnisky DA, Yu J, Hires SA, To MS, Bale M, Svoboda K, Golomb D (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "L4 cortical barrel NN model receiving thalamic input during whisking or touch (Gutnisky et al. 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 182505, + "object_name": "Neocortex layer 4 neuron" + }, + { + "object_id": 146805, + "object_name": "Abstract Wang-Buzsaki neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Excitatory neurons in layer 4 (L4) in the barrel cortex respond relatively strongly to touch but not to whisker movement (Yu et al., Nat. Neurosci. 2016). The model explains the mechanism underlying this effect. The network is settled to filter out most stationary inputs. Brief touch input passes through because it takes time until feed-forward inhibition silences excitatory neurons receiving brief and strong thalamic excitation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/228596", + "public_submitter_email": { + "attr_id": 309, + "value": "golomb@bgu.ac.il" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "David Golomb" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228597, + "object_name": "Barrel cortex" + }, + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 249926, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2019-02-26T10:24:08.613", + "ver_number": 10 + }, + "228599": { + "citation": { + "attr_id": 391, + "value": "https://doi.org/10.1101/084152" + }, + "class_id": 19, + "created": "2017-05-25T12:23:54.267", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 228599, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 260965, + "object_name": "Honnuraiah, Suraj [hs at ini.phys.ethz.ch]" + }, + { + "object_id": 260963, + "object_name": "Gutierrez, Adrian [agutie at ini.uzh.ch]" + }, + { + "object_id": 260964, + "object_name": "Soldado-Magraner, Saray [ssaray at ini.uzh.ch]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 261228, + "object_name": "Soldado-Magraner S, Brandalise F, Honnuraiah S, Pfeiffer M, Moulinier M, Gerber U, Douglas R (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Firing patterns of CA3 hippocampal neurons (Soldado-Magraner et al. 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Here we demonstrate that the intrinsic firing patterns of CA3 neurons of the rat hippocampus in vitro undergo rapid long-term plasticity in response to a few minutes of only subthreshold synaptic conditioning. This plasticity on the spike-timing could also be induced by intrasomatic injection of subthreshold depolarizing pulses and was blocked by kinase inhibitors, indicating that discharge dynamics are modulated locally. Cluster analysis of firing patterns before and after conditioning revealed systematic transitions towards adapting and intrinsic burst behaviours, irrespective of the patterns initially exhibited by the cells. We used a conductance-based model to decide appropriate pharmacological blockade, and found that the observed transitions are likely due to recruitment of low-voltage calcium and Kv7 potassium conductances. We conclude that CA3 neurons adapt their conductance profile to the subthreshold activity of their input, so that their intrinsic firing pattern is not a static signature, but rather a reflection of their history of subthreshold activity. In this way, recurrent output from CA3 neurons may collectively shape the temporal dynamics of their embedding circuits.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/228599", + "other_type": { + "attr_id": 305, + "value": "Single Compartment" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ssaray@ini.uzh.ch" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Saray Soldado-Magraner" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2019-11-22T17:37:00.167", + "ver_number": 26 + }, + "228604": { + "citation": { + "attr_id": 391, + "value": "Droste F, Lindner B, Exact analytical results for integrate-and-fire neurons driven\r\nby excitatory shot noise, J. Comp. Neurosci, 2017 (accepted)" + }, + "class_id": 19, + "created": "2017-05-30T14:08:48.09", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 228604, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 228605, + "object_name": "Droste, Felix [fedro at posteo.de]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 228606, + "object_name": "Droste F, Lindner B (2017)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Theory and simulation of integrate-and-fire neurons driven by shot noise (Droste & Lindner 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + }, + { + "object_id": 146806, + "object_name": "Abstract theta neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This archive contains source code for the paper \"Exact analytical results for integrate-and-fire neurons driven by excitatory shot noise\" by Droste and Lindner, 2017. Specifically, it contains a Python implementation of the analytical formulas derived in that paper (allowing to calculate firing rate, CV and stationary voltage distribution of general integrate-and-fire neurons driven by excitatory shot noise, as well as power spectrum and rate-response of leaky integrate-and-fire neurons with such input) and C++ code implementing a Monte-Carlo simulation to estimate these quantities. A sample Jupyter notebook to play around with the analytics is included, as are scripts to reproduce the figures from the paper." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/228604", + "other_implementer": { + "attr_id": 308, + "value": "Droste, Felix " + }, + "other_neurons": { + "attr_id": 301, + "value": "Abstract general integrate-and-fire neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "felix.droste@posteo.de" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Felix Droste" + }, + "ver_date": "2017-06-06T16:23:46.893", + "ver_number": 5 + }, + "229276": { + "class_id": 19, + "created": "2017-06-05T11:42:19.083", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122814, + "object_name": "Cav1.2 CACNA1C" + }, + { + "object_id": 122812, + "object_name": "Cav1.3 CACNA1D" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 229276, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 156119, + "object_name": "Simoes-de-Souza, Fabio [fabio.souza at ufabc.edu.br]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 87578, + "object_name": "STDP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 238763, + "object_name": "Antunes G, Faria da Silva SF, Simoes de Souza FM (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Mirror Neuron (Antunes et al 2017)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Modeling Mirror Neurons Through Spike-Timing Dependent Plasticity. 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Amplitude and decay time course of inhibitory synapses can be systematically changed for different distances between connected cells. Various forms of excitatory drives can be administered to the network including spatially structured input." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/229750", + "other_implementer": { + "attr_id": 308, + "value": "Michael Str\u00fcber" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "michael_strueber@hotmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Michael Str\u00fcber" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115947, + "object_name": "Dentate gyrus" + } + ] + }, + "ver_date": "2017-10-03T16:49:14.33", + "ver_number": 16 + }, + "230046": { + "citation": { + "attr_id": 391, + "value": "Melozzi et al. 2017" + }, + "class_id": 19, + "created": "2017-06-28T10:39:30.507", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 230046, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 230048, + "object_name": "Melozzi, Francesca " + }, + { + "object_id": 230049, + "object_name": "Jirsa, Viktor K " + }, + { + "object_id": 230050, + "object_name": "Bernard, Christophe " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 230044, + "object_name": "Melozzi F, Woodman MM, Jirsa VK, Bernard C (2017)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 230047, + "object_name": "The Virtual Mouse Brain (TVMB) (web link to model)" + } + ] + }, + "name": "The Virtual Mouse Brain (TVMB) (Melozzi et al. 2017)", + "notes": { + "attr_id": 24, + "value": "\"Connectome-based modeling of large-scale brain network dynamics enables causal in silico interrogation of the brain\u2019s\r\nstructure-function relationship, necessitating the close integration of diverse neuroinformatics fields.\r\nHere we extend the open-source simulation software The Virtual Brain to whole mouse brain network modeling\r\nbased on individual diffusion Magnetic Resonance Imaging (dMRI)-based or tracer-based detailed mouse connectomes.\r\nWe provide practical examples on how to use The Virtual Mouse Brain to simulate brain activity, such as seizure\r\npropagation and the switching behavior of the resting state dynamics in health and disease.\r\nThe Virtual Mouse Brain enables theoretically driven experimental planning and ways to test predictions in the\r\nnumerous strains of mice available to study brain function in normal and pathological conditions.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/230046", + "other_concept": { + "attr_id": 306, + "value": "resting state dynamics" + }, + "other_simulator": { + "attr_id": 307, + "value": "TVMB" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - 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Intensity-voltage curves, normalized conductance-voltage relationship, steady-state availability and recovery from inactivation are simulated." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/230137", + "other_concept": { + "attr_id": 306, + "value": "Markov-type channel model" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "piero.balbi@fsm.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pietro Balbi" + }, + "ver_date": "2023-03-06T11:26:02.817", + "ver_number": 24 + }, + "230138": { + "citation": { + "attr_id": 391, + "value": "Masquelier T (2017) STDP allows close-to-optimal spatiotemporal spike pattern detection by single coincidence detector neurons. Neuroscience.\r\nhttps://doi.org/10.1016/j.neuroscience.2017.06.032" + }, + "class_id": 19, + "created": "2017-06-30T05:35:55.203", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 230138, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 123929, + "object_name": "Masquelier, Tim [timothee.masquelier at alum.mit.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 83538, + "object_name": "Unsupervised Learning" + }, + { + "object_id": 180821, + "object_name": "Hebbian plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 3542, + "object_name": "Pattern Recognition" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 230144, + "object_name": "Masquelier T (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Optimal spatiotemporal spike pattern detection by STDP (Masquelier 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We simulate a LIF neuron equipped with STDP. A pattern repeats in its inputs. The LIF progressively becomes selective to the repeating pattern, in an optimal manner." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/230138", + "public_submitter_email": { + "attr_id": 309, + "value": "timothee.masquelier@alum.mit.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tim Masquelier" + }, + "ver_date": "2017-06-30T16:56:26.81", + "ver_number": 5 + }, + "230324": { + "class_id": 19, + "created": "2017-07-07T07:02:57.453", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 230324, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 230325, + "object_name": "Pernelle, Guillaume [g.pernelle14 at imperial.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 156115, + "object_name": "Gamma oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 239749, + "object_name": "Pernelle G, Nicola W, Clopath C (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Gap junction plasticity as a mechanism to regulate network-wide oscillations (Pernelle et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + }, + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Oscillations of neural activity emerge when many neurons repeatedly activate together and are observed in many brain regions, particularly during sleep and attention. Their functional role is still debated, but could be associated with normal cognitive processes such as memory formation or with pathologies such as schizophrenia and autism. Powerful oscillations are also a hallmark of epileptic seizures. Therefore, we wondered what mechanism could regulate oscillations. A type of neuronal coupling, called gap junctions, has been shown to promote synchronization between inhibitory neurons. Computational models show that when gap junctions are strong, neurons synchronize together. Moreover recent investigations show that the gap junction coupling strength is not static but plastic and dependent on the firing properties of the neurons. Thus, we developed a model of gap junction plasticity in a network of inhibitory and excitatory neurons. We show that gap junction plasticity can maintain the right amount of oscillations to prevent pathologies from emerging. Finally, we show that gap junction plasticity serves an additional functional role and allows for efficient and robust information transfer.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/230324", + "other_concept": { + "attr_id": 306, + "value": "Gap junction plasticity" + }, + "other_implementer": { + "attr_id": 308, + "value": "Guillaume Pernelle [g.pernelle14 at imperial.ac.uk]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "g.pernelle14@imperial.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Guillaume Pernelle" + }, + "ver_date": "2020-05-27T11:07:35.817", + "ver_number": 22 + }, + "230326": { + "citation": { + "attr_id": 391, + "value": "Hay, E., Hill, S., Sch\u00fcrmann, F., Markram, H., and Segev, I. (2011). Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties. PLoS Comput. Biol. 7, e1002107. doi:10.1371/journal.pcbi.1002107." + }, + "class_id": 19, + "created": "2017-07-07T08:56:13.373", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 88209, + "object_name": "Na/Ca exchanger" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 230326, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 185339, + "object_name": "Zylbertal, Asaph [asaph.zylbertal at mail.huji.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 145814, + "object_name": "Hay E, Hill S, Sch\u00fcrmann F, Markram H, Segev I (2011)" + }, + { + "object_id": 231916, + "object_name": "Zylbertal A, Yarom Y, Wagner S (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Cortical Layer 5b pyr. cell with [Na+]i mechanisms, from Hay et al 2011 (Zylbertal et al 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Based on a large body of experimental recordings from both the soma and dendrites of L5b pyramidal cells in adult rats, we characterized key features of the somatic and dendritic firing and quantified their statistics. We used these features to constrain the density of a set of ion channels over the soma and dendritic surface via multi-objective optimization with an evolutionary algorithm, thus generating a set of detailed conductance-based models that faithfully replicate the back-propagating action potential activated Ca(2+) spike firing and the perisomatic firing response to current steps, as well as the experimental variability of the properties. Furthermore, we show a useful way to analyze model parameters with our sets of models, which enabled us to identify some of the mechanisms responsible for the dynamic properties of L5b pyramidal cells as well as mechanisms that are sensitive to morphological changes. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/230326", + "other_concept": { + "attr_id": 306, + "value": "Intracellular Sodium" + }, + "other_neurons": { + "attr_id": 301, + "value": "Layer 5b pyramidal cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "asaph.zylbertal@mail.huji.ac.il" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Asaph Zylbertal" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2019-10-11T16:19:32.66", + "ver_number": 10 + }, + "230329": { + "class_id": 19, + "created": "2017-07-08T22:15:29.267", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 230329, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 260018, + "object_name": "Yi, Guosheng " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 231864, + "object_name": "Yi G, Wang J, Wei X, Deng B (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Dendritic properties control energy efficiency of APs in cortical pyramidal cells (Yi et al 2017)", + "notes": { + "attr_id": 24, + "value": "Neural computation is performed by transforming input signals into sequences of action potentials (APs), which is metabolically expensive and limited by the energy available to the brain. The energy efficiency of single AP has important consequences for the computational power of the cell, which is determined by its biophysical properties and morphologies. Here we adopt biophysically-based two-compartment models to investigate how dendrites affect energy efficiency of APs in cortical pyramidal neurons. We measure the Na+ entry during the spike and examine how it is efficiently used for generating AP depolarization. We show that increasing the proportion of dendritic area or coupling conductance between two chambers decreases Na+ entry efficiency of somatic AP. Activating inward Ca2+ current in dendrites results in dendritic spike, which increases AP efficiency. Activating Ca2+-activated outward K+ current in dendrites, however, decreases Na+ entry efficiency. We demonstrate that the active and passive dendrites take effects by altering the overlap between Na+ influx and internal current flowing from soma to dendrite. We explain a fundamental link between dendritic properties and AP efficiency, which is essential to interpret how neural computation consumes metabolic energy and how the biophysics and morphology contributes to such consumption." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/230329", + "other_implementer": { + "attr_id": 308, + "value": "Yi, Guosheng " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "guoshengyi@tju.edu.cn" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Guosheng Yi,Jiang Wang, Xile Wei, Bin Deng" + }, + "ver_date": "2019-10-14T17:29:52.947", + "ver_number": 10 + }, + "230400": { + "citation": { + "attr_id": 391, + "value": "von Reyn CR, Nern A, Williamson WR, Breads P, Wu M, Namiki S, Card GM.\r\nFeature Integration Drives Probabilistic Behavior in the Drosophila Escape Response.\r\nNeuron. 2017 Jun 21;94(6):1190-1204.e6. doi: 10.1016/j.neuron.2017.05.036.\r\nPMID: 28641115" + }, + "class_id": 19, + "created": "2017-07-10T14:26:38.353", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 230400, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 230423, + "object_name": "von Reyn, Catherine [crv33 at drexel.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 230422, + "object_name": "von Reyn CR, Nern A, Williamson WR, Breads P, Wu M, Namiki S, Card GM (2017)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Feature integration drives probabilistic behavior in Fly escape response (von Reyn et al 2017)", + "notes": { + "attr_id": 24, + "value": "\"... A Linear Model for Visual Feature Integration in the GF (Drosophila Giant Fiber)\r\nCircuit.\r\nTo test our hypothesis that the GFs linearly integrate the separately\r\nencoded features of looming stimulus size and angular\r\nvelocity, we developed a model to predict GF membrane potential\r\nacross visual stimuli (Figure 8A). In this four-component\r\nmodel, the GFs linearly sum two excitatory components\u2014\r\nnon-LC4(Type 4 lobula columnar neurons)-mediated angular size excitation and LC4-mediated\r\nangular velocity excitation\u2014and two inhibitory components\u2014\r\nnon-LC4- and LC4-mediated angular size inhibition.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/230400", + "public_submitter_email": { + "attr_id": 309, + "value": "crv33@drexel.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Catherine R von Reyn" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 206363, + "object_name": "Drosophila" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 120613, + "object_name": "Drosophila (fruit fly)" + } + ] + }, + "ver_date": "2019-02-26T10:15:23.237", + "ver_number": 7 + }, + "230562": { + "citation": { + "attr_id": 391, + "value": "M. Daneshzand, M. Faezipour and B. D. Barkana,\"Hyperbolic Modeling of Subthalamic Nucleus cells to investigate the effect of Dopamine Depletion \", Computational intelligence and Neuroscience. accepted July 2017." + }, + "class_id": 19, + "created": "2017-07-12T13:07:02.31", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 230562, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 230897, + "object_name": "Daneshzand, Mohammad [mdaneshz at my.bridgeport.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 238733, + "object_name": "Daneshzand M, Faezipour M, Barkana BD (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Hyperbolic model (Daneshzand et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A modified Izhikevich neuron model to address the switching patterns of neuronal firing seen in Parkinson's Disease." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/230562", + "public_submitter_email": { + "attr_id": 309, + "value": "mdaneshz@my.bridgeport.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Mohammad Daneshzand" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 116868, + "object_name": "Subthalamic Nucleus" + } + ] + }, + "ver_date": "2017-07-25T13:59:46.073", + "ver_number": 6 + }, + "230578": { + "citation": { + "attr_id": 391, + "value": "Caze et al 2017" + }, + "class_id": 19, + "created": "2017-07-12T17:19:51.737", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 230578, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 230581, + "object_name": "Stimulus selectivity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 229481, + "object_name": "Caz\u00e9 RD, Jarvis S, Foust AJ, Schultz SR (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Dendrites enable a robust mechanism for neuronal stimulus selectivity (Caze et al 2017)", + "notes": { + "attr_id": 24, + "value": "\"... Using a multi-subunit nonlinear model, we\r\ndemonstrate that stimulus selectivity can arise from the spatial\r\ndistribution of synapses. We propose this as a general mechanism for\r\ninformation processing by neurons possessing dendritic\r\ntrees. Moreover, we show that this implementation of stimulus\r\nselectivity increases the neuron's robustness to synaptic and\r\ndendritic failure. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/230578", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Thomas Morse" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2017-07-12T17:45:08.157", + "ver_number": 3 + }, + "230811": { + "class_id": 19, + "created": "2017-07-19T06:20:39.137", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 112843, + "object_name": "I R" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 88215, + "object_name": "I_Ks" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 230811, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 155061, + "object_name": "Papoutsi, Athanasia [athpapoutsi at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 230812, + "object_name": "Papoutsi A, Kastellakis G, Poirazi P (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "L5 PFC pyramidal neurons (Papoutsi et al. 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Here, we use a\r\nmodeling approach to investigate whether and how the morphology of the\r\nbasal tree mediates the functional output of neurons. We implemented\r\n57 basal tree morphologies of layer 5 prefrontal pyramidal neurons of\r\nthe rat and identified morphological types which were characterized by\r\ndifferent response features, forming distinct functional types. These\r\ntypes were robust to a wide range of manipulations (distribution of\r\nactive ionic mechanisms, NMDA conductance, somatic and apical tree\r\nmorphology or the number of activated synapses) and supported\r\ndifferent temporal coding schemes at both the single neuron and the\r\nmicrocircuit level.\r\nWe predict that the basal tree morphological\r\ndiversity among neurons of the same class mediates their segregation\r\ninto distinct functional pathways.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/230811", + "other_neurons": { + "attr_id": 301, + "value": "Prefrotnal layer 5 pyramidal" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "athpapoutsi@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Athanasia Papoutsi" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "ver_date": "2019-10-10T17:37:38.853", + "ver_number": 9 + }, + "230861": { + "citation": { + "attr_id": 391, + "value": "Canakci S, Inci AF, Toy MF, Liu X, and Kuzum D. \"Computational Analysis of Network Activity and Spatial Reach of Sharp Wave-Ripples\". PLOS ONE 2017" + }, + "class_id": 19, + "created": "2017-07-20T14:26:16.697", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 230861, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 230877, + "object_name": "Canakci, Sadullah [scanakci at bu.edu]" + }, + { + "object_id": 230878, + "object_name": "Inci, Ahmet F [afinci at sabanciuniv,edu]" + }, + { + "object_id": 230879, + "object_name": "Toy, Faruk [faruk.toy at metu.edu.tr]" + }, + { + "object_id": 230880, + "object_name": "Liu, Xin [xil432 at end.ucsd.edu]" + }, + { + "object_id": 230881, + "object_name": "Kuzum, Duygu [dkuzum at eng.ucsd.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 230882, + "object_name": "Canakci S, Toy MF, Inci AF, Liu X, Kuzum D (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Computational analysis of NN activity and spatial reach of sharp wave-ripples (Canakci et al 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 114079, + "object_name": "Hippocampus CA1 basket cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Network oscillations of different frequencies, durations and amplitudes are hypothesized to coordinate information processing and transfer across brain areas. Among these oscillations, hippocampal sharp wave-ripple complexes (SPW-Rs) are one of the most prominent. SPW-Rs occurring in the hippocampus are suggested to play essential roles in memory consolidation as well as information transfer to the neocortex. To-date, most of the knowledge about SPW-Rs comes from experimental studies averaging responses from neuronal populations monitored by conventional microelectrodes. In this work, we investigate spatiotemporal characteristics of SPW-Rs and how microelectrode size and distance influence SPW-R recordings using a biophysical model of hippocampus. We also explore contributions from neuronal spikes and synaptic potentials to SPW-Rs based on two different types of network activity. Our study suggests that neuronal spikes from pyramidal cells contribute significantly to ripples while high amplitude sharp waves mainly arise from synaptic activity. Our simulations on spatial reach of SPW-Rs show that the amplitudes of sharp waves and ripples exhibit a steep decrease with distance from the network and this effect is more prominent for smaller area electrodes. Furthermore, the amplitude of the signal decreases strongly with increasing electrode surface area as a result of averaging. The relative decrease is more pronounced when the recording electrode is closer to the source of the activity. Through simulations of field potentials across a high-density microelectrode array, we demonstrate the importance of finding the ideal spatial resolution for capturing SPW-Rs with great sensitivity. Our work provides insights on contributions from spikes and synaptic potentials to SPW-Rs and describes the effect of measurement configuration on LFPs to guide experimental studies towards improved SPW-R recordings." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/230861", + "other_implementer": { + "attr_id": 308, + "value": "Canakci, Sadullah [scanakci at bu.edu];\r\nInci, Ahmet F [afinci at sabanciuniv,edu];\r\nToy, Faruk [faruk.toy at metu.edu.tr];\r\nLiu, Xin [xil432 at end.ucsd.edu];\r\nKuzum, Duygu [dkuzum at eng.ucsd.edu];" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "scanakci@bu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Sadullah Canakci" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2017-08-30T10:43:10.03", + "ver_number": 15 + }, + "230888": { + "citation": { + "attr_id": 391, + "value": "chandler hodgkin 1965" + }, + "class_id": 19, + "created": "2017-07-21T19:02:00.497", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 230888, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33736, + "object_name": "Hines, Michael [Michael.Hines at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3642, + "object_name": "Tutorial/Teaching" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 230862, + "object_name": "Chandler WK, Hodgkin AL (1965)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "The cannula artifact (Chandler & Hodgkin 1965)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52426, + "object_name": "Squid axon" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Chandler and Hodgkin 1965 describes how using a high impedance electrode can lead to squid axon recordings that appear to overshoot the sodium reversal potential, thus resolving controversial recordings at the time." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/230888", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2017-07-21T19:17:22.613", + "ver_number": 5 + }, + "230929": { + "class_id": 19, + "created": "2017-07-25T17:20:37.91", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 230929, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 154777, + "object_name": "Cohen, Michael X [mikexcohen at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3642, + "object_name": "Tutorial/Teaching" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 84598, + "object_name": "Extracellular Fields" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 230928, + "object_name": "Cohen MX (2017)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "MATLAB for brain and cognitive scientists (Cohen 2017)", + "notes": { + "attr_id": 24, + "value": "\" ... MATLAB for Brain and Cognitive Scientists takes readers from beginning to intermediate and advanced levels of MATLAB programming, helping them gain real expertise in applications that they will use in their work.\r\n\r\nThe book offers a mix of instructive text and rigorous explanations of MATLAB code along with programming tips and tricks. The goal is to teach the reader how to program data analyses in neuroscience and psychology. Readers will learn not only how to but also how not to program, with examples of bad code that they are invited to correct or improve. Chapters end with exercises that test and develop the skills taught in each chapter. Interviews with neuroscientists and cognitive scientists who have made significant contributions to their field using MATLAB appear throughout the book. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/230929", + "ver_date": "2017-07-26T10:57:54.027", + "ver_number": 8 + }, + "231105": { + "citation": { + "attr_id": 391, + "value": "Tommer Argaman and David Golomb,\r\nDoes Layer 4 in the barrel cortex function as a balanced circuit when\r\nresponding to whisker movements?\r\nNeuroscience, in press." + }, + "class_id": 19, + "created": "2017-07-30T10:29:56.95", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 231105, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 231164, + "object_name": "Argaman, Tommer " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 245799, + "object_name": "Whisking" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 231162, + "object_name": "Argaman T, Golomb D (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Conductance-based model of Layer-4 in the barrel cortex (Argaman et Golomb 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 146805, + "object_name": "Abstract Wang-Buzsaki neuron" + }, + { + "object_id": 182505, + "object_name": "Neocortex layer 4 neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Layer 4 in the mouse barrel cortex includes hundreds of inhibitory PV neurons and thousands of excitatory neurons. Despite this fact, its dynamical state is similar to a balanced state of large neuronal circuits." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/231105", + "other_implementer": { + "attr_id": 308, + "value": "Tommer Argaman" + }, + "other_neurons": { + "attr_id": 301, + "value": "Neocortex layer 4 neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "golomb@bgu.ac.il" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "David Golomb" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228597, + "object_name": "Barrel cortex" + } + ] + }, + "ver_date": "2018-10-18T17:36:47.877", + "ver_number": 12 + }, + "231185": { + "class_id": 19, + "created": "2017-08-02T07:56:49.613", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 231185, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 155061, + "object_name": "Papoutsi, Athanasia [athpapoutsi at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 261389, + "object_name": "Park J, Papoutsi A, Ash RT, Marin MA, Poirazi P, Smirnakis SM (2019)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Orientation preference in L23 V1 pyramidal neurons (Park et al 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Pyramidal neurons integrate synaptic inputs from basal and apical dendrites to generate stimulus-specific responses. It has been proposed that feed-forward inputs to basal dendrites drive a neuron\u2019s stimulus preference, while feedback inputs to apical dendrites sharpen selectivity. However, how a neuron\u2019s dendritic domains relate to its functional selectivity has not been demonstrated experimentally. We performed 2-photon dendritic micro-dissection on layer-2/3 pyramidal neurons in mouse primary visual cortex. We found that removing the apical dendritic tuft did not alter orientation-tuning. Furthermore, orientation-tuning curves were remarkably robust to the removal of basal dendrites: ablation of 2 basal dendrites was needed to cause a small shift in orientation preference, without significantly altering tuning width. Computational modeling corroborated our results and put limits on how orientation preferences among basal dendrites differ in order to reproduce the post-ablation data. In conclusion, neuronal orientation-tuning appears remarkably robust to loss of dendritic input.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/231185", + "other_neurons": { + "attr_id": 301, + "value": "Neocortex V1 L23 pyramidal neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "athpapoutsi@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Athanasia Papoutsi" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2019-12-02T14:19:11.94", + "ver_number": 9 + }, + "231238": { + "citation": { + "attr_id": 391, + "value": "1 . Rothman JS, Manis PB (2003c) The roles potassium currents play in regulating the electrical activity of ventral cochlear nucleus neurons. J Neurophysiol 89:3097-113 [PubMed]" + }, + "class_id": 19, + "created": "2017-08-03T17:01:50.597", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 231238, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 231239, + "object_name": "McCall, Thomas [thomas.mccall at case.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 37966, + "object_name": "Rothman JS, Manis PB (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 231240, + "object_name": "Brian 2" + } + ] + }, + "name": "CN bushy, stellate neurons (Rothman, Manis 2003) (Brian 2)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 273, + "object_name": "Cochlear nucleus bushy GLU cell" + }, + { + "object_id": 88113, + "object_name": "CN stellate cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model is an updated version of Romain Brette's adaptation of Rothman & Manis (2003). The model now uses Brian 2 instead of Brian 1 and can be configured to use n cells instead of a single cell. The included figure shows that Brian 2 is more efficient than Brian 1 once the number of cells exceeds 1,000." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/231238", + "other_implementer": { + "attr_id": 308, + "value": "Thomas McCall" + }, + "other_simulator": { + "attr_id": 307, + "value": "Brian 2" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "thomas.mccall@case.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Thomas McCall" + }, + "ver_date": "2017-08-04T11:04:37.023", + "ver_number": 11 + }, + "231392": { + "citation": { + "attr_id": 391, + "value": "\"Self-organization of modular activity of grid cells\"; Eugenio Urdapilleta, Bailu Si, Alessandro Treves; Hippocampus 2017. DOI: 10.1002/hipo.22765" + }, + "class_id": 19, + "created": "2017-08-07T10:48:05.78", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 231392, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 231422, + "object_name": "Urdapilleta, Eugenio [urdapile at gmail.com]" + }, + { + "object_id": 231423, + "object_name": "Si, Bailu " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 194888, + "object_name": "Grid cell" + }, + { + "object_id": 180821, + "object_name": "Hebbian plasticity" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 231393, + "object_name": "Urdapilleta E, Si B, Treves A (2017)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Development of modular activity of grid cells (Urdapilleta et al 2017)", + "notes": { + "attr_id": 24, + "value": "This study explores the self-organization of modular activity of grid cells" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/231392", + "other_implementer": { + "attr_id": 308, + "value": "Eugenio Urdapilleta & Bailu Si" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "urdapile@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Eugenio Urdapilleta" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 119191, + "object_name": "Entorhinal cortex" + } + ] + }, + "ver_date": "2017-08-09T11:21:09.163", + "ver_number": 5 + }, + "231416": { + "citation": { + "attr_id": 391, + "value": "Cell-type\u2013specific inhibition of the dendritic plateau potential in striatal spiny projection neurons\r\nKai Du , Yu-Wei Wu , Robert Lindroos , Yu Liu , Bal\u00e1zs R\u00f3zsa , Gergely Katona , Jun B. Ding ,\r\nand Jeanette Hellgren Kotaleski , PNAS, in press" + }, + "class_id": 19, + "created": "2017-08-09T04:57:34.677", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + }, + { + "object_id": 167500, + "object_name": "Kir" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 112844, + "object_name": "I Q" + }, + { + "object_id": 112843, + "object_name": "I R" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122809, + "object_name": "Cav3.2 CACNA1H" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 231416, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 231421, + "object_name": "Du, Kai [kai.du at ki.se]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 235125, + "object_name": "Du K, Wu YW, Lindroos R, Liu Y, R\u00f3zsa B, Katona G, Ding JB, Kotaleski JH (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "Specific inhibition of dendritic plateau potential in striatal projection neurons (Du et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 150619, + "object_name": "Neostriatum spiny neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We explored dendritic plateau potentials in a biophysically detailed SPN model. We coupled the dendritic plateaus to different types of inhibitions (dendritic fast and slow inhibitions, perisomatic inhibition from FS interneurons , etc.) We found the inhibition provides precise control over the plateau potential, and thus the spiking output of SPNs." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/231416", + "other_currents": { + "attr_id": 303, + "value": "CaT(CaV3.3, CaV3.2),CaR,KDR, I L low thereshold" + }, + "other_implementer": { + "attr_id": 308, + "value": "Kai Du, Karolinska Institute, kai.du@ki.se" + }, + "other_receptors": { + "attr_id": 302, + "value": "slow GABAA" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "kai.du@ki.se" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Kai DU" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 206, + "object_name": 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"object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This work (published in \"Timed synaptic inhibition shapes NMDA spikes,\r\ninfluencing local dendritic processing\r\nand global I/O properties of cortical neurons\", Doron et al, Cell Reports, 2017), examines the effect of timed inhibition over dendritic NMDA spikes on L5PC (Based on Hay et al., 2011) and CA1 cell (Based on Grunditz et al. 2008 and Golding et al. 2001)." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/231427", + "other_implementer": { + "attr_id": 308, + "value": "Michael Doron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "michael.doron@mail.huji.ac.il" + }, + "public_submitter_name": { + "attr_id": 621, + "value": 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"model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 233501, + "object_name": "Wilson CD, Serrano GO, Koulakov AA, Rinberg D (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Model for concentration invariant odor coding based on primacy hypothesis (Wilson et al 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... Here we\r\npropose that, in olfaction, a small and relatively stable set\r\ncomprised of the earliest activated receptors forms a code for\r\nconcentration-invariant odor identity. One prediction of this \u201cprimacy\r\ncoding\u201d scheme is that decisions based on odor identity can be made\r\nsolely using early odor-evoked neural activity. Using an optogenetic\r\nmasking paradigm, we define the sensory integration time necessary for\r\nodor identification and demonstrate that animals can use information\r\noccurring <100ms after inhalation onset to identify odors.\r\n... We\r\npropose a computational model demonstrating how such a code can be\r\nread by neural circuits of the olfactory system.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/231814", + "other_concept": { + "attr_id": 306, + "value": "concentration invariant o" + }, + "other_implementer": { + "attr_id": 308, + "value": "Koulakov, Alexei (akula@cshl.edu)" + }, + "other_neurons": { + "attr_id": 301, + "value": "piriform cortex neuron" + }, + "other_neurotransmitter": { + "attr_id": 304, + "value": "AMPA" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "akula@cshl.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Alexei Koulakov" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 2372, + "object_name": "Gaseous Receptors" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 249926, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2019-02-26T10:24:37.18", + "ver_number": 10 + }, + "231815": { + "class_id": 19, + "created": "2017-08-22T05:55:09.31", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 231815, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 262438, + "object_name": "Dan, Ohad [Ohad.Dan at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262436, + "object_name": "Dan O, Hopp E, Borst A, Segev I (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Vertical system (VS) fly cells with biophysics (Dan et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 116967, + "object_name": "Fly lobular plate vertical system cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The fly visual system offers a unique opportunity to explore computations performed by single neurons. Two previous studies characterized, in vivo, the receptive field (RF) of the vertical system (VS) cells of the blowfly (calliphora vicina), both intracellularly in the axon, and, independently using Ca2+ imaging, in hundreds of distal dendritic branchlets. We integrated this information into detailed passive cable and compartmental models of 3D reconstructed VS cells. Within a given VS cell type, the transfer resistance (TR) from different branchlets to the axon differs substantially, suggesting that they contribute unequally to the shaping of the axonal RF. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/231815", + "public_submitter_email": { + "attr_id": 309, + "value": "Ohad.Dan@mail.huji.ac.il" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ohad Dan" + }, + "ver_date": "2020-03-08T11:17:02.027", + "ver_number": 8 + }, + "231818": { + "class_id": 19, + "created": "2017-08-22T13:01:05.837", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 185501, + "object_name": "Kir2 leak" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122814, + "object_name": "Cav1.2 CACNA1C" + }, + { + "object_id": 122812, + "object_name": "Cav1.3 CACNA1D" + }, + { + "object_id": 122820, + "object_name": "Cav2.2 CACNA1B" + }, + { + "object_id": 122809, + "object_name": "Cav3.2 CACNA1H" + }, + { + "object_id": 226057, + "object_name": "HCN1" + }, + { + "object_id": 92083, + "object_name": "Nav1.2 SCN2A" + }, + { + "object_id": 92087, + "object_name": "Nav1.6 SCN8A" + }, + { + "object_id": 185462, + "object_name": "Kir2.1 KCNJ2" + }, + { + "object_id": 122326, + "object_name": "Kv1.1 KCNA1" + }, + { + "object_id": 122804, + "object_name": "Kv1.4 KCNA4" + }, + { + "object_id": 122816, + "object_name": "Kv2.1 KCNB1" + }, + { + "object_id": 122808, + "object_name": "Kv3.3 KCNC3" + }, + { + "object_id": 123580, + "object_name": "Kv3.4 KCNC4" + }, + { + "object_id": 112842, + "object_name": "Kv4.2 KCND2" + }, + { + "object_id": 229638, + "object_name": "KCa1.1 KCNMA1" + }, + { + "object_id": 122821, + "object_name": "KCa2.2 KCNN2" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 231818, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 234825, + "object_name": "Beining, Marcel [beining at fias.uni-frankfurt.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 155599, + "object_name": "Neurogenesis" + }, + { + "object_id": 143636, + "object_name": "Conductance distributions" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 234812, + "object_name": "Beining M, Mongiat LA, Schwarzacher SW, Cuntz H, Jedlicka P (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "GC model (Beining et al 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A companion modeldb entry (NEURON only) to modeldb accession number 231862." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/231818", + "other_implementer": { + "attr_id": 308, + "value": "Beining, Marcel [marcel.beining at gmail.com]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "marcel.beining@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Marcel Beining" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115947, + "object_name": "Dentate gyrus" + } + ] + }, + "ver_date": "2018-07-11T16:57:02.553", + "ver_number": 6 + }, + "231849": { + "citation": { + "attr_id": 391, + "value": "G. Massobrio, S. Martinoia, P. Massobrio.\r\nEquivalent circuit of the neuro-electronic junction for signal recordings from planar and engulfed micro- nano-electrodes.\r\nunder review on IEEE Transactions on Biomedical Circuits and Systems" + }, + "class_id": 19, + "created": "2017-08-28T08:57:08.58", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 231849, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 256934, + "object_name": "Massobrio G, Martinoia S, Massobrio P (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 231850, + "object_name": "HSPICE" + }, + { + "object_id": 257024, + "object_name": "ngspice" + } + ] + }, + "name": "The neuro-electronic junction (planar and engulfed electrodes) (Massobrio et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Models of the neuron, planar (GP\u00b5E) and mushroom-shaped (GM\u00b5E) microelectrodes, neuro-electronic junction (microelectrode-electrolyte interface, cleft effect, and protein-glycocalyx electric double layer) are presented. Then, neuronal electrical activity is simulated by HSPICE software, and analyzed as a function of the most sensitive biophysical models parameters such as the neuron-microelectrode cleft width, spreading and seal resistances, ion-channel densities, double-layer properties, and microelectrode geometries." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/231849", + "other_simulator": { + "attr_id": 307, + "value": "HSPICE" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "paolo.massobrio@unige.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Paolo Massobrio" + }, + "ver_date": "2019-05-24T18:51:03.207", + "ver_number": 11 + }, + "231859": { + "citation": { + "attr_id": 391, + "value": "None yet" + }, + "class_id": 19, + "created": "2017-08-31T14:38:42.073", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 231859, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 233321, + "object_name": "de Wiljes, O. O. [otdewiljes at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 233365, + "object_name": "Early evolution" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 233363, + "object_name": "de Wiljes OO, van Elburg RAJ, Keijzer FA (2017)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 231240, + "object_name": "Brian 2" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Modelling the effects of short and random proto-neural elongations (de Wiljes et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"To understand how neurons and nervous systems first evolved, we need\r\nan account of the origins of neural elongations: why did neural\r\nelongations (axons and dendrites) first originate, such that they\r\ncould become the central component of both neurons and nervous\r\nsystems? Two contrasting conceptual accounts provide different answers\r\nto this question. Braitenberg's vehicles provide the iconic\r\nillustration of the dominant input-output (IO) view. Here, the basic\r\nrole of neural elongations is to connect sensors to effectors, both\r\nsituated at different positions within the body. For this function,\r\nneural elongations are thought of as comparatively long and specific\r\nconnections, which require an articulated body involving substantial\r\ndevelopmental processes to build. Internal coordination (IC) models\r\nstress a different function for early nervous systems. Here, the\r\ncoordination of activity across extended parts of a multicellular body\r\nis held central, in particular, for the contractions of (muscle)\r\ntissue. An IC perspective allows the hypothesis that the earliest\r\nproto-neural elongations could have been functional even when they\r\nwere initially simple, short and random connections, as long as they\r\nenhanced the patterning of contractile activity across a multicellular\r\nsurface. The present computational study provides a proof of concept\r\nthat such short and random neural elongations can play this\r\nrole. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/231859", + "other_concept": { + "attr_id": 306, + "value": "Early evolution" + }, + "other_implementer": { + "attr_id": 308, + "value": "O. O. de Wiljes" + }, + "other_type": { + "attr_id": 305, + "value": "Abstract network" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "otdewiljes@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ot de Wiljes" + }, + "ver_date": "2017-11-20T13:52:46.553", + "ver_number": 9 + }, + "231862": { + "class_id": 19, + "created": "2017-09-03T05:06:14.7", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 185501, + "object_name": "Kir2 leak" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122814, + "object_name": "Cav1.2 CACNA1C" + }, + { + "object_id": 122812, + "object_name": "Cav1.3 CACNA1D" + }, + { + "object_id": 122820, + "object_name": "Cav2.2 CACNA1B" + }, + { + "object_id": 122809, + "object_name": "Cav3.2 CACNA1H" + }, + { + "object_id": 226057, + "object_name": "HCN1" + }, + { + "object_id": 229638, + "object_name": "KCa1.1 KCNMA1" + }, + { + "object_id": 122821, + "object_name": "KCa2.2 KCNN2" + }, + { + "object_id": 185462, + "object_name": "Kir2.1 KCNJ2" + }, + { + "object_id": 122326, + "object_name": "Kv1.1 KCNA1" + }, + { + "object_id": 122804, + "object_name": "Kv1.4 KCNA4" + }, + { + "object_id": 122816, + "object_name": "Kv2.1 KCNB1" + }, + { + "object_id": 122808, + "object_name": "Kv3.3 KCNC3" + }, + { + "object_id": 123580, + "object_name": "Kv3.4 KCNC4" + }, + { + "object_id": 112842, + "object_name": "Kv4.2 KCND2" + }, + { + "object_id": 92083, + "object_name": "Nav1.2 SCN2A" + }, + { + "object_id": 92087, + "object_name": "Nav1.6 SCN8A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 231862, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 234825, + "object_name": "Beining, Marcel [beining at fias.uni-frankfurt.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 143636, + "object_name": "Conductance distributions" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 155599, + "object_name": "Neurogenesis" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 234812, + "object_name": "Beining M, Mongiat LA, Schwarzacher SW, Cuntz H, Jedlicka P (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 258845, + "object_name": "T2N (web link to model)" + }, + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Mature and young adult-born dentate granule cell models (T2N interface) (Beining et al. 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "... Here, we present T2N, a powerful\r\ninterface to control NEURON with Matlab and TREES toolbox, which\r\nsupports generating models stable over a broad range of reconstructed\r\nand synthetic morphologies. We illustrate this for a novel,\r\nhighly-detailed active model of dentate granule cells (GCs)\r\nreplicating a wide palette of experiments from various labs. By\r\nimplementing known differences in ion channel composition and\r\nmorphology, our model reproduces data from mouse or rat, mature or\r\nadult-born GCs as well as pharmacological interventions and epileptic\r\nconditions.\r\n\r\n... T2N is suitable for creating robust models useful for\r\nlarge-scale networks that could lead to novel predictions. ...\"\r\nSee modeldb accession number 231818 for NEURON only code." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/231862", + "other_implementer": { + "attr_id": 308, + "value": "Beining, Marcel [beining at fias.uni-frankfurt.de]" + }, + "other_simulator": { + "attr_id": 307, + "value": "T2N" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "marcel.beining@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Marcel Beining" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115947, + "object_name": "Dentate gyrus" + } + ] + }, + "ver_date": "2020-08-26T19:13:57.703", + "ver_number": 18 + }, + "232023": { + "class_id": 19, + "created": "2017-09-07T04:49:43.777", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 232023, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 153454, + "object_name": "Hong, Sungho [shhong at oist.jp]" + }, + { + "object_id": 152114, + "object_name": "Guo, Daqing [dqguo at uestc.edu.cn]" + }, + { + "object_id": 217983, + "object_name": "Raikov, Ivan [ivan.g.raikov at gmail.com]" + }, + { + "object_id": 124065, + "object_name": "Publio, Rodrigo [publio at oist.jp]" + }, + { + "object_id": 112689, + "object_name": "De Schutter, Erik [erik at oist.jp]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 83540, + "object_name": "Winner-take-all" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 232814, + "object_name": "Sudhakar SK, Hong S, Raikov I, Publio R, Lang C, Close T, Guo D, Negrello M, De Schutter E (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Model of the cerebellar granular network (Sudhakar et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 112687, + "object_name": "Cerebellum golgi cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The granular layer, which mainly consists of granule and Golgi cells, is the first stage of the cerebellar cortex and processes spatiotemporal information transmitted by mossy fiber inputs with a wide variety of firing patterns. To study its dynamics at multiple time scales in response to inputs approximating real spatiotemporal patterns, we constructed a large-scale 3D network model of the granular layer. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/232023", + "other_concept": { + "attr_id": 306, + "value": "Winner-keep-winning" + }, + "other_implementer": { + "attr_id": 308, + "value": "Sudhakar, Shyam Kumar [shyamk at umich.edu]\r\nClose, Tom [tom.close at monash.edu]\r\nLang, Claus [clauslang at posteo.de]\r\nNegrello, Mario [mnegrello at gmail.com]" + }, + "other_neurons": { + "attr_id": 301, + "value": "Cerebellum granule cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "shhong@oist.jp" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Sungho Hong" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2022-11-04T13:25:04.2", + "ver_number": 17 + }, + "232074": { + "class_id": 19, + "created": "2017-09-09T15:56:12.173", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 232074, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 225082, + "object_name": "Milstein, Aaron D. [aaronmil at stanford.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 180821, + "object_name": "Hebbian plasticity" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 98011, + "object_name": "Place cell/field" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 232073, + "object_name": "Bittner KC, Milstein AD, Grienberger C, Romani S, Magee JC (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "Behavioral time scale synaptic plasticity underlies CA1 place fields (Bittner et al. 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Place fields could be produced\r\nin vivo in a single trial by potentiation of input that arrived seconds before and after complex\r\nspiking.The potentiated synaptic input was not initially coincident with action potentials or\r\ndepolarization.This rule, named behavioral timescale synaptic plasticity, abruptly modifies inputs\r\nthat were neither causal nor close in time to postsynaptic activation. ...\", \" ... To determine if the above plasticity rule could\r\nbe observed under more realistic model conditions,\r\nwe constructed and optimized a biophysically\r\ndetailed model and attempted to fully account\r\nfor the experimental data. ... \"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/232074", + "other_implementer": { + "attr_id": 308, + "value": "A. D. Milstein" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2017-09-09T16:16:54.873", + "ver_number": 5 + }, + "232094": { + "class_id": 19, + "created": "2017-09-09T16:39:49.5", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 232094, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 232092, + "object_name": "McColgan, Thomas " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 232025, + "object_name": "McColgan T, Liu J, Kuokkanen PT, Carr CE, Wagner H, Kempter R (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Dipolar extracellular potentials generated by axonal projections (McColgan et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52424, + "object_name": "Nucleus laminaris neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Here, we established experimentally and\r\ntheoretically that contributions of axons to EFPs can be significant. Modeling action\r\npotentials propagating along axons, we showed that EFPs were prominent in the\r\npresence of terminal zones where axons branch and terminate in close succession, as\r\nfound in many brain regions. Our models predicted a dipolar far field and a polarity\r\nreversal at the center of the terminal zone. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/232094", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 183076, + "object_name": "Brainstem" + }, + { + "object_id": 143145, + "object_name": "Auditory brainstem" + } + ] + }, + "ver_date": "2017-09-09T16:44:41.203", + "ver_number": 4 + }, + "232096": { + "citation": { + "attr_id": 391, + "value": "Synaptic transmission optimisation predicts expression loci of long-term plasticity (to appear)" + }, + "class_id": 19, + "created": "2017-09-11T18:34:39.71", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 232096, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 149959, + "object_name": "Costa, Rui Ponte [ruipontecosta at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 242871, + "object_name": "Costa RP, Padamsey Z, D'Amour JA, Emptage NJ, Froemke RC, Vogels TP (2017)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Statistical Long-term Synaptic Plasticity (statLTSP) (Costa et al 2017)", + "notes": { + "attr_id": 24, + "value": "In this paper we introduce a new statistical view of long-term synaptic plasticity, in which the postsynaptic responses are optimised towards a bound (or target). This in turn explains a wide range of experimental data." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/232096", + "public_submitter_email": { + "attr_id": 309, + "value": "rui.ponte.costa@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Rui Ponte Costa" + }, + "ver_date": "2018-06-12T10:11:34.137", + "ver_number": 10 + }, + "232097": { + "citation": { + "attr_id": 391, + "value": "Li G, Cleland TA (2017) A coupled-oscillator model of olfactory bulb gamma oscillations. PLOS Computational Biology (In press)." + }, + "class_id": 19, + "created": "2017-09-12T16:06:09.64", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 232097, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 149751, + "object_name": "Li, Guoshi [guoshi_li at med.unc.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 232098, + "object_name": "Li G, Cleland TA (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "2D model of olfactory bulb gamma oscillations (Li and Cleland 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + }, + { + "object_id": 3979, + "object_name": "Olfactory bulb main interneuron granule MC GABA cell" + }, + { + "object_id": 3978, + "object_name": "Olfactory bulb main interneuron periglomerular GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a biophysical model of the olfactory bulb (OB) that contains three types of neurons: mitral cells, granule cells and periglomerular cells. The model is used to study the cellular and synaptic mechanisms of OB gamma oscillations. We concluded that OB gamma oscillations can be best modeled by the coupled oscillator architecture termed pyramidal resonance inhibition network gamma (PRING)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/232097", + "public_submitter_email": { + "attr_id": 309, + "value": "guoshi_li@med.unc.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Guoshi Li" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "ver_date": "2022-05-27T18:37:05.073", + "ver_number": 6 + }, + "232813": { + "class_id": 19, + "created": "2017-09-17T05:57:57.89", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 88118, + "object_name": "I_KLT" + }, + { + "object_id": 88117, + "object_name": "I_KHT" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122326, + "object_name": "Kv1.1 KCNA1" + }, + { + "object_id": 233511, + "object_name": "Kv7.4 KCNQ4" + }, + { + "object_id": 233512, + "object_name": "Kvb2 KCNAB2" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 232813, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 233499, + "object_name": "Watanabe, Takaki [wtakaki at m.u-tokyo.ac.jp]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 237168, + "object_name": "Watanabe T, Shimazaki T, Oda Y (2017)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Zebrafish Mauthner-cell model (Watanabe et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 189309, + "object_name": "Mauthner cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The NEURON model files encode the channel generator and firing simulator for simulating development and differentiation of the Mauthner cell (M-cell) excitability in zebrafish. The channel generator enables us to generate arbitrary Na+ and K+ channels by changing parameters of a Hodgkin-Huxley model under emulation of two-electrode voltage-clamp recordings in Xenopus oocyte system. The firing simulator simulates current-clamp recordings to generate firing patterns of the model M-cell, which are implemented with arbitrary-generated basic Na+ and K+ conductances and low-threshold K+ channels Kv7.4/KCNQ4 and sole Kv1.1 or Kv1.1 coexpressed with Kvbeta2." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/232813", + "other_gene": { + "attr_id": 477, + "value": "Kv7.4 KCNQ4" + }, + "other_implementer": { + "attr_id": 308, + "value": "Takaki Watanabe" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "wtakaki@m.u-tokyo.ac.jp" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Takaki Watanabe" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 183076, + "object_name": "Brainstem" + } + ] + }, + "ver_date": "2018-01-15T15:39:17.24", + "ver_number": 28 + }, + "232855": { + "citation": { + "attr_id": 391, + "value": "Stinchcombe et al. 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J. Spike and Burst Coding in Thalamocortical Relay Cells. Under review at PLoS Computational Biology." + }, + "class_id": 19, + "created": "2017-09-25T08:28:29.507", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 232876, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 150218, + "object_name": "Zeldenrust, Fleur [fleurzeldenrust at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 136098, + "object_name": "Information transfer" + }, + { + "object_id": 136252, + "object_name": "Rebound firing" + }, + { + "object_id": 182362, + "object_name": "Sensory coding" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 239073, + "object_name": "Zeldenrust F, Chameau P, Wadman WJ (2018)" + }, + { + "object_id": 28235, + "object_name": "Destexhe A, Bal T, McCormick DA, Sejnowski TJ (1996)" + }, + { + "object_id": 127386, + "object_name": "Huguenard JP, Mccormick DA (1994)" + }, + { + "object_id": 3882, + "object_name": "Destexhe A, Rudolph M, Fellous JM, Sejnowski TJ (2001)" + }, + { + "object_id": 83443, + "object_name": "Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Morrison A, Goodman PH, Harris FC, Zirpe M, Natschl\u00e4ger T, Pecevski D, Ermentrout B, Djurfeldt M, Lansner A, Rochel O, Vieville T, Muller E, Davison AP, El Boustani S, Destexhe A (2007)" + }, + { + "object_id": 58969, + "object_name": "Vogels TP, Abbott LF (2005)" + }, + { + "object_id": 4161, + "object_name": "Destexhe A, Neubig M, Ulrich D, Huguenard J (1998)" + }, + { + "object_id": 4346, + "object_name": "Destexhe A, Babloyantz A, Sejnowski TJ (1993)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Thalamocortical Relay cell under current clamp in high-conductance state (Zeldenrust et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Mammalian thalamocortical relay (TCR) neurons switch their firing activity between a tonic spiking and a bursting regime. In a combined experimental and computational study, we investigated the features in the input signal that single spikes and bursts in the output spike train represent and how this code is influenced by the membrane voltage state of the neuron. \r\n\r\nIdentical frozen Gaussian noise current traces were injected into TCR neurons in rat brain slices to adjust, fine-tune and validate a three-compartment TCR model cell (Destexhe et al. 1998, accession number 279). Three currents were added: an h-current (Destexhe et al. 1993,1996, accession number 3343), a high-threshold calcium current and a calcium-\r\nactivated potassium current (Huguenard & McCormick 1994, accession number 3808). \r\n\r\nThe information content carried by the various types of events in the signal as well as by the whole signal was calculated. Bursts phase-lock to and transfer information at lower frequencies than single spikes. On depolarization the neuron transits smoothly from the predominantly bursting regime to a spiking regime, in which it is more sensitive to high-frequency fluctuations. \r\n\r\nFinally, the model was used to in the more realistic \u201chigh-conductance state\u201d (Destexhe et al. 2001, accession number 8115), while being stimulated with a Poisson input (Brette et al. 2007, Vogels & Abbott 2005, accession number 83319), where fluctuations are caused by (synaptic) conductance changes instead of current injection. Under \u201cstandard\u201d conditions bursts are difficult to initiate, given the high degree of inactivation of the T-type calcium current. Strong and/or precisely timed inhibitory currents were able to remove this inactivation.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/232876", + "public_submitter_email": { + "attr_id": 309, + "value": "F.Zeldenrust@neurophysiology.nl" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Fleur Zeldenrust" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2022-11-04T13:02:12.19", + "ver_number": 23 + }, + "232913": { + "class_id": 19, + "created": "2017-09-25T17:19:33.807", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 232913, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 136814, + "object_name": "Huys QJ, Paninski L (2009)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Smoothing of, and parameter estimation from, noisy biophysical recordings (Huys & Paninski 2009)", + "notes": { + "attr_id": 24, + "value": "\" ... Sequential Monte Carlo (\u201cparticle filtering\u201d) methods, in combination with a detailed biophysical description of a cell, are used for principled, model-based smoothing of noisy recording data. We also provide an alternative formulation of smoothing where the neural nonlinearities are estimated in a non-parametric manner. Biophysically important parameters of detailed models (such as channel densities, intercompartmental conductances, input resistances, and observation noise) are inferred automatically from noisy data via expectation-maximisation. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/232913", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2017-09-26T16:19:09.61", + "ver_number": 5 + }, + "232914": { + "citation": { + "attr_id": 391, + "value": "doi:10.1038/s41467-017-00740-z" + }, + "class_id": 19, + "created": "2017-09-26T05:33:59.163", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 232914, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 232915, + "object_name": 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reduced neuron model with voltage-dependent plasticity." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/232914", + "other_neurons": { + "attr_id": 301, + "value": "Neocortex L5, Neocortex L2/3" + }, + "other_type": { + "attr_id": 305, + "value": "Single neuron and network" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jacopobono@hotmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Jacopo Bono" + }, + "ver_date": "2019-03-20T09:07:40.127", + "ver_number": 17 + }, + "232956": { + "citation": { + "attr_id": 391, + "value": "21781976\r\n25167790\r\n26796228" + }, + "class_id": 19, + "created": "2017-09-28T20:07:25.623", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 167500, + "object_name": "Kir" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + 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"model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A mathematical model of a neurovascular unit (Dormanns et al 2015, 2016) (Farrs & David 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 87589, + "object_name": "Astrocyte" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 226, + "object_name": "NO" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Here a lumped parameter numerical model of a neurovascular unit is presented, representing an intercellular communication system based on ion exchange through pumps and channels between neurons, astrocytes, smooth muscle cells, endothelial cells, and the spaces between these cells: the synaptic cleft between the neuron and astrocyte, the perivascular space between the astrocyte and SMC, and the extracellular space surrounding the cells. \r\nThe model contains various cellular and chemical pathways such as potassium, astrocytic calcium, and nitric oxide.\r\nThe model is able to simulate neurovascular coupling, the process characterised by an increase in neuronal activity followed by a rapid dilation of local blood vessels and hence increased blood supply providing oxygen and glucose to cells in need." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/232956", + "other_implementer": { + "attr_id": 308, + "value": "Allanah Kenny\r\nTim David" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "allanah.kenny@pg.canterbury.ac.nz" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Allanah Kenny" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 212, + "object_name": "Glutamate" + }, + { + "object_id": 2391, + "object_name": 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Sequential neuromodulation of Hebbian\r\nplasticity offers mechanism for effective reward-based navigation. eLife. 2017;6." + }, + "class_id": 19, + "created": "2017-10-04T10:48:57.567", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 233396, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 233400, + "object_name": "Zannone, Sara [s.zannone14 at imperial.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + }, + { + "object_id": 116858, + "object_name": "Reward-modulated STDP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 233399, + "object_name": "Brzosko Z, Zannone S, Schultz W, Clopath C, Paulsen O (2017)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Sequential neuromodulation of Hebbian plasticity in reward-based navigation (Brzosko et al 2017)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + }, + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ...Here, we\r\ndemonstrate that sequential neuromodulation of STDP by acetylcholine and dopamine offers an\r\nefficacious model of reward-based navigation. Specifically, our experimental data in mouse\r\nhippocampal slices show that acetylcholine biases STDP toward synaptic depression, whilst\r\nsubsequent application of dopamine converts this depression into potentiation. Incorporating this\r\nbidirectional neuromodulation-enabled correlational synaptic learning rule into a computational\r\nmodel yields effective navigation toward changing reward locations, as in natural foraging\r\nbehavior. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/233396", + "other_implementer": { + "attr_id": 308, + "value": "Sara Zannone (s.zannone14@imperial.ac.uk)" + }, + "other_neurons": { + "attr_id": 301, + "value": "Abstract Spike Response Model0 " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "s.zannone14@imperial.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Sara Znnone" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2017-10-04T13:26:07.663", + "ver_number": 7 + }, + "233509": { + "citation": { + "attr_id": 391, + "value": "Guoshi Li, Craig Henriquez and Flavio Fr\u00f6hlich (2017) Unified Thalamic Model Generates Multiple Distinct Oscillations with State-dependent Entrainment by Stimulation. PLOS Computational Biology (In press)." + }, + "class_id": 19, + "created": "2017-10-06T15:42:53.197", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 249, + "object_name": "I K,leak" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 233509, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 149751, + "object_name": "Li, Guoshi [guoshi_li at med.unc.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 93420, + "object_name": "Sleep" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 156115, + "object_name": "Gamma oscillations" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 139148, + "object_name": "Brain Rhythms" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 233510, + "object_name": "Li G, Henriquez C, Fr\u00f6hlich F (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "A unified thalamic model of multiple distinct oscillations (Li, Henriquez and Fr\u00f6hlich 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + }, + { + "object_id": 263, + "object_name": "Thalamus reticular nucleus GABA cell" + }, + { + "object_id": 140252, + "object_name": "Thalamus lateral geniculate nucleus interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + }, + { + "object_id": 230, + "object_name": "Norephinephrine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We present a unified model of the thalamus that is capable of independently generating multiple distinct oscillations (delta, spindle, alpha and gamma oscillations) under different levels of acetylcholine (ACh) and norepinephrine (NE) modulation corresponding to different physiological conditions (deep sleep, light sleep, relaxed wakefulness and attention). The model also shows that entrainment of thalamic oscillations is state-dependent." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/233509", + "public_submitter_email": { + "attr_id": 309, + "value": "guoshi_li@med.unc.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Guoshi Li" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2017-11-09T19:23:23.68", + "ver_number": 5 + }, + "234097": { + "citation": { + "attr_id": 391, + "value": "Hebbian Learning in a Random Network Captures Selectivity Properties of Prefrontal Cortex\r\nGrace W. Lindsay, Mattia Rigotti, Melissa R. Warden, Earl K. Miller and Stefano Fusi\r\nJournal of Neuroscience 6 October 2017, 1222-17; DOI: https://doi.org/10.1523/JNEUROSCI.1222-17.2017" + }, + "class_id": 19, + "created": "2017-10-17T19:26:48.29", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 234097, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83535, + "object_name": "Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 234110, + "object_name": "Lindsay GW, Rigotti M, Warden MR, Miller EK, Fusi S (2017)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Hebbian learning in a random network for PFC modeling (Lindsay, et al. 2017)", + "notes": { + "attr_id": 24, + "value": "Creates a random model that replicates the inputs and outputs of PFC cells during a complex task. Then executes Hebbian learning in the model and performs a set of analyses on the output. A portion of this model's analysis requires code from: https://github.com/brian-lau/highdim" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/234097", + "other_neurons": { + "attr_id": 301, + "value": "Prefrontal Cortical Neuron" + }, + "other_type": { + "attr_id": 305, + "value": "Firing rate" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "gracewlindsay@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Grace Lindsay" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "ver_date": "2017-10-21T19:42:53.587", + "ver_number": 7 + }, + "234101": { + "citation": { + "attr_id": 391, + "value": "Simulation software used in Danner SM, Shevtsova NA, Frigon A, Rybak IA.\r\nLong propriospinal neurons and gait expression in quadrupeds. eLife. submitted" + }, + "class_id": 19, + "created": "2017-10-18T14:12:57.77", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 234101, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 235164, + "object_name": "Danner, Simon M [simon.danner at gmail.com]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 235161, + "object_name": "Danner SM, Shevtsova NA, Frigon A, Rybak IA (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Spinal circuits controlling limb coordination and gaits in quadrupeds (Danner et al 2017)", + "notes": { + "attr_id": 24, + "value": "Simulation of spinal neural networks involved in the central control of interlimb coordination and speed-dependent gait expression in quadrupeds." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/234101", + "other_concept": { + "attr_id": 306, + "value": "central pattern generator" + }, + "other_currents": { + "attr_id": 303, + "value": "persistent-sodium current" + }, + "other_implementer": { + "attr_id": 308, + "value": "Simon Danner" + }, + "other_neurons": { + "attr_id": 301, + "value": "activitiy-based neuron model" + }, + "other_simulator": { + "attr_id": 307, + "value": "custom simulator" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "simon.danner@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Simon Danner" + }, + "ver_date": "2017-12-06T17:20:50.99", + "ver_number": 26 + }, + "234111": { + "citation": { + "attr_id": 391, + "value": "Joos B., Barlow B.M., Morris C.E. (2017) Calculating the Consequences of Left-Shifted Nav Channel Activity in Sick Excitable Cells. In: . Handbook of Experimental Pharmacology. Springer, Berlin, Heidelberg https://doi.org/10.1007/164_2017_63" + }, + "class_id": 19, + "created": "2017-10-20T14:30:58.59", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92087, + "object_name": "Nav1.6 SCN8A" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 234111, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 263686, + "object_name": "Barlow, Benjamin Stephen [BBarlow at uottawa.ca]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3642, + "object_name": "Tutorial/Teaching" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 234114, + "object_name": "Joos B, Barlow BM, Morris CE (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Calculating the consequences of left-shifted Nav channel activity in sick cells (Joos et al 2018)", + "notes": { + "attr_id": 24, + "value": "\"Two features common to diverse sick excitable cells are \u201cleaky\u201d Nav channels and bleb damage-damaged membranes. The bleb damage, we have argued, causes a channel kinetics based \u201cleakiness.\u201d Recombinant (node of Ranvier type) Nav1.6 channels voltage-clamped in mechanically-blebbed cell-attached patches undergo a damage intensity dependent kinetic change. Specifically, they experience a coupled hyperpolarizing (left) shift of the activation and inactivation processes. The biophysical observations on Nav1.6 currents formed the basis of Nav-Coupled Left Shift (Nav-CLS) theory. Node of Ranvier excitability can be modeled with Nav-CLS imposed at varying LS intensities and with varying fractions of total nodal membrane affected. Mild damage from which sick excitable cells might recover is of most interest pathologically. Accordingly, Na+/K+ ATPase (pump) activity was included in the modeling. As we described more fully in our other recent reviews, Nav-CLS in nodes with pumps proves sufficient to predict many of the pathological excitability phenomena reported for sick excitable cells. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/234111", + "public_submitter_email": { + "attr_id": 309, + "value": "BBarl039@uottawa.ca" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Benjamin M. Barlow" + }, + "ver_date": "2020-04-22T12:34:58.327", + "ver_number": 11 + }, + "234118": { + "citation": { + "attr_id": 391, + "value": "Sen-Bhattacharya, B., Serrano-Gotarredona, T., Balassa, L., Bhattacharya, A., Stokes, A. B., Rowley, A., \u2026 Furber, S. (2017). A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine. Frontiers in Neuroscience, 11, 454. http://doi.org/10.3389/fnins.2017.00454" + }, + "class_id": 19, + "created": "2017-10-23T06:58:28.16", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 234118, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 139147, + "object_name": "Sen-Bhattacharya, Basabdatta [basab.sen.b at gmail.com]" + }, + { + "object_id": 234129, + "object_name": "Serrano-Gotarredona T " + }, + { + "object_id": 234130, + "object_name": "Balassa L " + }, + { + "object_id": 234131, + "object_name": "Bhattacharya A " + }, + { + "object_id": 234132, + "object_name": "Stokes AB " + }, + { + "object_id": 234133, + "object_name": "Rowley A " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 234126, + "object_name": "Sen-Bhattacharya B, Serrano-Gotarredona T, Balassa L, Bhattacharya A, Stokes AB, Rowley A, Sugiarto I, Furber S (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 234127, + "object_name": "SpiNNaker" + } + ] + }, + "name": "A spiking neural network model of the Lateral Geniculate Nucleus (Sen-Bhattacharya et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Using Izhikevich's spiking neuron models, to build a network with a biologically informed synaptic layout emulating the Lateral Geniculate Nucleus." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/234118", + "public_submitter_email": { + "attr_id": 309, + "value": "basab.sen.b@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Basabdatta Sen Bhattacharya" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2017-10-23T10:22:33.58", + "ver_number": 7 + }, + "234233": { + "class_id": 19, + "created": "2017-10-26T11:45:11.937", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 234233, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 237602, + "object_name": "Knox, Andrew [knox at neurology.wisc.edu]" + }, + { + "object_id": 33744, + "object_name": "Destexhe, Alain [Destexhe at iaf.cnrs-gif.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 226617, + "object_name": "Spindles" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 237601, + "object_name": "Knox AT, Glauser T, Tenney J, Lytton WW, Holland K (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Knox implementation of Destexhe 1998 spike and wave oscillation model (Knox et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 263, + "object_name": "Thalamus reticular nucleus GABA cell" + }, + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + }, + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + }, + { + "object_id": 184128, + "object_name": "Neocortex layer 4 pyramidal cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ...The aim of this study was to use an established thalamocortical computer model to determine how T-type calcium channels work in concert with cortical excitability to contribute to pathogenesis and treatment response in CAE.\r\n\r\nMETHODS:\r\nThe model is comprised of cortical pyramidal, cortical inhibitory, thalamocortical relay, and thalamic reticular single-compartment neurons, implemented with Hodgkin-Huxley model ion channels and connected by AMPA, GABAA , and GABAB synapses. Network behavior was simulated for different combinations of T-type calcium channel conductance, inactivation time, steady state activation/inactivation shift, and cortical GABAA conductance.\r\n\r\nRESULTS:\r\nDecreasing cortical GABAA conductance and increasing T-type calcium channel conductance converted spindle to spike and wave oscillations; smaller changes were required if both were changed in concert. In contrast, left shift of steady state voltage activation/inactivation did not lead to spike and wave oscillations, whereas right shift reduced network propensity for oscillations of any type....\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/234233", + "public_submitter_email": { + "attr_id": 309, + "value": "Knox@neurology.wisc.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Andrew Knox" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2018-01-29T20:01:31.697", + "ver_number": 12 + }, + "234241": { + "citation": { + "attr_id": 391, + "value": "Wilmes, K. A., Schleimer, J.-H. and Schreiber, S. (2017), Spike-timing dependent inhibitory plasticity to learn a selective gating of backpropagating action potentials. Eur J Neurosci, 45: 1032\u20131043. doi:10.1111/ejn.13326" + }, + "class_id": 19, + "created": "2017-10-29T12:15:14.373", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 234241, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 234245, + "object_name": "Wilmes, Katharina A. [katharina.wilmes at googlemail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 87578, + "object_name": "STDP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 234243, + "object_name": "Wilmes KA, Schleimer JH, Schreiber S (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Spike-timing dependent inhibitory plasticity for gating bAPs (Wilmes et al 2017)", + "notes": { + "attr_id": 24, + "value": "\"Inhibition is known to influence the forward-directed flow of information within neurons. However, also regulation of backward-directed signals, such as backpropagating action potentials (bAPs), can enrich the functional repertoire of local circuits. Inhibitory\r\ncontrol of bAP spread, for example, can provide a switch for the plasticity of excitatory synapses. Although such a mechanism is\r\npossible, it requires a precise timing of inhibition to annihilate bAPs without impairment of forward-directed excitatory information flow. Here, we propose a specific learning rule for inhibitory synapses to automatically generate the correct timing to gate bAPs in pyramidal cells when embedded in a local circuit of feedforward inhibition. Based on computational modeling of multi-compartmental neurons with physiological properties, we demonstrate that a learning rule with anti-Hebbian shape can establish the\r\nrequired temporal precision. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/234241", + "other_neurons": { + "attr_id": 301, + "value": "Pyramidal Neurons, Interneuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "katharina.wilmes@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Katharina Anna Wilmes" + }, + "ver_date": "2017-11-02T18:39:28.983", + "ver_number": 15 + }, + "234313": { + "class_id": 19, + "created": "2017-11-02T04:48:22.547", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 234313, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 234436, + "object_name": "Salimi-Badr, Armin [armin.salimibadr at aut.ac.ir]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 236374, + "object_name": "Salimi-Badr A, Ebadzadeh MM, Darlot C (2018)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 83572, + "object_name": "Simulink" + } + ] + }, + "name": "Basal Ganglia motor-circuit for kinematic planning of arm movements (Salimi-Badr et al 2017)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A mathematical model of BG for kinematic planning." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/234313", + "other_type": { + "attr_id": 305, + "value": "System-level" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "armin.salimibadr@aut.ac.ir" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Armin Salimi-Badr" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2018-01-09T10:39:20.17", + "ver_number": 9 + }, + "234992": { + "citation": { + "attr_id": 391, + "value": "Zerlaut, Y., Chemla, S., Chavane, F. et al. J Comput Neurosci (2017). https://doi.org/10.1007/s10827-017-0668-2" + }, + "class_id": 19, + "created": "2017-11-29T10:55:44.277", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 234992, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 234994, + "object_name": "Zerlaut Y, Chemla S, Chavane F, Destexhe A (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 231240, + "object_name": "Brian 2" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Mesoscopic dynamics from AdEx recurrent networks (Zerlaut et al JCNS 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We present a mean-field model of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based synaptic interactions. We study a network of regular-spiking (RS) excitatory neurons and fast-spiking (FS) inhibitory neurons. We use a Master Equation formalism, together with a semi-analytic approach to the transfer function of AdEx neurons to describe the average dynamics of the coupled populations. We compare the predictions of this mean-field model to simulated networks of RS-FS cells, first at the level of the spontaneous activity of the network, which is well predicted by the analytical description. Second, we investigate the response of the network to time-varying external input, and show that the mean-field model predicts the response time course of the population. Finally, to model VSDi signals, we consider a one-dimensional ring model made of interconnected RS-FS mean-field units." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/234992", + "public_submitter_email": { + "attr_id": 309, + "value": "yann.zerlaut@iit.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Yann Zerlaut" + }, + "ver_date": "2020-04-21T08:55:57.04", + "ver_number": 15 + }, + "235002": { + "citation": { + "attr_id": 391, + "value": "https://link.springer.com/protocol/10.1007/978-1-4939-7321-7_17" + }, + "class_id": 19, + "created": "2017-11-30T10:58:59.563", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 235002, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 236412, + "object_name": "Keller AF, Bouteiller JC, Berger TW (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "name": "A computational approach/model to explore NMDA receptors functions (Keller et al 2017)", + "notes": { + "attr_id": 24, + "value": "\"...\r\nHere, we describe a general computational method\r\naiming at developing kinetic Markov-chain based models of NMDARs\r\nsubtypes capable of reproducing various experimental\r\nresults.\r\n\r\nThese models are then used to make predictions on\r\nadditional (non-obvious) properties and on their role in synaptic\r\nfunction under various physiological and pharmacological\r\nconditions.\r\n\r\n\r\nFor the purpose of this book chapter, we will focus\r\non the method used to develop a NMDAR model that includes\r\npharmacological site of action of different compounds. Notably,\r\nthis elementary model can subsequently be included in a neuron\r\nmodel (not described in detail here) to explore the impact of\r\ntheir differential distribution on synaptic functions.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/235002", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2018-01-19T11:30:16.463", + "ver_number": 9 + }, + "235052": { + "citation": { + "attr_id": 391, + "value": "A computational model for how the fast afterhyperpolarization paradoxically increases gain in regularly firing neurons, J. Neurophysiology (under review)\r\n\r\nD.B Jaffe, D.B.* and Brenner R.**\r\n*Department of Biology, UTSA Neurosciences Institute, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA.\r\n**Department of Cell and Integrative Physiology, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA." + }, + "class_id": 19, + "created": "2017-12-04T11:03:56.343", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 241, + "object_name": "I Na,p" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 235052, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 140473, + "object_name": "Jaffe, David B [david.jaffe at utsa.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 237472, + "object_name": "Jaffe DB, Brenner R (2018)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Paradoxical effect of fAHP amplitude on gain in dentate gyrus granule cells (Jaffe & Brenner 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The afterhyperpolarization (AHP) is canonically viewed as a major factor underlying the\r\nrefractory period, serving to limit neuronal firing rate. We recently reported (Wang et al, \r\nJ. Neurophys. 116:456, 2016) that enhancing the amplitude of the fast AHP in \r\na relatively slowly firing neuron (versus fast spiking neurons), augments neuronal excitability\r\nin dentate gyrus granule neurons expressing gain-of-function BK channels. Here we present a novel, \r\nquantitative hypothesis for how varying the amplitude of the fast AHP (fAHP) can, paradoxically, \r\ninfluence a subsequent spike tens of milliseconds later." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/235052", + "public_submitter_email": { + "attr_id": 309, + "value": "david.jaffe@utsa.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "David B. Jaffe" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115947, + "object_name": "Dentate gyrus" + } + ] + }, + "ver_date": "2019-01-04T17:27:36.23", + "ver_number": 14 + }, + "235053": { + "citation": { + "attr_id": 391, + "value": "PMC5653655" + }, + "class_id": 19, + "created": "2017-12-04T11:48:39.243", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 235053, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 235162, + "object_name": "Cayco Gajic, Alex [natasha.gajic at ucl.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 185463, + "object_name": "Pattern Separation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 235057, + "object_name": "Cayco-Gajic NA, Clopath C, Silver RA (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Sparse connectivity is required for decorrelation, pattern separation (Cayco-Gajic et al 2017)", + "notes": { + "attr_id": 24, + "value": "\" ... To investigate the structural and functional determinants of pattern separation we built models of the cerebellar input layer with spatially correlated input patterns, and systematically varied their synaptic connectivity. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/235053", + "public_submitter_email": { + "attr_id": 309, + "value": "natasha.gajic@ucl.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Alex Cayco Gajic" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2017-12-06T16:55:48.79", + "ver_number": 10 + }, + "235054": { + "class_id": 19, + "created": "2017-12-04T11:59:23.08", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 235054, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144508, + "object_name": "Orio, Patricio [patricio.orio at uv.cl]" + }, + { + "object_id": 235055, + "object_name": "Richard, Alexandre " + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 239191, + "object_name": "Richard A, Orio P, Tanr\u00e9 E (2018)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Perfect Integrate and fire with noisy adaptation or fractional noise (Richard et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Here we show that a purely Markovian integrate-and-fire (IF) model, with a noisy slow adaptation term, can generate interspike intervals (ISIs) that appear as having Long-range dependency (LRD). However a proper analysis shows that this is not the case asymptotically. For comparison, we also consider a new model of individual IF neuron with fractional (non-Markovian) noise. The correlations of its spike trains are studied and proven to have LRD, unlike classical IF models.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/235054", + "other_concept": { + "attr_id": 306, + "value": "fractional noise" + }, + "other_implementer": { + "attr_id": 308, + "value": "Alexandre Richard" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "patricio.orio@uv.cl" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Patricio Orio" + }, + "ver_date": "2018-03-27T20:09:39.163", + "ver_number": 12 + }, + "235123": { + "citation": { + "attr_id": 391, + "value": "http://journal.frontiersin.org/article/10.3389/fnins.2017.00341/full" + }, + "class_id": 19, + "created": "2017-12-04T18:33:35.397", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 235123, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 243973, + "object_name": "Vannucci, Lorenzo [l.vannucci at santannapisa.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 226617, + "object_name": "Spindles" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 243276, + "object_name": "Vannucci L, Falotico E, Laschi C (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 2832, + "object_name": "NEST" + }, + { + "object_id": 234127, + "object_name": "SpiNNaker" + } + ] + }, + "name": "Neuromorphic muscle spindle model (Vannucci et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 243977, + "object_name": "Dorsal Root Ganglion cell: Spinal cord muscle spindle type Ia sensory fiber" + }, + { + "object_id": 243978, + "object_name": "Dorsal Root Ganglion cell: Spinal cord muscle spindle type II sensory fiber" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A fully spike-based, biologically inspired mechanism for the translation of proprioceptive feedback." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/235123", + "other_implementer": { + "attr_id": 308, + "value": "Lorenzo Vannucci" + }, + "other_neurons": { + "attr_id": 301, + "value": "Spinal cord muscle spindle" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user" + }, + "ver_date": "2018-07-18T16:18:52.547", + "ver_number": 7 + }, + "235138": { + "citation": { + "attr_id": 391, + "value": "https://arxiv.org/pdf/1709.06824" + }, + "class_id": 19, + "created": "2017-12-05T11:57:20.027", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 235138, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 245202, + "object_name": "Van Pottelbergh, Tomas\r\n[tmjv2 at cam.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 143724, + "object_name": "Bifurcation" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 97752, + "object_name": "Delay" + }, + { + "object_id": 225299, + "object_name": "Multiscale" + }, + { + "object_id": 222956, + "object_name": "Neuromodulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 245201, + "object_name": "Van Pottelbergh T, Drion G, Sepulchre R (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113733, + "object_name": "Brian" + }, + { + "object_id": 231240, + "object_name": "Brian 2" + } + ] + }, + "name": "Robust modulation of integrate-and-fire models (Van Pottelbergh et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + }, + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + }, + { + "object_id": 240378, + "object_name": "Abstract integrate-and-fire neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"By controlling the state of neuronal populations, neuromodulators ultimately affect behavior. A key neuromodulation mechanism is the alteration of neuronal excitability via the modulation of ion channel expression. This type of neuromodulation is normally studied with conductance-based models, but those models are computationally challenging for large-scale network simulations needed in population studies. This article studies the modulation properties of the multiquadratic integrate-and-fire model, a generalization of the classical quadratic integrate-and-fire model. The model is shown to combine the computational economy of integrate-and-fire modeling and the physiological interpretability of conductance-based modeling. It is therefore a good candidate for affordable computational studies of neuromodulation in large networks.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/235138", + "other_concept": { + "attr_id": 306, + "value": "Robustness" + }, + "other_implementer": { + "attr_id": 308, + "value": "Van Pottelbergh, Tomas" + }, + "other_neurons": { + "attr_id": 301, + "value": "Abstract multi-quadratic integrate-and-fire (MQIF)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user" + }, + "ver_date": "2018-09-19T11:05:07.933", + "ver_number": 10 + }, + "235320": { + "citation": { + "attr_id": 391, + "value": "J. Neurosci ; 10.1523/JNEUROSCI.1432-17.2017" + }, + "class_id": 19, + "created": "2017-12-08T18:18:18.47", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 235320, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 235324, + "object_name": "Lopez-Jury, Luciana [lucianalopezjury at gmail.com]" + }, + { + "object_id": 33801, + "object_name": "Canavier, CC" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 227131, + "object_name": "Pacemaking mechanism" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 235323, + "object_name": "Meza RC, L\u00f3pez-Jury L, Canavier CC, Henny P (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Role of the AIS in the control of spontaneous frequency of dopaminergic neurons (Meza et al 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 261, + "object_name": "Substantia nigra pars compacta DA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Computational modeling showed that the\r\nsize of the Axon Initial Segment (AIS), but not its position within the somatodendritic domain, is the major causal determinant of the tonic firing rate in the intact model, by virtue of the higher intrinsic frequency of the isolated AIS. Further mechanistic analysis of the relationship between neuronal morphology and firing rate showed that dopaminergic neurons function as a coupled oscillator whose frequency of discharge results from a compromise between AIS and somatodendritic oscillators." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/235320", + "other_implementer": { + "attr_id": 308, + "value": "Luciana Lopez-Jury" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "lucianalopezjury@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Luciana Francisca Lopez Jury" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + }, + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 249926, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2019-02-26T10:25:05.01", + "ver_number": 10 + }, + "235376": { + 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Neuronal Models" + }, + { + "object_id": 151948, + "object_name": "Potassium buffering" + }, + { + "object_id": 167716, + "object_name": "Spreading depression" + }, + { + "object_id": 235462, + "object_name": "Spreading depolarization" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 235380, + "object_name": "Conte C, Lee R, Sarkar M, Terman D (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 120799, + "object_name": "Glia" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "A model for recurrent spreading depolarizations (Conte et al. 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 87589, + "object_name": "Astrocyte" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A detailed biophysical model for a neuron/astrocyte network is developed in order to explore mechanisms responsible for cortical spreading depolarizations. This includes a model for the Na+-glutamate transporter, which allows for a detailed description of reverse glutamate uptake. In particular, we consider the specific roles of elevated extracellular glutamate and K+ in the initiation, propagation and recurrence of spreading depolarizations." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/235377", + "other_concept": { + "attr_id": 306, + "value": "Spreading depolarization" + }, + "other_currents": { + "attr_id": 303, + "value": "Na+-glutamate transporter" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "terman.1@osu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "David Hillel Terman" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "ver_date": "2017-12-14T18:58:42.46", + "ver_number": 6 + }, + "235561": { + "citation": { + "attr_id": 391, + "value": "Hall et al 2017" + }, + "class_id": 19, + "created": "2017-12-13T11:09:07.987", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 235561, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33800, + "object_name": "Traub, Roger D [rtraub at us.ibm.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 235468, + "object_name": "Hall SP, Traub RD, Adams NE, Cunningham MO, Schofield I, Jenkins AJ, Whittington MA (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 45540, + "object_name": "FORTRAN" + } + ] + }, + "name": "The origin of different spike and wave-like events (Hall et al 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + }, + { + "object_id": 263, + "object_name": "Thalamus reticular nucleus GABA cell" + }, + { + "object_id": 167345, + "object_name": "Neocortex U1 L6 pyramidal corticalthalamic GLU cell" + }, + { + "object_id": 167344, + "object_name": "Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + }, + { + "object_id": 142232, + "object_name": "Neocortex spiking low threshold (LTS) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Acute In vitro models have revealed a great deal of information about\r\nmechanisms underlying many types of epileptiform activity. However,\r\nfew examples exist that shed light on spike and wave (SpW) patterns of\r\npathological activity. SpW are seen in many epilepsy syndromes, both\r\ngeneralised and focal, and manifest across the entire age\r\nspectrum. They are heterogeneous in terms of their severity, symptom\r\nburden and apparent anatomical origin (thalamic, neocortical or both),\r\nbut any relationship between this heterogeneity and underlying\r\npathology remains elusive. Here we demonstrate that physiological\r\ndelta frequency rhythms act as an effective substrate to permit\r\nmodelling of SpW of cortical origin and may help to address this\r\nissue.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/235561", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Roger Truab" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2017-12-13T11:09:57.903", + "ver_number": 2 + }, + "235768": { + "citation": { + "attr_id": 391, + "value": "Hall et al 2017" + }, + "class_id": 19, + "created": "2017-12-18T17:18:50.567", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 235768, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33800, + "object_name": "Traub, Roger D [rtraub at us.ibm.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 235505, + "object_name": "Hall S, Hunt M, Simon A, Cunnington LG, Carracedo LM, Schofield IS, Forsyth R, Traub RD, Whittington MA (2015)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 45540, + "object_name": "FORTRAN" + } + ] + }, + "name": "Unbalanced peptidergic inhibition in superficial cortex underlies seizure activity (Hall et al 2015)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + }, + { + "object_id": 263, + "object_name": "Thalamus reticular nucleus GABA cell" + }, + { + "object_id": 167344, + "object_name": "Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell" + }, + { + "object_id": 167345, + "object_name": "Neocortex U1 L6 pyramidal corticalthalamic GLU cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + }, + { + "object_id": 142232, + "object_name": "Neocortex spiking low threshold (LTS) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ...Loss of tonic neuromodulatory excitation, mediated by nicotinic acetylcholine or serotonin (5HT3A) receptors, of 5HT3-immunopositive interneurons caused an increase in amplitude and slowing of the delta rhythm until each period became the \"wave\" component of the spike and wave discharge. As with the normal delta rhythm, the wave of a spike and wave discharge originated in cortical layer 5. In contrast, the \"spike\" component of the spike and wave discharge originated from a relative failure of fast inhibition in layers 2/3-switching pyramidal cell action potential outputs from single, sparse spiking during delta rhythms to brief, intense burst spiking, phase-locked to the field spike. The mechanisms underlying this loss of superficial layer fast inhibition, and a concomitant increase in slow inhibition, appeared to be precipitated by a loss of neuropeptide Y (NPY)-mediated local circuit inhibition and a subsequent increase in vasoactive intestinal peptide (VIP)-mediated disinhibition. Blockade of NPY Y1 receptors was sufficient to generate spike and wave discharges, whereas blockade of VIP receptors almost completely abolished this form of epileptiform activity. These data suggest that aberrant, activity-dependent neuropeptide corelease can have catastrophic effects on neocortical dynamics.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/235768", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Roger Truab" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2017-12-19T12:07:13.227", + "ver_number": 9 + }, + "235769": { + "citation": { + "attr_id": 391, + "value": "28336534" + }, + "class_id": 19, + "created": "2017-12-19T00:27:49.23", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122812, + "object_name": "Cav1.3 CACNA1D" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 235769, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 235771, + "object_name": "Kim, Hojeong [hojeong.kim03 at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 235770, + "object_name": "Kim H (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A model of slow motor unit (Kim, 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 276, + "object_name": "Spinal cord lumbar motor neuron alpha ACh cell" + }, + { + "object_id": 105531, + "object_name": "Skeletal muscle cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Cav1.3 channels in motoneuron dendrites are actively involved during normal motor activities. To investigate the effects of the activation of motoneuron Cav1.3 channels on force production, a model motor unit was built based on best-available data. The simulation results suggest that force potentiation induced by Cav1.3 channel activation is strongly modulated not only by firing history of the motoneuron but also by length variation of the muscle as well as neuromodulation inputs from the brainstem." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/235769", + "other_implementer": { + "attr_id": 308, + "value": "Kim, Hojeong [hojeong.kim03 at gmail.com]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "hojeong.kim03@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Hojeong Kim" + }, + "ver_date": "2023-01-27T13:26:29.437", + "ver_number": 14 + }, + "235774": { + "biosimulations": { + "value": "modeldb-235774" + }, + "citation": { + "attr_id": 391, + "value": "The role of glutamate in neuronal ion homeostasis: A case study of spreading depolarization.\r\nPLoS Computational Biology, Oct 12 (2017), 13(10):e1005804.\r\nPMID: 29023523" + }, + "class_id": 19, + "created": "2017-12-19T13:27:12.593", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 235774, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144977, + "object_name": "Ullah, Ghanim [ghanim.phy at gmail.com]" + }, + { + "object_id": 235778, + "object_name": "Hubel, Niklas " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 235777, + "object_name": "H\u00fcbel N, Hosseini-Zare MS, \u017diburkus J, Ullah G (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "The role of glutamate in neuronal ion homeostasis: spreading depolarization (Hubel et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 87589, + "object_name": "Astrocyte" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model includes ion concentration dynamics (sodium, potassium, chloride) inside and outside the neuron, the exchange of ions with glia and blood vessels, volume dynamics of neuron, glia, and extracellular space, glutamate homeostasis involving release by neuron and uptake by both neuron and glia. Spreading depolarization is used as a case study." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/235774", + "other_implementer": { + "attr_id": 308, + "value": "Hubel, Niklas" + }, + "other_neurons": { + "attr_id": 301, + "value": "Neuron (general)" + }, + "other_simulator": { + "attr_id": 307, + "value": "AUTO" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "gullah@usf.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ghanim Ullah" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115960, + "object_name": "Generic" + }, + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2017-12-19T16:00:43.013", + "ver_number": 7 + }, + "236306": { + "class_id": 19, + "created": "2018-01-03T16:58:49.93", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 236306, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 236307, + "object_name": "Fountas, Zafeirios [zfountas at imperial.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 136098, + "object_name": "Information transfer" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 236143, + "object_name": "Fountas Z, Shanahan M (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113733, + "object_name": "Brian" + } + ] + }, + "name": "Cortical oscillations and the basal ganglia (Fountas & Shanahan 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 182735, + "object_name": "Subthalamic nucleus principal GABA cell" + }, + { + "object_id": 182591, + "object_name": "Globus pallidus principal GABA cell" + }, + { + "object_id": 182544, + "object_name": "Substantia nigra pars reticulata principal GABA cell" + }, + { + "object_id": 74300, + "object_name": "Subthalamus nucleus projection neuron" + }, + { + "object_id": 88217, + "object_name": "Globus pallidus neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Although brain oscillations involving the basal ganglia (BG) have been\r\nthe target of extensive research, the main focus lies\r\ndisproportionally on oscillations generated within the BG circuit\r\nrather than other sources, such as cortical areas. We remedy this here\r\nby investigating the influence of various cortical frequency bands on\r\nthe intrinsic effective connectivity of the BG, as well as the role of\r\nthe latter in regulating cortical behaviour. To do this, we construct\r\na detailed neural model of the complete BG circuit based on fine-tuned\r\nspiking neurons, with both electrical and chemical synapses as well as\r\nshort-term plasticity between structures. As a measure of effective\r\nconnectivity, we estimate information transfer between nuclei by means\r\nof transfer entropy. Our model successfully reproduces firing and\r\noscillatory behaviour found in both the healthy and Parkinsonian\r\nBG. We found that, indeed, effective connectivity changes dramatically\r\nfor different cortical frequency bands and phase offsets, which are\r\nable to modulate (or even block) information flow in the three major\r\nBG pathways. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/236306", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 116868, + "object_name": "Subthalamic Nucleus" + }, + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2018-01-03T17:05:23.633", + "ver_number": 4 + }, + "236310": { + "class_id": 19, + "created": "2018-01-05T10:03:58.133", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 236310, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 206291, + "object_name": "Membrane Properties" + }, + { + "object_id": 64175, + "object_name": "Electrotonus" + }, + { + "object_id": 143085, + "object_name": "Synaptic-input statistic" + }, + { + "object_id": 261622, + "object_name": "Huntington's" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 248831, + "object_name": "Goodliffe JW, Song H, Rubakovic A, Chang W, Medalla M, Weaver CM, Luebke JI (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Electrotonic transform and EPSCs for WT and Q175+/- spiny projection neurons (Goodliffe et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 150619, + "object_name": "Neostriatum spiny neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model achieves electrotonic transform and computes mean inward and outward attenuation from 0 to 500 Hz input; and randomly activates synapses along dendrites to simulate AMPAR mediated EPSCs.\r\nFor electrotonic analysis, in Elec folder, the entry file is MSNelec_transform.hoc. \r\nFor EPSC simulation, in Syn folder, the entry file is randomepsc.hoc. Run read_EPSCsims_mdb_alone.m next with the simulated parameter values specified to compute the mean EPSC." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/236310", + "public_submitter_email": { + "attr_id": 309, + "value": "hsong1@fandm.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Hanbing Song, Christina Weaver" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 138633, + "object_name": "Striatum" + } + ] + }, + "ver_date": "2020-01-22T11:55:07.203", + "ver_number": 9 + }, + "236429": { + "citation": { + "attr_id": 391, + "value": "https://academic.oup.com/cercor/article/doi/10.1093/cercor/bhx226/4159219/Comprehensive-Morpho-Electrotonic-Analysis-Shows-2" + }, + "class_id": 19, + "created": "2018-01-09T16:13:42.193", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 236429, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206286, + "object_name": "Eyal, Guy [guy.eyal at mail.huji.ac.il]" + }, + { + "object_id": 244923, + "object_name": "Deitcher, Yair [yair.deitcher at mail.huji.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 64175, + "object_name": "Electrotonus" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 243612, + "object_name": "Deitcher Y, Eyal G, Kanari L, Verhoog MB, Atenekeng Kahou GA, Mansvelder HD, de Kock CPJ, Segev I (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "2 Distinct Classes of L2 and L3 Pyramidal Neurons in Human Temporal Cortex (Deitcher et al 2017)", + "notes": { + "attr_id": 24, + "value": "\"There have been few quantitative characterizations of the\r\nmorphological, biophysical, and cable properties of neurons in the\r\nhuman neocortex. We employed feature-based statistical methods on a\r\nrare data set of 60 3D reconstructed pyramidal neurons from L2 and L3\r\nin the human temporal cortex (HL2/L3 PCs) removed after brain\r\nsurgery. Of these cells, 25 neurons were also characterized\r\nphysiologically. Thirty-two morphological features were analyzed\r\n(e.g., dendritic surface area, 36 333 \u00b1 18 157 \u00b5m2; number of basal\r\ntrees, 5.55 \u00b1 1.47; dendritic diameter, 0.76 \u00b1 0.28 \u00b5m).\r\n\r\n\r\n...\r\nA novel descriptor for apical dendritic\r\ntopology yielded 2 distinct classes, termed hereby as \u201cslim-tufted\u201d\r\nand \u201cprofuse-tufted\u201d HL2/L3 PCs; the latter class tends to fire at\r\nhigher rates. Thus, our morpho-electrotonic analysis shows 2 distinct\r\nclasses of HL2/L3 PCs.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/236429", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2018-09-13T14:13:48.517", + "ver_number": 16 + }, + "236439": { + "citation": { + "attr_id": 391, + "value": "http://dev.biologists.org/content/144/19/3465" + }, + "class_id": 19, + "created": "2018-01-10T13:36:10.61", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 236439, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 236519, + "object_name": "Boareto, Marcelo [marcelo.boareto at bsse.ethz.ch]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 144409, + "object_name": "Homeostasis" + }, + { + "object_id": 128875, + "object_name": "Development" + }, + { + "object_id": 155599, + "object_name": "Neurogenesis" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 236442, + "object_name": "Boareto M, Iber D, Taylor V (2017)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Differential interactions between Notch and ID factors control neurogenesis (Boareto et al 2017)", + "notes": { + "attr_id": 24, + "value": "\"During embryonic and adult neurogenesis, neural stem cells (NSCs)\r\ngenerate the correct number and types of neurons in a temporospatial\r\nfashion. Control of NSC activity and fate is crucial for brain\r\nformation and homeostasis. Neurogenesis in the embryonic and adult\r\nbrain differ considerably, but Notch signaling and inhibitor of\r\nDNA-binding (ID) factors are pivotal in both. Notch and ID factors\r\nregulate NSC maintenance; however, it has been difficult to evaluate\r\nhow these pathways potentially interact. Here, we combined\r\nmathematical modeling with analysis of single-cell transcriptomic data\r\nto elucidate unforeseen interactions between the Notch and ID factor\r\npathways. ...\"\r\n" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/236439", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user" + }, + "ver_date": "2018-01-10T17:41:55.46", + "ver_number": 7 + }, + "236446": { + "class_id": 19, + "created": "2018-01-10T14:43:19.433", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 236446, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 236445, + "object_name": "Evans, Benjamin D [benjamin.evans at imperial.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 236444, + "object_name": "Evans BD, Jarvis S, Schultz SR, Nikolic K (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + }, + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 236518, + "object_name": "Brian 2 (web link to model)" + } + ] + }, + "name": "PyRhO: A multiscale optogenetics simulation platform (Evans et al 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52426, + "object_name": "Squid axon" + }, + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... we present an integrated suite of open-source, multi-scale\r\ncomputational tools called PyRhO. The purpose of developing PyRhO is\r\nthree-fold: (i) to characterize new (and existing) opsins by\r\nautomatically fitting a minimal set of experimental data to three-,\r\nfour-, or six-state kinetic models, (ii) to simulate these models at\r\nthe channel, neuron and network levels, and (iii) provide functional\r\ninsights through model selection and virtual experiments in\r\nsilico. The module is written in Python with an additional\r\nIPython/Jupyter notebook based GUI, allowing models to be fit,\r\nsimulations to be run and results to be shared through simply\r\ninteracting with a webpage. The seamless integration of model fitting\r\nalgorithms with simulation environments (including NEURON and Brian2)\r\nfor these virtual opsins will enable neuroscientists to gain a\r\ncomprehensive understanding of their behavior and rapidly identify the\r\nmost suitable variant for application in a particular biological\r\nsystem. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/236446", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 222, + "object_name": "Opsins" + } + ] + }, + "ver_date": "2018-01-10T16:01:17.31", + "ver_number": 4 + }, + "237160": { + "citation": { + "attr_id": 391, + "value": "http://search.proquest.com/openview/59a7d10cf74f679c549d547f2cde66b3/1?pq-origsite=gscholar" + }, + "class_id": 19, + "created": "2018-01-11T15:44:03.673", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 237160, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 237161, + "object_name": "Stockton, David B [david.stockton at utsa.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 189732, + "object_name": "Stockton DB, Santamaria F (2015)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 154350, + "object_name": "NeuroML (web link to model)" + }, + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "NeuroManager: a workflow analysis based simulation management engine (Stockton & Santamaria 2015)", + "notes": { + "attr_id": 24, + "value": "\"We developed NeuroManager, an object-oriented simulation management\r\nsoftware engine for computational neuroscience. NeuroManager automates\r\nthe workflow of simulation job submissions when using heterogeneous\r\ncomputational resources, simulators, and simulation tasks. The\r\nobject-oriented approach (1) provides flexibility to adapt to a\r\nvariety of neuroscience simulators, (2) simplifies the use of\r\nheterogeneous computational resources, from desktops to super computer\r\nclusters, and (3) improves tracking of simulator/simulation\r\nevolution. We implemented NeuroManager in MATLAB, a widely used\r\nengineering and scientific language, for its signal and image\r\nprocessing tools, prevalence in electrophysiology analysis, and\r\nincreasing use in college Biology education. To design and develop\r\nNeuroManager we analyzed the workflow of simulation submission for a\r\nvariety of simulators, operating systems, and computational resources,\r\nincluding the handling of input parameters, data, models, results, and\r\nanalyses. ...\"\r\n" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/237160", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user" + }, + "ver_date": "2018-01-11T16:14:54.057", + "ver_number": 6 + }, + "237323": { + "citation": { + "attr_id": 391, + "value": "https://uwspace.uwaterloo.ca/bitstream/handle/10012/12393/Duggins_Peter.pdf?sequence=8" + }, + "class_id": 19, + "created": "2018-01-17T14:48:24.793", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 237323, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 237324, + "object_name": "Duggins, Peter [psipeter at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 82787, + "object_name": "Working memory" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 237274, + "object_name": "Duggins P, Stewart TC, Choo X, Eliasmith C (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Effects of Guanfacine and Phenylephrine on a model of working memory (Duggins et al 2017)", + "notes": { + "attr_id": 24, + "value": "\"We use a spiking neural network model of working memory (WM) capable of performing the spatial delayed response task (DRT) to investigate two drugs that affect WM: guanfacine (GFC) and phenylephrine (PHE). In this model, the loss of information over time results from changes in the spiking neural activity through recurrent connections. We reproduce the standard forgetting curve and then show that this curve changes in the presence of GFC and PHE, whose application is simulated by manipulating functional, neural, and biophysical properties of the model. ... We compare our model to both electrophysiological data from neurons in monkey dorsolateral prefrontal cortex and to behavioral evidence from monkeys performing the DRT.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/237323", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "ver_date": "2018-01-18T10:14:07.91", + "ver_number": 8 + }, + "237326": { + "citation": { + "attr_id": 391, + "value": "https://www.nature.com/neuro/journal/vaop/ncurrent/full/nn.4582.html" + }, + "class_id": 19, + "created": "2018-01-18T10:38:18.607", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 247, + "object_name": "I A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 237326, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 95963, + "object_name": "Schmidt-Hieber, Christoph [c.schmidt-hieber at ucl.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 182690, + "object_name": "Spatial Navigation" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 83539, + "object_name": "Attractor Neural Network" + }, + { + "object_id": 183719, + "object_name": "Synaptic noise" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 237329, + "object_name": "Schmidt-Hieber C, Toleikyte G, Aitchison L, Roth A, Clark BA, Branco T, H\u00e4usser M (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 237342, + "object_name": "Brian (web link to model)" + }, + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Active dendritic integration in robust and precise grid cell firing (Schmidt-Hieber et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 129068, + "object_name": "Entorhinal cortex stellate cell" + }, + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... Whether active dendrites contribute to the generation of the\r\ndual temporal and rate codes characteristic of grid cell output is\r\nunknown. We show that dendrites of medial entorhinal cortex neurons\r\nare highly excitable and exhibit a supralinear input\u2013output function\r\nin vitro, while in vivo recordings reveal membrane potential\r\nsignatures consistent with recruitment of active dendritic\r\nconductances. By incorporating these nonlinear dynamics into grid cell\r\nmodels, we show that they can sharpen the precision of the temporal\r\ncode and enhance the robustness of the rate code, thereby supporting a\r\nstable, accurate representation of space under varying environmental\r\nconditions. Our results suggest that active dendrites may therefore\r\nconstitute a key cellular mechanism for ensuring reliable spatial\r\nnavigation.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/237326", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 119191, + "object_name": "Entorhinal cortex" + } + ] + }, + "ver_date": "2018-01-18T11:57:29.987", + "ver_number": 8 + }, + "237348": { + "citation": { + "attr_id": 391, + "value": "http://www.sciencedirect.com/science/article/pii/S1388245717304728" + }, + "class_id": 19, + "created": "2018-01-19T12:50:14.793", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 237348, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 237349, + "object_name": "Ruijter, Barry J [b.j.ruijter at utwente.nl]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 22012, + "object_name": "Depression" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 237347, + "object_name": "Ruijter BJ, Hofmeijer J, Meijer HGE, van Putten MJAM (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Synaptic damage underlies EEG abnormalities in postanoxic encephalopathy (Ruijter et al 2017)", + "notes": { + "attr_id": 24, + "value": "\"... In postanoxic coma, EEG patterns indicate the severity of encephalopathy and typically evolve in time. We aim to improve the understanding of pathophysiological mechanisms underlying these EEG abnormalities.\r\n...\r\nWe used a mean field model comprising excitatory and inhibitory neurons, local synaptic connections, and input from thalamic afferents. Anoxic damage is modeled as aggravated short-term synaptic depression, with gradual recovery over many hours. Additionally, excitatory neurotransmission is potentiated, scaling with the severity of anoxic encephalopathy. Simulations were compared with continuous EEG recordings of 155 comatose patients after cardiac arrest. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/237348", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2018-01-19T14:12:03.92", + "ver_number": 4 + }, + "237466": { + "citation": { + "attr_id": 391, + "value": "28816645" + }, + "class_id": 19, + "created": "2018-01-22T08:21:09.81", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 237466, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 237467, + "object_name": "Sasidharakurup, Hemalatha [hemalathas at am.amrita.edu]" + }, + { + "object_id": 116853, + "object_name": "Diwakar, Shyam [shyam at amrita.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 236790, + "object_name": "Sasidharakurup H, Melethadathil N, Nair B, Diwakar S (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 237011, + "object_name": "CellDesigner" + } + ] + }, + "name": "A systems model of Parkinson\u2019s disease using biochemical systems theory (Sasidharakurup et al. 2017)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Major pathways involving in Parkinson's disease (PD) such as alphasynuclein aggregation, dopamine \r\nsynthesis, lewy body formation, tau phosphorylation, parkin, and apoptosis were modeled \r\nusing stochastic differential equations. Pathways were modeled and simulated using the \r\nbiochemical pathway visualization program CellDesigner, a modeling tool for gene-regulatory \r\nand biochemical networks that support graphical notation and listing of symbols. The model allows \r\na qualitative analysis of PD and a key signalling pathways for evaluating PD treatment conditions \r\nrelating pathophysiology to molecular concentration changes recorded in experiments." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/237466", + "other_implementer": { + "attr_id": 308, + "value": "Sasidharakurup, Hemalatha [hemalathas@am.amrita.edu]" + }, + "other_simulator": { + "attr_id": 307, + "value": "Cell Designer" + }, + "other_type": { + "attr_id": 305, + "value": "Biochemical SystemsTheory" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "shyam@amrita.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Shyam Diwakar" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115960, + "object_name": "Generic" + } + ] + }, + "ver_date": "2018-01-22T10:32:11.073", + "ver_number": 5 + }, + "237469": { + "class_id": 19, + "created": "2018-01-24T07:37:26.933", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122814, + "object_name": "Cav1.2 CACNA1C" + }, + { + "object_id": 122812, + "object_name": "Cav1.3 CACNA1D" + }, + { + "object_id": 122811, + "object_name": "Cav3.3 CACNA1I" + }, + { + "object_id": 226057, + "object_name": "HCN1" + }, + { + "object_id": 122816, + "object_name": "Kv2.1 KCNB1" + }, + { + "object_id": 92082, + "object_name": "Nav1.1 SCN1A" + }, + { + "object_id": 247185, + "object_name": "PMCA ATP2B2" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 237469, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 147118, + "object_name": "Maki-Marttunen, Tuomo [tuomomm at uio.no]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 112853, + "object_name": "Schizophrenia" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 247974, + "object_name": "M\u00e4ki-Marttunen T, Krull F, Bettella F, Hagen E, N\u00e6ss S, Ness TV, Moberget T, Elvs\u00e5shagen T, Metzner C, Devor A, Edwards AG, Fyhn M, Djurovic S, Dale AM, Andreassen OA, Einevoll GT (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 144398, + "object_name": "LFPy" + } + ] + }, + "name": "SCZ-associated variant effects on L5 pyr cell NN activity and delta osc. (Maki-Marttunen et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" \u2026 Here, using computational modeling,\r\nwe show that a common biomarker of schizophrenia, namely, an increase in delta-oscillation power, may be a direct\r\nconsequence of altered expression or kinetics of voltage-gated ion channels or calcium transporters. Our model of a circuit\r\nof layer V pyramidal cells highlights multiple types of schizophrenia-related variants that contribute to altered dynamics in\r\nthe delta frequency band. Moreover, our model predicts that the same membrane mechanisms that increase the layer V\r\npyramidal cell network gain and response to delta-frequency oscillations may also cause a decit in a single-cell correlate of\r\nthe prepulse inhibition, which is a behavioral biomarker highly associated with schizophrenia.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/237469", + "other_gene": { + "attr_id": 477, + "value": "PMCA ATP2B2" + }, + "other_neurons": { + "attr_id": 301, + "value": "Neocortex layer V pyramidal cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tuomomm@uio.no" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tuomo Maki-Marttunen" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2019-10-10T17:25:47.307", + "ver_number": 15 + }, + "237555": { + "citation": { + "attr_id": 391, + "value": "Detailed Dendritic Excitatory/Inhibitory Balance through Heterosynaptic Spike-Timing-Dependent Plasticity.\r\nHiratani N, Fukai T" + }, + "class_id": 19, + "created": "2018-01-26T13:52:20.563", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 237555, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 237554, + "object_name": "Hiratani,Naoki [N.Hiratani at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 237553, + "object_name": "Hiratani N, Fukai T (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Heterosynaptic Spike-Timing-Dependent Plasticity (Hiratani & Fukai 2017)", + "notes": { + "attr_id": 24, + "value": "\"The balance between excitatory and inhibitory inputs is a key feature of cortical dynamics. Such a balance is arguably preserved in dendritic branches, yet its underlying mechanism and functional roles remain unknown. In this study, we developed computational models of heterosynaptic spike-timing-dependent plasticity (STDP) to show that the excitatory/inhibitory balance in dendritic branches is robustly achieved through heterosynaptic interactions between excitatory and inhibitory synapses. The model reproduces key features of experimental heterosynaptic STDP well, and provides analytical insights. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/237555", + "other_implementer": { + "attr_id": 308, + "value": "Naoki Hiratani" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "ver_date": "2018-02-13T16:47:28.76", + "ver_number": 3 + }, + "237594": { + "citation": { + "attr_id": 391, + "value": "Singh, C., & Levy, W. B. (2017). A consensus layer V pyramidal neuron can sustain interpulse-interval coding. PloS one, 12(7), e0180839." + }, + "class_id": 19, + "created": "2018-01-28T21:49:44.297", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 237594, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 237603, + "object_name": "Singh, Chandan [chandan_singh at berkeley.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 237597, + "object_name": "Singh C, Levy WB (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Stochastic layer V pyramidal neuron: interpulse interval coding and noise (Singh & Levy 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Layer V pyramidal neuron with stochastic Na channels. Supports evidence for interpulse interval coding and has very detailed AIS with Nav1.2 and Nav1.6 channels." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/237594", + "other_neurons": { + "attr_id": 301, + "value": "Neocortex layer V pyramidal neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "chandan_singh@berkeley.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Chandan Singh" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2019-10-10T17:25:17.197", + "ver_number": 7 + }, + "237595": { + "class_id": 19, + "created": "2018-01-29T03:43:49.507", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 237595, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 258240, + "object_name": "Tzilivaki, Alexandra [alexandra.tzilivaki at charite.de]" + }, + { + "object_id": 206278, + "object_name": "Kastellakis, George [gkastel at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 258239, + "object_name": "Tzilivaki A, Kastellakis G, Poirazi P (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Fast Spiking Basket cells (Tzilivaki et al 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 154093, + "object_name": "Hippocampus CA3 interneuron basket GABA cell" + }, + { + "object_id": 184134, + "object_name": "Neocortex layer 5 interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Interneurons are critical for the proper functioning of neural circuits. While often morphologically complex, dendritic integration and its role in neuronal output have been ignored for decades, treating interneurons as linear point neurons. Exciting new findings suggest that interneuron dendrites support complex, nonlinear computations: sublinear integration of EPSPs in the cerebellum, coupled to supralinear calcium accumulations and supralinear voltage integration in the hippocampus. These findings challenge the point neuron dogma and call for a new theory of interneuron arithmetic. Using detailed, biophysically constrained models, we predict that dendrites of FS basket cells in both hippocampus and mPFC come in two flavors: supralinear, supporting local sodium spikes within large-volume branches and sublinear, in small-volume branches. Synaptic activation of varying sets of these dendrites leads to somatic firing variability that cannot be explained by the point neuron reduction. Instead, a 2-stage Artificial Neural Network (ANN), with both sub- and supralinear hidden nodes, captures most of the variance. We propose that FS basket cells have substantially expanded computational capabilities sub-served by their non-linear dendrites and act as a 2-layer ANN.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/237595", + "other_concept": { + "attr_id": 306, + "value": "Artificial Neural Network" + }, + "other_implementer": { + "attr_id": 308, + "value": "Tzilivaki, Alexandra [alexandra.tzilivaki@charite.de]\r\nKastellakis, George [gkastel@gmail.com]" + }, + "other_neurons": { + "attr_id": 301, + "value": "Multi-compartmental biophysical models" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "aletzil10@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Alexandra Tzilivaki" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + }, + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "ver_date": "2019-07-29T14:14:19.353", + "ver_number": 23 + }, + "237604": { + "citation": { + "attr_id": 391, + "value": "Kenny, A., Plank, M. J., & David, T. (2018). The role of astrocytic calcium and TRPV4 channels in neurovascular coupling. Journal of Computational Neuroscience, 44(1), 97\u2013114. http://doi.org/10.1007/s10827-017-0671-7\r\n\r\nMathias, E. J., Plank, M. J., & David, T. (2017). A model of neurovascular coupling and the BOLD response: PART I. Computer Methods in Biomechanics and Biomedical Engineering, 20(5), 508\u2013518. http://doi.org/10.1080/10255842.2016.1255732" + }, + "class_id": 19, + "created": "2018-01-29T19:21:07.6", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 237604, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 233062, + "object_name": "Kenny, Allanah [allanah.kenny at pg.canterbury.ac.nz]" + }, + { + "object_id": 237609, + "object_name": "Mathias, Elshin " + }, + { + "object_id": 237610, + "object_name": "Dormanns, Katharina " + }, + { + "object_id": 233063, + "object_name": "David, Tim " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 151948, + "object_name": "Potassium buffering" + } + ] + }, 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"Here a lumped parameter numerical model of a neurovascular unit is presented, representing an intercellular communication system based on ion exchange through pumps and channels between neurons, astrocytes, smooth muscle cells, endothelial cells, and the spaces between these cells: the synaptic cleft between the neuron and astrocyte, the perivascular space between the astrocyte and SMC, and the extracellular space surrounding the cells. \r\nThe model contains various cellular and chemical pathways such as potassium, astrocytic calcium, and nitric oxide. \r\nThe model is able to simulate neurovascular coupling, the process characterised by an increase in neuronal activity followed by a rapid dilation of local blood vessels and hence increased blood supply providing oxygen and glucose to cells in need. \r\nThe model also incorporates the BOLD response." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/237604", + "public_submitter_email": { + 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We also connect multiple channels to substrates of a dopamine induced subcellular cascade to highlight that the classical pathway is too slow to explain DA induced kinetics in the subsecond range (Howe and Dombeck, 2016. doi: 10.1038/nature18942)" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/237653", + "other_implementer": { + "attr_id": 308, + "value": "Lindroos, Robert [robert.lindroos at ki.se]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "robert.lindroos@ki.se" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Robert Lindroos" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 196, + "object_name": "D1" + }, + { + "object_id": 223, + "object_name": "Dopaminergic Receptor" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 213, + "object_name": "Gaba" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + }, + { + "object_id": 138633, + "object_name": "Striatum" + } + ] + }, + "ver_date": "2023-02-10T10:54:25.91", + "ver_number": 15 + }, + "237685": { + "citation": { + "attr_id": 391, + "value": "Ausborn J et al 2017" + }, + "class_id": 19, + "created": "2018-02-01T13:48:29.44", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 237685, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 235171, + "object_name": "Ausborn J, Snyder AC, Shevtsova NA, Rybak IA, Rubin JE (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 237684, + "object_name": "NSM (web link to model)" + } + ] + }, + "name": "State-dependent rhythmogenesis in a half-center locomotor CPG (Ausborn et al 2017)", + "notes": { + "attr_id": 24, + "value": "\"The spinal locomotor central pattern generator (CPG) generates\r\nrhythmic activity with alternating flexion and extension phases. This\r\nrhythmic pattern is likely to result from inhibitory interactions\r\nbetween neural populations representing flexor and extensor\r\nhalf-centers. However, it is unclear whether the flexor-extensor CPG\r\nhas a quasi-symmetric organization with both half-centers critically\r\ninvolved in rhythm generation, features an asymmetric organization\r\nwith flexor-driven rhythmogenesis, or comprises a pair of\r\nintrinsically rhythmic half-centers. There are experimental data that\r\nsupport each of the above concepts but appear to be inconsistent with\r\nthe others. In this theoretical/modeling study, we present and analyze\r\na CPG model architecture that can operate in different regimes\r\nconsistent with the above three concepts depending on conditions,\r\nwhich are defined by external excitatory drives to CPG\r\nhalf-centers. We show that control of frequency and phase durations\r\nwithin each regime depends on network dynamics, defined by the\r\nregime-dependent expression of the half-centers' intrinsic rhythmic\r\ncapabilities and the operating phase transition mechanisms (escape\r\nvs. release). Our study suggests state dependency in locomotor CPG\r\noperation and proposes explanations for seemingly contradictory\r\nexperimental data.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/237685", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2020-06-24T12:35:20.147", + "ver_number": 6 + }, + "237727": { + "citation": { + "attr_id": 391, + "value": "PMID: 29387993" + }, + "class_id": 19, + "created": "2018-02-04T19:54:23.783", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 237727, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 238732, + "object_name": "Hoshino, Osamu [osamu.hoshino.507 at vc.ibaraki.ac.jp]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 237731, + "object_name": "Hoshino O, Zheng M, Watanabe K (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Perceptual judgments via sensory-motor interaction assisted by cortical GABA (Hoshino et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 230188, + "object_name": "Neocortex M1 interneuron basket PV GABA cell" + }, + { + "object_id": 230198, + "object_name": "Neocortex U1 interneuron basket PV GABA cell" + }, + { + "object_id": 87589, + "object_name": "Astrocyte" + }, + { + "object_id": 184128, + "object_name": "Neocortex layer 4 pyramidal cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Recurrent input to sensory cortex, via long-range reciprocal projections between motor and sensory cortices, is essential\r\nfor accurate perceptual judgments. GABA levels in sensory cortices correlate with perceptual performance. We simulated\r\na neuron-astrocyte network model to investigate how top-down, feedback signaling from a motor network (Nmot) to a\r\nsensory network (Nsen) affects perceptual judgments in association with ambient (extracellular) GABA levels. In the Nsen,\r\nastrocytic transporters modulated ambient GABA levels around pyramidal cells. A simple perceptual task was implemented:\r\ndetection of a feature stimulus presented to the Nsen. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/237727", + "public_submitter_email": { + "attr_id": 309, + "value": "osamu.hoshino.507@vc.ibaraki.ac.jp" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Osamu Hoshino" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "ver_date": "2018-02-16T14:42:15.6", + "ver_number": 11 + }, + "237728": { + "class_id": 19, + "created": "2018-02-04T20:48:22.73", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 237728, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 238924, + "object_name": "Bloss EB, Cembrowski MS, Karsh B, Colonell J, Fetter RD, Spruston N (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Single excitatory axons form clustered synapses onto CA1 pyramidal cell dendrites (Bloss et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Here we show that single presynaptic axons form multiple, spatially clustered inputs onto the distal, but not proximal, dendrites of CA1 pyramidal neurons. These compound connections exhibit ultrastructural features indicative of strong synapses and occur much more commonly in entorhinal than in thalamic afferents. Computational simulations revealed that compound connections depolarize dendrites in a biophysically efficient manner, owing to their inherent spatiotemporal clustering. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/237728", + "public_submitter_email": { + "attr_id": 309, + "value": "cembrowskim@janelia.hhmi.org" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Mark Cembrowski" + }, + "ver_date": "2018-02-26T10:34:41.287", + "ver_number": 6 + }, + "238332": { + "class_id": 19, + "created": "2018-02-09T12:56:55.4", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 88215, + "object_name": "I_Ks" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 238332, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 239343, + "object_name": "Ferrario, Andrea [andrea.ferrario at plymouth.ac.uk]" + }, + { + "object_id": 79305, + "object_name": "Borisyuk, Roman [rborisyuk at plymouth.ac.uk]" + }, + { + "object_id": 239344, + "object_name": "Merrison-Hort, Robert " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 114660, + "object_name": "Connectivity matrix" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 239500, + "object_name": "Ferrario A, Merrison-Hort R, Soffe SR, Borisyuk R (2018)" + }, + { + "object_id": 238336, + "object_name": "Roberts A, Conte D, Hull M, Merrison-Hort R, al Azad AK, Buhl E, Borisyuk R, Soffe SR (2014)" + }, + { + "object_id": 239273, + "object_name": "Borisyuk R, Al Azad AK, Conte D, Roberts A, Soffe SR (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Model of the Xenopus tadpole swimming spinal network (Roberts et al. 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a NEURON-python and MATLAB simulation code for generating anatomical or probabilistic connectivity and simulating the neuronal dynamics of the neuronal network controlling swimming in Xenopus tadpoles. For more details about this model, see Ferrario et al, 2018, eLife and Roberts et al, 2014, J of Neurosci" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/238332", + "public_submitter_email": { + "attr_id": 309, + "value": "andrea.ferrario@plymouth.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Andrea Ferrario" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 213, + "object_name": "Gaba" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2018-04-10T14:27:07.403", + "ver_number": 11 + }, + "238338": { + "citation": { + "attr_id": 391, + "value": "Myung J, Schmal C, Hong S, Tsukizawa Y, Rose P, Zhang Y, Holtzman MJ, De Schutter E, Herzel H (2018) The Choroid Plexus is an Important Circadian Clock Component. Nat Commun, in press." + }, + "class_id": 19, + "created": "2018-02-10T06:19:09.947", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 238338, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 153454, + "object_name": "Hong, Sungho [shhong at oist.jp]" + }, + { + "object_id": 238345, + "object_name": "Schmal, Christoph " + }, + { + "object_id": 238346, + "object_name": "Myung, Jihwan " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 145886, + "object_name": "Circadian Rhythms" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 249013, + "object_name": "Myung J, Schmal C, Hong S, Tsukizawa Y, Rose P, Zhang Y, Holtzman MJ, De Schutter E, Herzel H, Bordyugov G, Takumi T (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 143722, + "object_name": "Mathematica" + } + ] + }, + "name": "Synchronized oscillations of clock gene expression in the choroid plexus (Myung et al 2018)", + "notes": { + "attr_id": 24, + "value": "Our model simulates synchronized rhythms in the clock gene expression found in the choroid plexus. These synchronized oscillations, primarily mediated by gap junctions, showed interesting relationships between their amplitude, oscillation frequency, and coupling strength (gap junction density) in our experimental data. The model is based on coupled Poincar\u00e9 oscillators and replicates this phenomenon via a non-zero \"twist\" in each cell." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/238338", + "other_concept": { + "attr_id": 306, + "value": "Circadian rhythm" + }, + "other_gene": { + "attr_id": 477, + "value": "Clock, Bmal, Per" + }, + "other_implementer": { + "attr_id": 308, + "value": "Schmal, Christoph\r\nMyung, Jihwan" + }, + "other_neurons": { + "attr_id": 301, + "value": "Abstract Poincar\u00e9 oscillator cell" + }, + "other_type": { + "attr_id": 305, + "value": "Oscillator network" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "shhong@oist.jp" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Sungho Hong" + }, + "ver_date": "2018-03-13T00:30:03.017", + "ver_number": 22 + }, + "238347": { + "citation": { + "attr_id": 391, + "value": "Eyal et al 2018, in preparation" + }, + "class_id": 19, + "created": "2018-02-12T13:13:52.077", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 238347, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206286, + "object_name": "Eyal, Guy [guy.eyal at mail.huji.ac.il]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 243596, + "object_name": "Eyal G, Verhoog MB, Testa-Silva G, Deitcher Y, Benavides-Piccione R, DeFelipe J, de Kock CPJ, Mansvelder HD, Segev I (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Comprehensive models of human cortical pyramidal neurons (Eyal et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"We present detailed models of pyramidal cells from human neocortex, including models on their excitatory synapses, dendritic spines, dendritic NMDA- and somatic/axonal Na+ spikes that provided new insights into signal processing and computational capabilities of these principal cells. Six human layer 2 and layer 3 pyramidal cells (HL2/L3 PCs) were modeled, integrating detailed anatomical and physiological data from both fresh and postmortem tissues from human temporal cortex. The models predicted particularly large AMPA- and NMDA- conductances per synaptic contact (0.88 nS and 1.31nS, respectively) and a steep dependence of the NMDA-conductance on voltage...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/238347", + "other_neurons": { + "attr_id": 301, + "value": "Neocortex L2/3 pyramidal cells" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "guy.eyal@mail.huji.ac.il" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Guy Eyal" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 227987, + "object_name": "Human" + }, + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2019-10-10T17:24:33.91", + "ver_number": 13 + }, + "238449": { + "class_id": 19, + "created": "2018-02-13T15:54:09.1", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 238449, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 238450, + "object_name": "Morschel, Konstantin " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 238331, + "object_name": "M\u00f6rschel K, Breit M, Queisser G (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Generating neuron geometries for detailed 3D simulations using AnaMorph (Morschel et al 2017)", + "notes": { + "attr_id": 24, + "value": "\"Generating realistic and complex computational domains for numerical simulations is often a challenging task. In neuroscientific research, more and more one-dimensional morphology data is becoming publicly available through databases. This data, however, only contains point and diameter information not suitable for detailed three-dimensional simulations. In this paper, we present a novel framework, AnaMorph, that automatically generates water-tight surface meshes from one-dimensional point-diameter files. These surface triangulations can be used to simulate the electrical and biochemical behavior of the underlying cell. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/238449", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2018-02-13T16:31:57.38", + "ver_number": 4 + }, + "238892": { + "class_id": 19, + "created": "2018-02-23T10:49:22.697", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 238892, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 247215, + "object_name": "Newton, Adam J H [adam.newton at yale.edu]" + }, + { + "object_id": 146951, + "object_name": "McDougal, Robert [robert.mcdougal at yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 247211, + "object_name": "Newton AJH, McDougal RA, Hines ML, Lytton WW (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Using NEURON for reaction-diffusion modeling of extracellular dynamics (Newton et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 236, + "object_name": "Ions" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Development of credible clinically-relevant brain simulations has been slowed due to a focus on electrophysiology in computational neuroscience, neglecting the multiscale whole-tissue modeling approach used for simulation in most other organ systems. We have now begun to extend the NEURON simulation platform in this direction by adding extracellular modeling. NEURON's extracellular reaction-diffusion is supported by an intuitive Python-based where/who/what command sequence, derived from that used for intracellular reaction diffusion, to support coarse-grained macroscopic extracellular models. This simulation specification separates the expression of the conceptual model and parameters from the underlying numerical methods. In the volume-averaging approach used, the macroscopic model of tissue is characterized by free volume fraction\u2014the proportion of space in which species are able to diffuse, and tortuosity\u2014the average increase in path length due to obstacles. These tissue characteristics can be defined within particular spatial regions, enabling the modeler to account for regional differences, due either to intrinsic organization, particularly gray vs. white matter, or to pathology such as edema. We illustrate simulation development using spreading depression, a pathological phenomenon thought to play roles in migraine, epilepsy and stroke." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/238892", + "public_submitter_email": { + "attr_id": 309, + "value": "adam.newton@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Adam John Hunter Newton" + }, + "ver_date": "2018-11-19T22:42:02.483", + "ver_number": 21 + }, + "238911": { + "citation": { + "attr_id": 391, + "value": "http://iopscience.iop.org/article/10.1088/1741-2552/aa86c8/meta" + }, + "class_id": 19, + "created": "2018-02-24T22:20:10.897", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 238911, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 252077, + "object_name": "Barth, Bradley [bradley.barth at duke.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 250131, + "object_name": "Barth BB, Henriquez CS, Grill WM, Shen X (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 231240, + "object_name": "Brian 2" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Neuromuscular network model of gut motility (Barth et al 2017)", + "notes": { + "attr_id": 24, + "value": "Here we develop an integrated neuromechanical model of the ENS and assess neurostimulation strategies for enhancing gut motility. The model includes a network of enteric neurons, smooth muscle fibers, and interstitial cells of Cajal, which regulate propulsion of a virtual pellet in a model of gut motility." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/238911", + "other_concept": { + "attr_id": 306, + "value": "Neuromuscular Motility" + }, + "other_implementer": { + "attr_id": 308, + "value": "Barth, Bradley [bradley.barth@duke.edu]" + }, + "other_neurons": { + "attr_id": 301, + "value": "Enteric neuron; smooth muscle fiber; ICC" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 340" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115955, + "object_name": "Enteric nervous system" + } + ] + }, + "ver_date": "2019-03-28T15:00:00.053", + "ver_number": 13 + }, + "238912": { + "citation": { + "attr_id": 391, + "value": "http://jn.physiology.org/content/early/2017/07/31/jn.00274.2017.abstract" + }, + "class_id": 19, + "created": "2018-02-24T22:22:48.953", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 238912, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 183719, + "object_name": "Synaptic noise" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 249852, + "object_name": "Feeney DF, Meyer FG, Noone N, Enoka RM (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A state-space model to quantify common input to motor neurons (Feeney et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 117818, + "object_name": "Spinal cord renshaw cell" + }, + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... We introduce a space-state model in which the discharge activity\r\nof motor neurons is modeled as inhomogeneous Poisson processes\r\nand propose a method to quantify an abstract latent trajectory\r\nthat represents the common input received by motor neurons. The\r\napproach also approximates the variation in synaptic noise in the\r\ncommon input signal. The model is validated with four data sets:\r\na simulation of 120 motor units, a pair of integrate-and-fire\r\nneurons with a Renshaw cell providing inhibitory feedback, the\r\ndischarge activity of 10 integrate-and-fire neurons, and the\r\ndischarge times of concurrently active motor units during an\r\nisometric voluntary contraction. The simulations revealed that a\r\nlatent state-space model is able to quantify the trajectory and\r\nvariability of the common input signal across all four\r\nconditions. When compared with the cumulative spike train method\r\nof characterizing common input, the state-space approach was more\r\nsensitive to the details of the common input current and was less\r\ninfluenced by the duration of the signal. The state-space\r\napproach appears to be capable of detecting rather modest changes\r\nin common input signals across conditions.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/238912", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 340" + }, + "ver_date": "2019-02-18T17:58:05.66", + "ver_number": 11 + }, + "238916": { + "citation": { + "attr_id": 391, + "value": "https://link.springer.com/article/10.1007/s00429-017-1541-9" + }, + "class_id": 19, + "created": "2018-02-25T19:19:42.507", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 238916, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 258647, + "object_name": "Nanda S, Das R, Bhattacharjee S, Cox DN, Ascoli GA (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53895, + "object_name": "Java" + } + ] + }, + "name": "Morphological determinants of dendritic arborization neurons in Drosophila larva (Nanda et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 258654, + "object_name": "Drosophila dendritic arborization neurons" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Pairing in vivo imaging and computational modeling of dendritic\r\narborization (da) neurons from the fruit fly larva provides a unique\r\nwindow into neuronal growth and underlying molecular processes. We\r\nimage, reconstruct, and analyze the morphology of wild-type,\r\nRNAi-silenced, and mutant da neurons. We then use local and global\r\nrule-based stochastic simulations to generate artificial arbors, and\r\nidentify the parameters that statistically best approximate the real\r\ndata. We observe structural homeostasis in all da classes, where an\r\nincrease in size of one dendritic stem is compensated by a reduction\r\nin the other stems of the same neuron. Local rule models show that\r\nbifurcation probability is determined by branch order, while branch\r\nlength depends on path distance from the soma. Global rule simulations\r\nsuggest that most complex morphologies tend to be constrained by\r\nresource optimization, while simpler neuron classes privilege path\r\ndistance conservation. Genetic manipulations affect both the local\r\nand global optimal parameters, demonstrating functional\r\nperturbations in growth mechanisms.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/238916", + "other_neurons": { + "attr_id": 301, + "value": "Drosophila dendritic arborization neurons" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 340" + }, + "ver_date": "2019-08-15T16:53:37.623", + "ver_number": 10 + }, + "238920": { + "citation": { + "attr_id": 391, + "value": "http://www.jneurosci.org/content/37/20/5123.abstract" + }, + "class_id": 19, + "created": "2018-02-25T19:28:41.237", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 238920, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 139996, + "object_name": "Evoked LFP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 251355, + "object_name": "Hagen E, Fossum JC, Pettersen KH, Alonso JM, Swadlow HA, Einevoll GT (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "LFP signature of monosynaptic thalamocortical connection (Hagen et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 184132, + "object_name": "Neocortex layer 4 interneuron" + }, + { + "object_id": 182505, + "object_name": "Neocortex layer 4 neuron" + }, + { + "object_id": 184128, + "object_name": "Neocortex layer 4 pyramidal cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"A resurgence has taken place in recent years in the use of the\r\nextracellularly recorded local field potential (LFP) to investigate\r\nneural network activity. To probe monosynaptic thalamic activation of\r\ncortical postsynaptic target cells, so called spike-trigger-averaged\r\nLFP (stLFP) signatures have been measured. In these experiments, the\r\ncortical LFP is measured by multielectrodes covering several cortical\r\nlamina and averaged on spontaneous spikes of thalamocortical (TC)\r\ncells. Using a well established forward-modeling scheme, we\r\ninvestigated the biophysical origin of this stLFP signature with\r\nsimultaneous synaptic activation of cortical layer-4 neurons,\r\nmimicking the effect of a single afferent spike from a single TC\r\nneuron.\r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/238920", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 340" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + }, + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2019-03-01T17:24:42.543", + "ver_number": 4 + }, + "238959": { + "class_id": 19, + "created": "2018-02-28T02:50:47.457", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 238959, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267631, + "object_name": "Kao KW, Lo CC (2020)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Short Term Depression, Presynaptic Inhib., Neuron Diversity Roles in Antennal Lobe (Wei & Lo 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + }, + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/238959", + "other_implementer": { + "attr_id": 308, + "value": "pre-synaptic inhibition \r\nshort-term depression " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "kkshxt@lolab-nthu.org" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Kao Kuo Wei" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 218, + "object_name": "Cholinergic Receptors" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 212, + "object_name": "Glutamate" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2023-02-09T10:49:06.97", + "ver_number": 10 + }, + "238985": { + "citation": { + "attr_id": 391, + "value": "http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005834" + }, + "class_id": 19, + "created": "2018-03-03T20:10:39.023", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 238985, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 245381, + "object_name": "Rakowski, Franciszek " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 245238, + "object_name": "Rakowski F, Karbowski J (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113428, + "object_name": "Java (web link to model)" + }, + { + "object_id": 206355, + "object_name": "Mathematica (web link to model)" + } + ] + }, + "name": "Optimal synaptic assignment for locomotory behavior in C. elegans (Rakowski & Karbowski 2017)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The detailed knowledge of C. elegans connectome for 3 decades has not contributed dramatically to our understanding of worm\u2019s behavior. One of main reasons for this situation has been the lack of data on the type of synaptic signaling between particular neurons in the worm\u2019s connectome. The aim of this study was to determine synaptic polarities for each connection in a small pre-motor circuit controlling locomotion. Even in this compact network of just 7 neurons the space of all possible patterns of connection types (excitation vs. inhibition) is huge. To deal effectively with this combinatorial problem we devised a novel and relatively fast technique based on genetic algorithms and large-scale parallel computations, which we combined with detailed neurophysiological modeling of interneuron dynamics and compared the theory to the available behavioral data. As a result of these massive computations, we found that the optimal connectivity pattern that matches the best locomotory data is the one in which all interneuron connections are inhibitory, even those terminating on motor neurons. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/238985", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 340" + }, + "ver_date": "2018-09-27T10:32:18.023", + "ver_number": 7 + }, + "239003": { + "citation": { + "attr_id": 391, + "value": "https://f1000research.com/articles/6-1222/v2" + }, + "class_id": 19, + "created": "2018-03-04T19:28:24.22", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 239003, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 256750, + "object_name": "Scheler, Gabriele [gscheler at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 256719, + "object_name": "Scheler G (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Logarithmic distributions prove that intrinsic learning is Hebbian (Scheler 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"In this paper, we present data for the lognormal distributions of spike rates,\r\nsynaptic weights and intrinsic excitability (gain) for neurons in various brain\r\nareas, such as auditory or visual cortex, hippocampus, cerebellum, striatum,\r\nmidbrain nuclei. We find a remarkable consistency of heavy-tailed, specifically\r\nlognormal, distributions for rates, weights and gains in all brain areas\r\nexamined. The difference between strongly recurrent and feed-forward\r\nconnectivity (cortex vs. striatum and cerebellum), neurotransmitter (GABA\r\n(striatum) or glutamate (cortex)) or the level of activation (low in cortex, high in\r\nPurkinje cells and midbrain nuclei) turns out to be irrelevant for this feature.\r\nLogarithmic scale distribution of weights and gains appears to be a general,\r\nfunctional property in all cases analyzed. We then created a generic neural\r\nmodel to investigate adaptive learning rules that create and maintain lognormal\r\ndistributions. We conclusively demonstrate that not only weights, but also\r\nintrinsic gains, need to have strong Hebbian learning in order to produce and\r\nmaintain the experimentally attested distributions. This provides a solution to\r\nthe long-standing question about the type of plasticity exhibited by intrinsic\r\nexcitability.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239003", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 340" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + }, + { + "object_id": 115952, + "object_name": "Basal ganglia" + }, + { + "object_id": 115953, + "object_name": "Cerebellum" + }, + { + "object_id": 138633, + "object_name": "Striatum" + } + ] + }, + "ver_date": "2019-05-20T15:38:19.877", + "ver_number": 6 + }, + "239006": { + "citation": { + "attr_id": 391, + "value": "27223870" + }, + "class_id": 19, + "created": "2018-03-05T10:27:44.78", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "icg_channel_details": { + "attr_id": 640, + "value": [ + { + "object_id": 239007, + "object_name": "ICG Detail_239006" + }, + { + "object_id": 239008, + "object_name": "ICG Detail_239006" + } + ] + }, + "id": 239006, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33735, + "object_name": "Carnevale, Ted [Ted.Carnevale at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 84598, + "object_name": "Extracellular Fields" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 239009, + "object_name": "Reilly JP (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Extracellular stimulation of myelinated axon (Reilly 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52425, + "object_name": "Myelinated neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is an implementation of an established \"electrostimulation model\" subjected to a set of stimulation protocols. Such models and protocols are used to predict the response of neural tissue to stimulation by electromagnetic fields or direct application of extracellular current in order to \"evaluate the efficacy and safety of medical devices, or to develop guidelines or standards on acceptible incidental exposure that may not be related to patient exposure for medical purposes.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239006", + "other_concept": { + "attr_id": 306, + "value": "Extracellular stimulation" + }, + "other_neurons": { + "attr_id": 301, + "value": "Xenopus axon" + }, + "other_type": { + "attr_id": 305, + "value": "A fiber" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ted.carnevale@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ted Carnevale" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 239013, + "object_name": "Xenopus" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 249928, + "object_name": "Xenopus" + } + ] + }, + "ver_date": "2019-02-26T10:29:17.057", + "ver_number": 13 + }, + "239039": { + "biosimulations": { + "value": "modeldb-239039" + }, + "citation": { + "attr_id": 391, + "value": "Hojeong Kim (2017), Impact of the localization of dendritic calcium persistent inward current on the input-output properties of spinal motoneuron pool: a computational study, J App Physiol 123(5):1166-1187.\r\n\r\nKim, H., Jones, K.E., and Heckman, C.J. (2014). Asymmetry in signal propagation between the soma and dendrites plays a key role in determining dendritic excitability in motoneurons. 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This issue was investigated developing a model motoneuron pool where model parameters were analytically determined to reflect key motoneuron type-specific properties experimentally identified. The simulation results point out the negative relationship between the distance of Ca-PIC source from the soma and cell recruitment threshold as a basis underlying the systematic variation in input-output properties of motoneurons over the motoneuron pool." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239039", + "other_simulator": { + "attr_id": 307, + "value": "XPPAUT" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "hojeong.kim03@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Hojeong Kim" + }, + "ver_date": "2018-03-08T10:50:07.287", + "ver_number": 7 + }, + "239072": { + "class_id": 19, + "created": "2018-03-10T14:45:51.69", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 239072, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 240413, + "object_name": "Kufel, Dominik [dominic.kufel at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 152639, + "object_name": "Temperature" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 240385, + "object_name": "Kufel DS, Wojcik GM (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 2835, + "object_name": "MCell" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Analytical modelling of temperature effects on an AMPA-type synapse (Kufel & Wojcik 2018)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This code was used in the construction of the model developed in the paper. It is a modified version of the simulation developed by Postlethwaite et al. 2007 - for details of modifications refer to the main body of Kufel & Wojcik (2018)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239072", + "public_submitter_email": { + "attr_id": 309, + "value": "dominic.kufel@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Dominik Kufel" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2018-05-18T13:45:58.337", + "ver_number": 7 + }, + "239103": { + "class_id": 19, + "created": "2018-03-13T14:15:10.887", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 239103, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 97751, + "object_name": "Stuttering" + }, + { + "object_id": 97752, + "object_name": "Delay" + }, + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 239101, + "object_name": "Venkadesh S, Komendantov AO, Listopad S, Scott EO, De Jong K, Krichmar JL, Ascoli GA (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 113428, + "object_name": "Java (web link to model)" + } + ] + }, + "name": "Evolving simple models of diverse dynamics in hippocampal neuron types (Venkadesh et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... we present an automated pipeline based on evolutionary algorithms to quantitatively reproduce features of various classes of neuronal spike patterns using the Izhikevich model. Employing experimental data from Hippocampome.org, a comprehensive knowledgebase of neuron types in the rodent hippocampus, we demonstrate that our approach reliably fit Izhikevich models to nine distinct classes of experimentally recorded spike patterns, including\r\ndelayed spiking, spiking with adaptation, stuttering, and bursting. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239103", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2018-03-13T16:20:42.26", + "ver_number": 4 + }, + "239145": { + "class_id": 19, + "created": "2018-03-18T21:42:43.357", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 239145, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 167501, + "object_name": "Egger, Robert [robert.egger at nyumc.org]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 230581, + "object_name": "Stimulus selectivity" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 261323, + "object_name": "Egger R, Narayanan RT, Guest JM, Bast A, Udvary D, Messore LF, Das S, de Kock CP, Oberlaender M (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Sensory-evoked responses of L5 pyramidal tract neurons (Egger et al 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is the L5 pyramidal tract neuron (L5PT) model from Egger, Narayanan et al., Neuron 2020.\r\n\r\nIt allows investigating how synaptic inputs evoked by different sensory stimuli are integrated by the complex intrinsic properties of L5PTs.\r\n\r\nThe model is constrained by anatomical measurements of the subcellular synaptic input patterns to L5PT neurons, in vivo measurements of sensory-evoked responses of different populations of neurons providing these synaptic inputs, and in vitro measurements constraining the biophysical properties of the soma, dendrites and axon (note: the biophysical model is based on the work by Hay et al., Plos Comp Biol 2011).\r\n\r\nThe model files provided here allow performing simulations and analyses presented in Figures 3, 4 and 5." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239145", + "other_neurons": { + "attr_id": 301, + "value": "Neocortex S1 L5 pyramidal tract neuron" + }, + "other_type": { + "attr_id": 305, + "value": "Multi-scale model" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "Robert.Egger@nyumc.org" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Robert Egger" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2019-11-27T14:35:38.803", + "ver_number": 42 + }, + "239146": { + "citation": { + "attr_id": 391, + "value": "D. 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In cirsiumNeuron, we utilize a general modeling framework for biological neuronal circuits which systematically captures the nonstationary stochastic behavior of the ion channels and the synaptic processes. In this framework, we employ fine-grained, discrete-state, continuous-time Markov Chain (MC) models of both ion channels and synaptic processes in a unified manner. Our modeling framework can automatically generate the corresponding coarse-grained, continuous-state, continuous-time Stochastic Differential Equation (SDE) models. In addition, for the stochastic characterization of neuronal variability and noise, we have implemented semi-analytical, non Monte Carlo analysis techniques that work both in time and frequency domains, which were previously developed for analog electronic circuits. In these semi-analytical noise evaluation schemes, (differential) equations that directly govern probabilistic characterizations in the form of correlation functions (time domain) or spectral densities (frequency domain) are first derived analytically, and then solved numerically. These semi-analytical noise analysis techniques correctly and accurately capture the second order statistics (mean, variance, autocorrelation, and power spectral density) of the underlying neuronal processes as compared with Monte Carlo simulations." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239146", + "other_implementer": { + "attr_id": 308, + "value": "Kilinc, Deniz [dkilinc at ku.edu.tr];\r\nMahmutoglu, A. 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It is designed to speed up and simplify the process of generating, sharing, and exploring network models of neurons with one or more compartments. Models can be specified by equations directly (similar to XPP or the Brian simulator) or by lists of predefined or custom model components. The higher-level specification supports arbitrarily complex population models and networks of interconnected populations. DynaSim also includes a large set of features that simplify exploring model dynamics over parameter spaces, running simulations in parallel using both multicore processors and high-performance computer clusters, and analyzing and plotting large numbers of simulated data sets in parallel. It also includes a graphical user interface (DynaSim GUI) that supports full functionality without requiring user programming. The software has been implemented in MATLAB to enable advanced neural modeling using MATLAB, given its popularity and a growing interest in modeling neural systems....\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239161", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "ver_date": "2018-03-22T12:11:51.58", + "ver_number": 5 + }, + "239177": { + "class_id": 19, + "created": "2018-03-21T20:47:13.587", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 239177, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 186894, + "object_name": "Tikidji-Hamburyan, Ruben [ruben.tikidji.hamburyan at gmail.com] " + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 253430, + "object_name": "Tikidji-Hamburyan RA, Leonik CA, Canavier CC (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Phase response theory in sparsely + strongly connected inhibitory NNs (Tikidji-Hamburyan et al 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 156124, + "object_name": "Abstract single compartment conductance based cell" + } + ] + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239177", + "public_submitter_email": { + "attr_id": 309, + "value": "ruben.tikidji.hamburyan@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ruben Tikidzhi-Khamburyan" + }, + "ver_date": "2022-05-27T12:54:29.677", + "ver_number": 8 + }, + "239388": { + "class_id": 19, + "created": "2018-03-26T19:09:42.713", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 239388, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 239426, + "object_name": "Heitmann S, Breakspear M (2018)" + }, + { + "object_id": 239348, + "object_name": "Heitmann S, Breakspear M (2018)" + }, + { + "object_id": 239360, + "object_name": "Heitmann S,Aburn MJ,Breakspear M (2017)" + }, + { + "object_id": 239425, + "object_name": "Heitmann S, Breakspear M (2017)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 239389, + "object_name": "Brain Dynamics Toolbox (web link to model)" + }, + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Brain Dynamics Toolbox (Heitmann & Breakspear 2016, 2017, 2018)", + "notes": { + "attr_id": 24, + "value": "\"The Brain Dynamics Toolbox is open-source software for simulating dynamical systems in neuroscience. It is for researchers and students who wish to explore mathematical models of brain function using Matlab. It includes a graphical tool for simulating dynamical systems in real-time as well as command-line tools for scripting large-scale simulations.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239388", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2018-04-03T19:35:02.56", + "ver_number": 6 + }, + "239413": { + "citation": { + "attr_id": 391, + "value": "http://www.jneurosci.org/content/early/2017/10/09/JNEUROSCI.0044-17.2017.abstract" + }, + "class_id": 19, + "created": "2018-04-01T19:32:51.7", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 239413, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 47316, + "object_name": "Invertebrate" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 252324, + "object_name": "Ai H, Kai K, Kumaraswamy A, Ikeno H, Wachtler T (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 236518, + "object_name": "Brian 2 (web link to model)" + } + ] + }, + "name": "Vibration-sensitive Honeybee interneurons (Ai et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Female honeybees use the \u201cwaggle dance\u201d to communicate the location of nectar sources to their hive mates. Distance information is encoded in the duration of the waggle phase (von Frisch, 1967). During the waggle phase, the dancer produces trains of vibration pulses, which are detected by the follower bees via Johnston's organ located on the antennae. To uncover the neural mechanisms underlying the encoding of distance information in the waggle dance follower, we investigated morphology, physiology, and immunohistochemistry of interneurons arborizing in the primary auditory center of the honeybee (Apis mellifera). We identified major interneuron types, named DL-Int-1, DL-Int-2, and bilateral DL-dSEG-LP, that responded with different spiking patterns to vibration pulses applied to the antennae. Experimental and computational analyses suggest that inhibitory connection plays a role in encoding and processing the duration of vibration pulse trains in the primary auditory center of the honeybee.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239413", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 340" + }, + "ver_date": "2019-03-08T17:41:24.063", + "ver_number": 5 + }, + "239418": { + "citation": { + "attr_id": 391, + "value": "https://link.springer.com/article/10.1007/s10827-017-0659-3" + }, + "class_id": 19, + "created": "2018-04-01T19:48:46.33", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 239418, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 253249, + "object_name": "Hu, Brian [bhu6 (AT) jhmi (DOT) edu]" + }, + { + "object_id": 253250, + "object_name": "Jeck, Danny " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 253091, + "object_name": "Hu B, Niebur E (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "NN for proto-object based contour integration and figure-ground segregation (Hu & Niebur 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 245940, + "object_name": "Abstract rate-based neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Visual processing of objects makes use of both\r\nfeedforward and feedback streams of information. However, the nature of feedback signals is largely unknown, as\r\nis the identity of the neuronal populations in lower visual\r\nareas that receive them. Here, we develop a recurrent neural\r\nmodel to address these questions in the context of contour\r\nintegration and figure-ground segregation. A key feature\r\nof our model is the use of grouping neurons whose activity represents tentative objects (\u201cproto-objects\u201d) based on\r\nthe integration of local feature information. Grouping neurons receive input from an organized set of local feature\r\nneurons, and project modulatory feedback to those same\r\nneurons. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239418", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 340" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2019-03-12T17:31:14.61", + "ver_number": 5 + }, + "239421": { + "citation": { + "attr_id": 391, + "value": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5602117/" + }, + "class_id": 19, + "created": "2018-04-01T19:52:44.04", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 239421, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 229586, + "object_name": "Masoli, Stefano [stefano.masoli at unipv.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 257190, + "object_name": "Masoli S, D'Angelo E (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Purkinje cell: Synaptic activation predicts voltage control of burst-pause (Masoli & D'Angelo 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The dendritic processing in cerebellar Purkinje cells (PCs), which integrate synaptic inputs coming from hundreds of thousands granule cells and molecular layer interneurons, is still unclear. Here we have tested a leading hypothesis maintaining that the significant PC output code is represented by burst-pause responses (BPRs), by simulating PC responses in a biophysically detailed model that allowed to systematically explore a broad range of input patterns. BPRs were generated by input bursts and were more prominent in Zebrin positive than Zebrin negative (Z+ and Z-) PCs. Different combinations of parallel fiber and molecular layer interneuron synapses explained type I, II and III responses observed in vivo. BPRs were generated intrinsically by Ca-dependent K channel activation in the somato-dendritic compartment and the pause was reinforced by molecular layer interneuron inhibition. BPRs faithfully reported the duration and intensity of synaptic inputs, such that synaptic conductance tuned the number of spikes and release probability tuned their regularity in the millisecond range. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239421", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 340" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2020-04-20T07:49:11.55", + "ver_number": 13 + }, + "239427": { + "citation": { + "attr_id": 391, + "value": "https://doi.org/10.1016/j.neuron.2018.01.047" + }, + "class_id": 19, + "created": "2018-04-04T13:15:10.84", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 239427, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 222955, + "object_name": "Pedrosa, Victor [v.pedrosa15 at imperial.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 93420, + "object_name": "Sleep" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 239497, + "object_name": "Gonz\u00e1lez-Rueda A, Pedrosa V, Feord RC, Clopath C, Paulsen O (2018)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Feedforward network undergoing Up-state-mediated plasticity (Gonzalez-Rueda et al. 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Using whole-cell recordings and optogenetic stimulation of presynaptic input in anaesthetized mice, we show that synaptic plasticity rules are gated by cortical dynamics. Up states are biased towards depression such that presynaptic stimulation alone leads to synaptic depression, while connections contributing to postsynaptic spiking are protected against this synaptic weakening. We\r\nfind that this novel activity-dependent and input-specific downscaling mechanism has two important computational advantages: 1) improved signal-to-noise ratio, and 2) preservation of previously stored information. Thus, these synaptic plasticity rules provide an attractive mechanism for SWS-related synaptic downscaling and circuit refinement.\r\n\r\nWe simulate a feedforward network of neurons undergoing Up-state-mediated plasticity. Under this plasticity rule, presynaptic spikes alone lead to synaptic depression, whereas those followed by postsynaptic spikes within 10 ms are not changed." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239427", + "public_submitter_email": { + "attr_id": 309, + "value": "v.pedrosa15@imperial.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Victor Pedrosa" + }, + "ver_date": "2018-04-09T14:24:58.85", + "ver_number": 8 + }, + "239435": { + "citation": { + "attr_id": 391, + "value": "Gruntman, E., Romani, S., & Reiser, M. B., Simple integration of fast excitation and offset, delayed inhibition computes directional selectivity in Drosophila. Nature Neuroscience 21, 250\u2013257 (2018)" + }, + "class_id": 19, + "created": "2018-04-07T20:14:17.737", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 239435, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 239438, + "object_name": "Gruntman, Eyal [ gruntmane at janelia.hhmi.org]" + }, + { + "object_id": 239439, + "object_name": "Romani, Sandro [ romanis at janelia.hhmi.org]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 239437, + "object_name": "Gruntman E, Romani S, Reiser MB (2018)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Drosophila T4 neuron (Gruntman et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 239440, + "object_name": "Fly lobula plate T4 neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Passive, multi-compartment conductance-based model of a T4 cell. The model reproduces the neuron's response to moving stimuli via integration of spatially offset fast excitatory and slow inhibitory inputs." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239435", + "other_implementer": { + "attr_id": 308, + "value": "Gruntman, Eyal [ gruntmane at janelia.hhmi.org]Romani, Sandro [ romanis at janelia.hhmi.org]" + }, + "other_neurons": { + "attr_id": 301, + "value": "Fly lobula plate T4 neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "gruntmane@janelia.hhmi.org" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Eyal Gruntman" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 206363, + "object_name": "Drosophila" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 120613, + "object_name": "Drosophila (fruit fly)" + } + ] + }, + "ver_date": "2019-02-26T10:15:43.84", + "ver_number": 3 + }, + "239530": { + "citation": { + "attr_id": 391, + "value": "M. Ursino, C. Baston, Aberrant learning in Parkinson's disease: a neurocomputational study on bradykinesia, to be published in European Journal of Neuroscience" + }, + "class_id": 19, + "created": "2018-04-11T06:15:07.99", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 239530, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 239531, + "object_name": "Ursino, Mauro [mauro.ursino at unibo.it]" + }, + { + "object_id": 239532, + "object_name": "Baston, Chiara [chiara.baston at unibo.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 241831, + "object_name": "Ursino M, Baston C (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A basal ganglia model of aberrant learning (Ursino et al. 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 260, + "object_name": "Neostriatum medium spiny direct pathway GABA cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 209, + "object_name": "Acetylcholine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A comprehensive, biologically inspired neurocomputational model of action selection in the Basal Ganglia allows simulation of dopamine induced aberrant learning in Parkinsonian subjects. In particular, the model simulates the Alternate Finger Tapping motor task as an indicator of bradykinesia." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239530", + "other_implementer": { + "attr_id": 308, + "value": "Ursino, Mauro (mauro.ursino@unibo.it)\r\nBaston, Chiara (chiara.baston@unibo.it)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "mauro.ursino@unibo.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Mauro Ursino" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 196, + "object_name": "D1" + }, + { + "object_id": 197, + "object_name": "D2" + }, + { + "object_id": 218, + "object_name": "Cholinergic Receptors" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2018-06-07T17:34:28.46", + "ver_number": 7 + }, + "239535": { + "citation": { + "attr_id": 391, + "value": "Kim H and Kim M (2018) PyMUS: Python-Based Simulation Software for Virtual Experiments on Motor Unit System. Front. Neuroinform. 12:15. doi: 10.3389/fninf.2018.00015" + }, + "class_id": 19, + "created": "2018-04-11T21:03:07.94", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 239535, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 235771, + "object_name": "Kim, Hojeong [hojeong.kim03 at gmail.com]" + }, + { + "object_id": 239536, + "object_name": "Kim, Minjung [reddkwl at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 186770, + "object_name": "Motor control" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 239537, + "object_name": "Kim H, Kim M (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "PyMUS: A Python based Motor Unit Simulator (Kim & Kim 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 276, + "object_name": "Spinal cord lumbar motor neuron alpha ACh cell" + }, + { + "object_id": 105531, + "object_name": "Skeletal muscle cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "PyMUS is a simulation software that allows for integrative investigations on the input-output processing of the motor unit system in a hierarchical manner from a single channel to the entire system behavior. Using PyMUS, a single motoneuron, muscle unit and motor unit can be separately simulated under a wide range of experimental input protocols." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239535", + "other_currents": { + "attr_id": 303, + "value": "I_Leak" + }, + "other_implementer": { + "attr_id": 308, + "value": "Kim, Minjung [reddkwl at gmail.com]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "hojeong.kim03@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Hojeong Kim" + }, + "ver_date": "2018-04-16T07:44:54.9", + "ver_number": 7 + }, + "239540": { + "class_id": 19, + "created": "2018-04-12T16:26:46.827", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 239540, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 243652, + "object_name": "Fiore, Vincenzo G. [vincenzo.g.fiore at gmail.com]" + }, + { + "object_id": 243653, + "object_name": "Ognibene, Dimitri " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 112854, + "object_name": "Addiction" + }, + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 243651, + "object_name": "Fiore VG, Ognibene D, Adinoff B, Gu X (2018)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Computational endophenotypes in addiction (Fiore et al 2018)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... here we simulated phenotypic\r\nvariations in addiction symptomology and responses to putative\r\ntreatments, using both a neural model, based on cortico-striatal\r\ncircuit dynamics, and an algorithmic model of reinforcement\r\nlearning. These simulations rely on the widely accepted assumption\r\nthat both the ventral, model-based, goal-directed system and the\r\ndorsal, model-free, habitual system are vulnerable to\r\nextra-physiologic dopamine reinforcements triggered by addictive\r\nrewards. We found that endophenotypic differences in the balance\r\nbetween the two circuit or control systems resulted in an inverted\r\nU-shape in optimal choice behavior. Specifically, greater unbalance\r\nled to a higher likelihood of developing addiction and more severe\r\ndrug-taking behaviors.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239540", + "public_submitter_email": { + "attr_id": 309, + "value": "vincenzo.g.fiore@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Vincenzo G Fiore" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 138633, + "object_name": "Striatum" + }, + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2018-07-10T14:21:53.373", + "ver_number": 9 + }, + "239541": { + "citation": { + "attr_id": 391, + "value": "Helfer, P. & Shultz, T.R. (2018)\r\nCoupled Molecular Feedback Loops Maintain Synaptic Long-Term Potentiation: A Computational Model\r\nSubmitted for publication" + }, + "class_id": 19, + "created": "2018-04-16T13:13:16.843", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 239541, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 239548, + "object_name": "Helfer, Peter [peter.helfer at mail.mcgill.ca]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 239587, + "object_name": "Helfer P, Shultz TR (2018)" + }, + { + "object_id": 239589, + "object_name": "Helfer P, Shultz TR (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "PKMZ synthesis and AMPAR regulation in late long-term synaptic potentiation (Helfer & Shultz 2018)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Stochastic simulation of a set of molecular reactions that implement late long-term potentiation (L-LTP). The model is able to account for a wide range of empirical results, including induction and maintenance of late-phase LTP, cellular memory reconsolidation and the effects of different pharmaceutical interventions." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239541", + "other_implementer": { + "attr_id": 308, + "value": "Helfer, Peter [peter.helfer@mail.mcgill.ca]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "peter.helfer@mail.mcgill.ca" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Peter Helfer" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2018-04-18T10:37:39.913", + "ver_number": 9 + }, + "239582": { + "class_id": 19, + "created": "2018-04-16T14:59:44.48", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 239582, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83510, + "object_name": "Powers, Randy [rkpowers at u.washington.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 239549, + "object_name": "Powers RK, Heckman CJ (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Motoneuron pool input-output function (Powers & Heckman 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 276, + "object_name": "Spinal cord lumbar motor neuron alpha ACh cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Although motoneurons have often been considered\r\nto be fairly linear transducers of synaptic input, recent evidence\r\nsuggests that strong persistent inward currents (PICs) in motoneurons\r\nallow neuromodulatory and inhibitory synaptic inputs to induce large\r\nnonlinearities in the relation between the level of excitatory input and\r\nmotor output. To try to estimate the possible extent of this nonlinearity,\r\nwe developed a pool of model motoneurons designed to replicate\r\nthe characteristics of motoneuron input-output properties measured in\r\nmedial gastrocnemius motoneurons in the decerebrate cat with voltage-\r\nclamp and current-clamp techniques. We drove the model pool\r\nwith a range of synaptic inputs consisting of various mixtures of\r\nexcitation, inhibition, and neuromodulation. We then looked at the\r\nrelation between excitatory drive and total pool output. Our results\r\nrevealed that the PICs not only enhance gain but also induce a strong\r\nnonlinearity in the relation between the average firing rate of the\r\nmotoneuron pool and the level of excitatory input. The relation\r\nbetween the total simulated force output and input was somewhat\r\nmore linear because of higher force outputs in later-recruited units. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239582", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2023-01-27T13:29:52.197", + "ver_number": 7 + }, + "239741": { + "citation": { + "attr_id": 391, + "value": "Vinci G., Ventura V., Smith M.A., and Kass R.E. (2018). Adjusted Regularization of Cortical Covariance. Journal of Computational Neuroscience." + }, + "class_id": 19, + "created": "2018-04-17T21:12:31.89", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 239741, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 245420, + "object_name": "Vinci G, Ventura V, Smith MA, Kass RE (2018)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 150209, + "object_name": "R" + } + ] + }, + "name": "Adjusted regularization of cortical covariance (Vinci et al 2018)", + "notes": { + "attr_id": 24, + "value": "Graphical Lasso with Adjusted Regularization (GAR) useful to estimate functional connectivity." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239741", + "public_submitter_email": { + "attr_id": 309, + "value": "giuseppevinci88@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Giuseppe Vinci" + }, + "ver_date": "2018-10-06T18:59:40.227", + "ver_number": 2 + }, + "239744": { + "citation": { + "attr_id": 391, + "value": "Molecular Mechanisms Underlying Striatal Synaptic Plasticity: Relevance to Chronic Alcohol Consumption and Seeking. Eur J Neurosci. 2018 PMID: 29602186" + }, + "class_id": 19, + "created": "2018-04-18T14:36:52.54", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 239744, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53429, + "object_name": "Blackwell, Avrama [avrama at gmu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 136106, + "object_name": "G-protein coupled" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 242863, + "object_name": "Alcohol Use Disorder" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 239748, + "object_name": "Blackwell KT, Salinas AG, Tewatia P, English B, Hellgren Kotaleski J, Lovinger DM (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 145174, + "object_name": "Molecular Network" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 139757, + "object_name": "NeuroRD" + } + ] + }, + "name": "Signaling pathways In D1R containing striatal spiny projection neurons (Blackwell et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 260, + "object_name": "Neostriatum medium spiny direct pathway GABA cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + }, + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 184489, + "object_name": "Endocannabinoid" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We implemented a mechanistic model of signaling pathways activated by dopamine D1 receptors, acetylcholine receptors, and glutamate. We use our novel, computationally efficient simulator, NeuroRD, to simulate stochastic interactions both within and between dendritic spines. Results show that the combined activity of several key plasticity molecules correctly predicts the occurrence of either LTP, LTD or no plasticity for numerous experimental protocols." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239744", + "other_simulator": { + "attr_id": 307, + "value": "NeuroRD" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "kblackw1@gmu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Kim Blackwell" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 196, + "object_name": "D1" + }, + { + "object_id": 188, + "object_name": "mGluR5" + }, + { + "object_id": 179, + "object_name": "M1" + }, + { + "object_id": 181, + "object_name": "M4" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + }, + { + "object_id": 138633, + "object_name": "Striatum" + } + ] + }, + "ver_date": "2018-11-20T14:28:03.513", + "ver_number": 14 + }, + "239878": { + "citation": { + "attr_id": 391, + "value": "Martinez-Ca\u00f1ada, P., Mobarhan, M. H., Halnes, G., Fyhn, M., Morillas, C., Pelayo, F., & Einevoll, G. T. (2018). Biophysical network modeling of the dLGN circuit: Effects of cortical feedback on spatial response properties of relay cells. PLoS computational biology, 14(1), e1005930." + }, + "class_id": 19, + "created": "2018-04-30T04:08:23.017", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 239878, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 225168, + "object_name": "Mart\u00ednez-Ca\u00f1ada, Pablo [pablomc at ugr.es]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 239880, + "object_name": "Mart\u00ednez-Ca\u00f1ada P, Mobarhan MH, Halnes G, Fyhn M, Morillas C, Pelayo F, Einevoll GT (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 144398, + "object_name": "LFPy" + }, + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 2832, + "object_name": "NEST" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Cortical feedback alters visual response properties of dLGN relay cells (Mart\u00ednez-Ca\u00f1ada et al 2018)", + "notes": { + "attr_id": 24, + "value": "Network model that includes biophysically detailed, single-compartment and multicompartment neuron models of relay-cells and interneurons in the dLGN and a population of orientation-selective layer 6 simple cells, consisting of pyramidal cells (PY). We have considered two different arrangements of synaptic feedback from the ON and OFF zones in the visual cortex to the dLGN: phase-reversed (\u2018push-pull\u2019) and phase-matched (\u2018push-push\u2019), as well as different spatial extents of the corticothalamic projection pattern. This project is the result of a research work and its associated publication is: (Mart\u00ednez-Ca\u00f1ada et al 2018).\r\nInstallation instructions as well as the latest version can be found in the Github repository: https://github.com/CINPLA/biophysical_thalamocortical_system" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/239878", + "public_submitter_email": { + "attr_id": 309, + "value": "pablomc@ugr.es" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pablo Martinez-Ca\u00f1ada" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2018-10-17T17:35:09.083", + "ver_number": 11 + }, + "240116": { + "class_id": 19, + "created": "2018-05-04T13:15:13.58", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 88215, + "object_name": "I_Ks" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 240116, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 153637, + "object_name": "Cavarretta, Francesco [francescocavarretta at hotmail.it]" + }, + { + "object_id": 33736, + "object_name": "Hines, Michael [Michael.Hines at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3542, + "object_name": "Pattern Recognition" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 182362, + "object_name": "Sensory coding" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 240117, + "object_name": "Cavarretta F, Burton SD, Igarashi KM, Shepherd GM, Hines ML, Migliore M (2018)" + }, + { + "object_id": 227988, + "object_name": "Cavarretta F, Marasco A, Hines ML, Shepherd GM, Migliore M (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Parallel odor processing by mitral and middle tufted cells in the OB (Cavarretta et al 2016, 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 153282, + "object_name": "Olfactory bulb main tufted middle GLU cell" + }, + { + "object_id": 3979, + "object_name": "Olfactory bulb main interneuron granule MC GABA cell" + }, + { + "object_id": 153283, + "object_name": "Olfactory bulb main interneuron granule TC GABA cell" + }, + { + "object_id": 185340, + "object_name": "Olfactory bulb (accessory) mitral cell" + }, + { + "object_id": 207694, + "object_name": "Olfactory bulb main tufted cell external" + }, + { + "object_id": 168419, + "object_name": "Olfactory bulb short axon cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"[...] experimental findings suggest that\r\nMC and mTC may encode parallel and complementary odor representations. We\r\nhave analyzed the functional roles of these pathways by using a morphologically\r\nand physiologically realistic three-dimensional model to explore the MC and\r\nmTC microcircuits in the glomerular layer and deeper plexiform layers. [...]\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/240116", + "public_submitter_email": { + "attr_id": 309, + "value": "francescocavarretta@hotmail.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Francesco Cavarretta" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "ver_date": "2022-11-04T13:05:48.653", + "ver_number": 25 + }, + "240364": { + "biosimulations": { + "value": "modeldb-240364" + }, + "class_id": 19, + "created": "2018-05-08T18:06:19.647", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 140826, + "object_name": "I Ca,p" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 240364, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 248171, + "object_name": "Viertel R, Borisyuk A (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "External Tufted Cell Model (Ryan Viertel, Alla Borisyuk 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 207694, + "object_name": "Olfactory bulb main tufted cell external" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "ODE model of the Mammalian External Tufted Cell" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/240364", + "public_submitter_email": { + "attr_id": 309, + "value": "rdviertel@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ryan Viertel" + }, + "ver_date": "2019-01-07T15:08:08.243", + "ver_number": 10 + }, + "240369": { + "citation": { + "attr_id": 391, + "value": "M. 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In the highest layers, each neuron\r\nwas assigned to an object category, and it was assumed that\r\nthe stimulus category was the category of the first neuron to\r\nfire. ...\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/240369", + "other_concept": { + "attr_id": 306, + "value": "Temporal Coding" + }, + "other_implementer": { + "attr_id": 308, + "value": "Mozafari, Milad [milad.mozafari at ut.ac.ir]" + }, + "other_simulator": { + "attr_id": 307, + "value": "Microsoft C# Program" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "milad.mozafari@ut.ac.ir" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Milad Mozafari" + }, + "ver_date": "2018-10-17T17:35:28.557", + "ver_number": 12 + }, + "240382": { + "class_id": 19, + "created": "2018-05-13T02:59:07.243", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 240382, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 241134, + "object_name": "Reneaux, Melissa [reneauxm5 at gmail.com]" + }, + { + "object_id": 207819, + "object_name": "Gupta, Rahul [gupta.sbt at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 143724, + "object_name": "Bifurcation" + }, + { + "object_id": 238958, + "object_name": "Stochastic simulation" + }, + { + "object_id": 82787, + "object_name": "Working memory" + }, + { + "object_id": 112853, + "object_name": "Schizophrenia" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 241409, + "object_name": "Reneaux M, Gupta R (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Effect of cortical D1 receptor sensitivity on working memory maintenance (Reneaux & Gupta 2018)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Alterations in cortical D1 receptor density and reactivity of dopamine-binding sites, collectively termed as D1 receptor-sensitivity in the present study, have been experimentally shown to affect the working memory maintenance during delay-period. However, computational models addressing the effect of D1 receptor-sensitivity are lacking. A quantitative neural mass model of the prefronto-mesoprefrontal system has been proposed to take into account the effect of variation in cortical D1 receptor-sensitivity on working memory maintenance during delay. The model computes the delay-associated equilibrium states/operational points of the system for different values of D1 receptor-sensitivity through the nullcline and bifurcation analysis. Further, to access the robustness of the working memory maintenance during delay in the presence of alteration in D1 receptor-sensitivity, numerical simulations of the stochastic formulation of the model are performed to obtain the global potential landscape of the dynamics.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/240382", + "other_concept": { + "attr_id": 306, + "value": "Signal-to-noise ratio" + }, + "other_implementer": { + "attr_id": 308, + "value": "Reneaux, Melissa [reneauxm5 at gmail.com]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "reneauxm5@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Melissa Reneaux" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 196, + "object_name": "D1" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "ver_date": "2018-05-30T10:46:57.29", + "ver_number": 15 + }, + "240954": { + "class_id": 19, + "created": "2018-05-15T11:59:10.45", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 240954, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 246829, + "object_name": "Ramirez-Mahaluf, Juan P [jpramirezmahaluf at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 156115, + "object_name": "Gamma oscillations" + }, + { + "object_id": 240955, + "object_name": "Major Depression Disease (MDD)" + }, + { + "object_id": 185022, + "object_name": "Beta oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 240534, + "object_name": "Ramirez-Mahaluf JP, Roxin A, Mayberg HS, Compte A (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 237342, + "object_name": "Brian (web link to model)" + } + ] + }, + "name": "MDD: the role of glutamate dysfunction on Cingulo-Frontal NN dynamics (Ramirez-Mahaluf et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 239, + "object_name": "Serotonin" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ...Currently, no mechanistic framework describes how network dynamics, glutamate, and serotonin interact to explain MDD symptoms and treatments. Here, we built a biophysical computational model of 2 areas (vACC and dlPFC) that can switch between emotional and cognitive processing. (Major Depression Disease) MDD networks were simulated by slowing glutamate decay in vACC and demonstrated sustained vACC activation. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/240954", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "ver_date": "2018-11-09T14:55:17.897", + "ver_number": 7 + }, + "240957": { + "class_id": 19, + "created": "2018-05-16T09:45:42.763", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 240957, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 226424, + "object_name": "Appukuttan, Shailesh [shailesh.appukuttan at unic.cnrs-gif.fr;\r\nappukuttan.shailesh at gmail.com;]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 84598, + "object_name": "Extracellular Fields" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 183995, + "object_name": "Ephaptic coupling" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 235132, + "object_name": "Appukuttan S, Brain KL, Manchanda R (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Extracellular fields for a three-dimensional network of cells using NEURON (Appukuttan et al 2017)", + "notes": { + "attr_id": 24, + "value": "\" ... In the present work, we demonstrate a technique to couple the extracellular fields of individual cells within the NEURON simulation environment. The existing features of the simulator are extended by explicitly defining current balance equations, resulting in the coupling of the extracellular fields of adjacent cells. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/240957", + "public_submitter_email": { + "attr_id": 309, + "value": "shailesh.a@iitb.ac.in" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Shailesh Appukuttan" + }, + "ver_date": "2018-06-05T11:12:55.523", + "ver_number": 15 + }, + "240960": { + "citation": { + "attr_id": 391, + "value": "Hsu CL, Zhao X, Milstein AD, Spruston N. (2018). Persistent sodium current mediates steep voltage dependence of spatial coding in hippocampal pyramidal neurons. 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A simple model built with realistic axo-somatic voltage-gated sodium channels in CA1 (Carter et al., 2012; Neuron 75, 1081\u20131093) demonstrates that the biophysics of persistent sodium current is sufficient to explain the synaptic amplification effects. A full model built previously (Grienberger et al., 2017; Nature Neuroscience, 20(3): 417\u2013426) with detailed morphology, ion channel types and biophysical properties of CA1 place cells naturally reproduces the steep voltage dependence of synaptic responses." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/240960", + "other_currents": { + "attr_id": 303, + "value": "I Leak, I delayed-rectifier" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "hsuc@janelia.hhmi.org" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ching-Lung Hsu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2018-06-15T16:47:44.087", + "ver_number": 25 + }, + "240961": { + "biosimulations": { + "value": "modeldb-240961" + }, + "citation": { + "attr_id": 391, + "value": "Huange et al. Multiple actions of rotenone, an inhibitor of mitochondrial respiratory chain, \r\non ionic currents and minature end-plate potential in mouse hippocampal (mHippoE-14) neurons.\r\nCell Physiol Biochem 2018;47:330-343." + }, + "class_id": 19, + "created": "2018-05-17T03:37:30.84", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 143445, + "object_name": "I_KD" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 240961, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 183995, + "object_name": "Ephaptic coupling" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 240965, + "object_name": "Huang CW, Lin KM, Hung TY, Chuang YC, Wu SN (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Actions of Rotenone on ionic currents and MEPPs in Mouse Hippocampal Neurons (Huang et al 2018) ", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... With the aid of patch-clamp technology and simulation modeling,\r\nthe effects of (Rotenone) Rot on membrane ion currents present in\r\nmHippoE-14 cells were investigated. Results: Addition of Rot produced\r\nan inhibitory action on the peak amplitude of INa ...; however,\r\nneither activation nor inactivation kinetics of INa was changed during\r\ncell exposure to this compound. Addition of Rot produced little or no\r\nmodifications in the steady-state inactivation curve of INa. Rot\r\nincreased the amplitude of Ca2+-activated Cl- current in response to\r\nmembrane depolarization ... . Moreover, when these cells were exposed\r\nto 10 \u00b5M Rot, a specific population of ATP-sensitive K+ channels\r\n... was measured, despite its inability to alter single-channel\r\nconductance. Under current clamp condition, the frequency of miniature\r\nend-plate potentials in mHippoE-14 cells was significantly raised in\r\nthe presence of Rot (10 \u00b5M) with no changes in their amplitude and\r\ntime course of rise and decay. In simulated model of hippocampal\r\nneurons incorporated with chemical autaptic connection, increased\r\nautaptic strength to mimic the action of Rot was noted to change the\r\nbursting pattern with emergence of subthreshold\r\npotentials. Conclusions: The Rot effects presented herein might exert\r\na significant action on functional activities of hippocampal neurons\r\noccurring in vivo. \"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/240961", + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Dr. Sheng-Nan Wu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2018-05-17T11:28:10.897", + "ver_number": 6 + }, + "241160": { + "citation": { + "attr_id": 391, + "value": "Anderson R, Farokhniaee A, Gunalan K., Howell B, McIntyre C., Action potential initiation, propagation, and cortical invasion in the hyperdirect pathway during subthalamic deep brain stimulation, Brain Stimulation (2018), https://doi.org/10.1016/j.brs.2018.05.008" + }, + "class_id": 19, + "created": "2018-05-21T10:37:24.173", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 241160, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 241163, + "object_name": "Anderson RW, Farokhniaee A, Gunalan K, Howell B, McIntyre CC (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "AP initiation, propagation, and cortical invasion in a Layer 5 pyramidal cell (Anderson et 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 167344, + "object_name": "Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... High frequency (~130 Hz) deep brain stimulation (DBS) of\r\nthe subthalamic region is an established clinical therapy for the\r\ntreatment of late stage Parkinson's disease (PD). Direct modulation of\r\nthe hyperdirect pathway, defined as cortical layer V pyramidal neurons\r\nthat send an axon collateral to the subthalamic nucleus (STN), has\r\nemerged as a possible component of the therapeutic mechanisms.\r\n...We found robust AP propagation throughout the complex axonal\r\narbor of the hyperdirect neuron. Even at therapeutic DBS frequencies,\r\nstimulation induced APs could reach all of the intracortical axon\r\nterminals with ~100% fidelity. The functional result of this high\r\nfrequency axonal driving of the thousands of synaptic connections made\r\nby each directly stimulated hyperdirect neuron is a profound synaptic\r\nsuppression that would effectively disconnect the neuron from the\r\ncortical circuitry. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/241160", + "public_submitter_email": { + "attr_id": 309, + "value": "anderson@case.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ross W. Anderson" + }, + "ver_date": "2018-05-23T18:33:06.703", + "ver_number": 6 + }, + "241165": { + "class_id": 19, + "created": "2018-05-21T17:09:25.047", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 241165, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 241166, + "object_name": "Aberra, Aman [aman.aberra at duke.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 244682, + "object_name": "Aberra AS, Peterchev AV, Grill WM (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Biophysically realistic neuron models for simulation of cortical stimulation (Aberra et al. 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52425, + "object_name": "Myelinated neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This archive instantiates the single-cell cortical models used in (Aberra et al. 2018) and sets up extracellular stimulation with either a point-current source, to simulate intracortical microstimulation (ICMS), or a uniform E-field distribution, with a monophasic, rectangular pulse waveform in both cases. " + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/241165", + "public_submitter_email": { + "attr_id": 309, + "value": "aman.aberra@duke.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Aman Aberra" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 228597, + "object_name": "Barrel cortex" + } + ] + }, + "ver_date": "2022-05-27T12:58:23.52", + "ver_number": 23 + }, + "241169": { + "class_id": 19, + "created": "2018-05-22T19:35:37.997", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 241169, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 240367, + "object_name": "Dewell, Richard Burkett [dewell at bcm.edu]" + }, + { + "object_id": 33804, + "object_name": "Gabbiani, F" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 206291, + "object_name": "Membrane Properties" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 256142, + "object_name": "Dewell RB, Gabbiani F (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "M-current in a collision detection neuron (LGMD model) (Dewell & Gabbiani 2018) ", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 116948, + "object_name": "Locust Lobula Giant Movement Detector (LGMD) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ...In this study, we demonstrate that the LGMD (Lobula Giant Movement Detector) neuron exhibits a large M\r\ncurrent, generated by noninactivating K\u0002 channels, that shortens the temporal window of dendritic integration, regulates a firing mode switch between burst and isolated spiking, increases the precision of spike timing, and increases the reliability of spike propagation to\r\ndownstream motor centers. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/241169", + "public_submitter_email": { + "attr_id": 309, + "value": "dewell@bcm.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Richard Dewell" + }, + "ver_date": "2023-06-22T18:35:28.462620", + "ver_number": 5 + }, + "241240": { + "citation": { + "attr_id": 391, + "value": "Holmes et al 2017" + }, + "class_id": 19, + "created": "2018-05-25T18:09:25.857", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 143445, + "object_name": "I_KD" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 241240, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 241772, + "object_name": "Holmes, William [holmes at ohio.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 234737, + "object_name": "Holmes WR, Huwe JA, Williams B, Rowe MH, Peterson EH (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Role of afferent-hair cell connectivity in determining spike train regularity (Holmes et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 139834, + "object_name": "Vestibular neuron" + }, + { + "object_id": 241766, + "object_name": "Turtle vestibular neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Vestibular bouton afferent terminals in turtle utricle\r\ncan be categorized into four types depending on their location and\r\nterminal arbor structure: lateral extrastriolar (LES), striolar, juxtastriolar,\r\nand medial extrastriolar (MES). The terminal arbors of these\r\nafferents differ in surface area, total length, collecting area, number of\r\nboutons, number of bouton contacts per hair cell, and axon diameter\r\n(Huwe JA, Logan CJ, Williams B, Rowe MH, Peterson EH. J\r\nNeurophysiol 113: 2420 \u20132433, 2015). To understand how differences\r\nin terminal morphology and the resulting hair cell inputs might affect\r\nafferent response properties, we modeled representative afferents\r\nfrom each region, using reconstructed bouton afferents. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/241240", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 241764, + "object_name": "Turtle vestibular system" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 249927, + "object_name": "Turtle" + } + ] + }, + "ver_date": "2022-05-27T13:02:41.427", + "ver_number": 15 + }, + "241796": { + "citation": { + "attr_id": 391, + "value": "Telenczuk et al 2018" + }, + "class_id": 19, + "created": "2018-05-31T15:30:04.6", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 241796, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 241794, + "object_name": "Telenczuk, Maria [mtelenczuk at unic.cnrs-gif.fr]" + }, + { + "object_id": 241795, + "object_name": "Telenczuk, Bartosz [bartosz.telenczuk at inaf.cnrs-gif.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 84598, + "object_name": "Extracellular Fields" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 241769, + "object_name": "Telenczuk M, Brette R, Destexhe A, Telenczuk B (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + }, + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "Contribution of the axon initial segment to APs recorded extracellularly (Telenczuk et al 2018)", + "notes": { + "attr_id": 24, + "value": "\"... It\r\nwas recently proposed that at onset of an (Action Potential) AP the soma and the (Axon Initial Segment) AIS form\r\na dipole.\r\n\r\nWe study the extracellular signature (the extracellular\r\naction potential, EAP) generated by such a dipole.\r\n\r\nFirst, we\r\ndemonstrate the formation of the dipole and its extracellular\r\nsignature in detailed morphological models of a reconstructed\r\npyramidal neuron.\r\n\r\nThen, we study the EAP waveform and its spatial\r\ndependence in models with axonal AP initiation and contrast it with\r\nthe EAP obtained in models with somatic AP initiation.\r\n\r\nWe show that in\r\nthe models with axonal AP initiation the dipole forms between\r\nsomatodendritic compartments and the AIS, and not between soma and\r\ndendrites as in the classical models.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/241796", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "ver_date": "2018-05-31T15:50:11.867", + "ver_number": 3 + }, + "241826": { + "citation": { + "attr_id": 391, + "value": "Pietak Levin 2017" + }, + "class_id": 19, + "created": "2018-05-31T17:30:16.257", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 241826, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 241827, + "object_name": "Pietak, Alexis " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 225300, + "object_name": "Electrical-chemical" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 241244, + "object_name": "Pietak A, Levin M (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 241828, + "object_name": "BETSE (web link to model)" + } + ] + }, + "name": "Genetic, biochemical and bioelectrical dynamics in pattern regulation (Pietak & Levin 2017)", + "notes": { + "attr_id": 24, + "value": "\"Gene regulatory networks (GRNs) describe interactions between gene\r\nproducts and transcription factors that control gene expression. In\r\ncombination with reaction\u2013diffusion models, GRNs have enhanced\r\ncomprehension of biological pattern formation. However, although it is\r\nwell known that biological systems exploit an interplay of genetic and\r\nphysical mechanisms, instructive factors such as transmembrane\r\npotential (Vmem) have not been integrated into full GRN models. Here\r\nwe extend regulatory networks to include bioelectric signalling,\r\ndeveloping a novel synthesis: the bioelectricity-integrated gene and\r\nreaction (BIGR) network.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/241826", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "ver_date": "2018-05-31T17:49:53.21", + "ver_number": 5 + }, + "241932": { + "citation": { + "attr_id": 391, + "value": "PMID: 28701481 DOI: 10.1523/JNEUROSCI.3490-16.2017" + }, + "class_id": 19, + "created": "2018-06-05T09:59:04.98", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 241932, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 241933, + "object_name": "Renn\u00f3-Costa, C\u00e9sar [rennocosta at neuro.ufrn.br]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 156115, + "object_name": "Gamma oscillations" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + }, + { + "object_id": 83540, + "object_name": "Winner-take-all" + }, + { + "object_id": 98011, + "object_name": "Place cell/field" + }, + { + "object_id": 185463, + "object_name": "Pattern Separation" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 241935, + "object_name": "Renn\u00f3-Costa C, Tort ABL (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Place and grid cells in a loop (Renn\u00f3-Costa & Tort 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model implements a loop circuit between place and grid cells. The model was used to explain place cell remapping and grid cell realignment. Grid cell model as a continuous attractor network. Place cells have recurrent attractor network. Rate models implemented with E%-MAX winner-take-all network dynamics, with gamma cycle time-step." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/241932", + "other_implementer": { + "attr_id": 308, + "value": "Renn\u00f3-Costa, C. [Federal Unviersity of Rio Grande do Norte, Brazil]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "rennocosta@neuro.ufrn.br" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "C\u00e9sar Renn\u00f3-Costa" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + }, + { + "object_id": 119191, + "object_name": "Entorhinal cortex" + } + ] + }, + "ver_date": "2018-06-06T15:44:27.55", + "ver_number": 9 + }, + "241979": { + "citation": { + "attr_id": 391, + "value": "PMID: 28895002 PMCID: PMC5691111 DOI: 10.1007/s10827-017-0657-5\r\n\r\nSailamul, Pachaya, Jaeson Jang, and Se-Bum Paik. \"Synaptic convergence regulates synchronization-dependent spike transfer in feedforward neural networks.\" Journal of computational neuroscience 43.3 (2017): 189-202." + }, + "class_id": 19, + "created": "2018-06-06T14:17:02.31", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 241979, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 242015, + "object_name": "Sailamul, Pachaya [pachaya_sailamul at brown.edu]" + }, + { + "object_id": 242016, + "object_name": "Jang, Jaeson " + }, + { + "object_id": 242017, + "object_name": "Paik, Se-Bum " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 136098, + "object_name": "Information transfer" + }, + { + "object_id": 242018, + "object_name": "Synaptic Convergence" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 241981, + "object_name": "Sailamul P, Jang J, Paik SB (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Convergence regulates synchronization-dependent AP transfer in feedforward NNs (Sailamul et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We study how synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. \r\n\r\nWe implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance. In the Uniform-Constant (UC) and Uniform-Exponential (UE) models, the connection probability density is a uniform constant within a certain range, but the connection strength is set as a constant value or an exponentially decaying function, respectively.\r\n\r\nThen we examined how the spike transfer function is modulated under these conditions, while static or synchronized input patterns were introduced to simulate different levels of feedforward spike synchronization. \r\nWe observed that the synchronization-dependent modulation of the transfer function appeared noticeably different for each convergence condition. The modulation of the spike transfer function was largest in the UC model, and smallest in the UE model. Our analysis showed that this difference was induced by the different spike weight distributions that was generated from convergent synapses in each model.\r\n\r\n Our results suggest that the structure of the feedforward convergence is a crucial factor for correlation-dependent spike control, thus must be considered important to understand the mechanism of information transfer in the brain." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/241979", + "other_concept": { + "attr_id": 306, + "value": "synaptic convergence" + }, + "other_implementer": { + "attr_id": 308, + "value": "Sailamul, Pachaya;\r\nJang, Jaeson;\r\nPaik, Se-Bum;" + }, + "other_type": { + "attr_id": 305, + "value": "Feedforward networks" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "pachaya_sailamul@brown.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pachaya Sailamul" + }, + "ver_date": "2018-06-06T17:29:32.85", + "ver_number": 6 + }, + "243212": { + "citation": { + "attr_id": 391, + "value": "Aerts et al 2018" + }, + "class_id": 19, + "created": "2018-06-12T17:28:14.847", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 243212, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 243213, + "object_name": "Brain Tumor" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 242982, + "object_name": "Aerts H, Schirner M, Jeurissen B, Van Roost D, Achten E, Ritter P, Marinazzo D (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 243210, + "object_name": "The Virtual Brain (web link to model)" + } + ] + }, + "name": "Modeling brain dynamics in brain tumor patients using the Virtual Brain (Aerts et al 2018)", + "notes": { + "attr_id": 24, + "value": "\"Presurgical planning for brain tumor resection aims at delineating\r\neloquent tissue in the vicinity of the lesion to spare during\r\nsurgery. \r\n... we simulated large-scale brain dynamics in 25\r\nhuman brain tumor patients and 11 human control participants using The\r\nVirtual Brain, an open-source neuroinformatics platform. Local and\r\nglobal model parameters of the Reduced Wong\u2013Wang model were\r\nindividually optimized and compared between brain tumor patients and\r\ncontrol subjects. In addition, the relationship between model\r\nparameters and structural network topology and cognitive performance\r\nwas assessed. Results showed (1) significantly improved prediction\r\naccuracy of individual functional connectivity when using individually\r\noptimized model parameters; (2) local model parameters that can\r\ndifferentiate between regions directly affected by a tumor, regions\r\ndistant from a tumor, and regions in a healthy brain; and (3)\r\ninteresting associations between individually optimized model\r\nparameters and structural network topology and cognitive performance.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/243212", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2018-06-12T17:34:25.763", + "ver_number": 3 + }, + "243350": { + "class_id": 19, + "created": "2018-06-15T05:52:21.787", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 243350, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 249460, + "object_name": "Aussel, Am\u00e9lie [amelie.aussel at loria.fr]" + }, + { + "object_id": 249461, + "object_name": "Buhry, Laure " + }, + { + "object_id": 249462, + "object_name": "Ranta, Radu " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 93420, + "object_name": "Sleep" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 156115, + "object_name": "Gamma oscillations" + }, + { + "object_id": 249458, + "object_name": "Theta oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 249429, + "object_name": "Aussel A, Buhry L, Tyvaert L, Ranta R (2018)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 231240, + "object_name": "Brian 2" + } + ] + }, + "name": "Model of the hippocampus over the sleep-wake cycle using Hodgkin-Huxley neurons (Aussel et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ...we propose a\r\ncomputational model of the hippocampal formation based on a\r\nrealistic topology and synaptic connectivity, and we analyze the\r\neffect of different changes on the network, namely the variation\r\nof synaptic conductances, the variations of the CAN channel\r\nconductance and the variation of inputs. By using a detailed\r\nsimulation of intracerebral recordings, we show that this is able\r\nto reproduce both the theta-nested gamma oscillations that are\r\nseen in awake brains and the sharp-wave ripple complexes measured\r\nduring slow-wave sleep. The results of our simulations support\r\nthe idea that the functional connectivity of the hippocampus,\r\nmodulated by the sleep-wake variations in Acetylcholine\r\nconcentration, is a key factor in controlling its rhythms.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/243350", + "other_simulator": { + "attr_id": 307, + "value": "Brian2" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "amelie.aussel@loria.fr" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Aussel Am\u00e9lie" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2019-01-18T15:19:15.48", + "ver_number": 9 + }, + "243446": { + "class_id": 19, + "created": "2018-06-19T03:53:29.17", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 243446, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 244383, + "object_name": "Zang, Yunliang " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 244387, + "object_name": "Zang Y, Dieudonn\u00e9 S, De Schutter E (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Voltage- and Branch-specific Climbing Fiber Responses in Purkinje Cells (Zang et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Climbing fibers (CFs) provide instructive signals driving cerebellar\r\nlearning, but mechanisms causing the variable CF responses in Purkinje\r\ncells (PCs) are not fully understood. Using a new experimentally\r\nvalidated PC model, we unveil the ionic mechanisms underlying\r\nCF-evoked distinct spike waveforms on different parts of the PC. We\r\ndemonstrate that voltage can gate both the amplitude and the spatial\r\nrange of CF-evoked Ca2+ influx by the availability of K+\r\ncurrents.\r\n...\r\nThe voltage- and\r\nbranch-specific CF responses can increase dendritic computational\r\ncapacity and enable PCs to actively integrate CF signals.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/243446", + "other_implementer": { + "attr_id": 308, + "value": "Zang, Yunliang" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "yunliang.zang@oist.jp" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Yunliang Zang" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2022-09-23T09:56:05.17", + "ver_number": 16 + }, + "243447": { + "class_id": 19, + "created": "2018-06-19T14:31:00.19", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 243447, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 146501, + "object_name": "Nicola, Wilten [wnicola at uwaterloo.ca]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 188516, + "object_name": "Memory" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 261826, + "object_name": "Nicola W, Clopath C (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "SHOT-CA3, RO-CA1 Training, & Simulation CODE in models of hippocampal replay (Nicola & Clopath 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In this code, we model the interaction between the medial septum and hippocampus as a FORCE trained, dual oscillator model. One oscillator corresponds to the medial septum and serves as an input, while a FORCE trained network of LIF neurons acts as a model of the CA3. We refer to this entire model as the Septal Hippocampal Oscillator Theta (or SHOT) network. \r\n\r\nThe code contained in this upload allows a user to train a SHOT network, train a population of reversion interneurons, and simulate the SHOT-CA3 and RO-CA1 networks after training. The code scripts are labeled to correspond to the figure from the manuscript. \r\n\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/243447", + "public_submitter_email": { + "attr_id": 309, + "value": "wnicola@imperial.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Wilten Nicola" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2019-07-08T16:29:09.34", + "ver_number": 19 + }, + "243448": { + "citation": { + "attr_id": 391, + "value": "A biophysically detailed computational model of bladder small DRG neuron soma- \r\nMandge and Manchanda, 2018 (accepted PLOS Computational Biology, DOI: 10.1371/journal.pcbi.1006293)" + }, + "class_id": 19, + "created": "2018-06-21T06:05:36.627", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 222730, + "object_name": "I Na, slow 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DRG neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Bladder small DRG neurons, which are putative nociceptors pivotal to urinary bladder function, express more than a dozen different ionic membrane mechanisms: ion channels, pumps and exchangers. Small-conductance Ca2+-activated K+ (SKCa) channels which were earlier thought to be gated solely by intracellular Ca2+ concentration ([Ca]i ) have recently been shown to exhibit inward rectification with respect to membrane potential. The effect of SKCa inward rectification on the excitability of these neurons is unknown. Furthermore, studies on the role of KCa channels in repetitive firing and their contributions to different types of afterhyperpolarization (AHP) in these neurons are lacking. In order to study these phenomena, we first constructed and validated a biophysically detailed single compartment model of bladder small DRG soma constrained by physiological data. The model includes twenty-two major known membrane mechanisms along with intracellular Ca2+ dynamics comprising Ca2+ diffusion, cytoplasmic buffering, and endoplasmic reticulum (ER) and mitochondrial mechanisms. Using modelling studies, we show that inward rectification of SKCa is an important parameter regulating neuronal repetitive firing and that its absence reduces action potential (AP) firing frequency. We also show that SKCa is more potent in reducing AP spiking than the large-conductance KCa channel (BKCa) in these neurons. Moreover, BKCa was found to contribute to the fast AHP (fAHP) and SKCa to the medium-duration (mAHP) and slow AHP (sAHP). We also report that the slow inactivating A-type K+ channel (slow KA) current in these neurons is composed of 2 components: an initial fast inactivating (time constant ~ 25-100 ms) and a slow inactivating (time constant ~ 200-800 ms) current. We discuss the implications of our findings, and how our detailed model can help further our understanding of the role of C-fibre afferents in the physiology of urinary bladder as well as in certain disorders." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/243448", + "other_currents": { + "attr_id": 303, + "value": "I Ca SOCC, IK Bkca, IK SKCa" + }, + "other_implementer": { + "attr_id": 308, + "value": "Darshan Mandge" + }, + "other_neurons": { + "attr_id": 301, + "value": "Urinary Bladder small-diameter DRG neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "darshanmandge@iitb.ac.in" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Darshan Mandge" + }, + "ver_date": "2019-06-04T16:30:52.917", + "ver_number": 14 + }, + "243508": { + "class_id": 19, + "created": "2018-06-22T11:35:49.943", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 167500, + "object_name": "Kir" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 243508, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 246010, + "object_name": "Savtchenko, Leonid P [leonid.savtchenko at ucl.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 150554, + "object_name": "Calcium waves" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 151948, + "object_name": "Potassium buffering" + }, + { + "object_id": 183421, + "object_name": "Volume transmission" + }, + { + "object_id": 206291, + "object_name": "Membrane Properties" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 246009, + "object_name": "Savtchenko LP, Bard L, Jensen TP, Reynolds JP, Kraev I, Medvedev N, Stewart MG, Henneberger C, Rusakov DA (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 120799, + "object_name": "Glia" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Disentangling astroglial physiology with a realistic cell model in silico (Savtchenko et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 87589, + "object_name": "Astrocyte" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Electrically non-excitable astroglia take up neurotransmitters, buffer extracellular K+ and generate Ca2+ signals that release molecular regulators of neural circuitry. The underlying machinery remains enigmatic, mainly because the sponge-like astrocyte morphology has been difficult to access experimentally or explore theoretically. Here, we systematically incorporate multi-scale, tri-dimensional astroglial architecture into a realistic multi-compartmental cell model, which we constrain by empirical tests and integrate into the NEURON computational biophysical environment. This approach is implemented as a flexible astrocyte-model builder ASTRO. As a proof-of-concept, we explore an in silico astrocyte to evaluate basic cell physiology features inaccessible experimentally. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/243508", + "other_concept": { + "attr_id": 306, + "value": "Morphology, Diffusion" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "leonid.savtchenko@ucl.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Leonid Savtchenko" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2018-10-24T10:04:28.15", + "ver_number": 21 + }, + "243510": { + "citation": { + "attr_id": 391, + "value": "C Vich, RW Berg, A Guillamon, S Ditlevsen - Frontiers in computational neuroscience, 2017" + }, + "class_id": 19, + "created": "2018-06-25T10:04:12.19", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 243510, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 184150, + "object_name": "Vich, Catalina [catalina.vich at uib.es]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 243515, + "object_name": "Conductances estimation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 243511, + "object_name": "Vich C, Berg RW, Guillamon A, Ditlevsen S (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "QIF method to estimate synaptic conductances (Vich et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 243514, + "object_name": "Abstract quadratic integrate-and-fire" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Subthreshold fluctuations in neuronal membrane potential traces\r\ncontain nonlinear components, and employing nonlinear models might\r\nimprove the statistical inference. We propose a new strategy to\r\nestimate synaptic conductances, which has been tested using in silico\r\ndata and applied to in vivo recordings. The model is constructed to\r\ncapture the nonlinearities caused by subthreshold activated currents,\r\nand the estimation procedure can discern between excitatory and\r\ninhibitory conductances using only one membrane potential trace.\r\n... The results show an improvement\r\ncompared to existent procedures for the models tested here.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/243510", + "other_concept": { + "attr_id": 306, + "value": "Conductances estimation" + }, + "other_currents": { + "attr_id": 303, + "value": "subthreshold ionic currents" + }, + "other_neurons": { + "attr_id": 301, + "value": "Quadratic Integrate-and-fire" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "catalina.vich@uib.es" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Catalina Vich" + }, + "ver_date": "2018-06-28T16:19:11.16", + "ver_number": 11 + }, + "243595": { + "class_id": 19, + "created": "2018-06-29T08:55:58.07", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 243595, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 258745, + "object_name": "Soman, Karthik [karthi.soman at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 258744, + "object_name": "Soman K, Muralidharan V, Chakravarthy VS (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "An oscillatory neural autoencoder based on frequency modulation and multiplexing (Soman et al 2018)", + "notes": { + "attr_id": 24, + "value": "\" ... We propose here an oscillatory neural network model that performs the function of an autoencoder. The model is a hybrid of rate-coded neurons and neural oscillators. Input signals modulate the frequency of the neural encoder oscillators. These signals are then multiplexed using a network of rate-code neurons that has afferent Hebbian and lateral anti-Hebbian connectivity, termed as Lateral Anti Hebbian Network (LAHN). Finally the LAHN output is de-multiplexed using an output neural layer which is a combination of adaptive Hopf and Kuramoto oscillators for the signal reconstruction. The Kuramoto-Hopf combination performing demodulation is a novel way of describing a neural phase-locked loop. The proposed model is tested using both synthetic signals and real world EEG signals. The proposed model arises out of the general motivation to construct biologically inspired, oscillatory versions of some of the standard neural network models, and presents itself as an autoencoder network based on oscillatory neurons applicable to time series signals. As a demonstration, the model is applied to compression of EEG signals.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/243595", + "public_submitter_email": { + "attr_id": 309, + "value": "karthi.soman@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Karthik Soman" + }, + "ver_date": "2019-08-28T17:50:55.393", + "ver_number": 9 + }, + "243841": { + "citation": { + "attr_id": 391, + "value": "PMID: 29946922 DOI: 10.1007/s10827-018-0689-5" + }, + "class_id": 19, + "created": "2018-07-05T13:49:55.337", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 243841, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 128875, + "object_name": "Development" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 245798, + "object_name": "Touch" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 243850, + "object_name": "Gaines JL, Finn KE, Slopsema JP, Heyboer LA, Polasek KH (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Human somatosensory and motor axon pair to compare thresholds (Gaines et al 2018)", + "notes": { + "attr_id": 24, + "value": "These motor and sensory axon models are based on the MRG axon model and the Howells motor and sensory compartment models. They take into account known differences in the channel properties between sensory and motor neurons." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/243841", + "other_neurons": { + "attr_id": 301, + "value": "Somatosensory" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "polasek@hope.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Katharine Polasek" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 227987, + "object_name": "Human" + } + ] + }, + "ver_date": "2018-10-18T17:30:19.787", + "ver_number": 9 + }, + "243842": { + "citation": { + "attr_id": 391, + "value": "Mahapatra C, Brain KL, Manchanda R (2018) A biophysically constrained computational model of the action potential of mouse urinary bladder smooth muscle PLOS One, accepted" + }, + "class_id": 19, + "created": "2018-07-08T15:11:21.097", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 87769, + "object_name": "ATP-senstive potassium current" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 243504, + "object_name": "IK Bkca" + }, + { + "object_id": 243506, + "object_name": "IK Skca" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "icg_channel_details": { + "attr_id": 640, + "value": [ + { + "object_id": 243851, + "object_name": "ICG Detail_243842" + }, + { + "object_id": 243852, + "object_name": "ICG Detail_243842" + }, + { + "object_id": 243853, + "object_name": "ICG Detail_243842" + }, + { + "object_id": 243854, + "object_name": "ICG Detail_243842" + } + ] + }, + "id": 243842, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 246185, + "object_name": "Mahapatra C, Brain KL, Manchanda R (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Action potential of mouse urinary bladder smooth muscle (Mahapatra et al 2018)", + "notes": { + "attr_id": 24, + "value": "Urinary incontinence is associated with enhanced spontaneous phasic contractions of the detrusor smooth muscle (DSM). Although a complete understanding of the etiology of these spontaneous contractions is not yet established, it is suggested that the spontaneously evoked action potentials (sAPs) in DSM cells initiate and modulate the contractions. In order to further our understanding of the ionic mechanisms underlying sAP generation, we present here a biophysically detailed computational model of a single DSM cell. First, we constructed mathematical models for nine ion channels found in DSM cells based on published experimental data: two voltage-gated Ca2+ ion channels, an hyperpolarization-activated ion channel, two voltage-gated K+ ion channels, three Ca2+-activated K+ ion channels and a non-specific background leak ion channel. Incorporating these channels, our DSM model is capable of reproducing experimentally recorded spike-type sAPs of varying configurations, ranging from sAPs displaying after-hyperpolarizations to sAPs displaying after-depolarizations. Our model, constrained heavily by physiological data, provides a powerful tool to investigate the ionic mechanisms underlying the genesis of DSM electrical activity, which can further shed light on certain aspects of urinary bladder function and dysfunction." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/243842", + "other_implementer": { + "attr_id": 308, + "value": "Chitaranjan Mahapatra (cmahapatra97@gmail.com)" + }, + "other_neurons": { + "attr_id": 301, + "value": "Urinary Bladder Smooth Muscle (Detrusor) Cell" + }, + "other_type": { + "attr_id": 305, + "value": "Smooth Muscle Cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "cmahapatra97@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Chitaranjan Mahapatra" + }, + "ver_date": "2018-07-13T12:10:23.353", + "ver_number": 16 + }, + "244202": { + "citation": { + "attr_id": 391, + "value": "27121577" + }, + "class_id": 19, + "created": "2018-07-24T19:06:21.827", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 88118, + "object_name": "I_KLT" + }, + { + "object_id": 88117, + "object_name": "I_KHT" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 244202, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 244203, + "object_name": "Hight, Ariel E. " + }, + { + "object_id": 244204, + "object_name": "Kalluri, Radha [radha at usc.edu]" + }, + { + "object_id": 244205, + "object_name": "Ventura, Christopher M. " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 195623, + "object_name": "Hight AE, Kalluri R (2016)" + }, + { + "object_id": 37966, + "object_name": "Rothman JS, Manis PB (2003)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A biophysical model of vestibular ganglion neurons (Hight & Kalluri 2016, Ventura & Kalluri 2018) ", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + }, + { + "object_id": 206251, + "object_name": "Abstract integrate-and-fire leaky neuron with dendritic subunits" + }, + { + "object_id": 156124, + "object_name": "Abstract single compartment conductance based cell" + }, + { + "object_id": 139834, + "object_name": "Vestibular neuron" + }, + { + "object_id": 241766, + "object_name": "Turtle vestibular neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A single compartment model in Matlab to represent vestibular ganglion neurons' somatic ion channels and their influence on firing patterns. Model is connected to a synthetic synaptic conductance to examine the relative influence of synaptic inputs and low-voltage gated potassium conductances on spike patterns." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/244202", + "other_implementer": { + "attr_id": 308, + "value": "Ariel E. Hight\r\nRadha Kalluri\r\nChristopher M. Ventura" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "radha@usc.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Radha Kalluri" + }, + "ver_date": "2023-03-23T15:21:49.137", + "ver_number": 8 + }, + "244261": { + "citation": { + "attr_id": 391, + "value": "Shimoura, R. O., Kamiji, N. L., Pena, R. F. O., Cordeiro, V. L., Ceballos, C. C., Cecilia, R., & Roque, A. C. (2018). [Re] The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. The ReScience Journal, 4.\r\nPotjans, T. C., & Diesmann, M. (2014). The cell-type specific cortical microcircuit: Relating structure and activity in a full-scale spiking network model. Cerebral Cortex, 24, 785\u2013806." + }, + "class_id": 19, + "created": "2018-08-02T15:47:15.143", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 244261, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 244263, + "object_name": "Shimoura, Renan [renanshimoura at usp.br]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 244268, + "object_name": "Shimoura RO, Kamiji NL, Pena RFO, Cordeiro VL, Ceballos CC, Cecilia R, Roque AC (2018)" + }, + { + "object_id": 187435, + "object_name": "Potjans TC, Diesmann M (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 231240, + "object_name": "Brian 2" + } + ] + }, + "name": "A full-scale cortical microcircuit spiking network model (Shimoura et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Reimplementation in BRIAN 2 simulator of a full-scale cortical microcircuit containing two cell types (excitatory and inhibitory) distributed in four layers, and represents the cortical network below a surface of 1 mm\u00b2 (Potjans & Diesmann, 2014)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/244261", + "other_concept": { + "attr_id": 306, + "value": "Network dynamics" + }, + "other_implementer": { + "attr_id": 308, + "value": "Shimoura, Renan [renanshimoura at usp.br]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "renanshimoura@usp.br" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Renan Oliveira Shimoura" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2018-08-03T12:02:54.523", + "ver_number": 6 + }, + "244262": { + "citation": { + "attr_id": 391, + "value": "29694280" + }, + "class_id": 19, + "created": "2018-08-03T00:00:26.933", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 143445, + "object_name": "I_KD" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 244262, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206236, + "object_name": "Kumaravelu, Karthik [kk192 at duke.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + }, + { + "object_id": 139996, + "object_name": "Evoked LFP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 244297, + "object_name": "Kumaravelu K, Oza CS, Behrend CE, Grill WM (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Deconstruction of cortical evoked potentials generated by subthalamic DBS (Kumaravelu et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 227830, + "object_name": "Neocortex M1 L6 pyramidal corticothalamic GLU cell" + }, + { + "object_id": 227970, + "object_name": "Neocortex M1 L5B pyramidal pyramidal tract GLU cell" + }, + { + "object_id": 230208, + "object_name": "Neocortex M1 L4 stellate GLU cell" + }, + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + }, + { + "object_id": 182505, + "object_name": "Neocortex layer 4 neuron" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 217749, + "object_name": "Neocortex primary motor area pyramidal layer 5 corticospinal cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"...\r\nHigh frequency deep brain stimulation (DBS) of the\r\nsubthalamic nucleus (STN) suppresses parkinsonian motor symptoms and\r\nmodulates cortical activity.\r\n\r\n...\r\n\r\nCortical evoked potentials (cEP) generated by STN DBS reflect\r\nthe response of cortex to subcortical stimulation, and the goal was to\r\ndetermine the neural origin of cEP using a two-step approach.\r\n\r\nFirst,\r\nwe recorded cEP over ipsilateral primary motor cortex during different\r\nfrequencies of STN DBS in awake healthy and unilateral 6-OHDA lesioned\r\nparkinsonian rats.\r\n\r\nSecond, we used a biophysically-based model of the\r\nthalamocortical network to deconstruct the neural origin of the\r\ncEP. The in vivo cEP included short (R1), intermediate (R2) and\r\nlong-latency (R3) responses. Model-based cortical responses to\r\nsimulated STN DBS matched remarkably well the in vivo responses.\r\n\r\nR1\r\nwas generated by antidromic activation of layer 5 pyramidal neurons,\r\nwhile recurrent activation of layer 5 pyramidal neurons via excitatory\r\naxon collaterals reproduced R2. R3 was generated by polysynaptic\r\nactivation of layer 2/3 pyramidal neurons via the\r\ncortico-thalamic-cortical pathway.\r\n\r\nAntidromic activation of the\r\nhyperdirect pathway and subsequent intracortical and\r\ncortico-thalamo-cortical synaptic interactions were sufficient to\r\ngenerate cEP by STN DBS, and orthodromic activation through basal\r\nganglia-thalamus-cortex pathways was not required. These results\r\ndemonstrate the utility of cEP to determine the neural elements\r\nactivated by STN DBS that might modulate cortical activity and\r\ncontribute to the suppression of parkinsonian symptoms.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/244262", + "other_neurons": { + "attr_id": 301, + "value": "TCR, nRT neurons" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "kk192@duke.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Karthik Kumaravelu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 213, + "object_name": "Gaba" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2023-04-30T16:01:51.493", + "ver_number": 16 + }, + "244384": { + "citation": { + "attr_id": 391, + "value": "Muddapu VR, Ramaswamy S, Chakravarthy SV (2018) A computational model of loss of dopaminergic cells in Parkinson\u2019s disease due to glutamate-induced excitotoxicity. bioRxiv Available at: http://biorxiv.org/content/early/2018/08/06/385138.abstract." + }, + "class_id": 19, + "created": "2018-08-07T10:05:59.653", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 244384, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 251109, + "object_name": "Muddapu, Vignayanandam R. [vignan.0009 at gmail.com]" + }, + { + "object_id": 251110, + "object_name": "Chakravarthy, Srinivasa V. [schakra at iitm.ac.in]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 250974, + "object_name": "Muddapu VR, Mandali A, Chakravarthy VS, Ramaswamy S (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Excitotoxic loss of dopaminergic cells in PD (Muddapu et al 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... A\r\ncouple of the proposed mechanisms, however, show\r\npotential for the\r\ndevelopment of a novel line of PD (Parkinson's disease) therapeutics. One of these\r\nmechanisms is the peculiar metabolic vulnerability of SNc (Substantia Nigra pars compacta) cells\r\ncompared to other dopaminergic clusters; the other is the SubThalamic\r\nNucleus (STN)-induced excitotoxicity in SNc. To investigate the latter\r\nhypothesis computationally, we developed a spiking neuron\r\nnetwork-model of SNc-STN-GPe system. In the model, prolonged\r\nstimulation of SNc cells by an overactive STN leads to an increase in\r\n\u2018stress\u2019 variable; when the stress in a SNc neuron exceeds a stress\r\nthreshold, the neuron dies. The model shows that the interaction\r\nbetween SNc and STN involves a positive-feedback due to which, an\r\ninitial loss of SNc cells that crosses a threshold causes a\r\nrunaway-effect, leading to an inexorable loss of SNc cells, strongly\r\nresembling the process of neurodegeneration. The model further\r\nsuggests a link between the two aforementioned mechanisms of SNc cell\r\nloss. Our simulation results show that the excitotoxic cause of SNc\r\ncell loss might initiate by weak-excitotoxicity mediated by energy\r\ndeficit, followed by strong-excitotoxicity, mediated by a disinhibited\r\nSTN. A variety of conventional therapies were simulated to test their\r\nefficacy in slowing down SNc cell loss. Among them, glutamate\r\ninhibition, dopamine restoration, subthalamotomy and deep brain\r\nstimulation showed superior neuroprotective-effects in the proposed\r\nmodel.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/244384", + "other_implementer": { + "attr_id": 308, + "value": "Vignayanandam R. Muddapu\r\nSrinivasa V. Chakravarthy" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "vignan.0009@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Vignayanandam Ravindernath Muddapu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 213, + "object_name": "Gaba" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + }, + { + "object_id": 116868, + "object_name": "Subthalamic Nucleus" + } + ] + }, + "ver_date": "2019-03-04T10:38:23.23", + "ver_number": 11 + }, + "244412": { + "class_id": 19, + "created": "2018-08-07T20:28:27.16", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 143445, + "object_name": "I_KD" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 244412, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 116577, + "object_name": "Solinas, Sergio [solinas at unipv.it]" + }, + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 22010, + "object_name": "Facilitation" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + }, + { + "object_id": 87578, + "object_name": "STDP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 256390, + "object_name": "Solinas SMG, Edelmann E, Le\u00dfmann V, Migliore M (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "STDP and BDNF in CA1 spines (Solinas et al. 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Storing memory traces in the brain is essential for learning and memory formation. Memory traces are created by joint electrical activity in neurons that are interconnected by synapses and allow transferring electrical activity from a sending (presynaptic) to a receiving (postsynaptic) neuron. During learning, neurons that are co-active can tune synapses to become more effective. This process is called synaptic plasticity or long-term potentiation (LTP). Timing-dependent LTP (t-LTP) is a physiologically relevant type of synaptic plasticity that results from repeated sequential firing of action potentials (APs) in pre- and postsynaptic neurons. T-LTP is observed during learning in vivo and is a cellular correlate of memory formation. T-LTP can be elicited by different rhythms of synaptic activity that recruit distinct synaptic growth processes underlying t-LTP. The protein brain-derived neurotrophic factor (BDNF) is released at synapses and mediates synaptic growth in response to specific rhythms of t-LTP stimulation, while other rhythms mediate BDNF-independent t-LTP. \r\nHere, we developed a realistic computational model that accounts for our previously published experimental results of BDNF-independent 1:1 t-LTP (pairing of 1 presynaptic with 1 postsynaptic AP) and BDNF-dependent 1:4 t-LTP (pairing of 1 presynaptic with 4 postsynaptic APs). The model explains the magnitude and time course of both t-LTP forms and allows predicting t-LTP properties that result from altered BDNF turnover. \r\nSince BDNF levels are decreased in demented patients, understanding the function of BDNF in memory processes is of utmost importance to counteract Alzheimer\u2019s disease." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/244412", + "other_implementer": { + "attr_id": 308, + "value": "Sergio Solinas\r\nMichele Migliore" + }, + "other_receptors": { + "attr_id": 302, + "value": "TrkB" + }, + "other_type": { + "attr_id": 305, + "value": "Spine" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "smgsolinas@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Sergio Solinas" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2023-01-30T13:32:23.027", + "ver_number": 39 + }, + "244414": { + "citation": { + "attr_id": 391, + "value": "PMID: 24068903" + }, + "class_id": 19, + "created": "2018-08-08T12:11:48.713", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 151550, + "object_name": "Channelrhodopsin (ChR)" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 244414, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 97968, + "object_name": "Giugliano, Michele [mgiugliano at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 151488, + "object_name": "Williams JC, Xu J, Lu Z, Klimas A, Chen X, Ambrosi CM, Cohen IS, Entcheva E (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Voltage and light-sensitive Channelrhodopsin-2 model (ChR2-H134R) (Williams et al. 2013) (NEURON)", + "notes": { + "attr_id": 24, + "value": "\" ... Focusing on one of the most widely used ChR2 mutants (H134R) with enhanced current, we collected a comprehensive experimental data set of the response of this ion channel to different irradiances and voltages, and used these data to develop a model of ChR2 with empirically-derived voltage- and irradiance- dependence, where parameters were fine-tuned via simulated annealing optimization. This ChR2 model offers: 1) accurate inward rectification in the current-voltage response across irradiances; 2) empirically-derived voltage- and light-dependent kinetics (activation, deactivation and recovery from inactivation); and 3) accurate amplitude and morphology of the response across voltage and irradiance settings. Temperature-scaling factors (Q10) were derived and model kinetics was adjusted to physiological temperatures. ... \" " + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/244414", + "other_currents": { + "attr_id": 303, + "value": "ChR2, mutant H134R" + }, + "other_implementer": { + "attr_id": 308, + "value": "Based on the MATLAB code by John C Williams." + }, + "public_submitter_email": { + "attr_id": 309, + "value": "mgiugliano@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Michele Giugliano" + }, + "ver_date": "2018-08-10T12:22:14.217", + "ver_number": 7 + }, + "244416": { + "citation": { + "attr_id": 391, + "value": "Combe CL, Canavier CC, Gasparini S. Intrinsic mechanisms of frequency selectivity in the proximal dendrites of CA1 pyramidal neurons. J Neurosci. 2018 Aug 3. pii: 0449-18. doi: 10.1523/JNEUROSCI.0449-18.2018. [Epub ahead of print] PMID: 30076213" + }, + "class_id": 19, + "created": "2018-08-13T11:50:22.217", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 244416, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33801, + "object_name": "Canavier, CC" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 156115, + "object_name": "Gamma oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 244418, + "object_name": "Combe CL, Canavier CC, Gasparini S (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "CA1 pyramidal neuron (Combe et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Gamma oscillations are thought to play a role in learning and memory. Two distinct bands, slow (25-50 Hz) and fast (65-100 Hz) gamma, have been identified in area CA1 of the rodent hippocampus. Slow gamma is phase-locked to activity in area CA3 and presumably driven by the Schaffer collaterals. We used a combination of computational modeling and in vitro electrophysiology in hippocampal slices of male rats to test whether CA1 neurons responded to Schaffer collateral stimulation selectively at slow gamma frequencies, and to identify the mechanisms involved. Both approaches demonstrated that in response to temporally precise input at Schaffer collaterals, CA1 pyramidal neurons fire preferentially in the slow gamma range regardless of whether the input is at fast or slow gamma frequencies, suggesting frequency selectivity in CA1 output with respect to CA3 input. In addition, phase-locking, assessed by the vector strength, was more precise for slow gamma than fast gamma input. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/244416", + "public_submitter_email": { + "attr_id": 309, + "value": "ccanav@lsuhsc.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Carmen Canavier" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2018-08-17T22:35:22.283", + "ver_number": 10 + }, + "244485": { + "citation": { + "attr_id": 391, + "value": "Gleit, Diniz Behn and Booth, J Biol Rhythms 28:339-355, 2013\r\nBooth, Xique and Diniz Behn, SIAM J Appl Dyn Sys 16(2):1089-1112, 2017" + }, + "class_id": 19, + "created": "2018-08-18T16:28:23.973", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 244485, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 244486, + "object_name": "Booth, Victoria [vbooth at umich.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 93420, + "object_name": "Sleep" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 226774, + "object_name": "Gleit RD, Diniz Behn CG, Booth V (2013)" + }, + { + "object_id": 244488, + "object_name": "Booth V, Xique I, Diniz Behn C (2017)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Human sleep-wake regulatory network model (Gleit et al 2013, Booth et al 2017)", + "notes": { + "attr_id": 24, + "value": "A physiologically-based mathematical model of a sleep-wake regulatory network model for human sleep. The model simulates neurotransmitter-mediated interactions among hypothalamic and brainstem neuronal populations that promote wake, rapid eye movement (REM) sleep and non-REM (NREM) sleep. A neuronal population firing rate model formalism is used. The circadian rhythm pacemaker neuronal population, the suprachiasmatic nucleus (SCN), modulates activity in the wake- and sleep-promoting populations to entrain sleep-wake behavior to the ~24h circadian rhythm. A circadian clock oscillator model drives a 24h variation in the SCN firing rate and can be entrained to an externally imposed light:dark cycle. The default parameters replicate typical human sleep entrained to an external 14h:10h light:dark cycle" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/244485", + "other_implementer": { + "attr_id": 308, + "value": "Booth, Victoria" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "vbooth@umich.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Victoria Booth" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 183076, + "object_name": "Brainstem" + } + ] + }, + "ver_date": "2018-08-20T23:00:58.053", + "ver_number": 7 + }, + "244599": { + "citation": { + "attr_id": 391, + "value": "PubMed: 30104340\r\nDOI:10.1523/JNEUROSCI.0651-18.2018" + }, + "class_id": 19, + "created": "2018-08-20T15:28:59.573", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 244599, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 235324, + "object_name": "Lopez-Jury, Luciana [lucianalopezjury at gmail.com]" + }, + { + "object_id": 33801, + "object_name": "Canavier, CC" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 227131, + "object_name": "Pacemaking mechanism" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 84598, + "object_name": "Extracellular Fields" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 244658, + "object_name": "L\u00f3pez-Jury L, Meza RC, Brown MTC, Henny P, Canavier CC (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Determinants of the intracellular and extracellular waveforms in DA neurons (Lopez-Jury et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 261, + "object_name": "Substantia nigra pars compacta DA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "To systematically address the contribution of AIS, dendritic and somatic compartments to shaping the two-component action potentials (APs), we modeled APs of male mouse and rat dopaminergic neurons. A parsimonious two-domain model, with high (AIS) and lower (dendro-somatic) Na+ conductance, reproduced the notch in the temporal derivatives, but not in the extracellular APs, regardless of morphology. The notch was only revealed when somatic active currents were reduced, constraining the model to three domains. Thus, an initial AIS spike is followed by an actively generated spike by the axon-bearing dendrite (ABD), in turn followed mostly passively by the soma. Larger AISs and thinner ABD (but not soma-to-AIS distance) accentuate the AIS component." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/244599", + "public_submitter_email": { + "attr_id": 309, + "value": "lucianalopezjury@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Luciana L\u00f3pez Jury" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2018-08-21T11:43:46.973", + "ver_number": 8 + }, + "244679": { + "class_id": 19, + "created": "2018-08-22T02:48:59.117", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 244679, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 244680, + "object_name": "Filipis, Luiza [luizafilipu at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 249423, + "object_name": "Ait Ouares K, Filipis L, Tzilivaki A, Poirazi P, Canepari M (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Cerebellum Purkinje cell: dendritic ion channels activated by climbing fibre (Ait Ouares et al 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"In cerebellar Purkinje neuron (PN) dendrites, the transient\r\ndepolarisation associated with a climbing fibre (CF) EPSP\r\nactivates voltage-gated Ca2+ channels (VGCCs), voltage-gated K+\r\nchannels (VGKCs) and Ca2+ activated SK and BK K+ channels. The\r\nresulting membrane potential (Vm) and Ca2+ transients play a\r\nfundamental role in dendritic integration and synaptic plasticity\r\nof parallel fibre inputs. Here we report a detailed investigation\r\nof the kinetics of dendritic Ca2+ and K+ channels activated by\r\nCF-EPSPs, based on optical measurements of Vm and Ca2+ transients\r\nand on a single-compartment NEURON model reproducing experimental\r\ndata.\r\n... \"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/244679", + "other_implementer": { + "attr_id": 308, + "value": "Luiza Filipis" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "luizafilipu@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "filipis luiza" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2022-09-23T09:51:40.033", + "ver_number": 22 + }, + "244684": { + "citation": { + "attr_id": 391, + "value": "Masquelier & Kheradpisheh (2018) Optimal localist and distributed coding of spatiotemporal spike patterns through STDP and coincidence detection. Frontiers in Computational Neuroscience." + }, + "class_id": 19, + "created": "2018-08-25T04:09:33.287", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 244684, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 123929, + "object_name": "Masquelier, Tim [timothee.masquelier at alum.mit.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 83538, + "object_name": "Unsupervised Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 244686, + "object_name": "Masquelier T, Saeed Reza Kheradpisheh SR (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Optimal Localist and Distributed Coding Through STDP (Masquelier & Kheradpisheh 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We show how a LIF neuron equipped with STDP can become optimally selective, in an unsupervised manner, to one or several repeating spike patterns, even when those patterns are hidden in Poisson spike trains." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/244684", + "public_submitter_email": { + "attr_id": 309, + "value": "timothee.masquelier@alum.mit.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tim Masquelier" + }, + "ver_date": "2018-09-13T14:32:06.327", + "ver_number": 4 + }, + "244687": { + "class_id": 19, + "created": "2018-08-26T04:56:04.933", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 244687, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 225817, + "object_name": "Aghvami, S. Sara [ssa.aghvami at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 3632, + "object_name": "Coincidence Detection" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 249597, + "object_name": "Aghvami SS, M\u00fcller M, Araabi BN, Egger V (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Coincident signals in Olfactory Bulb Granule Cell spines (Aghvami et al 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 3979, + "object_name": "Olfactory bulb main interneuron granule MC GABA cell" + }, + { + "object_id": 153283, + "object_name": "Olfactory bulb main interneuron granule TC GABA cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"In the mammalian olfactory bulb, the inhibitory axonless granule cells (GCs) feature reciprocal synapses that interconnect them with the principal neurons of the bulb, mitral, and tufted cells. These synapses are located within large excitable spines that can generate local action potentials (APs) upon synaptic input (\u201cspine spike\u201d). Moreover, GCs can fire global APs that propagate throughout the dendrite. Strikingly, local postsynaptic Ca2+ entry summates mostly linearly with Ca2+ entry due to coincident global APs generated by glomerular stimulation, although some underlying conductances should be inactivated. We investigated this phenomenon by constructing a compartmental GC model to simulate the pairing of local and global signals as a function of their temporal separation ?t. These simulations yield strongly sublinear summation of spine Ca2+ entry for the case of perfect coincidence ?t = 0 ms. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/244687", + "public_submitter_email": { + "attr_id": 309, + "value": "ssa.aghvami@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "S.Sara Aghvami" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "ver_date": "2019-01-27T07:24:51.857", + "ver_number": 5 + }, + "244688": { + "class_id": 19, + "created": "2018-08-26T10:15:34.483", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 143445, + "object_name": "I_KD" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 244688, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 170033, + "object_name": "Migliore, Rosanna [rosanna.migliore at cnr.it]" + }, + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 245017, + "object_name": "Migliore R, Lupascu CA, Bologna LL, Romani A, Courcol JD, Antonel S, Van Geit WAH, Thomson AM, Mercer A, Lange S, Falck J, Roessert CA, Shi Y, Hagens O, Pezzoli M, Freund TF, Kali S, Muller EB, Schuermann F, Markram H, Migliore M (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 245004, + "object_name": "BluePyOpt\r\n" + } + ] + }, + "name": "Channel density variability among CA1 neurons (Migliore et al. 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The peak conductance of many ion channel types measured in any given animal is highly variable across neurons, both within and between neuronal populations. The current view is that this occurs because a neuron needs to adapt its intrinsic electrophysiological properties either to maintain the same operative range in the presence of abnormal inputs or to compensate for the effects of pathological conditions. Limited experimental and modeling evidence suggests this might be implemented via the correlation and/or degeneracy in the function of multiple types of conductances. To study this mechanism in hippocampal CA1 neurons and interneurons, we systematically generated a set of morphologically and biophysically accurate models. We then analyzed the ensembles of peak conductance obtained for each model neuron. The results suggest that the set of conductances expressed in the various neuron types may be divided into two groups: one group is responsible for the major characteristics of the firing behavior in each population and the other more involved with degeneracy. These models provide experimentally testable predictions on the combination and relative proportion of the different conductance types that should be present in hippocampal CA1 pyramidal cells and interneurons." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/244688", + "other_concept": { + "attr_id": 306, + "value": "Parameter Optimizations" + }, + "other_currents": { + "attr_id": 303, + "value": "I CAT, I CAL" + }, + "other_neurons": { + "attr_id": 301, + "value": "Hippocampus CA1 interneurons" + }, + "other_simulator": { + "attr_id": 307, + "value": "BluePyOPt" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "michele.migliore@pa.ibf.cnr.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Michele Migliore" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2018-09-24T09:55:03.797", + "ver_number": 12 + }, + "244690": { + "citation": { + "attr_id": 391, + "value": "Smolen P, Baxter DA, Byrne JH (2018). Paradoxical LTP maintenance with inhibition of protein synthesis and the proteasome suggests a novel protein synthesis requirement for early LTP reversal. J. Theor. Biol. (2018). doi: 10.1016/j.jtbi.2018.08.026 ." + }, + "class_id": 19, + "created": "2018-08-27T14:26:20.587", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 244690, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 149176, + "object_name": "Smolen, Paul [paul.d.smolen at uth.tmc.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 244691, + "object_name": "Smolen P, Baxter DA, Byrne JH (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "name": "Long term potentiation, LTP, protein synthesis, proteasome (Smolen et al. 2018)", + "notes": { + "attr_id": 24, + "value": "The transition from early long-term potentiation (E-LTP) to late LTP (L-LTP) involves protein synthesis and degradation. L-LTP is blocked by inhibiting either protein synthesis or proteasome-dependent degradation prior to and during a tetanic stimulus, but paradoxically, L-LTP is not blocked when synthesis and degradation are inhibited simultaneously, suggesting counter-acting positive and negative proteins regulate L-LTP. To investigate this paradox, we modeled LTP at the Schaffer collateral synapse. Nine differential equations describe the levels of positive and negative regulator proteins (PP and NP) and transitions among five discrete synaptic states, a basal state (BAS), three E-LTP states (EP1, EP2, ED), and a L-LTP state (LP). A stimulus initiates the transition from BAS to EP1 and from EP1 to EP2, initiates the synthesis of PP and NP, and activates the ubiquitin-proteasome system (UPS). UPS mediates transitions of EP1 and EP2 to ED and the degradation of NP. The conversion of E-LTP to L-LTP is mediated by a PP-dependent transition from ED to LP. NP mediates reversal of EP2 to BAS. This model simulates empirical observations: 1) normal L-LTP, 2) block by either proteasome inhibitor or protein synthesis inhibitor alone, and 3) preservation of L-LTP when both inhibitors are applied together. Elements of this abstract model can be correlated with specific molecules and processes. Moreover, the model makes testable predictions, such as a unique synaptic state ED that precedes the transition to L-LTP, and a time window for the action of the UPS (during the transitions from EP1 and EP2 to ED). Tests of these predictions will provide insights into the processes of long-term synaptic plasticity." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/244690", + "public_submitter_email": { + "attr_id": 309, + "value": "Paul.D.Smolen@uth.tmc.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Paul Smolen" + }, + "ver_date": "2018-09-05T13:39:34.603", + "ver_number": 7 + }, + "244692": { + "citation": { + "attr_id": 391, + "value": "Appukuttan, S., Sathe, R., & Manchanda, R. (2016, January). Influence of gap junction subtypes on passive and active electrical properties of syncytial tissues. In Systems in Medicine and Biology (ICSMB), 2016 International Conference on (pp. 128-131). IEEE. [DOI: https://doi.org/10.1109/ICSMB.2016.7915104]" + }, + "class_id": 19, + "created": "2018-08-30T13:06:52.06", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 244692, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 226424, + "object_name": "Appukuttan, Shailesh [shailesh.appukuttan at unic.cnrs-gif.fr;\r\nappukuttan.shailesh at gmail.com;]" + }, + { + "object_id": 244695, + "object_name": "Sathe, Rohan " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 244694, + "object_name": "Appukuttan S, Sathe R, Manchanda R (2016)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Gap junction subtypes (Appukuttan et al 2016)", + "notes": { + "attr_id": 24, + "value": "Computational models of various gap junction sub-types including accommodating differences in their unitary conductances, voltage sensitivity and gating kinetics." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/244692", + "other_implementer": { + "attr_id": 308, + "value": "Rohan Sathe" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "shailesh.a@iitb.ac.in" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Shailesh Appukuttan" + }, + "ver_date": "2018-09-13T11:23:01.533", + "ver_number": 12 + }, + "244700": { + "citation": { + "attr_id": 391, + "value": "https://www.biorxiv.org/content/early/2017/05/14/137984" + }, + "class_id": 19, + "created": "2018-09-04T07:01:05.057", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 244700, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 247724, + "object_name": "G\u00f3rski, Tomasz [gorski at inaf.cnrs-gif.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 247723, + "object_name": "G\u00f3rski T, Veltz R, Galtier M, Fragnaud H, Goldman JS, Telenczuk B, Destexhe A (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 231240, + "object_name": "Brian 2" + } + ] + }, + "name": "Response to correlated synaptic input for HH/IF point neuron vs with dendrite (G\u00f3rski et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 240378, + "object_name": "Abstract integrate-and-fire neuron" + }, + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Here, we study computational models of neurons to investigate the functional effects of dendritic spikes. In agreement with previous studies, we found that point neurons or neurons with passive dendrites increase their somatic firing rate in response to the correlation of synaptic bombardment for a wide range of input conditions, i.e. input firing rates, synaptic conductances, or refractory periods. However, neurons with active dendrites show the opposite behavior: for a wide range of conditions the firing rate decreases as a function of correlation. We found this property in three types of models of dendritic excitability: a Hodgkin-Huxley model of dendritic spikes, a model with integrate and fire dendrites, and a discrete-state dendritic model. We conclude that fast dendritic spikes confer much broader computational properties to neurons, sometimes opposite to that of point neurons.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/244700", + "public_submitter_email": { + "attr_id": 309, + "value": "tomasz.gorski.14@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tomasz G\u00f3rski" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 213, + "object_name": "Gaba" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2018-12-14T12:21:32", + "ver_number": 19 + }, + "244848": { + "citation": { + "attr_id": 391, + "value": "Basak R, Narayanan R (in Press), Active dendrites regulate the spatiotemporal spread of signaling microdomains, Plos. Comp. Biol" + }, + "class_id": 19, + "created": "2018-09-10T04:02:47.887", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 88211, + "object_name": "I_SERCA" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 116966, + "object_name": "I_K,Na" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 244848, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 244851, + "object_name": "Basak, Reshma [reshmab at iisc.ac.in]" + }, + { + "object_id": 147758, + "object_name": "Narayanan, Rishikesh [rishi at iisc.ac.in]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 248776, + "object_name": "Basak R, Narayanan R (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Active dendrites shape signaling microdomains in hippocampal neurons (Basak & Narayanan 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The spatiotemporal spread of biochemical signals in neurons and other cells regulate signaling specificity, tuning of signal propagation, along with specificity and clustering of adaptive plasticity. Theoretical and experimental studies have demonstrated a critical role for cellular morphology and the topology of signaling networks in regulating this spread. In this study, we add a significantly complex dimension to this narrative by demonstrating that voltage-gated ion channels (A-type Potassium channels and T-type Calcium channels) on the plasma membrane could actively amplify or suppress the strength and spread of downstream signaling components. We employed a multiscale, multicompartmental, morphologically realistic, conductance-based model that accounted for the biophysics of electrical signaling and the biochemistry of calcium handling and downstream enzymatic signaling in a hippocampal pyramidal neuron. We chose the calcium \u2013 calmodulin \u2013 calcium/calmodulin-dependent protein kinase II (CaMKII) \u2013 protein phosphatase 1 (PP1) signaling pathway owing to its critical importance to several forms of neuronal plasticity, and employed physiologically relevant theta-burst stimulation (TBS) or theta-burst pairing (TBP) protocol to initiate a calcium microdomain through NMDAR activation at a synapse." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/244848", + "other_concept": { + "attr_id": 306, + "value": "Calcium Microdomain" + }, + "other_implementer": { + "attr_id": 308, + "value": "Reshma Basak (reshmab@iisc.ac.in),\r\nRishikesh Narayanan (rishi@iisc.ac.in)" + }, + "other_type": { + "attr_id": 305, + "value": "Realistic morphology" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "reshmab@iisc.ac.in" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Reshma Basak" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2022-05-27T13:18:01.07", + "ver_number": 13 + }, + "244922": { + "citation": { + "attr_id": 391, + "value": "Mukunda, C. L. and Narayanan, R. (2017), Degeneracy in the regulation of short-term plasticity and synaptic filtering by presynaptic mechanisms. J Physiol, 595: 2611-2637. doi:10.1113/JP273482" + }, + "class_id": 19, + "created": "2018-09-13T11:03:43.303", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 255, + "object_name": "I CAN" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 244922, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 245002, + "object_name": "Mukunda, Chinmayee L [chinmayeelm at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 143636, + "object_name": "Conductance distributions" + }, + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 245160, + "object_name": "Neurotransmitter dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 245001, + "object_name": "Mukunda CL, Narayanan R (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Conductance based model for short term plasticity at CA3-CA1 synapses (Mukunda & Narayanan 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We develop a new biophysically rooted, physiologically constrained conductance-based synaptic model to mechanistically account for short-term facilitation and depression, respectively through residual calcium and transmitter depletion kinetics. The model exhibits different synaptic filtering profiles upon changing certain parameters in the base model. We show degenercy in achieving similar plasticity profiles with different presynaptic parameters. Finally, by virtually knocking out certain conductances, we show the differential contribution of conductances." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/244922", + "other_concept": { + "attr_id": 306, + "value": "Neurotransmitter dynamics" + }, + "other_currents": { + "attr_id": 303, + "value": "I Na, I CaL" + }, + "other_implementer": { + "attr_id": 308, + "value": "Chinmayee L Mukunda" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "chinmayeelm@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Chinmayee L M" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2018-09-18T14:28:14.2", + "ver_number": 13 + }, + "245018": { + "class_id": 19, + "created": "2018-09-17T11:25:25.633", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 245018, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 233400, + "object_name": "Zannone, Sara [s.zannone14 at imperial.ac.uk]" + }, + { + "object_id": 144567, + "object_name": "Clopath, Claudia [c.clopath at imperial.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 116858, + "object_name": "Reward-modulated STDP" + }, + { + "object_id": 180821, + "object_name": "Hebbian plasticity" + }, + { + "object_id": 182690, + "object_name": "Spatial Navigation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 244600, + "object_name": "Zannone S, Brzosko Z, Paulsen O, Clopath C (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Acetylcholine-modulated plasticity in reward-driven navigation (Zannone et al 2018)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + }, + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Neuromodulation plays a fundamental role in the acquisition of new behaviours. In previous\r\nexperimental work, we showed that acetylcholine biases hippocampal synaptic plasticity towards\r\ndepression, and the subsequent application of dopamine can retroactively convert depression into\r\npotentiation. We also demonstrated that incorporating this sequentially neuromodulated Spike-\r\nTiming-Dependent Plasticity (STDP) rule in a network model of navigation yields effective learning\r\nof changing reward locations. Here, we employ computational modelling to further characterize the\r\neffects of cholinergic depression on behaviour. We find that acetylcholine, by allowing learning from\r\nnegative outcomes, enhances exploration over the action space. We show that this results in a variety\r\nof effects, depending on the structure of the model, the environment and the task. Interestingly,\r\nsequentially neuromodulated STDP also yields flexible learning, surpassing the performance of other\r\nreward-modulated plasticity rules.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/245018", + "other_implementer": { + "attr_id": 308, + "value": "Sara Zannone, Claudia Clopath" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "s.zannone14@imperial.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Sara Zannone" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 249926, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2019-02-26T10:25:41.13", + "ver_number": 12 + }, + "245071": { + "class_id": 19, + "created": "2018-09-17T17:59:16.11", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 222730, + "object_name": "I Na, slow inactivation" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 245071, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266512, + "object_name": "Berteau, Stefan [berteau at bu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + }, + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266511, + "object_name": "Berteau S, Bullock D (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 223029, + "object_name": "Predictive Coding Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Hippocampal CA1 pyramidal cell demonstrating dynamic mode switching (Berteau & Bullock 2020)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 209, + "object_name": "Acetylcholine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A simulated proposed single-cell mechanism for CA1\u2019s behavior as an associative mismatch detector. Shifts in spiking mode (accomplished via KCNQ interaction with chloride leak currents) signal matches vs. mismatches." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/245071", + "other_concept": { + "attr_id": 306, + "value": "Associative Mismatch" + }, + "other_implementer": { + "attr_id": 308, + "value": "Berteau, Stefan" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "stefan.berteau@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Stefan Berteau" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 213, + "object_name": "Gaba" + }, + { + "object_id": 212, + "object_name": "Glutamate" + }, + { + "object_id": 218, + "object_name": "Cholinergic Receptors" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2020-06-03T13:53:47.04", + "ver_number": 9 + }, + "245409": { + "citation": { + "attr_id": 391, + "value": "Chauhan, T., Masquelier, T., Montlibert, A., & Cottereau, B. R. (2018). Emergence of binocular disparity selectivity through Hebbian learning. Journal of Neuroscience, 1259-18." + }, + "class_id": 19, + "created": "2018-09-30T16:42:59.02", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 245409, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 245410, + "object_name": "Chauhan, Tushar [research at tusharchauhan DOT com]" + }, + { + "object_id": 245443, + "object_name": "Montlibert, Alexandre [Alexandre.Montlibert at cnrs.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 180821, + "object_name": "Hebbian plasticity" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 245442, + "object_name": "Chauhan T, Masquelier T, Montlibert A, Cottereau BR (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Hebbian STDP for modelling the emergence of disparity selectivity (Chauhan et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 240378, + "object_name": "Abstract integrate-and-fire neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This code shows how Hebbian learning mediated by STDP mechanisms could explain the emergence of disparity selectivity in the early visual system. This upload is a snapshot of the code at the time of acceptance of the paper. For a link to a soon-to-come git repository, consult the author's website: www.tusharchauhan.com/research/ .\r\n\r\n\r\nThe datasets used in the paper are not provided due to size, but download links and expected directory-structures are. The user can (and is strongly encouraged to) experiment with their own dataset. Let me know if you find something interesting!\r\n\r\n\r\nFinally, I am very keen on a redesign/restructure/adaptation of the code to more applied problems in AI and robotics (or any other field where a spiking non-linear approach makes sense). If you have a serious proposal, don't hesitate to contact me [research AT tusharchauhan DOT com ]. \r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/245409", + "public_submitter_email": { + "attr_id": 309, + "value": "research@tusharchauhan.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tushar Chauhan" + }, + "ver_date": "2018-10-17T17:35:43.337", + "ver_number": 8 + }, + "245411": { + "class_id": 19, + "created": "2018-10-01T16:07:02.313", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 167500, + "object_name": "Kir" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 112841, + "object_name": "I Krp" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 112843, + "object_name": "I R" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 243504, + "object_name": "IK Bkca" + }, + { + "object_id": 243506, + "object_name": "IK Skca" + }, + { + "object_id": 88209, + "object_name": "Na/Ca exchanger" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122809, + "object_name": "Cav3.2 CACNA1H" + }, + { + "object_id": 122811, + "object_name": "Cav3.3 CACNA1I" + }, + { + "object_id": 122814, + "object_name": "Cav1.2 CACNA1C" + }, + { + "object_id": 122812, + "object_name": "Cav1.3 CACNA1D" + }, + { + "object_id": 122820, + "object_name": "Cav2.2 CACNA1B" + }, + { + "object_id": 112842, + "object_name": "Kv4.2 KCND2" + }, + { + "object_id": 185462, + "object_name": "Kir2.1 KCNJ2" + }, + { + "object_id": 122816, + "object_name": "Kv2.1 KCNB1" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 245411, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 189722, + "object_name": "Dorman, Daniel B " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 246842, + "object_name": "Dorman DB, Jedrzejewska-Szmek J, Blackwell KT (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "Striatal Spiny Projection Neuron, inhibition enhances spatial specificity (Dorman et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 150619, + "object_name": "Neostriatum spiny neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We use a computational model of a striatal spiny projection neuron to investigate dendritic spine calcium dynamics in response to spatiotemporal patterns of synaptic inputs. We show that spine calcium elevation is stimulus-specific, with supralinear calcium elevation in cooperatively stimulated spines. Intermediate calcium elevation occurs in neighboring non-stimulated dendritic spines, predicting heterosynaptic effects. Inhibitory synaptic inputs enhance the difference between peak calcium in stimulated spines, and peak calcium in non-stimulated spines, thereby enhancing stimulus specificity." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/245411", + "public_submitter_email": { + "attr_id": 309, + "value": "ddorman@gmu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Daniel Dorman" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2018-12-12T16:06:18.857", + "ver_number": 15 + }, + "245412": { + "class_id": 19, + "created": "2018-10-02T11:30:16.58", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 245412, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 245413, + "object_name": "Zamora, Criseida [criseida.chimal at oist.jp]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 238958, + "object_name": "Stochastic simulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 248648, + "object_name": "Zamora Chimal CG, De Schutter E (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 145174, + "object_name": "Molecular Network" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 144556, + "object_name": "STEPS" + } + ] + }, + "name": "Ca2+ requirements for Long-Term Depression in Purkinje Cells (Criseida Zamora et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "An updated stochastic model of cerebellar Long-Term Depression (LTD) to study the requirements of calcium to induce LTD. Calcium signal is generated as a train of calcium pulses and this can be modulated by its amplitude, frequency, width and number of pulses.\r\nCaMKII activation and its regulatory pathway are added to an earlier published model to study the sensitivity to calcium frequency. The model is useful to investigate systematically the dependence of LTD induction on calcium stimuli parameters." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/245412", + "other_implementer": { + "attr_id": 308, + "value": "Zamora Criseida (criseida.chimal at oist.jp)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "criseida.chimal@oist.jp" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Criseida Zamora" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2020-02-27T19:06:47.2", + "ver_number": 13 + }, + "245415": { + "class_id": 19, + "created": "2018-10-04T05:24:32.583", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 245415, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 185339, + "object_name": "Zylbertal, Asaph [asaph.zylbertal at mail.huji.ac.il]" + }, + { + "object_id": 245416, + "object_name": "Augustin, Hrvoje " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 47316, + "object_name": "Invertebrate" + }, + { + "object_id": 97752, + "object_name": "Delay" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 255584, + "object_name": "Augustin H, Zylbertal A, Partridge L (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Escape response latency in the Giant Fiber System of Drosophila melanogastor (Augustin et al 2019)", + "notes": { + "attr_id": 24, + "value": "\"The Giant Fiber System (GFS) is a multi-component neuronal pathway mediating rapid escape response in the adult fruit-fly Drosophila melanogaster, usually in the face of a threatening visual stimulus. Two branches of the circuit promote the response by stimulating an escape jump followed by flight initiation. Our recent work demonstrated an age-associated decline in the speed of signal propagation through the circuit, measured as the stimulus-to-muscle depolarization response latency. The decline is likely due to the diminishing number of inter-neuronal gap junctions in the GFS of ageing flies. In this work, we presented a realistic conductance-based, computational model of the GFS that recapitulates our experimental results and identifies some of the critical anatomical and physiological components governing the circuit's response latency. According to our model, anatomical properties of the GFS neurons have a stronger impact on the transmission than neuronal membrane conductance densities. The model provides testable predictions for the effect of experimental interventions on the circuit's performance in young and ageing flies.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/245415", + "other_implementer": { + "attr_id": 308, + "value": "Augustin, Hrvoje" + }, + "other_neurons": { + "attr_id": 301, + "value": "Drosophila giant fiber, drosophila motoneurons" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "asaph.zylbertal@mail.huji.ac.il" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Asaph Zylbertal" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 206363, + "object_name": "Drosophila" + } + ] + }, + "ver_date": "2019-04-05T18:21:35.307", + "ver_number": 9 + }, + "245417": { + "class_id": 19, + "created": "2018-10-05T09:23:11.613", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 245417, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + }, + { + "object_id": 253930, + "object_name": "Giacalone, Elisabetta [elisabetta.giacalone at pa.ibf.cnr.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 259335, + "object_name": "Martinello K, Giacalone E, Migliore M, Brown DA, Shah MM (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Hippocampal Mossy Fiber bouton: presynaptic KV7 channel function (Martinello et al 2019)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/245417", + "other_neurons": { + "attr_id": 301, + "value": "Hippocampal Mossy-fiber bouton" + }, + "other_type": { + "attr_id": 305, + "value": "Synaptic bouton" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "michele.migliore@pa.ibf.cnr.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Michele Migliore" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2019-04-24T09:50:02.773", + "ver_number": 12 + }, + "245424": { + "citation": { + "attr_id": 391, + "value": "Function and energy consumption constrain neuronal biophysics in a canonical computation: coincidence detection\r\nMichiel W.H. Remme, John Rinzel, Susanne Schreiber\r\nPLoS Comp Bio, under review" + }, + "class_id": 19, + "created": "2018-10-07T12:12:16.583", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 88118, + "object_name": "I_KLT" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 245424, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 245425, + "object_name": "Remme, Michiel [michiel.remme at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 182362, + "object_name": "Sensory coding" + }, + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 206291, + "object_name": "Membrane Properties" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 249297, + "object_name": "Remme MWH, Rinzel J, Schreiber S (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Function and energy constrain neuronal biophysics in coincidence detection (Remme et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88110, + "object_name": "Medial Superior Olive (MSO) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... We use models of conductance-based neurons constrained by experimentally observed characteristics with parameters varied within a physiologically realistic range. Our study shows that neuronal design of MSO cells does not compromise on function, but favors energetically less costly cell properties where possible without interfering with function.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/245424", + "other_implementer": { + "attr_id": 308, + "value": "Remme, Michiel W.H." + }, + "public_submitter_email": { + "attr_id": 309, + "value": "michiel.remme@hu-berlin.de" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Michele Remme" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 183076, + "object_name": "Brainstem" + } + ] + }, + "ver_date": "2019-01-09T11:24:24.87", + "ver_number": 12 + }, + "245427": { + "class_id": 19, + "created": "2018-10-08T10:05:00.693", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 245427, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 253928, + "object_name": "Tell, Fabien [fabien.tell at univ-amu.fr]" + }, + { + "object_id": 253929, + "object_name": "Moubarak, Estelle " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 227131, + "object_name": "Pacemaking mechanism" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 259310, + "object_name": "Moubarak E, Engel D, Dufour MA, Tapia M, Tell F, Goaillard JM (2019)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Dopaminergic subtantia nigra neuron (Moubarak et al 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 261, + "object_name": "Substantia nigra pars compacta DA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Axon initial segment (AIS) geometry critically influences neuronal excitability. Interestingly, the axon of substantia nigra pars compacta (SNc) dopaminergic (DA) neurons displays a highly variable location and most often arises from an axon-bearing dendrite (ABD). We combined current-clamp somatic and dendritic recordings, outside-out recordings of dendritic sodium and potassium currents, morphological reconstructions and multi-compartment modelling to determine cell-to-cell variations in AIS and ABD geometry and their influence on neuronal output (spontaneous pacemaking frequency, AP shape). Both AIS and ABD geometries are highly variable between SNc DA neurons. Surprisingly, we found that AP shape and pacemaking frequency were independent of AIS geometry. Modelling realistic morphological and biophysical variations clarify this result: in SNc DA neurons, the complexity of the ABD combined with its excitability predominantly define pacemaking frequency and AP shape, such that large variations in AIS geometry negligibly affect neuronal output, and are tolerated." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/245427", + "other_implementer": { + "attr_id": 308, + "value": "Tell Fabien\r\nMoubarak estelle " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "fabien.tell@univ-amu.fr" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "TELL" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2019-03-29T13:55:30.543", + "ver_number": 13 + }, + "245445": { + "citation": { + "attr_id": 391, + "value": "DOI: 10.1371/journal.pcbi.1006536" + }, + "class_id": 19, + "created": "2018-10-10T07:14:05.257", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 245445, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 245448, + "object_name": "Chan, Ho Ka [chanhoka911212 at yahoo.com.hk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245690, + "object_name": "Olfaction" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 248676, + "object_name": "Chan HK, Hersperger F, Marachlian E, Smith BH, Locatelli F, Szyszka P, Nowotny T (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Honey bee receptor and antennal lobe model (Chan et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 268, + "object_name": "Olfactory receptor GLU cell" + }, + { + "object_id": 3266, + "object_name": "Antennal lobe olfactory projection neuron (insect)" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model consists of the full repertoire of honey bees' receptors and glomeruli. It fits to the statistics of data from Galizia et al (1999) and Gremiaux et al (2012). Parameters can be changed to (statistically) fit to other data sets." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/245445", + "other_implementer": { + "attr_id": 308, + "value": "Chan, Ho Ka" + }, + "other_receptors": { + "attr_id": 302, + "value": "insect olfactory receptor" + }, + "other_type": { + "attr_id": 305, + "value": "IF neuron network" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "chanhoka911212@yahoo.com.hk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ho Ka Chan" + }, + "ver_date": "2018-10-17T16:22:24.033", + "ver_number": 9 + }, + "245529": { + "citation": { + "attr_id": 391, + "value": "27523748" + }, + "class_id": 19, + "created": "2018-10-15T15:15:09.557", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 245529, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53429, + "object_name": "Blackwell, Avrama [avrama at gmu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 136106, + "object_name": "G-protein coupled" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 231303, + "object_name": "Luczak V, Blackwell KT, Abel T, Girault JA, Gervasi N (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "name": "Compartmental differences in cAMP signaling pathways in hippocam. CA1 pyr. cells (Luczak et al 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 230, + "object_name": "Norephinephrine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Model of cAMP signaling pathways in hippocampal CA1 pyramidal neurons investigate mechanisms underlying the experimentally observed difference in cAMP and PKA FRET between proximal and distal dendrites. Simulations show that compartmental difference in PKA activity required enrichment of protein phosphatase in small compartments; neither reduced PKA subunits nor increased PKA substrates were sufficient." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/245529", + "other_simulator": { + "attr_id": 307, + "value": "NeuroRD-3.0.0.8" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "kblackw1@gmu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Kim Blackwell" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 215, + "object_name": "Adrenergic" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + }, + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "species": { + "attr_id": 689, + "value": [ + { + "object_id": 249926, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2019-02-26T10:26:01.88", + "ver_number": 10 + }, + "245563": { + "citation": { + "attr_id": 391, + "value": "Jedrzejewski-Szmek et al 2018" + }, + "class_id": 19, + "created": "2018-10-16T13:48:05.18", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 245563, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 245561, + "object_name": "Jedrzejewski-Szmek, Zbigniew " + }, + { + "object_id": 189720, + "object_name": "Jedrzejewska-Szmek, Joanna " + }, + { + "object_id": 53429, + "object_name": "Blackwell, Avrama [avrama at gmu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 245531, + "object_name": "Jedrzejewski-Szmek Z, Abrahao KP, Jedrzejewska-Szmek J, Lovinger DM, Blackwell KT (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 186718, + "object_name": "MOOSE/PyMOOSE" + } + ] + }, + "name": "Parameter optimization using CMA-ES (Jedrzejewski-Szmek et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 260, + "object_name": "Neostriatum medium spiny direct pathway GABA cell" + }, + { + "object_id": 153360, + "object_name": "Neostriatum medium spiny indirect pathway GABA cell" + }, + { + "object_id": 88217, + "object_name": "Globus pallidus neuron" + }, + { + "object_id": 150619, + "object_name": "Neostriatum spiny neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Computational models in neuroscience can be used to predict causal\r\nrelationships between biological mechanisms in neurons and networks,\r\nsuch as the effect of blocking an ion channel or synaptic connection\r\non neuron activity. Since developing a biophysically realistic, single\r\nneuron model is exceedingly difficult, software has been developed for\r\nautomatically adjusting parameters of computational neuronal\r\nmodels. The ideal optimization software should work with commonly used\r\nneural simulation software; thus, we present software which works with\r\nmodels specified in declarative format for the MOOSE\r\nsimulator. Experimental data can be specified using one of two\r\ndifferent file formats. The fitness function is customizable as a\r\nweighted combination of feature differences. The optimization itself\r\nuses the covariance matrix adaptation-evolutionary strategy, because\r\nit is robust in the face of local fluctuations of the fitness\r\nfunction, and deals well with a high-dimensional and discontinuous\r\nfitness landscape. We demonstrate the versatility of the software by\r\ncreating several model examples of each of four types of neurons (two\r\nsubtypes of spiny projection neurons and two subtypes of globus\r\npallidus neurons) by tuning to current clamp data.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/245563", + "public_submitter_email": { + "attr_id": 309, + "value": "kblackw1@gmu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Avrama Blackwell" + }, + "ver_date": "2019-02-21T17:27:28.983", + "ver_number": 5 + }, + "245805": { + "citation": { + "attr_id": 391, + "value": "smith sl et al 2013" + }, + "class_id": 19, + "created": "2018-10-19T16:59:50.473", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 245805, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 245806, + "object_name": "Branco, Tiago " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 147931, + "object_name": "Orientation selectivity" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 156249, + "object_name": "Smith SL, Smith IT, Branco T, H\u00e4usser M (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "Dendritic spikes enhance stimulus selectivity in cortical neurons in vivo (Smith et al 2013)", + "notes": { + "attr_id": 24, + "value": "\"Neuronal dendrites are electrically excitable: they can generate regenerative events such as dendritic spikes in response to sufficiently strong synaptic input. Although such events have been observed in many neuronal types, it is not well understood how active dendrites contribute to the tuning of neuronal output in vivo. Here we show that dendritic spikes increase the selectivity of neuronal responses to the orientation of a visual stimulus (orientation tuning). ...\"." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/245805", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2018-10-19T17:06:05.687", + "ver_number": 3 + }, + "245818": { + "citation": { + "attr_id": 391, + "value": "M. Chessa and F. Solari. \r\nA Computational Model for the Neural Representation and Estimation of the \r\nBinocular Vector Disparity from Convergent Stereo Image Pairs. \r\nInternational Journal of Neural Systems, 28, art. no. 1850029, 2018\r\nDOI: https://doi.org/10.1142/S0129065718500296" + }, + "class_id": 19, + "created": "2018-10-23T08:57:46.287", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 245818, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 181037, + "object_name": "Solari, Fabio [fabio.solari at unige.it]" + }, + { + "object_id": 181036, + "object_name": "Chessa, Manuela [manuela.chessa at unige.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245691, + "object_name": "Vision" + }, + { + "object_id": 245934, + "object_name": "Disparity estimation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + 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"2018-10-23T11:14:00.963", + "ver_number": 5 + }, + "245879": { + "citation": { + "attr_id": 391, + "value": "ON-OFF receptive fields in auditory cortex diverge during development and contribute to directional sweep selectivity\r\nSollini, Chapuis, Clopath, Chadderton \r\nNature Communications volume 9, Article number: 2084 (2018) \r\n\r\nhttps://www.nature.com/articles/s41467-018-04548-3" + }, + "class_id": 19, + "created": "2018-10-23T10:08:27.067", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 245879, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144567, + "object_name": "Clopath, Claudia [c.clopath at imperial.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 116907, + "object_name": "Direction Selectivity" + }, + { + "object_id": 245692, + "object_name": "Audition" + } + ] + }, + "model_paper": { + 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We used whole-cell recordings and computational modeling to provide broad insight on intrinsic cellular mechanisms controlling excitability and capacity for synaptic integration. Compared to past intracellular recordings using microelectrode impalement, we observed dramatically higher membrane resistivity with primacy in controlling enhanced tSPN excitability and recruitment via synaptic integration. Compared to reported phasic firing, all tSPNs fire repetitively and linearly encode injected current magnitude to firing frequency over a broad range. Modeling studies suggest microelectrode impalement injury accounts for differences in tSPN properties previously observed. Overall, intrinsic tSPN excitability plays a much greater role in the integration and maintenance of sympathetic output than previously thought.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/245926", + "other_currents": { + "attr_id": 303, + "value": "I CaL" + }, + "other_implementer": { + "attr_id": 308, + "value": "Kun Tian (io.kuntian at gmail.com)" + }, + "other_neurons": { + "attr_id": 301, + "value": "Sympathetic postganglionic neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "io.kuntian@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Kun Tian" + }, + "ver_date": "2020-05-08T15:04:59.287", + "ver_number": 7 + }, + "246004": { + "citation": { + "attr_id": 391, + "value": "Jake P. Stroud, Mason A. Porter, Guillaume Hennequin, Tim P. Vogels, Motor primitives in space and time via targeted gain modulation in cortical networks, Nature Neuroscience, 2018." + }, + "class_id": 19, + "created": "2018-10-24T05:50:37.353", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 246004, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 246128, + "object_name": "Stroud, Jake P [jp.stroud at hotmail.com]" + }, + { + "object_id": 246129, + "object_name": "Hennequin, Guillaume " + }, + { + "object_id": 143822, + "object_name": "Vogels, Tim [tim.vogels at epfl.ch]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83535, + "object_name": "Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 246013, + "object_name": "Stroud JP, Porter MA, Hennequin G, Vogels TP (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Modelling gain modulation in stability-optimised circuits (Stroud et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 245940, + "object_name": "Abstract rate-based neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We supply Matlab code to create 'stability-optimised circuits'. These networks can give rise to rich neural activity transients that resemble primary motor cortex recordings in monkeys during reaching. We also supply code that allows one to learn new network outputs by changing the input-output gain of neurons in a stability-optimised network. Our code recreates the main results of Figure 1 in our related publication." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/246004", + "public_submitter_email": { + "attr_id": 309, + "value": "jp.stroud@hotmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Jake P Stroud" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 179, + "object_name": "M1" + } + ] + }, + "ver_date": "2018-10-24T11:16:16.693", + "ver_number": 6 + }, + "246535": { + "citation": { + "attr_id": 391, + "value": "Verhulst, S., Alto\u00e8, A., & Vasilkov, V. (2018). Computational modeling of the human auditory periphery: Auditory-nerve responses, evoked potentials and hearing loss. Hearing research, 360, 55-75.\r\n\r\nAlto\u00e8, A., Pulkki, V., & Verhulst, S. (2018). The effects of the activation of the inner-hair-cell basolateral K+ channels on auditory nerve responses. Hearing research, 364, 68-80.\r\n\r\nAlto\u00e8, A., Pulkki, V., & Verhulst, S. (2014). Transmission line cochlear models: improved accuracy and efficiency. The Journal of the Acoustical Society of America, 136(4), EL302-EL308.\r\n\r\nVerhulst, S., Dau, T., & Shera, C. A. (2012). Nonlinear time-domain cochlear model for transient stimulation and human otoacoustic emission. The Journal of the Acoustical Society of America, 132(6), 3842-3848." + }, + "class_id": 19, + "created": "2018-11-04T13:09:56.173", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 246535, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 246539, + "object_name": "Verhulst, Sarah [s.verhulst at ugent.be]" + }, + { + "object_id": 246540, + "object_name": "Alto\u00e9, Alessandro " + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 246538, + "object_name": "Verhulst S, Alto\u00e8 A, Vasilkov V (2018)" + }, + { + "object_id": 246542, + "object_name": "Alto\u00e8 A, Pulkki V, Verhulst S (2018)" + }, + { + "object_id": 246543, + "object_name": "Alto\u00e8 A, Pulkki V, Verhulst S (2014)" + }, + { + "object_id": 246544, + "object_name": "Verhulst S, Dau T, Shera CA (2012)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Human auditory periphery model: cochlea, IHC-AN, auditory brainstem responses (Verhulst et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 238352, + "object_name": "Cochlea hair inner GLU cell" + }, + { + "object_id": 273, + "object_name": "Cochlear nucleus bushy GLU cell" + }, + { + "object_id": 52419, + "object_name": "Auditory nerve" + }, + { + "object_id": 185139, + "object_name": "Brainstem neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The human auditory periphery model can simulate single-unit response of basilar-membrane vibration, IHC receptor potential, instantaneous AN/CN/IC firing rates, as well as population responses such as otoacoustic emissions, auditory brainstem responses. The neuron models (IHC, AN,CN,IC) can be run independently to relate their responses to electrophysiology, or be simulated as part of the human auditory periphery." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/246535", + "other_implementer": { + "attr_id": 308, + "value": "Sarah Verhulst\r\nAlessandro Alto\u00e9" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "s.verhulst@ugent.be" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Sarah Verhulst" + }, + "ver_date": "2019-06-04T15:44:12.963", + "ver_number": 6 + }, + "246546": { + "class_id": 19, + "created": "2018-11-08T12:03:55.377", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 246546, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206377, + "object_name": "Chavlis, Spyridon [schavlis at imbb.forth.gr]" + }, + { + "object_id": 249698, + "object_name": "Pandi, Ioanna " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98011, + "object_name": "Place cell/field" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 249697, + "object_name": "Turi GF, Li W, Chavlis S, Pandi I, O\u2019Hare J, Priestley JB, Grosmark AD, Liao Z, Ladow M, Zhang JF, Zemelman BV, Poirazi P, Losonczy A (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 113733, + "object_name": "Brian" + } + ] + }, + "name": "CA1 network model for place cell dynamics (Turi et al 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 114079, + "object_name": "Hippocampus CA1 basket cell" + }, + { + "object_id": 227332, + "object_name": "Hippocampus CA1 basket cell - CCK/VIP" + }, + { + "object_id": 151329, + "object_name": "Hippocampus CA1 bistratified cell" + }, + { + "object_id": 151330, + "object_name": "Hippocampus CA1 axo-axonic cell" + }, + { + "object_id": 190560, + "object_name": "Hippocampus CA1 stratum oriens lacunosum-moleculare interneuron " + }, + { + "object_id": 249699, + "object_name": "Hippocampal CA1 CR/VIP cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Biophysical model of CA1 hippocampal region. The model simulates place cells/fields and explores the place cell dynamics as function of VIP+ interneurons." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/246546", + "other_neurons": { + "attr_id": 301, + "value": "Hippocampal CA1 CR/VIP cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "schavlis@imbb.forth.gr" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Spyridon Chavlis" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + }, + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2023-05-02T14:52:08.907", + "ver_number": 9 + }, + "246837": { + "citation": { + "attr_id": 391, + "value": "LUBBA ET AL 2018" + }, + "class_id": 19, + "created": "2018-11-12T12:22:24.093", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 246837, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 246838, + "object_name": "Lubba, Carl Henning [c.lubba15 at imperial.ac.uk]" + }, + { + "object_id": 246839, + "object_name": "Le Guen, Yann " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 246835, + "object_name": "Lubba CH, Le Guen Y, Jarvis S, Jones NS, Cork SC, Eftekhar A, Schultz SR (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + }, + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "PyPNS: Multiscale Simulation of a Peripheral Nerve in Python (Lubba et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52425, + "object_name": "Myelinated neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... To reduce experimentation load and allow for a faster, more detailed analysis of peripheral nerve stimulation and recording, computational models incorporating experimental insights will be of great help. We present a peripheral nerve simulator that combines biophysical axon models and numerically solved and idealised extracellular space models in one environment. We modelled the extracellular space as a three-dimensional resistive continuum governed by the electro-quasistatic approximation of the Maxwell equations. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/246837", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@YALE.EDU" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom MOrse" + }, + "ver_date": "2018-11-12T14:02:17.153", + "ver_number": 5 + }, + "247179": { + "class_id": 19, + "created": "2018-11-15T13:53:39.597", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247179, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 150219, + "object_name": "Reliability" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 247178, + "object_name": "Sadashivaiah V, Sacr\u00e9 P, Guan Y, Anderson WS, Sarma SV (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + }, + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "Stimulated and physiologically induced APs: frequency and fiber diameter (Sadashivaiah et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52425, + "object_name": "Myelinated neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"...\r\nIn this study, we\r\naim to quantify the effects of stimulation frequency and fiber\r\ndiameter on AP (Action Potential) interactions involving collisions and loss of\r\nexcitability. We constructed a mechanistic model of a myelinated nerve\r\nfiber receiving two inputs: the underlying physiological activity at\r\nthe terminal end of the fiber, and an external stimulus applied to the\r\nmiddle of the fiber. We define conduction reliability as the\r\npercentage of physiological APs that make it to the somatic end of the\r\nnerve fiber. At low input frequencies, conduction reliability is\r\ngreater than 95% and decreases with increasing frequency due to an\r\nincrease in AP interactions. Conduction reliability is less sensitive\r\nto fiber diameter and only decreases slightly with increasing fiber\r\ndiameter. Finally, both the number and type of AP interactions\r\nsignificantly vary with both input frequencies and fiber\r\ndiameter.\r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247179", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2018-11-15T13:55:32.58", + "ver_number": 3 + }, + "247188": { + "citation": { + "attr_id": 391, + "value": "Top-down inputs drive neuronal network rewiring and context-enhanced sensory processing in olfaction,\r\nWayne Adams, James N. Graham, Xuchen Han, Hermann Riecke,\r\nPLoS Computational Biology (in print)" + }, + "class_id": 19, + "created": "2018-11-18T15:20:07.37", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247188, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 247195, + "object_name": "Riecke, Hermann [h-riecke at northwestern.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 155599, + "object_name": "Neurogenesis" + }, + { + "object_id": 188516, + "object_name": "Memory" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + }, + { + "object_id": 185463, + "object_name": "Pattern Separation" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 247213, + "object_name": "Top-down input" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 253326, + "object_name": "Adams W, Graham JN, Han X, Riecke H (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Neurogenesis in the olfactory bulb controlled by top-down input (Adams et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 240378, + "object_name": "Abstract integrate-and-fire neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This code implements a model for adult neurogenesis of granule cells in the olfactory system. The granule cells receive sensory input via the mitral cells and top-down input from a cortical area. That cortical area also receives olfactory input from the mitral cells as well as contextual input. This plasticity leads to a network structure consisting of bidirectional connections between bulbar and cortical odor representations. The top-down input enhances stimulus discrimination based on contextual input." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247188", + "other_concept": { + "attr_id": 306, + "value": "Top-down input" + }, + "other_implementer": { + "attr_id": 308, + "value": "Riecke, Hermann" + }, + "other_type": { + "attr_id": 305, + "value": "Neuronal Network" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "h-riecke@northwestern.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Hermann Riecke" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + }, + { + "object_id": 115949, + "object_name": "Olfactory cortex" + } + ] + }, + "ver_date": "2018-11-19T22:29:50.453", + "ver_number": 7 + }, + "247191": { + "citation": { + "attr_id": 391, + "value": "Brette 2013" + }, + "class_id": 19, + "created": "2018-11-19T13:09:55.79", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247191, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83453, + "object_name": "Brette R" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 184071, + "object_name": "Brette R (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 236518, + "object_name": "Brian 2 (web link to model)" + } + ] + }, + "name": "Sharpness of spike initiation in neurons explained by compartmentalization (Brette 2013)", + "notes": { + "attr_id": 24, + "value": "\"Spike initiation determines how the combined inputs to a neuron are converted to an output. Since the pioneering work of Hodgkin and Huxley, it is known that spikes are generated by the opening of sodium channels with depolarization. According to this standard theory, these channels should open gradually when the membrane potential increases, but spikes measured at the soma appear to suddenly rise from rest. This apparent contradiction has triggered a controversy about the origin of spike \u201csharpness.\u201d This study shows with biophysical modelling that if sodium channels are placed in the axon rather than in the soma, they open all at once when the somatic membrane potential exceeds a critical value. This work explains the sharpness of spike initiation and provides another demonstration that morphology plays a critical role in neural function.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247191", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Mrose" + }, + "ver_date": "2018-11-19T13:25:23.513", + "ver_number": 2 + }, + "247196": { + "citation": { + "attr_id": 391, + "value": "hindmarsh rose 1984" + }, + "class_id": 19, + "created": "2018-11-19T15:08:30.933", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247196, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83453, + "object_name": "Brette R" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 117085, + "object_name": "Hindmarsh JL, Rose RM (1984)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 236518, + "object_name": "Brian 2 (web link to model)" + } + ] + }, + "name": "A model of neuronal bursting using three coupled first order diff. eqs. (Hindmarsh & Rose 1984)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 188553, + "object_name": "Abstract Hindmarsh-Rose neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "R Brette's Brian 2 implementation of the classic Hindmarsh-Rose 1984 dynamical system representing neuronal bursting." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247196", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2018-11-19T15:29:03.26", + "ver_number": 3 + }, + "247209": { + "class_id": 19, + "created": "2018-11-19T15:30:13.14", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247209, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83453, + "object_name": "Brette R" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 136252, + "object_name": "Rebound firing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 4161, + "object_name": "Destexhe A, Neubig M, Ulrich D, Huguenard J (1998)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 236518, + "object_name": "Brian 2 (web link to model)" + } + ] + }, + "name": "Low Threshold Calcium Currents in TC cells (Destexhe et al 1998) (Brian)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "R Brette's implementation in Brian 2 of Destexhe et al 1998's model. The author's original code is also available from ModelDB with accession number 279 (yes, was one of the first models in ModelDB)!" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247209", + "runprotocols": { + "attr_id": 613, + "value": [ + { + "object_id": 181344, + "object_name": "279_4" + }, + { + "object_id": 181345, + "object_name": "279_5" + }, + { + "object_id": 181346, + "object_name": "279_2" + }, + { + "object_id": 181347, + "object_name": "279_3" + }, + { + "object_id": 181348, + "object_name": "279_1" + } + ] + }, + "ver_date": "2018-11-19T15:51:50.83", + "ver_number": 7 + }, + "247310": { + "class_id": 19, + "created": "2018-11-26T16:21:01.65", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247310, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 65417, + "object_name": "Parkinson's" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 248205, + "object_name": "Grado LL, Johnson MD, Netoff TI (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Adaptive dual control of deep brain stimulation in Parkinsons disease simulations (Grado et al 2018)", + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247310", + "other_neurons": { + "attr_id": 301, + "value": "Mean Field Model" + }, + "other_type": { + "attr_id": 305, + "value": "Mean Field Model" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "grado@umn.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Logan Grado" + }, + "ver_date": "2018-11-26T17:10:02.167", + "ver_number": 3 + }, + "247646": { + "citation": { + "attr_id": 391, + "value": "bertram et al 2008" + }, + "class_id": 19, + "created": "2018-11-28T11:20:00.3", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247646, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 247222, + "object_name": "Bertram R, Rhoads J, Cimbora WP (2008)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 91894, + "object_name": "XPP (web link to model)" + }, + { + "object_id": 120321, + "object_name": "CellML (web link to model)" + } + ] + }, + "name": "A phantom bursting mechanism for episodic bursting (Bertram et al 2008)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"We describe a novel dynamic mechanism for episodic or compound bursting\r\noscillations, in which bursts of electrical impulses are clustered together into episodes,\r\nseparated by long silent phases. We demonstrate the mechanism for episodic bursting\r\nusing a minimal mathematical model for \u201cphantom bursting.\u201d Depending on the location\r\nin parameter space, this model can produce fast, medium, or slow bursting, or in the\r\npresent case, fast, slow, and episodic bursting. The episodic bursting is modestly robust\r\nto noise and to parameter variation, and the effect that noise has on the episodic bursting\r\npattern is quite different from that of an alternate episodic burst mechanism in which\r\nthe slow envelope is produced by metabolic oscillations. This mechanism could account\r\nfor episodic bursting produced in endocrine cells or neurons, such as pancreatic islets or\r\ngonadotropin releasing neurons of the hypothalamus.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247646", + "other_currents": { + "attr_id": 303, + "value": "leak current with V=-40?" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2018-11-28T16:50:26.427", + "ver_number": 4 + }, + "247647": { + "citation": { + "attr_id": 391, + "value": "Butera et al 1999" + }, + "class_id": 19, + "created": "2018-11-28T11:57:10.673", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 88215, + "object_name": "I_Ks" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247647, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 28528, + "object_name": "Butera RJ, Rinzel J, Smith JC (1999)" + } + ] + }, + "name": "Respiratory pacemaker neurons (Butera et al 1999)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A network of oscillatory bursting neurons with excitatory coupling is hypothesized to define the primary kernel for respiratory rhythm\r\ngeneration in the pre-Botzinger complex (pre-BotC) in mammals.\r\nTwo minimal models of these neurons are proposed. In model 1, bursting arises via fast activation and slow inactivation of a persistent Na current INaP-h. In model 2, bursting arises via a fast-activating\r\npersistent Na current INaP and slow activation of a K1 current IKS.\r\nIn both models, action potentials are generated via fast Na and K\r\ncurrents. The two models have few differences in parameters to facilitate a rigorous comparison of the two different burst-generating mechanisms. Both models are consistent with many of the dynamic\r\nfeatures of electrophysiological recordings from pre-BotC oscillatory\r\nbursting neurons in vitro, including voltage-dependent activity modes\r\n(silence, bursting, and beating), a voltage-dependent burst frequency\r\nthat can vary from 0.05 to .1 Hz, and a decaying spike frequency\r\nduring bursting. These results are robust and persist across a wide range of parameter values for both models. However, the dynamics of model 1 are more consistent with experimental data in that the burst\r\nduration decreases as the baseline membrane potential is depolarized and the model has a relatively flat membrane potential trajectory during the interburst interval. We propose several experimental tests\r\nto demonstrate the validity of either model and to differentiate between the two mechanisms." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247647", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2018-11-28T11:58:48.393", + "ver_number": 2 + }, + "247648": { + "citation": { + "attr_id": 391, + "value": "Cloutier et al 2009" + }, + "class_id": 19, + "created": "2018-11-28T17:25:38.75", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247648, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 237616, + "object_name": "Cloutier M, Bolger FB, Lowry JP, Wellstead P (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 120321, + "object_name": "CellML (web link to model)" + } + ] + }, + "name": "An integrative dynamic model of brain energy metabolism (Coultier et al 2009)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "An integrative, systems approach to the modelling of brain energy metabolism is presented. Mechanisms such as glutamate cycling between neurons and astrocytes and glycogen storage in astrocytes have been implemented. A unique feature of the model is its calibration using in vivo data of brain glucose and lactate from freely moving rats under various stimuli. The model has been used to perform simulated perturbation experiments that show that glycogen breakdown in astrocytes is significantly activated during sensory (tail pinch) stimulation. This mechanism provides an additional input of energy substrate during high consumption phases. By way of validation, data from the perfusion of 50?\u00b5M propranolol in the rat brain was compared with the model outputs. Propranolol affects the glucose dynamics during stimulation, and this was accurately reproduced in the model by a reduction in the glycogen breakdown in astrocytes. The model\u2019s predictive capacity was verified by using data from a sensory stimulation (restraint) that was not used for model calibration. Finally, a sensitivity analysis was conducted on the model parameters, this showed that the control of energy metabolism and transport processes are critical in the metabolic behaviour of cerebral tissue." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247648", + "other_concept": { + "attr_id": 306, + "value": "Energy" + }, + "other_type": { + "attr_id": 305, + "value": "astrocyte" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2018-11-28T17:26:39.36", + "ver_number": 2 + }, + "247655": { + "citation": { + "attr_id": 391, + "value": "friel 1995" + }, + "class_id": 19, + "created": "2018-11-29T11:46:07.813", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247655, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 28582, + "object_name": "Friel DD (1995)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 120321, + "object_name": "CellML (web link to model)" + } + ] + }, + "name": "Ca2+ Oscillations in Sympathetic neurons (Friel 1995)", + "notes": { + "attr_id": 24, + "value": "\" ... This\r\nstudy focuses on caffeine-induced [Ca2+]i oscillations in sympathetic\r\nneurons. ... The aim of the study\r\nwas to understand the mechanism responsible for the oscillations. As a\r\nstarting point, [Ca2+]i relaxations were examined after membrane\r\ndepolarization and exposure to caffeine. For both stimuli,\r\npost-stimulus relaxations could be described by the sum of two\r\ndecaying exponential functions, consistent with a one-pool system in\r\nwhich Ca2+ transport between compartments is regulated by linear Ca2+\r\npumps and leaks. After modifying the store to include a\r\n[Ca2+]i-sensitive leak, the model also exhibits oscillations such as\r\nthose observed experimentally.\r\n\r\n\r\n... Thus, a one-pool model with a single\r\n[Ca2+]i-sensitive Ca2+ permeability is adequate to account for many of\r\nthe quantitative properties of steady-state [Ca2+]i oscillations in\r\nsympathetic neurons. ...\"\r\n" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247655", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2018-11-29T13:44:11.9", + "ver_number": 2 + }, + "247656": { + "citation": { + "attr_id": 391, + "value": "Li et al 1996" + }, + "class_id": 19, + "created": "2018-11-29T16:42:36.7", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247656, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 120695, + "object_name": "Sodium pump" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 6977, + "object_name": "Li YX, Bertram R, Rinzel J (1996)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 120321, + "object_name": "CellML (web link to model)" + } + ] + }, + "name": "Bursting in dopamine neurons (Li YX et al 1996)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Burst firing of dopaminergic neurons of the substantia nigra pars\r\ncompacta can be induced in vitro by the glutamate agonist\r\nN-methyl-D-aspartate. It has been suggested that the interburst\r\nhyperpolarization is due to Na+ extrusion by a ouabain-sensitive pump\r\n(Johnson et al. (1992) Science 258, 665-667). We formulate and explore\r\na theoretical model, with a minimal number of currents, for this novel\r\nmechanism of burst generation. This minimal model is further developed\r\ninto a more elaborate model based on observations of additional\r\ncurrents and hypotheses about their spatial distribution in\r\ndopaminergic neurons ... Responses of the model to a number of\r\nelectrophysiological and pharmacological stimuli are consistent with\r\nknown responses observed under similar conditions. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247656", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2018-11-29T16:44:03.937", + "ver_number": 3 + }, + "247657": { + "class_id": 19, + "created": "2018-11-29T17:41:57.98", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247657, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 151961, + "object_name": "\u00d8yehaug L, \u00d8stby I, Lloyd CM, Omholt SW, Einevoll GT (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 120799, + "object_name": "Glia" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 120321, + "object_name": "CellML (web link to model)" + } + ] + }, + "name": "Dependence of neuronal firing on astroglial membrane transport mechanisms (Oyehaug et al 2012)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + }, + { + "object_id": 87589, + "object_name": "Astrocyte" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Exposed to a sufficiently high extracellular potassium concentration ([K?+?]o), the neuron can fire spontaneous discharges or even become inactivated due to membrane depolarisation (\u2018depolarisation block\u2019). Since these phenomena likely are related to the maintenance and propagation of seizure discharges, it is of considerable importance to understand the conditions under which excess [K?+?]o causes them. To address the putative effect of glial buffering on neuronal activity under elevated [K?+?]o conditions, we combined a recently developed dynamical model of glial membrane ion and water transport with a Hodgkin\u2013Huxley type neuron model. In this interconnected glia-neuron model we investigated the effects of natural heterogeneity or pathological changes in glial membrane transporter density by considering a large set of models with different, yet empirically plausible, sets of model parameters. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247657", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2018-11-29T17:43:02.287", + "ver_number": 2 + }, + "247664": { + "class_id": 19, + "created": "2018-11-30T18:52:45.027", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 146506, + "object_name": "NKCC1" + }, + { + "object_id": 247662, + "object_name": "KCC1" + }, + { + "object_id": 247663, + "object_name": "NBC" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247664, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 144383, + "object_name": "Cellular volume dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 124403, + "object_name": "\u00d8stby I, \u00d8yehaug L, Einevoll GT, Nagelhus EA, Plahte E, Zeuthen T, Lloyd CM, Ottersen OP, Omholt SW (2009)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 120321, + "object_name": "CellML (web link to model)" + } + ] + }, + "name": "Mechanisms of extraneuronal space shrinkage (Ostby et al 2009)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 87589, + "object_name": "Astrocyte" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Neuronal stimulation causes ~30% shrinkage of the extracellular space\r\n(ECS) between neurons and surrounding astrocytes in grey and white\r\nmatter under experimental conditions. Despite its possible\r\nimplications for a proper understanding of basic aspects of potassium\r\nclearance and astrocyte function, the phenomenon remains\r\nunexplained. Here we present a dynamic model that accounts for current\r\nexperimental data related to the shrinkage phenomenon in wild-type as\r\nwell as in gene knockout individuals.\r\n...\r\nConsidering the current\r\nstate of knowledge, the model framework appears sufficiently detailed\r\nand constrained to guide future key experiments and pave the way for\r\nmore comprehensive astroglia\u2013neuron interaction models for normal as\r\nwell as pathophysiological situations.\r\n\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247664", + "other_currents": { + "attr_id": 303, + "value": "NaKATPase, I Cl," + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2018-11-30T18:56:45.403", + "ver_number": 2 + }, + "247694": { + "biosimulations": { + "value": "modeldb-247694" + }, + "class_id": 19, + "created": "2018-12-04T03:43:32.953", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247694, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 247695, + "object_name": "Denizot, Audrey [audrey.denizot at inria.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 259076, + "object_name": "Denizot A, Arizono M, N\u00e4gerl UV, Soula H, Berry H (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 120799, + "object_name": "Glia" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 144556, + "object_name": "STEPS" + }, + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Simulation of calcium signaling in fine astrocytic processes (Denizot et al 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 87589, + "object_name": "Astrocyte" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model corresponds to the model presented in Denizot et al, 2019. The model indicates that the frequency of calcium signals crucially depends on the spatial organization of the IP3R channels, including their clustering and co-localization with the other sources of calcium influx to the cytosol. Spontaneous calcium signals generated by the model with realistic PAPs volume and calcium concentration successfully reproduce spontaneous calcium transients that we measured in calcium micro-domains with confocal microscopy. To our knowledge, this model is the first model suited to the investigation of spontaneous calcium dynamics in fine astrocytic processes, a crucial step towards a better understanding of the spatio-temporal integration of astrocyte signals in response to neuronal activity." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247694", + "other_implementer": { + "attr_id": 308, + "value": "Audrey Denizot (audrey.denizot@inria.fr" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "audrey.denizot@inria.fr" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Audrey Denizot" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 153715, + "object_name": "IP3" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115960, + "object_name": "Generic" + } + ] + }, + "ver_date": "2019-07-14T19:20:13.733", + "ver_number": 12 + }, + "247696": { + "citation": { + "attr_id": 391, + "value": "Phillips Johnson 2007" + }, + "class_id": 19, + "created": "2018-12-04T14:33:56.913", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247696, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 145886, + "object_name": "Circadian Rhythms" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 93420, + "object_name": "Sleep" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 226757, + "object_name": "Phillips AJ, Robinson PA (2007)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 120321, + "object_name": "CellML (web link to model)" + } + ] + }, + "name": "Quantitative model of sleep-wake dynamics (Phillips & Robinson 2007)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"A quantitative, physiology-based model of the ascending arousal system\r\nis developed, using continuum neuronal population modeling, which\r\ninvolves averaging properties such as firing rates across neurons in\r\neach population. The model includes the ventrolateral preoptic area\r\n(VLPO), where circadian and homeostatic drives enter the system, the\r\nmonoaminergic and cholinergic nuclei of the ascending arousal system,\r\nand their interconnections. The human sleep-wake cycle is governed by\r\nthe activities of these nuclei, which modulate the behavioral state of\r\nthe brain via diffuse neuromodulatory projections.\r\n\r\n...\r\nThe model behavior is robust across\r\nthe constrained parameter ranges, but with sufficient flexibility to\r\ndescribe a wide range of observed phenomena.\r\n\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247696", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2018-12-04T14:41:31.49", + "ver_number": 2 + }, + "247698": { + "citation": { + "attr_id": 391, + "value": "Phillips Robinson 2008" + }, + "class_id": 19, + "created": "2018-12-04T15:57:00.87", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247698, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 145886, + "object_name": "Circadian Rhythms" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 93420, + "object_name": "Sleep" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 247502, + "object_name": "Phillips AJ, Robinson PA (2008)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 120321, + "object_name": "CellML (web link to model)" + } + ] + }, + "name": "Sleep deprivation in the ascending arousal system (Phillips & Robinson 2008)", + "notes": { + "attr_id": 24, + "value": "\"A physiologically based quantitative model of the human ascending\r\narousal system is used to study sleep deprivation after being\r\ncalibrated on a small set of experimentally based criteria. The model\r\nincludes the sleep\u2013wake switch of mutual inhibition between nuclei\r\nwhich use monoaminergic neuromodulators, and the ventrolateral\r\npreoptic area. The system is driven by the circadian rhythm and sleep\r\nhomeostasis.\r\nWe use a small number of experimentally derived criteria\r\nto calibrate the model for sleep deprivation, then investigate model\r\npredictions for other experiments, demonstrating the scope of\r\napplication.\r\n\r\n...\r\n\r\nThe form of the homeostatic drive suggests\r\nthat periods of wake during recovery from sleep deprivation are phases\r\nof relative recovery, in the sense that the homeostatic drive\r\ncontinues to converge toward baseline levels.\r\nThis undermines the\r\nconcept of sleep debt, and is in agreement with experimentally\r\nrestricted recovery protocols. Finally, we compare our model to the\r\ntwo-process model, and demonstrate the power of physiologically based\r\nmodeling by correctly predicting sleep latency times following\r\ndeprivation from experimental data.\r\n\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247698", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2018-12-04T16:01:36.32", + "ver_number": 3 + }, + "247702": { + "citation": { + "attr_id": 391, + "value": "Purvis Butera 2005" + }, + "class_id": 19, + "created": "2018-12-04T17:14:25.26", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247702, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 115098, + "object_name": "Purvis LK, Butera RJ (2005)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 120321, + "object_name": "CellML (web link to model)" + } + ] + }, + "name": "Ionic current model of a Hypoglossal Motoneuron (Purvis & Butera 2005)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 247700, + "object_name": "Hypoglossal motor neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"We have developed a single-compartment, electrophysiological,\r\nhypoglossal motoneuron (HM) model based primarily on experimental data\r\nfrom neonatal rat HMs. The model is able to reproduce the fine\r\nfeatures of the HM action potential: the fast afterhyperpolarization,\r\nthe afterdepolarization, and the medium-duration\r\nafterhyperpolarization (mAHP). The model also reproduces the\r\nrepetitive firing properties seen in neonatal HMs and replicates the\r\nneuron\u2019s response to pharmacological experiments. The model was used\r\nto study the role of specific ionic currents in HM firing and how\r\nvariations in the densities of these currents may account for\r\nage dependent changes in excitability seen in HMs. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247702", + "other_neurons": { + "attr_id": 301, + "value": "Hypoglossal motor neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2018-12-05T10:15:45.54", + "ver_number": 4 + }, + "247704": { + "biosimulations": { + "value": "modeldb-247704" + }, + "citation": { + "attr_id": 391, + "value": "Y. Liu, J. Milton and S.A. Campbell (2018) Outgrowing seizures in Childhood Absence Epilepsy: Time delays and bistability." + }, + "class_id": 19, + "created": "2018-12-05T09:20:43.717", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247704, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 247705, + "object_name": "Liu, Yu " + }, + { + "object_id": 247706, + "object_name": "Campbell, Sue Ann [sacampbell at uwaterloo.ca]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 249721, + "object_name": "Liu Y, Milton J, Campbell SA (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Thalamocortical loop with delay for investigation of absence epilepsy (Liu et al 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 263, + "object_name": "Thalamus reticular nucleus GABA cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Conductance based network model of one thalamic reticular neuron, one thalamic pyramidal neuron and one cortical pyramidal neuron. Used to show that large delay in the corticothalamic connection can lead to multistability." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247704", + "other_implementer": { + "attr_id": 308, + "value": "Yu Liu\r\nSue Ann Campbell sacampbell at uwaterloo.ca " + }, + "other_neurons": { + "attr_id": 301, + "value": "Thalamic relay, cortical pyramidal" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "sacampbell@uwaterloo.ca" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Sue Ann Campbell" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2019-02-15T14:29:43.043", + "ver_number": 20 + }, + "247707": { + "class_id": 19, + "created": "2018-12-05T11:34:17.56", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247707, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 227131, + "object_name": "Pacemaking mechanism" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 97932, + "object_name": "Purvis LK, Smith JC, Koizumi H, Butera RJ (2007)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 120321, + "object_name": "CellML (web link to model)" + } + ] + }, + "name": "Pacemaker neurons and respiratory rhythm generation (Purvis et al 2007)", + "notes": { + "attr_id": 24, + "value": "\"The pre-Botzinger complex (pBC) is a vital subcircuit of the\r\nrespiratory central pattern generator. Although the existence of\r\nneurons with pacemaker-like bursting properties in this network is not\r\nquestioned, their role in network rhythmogenesis is\r\nunresolved.\r\n\r\n...\r\nWe modeled the parameter\r\nvariability of experimental data from pBC bursting pacemaker and\r\nnonpacemaker neurons using a modified version of our previously\r\ndeveloped pBC neuron and network models.\r\n\r\n...\r\n\"\r\nThe paper contains network modeling results that are not represented in this model entry. Only the neuron models are included in this modeldb entry.\r\n" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247707", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2018-12-05T11:46:40.11", + "ver_number": 5 + }, + "247709": { + "citation": { + "attr_id": 391, + "value": "Tabak" + }, + "class_id": 19, + "created": "2018-12-05T16:41:36.583", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247709, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 128875, + "object_name": "Development" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 247580, + "object_name": "Tabak J, Mascagni M, Bertram R (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 91894, + "object_name": "XPP (web link to model)" + } + ] + }, + "name": "Universal feature of developing networks (Tabak et al 2010)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + }, + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Spontaneous episodic activity is a fundamental mode of operation of\r\ndeveloping networks. Surprisingly, the duration of an episode of\r\nactivity correlates with the length of the silent interval that\r\nprecedes it, but not with the interval that follows. \r\n\r\n... We thus developed simple models\r\nincorporating excitatory coupling between heterogeneous neurons and\r\nactivity-dependent synaptic depression. These models robustly\r\ngenerated episodic activity with the correct correlation pattern.\r\n\r\nThe\r\ncorrelation pattern resulted from episodes being triggered at random\r\nlevels of recovery from depression while they terminated around the\r\nsame level of depression. To explain this fundamental difference\r\nbetween episode onset and termination, we used a mean field model,\r\nwhere only average activity and average level of recovery from\r\nsynaptic depression are considered.\r\n...\r\nThis work further shows that networks with widely different\r\narchitectures, different cell types, and different functions all\r\noperate according to the same general mechanism early in their\r\ndevelopment.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247709", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2018-12-05T16:42:11.787", + "ver_number": 2 + }, + "247711": { + "citation": { + "attr_id": 391, + "value": "Tabak" + }, + "class_id": 19, + "created": "2018-12-05T16:44:15.15", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247711, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 128875, + "object_name": "Development" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 247580, + "object_name": "Tabak J, Mascagni M, Bertram R (2010)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 120321, + "object_name": "CellML (web link to model)" + } + ] + }, + "name": "Universal feature of developing networks (Tabak et al 2010) (CellML)", + "notes": { + "attr_id": 24, + "value": "\"Spontaneous episodic activity is a fundamental mode of operation of\r\ndeveloping networks. Surprisingly, the duration of an episode of\r\nactivity correlates with the length of the silent interval that\r\nprecedes it, but not with the interval that follows. \r\n\r\n... We thus developed simple models\r\nincorporating excitatory coupling between heterogeneous neurons and\r\nactivity-dependent synaptic depression. These models robustly\r\ngenerated episodic activity with the correct correlation pattern.\r\n\r\nThe\r\ncorrelation pattern resulted from episodes being triggered at random\r\nlevels of recovery from depression while they terminated around the\r\nsame level of depression. To explain this fundamental difference\r\nbetween episode onset and termination, we used a mean field model,\r\nwhere only average activity and average level of recovery from\r\nsynaptic depression are considered.\r\n...\r\nThis work further shows that networks with widely different\r\narchitectures, different cell types, and different functions all\r\noperate according to the same general mechanism early in their\r\ndevelopment.\" This modeldb entry only has the mean field model as networks are not implementable currently in CellML." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247711", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2018-12-05T16:48:01.117", + "ver_number": 4 + }, + "247713": { + "class_id": 19, + "created": "2018-12-06T17:07:20.93", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247713, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 145886, + "object_name": "Circadian Rhythms" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 246721, + "object_name": "Vasalou C, Henson MA (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 120321, + "object_name": "CellML (web link to model)" + } + ] + }, + "name": "A multiscale approach to analyze circadian rhythms (Vasalou & Henson, 2010) (CellML)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 145884, + "object_name": "Suprachiasmatic nucleus (SCN) neuron" + }, + { + "object_id": 245940, + "object_name": "Abstract rate-based neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... We developed a firing rate code model to incorporate known\r\nelectrophysiological properties of SCN (suprachiasmatic nucleus)\r\npacemaker cells, including circadian dependent changes in membrane\r\nvoltage and ion conductances. Calcium dynamics were included in the\r\nmodel as the putative link between electrical firing and gene\r\nexpression. Individual ion currents exhibited oscillatory patterns\r\nmatching experimental data both in current levels and phase\r\nrelationships. VIP and GABA neurotransmitters, which encode synaptic\r\nsignals across the SCN, were found to play critical roles in daily\r\noscillations of membrane excitability and gene expression. Blocking\r\nvarious mechanisms of intracellular calcium accumulation by simulated\r\npharmacological agents (nimodipine, IP3- and ryanodine-blockers)\r\nreproduced experimentally observed trends in firing rate dynamics and\r\ncore-clock gene transcription. The intracellular calcium concentration\r\nwas shown to regulate diverse circadian processes such as firing\r\nfrequency, gene expression and system periodicity. The model predicted\r\na direct relationship between firing frequency and gene expression\r\namplitudes, demonstrated the importance of intracellular pathways for\r\nsingle cell behavior and provided a novel multiscale framework which\r\ncaptured characteristics of the SCN at both the electrophysiological\r\nand gene regulatory levels.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247713", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 153715, + "object_name": "IP3" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2018-12-06T17:42:10.683", + "ver_number": 4 + }, + "247719": { + "class_id": 19, + "created": "2018-12-11T11:11:04.27", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247719, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 145886, + "object_name": "Circadian Rhythms" + }, + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 246721, + "object_name": "Vasalou C, Henson MA (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 125746, + "object_name": "SBML (web link to model)" + } + ] + }, + "name": "A multiscale approach to analyze circadian rhythms (Vasalou & Henson, 2010) (SBML)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 145884, + "object_name": "Suprachiasmatic nucleus (SCN) neuron" + }, + { + "object_id": 245940, + "object_name": "Abstract rate-based neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... We developed a firing rate code model to incorporate known\r\nelectrophysiological properties of SCN (suprachiasmatic nucleus)\r\npacemaker cells, including circadian dependent changes in membrane\r\nvoltage and ion conductances. Calcium dynamics were included in the\r\nmodel as the putative link between electrical firing and gene\r\nexpression. Individual ion currents exhibited oscillatory patterns\r\nmatching experimental data both in current levels and phase\r\nrelationships. VIP and GABA neurotransmitters, which encode synaptic\r\nsignals across the SCN, were found to play critical roles in daily\r\noscillations of membrane excitability and gene expression. Blocking\r\nvarious mechanisms of intracellular calcium accumulation by simulated\r\npharmacological agents (nimodipine, IP3- and ryanodine-blockers)\r\nreproduced experimentally observed trends in firing rate dynamics and\r\ncore-clock gene transcription. The intracellular calcium concentration\r\nwas shown to regulate diverse circadian processes such as firing\r\nfrequency, gene expression and system periodicity. The model predicted\r\na direct relationship between firing frequency and gene expression\r\namplitudes, demonstrated the importance of intracellular pathways for\r\nsingle cell behavior and provided a novel multiscale framework which\r\ncaptured characteristics of the SCN at both the electrophysiological\r\nand gene regulatory levels.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247719", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 153715, + "object_name": "IP3" + } + ] + }, + "ver_date": "2018-12-12T17:56:52.803", + "ver_number": 7 + }, + "247848": { + "citation": { + "attr_id": 391, + "value": "Arkhipov et al 2018" + }, + "class_id": 19, + "created": "2018-12-19T11:33:38.25", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 112841, + "object_name": "I Krp" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243506, + "object_name": "IK Skca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247848, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 139148, + "object_name": "Brain Rhythms" + }, + { + "object_id": 156115, + "object_name": "Gamma oscillations" + }, + { + "object_id": 152968, + "object_name": "Triggered activity" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 147931, + "object_name": "Orientation selectivity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 247732, + "object_name": "Arkhipov A, Gouwens NW, Billeh YN, Gratiy S, Iyer R, Wei Z, Xu Z, Abbasi-Asl R, Berg J, Buice M, Cain N, da Costa N, de Vries S, Denman D, Durand S, Feng D, Jarsky T, Lecoq J, Lee B, Li L, Mihalas S, Ocker GK, Olsen SR, Reid RC, Soler-Llavina G, Sorensen SA, Wang Q, Waters J, Scanziani M, Koch C (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 247847, + "object_name": "BioNet (web link to model)" + } + ] + }, + "name": "Visual physiology of the layer 4 cortical circuit in silico (Arkhipov et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 230185, + "object_name": "Neocortex V1 interneuron chandelier SOM GABA cell" + }, + { + "object_id": 7387, + "object_name": "Neocortex V1 interneuron basket PV GABA cell" + }, + { + "object_id": 184128, + "object_name": "Neocortex layer 4 pyramidal cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Despite advances in experimental techniques and accumulation of large datasets concerning\r\nthe composition and properties of the cortex, quantitative modeling of cortical circuits\r\nunder in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically\r\ndetailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-\r\ncortical visual inputs. The 45,000-neuron model was subjected to a battery of visual\r\nstimuli, and results were compared to published work and new in vivo experiments. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247848", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2018-12-21T13:34:39.693", + "ver_number": 4 + }, + "247968": { + "class_id": 19, + "created": "2018-12-28T23:44:36.007", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247968, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 247969, + "object_name": "Feng, Feng [ffvxb at mail.missouri.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 156115, + "object_name": "Gamma oscillations" + }, + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 247972, + "object_name": "Feng F, Headley DB , Amir A, Kanta V, Chen Z, Pare D, Nair S (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Gamma genesis in the basolateral amygdala (Feng et al 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 230, + "object_name": "Norephinephrine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Using in vitro and in vivo data we develop the first large-scale biophysically and anatomically realistic model of the basolateral amygdala nucleus (BL), which reproduces the dynamics of the in vivo local field potential (LFP). Significantly, it predicts that BL intrinsically generates the transient gamma oscillations observed in vivo. The model permitted exploration of the poorly understood synaptic mechanisms underlying gamma genesis in BL, and the model's ability to compute LFPs at arbitrary numbers of recording sites provided insights into the characteristics of the spatial properties of gamma bursts. Furthermore, we show how gamma synchronizes principal cells to overcome their low firing rates while simultaneously promoting competition, potentially impacting their afferent selectivity and efferent drive, and thus emotional behavior." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247968", + "other_currents": { + "attr_id": 303, + "value": "I K-DR" + }, + "other_implementer": { + "attr_id": 308, + "value": "ffvxb@mail.missouri.edu" + }, + "other_neurons": { + "attr_id": 301, + "value": "basolateral amygdala neurons" + }, + "other_type": { + "attr_id": 305, + "value": "LFP" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ffvxb@mail.missouri.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Feng Feng" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + }, + { + "object_id": 223, + "object_name": "Dopaminergic Receptor" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 142628, + "object_name": "Amygdala" + } + ] + }, + "ver_date": "2022-05-27T13:21:49.687", + "ver_number": 11 + }, + "247970": { + "class_id": 19, + "created": "2019-01-02T01:10:58.037", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 247970, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 253470, + "object_name": "Lian, Yanbo [yanbol at student.unimelb.edu.au]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 180821, + "object_name": "Hebbian plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 253469, + "object_name": "Lian Y, Grayden DB, Kameneva T, Meffin H, Burkitt AN (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Towards a biologically plausible model of LGN-V1 pathways (Lian et al 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 245940, + "object_name": "Abstract rate-based neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Increasing evidence supports the hypothesis that the visual system\r\nemploys a sparse code to represent visual stimuli, where information\r\nis encoded in an efficient way by a small population of cells that\r\nrespond to sensory input at a given time. This includes simple cells\r\nin primary visual cortex (V1), which are defined by their linear\r\nspatial integration of visual stimuli. Various models of sparse coding\r\nhave been proposed to explain physiological phenomena observed in\r\nsimple cells. However, these models have usually made the simplifying\r\nassumption that inputs to simple cells already incorporate linear\r\nspatial summation. This overlooks the fact that these inputs are known\r\nto have strong non-linearities such as the separation of ON and OFF\r\npathways, or separation of excitatory and inhibitory\r\nneurons. Consequently these models ignore a range of important\r\nexperimental phenomena that are related to the emergence of linear\r\nspatial summation from non-linear inputs, such as segregation of ON\r\nand OFF sub-regions of simple cell receptive fields, the push-pull\r\neffect of excitation and inhibition, and phase-reversed\r\ncortico-thalamic feedback. Here, we demonstrate that a two-layer model\r\nof the visual pathway from the lateral geniculate nucleus to V1 that\r\nincorporates these biological constraints on the neural circuits and\r\nis based on sparse coding can account for the emergence of these\r\nexperimental phenomena, diverse shapes of receptive fields and\r\ncontrast invariance of orientation tuning of simple cells when the\r\nmodel is trained on natural images. The model suggests that sparse\r\ncoding can be implemented by the V1 simple cells using neural circuits\r\nwith a simple biologically plausible architecture.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/247970", + "other_implementer": { + "attr_id": 308, + "value": "Yanbo Lian" + }, + "other_neurons": { + "attr_id": 301, + "value": "V1 simple cells" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "yanbol@student.unimelb.edu.au" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Yanbo Lian" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2019-03-15T15:04:09.13", + "ver_number": 12 + }, + "248313": { + "class_id": 19, + "created": "2019-01-05T15:08:17.32", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 87769, + "object_name": "ATP-senstive potassium current" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 248313, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 253076, + "object_name": "Althaf, Athil A Z " + }, + { + "object_id": 253077, + "object_name": "Mishra, Urvi " + }, + { + "object_id": 253078, + "object_name": "Konnur, Seeraja [seeraja.dsa at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 225300, + "object_name": "Electrical-chemical" + }, + { + "object_id": 136106, + "object_name": "G-protein coupled" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 252391, + "object_name": "Fridlyand LE, Philipson LH (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Pancreatic Beta Cell signalling pathways (Fridlyand & Philipson 2016) (MATLAB)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + }, + { + "object_id": 230, + "object_name": "Norephinephrine" + }, + { + "object_id": 229, + "object_name": "Ephinephrine" + }, + { + "object_id": 236, + "object_name": "Ions" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a 3rd party implementation of Fridlyand & Philipson 2016 who's abstract begins \"Insulin secretory in pancreatic beta-cells responses to nutrient\r\nstimuli and hormonal modulators include multiple messengers and\r\nsignaling pathways with complex interdependencies. Here we present a\r\ncomputational model that incorporates recent data on glucose\r\nmetabolism, plasma membrane potential, G-protein-coupled-receptors\r\n(GPCR), cytoplasmic and endoplasmic reticulum calcium dynamics, cAMP\r\nand phospholipase C pathways that regulate interactions between second\r\nmessengers in pancreatic beta-cells. The values of key model\r\nparameters were inferred from published experimental data. The model\r\ngives a reasonable fit to important aspects of experimentally measured\r\nmetabolic and second messenger concentrations and provides a framework\r\nfor analyzing the role of metabolic, hormones and neurotransmitters\r\nchanges on insulin secretion. Our analysis of the dynamic data\r\nprovides support for the hypothesis that activation of Ca2+-dependent\r\nadenylyl cyclases play a critical role in modulating the effects of\r\nglucagon-like peptide 1 (GLP-1), glucose-dependent insulinotropic\r\npolypeptide (GIP) and catecholamines. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/248313", + "other_currents": { + "attr_id": 303, + "value": "Na+, K+ channels" + }, + "other_implementer": { + "attr_id": 308, + "value": "Athil Althaf A Z, Urvi Mishra and Seeraja Konnur" + }, + "other_receptors": { + "attr_id": 302, + "value": "GLP-1R, GIPR (GPCR)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "seeraja.dsa@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Seeraja Konnur" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 215, + "object_name": "Adrenergic" + }, + { + "object_id": 204, + "object_name": "Muscarinic" + }, + { + "object_id": 153715, + "object_name": "IP3" + }, + { + "object_id": 180, + "object_name": "M3" + }, + { + "object_id": 218, + "object_name": "Cholinergic Receptors" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 227987, + "object_name": "Human" + } + ] + }, + "ver_date": "2019-03-11T14:56:31.433", + "ver_number": 33 + }, + "249404": { + "citation": { + "attr_id": 391, + "value": "Fleidervish IA, Lasser-Ross N, Gutnick MJ, Ross WN (2010) Na+ imaging reveals little difference in action potential-evoked Na+ influx between axon and soma. Nat Neurosci 13:852-60" + }, + "class_id": 19, + "created": "2019-01-10T12:41:18.737", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 249404, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 146951, + "object_name": "McDougal, Robert [robert.mcdougal at yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 127651, + "object_name": "Fleidervish IA, Lasser-Ross N, Gutnick MJ, Ross WN (2010)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Action potential-evoked Na+ influx similar in axon and soma (Fleidervish et al. 2010) (Python)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 167343, + "object_name": "Neocortex U1 L5B pyramidal pyramidal tract GLU cell" + }, + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"In cortical pyramidal neurons, the axon initial segment (AIS) is pivotal in synaptic integration. It has been asserted that this is because there is a high density of Na+ channels in the AIS. However, we found that action potential-associated Na+ flux, as measured by high-speed fluorescence Na+ imaging, was about threefold larger in the rat AIS than in the soma. Spike-evoked Na+ flux in the AIS and the first node of Ranvier was similar and was eightfold lower in basal dendrites. ... In computer simulations, these data were consistent with the known features of action potential generation in these neurons.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/249404", + "other_neurons": { + "attr_id": 301, + "value": "L5 pyramidal neurons from somatosensory cortex" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "robert.mcdougal@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Robert A McDougal" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2019-10-10T17:21:30.837", + "ver_number": 11 + }, + "249405": { + "class_id": 19, + "created": "2019-01-10T18:29:08.083", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 249405, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 22012, + "object_name": "Depression" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 249417, + "object_name": "Pinto TM, Schilstra MJ, Roque AC, Steuber V (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "A Computational Model of Bidirectional Plasticity Regulation by betaCaMKII (Pinto et al. 2019)", + "notes": { + "attr_id": 24, + "value": "We present a computational model that suggests how calcium-calmodulin dependent protein kinase II can act as a molecular switch in synaptic plasticity induction at an important cerebellar synapse (between parallel fibres and Purkinje cells). Our simulation results provide a potential explanation for experimental data by van Woerden et al (Van Woerden G, Hoebeek F, Gao Z, Nagaraja R, Hoogenraad C, Kushner S, et al. [beta]CaMKII controls the direction of plasticity at parallel fiber-Purkinje cell synapses. Nat Neurosci. 2009;12(7):823-825). These experiments were performed in the lab led by Professor Chris De Zeeuw." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/249405", + "public_submitter_email": { + "attr_id": 309, + "value": "thiago.pinto@ifrj.edu.br" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Thiago Matos Pinto" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2019-01-14T12:25:12.163", + "ver_number": 5 + }, + "249408": { + "citation": { + "attr_id": 391, + "value": "PMID: 30414556" + }, + "class_id": 19, + "created": "2019-01-11T09:34:36.687", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 249408, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 249412, + "object_name": "Suryanarayana, Shreyas [shreyas.suryanarayana at ki.se]" + }, + { + "object_id": 33786, + "object_name": "Gurney, Kevin [K.Gurney at shef.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 249401, + "object_name": "Suryanarayana SM, Hellgren Kotaleski J, Grillner S, Gurney KN (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A computational model of action selection in the basal ganglia (Suryanarayana et al 2019)", + "notes": { + "attr_id": 24, + "value": "\" ... Here, we incorporate newly revealed subgroups of neurons\r\nwithin the GPe into an existing computational model of the basal\r\nganglia, and investigate their role in action selection. Three\r\nmain results ensued. First, using previously used metrics for\r\nselection, the new extended connectivity improved the action\r\nselection performance of the model. Second, low frequency theta\r\noscillations were observed in the subpopulation of the GPe (the\r\nTA or \u2018arkypallidal\u2019 neurons) which project exclusively to the\r\nstriatum. These oscillations were suppressed by increased\r\ndopamine activity \u2014 revealing a possible link with symptoms of\r\nParkinson\u2019s disease. Third, a new phenomenon was observed in\r\nwhich the usual monotonic relationship between input to the basal\r\nganglia and its output within an action \u2018channel\u2019 was, under some\r\ncircumstances, reversed.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/249408", + "public_submitter_email": { + "attr_id": 309, + "value": "shreyas.suryanarayana@ki.se" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Shreyas Suryanarayana" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 138633, + "object_name": "Striatum" + }, + { + "object_id": 115952, + "object_name": "Basal ganglia" + }, + { + "object_id": 249414, + "object_name": "Globus pallidus externa (GPe)" + } + ] + }, + "ver_date": "2019-01-12T10:30:38.237", + "ver_number": 9 + }, + "249463": { + "class_id": 19, + "created": "2019-01-17T11:12:58.1", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 249463, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 147118, + "object_name": "Maki-Marttunen, Tuomo [tuomomm at uio.no]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 112853, + "object_name": "Schizophrenia" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 258952, + "object_name": "M\u00e4ki-Marttunen T, Devor A, Phillips WA, Dale AM, Andreassen OA, Einevoll GT (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Layer V pyramidal cell functions and schizophrenia genetics (M\u00e4ki-Marttunen et al 2019)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Study on how GWAS-identified risk genes of shizophrenia affect excitability and integration of inputs in thick-tufted layer V pyramidal cells" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/249463", + "public_submitter_email": { + "attr_id": 309, + "value": "tuomo@simula.no" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tuomo M\u00e4ki-Marttunen" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2023-01-27T10:37:21.41", + "ver_number": 11 + }, + "249589": { + "citation": { + "attr_id": 391, + "value": "J. F. Mejias, J. D. Murray, H. Kennedy and X.-J Wang, Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex, Science Advances, 2, e1601335, 2016." + }, + "class_id": 19, + "created": "2019-01-24T11:39:40.743", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 249589, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 249591, + "object_name": "Mejias, Jorge [j.f.mejias at uva.nl]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 249590, + "object_name": "Mejias JF, Murray JD, Kennedy H, Wang XJ (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Large-scale laminar model of macaque cortex (Mejias et al 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 245940, + "object_name": "Abstract rate-based neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This code reproduces the large-scale cortical model with laminar structure presented in Mejias et al., Science Advances 2016. The model includes different scales (intra-laminar, inter-laminar, inter-areal and large-scale) across macaque neocortex and reproduces experimentally observed dynamics of gamma and alpha/beta oscillations across these scales. It makes use of real anatomical data from the macaque cortex. Some parts of the code require external packages or data (see readme file for details)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/249589", + "other_implementer": { + "attr_id": 308, + "value": "Jorge Mejias" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "j.f.mejias@uva.nl" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Jorge Mejias" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2019-01-28T15:22:47.053", + "ver_number": 4 + }, + "249705": { + "class_id": 19, + "created": "2019-02-08T17:15:02.497", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 249705, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33780, + "object_name": "Antic, Srdjan [antic at neuron.uchc.edu]" + }, + { + "object_id": 249707, + "object_name": "Gao, Peng [peng at uchc.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 169987, + "object_name": "Dendritic Bistability" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 206291, + "object_name": "Membrane Properties" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266826, + "object_name": "Gao PP, Graham JW, Zhou WL, Jang J, Angulo SL, Dura-Bernal S, Hines ML, Lytton W, Antic SD (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Glutamate mediated dendritic and somatic plateau potentials in cortical L5 pyr cells (Gao et al '20)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Our model was built on a reconstructed Layer 5 pyramidal neuron of the rat medial prefrontal cortex, and constrained by 4 sets of experimental data: (i) voltage waveforms obtained at the site of the glutamatergic input in distal basal dendrite, including initial sodium spikelet, fast rise, plateau phase and abrupt collapse of the plateau; (ii) a family of voltage traces describing dendritic membrane responses to gradually increasing intensity of glutamatergic stimulation; (iii) voltage waveforms of backpropagating action potentials in basal dendrites (Antic, 2003); and (iv) the change of backpropagating action potential amplitude in response to drugs that block Na+ or K+ channels (Acker and Antic, 2009). Both, synaptic AMPA/NMDA and extrasynaptic NMDA inputs were placed on basal dendrites to model the induction of local regenerative potentials termed \"glutamate-mediated dendritic plateau potentials\". The active properties of the cell were tuned to match the voltage waveform, amplitude and duration of experimentally observed plateau potentials. The effects of input location, receptor conductance, channel properties and membrane time constant during plateau were explored. The new model predicted that during dendritic plateau potential the somatic membrane time constant is reduced. This and other model predictions were then tested in real neurons. Overall, the results support our theoretical framework that dendritic plateau potentials bring neuronal cell body into a depolarized state (\"UP state\"), which lasts 200 - 500 ms, or more. Plateau potentials profoundly change neuronal state -- a plateau potential triggered in one basal dendrite depolarizes the soma and shortens membrane time constant, making the cell more susceptible to action potential firing triggered by other afferent inputs. Plateau potentials may allow cortical pyramidal neurons to tune into ongoing network activity and potentially enable synchronized firing, to form active neural ensembles." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/249705", + "other_implementer": { + "attr_id": 308, + "value": "Gao, Peng [penggao.1987 at mail.com]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "penggao.1987@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Peng Penny Gao" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 212, + "object_name": "Glutamate" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + }, + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2020-10-28T12:26:49.15", + "ver_number": 10 + }, + "249706": { + "class_id": 19, + "created": "2019-02-09T16:07:29.21", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 249706, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 253378, + "object_name": "Bono J, Clopath C (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Inhibitory neuron plasticity as a mechanism for ocular dominance plasticity (Bono & Clopath 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 245940, + "object_name": "Abstract rate-based neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Ocular dominance plasticity is a well-documented phenomenon\r\nallowing us to study properties of cortical\r\nmaturation. Understanding this maturation might be an important\r\nstep towards unravelling how cortical circuits function. However,\r\nit is still not fully understood which mechanisms are responsible\r\nfor the opening and closing of the critical period for ocular\r\ndominance and how changes in cortical responsiveness arise after\r\nvisual deprivation. In this article, we present a theory of\r\nocular dominance plasticity. Following recent experimental work,\r\nwe propose a framework where a reduction in inhibition is\r\nnecessary for ocular dominance plasticity in both juvenile and\r\nadult animals. In this framework, two ingredients are crucial to\r\nobserve ocular dominance shifts: a sufficient level of inhibition\r\nas well as excitatory-to-inhibitory synaptic plasticity. In our\r\nmodel, the former is responsible for the opening of the critical\r\nperiod, while the latter limits the plasticity in adult\r\nanimals. Finally, we also provide a possible explanation for the\r\nvariability in ocular dominance shifts observed in individual\r\nneurons and for the counter-intuitive shifts towards the closed\r\neye.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/249706", + "public_submitter_email": { + "attr_id": 309, + "value": "jacopobono@hotmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Jacopo Bono" + }, + "ver_date": "2019-03-14T12:20:27", + "ver_number": 5 + }, + "249847": { + "citation": { + "attr_id": 391, + "value": "Solbra et al" + }, + "class_id": 19, + "created": "2019-02-13T13:54:13.14", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 249847, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 84598, + "object_name": "Extracellular Fields" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 249827, + "object_name": "Solbr\u00e5 A, Bergersen AW, van den Brink J, Malthe-S\u00f8renssen A, Einevoll GT, Halnes G (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 249848, + "object_name": "KNPsim (web link to method)" + }, + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "Modelling large scale electrodiffusion near morphologically detailed neurons (Solbra et al 2018)", + "notes": { + "attr_id": 24, + "value": "\" ... Here, we present the 3-D Kirchhoff-Nernst-Planck (KNP) framework, tailored to explore electrodiffusive effects on large spatiotemporal scales. By assuming electroneutrality, the KNP-framework circumvents charge-relaxation processes on the spatiotemporal scales of nanometers and nanoseconds, and makes it feasible to run simulations on the spatiotemporal scales of millimeters and seconds on a standard desktop computer. In the present work, we use the 3-D KNP framework to simulate the dynamics of ion concentrations and the electrical potential surrounding a morphologically detailed pyramidal cell. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/249847", + "other_simulator": { + "attr_id": 307, + "value": "KNPsim" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2019-02-13T14:31:01.057", + "ver_number": 3 + }, + "251493": { + "class_id": 19, + "created": "2019-03-05T11:36:30.043", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 251493, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 261589, + "object_name": "Ebner, Christian [ebner at fias.uni-frankfurt.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 261588, + "object_name": "Ebner C, Clopath C, Jedlicka P, Cuntz H (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Four-pathway phenomenological synaptic plasticity model (Ebner et al. 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 227971, + "object_name": "Neocortex V1 L5B pyramidal pyramidal tract GLU cell" + } + ] + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/251493", + "public_submitter_email": { + "attr_id": 309, + "value": "ebner@fias.uni-frankfurt.de" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Christian Ebner" + }, + "ver_date": "2020-01-16T18:31:36.46", + "ver_number": 13 + }, + "251881": { + "citation": { + "attr_id": 391, + "value": "Iavarone, Elisabetta, Jane Yi, Ying Shi, Bas-Jan Zandt, Christian O'reilly, Werner Van Geit, Christian R\u00f6ssert, Henry Markram, and Sean L. Hill. \"Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons.\" BioRxiv (2019)." + }, + "class_id": 19, + "created": "2019-03-06T04:33:26.46", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 251881, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 256561, + "object_name": "Iavarone, Elisabetta [elisabetta.iavarone01 at ateneopv.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 116962, + "object_name": "Parameter sensitivity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 259033, + "object_name": "Iavarone E, Yi J, Shi Y, Zandt BJ, O'Reilly C, Van Geit W, R\u00f6ssert C, Markram H, Hill SL (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 245004, + "object_name": "BluePyOpt\r\n" + } + ] + }, + "name": "Thalamocortical relay neuron models constrained by experiment and optimization (Iavarone et al 2019)", + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/251881", + "other_concept": { + "attr_id": 306, + "value": "Rat" + }, + "other_currents": { + "attr_id": 303, + "value": "I K,Ca SK-type" + }, + "other_implementer": { + "attr_id": 308, + "value": "Iavarone, Elisabetta [elisabetta.iavarone01 at ateneopv.it]" + }, + "other_neurons": { + "attr_id": 301, + "value": "Thalamus ventrobasal nucleus neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "elisabetta.iavarone@epfl.ch" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Elisabetta Iavarone" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2019-05-03T07:24:21.813", + "ver_number": 27 + }, + "253369": { + "citation": { + "attr_id": 391, + "value": "A Lombardi, P Jedlicka, HJ Luhmann, W Kilb\r\nInteractions between membrane resistance, GABA-A receptor properties, bicarbonate dynamics and Cl--transport shape activity-dependent changes of intra-cellular Cl- concentration.\r\nINt J Mol Sci (2019) in revision" + }, + "class_id": 19, + "created": "2019-03-14T05:58:48.69", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 253369, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 253492, + "object_name": "Lombardi A, Jedlicka P, Luhmann HJ, Kilb W (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Factors contribution to GDP-induced [Cl-]i transients (Lombardi et al 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This models are used to evaluate which factors influence the GDP (giant depolarizing potential) induced [Cl-]I transients based on a initial model of P. Jedlicka" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/253369", + "other_concept": { + "attr_id": 306, + "value": "Ionic plasticity, [Cl-]i-" + }, + "other_gene": { + "attr_id": 477, + "value": "NKCC1 (SLC12A2)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "wkilb@uni-mainz.de" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Werner Kilb" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228598, + "object_name": "Mouse" + }, + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2019-03-19T17:45:41.087", + "ver_number": 5 + }, + "253624": { + "citation": { + "attr_id": 391, + "value": "Bittner SR, Williamson RC, Snyder AC, Litwin-Kumar A, Doiron B, Chase SM, Smith MA, Yu BM (2017) Population activity structure of excitatory and inhibitory neurons. PLoS One 12:e0181773" + }, + "class_id": 19, + "created": "2019-03-22T15:50:05.793", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 253624, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 253612, + "object_name": "Bittner SR, Williamson RC, Snyder AC, Litwin-Kumar A, Doiron B, Chase SM, Smith MA, Yu BM (2017)" + }, + { + "object_id": 155094, + "object_name": "Litwin-Kumar A, Doiron B (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 253623, + "object_name": "Julia (web link to model)" + } + ] + }, + "name": "Excitatory and inhibitory population activity (Bittner et al 2017) (Litwin-Kumar & Doiron 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Many studies use population analysis approaches, such as\r\ndimensionality reduction, to characterize the activity of large groups\r\nof neurons. To date, these methods have treated each neuron equally,\r\nwithout taking into account whether neurons are excitatory or\r\ninhibitory. We studied population activity structure as a function of\r\nneuron type by applying factor analysis to spontaneous activity from\r\nspiking networks with balanced excitation and inhibition.\r\nThroughout\r\nthe study, we characterized population activity structure by measuring\r\nits dimensionality and the percentage of overall activity variance\r\nthat is shared among neurons. First, by sampling only excitatory or\r\nonly inhibitory neurons, we found that the activity structures of\r\nthese two populations in balanced networks are measurably\r\ndifferent. We also found that the population activity structure is\r\ndependent on the ratio of excitatory to inhibitory neurons\r\nsampled. Finally we classified neurons from extracellular recordings\r\nin the primary visual cortex of anesthetized macaques as putative\r\nexcitatory or inhibitory using waveform classification, and found\r\nsimilarities with the neuron type-specific population activity\r\nstructure of a balanced network with excitatory clustering. These\r\nresults imply that knowledge of neuron type is important, and allows\r\nfor stronger statistical tests, when interpreting population activity\r\nstructure.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/253624", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2019-03-22T16:24:32.067", + "ver_number": 5 + }, + "253991": { + "citation": { + "attr_id": 391, + "value": "Djurisic et al 2004" + }, + "class_id": 19, + "created": "2019-03-29T17:33:38.613", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 253991, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 64427, + "object_name": "Djurisic M, Antic S, Chen WR, Zecevic D (2004)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "name": "Voltage imaging calibration in tuft dendrites of mitral cells (Djurisic et al 2004)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A detailed morphology of a tuft is provided in a reconstruction of a mitral cell that was used to place simulated estimates on for the calibration of EPSPs as recorded in voltage imaging in the real cells (estimated to be within +12% to -18% of the actual amplitude)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/253991", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - 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Some neurons can store information for short periods (seconds to minutes) by continuing to spike after a stimulus ends, thus enabling working memory. This so-called \u201cpersistent\u201d spiking occurs in many brain areas and has been linked to activation of canonical transient receptor potential (TRPC) channels. However, TRPC activation alone is insufficient to explain many aspects of persistent spiking such as resumption of spiking after periods of imposed quiescence. Using experiments and simulations, we show that calcium influx caused by spiking is necessary and sufficient to activate TRPC channels and that the ensuing positive feedback interaction between intracellular calcium and TRPC channel activation can account for many hitherto unexplained aspects of persistent spiking.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/255569", + "other_neurons": { + "attr_id": 301, + "value": "ACC neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "ver_date": "2019-04-05T18:00:28.543", + "ver_number": 10 + }, + "256021": { + "class_id": 19, + "created": "2019-04-08T16:33:13.853", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 246, + "object_name": "I p,q" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 88212, + "object_name": "KCNQ1" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 167500, + "object_name": "Kir" + }, + { + "object_id": 88215, + "object_name": "I_Ks" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 88117, + "object_name": "I_KHT" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92082, + "object_name": "Nav1.1 SCN1A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 256021, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33781, + "object_name": "Manis, Paul B [PManis at med.unc.edu]" + }, + { + "object_id": 256022, + "object_name": "Compagnola, Luke " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 136098, + "object_name": "Information transfer" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 255940, + "object_name": "Manis PB, Campagnola L (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Modelling platform of the cochlear nucleus and other auditory circuits (Manis & Compagnola 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 274, + "object_name": "Cochlear nucleus pyramidal/fusiform GLU cell" + }, + { + "object_id": 277, + "object_name": "Cochlear nucleus octopus GLU cell" + }, + { + "object_id": 273, + "object_name": "Cochlear nucleus bushy GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Models of the auditory brainstem have been an invaluable tool for testing hypotheses about auditory information processing and for highlighting the most important gaps in the experimental literature. Due to the complexity of the auditory brainstem, and indeed most brain circuits, the dynamic behavior of the system may be difficult to predict without a detailed, biologically realistic computational model. Despite the sensitivity of models to their exact construction and parameters, most prior models of the cochlear nucleus have incorporated only a small subset of the known biological properties. This confounds the interpretation of modelling results and also limits the potential future uses of these models, which require a large effort to develop. To address these issues, we have developed a general purpose, bio-physically detailed model of the cochlear nucleus for use both in testing hypotheses about cochlear nucleus function and also as an input to models of downstream auditory nuclei. The model implements conductance-based Hodgkin-Huxley representations of cells using a Python-based interface to the NEURON simulator. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/256021", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 1361, + "object_name": "Glycine" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 183076, + "object_name": "Brainstem" + }, + { + "object_id": 143145, + "object_name": "Auditory brainstem" + } + ] + }, + "ver_date": "2019-04-08T16:41:52.12", + "ver_number": 3 + }, + "256024": { + "class_id": 19, + "created": "2019-04-08T22:47:29.087", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 256024, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 240367, + "object_name": "Dewell, Richard Burkett [dewell at bcm.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 206291, + "object_name": "Membrane Properties" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 258232, + "object_name": "Dewell RB, Gabbiani F (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "LGMD impedance (Dewell & Gabbiani 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 116948, + "object_name": "Locust Lobula Giant Movement Detector (LGMD) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"How neurons filter and integrate their complex patterns of synaptic inputs is central to their role in neural information processing . Synaptic filtering and integration are shaped by the frequency-dependent neuronal membrane impedance. Using single and dual dendritic recordings in vivo, pharmacology, and computational modeling, we characterized the membrane impedance of a collision detection neuron in the grasshopper, Schistocerca americana. This neuron, the lobula giant movement detector (LGMD), exhibits consistent impedance properties across frequencies and membrane potentials. Two common active conductances gH and gM, mediated respectively by hyperpolarization-activated cyclic nucleotide gated (HCN) channels and by muscarine sensitive M-type K+ channels, promote broadband integration with high temporal precision over the LGMD's natural range of membrane potentials and synaptic input frequencies. Additionally, we found that a model based on the LGMD's branching morphology increased the gain and decreased the delay associated with the mapping of synaptic input currents to membrane potential. More generally, this was true for a wide range of model neuron morphologies, including those of neocortical pyramidal neurons and cerebellar Purkinje cells. These findings show the unexpected role played by two widespread active conductances and by dendritic morphology in shaping synaptic integration.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/256024", + "other_currents": { + "attr_id": 303, + "value": "inductive leak" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "dewell@bcm.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Richard Dewell" + }, + "ver_date": "2019-08-15T16:03:40.203", + "ver_number": 14 + }, + "256028": { + "citation": { + "attr_id": 391, + "value": "Telenczuk M, Fontaine B, Brette R (2017) The basis of sharp spike onset in standard biophysical models. PLoS One 12:e0175362" + }, + "class_id": 19, + "created": "2019-04-09T15:51:37.597", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 256028, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 241794, + "object_name": "Telenczuk, Maria [mtelenczuk at unic.cnrs-gif.fr]" + }, + { + "object_id": 256032, + "object_name": "Fontaine, Bertrand " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 241823, + "object_name": "Telenczuk M, Fontaine B, Brette R (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "The basis of sharp spike onset in standard biophysical models (Telenczuk et al 2017)", + "notes": { + "attr_id": 24, + "value": "\"In most vertebrate neurons, spikes initiate in the axonal initial segment (AIS). When recorded in the soma, they have a surprisingly sharp onset, as if sodium (Na) channels opened abruptly. The main view stipulates that spikes initiate in a conventional manner at the distal end of the AIS, then progressively sharpen as they backpropagate to the soma. We examined the biophysical models used to substantiate this view, and we found that spikes do not initiate through a local axonal current loop that propagates along the axon, but through a global current loop encompassing the AIS and soma, which forms an electrical dipole. Therefore, the phenomenon is not adequately modeled as the backpropagation of an electrical wave along the axon, since the wavelength would be as large as the entire system. Instead, in these models, we found that spike initiation rather follows the critical resistive coupling model proposed recently, where the Na current entering the AIS is matched by the axial resistive current flowing to the soma. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/256028", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2019-04-09T17:40:23", + "ver_number": 5 + }, + "256140": { + "class_id": 19, + "created": "2019-04-15T14:44:20.257", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 256140, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 150226, + "object_name": "Luque, Niceto R. [nluque at ugr.es]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 93420, + "object_name": "Sleep" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 245693, + "object_name": "Vestibular" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 256062, + "object_name": "Luque NR, Naveros F, Carrillo RR, Ros E, Arleo A (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 150203, + "object_name": "EDLUT" + }, + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation (Luque et al 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + }, + { + "object_id": 7346, + "object_name": "Cerebellum interneuron granule GLU cell" + }, + { + "object_id": 139834, + "object_name": "Vestibular neuron" + }, + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Cerebellar Purkinje cells mediate accurate eye movement\r\ncoordination. However, it remains unclear how oculomotor\r\nadaptation depends on the interplay between the characteristic\r\nPurkinje cell response patterns, namely tonic, bursting, and\r\nspike pauses. Here, a spiking cerebellar model assesses the role\r\nof Purkinje cell firing patterns in vestibular ocular\r\nreflex (VOR) adaptation. The model captures the cerebellar\r\nmicrocircuit properties and it incorporates spike-based synaptic\r\nplasticity at multiple cerebellar sites. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/256140", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2019-04-22T10:57:22.16", + "ver_number": 8 + }, + "256311": { + "class_id": 19, + "created": "2019-04-25T07:24:41.113", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 256311, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206377, + "object_name": "Chavlis, Spyridon [schavlis at imbb.forth.gr]" + }, + { + "object_id": 249698, + "object_name": "Pandi, Ioanna " + }, + { + "object_id": 33746, + "object_name": "Poirazi, Panayiota [poirazi at imbb.forth.gr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 182690, + "object_name": "Spatial Navigation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 261938, + "object_name": "Shuman T, Aharoni D, Cai DJ, Lee CR, Chavlis S, Page-Harley L, Vetere LM, Feng Y, Yang CY, Mollinedo-Gajate I, Chen L, Pennington ZT, Taxidis J, Flores SE, Cheng K, Javaherian M, Kaba CC, Rao N, La-Vu M, Pandi I, Shtrahman M, Bakhurin KI, Masmanidis SC, Khakh BS, Poirazi P, Silva AJ, Golshani P (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 113733, + "object_name": "Brian" + } + ] + }, + "name": "CA1 network model: interneuron contributions to epileptic deficits (Shuman et al 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 249699, + "object_name": "Hippocampal CA1 CR/VIP cell" + }, + { + "object_id": 151330, + "object_name": "Hippocampus CA1 axo-axonic cell" + }, + { + "object_id": 114079, + "object_name": "Hippocampus CA1 basket cell" + }, + { + "object_id": 227332, + "object_name": "Hippocampus CA1 basket cell - CCK/VIP" + }, + { + "object_id": 190560, + "object_name": "Hippocampus CA1 stratum oriens lacunosum-moleculare interneuron " + }, + { + "object_id": 151329, + "object_name": "Hippocampus CA1 bistratified cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Temporal lobe epilepsy causes significant cognitive deficits in both humans and rodents, yet the specific circuit mechanisms underlying these deficits remain unknown. There are profound and selective interneuron death and axonal reorganization within the hippocampus of both humans and animal models of temporal lobe epilepsy.\r\nTo assess the specific contribution of these mechanisms on spatial coding, we developed a biophysically constrained network model of the CA1 region that consists of different subtypes of interneurons. More specifically, our network consists of 150 cells, 130 excitatory pyramidal cells and 20 interneurons (Fig. 1A). To simulate place cell formation in the network model, we generated grid cell and place cell inputs from the Entorhinal Cortex (ECLIII) and CA3 regions, respectively, activated in a realistic manner as observed when an animal transverses a linear track. Realistic place fields emerged in a subpopulation of pyramidal cells (40-50%), in which similar EC and CA3 grid cell inputs converged onto distal/proximal apical and basal dendrites. The tuning properties of these cells are very similar to the ones observed experimentally in awake, behaving animals\r\nTo examine the role of interneuron death and axonal reorganization in the formation and/or tuning properties of place fields we selectively varied the contribution of each interneuron type and desynchronized the two excitatory inputs. We found that desynchronized inputs were critical in reproducing the experimental data, namely the profound reduction in place cell numbers, stability and information content. These results demonstrate that the desynchronized firing of hippocampal neuronal populations contributes to poor spatial processing in epileptic mice, during behavior. Given the lack of experimental data on the selective contributions of interneuron death and axonal reorganization in spatial memory, our model findings predict the mechanistic effects of these alterations at the cellular and network levels." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/256311", + "public_submitter_email": { + "attr_id": 309, + "value": "schavlis@ics.forth.gr" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Spyridon Chavlis" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2023-01-30T10:48:53.72", + "ver_number": 9 + }, + "256370": { + "citation": { + "attr_id": 391, + "value": "Masse et al 2018" + }, + "class_id": 19, + "created": "2019-04-25T11:33:38.52", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 256370, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 256368, + "object_name": "Masse, Nicolas Y [masse at uchicago.edu]" + }, + { + "object_id": 256369, + "object_name": "Grant, Gregory D [dfreedman at uchicago.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 256314, + "object_name": "Masse NY, Grant GD, Freedman DJ (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Alleviating catastrophic forgetting: context gating and synaptic stabilization (Masse et al 2018)", + "notes": { + "attr_id": 24, + "value": "\"Artificial neural networks can suffer from catastrophic forgetting, in which learning a new task causes the network to forget how to perform previous tasks. While previous studies have proposed various methods that can alleviate forgetting over small numbers (<10) of tasks, it is uncertain whether they can prevent forgetting across larger numbers of tasks. In this study, we propose a neuroscience-inspired scheme, called \u201ccontext-dependent gating,\u201d in which mostly nonoverlapping sets of units are active for any one task. Importantly, context-dependent gating has a straightforward implementation, requires little extra computational overhead, and when combined with previous methods to stabilize connection weights, can allow networks to maintain high performance across large numbers of sequentially presented tasks.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/256370", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "ver_date": "2019-04-25T11:44:45.967", + "ver_number": 3 + }, + "256388": { + "class_id": 19, + "created": "2019-04-29T05:01:23.753", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 256388, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 143720, + "object_name": "Bianchi, Daniela [danielabianchi12 -at- gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 112047, + "object_name": "Aging/Alzheimer`s" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 188516, + "object_name": "Memory" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 260767, + "object_name": "Pousinha PA, Mouska X, Bianchi D, Temido-Ferreira M, Raj\u00e3o-Saraiva J, Gomes R, Fernandez SP, Salgueiro-Pereira AR, Gandin C, Raymond EF, Barik J, Goutagny R, Bethus I, Lopes LV, Migliore M, Marie H (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "The APP in C-terminal domain alters CA1 neuron firing (Pousinha et al 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The amyloid precursor protein (APP) is central to AD pathogenesis and we recently showed that its intracellular domain (AICD) could modify synaptic signal integration. We now hypothezise that AICD modifies neuron firing activity, thus contributing to the disruption of memory processes. Using cellular, electrophysiological and behavioural techniques, we showed that pathological AICD levels weakens CA1 neuron firing activity through a gene transcription-dependent mechanism. Furthermore, increased AICD production in hippocampal neurons modifies oscillatory activity, specifically in the gamma frequency range, and disrupts spatial memory task. Collectively, our data suggest that AICD pathological levels, observed in AD mouse models and in human patients, might contribute to progressive neuron homeostatic failure, driving the shift from normal ageing to AD.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/256388", + "public_submitter_email": { + "attr_id": 309, + "value": "danielabianchi12@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Daniela Bianchi" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2022-05-27T15:26:16.007", + "ver_number": 8 + }, + "256610": { + "class_id": 19, + "created": "2019-05-03T12:38:35.327", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 256610, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 261492, + "object_name": "Ibanez, Sara [sibanezs at bu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 82787, + "object_name": "Working memory" + }, + { + "object_id": 83539, + "object_name": "Attractor Neural Network" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263611, + "object_name": "Iba\u00f1ez S, Luebke JI, Chang W, Draguljic D, Weaver CM (2019)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Bump Attractor Models: Delayed Response & Recognition Span - spatial condition (Ibanez et al 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 245940, + "object_name": "Abstract rate-based neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The archive contains examples of two spatial working memory tasks: the Delayed Response Task (DRT) or oculomotor task & the Delayed Recognition Span Task in the spatial condition (DRSTsp).\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/256610", + "public_submitter_email": { + "attr_id": 309, + "value": "sibanezs@bu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Sara Ibanez" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "ver_date": "2020-04-01T17:26:22.867", + "ver_number": 16 + }, + "256624": { + "citation": { + "attr_id": 391, + "value": "Tabas A, Andermann M, Schuberth V, Riedel H, Balaguer-Ballester E, Rupp A (2019) Modeling and MEG evidence of early consonance processing in auditory cortex. PLoS Comput Biol 15:e1006820" + }, + "class_id": 19, + "created": "2019-05-07T17:10:51.85", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 256624, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 256625, + "object_name": "Tabas, Alejandro [tabas at cbs.mpg.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 113742, + "object_name": "Magnetoencephalography" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 254079, + "object_name": "Tabas A, Andermann M, Schuberth V, Riedel H, Balaguer-Ballester E, Rupp A (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Modeling and MEG evidence of early consonance processing in auditory cortex (Tabas et al 2019)", + "notes": { + "attr_id": 24, + "value": "Pitch is a fundamental attribute of auditory perception. The interaction of concurrent pitches gives rise to a sensation that can be characterized by its degree of consonance or dissonance. In this work, we propose that human auditory cortex (AC) processes pitch and consonance through a common neural network mechanism operating at early cortical levels. First, we developed a new model of neural ensembles incorporating realistic neuronal and synaptic parameters to assess pitch processing mechanisms at early stages of AC. Next, we designed a magnetoencephalography (MEG) experiment to measure the neuromagnetic activity evoked by dyads with varying degrees of consonance or dissonance. MEG results show that dissonant dyads evoke a pitch onset response (POR) with a latency up to 36 ms longer than consonant dyads. Additionally, we used the model to predict the processing time of concurrent pitches; here, consonant pitch combinations were decoded faster than dissonant combinations, in line with the experimental observations. Specifically, we found a striking match between the predicted and the observed latency of the POR as elicited by the dyads. These novel results suggest that consonance processing starts early in human auditory cortex and may share the network mechanisms that are responsible for (single) pitch processing." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/256624", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 144485, + "object_name": "Auditory cortex" + } + ] + }, + "ver_date": "2019-05-14T15:57:40.727", + "ver_number": 6 + }, + "256627": { + "biosimulations": { + "value": "modeldb-256627" + }, + "citation": { + "attr_id": 391, + "value": "Ratte et al 2014" + }, + "class_id": 19, + "created": "2019-05-10T17:40:46.867", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 256627, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 254354, + "object_name": "Ratt\u00e9 S, Zhu Y, Lee KY, Prescott SA (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Criticality,degeneracy in injury-induced changes in primary afferent excitability (Ratte et al 2014)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 150222, + "object_name": "Abstract Morris-Lecar neuron" + }, + { + "object_id": 136307, + "object_name": "Dorsal Root Ganglion (DRG) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Neuropathic pain remains notoriously difficult to treat despite numerous drug targets. Here, we offer a novel explanation for this intractability. Computer simulations predicted that qualitative changes in primary afferent excitability linked to neuropathic pain arise through a switch in spike initiation dynamics when molecular pathologies reach a tipping point (criticality), and that this tipping point can be reached via several different molecular pathologies (degeneracy). ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/256627", + "other_concept": { + "attr_id": 306, + "value": "pain" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom MOrse" + }, + "ver_date": "2019-05-10T23:15:43.56", + "ver_number": 3 + }, + "256628": { + "citation": { + "attr_id": 391, + "value": "Balachandar and Prescott 2018" + }, + "class_id": 19, + "created": "2019-05-10T18:38:15.477", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 256628, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 143724, + "object_name": "Bifurcation" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 116962, + "object_name": "Parameter sensitivity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 254219, + "object_name": "Balachandar A, Prescott SA (2018)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Origin of heterogeneous spiking patterns in spinal dorsal horn neurons (Balachandar & Prescott 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 150222, + "object_name": "Abstract Morris-Lecar neuron" + }, + { + "object_id": 136307, + "object_name": "Dorsal Root Ganglion (DRG) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Neurons are often classified by spiking pattern. Yet, some\r\nneurons exhibit distinct patterns under subtly different test\r\nconditions, which suggests that they operate near an abrupt\r\ntransition, or bifurcation. A set of such neurons may exhibit\r\nheterogeneous spiking patterns not because of qualitative\r\ndifferences in which ion channels they express, but rather\r\nbecause quantitative differences in expression levels cause\r\nneurons to operate on opposite sides of a bifurcation. Neurons in\r\nthe spinal dorsal horn, for example, respond to somatic current\r\ninjection with patterns that include tonic, single, gap, delayed\r\nand reluctant spiking. It is unclear whether these patterns\r\nreflect five cell populations (defined by distinct ion channel\r\nexpression patterns), heterogeneity within a single population,\r\nor some combination thereof. We reproduced all five spiking\r\npatterns in a computational model by varying the densities of a\r\nlow-threshold (KV1-type) potassium conductance and an\r\ninactivating (A-type) potassium conductance and found that\r\nsingle, gap, delayed and reluctant spiking arise when the joint\r\nprobability distribution of those channel densities spans two\r\nintersecting bifurcations that divide the parameter space into\r\nquadrants, each associated with a different spiking\r\npattern.\r\n...\r\n\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/256628", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "tom morse" + }, + "ver_date": "2019-05-10T18:39:35.367", + "ver_number": 2 + }, + "256632": { + "class_id": 19, + "created": "2019-05-12T17:39:15.053", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 247, + "object_name": "I A" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92082, + "object_name": "Nav1.1 SCN1A" + }, + { + "object_id": 92087, + "object_name": "Nav1.6 SCN8A" + }, + { + "object_id": 92088, + "object_name": "Nav1.7 SCN9A" + }, + { + "object_id": 92089, + "object_name": "Nav1.8 SCN10A" + }, + { + "object_id": 122326, + "object_name": "Kv1.1 KCNA1" + }, + { + "object_id": 122327, + "object_name": "Kv1.2 KCNA2" + }, + { + "object_id": 122816, + "object_name": "Kv2.1 KCNB1" + }, + { + "object_id": 122817, + "object_name": "Kv3.1 KCNC1" + }, + { + "object_id": 122808, + "object_name": "Kv3.3 KCNC3" + }, + { + "object_id": 123580, + "object_name": "Kv3.4 KCNC4" + }, + { + "object_id": 122803, + "object_name": "Kv4.3 KCND3" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 256632, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 257396, + "object_name": "Zheng, Yang [zylittlep at gmail.com]" + }, + { + "object_id": 48423, + "object_name": "Bean, Bruce [bruce_bean at hms.harvard.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 97752, + "object_name": "Delay" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 206291, + "object_name": "Membrane Properties" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 257395, + "object_name": "Zheng Y, Liu P, Bai L, Trimmer JS, Bean BP, Ginty DD (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 150209, + "object_name": "R" + } + ] + }, + "name": "DRG neuron models investigate how ion channel levels regulate firing properties (Zheng et al 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 136307, + "object_name": "Dorsal Root Ganglion (DRG) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We present computational models for an Abeta-LTMR (low-threshold mechanoreceptor) and a C-LTMR expressing four Na channels and four K channels to investigate how the expression level of Kv1 and Kv4 regulate number of spikes (repetitive firing) and onset latency to action potentials in Abeta-LTMRs and C-LTMRs, respectively. \r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/256632", + "other_gene": { + "attr_id": 477, + "value": "KCNC2; KCNA6" + }, + "other_implementer": { + "attr_id": 308, + "value": "Yang Zheng\r\nBruce P. Bean" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "zylittlep@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Yang Zheng" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2019-06-24T17:07:51.94", + "ver_number": 13 + }, + "257027": { + "class_id": 19, + "created": "2019-05-30T05:55:08.503", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 257027, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 170033, + "object_name": "Migliore, Rosanna [rosanna.migliore at cnr.it]" + }, + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266924, + "object_name": "McCauley JP, Petroccione MA, D'Brant LY, Todd GC, Affinnih N, Wisnoski JJ, Zahid S, Shree S, Sousa AA, De Guzman RM, Migliore R, Brazhe A, Leapman RD, Khmaladze A, Semyanov A, Zuloaga DG, Migliore M, Scimemi A (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Circadian rhythmicity shapes astrocyte morphology and neuronal function in CA1 (McCauley et al 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 87589, + "object_name": "Astrocyte" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Most animal species operate according to a 24-hour period set by the suprachiasmatic nucleus (SCN) of the hypothalamus. The rhythmic activity of the SCN modulates hippocampal-dependent memory, but the molecular and cellular mechanisms that account for this effect remain largely unknown. In McCauley et al. 2020 [1], we identify cell-type specific structural and functional changes that occur with circadian rhythmicity in neurons and astrocytes in hippocampal area CA1. Pyramidal neurons change the surface expression of NMDA receptors. Astrocytes change their proximity clustered excitatory synaptic inputs, ultimately shaping hippocampal-dependent learning in vivo. We identify to synapses. Together, these phenomena alter glutamate clearance, receptor activation and integration of temporally corticosterone as a key contributor to changes in synaptic strength. These findings highlight important mechanisms through which neurons and astrocytes modify the molecular composition and structure of the synaptic environment, contribute to the local storage of information in the hippocampus and alter the temporal dynamics of cognitive processing. \r\n\r\n[1] \"Circadian modulation of neurons and astrocytes controls synaptic plasticity in hippocampal area CA1\" by J.P. McCauley, M.A. Petroccione, L.Y. D\u2019Brant, G.C. Todd, N. Affinnih, J.J. Wisnoski, S. Zahid, S. Shree, A.A. Sousa, R.M. De Guzman, R. Migliore, A. Brazhe, R.D. Leapman, A. Khmaladze, A. Semyanov, D.G. Zuloaga, M. Migliore and A. Scimemi. \r\nCell Reports (2020), https://doi.org/10.1016/j.celrep.2020.108255\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/257027", + "other_concept": { + "attr_id": 306, + "value": "CIRCADIAN RHYTHMICITY" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "michele.migliore@pa.ibf.cnr.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Michele Migliore" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2020-10-14T12:50:47.717", + "ver_number": 19 + }, + "257028": { + "citation": { + "attr_id": 391, + "value": "X. Zhang and S. Santaniello, Role of cerebellar GABAergic dysfunctions in the origins of essential tremor, Proc. Nat. Acad. Sci. USA (2019, in press)" + }, + "class_id": 19, + "created": "2019-06-04T20:55:07.47", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 257028, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 257222, + "object_name": "Zhang X, Santaniello S (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A cortico-cerebello-thalamo-cortical loop model under essential tremor (Zhang & Santaniello 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + }, + { + "object_id": 136179, + "object_name": "Cerebellum deep nucleus neuron" + }, + { + "object_id": 144503, + "object_name": "Inferior olive neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We investigated the origins of oscillations under essential tremor (ET) by building a computational model of the cortico-cerebello-thalamo-cortical loop. It showed that an alteration of amplitudes and decay times of the GABAergic currents to the dentate nucleus can facilitate sustained oscillatory activity at tremor frequency throughout the network as well as a robust bursting activity in the thalamus, which is consistent with observations of thalamic tremor cells in ET patients. Tremor-related oscillations initiated in small neural populations and spread to a larger network as the \r\nsynaptic dysfunction increased, while thalamic high-frequency stimulation suppressed tremor-related activity in thalamus but increased the oscillation frequency in the olivocerebellar loop." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/257028", + "other_neurons": { + "attr_id": 301, + "value": "Thalamocortical neurons" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "xu.3.zhang@uconn.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Xu Zhang" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + }, + { + "object_id": 115951, + "object_name": "Thalamus" + }, + { + "object_id": 183076, + "object_name": "Brainstem" + } + ] + }, + "ver_date": "2019-06-20T15:56:50.627", + "ver_number": 22 + }, + "257608": { + "biosimulations": { + "value": "modeldb-257608" + }, + "citation": { + "attr_id": 391, + "value": "J.E. Rubin and J.C. Smith, \"Robustness of respiratory rhythm generation across dynamic regimes\", PLoS Computational Biology, 2019" + }, + "class_id": 19, + "created": "2019-07-03T12:20:44.227", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 257608, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 125612, + "object_name": "Rubin, Jonathan E [jonrubin at pitt.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 232102, + "object_name": "Respiratory control" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 259059, + "object_name": "Rubin JE, Smith JC (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Respiratory central pattern generator (mammalian brainstem) (Rubin & Smith 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 232103, + "object_name": "PreBotzinger complex neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model includes a conditional respiratory pacemaker unit (representing the pre-Botzinger Complex), which can be tuned across oscillatory and non-oscillatory dynamic regimes in isolation, embedded into a full respiratory network. The work shows that under this embedding, the pacemaker unit's dynamics become masked: the network exhibits similar dynamical properties regardless of the conditional pacemaker node's tuning, and that node's outputs are dominated by\r\nnetwork influences." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/257608", + "other_concept": { + "attr_id": 306, + "value": "rhythm generation" + }, + "other_currents": { + "attr_id": 303, + "value": "adaptation current" + }, + "other_neurons": { + "attr_id": 301, + "value": "Respiratory rhythm generator neurons" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jonrubin@pitt.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Jonathan Rubin" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 183076, + "object_name": "Brainstem" + } + ] + }, + "ver_date": "2019-07-14T23:03:38.517", + "ver_number": 7 + }, + "257609": { + "class_id": 19, + "created": "2019-07-04T14:14:16.637", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 257609, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 261615, + "object_name": "Maes, Amadeus [amadeus.maes at gmail.com]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 261804, + "object_name": "Maes A, Barahona M, Clopath C (2020)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 253622, + "object_name": "Julia" + } + ] + }, + "name": "Learning spatiotemporal sequences using recurrent spiking NN that discretizes time (Maes et al 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + }, + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Learning to produce spatiotemporal sequences is a common task that the brain has to solve. The same neural substrate may be used by the brain to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory biophysical neurons drives a read-out layer: the dynamics of the driver recurrent network is trained to encode time which is then mapped through the read-out neurons to encode another dimension, such as space or a phase. Different spatiotemporal patterns can be learned and encoded through the synaptic weights to the read-out neurons that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on time scales that are behaviourally relevant and we show that the learned sequences are robustly replayed during a regime of spontaneous activity.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/257609", + "public_submitter_email": { + "attr_id": 309, + "value": "amadeus.maes@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Amadeus Maes" + }, + "ver_date": "2020-02-05T17:25:40.8", + "ver_number": 5 + }, + "257610": { + "citation": { + "attr_id": 391, + "value": "Brette, R. and Gerstner, W. (2005). Adaptive exponential integrate-and-fire model\r\nas an effective description of neuronal activity. Journal of Neurophysiology,\r\n94(5):3637\u20133642. PMID: 16014787.\r\n\r\nDeFelipe, J., Ballesteros-Y\u00e1\u00f1ez, I., Inda, M. C., and Mu\u00f1oz, A. (2006). Double-bouquet\r\ncells in the monkey and human cerebral cortex with special reference to areas 17\r\nand 18. Progress in brain research, 154:15\u201332.\r\n\r\nDeFelipe, J., Hendry, S., and Jones, E. (1989). Synapses of double bouquet cells in\r\nmonkey cerebral cortex visualized by calbindin immunoreactivity. Brain research,\r\n503(1):49\u201354.\r\n\r\nFiebig, F. and Lansner, A. (2017). A spiking working memory model based on hebbian\r\nshort-term potentiation. Journal of Neuroscience, 37(1):83\u201396.\r\n\r\nGewaltig, M.-O. and Diesmann, M. (2007). Nest (neural simulation tool). Scholarpedia, 2(4):1430.\r\n\r\nKawaguchi, Y. and Kubota, Y. (1996). Physiological and morphological identification \r\nof somatostatin-or vasoactive intestinal polypeptide-containing cells among\r\ngabaergic cell subtypes in rat frontal cortex. Journal of Neuroscience, 16(8):2701\u2013\r\n2715.\r\n\r\nKawaguchi, Y. and Kubota, Y. (1997). Gabaergic cell subtypes and their synaptic connections \r\nin rat frontal cortex. Cerebral cortex, 7 (6):476\u201386.\r\n\r\nKelsom, C. and Lu, W. (2013). Development and specification of gabaergic cortical\r\ninterneurons. Cell & Bioscience, 3(1):19.\r\n\r\nKrimer, L. S., Zaitsev, A. V., Czanner, G., Kroner, S., Gonz\u00e1lez-Burgos, G., Povysheva,\r\nN. V., Iyengar, S., Barrionuevo, G., and Lewis, D. A. (2005). Cluster analysis\u2013based \r\nphysiological classification and morphological properties of inhibitory neurons in layers 2\u20133 of monkey dorsolateral prefrontal cortex. Journal of neurophysiology, 94(5):3009\u20133022.\r\n\r\nLansner, A. (2009). Associative memory models: from the cell-assembly theory to\r\nbiophysically detailed cortex simulations. Trends in neurosciences, 32 (3):178\u2013\r\n186.\r\n\r\nMari\u00b4a, R. and DeFelipe, J. (1995). A light and electron microscopic study of calbindin\r\nd-28k immunoreactive double bouquet cells in the human temporal cortex. Brain\r\nresearch, 690(1):133\u2013140.\r\n\r\nMarkram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., and Wu,\r\nC. (2004). Interneurons of the neocortical inhibitory system. Nature Reviews\r\nNeuroscience, 5(10):793\u2013807.\r\n\r\nSilberberg, G. and Markram, H. (2007). Disynaptic inhibition between neocortical\r\npyramidal cells mediated by martinotti cells. Neuron, 53(5):735\u2013746.\r\n\r\nStrata, P. and Harvey, R. (1999). Dales principle. Brain research bulletin, 50(5-6):349\u2013\r\n350.\r\n\r\nTamas, G., Buhl, E., and Somogyi, P. (1997). Fast ipsps elicited via multiple synaptic\r\nrelease sites by different types of gabaergic neurone in the cat visual cortex. The\r\nJournal of physiology, 500(3):715\u2013738.\r\n\r\nTsodyks, M. V. and Markram, H. (1997). The neural code between neocortical pyra-\r\nmidal neurons depends on neurotransmitter release probability. Proceedings of the\r\nNational Academy of Sciences, 94(2):719-723.\r\n\r\nTully, P. J., Hennig, M. H., and Lansner, A. (2014). Synaptic and nonsynaptic plasticity\r\napproximating probabilistic inference. Frontiers in Synaptic Neuroscience, (6):8.\r\n\r\nTully, P. J., Lind\u00e9n, H., Hennig, M. H., and Lansner, A. (2016). Spike-based bayesian-\r\nhebbian learning of temporal sequences. PLoS computational biology, 12(5):1\u201335.\r\n\r\nY\u00e1\u00f1ez, I. B., Mu\u00f1oz, A., Contreras, J., Gonzalez, J., Rodri\u00b4guez-Veiga, E., and DeFelipe,\r\nJ. (2005). Double bouquet cell in the human cerebral cortex and a comparison with\r\nother mammals. The Journal of comparative neurology, 486 (4):344\u2013360.\r\n\r\nZaitsev, A. V., Povysheva, N. V., Gonzalez-Burgos, G., Rotaru, D., Fish, K. N., Krimer,\r\nL. S., and Lewis, D. A. (2008). Interneuron diversity in layers 2\u20133 of monkey\r\nprefrontal cortex. Cerebral cortex, 19(7):1597\u20131615.\r\n\r\nWulfram Gerstner and Richard Naud. How good are neuron models? Science,\r\n326(5951):379\u2013380, 2009.\r\n\r\nSandberg, A., Lansner, A., Petersson, K. M., and Ekeberg. (2002). A bayesian attractor \r\nnetwork with incremental learning. Network: Computation in neural systems,\r\n13(2):179\u2013194.\r\n\r\nMuir, D. R., Da Costa, N. M., Girardin, C. C., Naaman, S., Omer, D. B., Ruesch, E.,\r\nGrinvald, A., and Douglas, R. J. (2011). Embedding of cortical representations by\r\nthe superficial patch system. Cerebral Cortex, 21(10):2244\u20132260.\r\n\r\nYoshimura, Y. and Callaway, E. M. (2005). Fine-scale specificity of cortical networks\r\ndepends on inhibitory cell type and connectivity. Nature neuroscience, 8(11):1552.\r\n\r\nStettler, D. D., Das, A., Bennett, J., and Gilbert, C. D. (2002). Lateral connectivity and\r\ncontextual interactions in macaque primary visual cortex. Neuron, 36(4):739\u2013750.\r\n\r\nBinzegger, T., Douglas, R., and Martin, K. (2009). Topology and dynamics of the\r\ncanonical circuit of cat v1. Neural Networks, 22(8):1071 \u2013 1078. Cortical Microcircuits.\r\n\r\nThomson, A. M., West, D. C., Wang, Y., and Bannister, A. P. (2002). Synaptic \r\nConnections and Small Circuits Involving Excitatory and Inhibitory Neurons in Layers\r\n25 of Adult Rat and Cat Neocortex: Triple Intracellular Recordings and Biocytin\r\nLabelling In Vitro. Cerebral Cortex, 12(9):936\u2013953.\r\n\r\nLansner, A. and Ekeberg, . (1989). A one-layer feedback artificial neural network with\r\na bayesian learning rule.\r\n\r\nCaporale, N. and Dan, Y. (2008). Spike timing\u2013dependent plasticity: a hebbian learning\r\nrule. Annu. Rev. Neurosci., 31:25\u201346. International Journal of Neural Systems,\r\n01(01):77\u201387.\r\n\r\nRobert A. McDougal, Thomas M. Morse, Ted Carnevale, Luis Marenco, Rixin Wang,\r\nMichele Migliore, Perry L. Miller, Gordon M. Shepherd, and Michael L. Hines.\r\nTwenty years of modeldb and beyond: building essential modeling tools for the\r\nfuture of neuroscience. Journal of Computational Neuroscience, 42(1):1\u201310, Feb\r\n2017." + }, + "class_id": 19, + "created": "2019-07-04T21:02:59.283", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 257610, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 258851, + "object_name": "Chrysanthidis, Nikolaos [nchr at kth.se]" + }, + { + "object_id": 258852, + "object_name": "Fiebig, Florian [fiebig at kth.se]" + }, + { + "object_id": 83465, + "object_name": "Lansner, Anders [ala at kth.se]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83535, + "object_name": "Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 258850, + "object_name": "Chrysanthidis N, Fiebig F, Lansner A (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 2832, + "object_name": "NEST" + } + ] + }, + "name": "An electrophysiological model of GABAergic double bouquet cells (Chrysanthidis et al. 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 230198, + "object_name": "Neocortex U1 interneuron basket PV GABA cell" + }, + { + "object_id": 167344, + "object_name": "Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell" + }, + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + }, + { + "object_id": 184130, + "object_name": "Neocortex layer 2-3 interneuron" + }, + { + "object_id": 93384, + "object_name": "Neocortex bitufted interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We present an electrophysiological model of double bouquet cells (DBCs) and integrate them into an established cortical columnar microcircuit model that implements a BCPNN (Bayesian Confidence Propagation Neural Network) learning rule. The proposed architecture effectively solves the problem of duplexed learning of inhibition and excitation by replacing recurrent inhibition between pyramidal cells in functional columns of different stimulus selectivity with a plastic disynaptic pathway. The introduction of DBCs improves the biological plausibility of our model, without affecting the model's spiking activity, basic operation, and learning abilities." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/257610", + "other_implementer": { + "attr_id": 308, + "value": "Chrysanthidis, Nikolaos [nchr at kth.se]; Fiebig, Florian [fiebig at kth.se]; Lansner, Anders [ala at kth.se]" + }, + "other_neurons": { + "attr_id": 301, + "value": "GABAergic interneuron;" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "nchr@kth.se" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Nikolaos Chrysanthidis" + }, + "ver_date": "2019-09-11T15:25:32.197", + "ver_number": 9 + }, + "257631": { + "class_id": 19, + "created": "2019-07-08T18:03:21.04", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 257631, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 257630, + "object_name": "Keane, Adam " + }, + { + "object_id": 257629, + "object_name": "Gong, Pulin [pulin.gong at sydney.edu.au]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 257620, + "object_name": "Keane A, Henderson JA, Gong P (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Dynamical patterns underlying response properties of cortical circuits (Keane et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Recent experimental studies show cortical circuit responses to external stimuli display varied dynamical properties. These include stimulus strength-dependent population response patterns, a shift from synchronous to asynchronous states and a decline in neural variability. To elucidate the mechanisms underlying these response properties and explore how they are mechanistically related, we develop a neural circuit model that incorporates two essential features widely observed in the cerebral cortex. The first feature is a balance between excitatory and inhibitory inputs to individual neurons; the second feature is distance-dependent connectivity. We show that applying a weak external stimulus to the model evokes a wave pattern propagating along lateral connections, but a strong external stimulus triggers a localized pattern; these stimulus strength-dependent population response patterns are quantitatively comparable with those measured in experimental studies. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/257631", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2019-07-08T18:05:10.37", + "ver_number": 2 + }, + "257747": { + "class_id": 19, + "created": "2019-07-17T03:39:22.247", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92086, + "object_name": "Nav1.5 SCN5A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 257747, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 261314, + "object_name": "Carannante, Ilaria [ilariac at kth.se]" + }, + { + "object_id": 151444, + "object_name": "Balbi, Pietro [piero.balbi at fsm.it]" + }, + { + "object_id": 261315, + "object_name": "Andreozzi, Emilio [emilio.andreozzi at unina.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 238957, + "object_name": "Markov-type model" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 261320, + "object_name": "Andreozzi E, Carannante I, D'Addio G, Cesarelli M, Balbi P (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Phenomenological models of NaV1.5: Hodgkin-Huxley and kinetic formalisms (Andreozzi et al 2019)", + "notes": { + "attr_id": 24, + "value": "\"Computational models of ion channels represent the building blocks of conductance-based, biologically inspired models of neurons and neural networks. Ion channels are still widely modelled by means of the formalism developed by the seminal work of Hodgkin and Huxley (HH), although the electrophysiological features of the channels are currently known to be better fitted by means of kinetic Markov-type models. The present study is aimed at showing why simplified Markov-type kinetic models are more suitable for ion channels modelling as compared to HH ones, and how a manual optimization process can be rationally carried out for both. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/257747", + "other_implementer": { + "attr_id": 308, + "value": "Carannante, Ilaria [ilariac at kth.se]\r\nBalbi, Pietro [piero.balbi at icsmaugeri.it]\r\nAndreozzi, Emilio [emilio.andreozzi at unina.it]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ilariac@kth.se" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ilaria Carannante" + }, + "ver_date": "2019-11-26T13:25:07.477", + "ver_number": 5 + }, + "257877": { + "class_id": 19, + "created": "2019-07-22T05:48:02.737", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 257877, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262030, + "object_name": "Assisi C, Stopfer M, Bazhenov M (2020)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Optimal sparse olfactory representations persist in a plastic network (Assisi et al 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 261422, + "object_name": "Abstract Rulkov-Bazhenov map neurons" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Kenyon cells (KCs) of the mushroom body represent odors as a sparse code. When viewed from the perspective of follower neurons, mushroom body output neurons (MBONs) reveal an optimal level of coding sparseness that maximally separates the representations of odors. However, the KC\u2013MBON synapse is highly plastic and may be potentiated or depressed by odor\u2013driven experience that could, in turn, disrupt the optimality formed by pre\u2013synaptic circuits. Contrary to this expectation, we show that synaptic plasticity based on spike timing of pre- and postsynaptic neurons improves the ability of the system to distinguish between the representations of similar odors while preserving the optimality determined by pre\u2013synaptic circuits.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/257877", + "other_neurons": { + "attr_id": 301, + "value": "Abstract Rulkov-Bazhenov map neurons" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "collins@iiserpune.ac.in" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Collins Assisi" + }, + "ver_date": "2020-05-13T09:40:17.507", + "ver_number": 6 + }, + "257940": { + "citation": { + "attr_id": 391, + "value": "Cleland, TA., and Linster C. (2012) On-center/inhibitory surround decorrelation vai intraglomerular inhibition in the olfactory bulb glomerular layer. Front Integr Neurosc. 6:5. \r\n\r\nLinster, C. Nai, Q. and Ennis, M. (2011) Non-linear effects of noradrenergic modulation of olfactory bulb function in adult rodents. J. Neurophysiol. 105(4):1432-43. \r\n\r\nLinster, C. and Cleland, TA. (2011) Decorrelation of odor respresentations via spike timing dependent plasticity. Frontiers in Computational Neuroscience. 4: 157. \r\n\r\nEscanilla, O., Arrellanos, A., Karnow, A., Ennis, M. and Linster, C. (2010) Noradrenergic modulation of odor detection and discrimination thresholds. Eur J Neuroscience 32(3):458-68. \r\n\r\nLinster, C., Menon, AV, Singh, CY, Wilson, DA (2009) Odor specific habituation arises from interaction of afferent synaptic adaptation and intrinsic synaptic potentiation in olfactory cortex. Learn Mem. 16)7): 452-9 \r\n\r\nLinster and Cleland (2004) Configurational and elemental olfactory mixture perception can arise from lateral inhibition J Comput Neurosci. 16(1):39-47." + }, + "class_id": 19, + "created": "2019-07-23T14:21:09.563", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 257940, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 258741, + "object_name": "Linster, Christiane [cl243 at cornell.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 258740, + "object_name": "Linster C, Kelsch W (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "A computational model of oxytocin modulation of olfactory recognition memory (Linster & Kelsch 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + }, + { + "object_id": 3978, + "object_name": "Olfactory bulb main interneuron periglomerular GABA cell" + }, + { + "object_id": 3979, + "object_name": "Olfactory bulb main interneuron granule MC GABA cell" + }, + { + "object_id": 153283, + "object_name": "Olfactory bulb main interneuron granule TC GABA cell" + }, + { + "object_id": 207694, + "object_name": "Olfactory bulb main tufted cell external" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Model of olfactory bulb (OB) and anterior olfactory nucleus (AON) pyramidal cells. Includes olfactory sensory neurons, mitral cells, periglomerular, external tufted and granule interneurons and pyramidal cells. Can be built to include a feedback loop between OB and AON. Output consists of voltage and spikes over time in all neurons. Model can be stimulated with simulated odorants. The code submitted here has served for a number of modeling explorations of olfactory bulb and cortex. \r\nThe model architecture is defined in \"bulb.dat\" with synapses defined in \"channels.dat\". The main function to run the model can be found in \"neuron.c\". Model architecture is constructed in \"set.c\" from types defined in \"sim.c\". A make file to create an executable is located in \"neuron.mak\"." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/257940", + "other_implementer": { + "attr_id": 308, + "value": "Christiane Linster" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "cl243@cornell.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Christiane Linster" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "ver_date": "2019-10-09T17:43:08.71", + "ver_number": 9 + }, + "257965": { + "class_id": 19, + "created": "2019-07-24T17:39:31.88", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 257965, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 258230, + "object_name": "Bell, Miriam " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 257879, + "object_name": "Bell M, Bartol T, Sejnowski T, Rangamani P (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 147741, + "object_name": "COMSOL (web link to model)" + } + ] + }, + "name": "Dendritic spine geometry, spine apparatus organization: spatiotemporal Ca dynamics (Bell et al 2019)", + "notes": { + "attr_id": 24, + "value": "\" ... we systematically investigated the relationship between the shape and size of both the spine head and spine apparatus, a specialized endoplasmic reticulum compartment within the spine head, in modulating rapid calcium dynamics using mathematical modeling. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/257965", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2019-07-25T18:19:28.17", + "ver_number": 3 + }, + "258234": { + "biosimulations": { + "value": "modeldb-258234" + }, + "citation": { + "attr_id": 391, + "value": "Modulation of inhibitory strength and kinetics facilitates regulation of persistent inward currents and motoneuron excitability following spinal cord injury.\r\nVenugopal S, Hamm TM, Crook SM, Jung R.\r\nJ Neurophysiol. 2011 Nov;106(5):2167-79. doi: 10.1152/jn.00359.2011. Epub 2011 Jul 20.\r\nPMID: 21775715" + }, + "class_id": 19, + "created": "2019-07-27T00:59:39.96", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 258234, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 258326, + "object_name": "Venugopal, Sharmila [vsharmila at g.ucla.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 188674, + "object_name": "Venugopal S, Hamm TM, Crook SM, Jung R (2011)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Inhibitory control of motoneuron excitability (Venugopal et al 2011)", + "notes": { + "attr_id": 24, + "value": "A two-compartment model for a motor neuron following chronic spinal cord injury with excessive dendritic persistent Ca2+ current." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/258234", + "other_simulator": { + "attr_id": 307, + "value": "XPPAUT" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "vsharmila@g.ucla.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Sharmila Venugopal" + }, + "ver_date": "2019-07-29T17:34:39.17", + "ver_number": 9 + }, + "258235": { + "citation": { + "attr_id": 391, + "value": "S Venugopal*, S Seki, DH Terman, A Pantazis, R Olcese, M Wiedau-Pazos, SH Chandler, \u201cResurgent Na+ current offers noise modulation in bursting neurons\u201d, PLoS Computational Biology, June 2019, 15(6): e1007154. doi: 10.1371/journal.pcbi.1007154." + }, + "class_id": 19, + "created": "2019-07-27T02:26:11.673", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 258235, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 258326, + "object_name": "Venugopal, Sharmila [vsharmila at g.ucla.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 258238, + "object_name": "Venugopal S, Seki S, Terman DH, Pantazis A, Olcese R, Wiedau-Pazos M, Chandler SH (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Resurgent Na+ current offers noise modulation in bursting neurons (Venugopal et al 2019)", + "notes": { + "attr_id": 24, + "value": "\"Neurons utilize bursts of action potentials as an efficient and reliable way to encode information. It is likely that the intrinsic membrane properties of neurons involved in burst generation may also participate in preserving its temporal features. Here we examined the contribution of the persistent and resurgent components of voltage-gated Na+ currents in modulating the burst discharge in sensory neurons. Using mathematical modeling, theory and dynamic-clamp electrophysiology, we show that, distinct from the persistent Na+ component which is important for membrane resonance and burst generation, the resurgent Na+ can help stabilize burst timing features including the duration and intervals. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/258235", + "public_submitter_email": { + "attr_id": 309, + "value": "vsharmila@g.ucla.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Sharmila Venugopal" + }, + "ver_date": "2019-07-29T17:33:14.613", + "ver_number": 7 + }, + "258478": { + "class_id": 19, + "created": "2019-08-01T14:46:48.467", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 258478, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 258479, + "object_name": "Burka, D\u00e1vid " + }, + { + "object_id": 258480, + "object_name": "Emri, Zsuzsa " + }, + { + "object_id": 258481, + "object_name": "K\u00e1li, Szabolcs " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 152639, + "object_name": "Temperature" + }, + { + "object_id": 139148, + "object_name": "Brain Rhythms" + }, + { + "object_id": 93420, + "object_name": "Sleep" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 258361, + "object_name": "Csernai M, Borb\u00e9ly S, Kocsis K, Burka D, Fekete Z, Balogh V, K\u00e1li S, Emri Z, Barth\u00f3 P (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Dynamics of sleep oscillations coupled to brain temperature on multiple scales (Csernai et al 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 263, + "object_name": "Thalamus reticular nucleus GABA cell" + }, + { + "object_id": 167345, + "object_name": "Neocortex U1 L6 pyramidal corticalthalamic GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Every form of neural activity depends on temperature, yet its\r\nrelationship to brain rhythms is poorly understood. In this work we\r\nexamined how sleep spindles are influenced by changing brain\r\ntemperatures and how brain temperature is influenced by sleep\r\noscillations. We employed a novel thermoelectrode designed for\r\nmeasuring temperature while recording neural activity. We found that\r\nspindle frequency is positively correlated and duration negatively\r\ncorrelated with brain temperature. Local heating of the thalamus\r\nreplicated the temperature dependence of spindle parameters in the\r\nheated area only, suggesting biophysical rather than global modulatory\r\nmechanisms, a finding also supported by a thalamic network\r\nmodel. Finally, we show that switches between oscillatory states also\r\ninfluence brain temperature on a shorter and smaller scale. Epochs of\r\nparadoxical sleep as well as the infra-slow oscillation were\r\nassociated with brain temperature fluctuations below 0.2\u00b0C. Our\r\nresults highlight that brain temperature is massively intertwined with\r\nsleep oscillations on various time scales.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/258478", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2019-08-01T17:07:10.267", + "ver_number": 6 + }, + "258631": { + "class_id": 19, + "created": "2019-08-09T00:54:29.75", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 258631, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 266891, + "object_name": "Paranoia" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266887, + "object_name": "Reed EJ, Uddenberg S, Suthaharan P, Mathys CD, Taylor JR, Groman SM, Corlett PR (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 266894, + "object_name": "Multilayer Hierarchical Gaussian filter (HGF)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 150209, + "object_name": "R" + } + ] + }, + "name": "Paranoia as a deficit in non-social belief updating (Reed et al 2020)", + "notes": { + "attr_id": 24, + "value": "Model fit to human and rodent data showing effects of paranoia and methamphetamine on behavior and model parameters." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/258631", + "public_submitter_email": { + "attr_id": 309, + "value": "erin.reed@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Erin Reed" + }, + "ver_date": "2022-10-10T10:08:16.85", + "ver_number": 23 + }, + "258643": { + "class_id": 19, + "created": "2019-08-13T16:36:31.3", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 258643, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 258644, + "object_name": "Rumbell, Tim [timrumbell at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 227131, + "object_name": "Pacemaking mechanism" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 116962, + "object_name": "Parameter sensitivity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 260749, + "object_name": "Rumbell T, Kozloski J (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Control of oscillations and spontaneous firing in dopamine neurons (Rumbell & Kozloski 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 261, + "object_name": "Substantia nigra pars compacta DA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Model of Substantia Nigra pars Compacta Dopamine Neuron.\r\n'Toy' morphology with 4 dendrites, one of which is the axon-bearing dendrite, with an axon branching from it. The axon is a short 'axon initial segment' compartment, followed by a longer 'axon'.\r\n727 parameter sets for ion channel conductance and kinetic parameters were found using evolutionary optimization, all of which are viable candidates representing a plausible model of a SNc DA." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/258643", + "other_implementer": { + "attr_id": 308, + "value": "Tim Rumbell (timrumbell@gmail.com)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "timrumbell@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tim Rumbell" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2019-09-13T16:26:29.26", + "ver_number": 9 + }, + "258738": { + "class_id": 19, + "created": "2019-08-20T10:58:25.467", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122826, + "object_name": "NR2A GRIN2A" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 258738, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 168951, + "object_name": "Sherif, Mohamed [mohamed.sherif.md at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 112853, + "object_name": "Schizophrenia" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267080, + "object_name": "Sherif MA, Neymotin SA, Lytton WW (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "In silico hippocampal modeling for multi-target pharmacotherapy in schizophrenia (Sherif et al 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + }, + { + "object_id": 154093, + "object_name": "Hippocampus CA3 interneuron basket GABA cell" + }, + { + "object_id": 139885, + "object_name": "Hippocampus CA3 stratum oriens lacunosum-moleculare interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Using a hippocampal CA3 computer model with 1200 neurons, we examined the effects of alterations in NMDAR, HCN (Ih current), and GABAAR on information flow (measured with normalized transfer entropy), and in gamma activity in local field potential (LFP). We found that altering NMDARs, GABAAR, Ih, individually or in combination, modified information flow in an inverted-U shape manner, with information flow reduced at low and high levels of these parameters. Theta-gamma phase-amplitude coupling also had an inverted-U shape relationship with NMDAR augmentation. The strong information flow was associated with an intermediate level of synchrony, seen as an intermediate level of gamma activity in the LFP, and an intermediate level of pyramidal cell excitability\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/258738", + "public_submitter_email": { + "attr_id": 309, + "value": "mohamed.sherif.md@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Mohamed A. 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Richmond\r\n and Shigeru Shinomoto to appear in Nature Communications in 2019" + }, + "class_id": 19, + "created": "2019-08-28T15:49:28.493", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 258807, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 258811, + "object_name": "Kurth, Anno [a.kurth at fz-juelich.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 260903, + "object_name": "Kobayashi R, Kurita S, Kurth A, Kitano K, Mizuseki K, Diesmann M, Richmond BJ, Shinomoto S (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 2832, + "object_name": "NEST" + } + ] + }, + "name": "GLMCC validation neural network model (Kobayashi et al. 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 258847, + "object_name": "Abstract integrate-and-fire leaky neuron with exponential post-synaptic current" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Network model of two populations of randomly connected inhibitory and excitatory neurons to validate method for reconstructing the neural circuitry developed in \"Reconstructing Neuronal Circuitry from Parallel Spike Trains\" by Ryota Kobayashi, Shuhei Kurita, Anno Kurth, Katsunori Kitano, Kenji Mizuseki, Markus Diesmann, Barry J. 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H., Cluff T., 2019, \"Robust control of human reaching movements: a model-free strategy to compensate for unpredictable disturbances\"\r\nJournal of Neuroscience, DOI: https://doi.org/10.1523/JNEUROSCI.0770-19.2019" + }, + "class_id": 19, + "created": "2019-09-06T04:48:13.843", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 258846, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 259370, + "object_name": "Crevecoeur, Fr\u00e9d\u00e9ric " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 186770, + "object_name": "Motor control" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 259369, + "object_name": "Crevecoeur F, Scott SH, Cluff T (2019)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Reaching movements with robust or stochastic optimal control models (Crevecoeur et al 2019)", + "notes": { + "attr_id": 24, + "value": "\"We explored the hypothesis that compensation for unmodelled disturbances was supported\r\nby a robust neural control strategy. We studied the predictions of stochastic optimal control (LQG) (Linear Quadratic Gaussian) (Todorov, 2005) and a robust control design that can equivalently be described as a \u201cmin-max\u201d or\r\nworst-case strategy (Basar and Bernhard, 1991) applied to linear models of planar reaching\r\nmovements. The robust controller displayed an increase in control gains, resulting in faster\r\nmovements towards the target and more vigorous responses to perturbations. Our experimental\r\nresults supported these predictions: the occurrence of unexpected force field disturbances evoked\r\nboth faster movements and more vigorous responses to perturbations. Thus, the neural controller\r\nwas more robust in the sense that the feedback responses reduced the impact of the perturbations\r\n(step and force field). Thus the compensation for disturbances involved a \u201cmodel-free\u201d component.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/258846", + "other_implementer": { + "attr_id": 308, + "value": "Fr\u00e9d\u00e9ric Crevecoeur " + }, + "other_type": { + "attr_id": 305, + "value": "Reaching Control" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "frederic.crevecoeur@uclouvain.be" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Crevecoeur" + }, + "ver_date": "2019-09-24T15:43:13.897", + "ver_number": 9 + }, + "258854": { + "class_id": 19, + "created": "2019-09-10T10:45:23.17", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 263960, + "object_name": "I C" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 258854, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 263954, + "object_name": "Navas-Olive, Andrea [acnavasolive at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266759, + "object_name": "Navas-Olive A, Valero M, Jurado-Parras T, de Salas-Quiroga A, Averkin RG, Gambino G, Cid E, de la Prida LM (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "LCN-HippoModel: model of CA1 PCs deep-superficial theta firing dynamics (Navas-Olive et al 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Using a biophysically realistic model of CA1 pyramidal cells together with a combination of single-cell and multisite electrophysiological recordings, we have studied factors underlying the internal theta phase preference of identified cell types from the dorsal CA1.\r\nWe found that perisomatic inhibition delivered by complementary populations of basket cells interacts with input pathways to shape phase-locked specificity of deep and superficial CA1 pyramidal cells. Somatodendritic integration of fluctuating glutamatergic inputs defined cycle-by-cycle by nested waveforms demonstrated that firing selection is tuneable across sublayers under the relevant influence of intrinsic factors. Our data identify a set of testable physiological mechanisms underlying a phase specific firing reservoir that can be repurposed for high-level flexible dynamical representations. Documentation in https://acnavasolive.github.io/LCN-HippoModel/. More info: http://hippo-circuitlab.es/" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/258854", + "other_currents": { + "attr_id": 303, + "value": "I AHPs, I C, I Ca L, iCa T, I KDR" + }, + "other_implementer": { + "attr_id": 308, + "value": "Andrea Navas-Olive" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "acnavasolive@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Andrea Navas-Olive" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 212, + "object_name": "Glutamate" + }, + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2020-04-14T12:14:37.203", + "ver_number": 18 + }, + "258867": { + "citation": { + "attr_id": 391, + "value": "Schulz JM, Knoflach F, Hernandez MC, Bischofberger J.\r\nDendrite-targeting interneurons control synaptic NMDA-receptor activation via nonlinear a5-GABAA receptors.\r\nNat Commun. 2018 Sep 3;9(1):3576. doi: 10.1038/s41467-018-06004-8.\r\n\r\nPMID: 30177704" + }, + "class_id": 19, + "created": "2019-09-11T16:02:23.86", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 247, + "object_name": "I A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 258867, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 258947, + "object_name": "Schulz, Jan M [j.schulz at unibas.ch]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 258870, + "object_name": "Schulz JM, Knoflach F, Hernandez MC, Bischofberger J (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "CA1 pyramidal neuron: nonlinear a5-GABAAR controls synaptic NMDAR activation (Schulz et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The study shows that IPSCs mediated by a5-subunit containing GABAA receptors are strongly outward-rectifying generating 4-fold larger conductances above -50?mV than at rest. Experiments and modeling show that synaptic activation of these receptors can very effectively control voltage-dependent NMDA-receptor activation in a spatiotemporally controlled manner in fine dendrites of CA1 pyramidal cells.\r\n\r\nThe files contain the NEURON code for Fig.8, Fig.S8 and Fig.S9 of the paper. The model is based on the model published by Bloss et al., 2017. Physiological properties of GABA synapses were modified as determined by optogenetic activation of inputs during voltage-clamp recordings in Schulz et al. 2018. Other changes include stochastic synaptic release and short-term synaptic plasticity. All changes of mechanisms and parameters are detailed in the Methods of the paper.\r\n\r\nSimulation can be run by starting start_simulation.hoc after running mknrndll. The files that model the individual figures have to be uncommented in start_simulation.hoc beforehand." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/258867", + "other_concept": { + "attr_id": 306, + "value": "inhibition of NMDA spikes" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "j.schulz@unibas.ch" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Jan M Schulz" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 203, + "object_name": "GabaB" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + }, + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2019-09-13T18:25:30.233", + "ver_number": 7 + }, + "258946": { + "citation": { + "attr_id": 391, + "value": "Schulz JM, Knoflach F, Hernandez MC, Bischofberger J.\r\nDendrite-targeting interneurons control synaptic NMDA-receptor activation via nonlinear a5-GABAA receptors.\r\nNat Commun. 2018 Sep 3;9(1):3576. doi: 10.1038/s41467-018-06004-8.\r\n\r\nPMID: 30177704" + }, + "class_id": 19, + "created": "2019-09-12T06:05:49.673", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 258946, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 258947, + "object_name": "Schulz, Jan M [j.schulz at unibas.ch]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 258870, + "object_name": "Schulz JM, Knoflach F, Hernandez MC, Bischofberger J (2018)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Single compartment: nonlinear a5-GABAAR controls synaptic NMDAR activation (Schulz et al 2018)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This study shows that IPSCs mediated by a5-subunit containing GABAA receptors are strongly outward-rectifying generating 4-fold larger conductances above -50?mV than at rest. This model shows that synaptic activation of these receptors can very effectively control voltage-dependent NMDA-receptor activation.\r\n\r\nThe files contain the NEURON code for Fig.6 and Fig.7. The model is a single dendritic compartment with one glutamatergic and GABAergic synapse. Physiological properties of GABA synapses were modeled as determined by optogenetic activation of inputs during voltage-clamp recordings in Schulz et al. 2018. " + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/258946", + "public_submitter_email": { + "attr_id": 309, + "value": "j.schulz@unibas.ch" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Jan M Schulz" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2019-09-12T18:49:42.727", + "ver_number": 7 + }, + "258949": { + "citation": { + "attr_id": 391, + "value": "https://arxiv.org/ftp/arxiv/papers/1703/1703.01357.pdf" + }, + "class_id": 19, + "created": "2019-09-12T22:25:19.497", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 258949, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 239548, + "object_name": "Helfer, Peter [peter.helfer at mail.mcgill.ca]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 188516, + "object_name": "Memory" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262031, + "object_name": "Helfer P, Shultz TR (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "A computational model of systems memory consolidation and reconsolidation (Helfer & Shultz 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 240378, + "object_name": "Abstract integrate-and-fire neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A neural-network framework for modeling systems memory consolidation and reconsolidation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/258949", + "public_submitter_email": { + "attr_id": 309, + "value": "peter.helfer@mail.mcgill.ca" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Peter Helfer" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + }, + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2019-12-06T16:49:08.7", + "ver_number": 6 + }, + "259261": { + "class_id": 19, + "created": "2019-09-17T18:58:50.603", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 259261, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 260748, + "object_name": "Muller, Salomon Z [szm2106 at columbia.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 261229, + "object_name": "Muller SZ, Zadina AN, Abbott LF, Sawtell NB (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "ELL Medium Ganglion cell (Muller et al 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 260746, + "object_name": "ELL Medium Ganglion cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Distributing learning across multiple layers has proven extremely powerful in artificial neural networks. However, little is known about how multi-layer learning is implemented in the brain. Here, we provide an account of learning across multiple processing layers in the electrosensory lobe (ELL) of mormyrid fish and report how it solves problems well known from machine learning. Because the ELL operates and learns continuously, it must reconcile learning and signaling functions without switching its mode of operation. We show that this is accomplished through a functional compartmentalization within intermediate layer neurons in which inputs driving learning differentially affect dendritic and axonal spikes. We also find that connectivity based on learning rather than sensory response selectivity assures that plasticity at synapses onto intermediate-layer neurons is matched to the requirements of output neurons. The mechanisms we uncover have relevance to learning in the cerebellum, hippocampus, and cerebral cortex, as well as in artificial systems.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/259261", + "other_concept": { + "attr_id": 306, + "value": "Separation of responses" + }, + "other_implementer": { + "attr_id": 308, + "value": "Muller, Salomon Z (szm2106@columbia.edu)" + }, + "other_neurons": { + "attr_id": 301, + "value": "ELL Medium Ganglion cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "szm2106@columbia.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Salomon Muller" + }, + "ver_date": "2019-11-12T15:44:37.863", + "ver_number": 22 + }, + "259366": { + "class_id": 19, + "created": "2019-09-20T10:53:32.907", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 259366, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 186894, + "object_name": "Tikidji-Hamburyan, Ruben [ruben.tikidji.hamburyan at gmail.com] " + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263161, + "object_name": "Tikidji-Hamburyan RA, Canavier CC (2020)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Cycle skipping in ING Type 1 / Type 2 networks (Tikidji-Hamburyan & Canavier 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 156124, + "object_name": "Abstract single compartment conductance based cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"All-to-all homogeneous networks of inhibitory neurons synchronize completely under the right conditions; however, many modeling studies have shown that biological levels of heterogeneity disrupt synchrony. Our fundamental scientific question is \u201chow can neurons maintain partial synchrony in the presence of heterogeneity and noise?\u201d A particular subset of strongly interconnected interneurons, the PV+ fast spiking basket neurons, are strongly implicated in gamma oscillations and in phase locking of nested gamma oscillations to theta. Their excitability type apparently varies between brain regions: in CA1 and the dentate gyrus they have type 1 excitability, meaning that they can fire arbitrarily slowly, whereas in the striatum and cortex they have type 2 excitability, meaning that there is a frequency threshold below which they cannot sustain repetitive firing. We constrained the models to study the effect of excitability type (more precisely bifurcation type) in isolation from all other factors. We use sparsely connected, heterogeneous, noisy networks with synaptic delays to show that synchronization properties, namely the resistance to suppression and the strength of theta phase to gamma amplitude coupling, are strongly dependent on the pairing of excitability type with the type of inhibition. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/259366", + "public_submitter_email": { + "attr_id": 309, + "value": "ruben.tikidji.hamburyan@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ruben Tikidji-Hamburyan" + }, + "ver_date": "2022-05-27T15:33:06.343", + "ver_number": 14 + }, + "259542": { + "class_id": 19, + "created": "2019-09-27T16:07:30.147", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 259542, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 259543, + "object_name": "Kim, Jimin " + }, + { + "object_id": 259544, + "object_name": "Leahy, William " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 47316, + "object_name": "Invertebrate" + }, + { + "object_id": 114660, + "object_name": "Connectivity matrix" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 259484, + "object_name": "Kim J, Leahy W, Shlizerman E (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "name": "Neural Interactome: interactive simulation of a neuronal system (Kim et al 2019)", + "notes": { + "attr_id": 24, + "value": "\"\"Connectivity and biophysical processes determine the functionality of\r\nneuronal networks. We, therefore, developed a real-time framework,\r\ncalled Neural Interactome, to simultaneously visualize and interact\r\nwith the structure and dynamics of such networks. Neural Interactome\r\nis a cross-platform framework, which combines graph visualization with\r\nthe simulation of neural dynamics, or experimentally recorded multi\r\nneural time series, to allow application of stimuli to neurons to\r\nexamine network responses. In addition, Neural Interactome supports\r\nstructural changes, such as disconnection of neurons from the network\r\n(ablation feature). Neural dynamics can be explored on a single neuron\r\nlevel (using a zoom feature), back in time (using a review feature),\r\nand recorded (using presets feature). The development of the Neural\r\nInteractome was guided by generic concepts to be applicable to\r\nneuronal networks with different neural connectivity and dynamics. We\r\nimplement the framework using a model of the nervous system of\r\nCaenorhabditis elegans (C. elegans) nematode, a model organism with\r\nresolved connectome and neural dynamics. We show that Neural\r\nInteractome assists in studying neural response patterns associated\r\nwith locomotion and other stimuli. In particular, we demonstrate how\r\nstimulation and ablation help in identifying neurons that shape\r\nparticular dynamics. We examine scenarios that were experimentally\r\nstudied, such as touch response circuit, and explore new scenarios\r\nthat did not undergo elaborate experimental studies.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/259542", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2019-09-27T16:14:04.297", + "ver_number": 4 + }, + "259546": { + "class_id": 19, + "created": "2019-09-30T12:33:03.643", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 259546, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 234245, + "object_name": "Wilmes, Katharina A. [katharina.wilmes at googlemail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 182362, + "object_name": "Sensory coding" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 264841, + "object_name": "Wilmes KA, Clopath C (2019)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 231240, + "object_name": "Brian 2" + } + ] + }, + "name": "Inhibitory microcircuits for top-down plasticity of sensory representations (Wilmes & Clopath 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + }, + { + "object_id": 230185, + "object_name": "Neocortex V1 interneuron chandelier SOM GABA cell" + }, + { + "object_id": 7387, + "object_name": "Neocortex V1 interneuron basket PV GABA cell" + }, + { + "object_id": 230183, + "object_name": "Neocortex V1 interneuron bipolar VIP/CR GABA cell" + }, + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Rewards influence plasticity of early sensory representations, but the underlying changes in circuitry are unclear. Recent experimental findings suggest that inhibitory circuits regulate learning. In addition, inhibitory neurons are highly modulated by diverse long-range inputs, including reward signals. We, therefore, hypothesise that inhibitory plasticity plays a major role in adjusting stimulus representations. We investigate how top-down modulation by rewards interacts with local plasticity to induce long-lasting changes in circuitry. Using a computational model of layer 2/3 primary visual cortex, we demonstrate how interneuron circuits can store information about rewarded stimuli to instruct long-term changes in excitatory connectivity in the absence of further reward. In our model, stimulus-tuned somatostatin-positive interneurons develop strong connections to parvalbumin-positive interneurons during reward such that they selectively disinhibit the pyramidal layer henceforth. This triggers excitatory plasticity, leading to increased stimulus representation. We make specific testable predictions and show that this two-stage model allows for translation invariance of the learned representation.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/259546", + "public_submitter_email": { + "attr_id": 309, + "value": "katharina.wilmes@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Katharina Anna Wilmes" + }, + "ver_date": "2020-04-24T11:56:08.74", + "ver_number": 7 + }, + "259620": { + "class_id": 19, + "created": "2019-10-01T10:59:10.17", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 259620, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 182782, + "object_name": "Sadeh, Sadra [s.sadeh at ucl.ac.uk]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 261906, + "object_name": "Sadeh S, Clopath C (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Inhibition perturbations reveals dynamical structure of neural processing (Sadeh & Clopath 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Perturbation of neuronal activity is key to understanding the brain's functional properties, however, intervention studies typically perturb neurons in a nonspecific manner. Recent optogenetics techniques have enabled patterned perturbations, in which specific patterns of activity can be invoked in identified target neurons to reveal more specific cortical function. Here, we argue that patterned perturbation of neurons is in fact necessary to reveal the specific dynamics of inhibitory stabilization, emerging in cortical networks with strong excitatory and inhibitory functional subnetworks, as recently reported in mouse visual cortex. We propose a specific perturbative signature of these networks and investigate how this can be measured under different experimental conditions. Functionally, rapid spontaneous transitions between selective ensembles of neurons emerge in such networks, consistent with experimental results. Our study outlines the dynamical and functional properties of feature-specific inhibitory-stabilized networks, and suggests experimental protocols that can be used to detect them in the intact cortex.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/259620", + "public_submitter_email": { + "attr_id": 309, + "value": "s.sadeh@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Sadra Sadeh" + }, + "ver_date": "2020-02-19T17:20:39.433", + "ver_number": 8 + }, + "259732": { + "citation": { + "attr_id": 391, + "value": "31558617" + }, + "class_id": 19, + "created": "2019-10-07T13:36:13.727", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 259732, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 124046, + "object_name": "Polsky, Alon [alonpol at tx.technion.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 136098, + "object_name": "Information transfer" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 259733, + "object_name": "Poleg-Polsky A (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Distinct integration properties of noisy inputs in active dendritic subunits (Poleg-Polsky 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + }, + { + "object_id": 217749, + "object_name": "Neocortex primary motor area pyramidal layer 5 corticospinal cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The brain operates surprisingly well despite the noisy nature of individual neurons. The central mechanism for noise mitigation in the nervous system is thought to involve averaging over multiple noise-corrupted inputs. Subsequently, there has been considerable interest recently to identify noise structures that can be integrated linearly in a way that preserves reliable signal encoding. By analyzing realistic synaptic integration in biophysically accurate neuronal models, I report a complementary de-noising approach that is mediated by focal dendritic spikes. Dendritic spikes might seem to be unlikely candidates for noise reduction due to their miniscule integration compartments and poor averaging abilities. Nonetheless, the extra thresholding step introduced by dendritic spike generation increases neuronal tolerance for a broad category of noise structures, some of which cannot be resolved well with averaging. This property of active dendrites compensates for compartment size constraints and expands the repertoire of conditions that can be processed by neuronal populations." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/259732", + "other_implementer": { + "attr_id": 308, + "value": "Poleg-Polsky, Alon" + }, + "other_neurons": { + "attr_id": 301, + "value": "Neocortex layer 2-3 pyramidal" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ALON.POLEG-POLSKY@UCDENVER.EDU" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Alon Poleg-Polsky" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2019-10-10T17:20:49.837", + "ver_number": 5 + }, + "259786": { + "biosimulations": { + "value": "modeldb-259786" + }, + "citation": { + "attr_id": 391, + "value": "Wittman, S., Abdala, A. P., & Rubin, J. E. (2019). Reduced computational modelling of K\u00f6lliker-Fuse contributions to breathing patterns in Rett syndrome. The Journal of Physiology, 597(10), 2651-2672." + }, + "class_id": 19, + "created": "2019-10-07T22:09:27.397", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 259786, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 259789, + "object_name": "Wittman, Samuel [swittman52 at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 232102, + "object_name": "Respiratory control" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 259788, + "object_name": "Wittman S, Abdala AP, Rubin JE (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Respiratory central pattern generator including Kolliker-Fuse nucleus (Wittman et al 2019)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 214, + "object_name": "Glycine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We present three highly reduced conductance-based models for the core of the respiratory CPG. 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(2005), modified to run on PGENESIS on supercomputing resources. \"In this paper (Crone et al 2019), we evaluate the computational performance of the GEneral NEural SImulation System (GENESIS) for large scale simulations of neural networks. While many benchmark studies have been performed for large scale simulations with leaky integrate-and-fire neurons or neuronal models with only a few compartments, this work focuses on higher fidelity neuronal models represented by 50\u201374 compartments per neuron. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/260267", + "other_implementer": { + "attr_id": 308, + "value": "Crone, J; Vindiola, MM; Yu, AB; Boothe, DL; Oie, KS; Franaszczuk, PJ" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "alfred.b.yu.civ@mail.mil" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Alfred Yu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2019-11-18T17:19:44.577", + "ver_number": 4 + }, + "260596": { + "citation": { + "attr_id": 391, + "value": "Franci et al 2018" + }, + "class_id": 19, + "created": "2019-10-30T16:28:59.7", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 260596, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 245212, + "object_name": "Franci A, Drion G, Sepulchre R (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 253623, + "object_name": "Julia (web link to model)" + } + ] + }, + "name": "Robust and tunable bursting requires slow positive feedback (Franci et al 2018)", + "notes": { + "attr_id": 24, + "value": "\"We highlight that the robustness and tunability of a bursting model critically rely on currents that provide slow positive feedback to the membrane potential. Such currents have the ability to make the total conductance of the circuit negative in a timescale that is termed \u201cslow\u201d because it is intermediate between the fast timescale of the spike upstroke and the ultraslow timescale of even slower adaptation currents. We discuss how such currents can be assessed either in voltage-clamp experiments or in computational models. We show that, while frequent in the literature, mathematical and computational models of bursting that lack the slow negative conductance are fragile and rigid. Our results suggest that modeling the slow negative conductance of cellular models is important when studying the neuromodulation of rhythmic circuits at any broader scale.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/260596", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "ver_date": "2019-10-30T16:33:51.843", + "ver_number": 2 + }, + "260653": { + "citation": { + "attr_id": 391, + "value": "https://www.nature.com/articles/s41598-017-14357-1" + }, + "class_id": 19, + "created": "2019-10-31T14:21:21.407", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 260653, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 262111, + "object_name": "Tsai, David [d.tsai at unsw.edu.au]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 128875, + "object_name": "Development" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262388, + "object_name": "Guo T, Tsai D, Morley JW, Suaning GJ, Kameneva T, Lovell NH, Dokos S (2016)" + }, + { + "object_id": 262387, + "object_name": "Tsai D, Chen S, Protti DA, Morley JW, Suaning GJ, Lovell NH (2012)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Retinal ganglion cells responses and activity (Tsai et al 2012, Guo et al 2016)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 270, + "object_name": "Retina ganglion GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "From the abstracts: \"Retinal ganglion cells (RGCs), which survive in large numbers following neurodegenerative diseases, could be stimulated with extracellular electric pulses to elicit artificial percepts. How do the RGCs respond to electrical stimulation at the sub-cellular level under different stimulus configurations, and how does this influence the whole-cell response? At the population level, why have experiments yielded conflicting evidence regarding the extent of passing axon activation? We addressed these questions through simulations of morphologically and biophysically detailed computational RGC models on high performance computing clusters. We conducted the analyses on both large-field RGCs and small-field midget RGCs. ...\", \"... In this study, an existing RGC ionic model was extended by including a hyperpolarization activated non-selective cationic current as well as a T-type calcium current identified in recent experimental findings. Biophysically-defined model parameters were simultaneously optimized against multiple experimental recordings from ON and OFF RGCs. ..." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/260653", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 118529, + "object_name": "Retina" + } + ] + }, + "ver_date": "2020-03-02T19:10:47.587", + "ver_number": 9 + }, + "260730": { + "biosimulations": { + "value": "modeldb-260730" + }, + "citation": { + "attr_id": 391, + "value": "https://mathematical-neuroscience.springeropen.com/articles/10.1186/s13408-017-0047-3" + }, + "class_id": 19, + "created": "2019-11-04T10:49:59.467", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 260730, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 262615, + "object_name": "Sherman, Arthur [arthurs at niddk.nih.gov]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 143724, + "object_name": "Bifurcation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262614, + "object_name": "Sherman AS, Ha J (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "How adaptation makes low firing rates robust (Sherman & Ha 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 150222, + "object_name": "Abstract Morris-Lecar neuron" + }, + { + "object_id": 188553, + "object_name": "Abstract Hindmarsh-Rose neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Low frequency firing is modeled by Type 1 neurons with a SNIC (saddle node on an invariant circle), but, because of the vertical slope of the square-root-like f\u2013I curve, low f only occurs over a narrow range of I. When an adaptive current is added, however, the f\u2013I curve is linearized, and low f occurs robustly over a large I range. Ermentrout (Neural Comput. 10(7):1721-1729, 1998) showed that this feature of adaptation paradoxically arises from the SNIC that is responsible for the vertical slope. We show, using a simplified Hindmarsh\u2013Rose neuron with negative feedback acting directly on the adaptation current, that whereas a SNIC contributes to linearization, in practice linearization over a large interval may require strong adaptation strength. We also find that a type 2 neuron with threshold generated by a Hopf bifurcation can also show linearization if adaptation strength is strong. Thus, a SNIC is not necessary. More fundamental than a SNIC is stretching the steep region near threshold, which stems from sufficiently strong adaptation, though a SNIC contributes if present. In a more realistic conductance-based model, Morris\u2013Lecar, with negative feedback acting on the adaptation conductance, an additional assumption that the driving force of the adaptation current is independent of I is needed. If this holds, strong adaptive conductance is both necessary and sufficient for linearization of f\u2013I curves of type 2 f\u2013I curves.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/260730", + "other_implementer": { + "attr_id": 308, + "value": "Arthur Sherman" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "ver_date": "2020-03-25T11:40:47.85", + "ver_number": 14 + }, + "260740": { + "class_id": 19, + "created": "2019-11-05T16:16:37.007", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 260740, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 260739, + "object_name": "Rankin, James [james.rankin at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 147931, + "object_name": "Orientation selectivity" + }, + { + "object_id": 128875, + "object_name": "Development" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 260599, + "object_name": "Rankin J, Chavane F (2017)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Neural field model to reconcile structure with function in V1 (Rankin & Chavane 2017)", + "notes": { + "attr_id": 24, + "value": "\"Voltage-sensitive dye imaging experiments in primary visual cortex (V1) have shown that local, oriented visual stimuli elicit stable orientation-selective activation within the stimulus retinotopic footprint. The cortical activation dynamically extends far beyond the retinotopic footprint, but the peripheral spread stays non-selective\u2014a surprising finding given a number of anatomo-functional studies showing the orientation specificity of long-range connections. Here we use a computational model to investigate this apparent discrepancy by studying the expected population response using known published anatomical constraints. The dynamics of input-driven localized states were simulated in a planar neural field model with multiple sub-populations encoding orientation. The realistic connectivity profile has parameters controlling the clustering of long-range connections and their orientation bias. We found substantial overlap between the anatomically relevant parameter range and a steep decay in orientation selective activation that is consistent with the imaging experiments. In this way our study reconciles the reported orientation bias of long-range connections with the functional expression of orientation selective neural activity. Our results demonstrate this sharp decay is contingent on three factors, that long-range connections are sufficiently diffuse, that the orientation bias of these connections is in an intermediate range (consistent with anatomy) and that excitation is sufficiently balanced by inhibition. Conversely, our modelling results predict that, for reduced inhibition strength, spurious orientation selective activation could be generated through long-range lateral connections. Furthermore, if the orientation bias of lateral connections is very strong, or if inhibition is particularly weak, the network operates close to an instability leading to unbounded cortical activation. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/260740", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2019-11-05T16:48:07.347", + "ver_number": 4 + }, + "260949": { + "citation": { + "attr_id": 391, + "value": "http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0182884" + }, + "class_id": 19, + "created": "2019-11-08T11:37:13.863", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 260949, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 263444, + "object_name": "Velarde, Osva [osva.m.velarde at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263443, + "object_name": "Velarde OM, Mato G, Dellavale D (2017)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Mechanisms for pattern specificity of DBS in Parkinson's disease (Velarde et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 240378, + "object_name": "Abstract integrate-and-fire neuron" + } + ] + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/260949", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + }, + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2020-06-13T11:09:57.937", + "ver_number": 12 + }, + "260960": { + "citation": { + "attr_id": 391, + "value": "http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005879" + }, + "class_id": 19, + "created": "2019-11-08T12:23:20.557", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 260960, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263030, + "object_name": "Soplata AE, McCarthy MM, Sherfey J, Lee S, Purdon PL, Brown EN, Kopell N (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 239393, + "object_name": "DynaSim" + } + ] + }, + "name": "Thalamocortical control of propofol phase-amplitude coupling (Soplata et al 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The anesthetic propofol elicits many different spectral properties on the EEG, including alpha oscillations (8-12 Hz), Slow Wave Oscillations (SWO, 0.1-1.5 Hz), and dose-dependent phase-amplitude coupling (PAC) between alpha and SWO. Propofol is known to increase GABAA inhibition and decrease H-current strength, but how it generates these rhythms and their interactions is still unknown. To investigate both generation of the alpha rhythm and its PAC to SWO, we simulate a Hodgkin-Huxley network model of a hyperpolarized thalamus and corticothalamic inputs. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/260960", + "other_neurons": { + "attr_id": 301, + "value": "Thalamocortical cell, Thalamic Reticular Nucleus c" + }, + "other_neurotransmitter": { + "attr_id": 304, + "value": "AMPA" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 203, + "object_name": "GabaB" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2020-03-12T13:30:33.54", + "ver_number": 5 + }, + "260967": { + "class_id": 19, + "created": "2019-11-11T09:01:17.97", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 260967, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 261475, + "object_name": "Cohen, Charles CH [c.cohen at gmx.com]" + }, + { + "object_id": 114397, + "object_name": "Kole, Maarten [m.kole at nin.knaw.nl]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 143636, + "object_name": "Conductance distributions" + }, + { + "object_id": 243515, + "object_name": "Conductances estimation" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 64175, + "object_name": "Electrotonus" + }, + { + "object_id": 84598, + "object_name": "Extracellular Fields" + }, + { + "object_id": 206291, + "object_name": "Membrane Properties" + }, + { + "object_id": 52406, + "object_name": "Multiple sclerosis" + }, + { + "object_id": 116962, + "object_name": "Parameter sensitivity" + }, + { + "object_id": 261476, + "object_name": "Double cable" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 261474, + "object_name": "Cohen CCH, Popovic MA, Klooster J, Weil M, M\u00f6bius W, Nave K, Kole MHP (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 120799, + "object_name": "Glia" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Double cable myelinated axon (Layer 5 pyramidal neuron; Cohen et al 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 52425, + "object_name": "Myelinated neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The periaxonal space in myelinated axons is conductive (~50 ohm cm). Together with a rapidly charging myelin sheath and relatively sealed paranodes, periaxonal conduction shapes the saltating voltage profiles of transaxonal (Vm), transmyelin (Vmy) and transfibre (Vmym) potentials. This model exemplifies double cable saltatory conduction across both time and space, and is the same cell (#6) as seen in Movie S4 of Cohen et al. 2020. This model version allows one to visualize and manipulate the controlling parameters of a propagating action potential.\r\n\r\nFurther notes: The corresponding potentials in NEURON to those named above are v, vext (or vext[0]) and v+vext, respectively. The loaded biophysical parameters were those optimized for this cell (Cohen et al. 2020)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/260967", + "other_concept": { + "attr_id": 306, + "value": "Double Cable" + }, + "other_currents": { + "attr_id": 303, + "value": "I Ca (LVA), I Ca (HVA), I Kv7, I Kv1" + }, + "other_implementer": { + "attr_id": 308, + "value": "Cohen, Charles CH [c.cohen at gmx.com]" + }, + "other_neurons": { + "attr_id": 301, + "value": "Layer 5/6 pyramidal neuron (S1)" + }, + "other_type": { + "attr_id": 305, + "value": "periaxonal space, myelin" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "c.cohen@gmx.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Charles C.H. 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We built a biochemically detailed model of post-synaptic plasticity describing CaMKII, PKA, and PKC pathways and their contribution to synaptic potentiation or depression. We developed a statistical AMPA-receptor-tetramer model, which permits the estimation of the AMPA-receptor-mediated maximal synaptic conductance based on numbers of GluR1s and GluR2s predicted by the biochemical signalling model. We show that our model reproduces neuromodulator-gated spike-timing-dependent plasticity as observed in the visual cortex and can be fit to data from many cortical areas, uncovering the biochemical contributions of the pathways pinpointed by the underlying experimental studies. Our model explains the dependence of different forms of plasticity on the availability of different proteins and can be used for the study of mental disorder-associated impairments of cortical plasticity.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/260971", + "public_submitter_email": { + "attr_id": 309, + "value": "tuomo@simula.no" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tuomo M\u00e4ki-Marttunen" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2020-08-09T12:48:47.21", + "ver_number": 9 + }, + "260972": { + "class_id": 19, + "created": "2019-11-13T12:11:37.157", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 260972, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266748, + "object_name": "Demaegd, Maggie [mdemaeg at ilstu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 114307, + "object_name": "Conduction failure" + }, + { + "object_id": 152639, + "object_name": "Temperature" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267632, + "object_name": "DeMaegd ML, Stein W (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Temperature sensitive axon models (DeMaegd & Stein 2020)", + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/260972", + "public_submitter_email": { + "attr_id": 309, + "value": "mdemaeg@ilstu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Margaret DeMaegd" + }, + "ver_date": "2023-02-09T09:37:50.597", + "ver_number": 4 + }, + "261078": { + "citation": { + "attr_id": 391, + "value": "PMID:31720999" + }, + "class_id": 19, + "created": "2019-11-14T20:59:09.693", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 261078, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 238732, + "object_name": "Hoshino, Osamu [osamu.hoshino.507 at vc.ibaraki.ac.jp]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 261080, + "object_name": "Hoshino O, Kameno R, Watanabe K (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Reducing variability in motor cortex activity by GABA (Hoshino et al. 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 230188, + "object_name": "Neocortex M1 interneuron basket PV GABA cell" + }, + { + "object_id": 217749, + "object_name": "Neocortex primary motor area pyramidal layer 5 corticospinal cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Interaction between sensory and motor cortices is crucial for perceptual decision-making, in which intracortical inhibition might have an important role. We simulated a neural network model consisting of a sensory network (NS) and a motor network (NM) to elucidate the significance of their interaction in perceptual decision-making in association with the level of GABA in extracellular space: extracellular GABA concentration. Extracellular GABA molecules acted on extrasynaptic receptors embedded in membranes of pyramidal cells and suppressed them. A reduction in extracellular GABA concentration either in NS or NM increased the rate of errors in perceptual decision-making, for which an increase in ongoing-spontaneous fluctuations in subthreshold neuronal activity in NM prior to sensory stimulation was responsible. Feedback (NM-to-NS) signaling enhanced selective neuronal responses in NS, which in turn increased stimulus-evoked neuronal activity in NM. We suggest that GABA in extracellular space contributes to reducing variability in motor cortex activity at a resting state\r\nand thereby the motor cortex can respond correctly to a subsequent sensory stimulus. Feedback signaling from the motor cortex improves the selective responsiveness of the sensory cortex, which ensures the fidelity of information transmission to the motor cortex, leading to reliable perceptual decision-making." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261078", + "other_concept": { + "attr_id": 306, + "value": "motor performance" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "osamu.hoshino.507@vc.ibaraki.ac.jp" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "osamu hoshino" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2019-12-16T11:10:01.613", + "ver_number": 15 + }, + "261423": { + "citation": { + "attr_id": 391, + "value": "Beeman D, Yu A, Crone J (2019) Studying evoked potentials in large cortical\r\nnetworks with PGENESIS 2.4. BMC Neuroscience 2019, 20(Suppl 1):P46\r\nhttps://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-019-0538-0#Sec281\r\n\r\nCrone JC, Vindiola MM, Yu AB, Boothe DL, Beeman D, Oie KS, Franaszczuk PJ (2019)\r\nEnabling Large-Scale Simulations With the GENESIS Neuronal Simulator.\r\nFront. Neuroinform. 13:69. doi: 10.3389/fninf.2019.00069\r\n(Model available from ModelDB, Accession number: 260267)" + }, + "class_id": 19, + "created": "2019-12-06T14:13:19.017", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 261423, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 83455, + "object_name": "Beeman, Dave" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 261425, + "object_name": "Beeman D, Yu A, Crone J (2019)" + }, + { + "object_id": 261132, + "object_name": "Crone JC, Vindiola MM, Yu AB, Boothe DL, Beeman D, Oie KS, Franaszczuk PJ (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 140257, + "object_name": "PGENESIS" + }, + { + "object_id": 1883, + "object_name": "GENESIS" + } + ] + }, + "name": "ACnet23 primary auditory cortex model (Beeman et al 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "These scripts were used to model a patch of layer 2/3 primary auditory cortex,\r\nmaking use of the the improvements to PGENESIS by Crone, et al. (2019).\r\nThis single layer model contains a 48 x 48 grid of pyramidal cells (PCs)\r\nand a 24 x 24 grid of basket cells (BCs). The reduced PC models have 17\r\ncompartments with dimensions and passive properties that were fit to human\r\ncortical PC reconstructions. This parallel version of the simulation was used\r\nby Beeman, et al. (2019) to understand the effects of inhibition of PCs by\r\nBCs on auditory evoked potentials.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261423", + "public_submitter_email": { + "attr_id": 309, + "value": "dbeeman@colorado.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "David Beeman" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 144485, + "object_name": "Auditory cortex" + } + ] + }, + "ver_date": "2019-12-08T16:05:07.523", + "ver_number": 6 + }, + "261435": { + "class_id": 19, + "created": "2019-12-09T20:18:10.347", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 261435, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267722, + "object_name": "Knowlton CJ, Baxter DA, Byrne JH., Canavier CC (2020)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Repetitive Action Potential Firing (Knowlton et al. 2020)", + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261435", + "public_submitter_email": { + "attr_id": 309, + "value": "cknow1@lsuhsc.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Christopher Knowlton" + }, + "ver_date": "2023-04-26T11:27:15.803", + "ver_number": 5 + }, + "261436": { + "citation": { + "attr_id": 391, + "value": "7675638" + }, + "class_id": 19, + "created": "2019-12-11T10:20:13.69", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 139268, + "object_name": "I Na, leak" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 261436, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 261455, + "object_name": "Zeberg, Hugo [hugo.zeberg at ki.se]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 5558, + "object_name": "Schwarz JR, Reid G, Bostock H (1995)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Peripheral nerve:Morris-Lecar implementation of (Schwarz et al 1995)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 136307, + "object_name": "Dorsal Root Ganglion (DRG) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a Morris-Lecar version of the model in Schwarz et al 1995. The original model in the paper was implemented in the Hodgkin-Huxley style." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261436", + "public_submitter_email": { + "attr_id": 309, + "value": "hugo.zeberg@ki.se" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Hugo Zeberg" + }, + "ver_date": "2019-12-11T17:38:23.267", + "ver_number": 8 + }, + "261460": { + "citation": { + "attr_id": 391, + "value": "https://www.biorxiv.org/content/10.1101/395384v1.abstract" + }, + "class_id": 19, + "created": "2019-12-14T08:23:34.817", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 261460, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 231429, + "object_name": "Doron, Michael [michael.doron at mail.huji.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 143085, + "object_name": "Synaptic-input statistic" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263064, + "object_name": "Iascone DM, Li Y, S\u00fcmb\u00fcl U, Doron M, Chen H, Andreu V, Goudy F, Blockus H, Abbott LF, Segev I, Peng H, Polleux F (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Mice Somatosensory L2/3 Pyramidal cells (Iascone et al 2020)", + "notes": { + "attr_id": 24, + "value": "Mice L2/3 pyramidal cells with full excitatory and inhibitory synaptic maps (Models used in Whole-neuron synaptic mapping reveals local balance between excitatory and inhibitory synapse organization - Iascone et at 2020)" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261460", + "public_submitter_email": { + "attr_id": 309, + "value": "michael.doron@mail.huji.ac.il" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Michael Doron" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2020-04-01T11:00:59.28", + "ver_number": 10 + }, + "261461": { + "citation": { + "attr_id": 391, + "value": "A biophysical model of striatal microcircuits suggests theta-rhythmically interleaved gamma and beta oscillations mediate periodicity in motor control\r\nJulia A. K. Chartove, Michelle M. McCarthy, Benjamin R. Pittman-Polletta, Nancy J. Kopell\r\nbioRxiv 725416; doi: https://doi.org/10.1101/725416" + }, + "class_id": 19, + "created": "2019-12-16T21:52:33.29", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 261461, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 261869, + "object_name": "Chartove, Julia A K [chartove at bu dot edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 185022, + "object_name": "Beta oscillations" + }, + { + "object_id": 156115, + "object_name": "Gamma oscillations" + }, + { + "object_id": 249458, + "object_name": "Theta oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263616, + "object_name": "Chartove JA, McCarthy MM, Pittman-Polletta BR, Kopell NJ (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 239393, + "object_name": "DynaSim" + } + ] + }, + "name": "Striatal FSI and SPN oscillation model (Chartove et al. 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 260, + "object_name": "Neostriatum medium spiny direct pathway GABA cell" + }, + { + "object_id": 153360, + "object_name": "Neostriatum medium spiny indirect pathway GABA cell" + }, + { + "object_id": 118395, + "object_name": "Neostriatum fast spiking interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Our model consists of three interconnected populations of single or double compartment Hodgkin-Huxley neurons: a feedforward network of FSIs, and two networks of SPNs (the D1 receptor-expressing \"direct pathway\" subnetwork and the D2 receptor-expressing \"indirect pathway\" subnetwork)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261461", + "other_concept": { + "attr_id": 306, + "value": "Delta oscillations" + }, + "other_currents": { + "attr_id": 303, + "value": "D-type potassium current, M-type potassium current" + }, + "other_implementer": { + "attr_id": 308, + "value": "Chartove, Julia A K [chartove at bu dot edu]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "chartove@bu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Julia Chartove" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 223, + "object_name": "Dopaminergic Receptor" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 138633, + "object_name": "Striatum" + } + ] + }, + "ver_date": "2020-02-06T23:01:21.533", + "ver_number": 6 + }, + "261466": { + "citation": { + "attr_id": 391, + "value": "https://www.biorxiv.org/content/10.1101/269134v5\r\nA very fast timescale of human motor adaptation: within movements adjustments of internal representations during reaching. To appear at eNeuro" + }, + "class_id": 19, + "created": "2019-12-23T04:38:59.93", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 261466, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 259370, + "object_name": "Crevecoeur, Fr\u00e9d\u00e9ric " + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263610, + "object_name": "Crevecoeur F, Thonnard JL, Lef\u00e8vre P (2020)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Within movement adjustments of internal representations during reaching (Crevecoeur et al 2020)", + "notes": { + "attr_id": 24, + "value": "\"An important function of the nervous system is to adapt motor commands in anticipation of predictable disturbances, which supports motor learning when we move in novel environments such as force fields (FFs). Here, we show that movement control when exposed to unpredictable disturbances exhibit similar traits: motor corrections become tuned to the FF, and they evoke after effects within an ongoing sequence of movements. We propose and discuss the framework of adaptive control to explain these results: a real-time learning algorithm, which complements feedback control in the presence of model errors. This candidate model potentially links movement control and trial-by-trial adaptation of motor commands.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261466", + "public_submitter_email": { + "attr_id": 309, + "value": "frederic.crevecoeur@uclouvain.be" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Frederic Crevecoeur" + }, + "ver_date": "2020-04-01T17:15:27.76", + "ver_number": 8 + }, + "261483": { + "class_id": 19, + "created": "2020-01-06T15:48:36.783", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 261483, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 261885, + "object_name": "Eckmann, Samuel [ec.sam at outlook.com]" + }, + { + "object_id": 261886, + "object_name": "Klimmasch, Lukas [klimmasch at fias.uni-frankfurt.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + }, + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + }, + { + "object_id": 83538, + "object_name": "Unsupervised Learning" + }, + { + "object_id": 262441, + "object_name": "Amblyopia" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262440, + "object_name": "Eckmann S, Klimmasch L, Shi BE, Triesch J (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 223029, + "object_name": "Predictive Coding Network" + }, + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Coding explains development of binocular vision and its failure in Amblyopia (Eckmann et al 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 245940, + "object_name": "Abstract rate-based neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is the MATLAB code for the Active Efficient Coding model introduced in Eckmann et al 2020.\r\nIt simulates an agent that self-calibrates vergence and accommodation eye movements in a simple visual environment. All algorithms are explained in detail in the main manuscript and the supplementary material of the paper." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261483", + "other_implementer": { + "attr_id": 308, + "value": "Eckmann, Samuel [ec.sam at outlook.com]\r\nKlimmasch, Lukas [klimmasch at fias.uni-frankfurt.de]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ec.sam@outlook.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Samuel Eckmann" + }, + "ver_date": "2020-03-04T11:53:55.88", + "ver_number": 10 + }, + "261489": { + "class_id": 19, + "created": "2020-01-09T15:28:51.913", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 261489, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 265917, + "object_name": "Costa, Renan M " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 188516, + "object_name": "Memory" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 265918, + "object_name": "Costa RM, Baxter DA, Byrne JH (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33983, + "object_name": "SNNAP" + } + ] + }, + "name": "Computational model of the distributed representation of operant reward memory (Costa et al. 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 65418, + "object_name": "Aplysia feeding CPG neurons" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Operant reward learning of feeding behavior in Aplysia increases the frequency and regularity of biting, as well as biases\r\nbuccal motor patterns (BMPs) toward ingestion-like BMPs (iBMPs). The engram underlying this memory comprises cells\r\nthat are part of a central pattern generating (CPG) circuit and includes increases in the intrinsic excitability of identified\r\ncells B30, B51, B63, and B65, and increases in B63\u2013B30 and B63\u2013B65 electrical synaptic coupling. To examine the ways in\r\nwhich sites of plasticity (individually and in combination) contribute to memory expression, a model of the CPG was developed.\r\nThe model included conductance-based descriptions of cells CBI-2, B4, B8, B20, B30, B31, B34, B40, B51, B52, B63,\r\nB64, and B65, and their synaptic connections. The model generated patterned activity that resembled physiological BMPs,\r\nand implementation of the engram reproduced increases in frequency, regularity, and bias. Combined enhancement of\r\nB30, B63, and B65 excitabilities increased BMP frequency and regularity, but not bias toward iBMPs. Individually, B30 increased\r\nregularity and bias, B51 increased bias, B63 increased frequency, and B65 decreased all three BMP features.\r\nCombined synaptic plasticity contributed primarily to regularity, but also to frequency and bias. B63\u2013B30 coupling contributed\r\nto regularity and bias, and B63\u2013B65 coupling contributed to all BMP features. Each site of plasticity altered multiple\r\nBMP features simultaneously. Moreover, plasticity loci exhibited mutual dependence and synergism. These results indicate\r\nthat the memory for operant reward learning emerged from the combinatoric engagement of multiple sites of plasticity." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261489", + "other_implementer": { + "attr_id": 308, + "value": "Costa, Renan M" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "renan.murillo.costa@uth.tmc.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Renan M. Costa" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115956, + "object_name": "Aplysia" + } + ] + }, + "ver_date": "2020-05-15T14:24:14.027", + "ver_number": 5 + }, + "261585": { + "class_id": 19, + "created": "2020-01-15T16:53:06.367", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 261585, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 261586, + "object_name": "Kim, Sung Soo [sungsoo at ucsb.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 182690, + "object_name": "Spatial Navigation" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 180821, + "object_name": "Hebbian plasticity" + }, + { + "object_id": 83539, + "object_name": "Attractor Neural Network" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 261495, + "object_name": "Kim SS, Hermundstad AM, Romani S, Abbott LF, Jayaraman V (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Generation of stable heading representations in diverse visual scenes (Kim et al 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 245940, + "object_name": "Abstract rate-based neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Many animals rely on an internal heading representation when\r\nnavigating in varied environments. How this\r\nrepresentation is linked to the sensory cues that define different\r\nsurroundings is unclear. In the fly brain, heading is represented by\r\n\u2018compass\u2019 neurons that innervate a ring-shaped structure known as the\r\nellipsoid body. Each compass neuron receives inputs from \u2018ring\u2019\r\nneurons that are selective for particular visual features;\r\nthis combination provides an ideal substrate for the extraction of\r\ndirectional information from a visual scene. Here we combine\r\ntwo-photon calcium imaging and optogenetics in tethered flying flies\r\nwith circuit modelling, and show how the correlated activity of\r\ncompass and visual neurons drives plasticity, which\r\nflexibly transforms two-dimensional visual cues into a stable heading\r\nrepresentation. ... \"\r\nSee the supplementary information for model details." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261585", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 206363, + "object_name": "Drosophila" + } + ] + }, + "ver_date": "2020-01-15T17:37:10.597", + "ver_number": 7 + }, + "261616": { + "citation": { + "attr_id": 391, + "value": "G. Mulcahy, B. Atwood and A. Kuznetsov. Basal Ganglia role in learning rewarded actions and executing previously learned choices: healthy and diseased states. PLoS ONE 2019. In press." + }, + "class_id": 19, + "created": "2020-01-22T05:35:35.253", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 261616, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 58584, + "object_name": "Rate-coding model neurons" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + }, + { + "object_id": 261622, + "object_name": "Huntington's" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263612, + "object_name": "Mulcahy G, Atwood B, Kuznetsov A (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Cortico - Basal Ganglia Loop (Mulcahy et al 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 245940, + "object_name": "Abstract rate-based neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model represents learning and reversal tasks and shows performance in control, Parkinsonian and Huntington disease conditions" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261616", + "public_submitter_email": { + "attr_id": 309, + "value": "askuznet@iupui.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Alexey Kuznetsov" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + }, + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2020-01-22T11:58:06.373", + "ver_number": 7 + }, + "261623": { + "citation": { + "attr_id": 391, + "value": "30446533" + }, + "class_id": 19, + "created": "2020-01-23T10:57:20.127", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 116966, + "object_name": "I_K,Na" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 261623, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 261707, + "object_name": "Jacob, Theju " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 114660, + "object_name": "Connectivity matrix" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 261631, + "object_name": "Jacob T, Lillis KP, Wang Z, Swiercz W, Rahmati N, Staley KJ (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "A NN with synaptic depression for testing the effects of connectivity on dynamics (Jacob et al 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 240378, + "object_name": "Abstract integrate-and-fire neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Here we used a 10,000 neuron model. The neurons are a mixture of excitatory and inhibitory integrate-and-fire neurons connected with synapses that exhibit synaptic depression. Three different connectivity paradigms were tested to look for spontaneous transition between interictal spiking and seizure: uniform, small-world network, and scale-free. All three model types are included here." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261623", + "other_concept": { + "attr_id": 306, + "value": "Seizure" + }, + "other_implementer": { + "attr_id": 308, + "value": "Jacob, Theju" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "klillis@mgh.harvard.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Kyle Lillis" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228598, + "object_name": "Mouse" + }, + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2020-02-04T13:56:26.96", + "ver_number": 5 + }, + "261624": { + "citation": { + "attr_id": 391, + "value": "\"MATHEMATICAL MODELING AND PARAMETER ESTIMATION OF LEVODOPA MOTOR RESPONSE IN PATIENTS WITH PARKINSON DISEASE\" by Ursino M., Magosso E., et al., accepted with revision by Plos One." + }, + "class_id": 19, + "created": "2020-01-29T08:23:00.027", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 261624, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 239531, + "object_name": "Ursino, Mauro [mauro.ursino at unibo.it]" + }, + { + "object_id": 261628, + "object_name": "Magosso, Elisa [elisa.magosso at unibo.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 180821, + "object_name": "Hebbian plasticity" + }, + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263614, + "object_name": "Ursino M, Magosso E, Lopane G, Calandra-Buonaura G, Cortelli P, Contin M (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Basal Ganglia and Levodopa Pharmacodynamics model for parameter estimation in PD (Ursino et al 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 245940, + "object_name": "Abstract rate-based neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 209, + "object_name": "Acetylcholine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Parkinson disease (PD) is characterized by a clear beneficial motor response to levodopa (LD) treatment. However, with disease progression and longer LD exposure, drug-related motor fluctuations usually occur. Recognition of the individual relationship between LD concentration and its effect may be difficult, due to the complexity and variability of the mechanisms involved. This work proposes an innovative procedure for the automatic estimation of LD pharmacokinetics and pharmacodynamics parameters, by a biologically-inspired mathematical model. An original issue, compared with previous similar studies, is that the model comprises not only a compartmental description of LD pharmacokinetics in plasma and its effect on the striatal neurons, but also a neurocomputational model of basal ganglia action selection. Parameter estimation was achieved on 26 patients (13 with stable and 13 with fluctuating LD response) to mimic plasma LD concentration and alternate finger tapping frequency along four hours after LD administration, automatically minimizing a cost function of the difference between simulated and clinical data points. Results show that individual data can be satisfactorily simulated in all patients and that significant differences exist in the estimated parameters between the two groups. Specifically, the drug removal rate from the effect compartment, and the Hill coefficient of the concentration-effect relationship were significantly higher in the fluctuating than in the stable group. \r\nThe model, with individualized parameters, may be used to reach a deeper comprehension of the PD mechanisms, mimic the effect of medication, and, based on the predicted neural responses, plan the correct management and design innovative therapeutic procedures." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261624", + "other_implementer": { + "attr_id": 308, + "value": "Magosso Elisa (elisa.magosso at unibo.it)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "mauro.ursino@unibo.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Mauro Ursino" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 196, + "object_name": "D1" + }, + { + "object_id": 197, + "object_name": "D2" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2020-01-31T17:23:23.423", + "ver_number": 6 + }, + "261709": { + "citation": { + "attr_id": 391, + "value": "Int J Mol Sci. 2019 Dec 27;21(1). pii: E206" + }, + "class_id": 19, + "created": "2020-01-31T05:40:11.247", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 261709, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 261712, + "object_name": "Gall, David " + }, + { + "object_id": 261713, + "object_name": "Dupont, Genevi\u00e8ve " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 188516, + "object_name": "Memory" + }, + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 112047, + "object_name": "Aging/Alzheimer`s" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 261710, + "object_name": "Gall D, Dupont G (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 45540, + "object_name": "FORTRAN" + } + ] + }, + "name": "Tonic activation of extrasynaptic NMDA-R promotes bistability (Gall & Dupont 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 7346, + "object_name": "Cerebellum interneuron granule GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Our theoretical model provides a simple description of neuronal electrical activity that takes into account the tonic activity of extrasynaptic NMDA receptors and a cytosolic calcium compartment. We show that calcium influx mediated by the tonic activity of NMDA-R can be coupled directly to the activation of calcium-activated potassium channels, resulting in an overall inhibitory effect on neuronal excitability. Furthermore, the presence of tonic NMDA-R activity promotes bistability in electrical activity by dramatically increasing the stimulus interval where both a stable steady state and repetitive firing can coexist. These results could provide an intrinsic mechanism for the constitution of memory traces in neuronal circuits." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261709", + "other_implementer": { + "attr_id": 308, + "value": "David Gall & Genevi\u00e8ve Dupont" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "dgall@ulb.ac.be" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "David Gall" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2020-02-05T16:11:43.883", + "ver_number": 8 + }, + "261714": { + "class_id": 19, + "created": "2020-02-01T13:35:42.063", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 261714, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 238957, + "object_name": "Markov-type model" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 149383, + "object_name": "Drug binding" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266418, + "object_name": "Neumaier F, Apldogan S, Hescheler J and Schneider T (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A Markov model of human Cav2.3 channels and their modulation by Zn2+ (Neumaier et al 2020)", + "notes": { + "attr_id": 24, + "value": "The Markov model for Cav2.3 channel gating in the absence of trace metals was developed based on channel structure, previous modeling studies and the ability to fit the data. Model parameters were optimized by fitting the model to macroscopic currents recorded with various electrophysiological protocols from HEK-293 cells stably transfected with human Cav2.3+\u00df3 channel subunits. The effects of Zn2+ were implemented by assuming that Zn2+ binding to a first site (KZn=0.003 mM) leads to electrostatic modification and mechanical slowing of one of the voltage-sensors while Zn2+-binding to a second, intra-pore site (KZn=0.1 mM) blocks the channel and modifies the opening and closing transitions." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261714", + "other_gene": { + "attr_id": 477, + "value": "Cav2.3 CACNA1E" + }, + "other_implementer": { + "attr_id": 308, + "value": "Felix Neumaier [felix at neumaier-net.de]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "felix@neumaier-net.de" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Felix Neumaier" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 227987, + "object_name": "Human" + } + ] + }, + "ver_date": "2020-06-10T09:31:55.673", + "ver_number": 43 + }, + "261864": { + "citation": { + "attr_id": 391, + "value": "Imam N, Cleland TA (2020) Rapid learning and robust recall in a neuromorphic olfactory circuit. Nature Machine Intelligence (in press)." + }, + "class_id": 19, + "created": "2020-02-06T11:04:52.997", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 261864, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 261866, + "object_name": "Imam, Nabil " + }, + { + "object_id": 261867, + "object_name": "Cleland, Thomas [tac29 at cornell.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 155599, + "object_name": "Neurogenesis" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 156115, + "object_name": "Gamma oscillations" + }, + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 97752, + "object_name": "Delay" + }, + { + "object_id": 180821, + "object_name": "Hebbian plasticity" + }, + { + "object_id": 188516, + "object_name": "Memory" + }, + { + "object_id": 245690, + "object_name": "Olfaction" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3542, + "object_name": "Pattern Recognition" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263615, + "object_name": "Imam N, Cleland TA (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Online learning model of olfactory bulb external plexiform layer network (Imam & Cleland 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 267, + "object_name": "Olfactory bulb main mitral GLU cell" + }, + { + "object_id": 3979, + "object_name": "Olfactory bulb main interneuron granule MC GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model illustrates the rapid online learning of odor representations, and their recognition despite high levels of interference (other competing odorants), in a model of the olfactory bulb external plexiform layer (EPL) network. The computational principles embedded in this model are based on the those developed in the biophysical models of Li and Cleland (2013, 2017). \r\n\r\nThis is a standard Python version of a model written for Intel's Loihi neuromorphic hardware platform (The Loihi code is available at https://github.com/intel-nrc-ecosystem/models/tree/master/official/epl)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261864", + "other_concept": { + "attr_id": 306, + "value": "Online learning" + }, + "other_implementer": { + "attr_id": 308, + "value": "Imam, Nabil\r\nClelalnd, Thomas" + }, + "other_type": { + "attr_id": 305, + "value": "Neuromorphic network" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tac29@cornell.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Thomas Cleland" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "ver_date": "2020-02-06T12:36:00.033", + "ver_number": 3 + }, + "261873": { + "citation": { + "attr_id": 391, + "value": "Farokhniaee, A., & McIntyre, C. C. (2019). Theoretical principles of deep brain stimulation induced synaptic suppression. Brain Stimulation, 12(6), 1402\u20131409. https://doi.org/10.1016/j.brs.2019.07.005" + }, + "class_id": 19, + "created": "2020-02-08T14:03:50.463", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 261873, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 261880, + "object_name": "Farokhniaee, AmirAli [aafarokh at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 261855, + "object_name": "Farokhniaee A, McIntyre CC (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Theoretical principles of DBS induced synaptic suppression (Farokhniaee & McIntyre 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Deep brain stimulation (DBS) is a successful clinical therapy for a wide range of neurological disorders; however, the physiological mechanisms of DBS remain unresolved. While many different hypotheses currently exist, our analyses suggest that high frequency (~100?Hz) stimulation-induced synaptic suppression represents the most basic concept that can be directly reconciled with experimental recordings of spiking activity in neurons that are being driven by DBS inputs.\r\n\r\nObjective\r\nThe goal of this project was to develop a simple model system to characterize the excitatory post-synaptic currents (EPSCs) and action potential signaling generated in a neuron that is strongly connected to pre-synaptic glutamatergic inputs that are being directly activated by DBS.\r\n\r\nMethods\r\nWe used the Tsodyks-Markram (TM) phenomenological synapse model to represent depressing, facilitating, and pseudo-linear synapses driven by DBS over a wide range of stimulation frequencies. The EPSCs were then used as inputs to a leaky integrate-and-fire neuron model and we measured the DBS-triggered post-synaptic spiking activity.\r\n\r\nResults\r\nSynaptic suppression was a robust feature of high frequency stimulation, independent of the synapse type. As such, the TM equations were used to define alternative DBS pulsing strategies that maximized synaptic suppression with the minimum number of stimuli.\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261873", + "public_submitter_email": { + "attr_id": 309, + "value": "aafarokh@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "AmirAli Farokhniaee" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2020-02-12T17:57:44.637", + "ver_number": 6 + }, + "261877": { + "class_id": 19, + "created": "2020-02-10T20:44:53.907", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 261877, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 265590, + "object_name": "Rajagopalan, Adithya [adiraj95 at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 265589, + "object_name": "Rajagopalan A, Assisi C (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Effect of circuit structure on odor representation in insect olfaction (Rajagopalan & Assisi 2020)", + "notes": { + "attr_id": 24, + "value": "\"How does the structure of a network affect its function? We address this question in the context of two olfactory systems that serve the same function, to distinguish the attributes of different odorants, but do so using markedly distinct architectures. In the locust, the probability of connections between projection neurons and Kenyon cells - a layer downstream - is nearly 50%. In contrast, this number is merely 5% in drosophila. We developed computational models of these networks to understand the relative advantages of each connectivity. Our analysis reveals that the two systems exist along a continuum of possibilities that balance two conflicting goals \u2013 separating the representations of similar odors while grouping together noisy variants of the same odor.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261877", + "public_submitter_email": { + "attr_id": 309, + "value": "adiraj95@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Adithya E Rajagopalan" + }, + "ver_date": "2020-07-29T19:57:01.933", + "ver_number": 5 + }, + "261878": { + "citation": { + "attr_id": 391, + "value": "Bush D, Burgess N (in press) Detection and Advantages of Phase Coding in the Absence of Rhythmicity. Hippocampus" + }, + "class_id": 19, + "created": "2020-02-12T12:10:59.77", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 261878, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 183714, + "object_name": "Bush, Daniel [drdanielbush @ gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 194888, + "object_name": "Grid cell" + }, + { + "object_id": 98011, + "object_name": "Place cell/field" + }, + { + "object_id": 249458, + "object_name": "Theta oscillations" + }, + { + "object_id": 182690, + "object_name": "Spatial Navigation" + }, + { + "object_id": 240376, + "object_name": "Temporal Coding" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263617, + "object_name": "Bush D, Burgess N (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Decoding movement trajectory from simulated grid cell population activity (Bush & Burgess 2019)", + "notes": { + "attr_id": 24, + "value": "Matlab code to simulate a population of grid cells that exhibit both a rate and phase code for location in 1D or 2D environments, and are modulated by a human hippocampal LFP signal with highly variable frequency; then subsequently decode location, running speed, movement direction and an arbitrary fourth variable from population firing rates and phases in each oscillatory cycle." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261878", + "public_submitter_email": { + "attr_id": 309, + "value": "drdanielbush@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Daniel Bush" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 119191, + "object_name": "Entorhinal cortex" + }, + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2020-02-19T12:34:08.597", + "ver_number": 9 + }, + "261881": { + "class_id": 19, + "created": "2020-02-13T02:43:18.827", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 261881, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 262437, + "object_name": "Giacopelli, Giuseppe [giuseppe.giacopelli at unipa.it]" + }, + { + "object_id": 262439, + "object_name": "Tegolo, Domenico [domenico.tegolo at unipa.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 114660, + "object_name": "Connectivity matrix" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262434, + "object_name": "Giacopelli G, Migliore M, Tegolo D (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Graph-theoretical Derivation of Brain Structural Connectivity (Giacopelli et al 2020)", + "notes": { + "attr_id": 24, + "value": "Brain connectivity at the single neuron level can provide fundamental insights into how information is integrated and propagated within and between brain regions. However, it is almost impossible to adequately study this problem experimentally and, despite intense efforts in the field, no mathematical description has been obtained so far. Here, we present a mathematical framework based on a graph-theoretical approach that, starting from experimental data obtained from a few small subsets of neurons, can quantitatively explain and predict the corresponding full network properties. This model also changes the paradigm with which large-scale model networks can be built, from using probabilistic/empiric connections or limited data, to a process that can algorithmically generate neuronal networks connected as in the real system." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261881", + "other_neurons": { + "attr_id": 301, + "value": "any neuronal network" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "giuseppe.giacopelli@unipa.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Giuseppe Giacopelli" + }, + "ver_date": "2020-03-04T10:17:28.397", + "ver_number": 13 + }, + "261882": { + "citation": { + "attr_id": 391, + "value": "Yousif N, Bain P, Nandi D and Borisyuk R (2020) A Population Model of Deep Brain Stimulation in Movement Disorders From Circuits to Cells.\r\nFront. Hum. Neurosci. 14:55. doi: 10.3389/fnhum.2020.00055" + }, + "class_id": 19, + "created": "2020-02-14T06:46:30.737", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 261882, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263618, + "object_name": "Yousif N, Bain PG, Nandi D, Borisyuk R (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Network model of movement disorders (Yousif et al 2020)", + "notes": { + "attr_id": 24, + "value": "This is a Wilson-Cowan model of the basal ganglia thalamocortical cerebellar network that demonstrates healthy gamma band oscillations, Parkinsonian oscillations in the beta band and oscillations in the tremor frequency range arising from the dynamics of the network." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261882", + "public_submitter_email": { + "attr_id": 309, + "value": "n.yousif@herts.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Nada Yousif" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + }, + { + "object_id": 115953, + "object_name": "Cerebellum" + }, + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2020-02-15T11:33:15.863", + "ver_number": 6 + }, + "261899": { + "citation": { + "attr_id": 391, + "value": "https://www.jneurosci.org/content/early/2019/07/25/JNEUROSCI.3169-18.2019.abstract" + }, + "class_id": 19, + "created": "2020-02-18T14:07:57.743", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 261899, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 262122, + "object_name": "Ocker, Gabriel K " + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262090, + "object_name": "Linaro D, Ocker GK, Doiron B, Giugliano M (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "L5 cortical neurons with recreated synaptic inputs in vitro correlation transfer (Linaro et al 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"...We studied pyramidal neurons and two classes of GABAergic interneurons of layer 5 in neocortical brain slices obtained from rats of both sexes, and we stimulated them with biophysically realistic correlated inputs, generated using dynamic clamp. We found that the physiological differences between cell types manifested unique features in their capacity to transfer correlated inputs. We used linear response theory and computational modeling to gain clear insights into how cellular properties determine both the gain and timescale of correlation transfer, thus tying single-cell features with network interactions. Our results provide further ground for the functionally distinct roles played by various types of neuronal cells in the cortical microcircuit...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/261899", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2020-07-29T20:01:44.967", + "ver_number": 7 + }, + "262045": { + "class_id": 19, + "created": "2020-02-20T07:33:07.093", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262045, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 182782, + "object_name": "Sadeh, Sadra [s.sadeh at ucl.ac.uk]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267633, + "object_name": "Sadeh S, Clopath C (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A computational model of single-neuron perturbations (Sadeh and Clopath 2020)", + "notes": { + "attr_id": 24, + "value": "A computational model to study the effect of single-neuron perturbations in large-scale excitatory-inhibitory networks of the primary visual cortex. Neuronal receptive fields and connectivity are constrained by experimental literature. The model addresses how the influence of perturbing an excitatory neuron (\"influencer\") in the network on other neurons (\"influencees\") depends on the similarity of their receptive fields. Specifically, in which regimes this influence is dominated by amplification or suppression, and how it relates to functional properties of neurons." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262045", + "public_submitter_email": { + "attr_id": 309, + "value": "s.sadeh@ucl.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Sadra Sadeh" + }, + "ver_date": "2023-02-09T09:44:56.807", + "ver_number": 8 + }, + "262046": { + "class_id": 19, + "created": "2020-02-20T08:58:22.7", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262046, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 263054, + "object_name": "John E. 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The cortical basal ganglia network model incorporates the (i) the extracellular DBS electric field, (ii) antidromic and orthodromic activation of STN afferent fibers, (iii) the LFP detected at non-stimulating contacts on the DBS electrode and (iv) temporal variation of network beta-band activity within the thalamo-cortico-basal ganglia loop. The model facilitates investigation of clinically-viable closed-loop DBS control approaches, modulating either DBS amplitude or frequency, using an LFP derived measure of network beta-activity." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262046", + "other_concept": { + "attr_id": 306, + "value": "Beta Burst Durations" + }, + "other_implementer": { + "attr_id": 308, + "value": "John E. Fleming" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "john.fleming@ucdconnect.ie" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "John E. Fleming" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + }, + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2020-03-17T16:40:09.873", + "ver_number": 5 + }, + "262059": { + "citation": { + "attr_id": 391, + "value": "Hazan L, Ziv NE (2020) Activity dependent and independent determinants of synaptic size diversity. J Neuroscience (in press)" + }, + "class_id": 19, + "created": "2020-02-21T09:37:06.277", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262059, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 262078, + "object_name": "Ziv, Noam [noamz at netvision.net.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 238958, + "object_name": "Stochastic simulation" + }, + { + "object_id": 183719, + "object_name": "Synaptic noise" + }, + { + "object_id": 262152, + "object_name": "Kesten Process\r\n" + }, + { + "object_id": 262153, + "object_name": "Langevin process" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263619, + "object_name": "Hazan L, Ziv NE (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 58965, + "object_name": "QBasic/QuickBasic/Turbo Basic/VBA" + } + ] + }, + "name": "Kesten and Langevin synaptic size fluctuation simulator (Hazan & Ziv 2020)", + "notes": { + "attr_id": 24, + "value": "Sizes of glutamatergic synapses vary tremendously, even when formed on the same neuron. This diversity is commonly thought to reflect the outcome of activity-dependent forms of synaptic plasticity, yet activity-independent processes might also play some part. In this paper we show that in neurons with no history of activity whatsoever, synaptic sizes are no less diverse. We show that this diversity is the product of activity-independent size fluctuations, which are sufficient to generate a full repertoire of synaptic sizes at correct proportions. This simulator shows how synaptic size fluctuations governed by a stochastic process known as a Kesten process (as well as a specific form of a non-linear Langevin process) can give rise to this size diversity." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262059", + "other_concept": { + "attr_id": 306, + "value": "Kesten Process" + }, + "other_implementer": { + "attr_id": 308, + "value": "Noam Ziv" + }, + "other_simulator": { + "attr_id": 307, + "value": "VBA" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "noamz@netvision.net.il" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Noam Ziv" + }, + "ver_date": "2020-02-26T18:10:37.89", + "ver_number": 10 + }, + "262060": { + "citation": { + "attr_id": 391, + "value": "Hazan L, Ziv NE (2020) Activity dependent and independent determinants of synaptic size diversity. J Neuroscience (in press)" + }, + "class_id": 19, + "created": "2020-02-21T09:44:31.8", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262060, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 262078, + "object_name": "Ziv, Noam [noamz at netvision.net.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 238958, + "object_name": "Stochastic simulation" + }, + { + "object_id": 183719, + "object_name": "Synaptic noise" + }, + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263619, + "object_name": "Hazan L, Ziv NE (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Mesoscopic model of spontaneous synaptic size fluctuations (Hazan & Ziv 2020)", + "notes": { + "attr_id": 24, + "value": "Sizes of glutamatergic synapses vary tremendously, even when formed on the same neuron. This diversity is commonly thought to reflect the outcome of activity-dependent forms of synaptic plasticity, yet activity-independent processes might also play some part. Here we show that in neurons with no history of activity whatsoever, synaptic sizes are no less diverse. We show that this diversity is the product of activity-independent size fluctuations, which are sufficient to generate a full repertoire of synaptic sizes at correct proportions. By combining modeling and experimentation we expose reciprocal relationships between size fluctuations, synaptic sizes and synaptic counts, and show how these phenomena might be connected through the dynamics of synaptic molecules as they move in, out and between synapses." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262060", + "other_implementer": { + "attr_id": 308, + "value": "Noam Ziv" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "noamz@netvision.net.il" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Noam Ziv" + }, + "ver_date": "2020-02-21T19:01:07.24", + "ver_number": 6 + }, + "262081": { + "class_id": 19, + "created": "2020-02-24T04:33:05.933", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 88215, + "object_name": "I_Ks" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 88210, + "object_name": "I_Na,Ca" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262081, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 262089, + "object_name": "Sircan, Ahmet K\u00fcrsad [ahmetkursadsircan at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262088, + "object_name": "Sengul S, Sircan AK, Abewa M, Kurt A, Dalaman U, Yaras N (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "The ventricular AP and effects of the isoproterenol-induced cardiac hypertrophy (Sengul et al 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88205, + "object_name": "Cardiac ventricular cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model reproduces Action Potential (AP) of Rat Ventricular Myocytes according to the experimental AP and Voltage Clamp recordings." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262081", + "other_currents": { + "attr_id": 303, + "value": "I Ca, L; I Ca T; I to; I Kr; I K1; I Ca, leak; If" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ahmetkursadsircan@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ahmet K\u00fcrsad SIRCAN" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2023-02-09T09:53:04.003", + "ver_number": 7 + }, + "262114": { + "class_id": 19, + "created": "2020-02-24T18:48:33.44", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262114, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 262121, + "object_name": "Renaud, Jolivet [renaud.jolivet at unige.ch]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 136098, + "object_name": "Information transfer" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262113, + "object_name": "Harris JJ, Engl E, Attwell D, Jolivet RB (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Energy-efficient information transfer at thalamocortical synapses (Harris et al 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114635, + "object_name": "Neocortex spiny stellate cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Using both multicompartment Hodgkin-Huxley-type simulations and electrophysiological recordings in rodent brain slices, we find that increasing or decreasing the postsynaptic conductance of the set of thalamocortical inputs to one L4SS (Layer 4 Spiny Stellate) cell decreases the energy efficiency of information transmission from a single thalamocortical input. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262114", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2020-02-25T08:24:42.87", + "ver_number": 5 + }, + "262115": { + "class_id": 19, + "created": "2020-02-24T19:04:46.75", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262115, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33744, + "object_name": "Destexhe, Alain [Destexhe at iaf.cnrs-gif.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 4119, + "object_name": "Destexhe A, Par\u00e9 D (1999)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "NN activity impact on neocortical pyr. neurons integrative properties in vivo (Destexhe & Pare 1999)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + }, + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"During wakefulness, neocortical neurons are subjected to an intense\r\nsynaptic bombardment. To assess the consequences of this background\r\nactivity for the integrative properties of pyramidal neurons, we\r\nconstrained biophysical models with in vivo intracellular data\r\nobtained in anesthetized cats during periods of intense network\r\nactivity similar to that observed in the waking state. In pyramidal\r\ncells of the parietal cortex (area 5\u20137), synaptic activity was\r\nresponsible for an approximately fivefold decrease in input resistance\r\n(Rin), a more depolarized membrane potential (Vm), and a marked\r\nincrease in the amplitude of Vm fluctuations, as determined by\r\ncomparing the same cells before and after microperfusion of\r\ntetrodotoxin (TTX).\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262115", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2022-11-04T17:58:18.407", + "ver_number": 7 + }, + "262138": { + "class_id": 19, + "created": "2020-02-26T10:00:24.9", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262138, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 262139, + "object_name": "Bingham, Clayton S [clayton.bingham at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262137, + "object_name": "Bingham CS, Mergenthal A, Bouteiller JC, Song D, Lazzi G, Berger TW (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "ROOTS: An Algorithm to Generate Biologically Realistic Cortical Axons (Bingham et al 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 129068, + "object_name": "Entorhinal cortex stellate cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... a Ruled-Optimum Ordered Tree System (ROOTS) was developed\r\nthat extends the capability of neuronal morphology generative\r\nmethods to include highly branched cortical axon terminal\r\narbors. Further, this study presents and explores a clear\r\nuse-case for such models in the prediction of cortical tissue\r\nresponse to externally applied electric fields. The results\r\npresented herein comprise (i) a quantitative and qualitative\r\nanalysis of the generative algorithm proposed, (ii) a comparison\r\nof generated fibers with those observed in histological\r\nstudies, (iii) a study of the requisite spatial and morphological\r\ncomplexity of axonal arbors for accurate prediction of neuronal\r\nresponse to extracellular electrical stimulation, and (iv) an\r\nextracellular electrical stimulation strength\u2013duration analysis\r\nto explore probable thresholds of excitation of the dentate\r\nperforant path under controlled conditions.\r\n...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262138", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2020-02-26T10:06:56.65", + "ver_number": 5 + }, + "262187": { + "citation": { + "attr_id": 391, + "value": "28968386" + }, + "class_id": 19, + "created": "2020-02-27T10:31:06.25", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262187, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 194888, + "object_name": "Grid cell" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262209, + "object_name": "D'Albis T, Kempter R (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "A single-cell spiking model for the origin of grid-cell patterns (D'Albis & Kempter 2017)", + "notes": { + "attr_id": 24, + "value": "A single-cell spiking model explaining the formation of grid-cell pattern in a feed-forward network. Patterns emerge via spatially-tuned feedforward inputs, synaptic plasticity, and spike-rate adaptation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262187", + "public_submitter_email": { + "attr_id": 309, + "value": "tiziano.dalbis@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tiziano D'Albis" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 119191, + "object_name": "Entorhinal cortex" + } + ] + }, + "ver_date": "2020-03-03T14:00:15.227", + "ver_number": 5 + }, + "262233": { + "citation": { + "attr_id": 391, + "value": "https://www.liebertpub.com/doi/abs/10.1089/neu.2018.6065" + }, + "class_id": 19, + "created": "2020-02-28T13:45:22.257", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 146506, + "object_name": "NKCC1" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262233, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 261487, + "object_name": "Sudhakar, Shyam Kumar [shyamk at umich.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 121254, + "object_name": "Depolarization block" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262391, + "object_name": "Sudhakar SK, Choi TJ, Ahmed OJ (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "name": "Biophysical modeling of pathological brain states (Sudhakar et al 2019)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Traumatic brain injuries (TBI) lead to dramatic changes in the surviving brain tissue. Altered ion concentrations, coupled with changes in the expression of membrane-spanning proteins, create a post-TBI brain state that can lead to further neuronal loss caused by secondary excitotoxicity. Several GABA receptor agonists have been tested in the search for neuroprotection immediately after an injury, with paradoxical results. These drugs not only fail to offer neuroprotection, but can also slow down functional recovery after TBI. Here, using computational modeling, we provide a biophysical hypothesis to explain these observations. We show that the accumulation of intracellular chloride ions caused by a transient upregulation of Na+-K+-2Cl- (NKCC1) co-transporters as observed following TBI, causes GABA receptor agonists to lead to excitation and depolarization block, rather than the expected hyperpolarization. The likelihood of prolonged, excitotoxic depolarization block is further exacerbated by the extremely high levels of extracellular potassium seen after TBI. Our modeling results predict that the neuroprotective efficacy of GABA receptor agonists can be substantially enhanced when they are combined with NKCC1 co-transporter inhibitors. This suggests a rational, biophysically principled method for identifying drug combinations for neuroprotection after TBI.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262233", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "ver_date": "2020-03-02T19:44:45.937", + "ver_number": 7 + }, + "262356": { + "class_id": 19, + "created": "2020-03-02T05:37:50.88", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262356, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 194888, + "object_name": "Grid cell" + }, + { + "object_id": 83539, + "object_name": "Attractor Neural Network" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266813, + "object_name": "D'Albis T, Kempter R (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Recurrent amplification of grid-cell activity (D'Albis and Kempter 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 245940, + "object_name": "Abstract rate-based neuron" + } + ] + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262356", + "other_concept": { + "attr_id": 306, + "value": "Network Amplification" + }, + "other_implementer": { + "attr_id": 308, + "value": "D'Albis Tiziano" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tiziano.dalbis@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tiziano D'Albis" + }, + "ver_date": "2020-09-17T11:44:31.97", + "ver_number": 6 + }, + "262368": { + "citation": { + "attr_id": 391, + "value": "https://www.physiology.org/doi/abs/10.1152/jn.00067.2018" + }, + "class_id": 19, + "created": "2020-03-02T12:14:33.607", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262368, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 262442, + "object_name": "Maling, Nicholas [nickmaling at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262433, + "object_name": "Maling N, Lempka SF, Blumenfeld Z, Bronte-Stewart H, McIntyre CC (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "Biophysical basis of Subthalamic LFPs Recorded from DBS electrodes (Maling et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 74300, + "object_name": "Subthalamus nucleus projection neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Clinical deep brain stimulation (DBS) technology is evolving to enable chronic recording of local field potentials (LFPs) that represent electrophysiological biomarkers of the underlying disease state. However, little is known about the biophysical basis of LFPs, or how the patient\u2019s unique brain anatomy and electrode placement impact the recordings. Therefore, we developed a patient-specific computational framework to analyze LFP recordings within a clinical DBS context. We selected a subject with Parkinson\u2019s disease implanted with a Medtronic Activa PC+S DBS system and reconstructed their subthalamic nucleus (STN) and DBS electrode location using medical imaging data. The patient-specific STN volume was populated with 235,280 multicompartment STN neuron models, providing a neuron density consistent with histological measurements. Each neuron received time-varying synaptic inputs and generated transmembrane currents that gave rise to the LFP signal recorded at DBS electrode contacts residing in a finite element volume conductor model. We then used the model to study the role of synchronous beta-band inputs to the STN neurons on the recorded power spectrum. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262368", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + }, + { + "object_id": 116868, + "object_name": "Subthalamic Nucleus" + } + ] + }, + "ver_date": "2020-03-04T12:19:42.39", + "ver_number": 6 + }, + "262373": { + "citation": { + "attr_id": 391, + "value": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006392" + }, + "class_id": 19, + "created": "2020-03-02T13:26:01.5", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262373, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 263017, + "object_name": "Coggan, Jay S [jay.coggan at epfl.ch]" + }, + { + "object_id": 238005, + "object_name": "Keller, Daniel " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 183421, + "object_name": "Volume transmission" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262997, + "object_name": "Coggan JS, Keller D, Cal\u00ec C, Lehv\u00e4slaiho H, Markram H, Sch\u00fcrmann F, Magistretti PJ (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 120799, + "object_name": "Glia" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Norepinephrine stimulates glycogenolysis in astrocytes to fuel neurons (Coggan et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 145165, + "object_name": "Locus Coeruleus neuron" + }, + { + "object_id": 87589, + "object_name": "Astrocyte" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The mechanism of rapid energy supply to the brain, especially to accommodate the heightened metabolic activity of excited states, is not well-understood. We explored the role of glycogen as a fuel source for neuromodulation using the noradrenergic stimulation of glia in a computational model of the neural-glial-vasculature ensemble (NGV). The detection of norepinephrine (NE) by the astrocyte and the coupled cAMP signal are rapid and largely insensitive to the distance of the locus coeruleus projection release sites from the glia, implying a diminished impact for volume transmission in high affinity receptor transduction systems. Glucosyl-conjugated units liberated from glial glycogen by NE-elicited cAMP second messenger transduction winds sequentially through the glycolytic cascade, generating robust increases in NADH and ATP before pyruvate is finally transformed into lactate. This astrocytic lactate is rapidly exported by monocarboxylate transporters to the associated neuron, demonstrating that the astrocyte-to-neuron lactate shuttle activated by glycogenolysis is a likely fuel source for neuromodulation and enhanced neural activity. Altogether, the energy supply for both astrocytes and neurons can be supplied rapidly by glycogenolysis upon neuromodulatory stimulus.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262373", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "ver_date": "2020-03-11T18:36:07.62", + "ver_number": 7 + }, + "262389": { + "citation": { + "attr_id": 391, + "value": "https://www.nature.com/articles/s41598-017-14357-1" + }, + "class_id": 19, + "created": "2020-03-02T19:14:40.75", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262389, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 262111, + "object_name": "Tsai, David [d.tsai at unsw.edu.au]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262110, + "object_name": "Tsai D, Morley JW, Suaning GJ, Lovell NH (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Survey of electrically evoked responses in the retina (Tsai et al 2017)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 1966, + "object_name": "Retina photoreceptor cone GLU cell" + }, + { + "object_id": 225100, + "object_name": "Retina horizontal cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Cones and horizontal cells are interconnected to adjacent cones and horizontal cells, respectively, with gap junctions. In particular, the horizontal cell gap junctional conductance is modulated by exogenous factors. What roles does this conductance play in the electrically evoked responses of horizontal cells? To address this question, we constructed a computational model consisting of the cone and horizontal cell layer...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262389", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 118529, + "object_name": "Retina" + } + ] + }, + "ver_date": "2020-03-02T19:25:41.137", + "ver_number": 5 + }, + "262422": { + "citation": { + "attr_id": 391, + "value": "https://www.physiology.org/doi/abs/10.1152/jn.00340.2018" + }, + "class_id": 19, + "created": "2020-03-03T14:53:17.487", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 88215, + "object_name": "I_Ks" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262422, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 263070, + "object_name": "Medlock, Laura [laura.medlock at mail.utoronto.ca]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263069, + "object_name": "Medlock L, Shute L, Fry M, Standage D, Ferguson AV (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Burst and tonic firing behaviour in subfornical organ (SFO) neurons (Medlock et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Subfornical organ (SFO) neurons exhibit heterogeneity in current expression and spiking behavior,\r\nwhere the two major spiking phenotypes appear as tonic and burst firing. Insight into the mechanisms behind\r\nthis heterogeneity is critical for understanding how the SFO, a sensory circumventricular organ, integrates and\r\nselectively influences physiological function. To integrate efficient methods for studying this heterogeneity,\r\nwe built a single-compartment, Hodgkin-Huxley-type model of an SFO neuron that is parameterized by SFO-specific in vitro patch-clamp data. The model accounts for the membrane potential distribution and spike train variability of both tonic and burst firing SFO neurons. Analysis of model dynamics confirms that a persistent Na+ and Ca2+ currents are required for burst initiation and maintenance and suggests that a slow-activating K+ current may be responsible for burst termination in SFO neurons. Additionally, the model suggests that heterogeneity in current expression and subsequent influence on spike afterpotential underlie the behavioral differences between tonic and burst firing SFO neurons. Future use of this model in coordination with single neuron patch-clamp electrophysiology provides a platform for explaining and predicting the response of SFO neurons to various combinations of circulating signals, thus elucidating the mechanisms underlying physiological signal integration within the SFO.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262422", + "other_currents": { + "attr_id": 303, + "value": "Non-selective cationic current" + }, + "other_implementer": { + "attr_id": 308, + "value": "L Medlock" + }, + "other_neurons": { + "attr_id": 301, + "value": "Subfornical organ neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "ver_date": "2022-06-14T14:08:05.513", + "ver_number": 9 + }, + "262431": { + "citation": { + "attr_id": 391, + "value": "https://europepmc.org/abstract/med/30188811" + }, + "class_id": 19, + "created": "2020-03-03T16:06:16.14", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262431, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 262488, + "object_name": "Tarnaud, Thomas [Thomas.Tarnaud at UGent.be]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262487, + "object_name": "Tarnaud T, Joseph W, Martens L, Tanghe E (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Computational modeling of ultrasonic Subthalamic Nucleus stimulation (Tarnaud et al 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 74300, + "object_name": "Subthalamus nucleus projection neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Objective: To explore the potential of ultrasonic modulation of plateau-potential generating subthalamic nucleus neurons (STN), by modeling their interaction with continuous and pulsed ultrasonic waves. Methods: A computational model for ultrasonic stimulation of the STN is created by combining the Otsuka-model with the bilayer sonophore model. The neuronal response to continuous and pulsed ultrasonic waves is computed in parallel for a range of frequencies, duty cycles, pulse repetition frequencies, and intensities. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262431", + "other_concept": { + "attr_id": 306, + "value": "Ultrasonic modulation" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + }, + { + "object_id": 116868, + "object_name": "Subthalamic Nucleus" + } + ] + }, + "ver_date": "2020-03-06T12:55:20.873", + "ver_number": 5 + }, + "262452": { + "citation": { + "attr_id": 391, + "value": "A Self-Regulating Gap Junction Network of Amacrine Cells Controls Nitric Oxide Release in the Retina.\r\nJacoby J1, Nath A2, Jessen ZF3, Schwartz GW4." + }, + "class_id": 19, + "created": "2020-03-04T15:46:45.233", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262452, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 262459, + "object_name": "Jessen, Zachary F " + }, + { + "object_id": 262460, + "object_name": "Schwartz, Gregory W [greg.schwartz at northwestern.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262450, + "object_name": "Jacoby J, Nath A, Jessen ZF, Schwartz GW (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + }, + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "A gap junction network of Amacrine Cells controls Nitric Oxide release (Jacoby et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 223953, + "object_name": "Retina amacrine cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 226, + "object_name": "NO" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... The effects of the neuromodulator nitric oxide (NO) have\r\nbeen studied in many circuits, including in the vertebrate\r\nretina, where it regulates synaptic release, gap junction\r\ncoupling, and blood vessel dilation, but little is known about\r\nthe cells that release NO. We show that a single type of amacrine\r\ncell (AC) controls NO release in the inner retina, and we report\r\nits light responses, electrical properties, and calcium\r\ndynamics. We discover that this AC forms a dense gap junction\r\nnetwork and that the strength of electrical coupling in the\r\nnetwork is regulated by light through NO. A model of the network\r\noffers insights into the biophysical specializations leading to\r\nauto-regulation of NO release within the network.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262452", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2020-03-04T16:37:29.83", + "ver_number": 5 + }, + "262456": { + "citation": { + "attr_id": 391, + "value": "https://www.frontiersin.org/articles/10.3389/fninf.2018.00088/full" + }, + "class_id": 19, + "created": "2020-03-04T16:18:34.773", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262456, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 263577, + "object_name": "Geminiani, Alice [alice.geminiani at unipv.it]" + }, + { + "object_id": 263578, + "object_name": "Casellato, Claudia [claudia.casellato at unipv.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263576, + "object_name": "Geminiani A, Casellato C, Locatelli F, Prestori F, Pedrocchi A, D'Angelo E (2018)" + }, + { + "object_id": 263579, + "object_name": "Geminiani A, Casellato C, D'Angelo E, Pedrocchi A (2019)" + }, + { + "object_id": 263580, + "object_name": "Geminiani A, Pedrocchi A, D'Angelo E, Casellato C (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 2832, + "object_name": "NEST" + } + ] + }, + "name": "Complex dynamics: reproducing Golgi cell electroresponsiveness (Geminiani et al 2018, 2019ab)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Excerpts from three papers abstracts: \"Brain neurons exhibit complex electroresponsive properties \u2013 including intrinsic subthreshold oscillations and pacemaking, resonance and phase-reset \u2013 which are thought to play a critical role in controlling neural network dynamics. Although these properties emerge from detailed representations of molecular-level mechanisms in \u201crealistic\u201d models, they cannot usually be generated by simplified neuronal models (although these may show spike-frequency adaptation and bursting). We report here that this whole set of properties can be generated by the extended generalized leaky integrate-and-fire (E-GLIF) neuron model. ...\" \"... In order to reproduce these properties in single-point neuron models, we have optimized the Extended-Generalized Leaky Integrate and Fire (E-GLIF) neuron through a multi-objective gradient-based algorithm targeting the desired input\u2013output relationships. ...\" \" ... In order to investigate how single neuron dynamics and geometrical modular connectivity affect cerebellar processing, we have built an olivocerebellar Spiking Neural Network (SNN) based on a novel simplification algorithm for single point models (Extended Generalized Leaky Integrate and Fire, EGLIF) capturing essential non-linear neuronal dynamics (e.g., pacemaking, bursting, adaptation, oscillation and resonance). ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262456", + "other_implementer": { + "attr_id": 308, + "value": "Geminiani, Alice [alice.geminiani at unipv.it]Casellato, Claudia [claudia.casellato at unipv.it]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2020-04-01T10:29:38.957", + "ver_number": 14 + }, + "262457": { + "citation": { + "attr_id": 391, + "value": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006359" + }, + "class_id": 19, + "created": "2020-03-04T16:20:26.62", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262457, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 262714, + "object_name": "Schmidt, Maximilian [schmidt.maximilian at posteo.de]" + }, + { + "object_id": 262715, + "object_name": "Schuecker, Jannis " + }, + { + "object_id": 262716, + "object_name": "van Albada, Sacha Jennifer [s.van.albada at fz-juelich.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 114660, + "object_name": "Connectivity matrix" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 225299, + "object_name": "Multiscale" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262711, + "object_name": "Schmidt M, Bakker R, Shen K, Bezgin G, Diesmann M, van Albada SJ (2018)" + }, + { + "object_id": 262712, + "object_name": "Schmidt M, Bakker R, Hilgetag CC, Diesmann M, van Albada SJ (2018)" + }, + { + "object_id": 262713, + "object_name": "Schuecker J, Schmidt M, van Albada SJ, Diesmann M, Helias M (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 2832, + "object_name": "NEST" + } + ] + }, + "name": "Multi-area layer-resolved spiking network model of resting-state dynamics in macaque visual cortex", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 258847, + "object_name": "Abstract integrate-and-fire leaky neuron with exponential post-synaptic current" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "See https://inm-6.github.io/multi-area-model/ for any updates." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262457", + "other_concept": { + "attr_id": 306, + "value": "Resting-state activity" + }, + "other_implementer": { + "attr_id": 308, + "value": "Schmidt, Maximilian [schmidt.maximilian@posteo.de]\r\nSchuecker, Jannis\r\nvan Albada, Sacha Jennifer [s.van.albada@fz-juelich.de]\r\n" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 266986, + "object_name": "Visual cortex" + } + ] + }, + "ver_date": "2021-03-23T06:15:39.157", + "ver_number": 9 + }, + "262464": { + "citation": { + "attr_id": 391, + "value": "Cellular function given parametric variation in the Hodgkin and Huxley model of excitability\r\nHillel Ori, View ORCID ProfileEve Marder, and View ORCID ProfileShimon Marom\r\nPNAS August 28, 2018 115 (35) E8211-E8218; first published August 15, 2018 https://doi.org/10.1073/pnas.1808552115" + }, + "class_id": 19, + "created": "2020-03-04T17:30:05.233", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262464, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 262483, + "object_name": "Marom, Shimon [marom at technion.ac.il]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 116962, + "object_name": "Parameter sensitivity" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262444, + "object_name": "Ori H, Marder E, Marom S (2018)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 206355, + "object_name": "Mathematica (web link to model)" + } + ] + }, + "name": "Cellular function given parametric variation in the HH model of excitability (Ori et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"How is reliable physiological function maintained in cells despite considerable variability in the values of key parameters of multiple interacting processes that govern that function? Here, we use the classic Hodgkin\u2013Huxley formulation of the squid giant axon action potential to propose a possible approach to this problem. Although the full Hodgkin\u2013Huxley model is very sensitive to fluctuations that independently occur in its many parameters, the outcome is in fact determined by simple combinations of these parameters along two physiological dimensions: structural and kinetic (denoted S and K, respectively). Structural parameters describe the properties of the cell, including its capacitance and the densities of its ion channels. Kinetic parameters are those that describe the opening and closing of the voltage-dependent conductances. The impacts of parametric fluctuations on the dynamics of the system\u2014seemingly complex in the high-dimensional representation of the Hodgkin\u2013Huxley model\u2014are tractable when examined within the S\u2013K plane. We demonstrate that slow inactivation, a ubiquitous activity-dependent feature of ionic channels, is a powerful local homeostatic control mechanism that stabilizes excitability amid changes in structural and kinetic parameters.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262464", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "ver_date": "2020-03-05T06:40:55.83", + "ver_number": 3 + }, + "262465": { + "citation": { + "attr_id": 391, + "value": "Xize Xu, Jianhua Cang, Hermann Riecke, J. Neurophysiology 2020 accepted" + }, + "class_id": 19, + "created": "2020-03-04T17:52:14.393", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262465, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 247195, + "object_name": "Riecke, Hermann [h-riecke at northwestern.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 128875, + "object_name": "Development" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 147931, + "object_name": "Orientation selectivity" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262485, + "object_name": "Xu X, Cang J', Riecke H (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Development and Binocular Matching of Orientation Selectivity in Visual Cortex (Xu et al 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model investigates the development of orientation selectivity and its binocular matching in visual cortex by implementing a neuron that has plastic synapses for its inputs from the left and right eye. The plasticity is taken to be voltage-based with homeostasis (Clopath et al 2010). The neuron is modeled as an adaptive exponential integrate-fire neuron. The uploaded model has been used in Xu, Cang & Riecke (2020) to analyze the impact of ocular dominance and orientation selectivity on the matching process. There it has been found that the matching can proceed by a slow shifting or a sudden switching of the preferred orientation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262465", + "other_implementer": { + "attr_id": 308, + "value": "Xize Xu" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "xizexu2016@u.northwestern.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Xize Xu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2020-03-05T16:08:00.55", + "ver_number": 4 + }, + "262670": { + "class_id": 19, + "created": "2020-03-10T16:49:52.23", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 262670, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 262704, + "object_name": "Ray, Subhasis [ray.subhasis at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245690, + "object_name": "Olfaction" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263621, + "object_name": "Ray S, Aldworth ZN, Stopfer MA (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Locust olfactory network with GGN and full KC population in the mushroom body (Ray et al 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114295, + "object_name": "Honeybee kenyon cell" + }, + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + }, + { + "object_id": 262703, + "object_name": "Locust Giant GABAergic Neuron (GGN)" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 209, + "object_name": "Acetylcholine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We reconstructed the GGN (giant GABAergic neuron) morphology from 3D confocal image stack, and built a passive model based on the morphology to study signal attenuation across this giant neuron. In order to study the effect of feedback inhibition from this cell on odor information processing, we created a model of the olfactory network in the locust mushroom body with 50,000 KCs (Kenyon cell) reciprocally connected to this neuron. Finally, we added a model of the IG (Inhibitor of GGN) to reproduce in vivo odor responses in GGN." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/262670", + "other_implementer": { + "attr_id": 308, + "value": "Subhasis Ray" + }, + "other_neurons": { + "attr_id": 301, + "value": "Locust Giant GABAergic Neuron (GGN)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ray.subhasis@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Subhasis Ray" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 262706, + "object_name": "Mushroom body" + }, + { + "object_id": 262707, + "object_name": "Locust" + } + ] + }, + "ver_date": "2020-10-02T17:03:34.543", + "ver_number": 21 + }, + "263034": { + "citation": { + "attr_id": 391, + "value": "http://www.eneuro.org/content/eneuro/early/2018/08/09/ENEURO.0254-18.2018.full.pdf" + }, + "class_id": 19, + "created": "2020-03-12T15:53:11.5", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263034, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266539, + "object_name": "Kamiya, Haruyuki [kamiya at med.hokudai.ac.j]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266538, + "object_name": "Ohura S, Kamiya H (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Evaluation of passive component of propagating AP in mossy fiber axons (Ohura & Kamiya 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Action potentials propagating along axons are often followed by prolonged afterdepolarization (ADP) lasting for several tens of milliseconds. Axonal ADP is thought to be an important factor in modulating the fidelity of spike propagation during repetitive firings. However, the mechanism as well as the functional significance of axonal ADP remain unclear, partly due to inaccessibility to small structures of axon for direct electrophysiological recordings. Here, we examined the ionic and electrical mechanisms underlying axonal ADP using whole-bouton recording from mossy fiber terminals in mice hippocampal slices. ADP following axonal action potentials was strongly enhanced by focal application of veratridine, an inhibitor of Na+ channel inactivation. In contrast, tetrodotoxin (TTX) partly suppressed ADP, suggesting that a Na+ channel\u2013dependent component is involved in axonal ADP. The remaining TTX-resistant Na+ channel\u2013independent component represents slow capacitive discharge reflecting the shape and electrical properties of the axonal membrane. We also addressed the functional impact of axonal ADP on presynaptic function. In paired-pulse stimuli, we found that axonal ADP minimally affected the peak height of subsequent action potentials, although the rising phase of action potentials was slightly slowed, possibly due to steady-state inactivation of Na+ channels by prolonged depolarization. Voltage clamp analysis of Ca2+ current elicited by action potential waveform commands revealed that axonal ADP assists short-term facilitation of Ca2+ entry into the presynaptic terminals. Taken together, these data show that axonal ADP maintains reliable firing during repetitive stimuli and plays important roles in the fine-tuning of short-term plasticity of transmitter release by modulating Ca2+ entry into presynaptic terminals.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263034", + "other_implementer": { + "attr_id": 308, + "value": "Kamiya, Haruyuki" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + }, + { + "object_id": 115947, + "object_name": "Dentate gyrus" + } + ] + }, + "ver_date": "2020-06-09T09:26:41.923", + "ver_number": 11 + }, + "263042": { + "citation": { + "attr_id": 391, + "value": "https://www.jneurosci.org/content/jneuro/39/36/7132.full-text.pdf" + }, + "class_id": 19, + "created": "2020-03-15T11:17:57.433", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263042, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 136098, + "object_name": "Information transfer" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263277, + "object_name": "Li X, Abou Tayoun A, Song Z, Dau A, Rien D, Jaciuch D, Dongre S, Blanchard F, Nikolaev A, Zheng L, Bollepalli MK, Chu B, Hardie RC, Dolph PJ, Juusola M (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "name": "How BK and SK channels benefit early vision (Li X et al 2019)", + "notes": { + "attr_id": 24, + "value": "\"Ca2+-activated K+ channels (BK and SK) are ubiquitous in synaptic circuits, but their role in network adaptation and sensory perception remains largely unknown. Using electrophysiological and behavioral assays and biophysical modeling, we discover how visual information transfer in mutants lacking the BK channel (dSlo- ), SK channel (dSK- ), or both (dSK- ;; dSlo- ) is shaped in the female fruit fly (Drosophila melanogaster) R1-R6 photoreceptor-LMC circuits (R-LMC-R system) through synaptic feedforward-feedback interactions and reduced R1-R6 Shaker and Shab K+ conductances. This homeostatic compensation is specific for each mutant, leading to distinctive adaptive dynamics. We show how these dynamics inescapably increase the energy cost of information and promote the mutants' distorted motion perception, determining the true price and limits of chronic homeostatic compensation in an in vivo genetic animal model. These results reveal why Ca2+-activated K+ channels reduce network excitability (energetics), improving neural adaptability for transmitting and perceiving sensory information. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263042", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 206363, + "object_name": "Drosophila" + } + ] + }, + "ver_date": "2020-04-22T09:14:48.65", + "ver_number": 6 + }, + "263053": { + "citation": { + "attr_id": 391, + "value": "doi.org/10.3389/fncel.2020.00040" + }, + "class_id": 19, + "created": "2020-03-17T12:37:04.23", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263053, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 263057, + "object_name": "Zbili, Mickael [zbili.mickael at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263056, + "object_name": "Zbili M, Debanne D (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "L5 pyramidal neuron myelination increases analog-digital facilitation extent (Zbili & Debanne 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 52425, + "object_name": "Myelinated neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Analog-digital facilitations (ADFs) correspond to a class of phenomena describing how subthreshold variations of the presynaptic membrane potential influence the synaptic transmission. ADFs rely on the propagation of somatic membrane potential fluctuations to the presynaptic bouton where they modulate ion channels availability, inducing modifications of the presynaptic spike waveform, and threfore modifying the neurotransmitter release. In this simulation, we show that myelination can promote the propagation of somatic voltage subtheshold fluctuations into the axon, allowing the ADFs to impact distal presynaptic bouton (up to 3mm from the soma)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263053", + "other_implementer": { + "attr_id": 308, + "value": "Zbili Mickael" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "zbili.mickael@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "mickael zbili" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2020-03-23T08:48:01.053", + "ver_number": 28 + }, + "263074": { + "citation": { + "attr_id": 391, + "value": "http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006186" + }, + "class_id": 19, + "created": "2020-03-20T16:40:01.99", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263074, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 263164, + "object_name": "Chizhov, Anton [anton.chizhov at mail.ioffe.ru]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 22012, + "object_name": "Depression" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263147, + "object_name": "Chizhov AV, Zefirov AV, Amakhin DV, Smirnova EY, Zaitsev AV (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 117353, + "object_name": "Pascal/Delphi" + }, + { + "object_id": 143722, + "object_name": "Mathematica" + }, + { + "object_id": 263162, + "object_name": "Javascript" + } + ] + }, + "name": "Minimal model of interictal and ictal discharges \u201cEpileptor-2\u201d (Chizhov et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Seizures occur in a recurrent manner with intermittent states of interictal and ictal discharges (IIDs and IDs). The transitions to and from IDs are determined by a set of processes, including synaptic interaction and ionic dynamics. Although mathematical models of separate types of epileptic discharges have been developed, modeling the transitions between states remains a challenge. A simple generic mathematical model of seizure dynamics (Epileptor) has recently been proposed by Jirsa et al. (2014); however, it is formulated in terms of abstract variables. In this paper, a minimal population-type model of IIDs and IDs is proposed that is as simple to use as the Epileptor, but the suggested model attributes physical meaning to the variables. The model is expressed in ordinary differential equations for extracellular potassium and intracellular sodium concentrations, membrane potential, and short-term synaptic depression variables. A quadratic integrate-and-fire model driven by the population input current is used to reproduce spike trains in a representative neuron. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263074", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "ver_date": "2020-05-14T13:28:08.057", + "ver_number": 11 + }, + "263130": { + "citation": { + "attr_id": 391, + "value": "https://www.nature.com/articles/s41598-018-28581-w" + }, + "class_id": 19, + "created": "2020-03-23T14:19:34.327", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263130, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 144557, + "object_name": "Antunes, Gabriela [gabri_antunes at hotmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 22012, + "object_name": "Depression" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263984, + "object_name": "Antunes G, Simoes-de-Souza FM (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 2835, + "object_name": "MCell" + } + ] + }, + "name": "AMPA receptor trafficking and its role in heterosynaptic plasticity (Antunes et al 2018)", + "notes": { + "attr_id": 24, + "value": "\"... cumulative experimental and theoretical data have\r\ndemonstrated that long-term potentiation (LTP) and long-term\r\ndepression (LTD) can promote compensatory alterations in\r\nnon-stimulated synapses. In this work, we have developed a (MCELL)\r\ncomputational model of a (3D) spiny dendritic segment\r\nto investigate the role of AMPA receptor (AMPAR) trafficking\r\nduring synaptic plasticity at specific synapses and its\r\nconsequences for the populations of AMPAR at nearby synapses. Our\r\nresults demonstrated that the mechanisms of AMPAR trafficking\r\ninvolved with LTP and LTD can promote heterosynaptic plasticity\r\nat non-stimulated synapses. These alterations are compensatory\r\nand arise from molecular competition. Moreover, the\r\nheterosynaptic changes observed in our model can modulate further\r\nactivity-driven inductions of synaptic plasticity.\" The model requires an installed version of MCell and CellBlender." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263130", + "other_implementer": { + "attr_id": 308, + "value": "Gabriela Antunes" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2020-04-15T10:15:56.897", + "ver_number": 7 + }, + "263193": { + "citation": { + "attr_id": 391, + "value": "https://aip.scitation.org/doi/abs/10.1063/1.5040288" + }, + "class_id": 19, + "created": "2020-03-26T12:56:05.313", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 263193, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 263686, + "object_name": "Barlow, Benjamin Stephen [BBarlow at uottawa.ca]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 143724, + "object_name": "Bifurcation" + }, + { + "object_id": 152639, + "object_name": "Temperature" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263685, + "object_name": "Barlow BM, Joos B, Trinh AK, Longtin A (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Cooling reverses pathological spontaneous firing caused by mild traumatic injury (Barlow et al 2018)", + "notes": { + "attr_id": 24, + "value": "\"Mild traumatic injury can modify the key sodium (Na+) current underlying the excitability of neurons. It causes the activation and inactivation properties of this current to become shifted to more negative trans-membrane voltages. This so-called coupled left shift (CLS) leads to a chronic influx of Na+ into the cell that eventually causes spontaneous or \u201cectopic\u201d firing along the axon, even in the absence of stimuli. The bifurcations underlying this enhanced excitability have been worked out in full ionic models of this effect. Here, we present computational evidence that increased temperature T can exacerbate this pathological state. Conversely, and perhaps of clinical relevance, mild cooling is shown to move the naturally quiescent cell further away from the threshold of ectopic behavior. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263193", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "ver_date": "2020-04-08T22:35:24.543", + "ver_number": 12 + }, + "263196": { + "citation": { + "attr_id": 391, + "value": "PMID: 29769701\r\nA Drosophila Model of Essential Tremor.\r\nSmith P, Arias R, Sonti S, Odgerel Z, Santa-Maria I, McCabe BD, Tsaneva-Atanasova K, Louis ED, Hodge JJL, Clark LN.\r\nSci Rep. 2018 May 16;8(1):7664. doi: 10.1038/s41598-018-25949-w." + }, + "class_id": 19, + "created": "2020-03-26T13:09:55.523", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263196, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 263210, + "object_name": "Smith, Philip [smithpj92 at hotmail.co.uk]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263209, + "object_name": "Smith P, Arias R, Sonti S, Odgerel Z, Santa-Maria I, McCabe BD, Tsaneva-Atanasova K, Louis ED, Hodge JJL, Clark LN (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Drosophila circadian clock neurone model of essential tremor (Smith et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 263211, + "object_name": "Drosophila ventral lateral neuron (LNV)" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Model of Drosophila ventral lateral neuron (LNV) used to study a human ion channel associated with essential tremor." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263196", + "other_implementer": { + "attr_id": 308, + "value": "Dr Philip Smith (smithpj92@hotmail.co.uk)" + }, + "other_neurons": { + "attr_id": 301, + "value": "Drosophila LNV neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "k.tsaneva-atanasova@exeter.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Krasimira Tsaneva-Atanasova" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 206363, + "object_name": "Drosophila" + } + ] + }, + "ver_date": "2020-03-26T20:09:07.5", + "ver_number": 11 + }, + "263199": { + "citation": { + "attr_id": 391, + "value": "PMID: 31612994\r\nShaw and Shal voltage-gated potassium channels mediate circadian changes in Drosophila clock neuron excitability.\r\nSmith P, Buhl E, Tsaneva-Atanasova K, Hodge JJL.\r\nJ Physiol. 2019 Dec;597(23):5707-5722. doi: 10.1113/JP278826." + }, + "class_id": 19, + "created": "2020-03-26T13:20:59.777", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263199, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 263210, + "object_name": "Smith, Philip [smithpj92 at hotmail.co.uk]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263202, + "object_name": "Smith P, Buhl E, Tsaneva-Atanasova K, Hodge JJL (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Drosophila lateral ventral clock neuron (LNV) model (Smith et al 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 263211, + "object_name": "Drosophila ventral lateral neuron (LNV)" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "LNVmodel models the activity of a Drosophila lateral ventral clock neurons (LNV) neurone." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263199", + "other_currents": { + "attr_id": 303, + "value": "Shaw and Shal voltage-gated potassium currents" + }, + "other_implementer": { + "attr_id": 308, + "value": "Philip Smith (smithpj92@hotmail.co.uk)" + }, + "other_neurons": { + "attr_id": 301, + "value": "Drosophila LNV neuron ion channels" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "k.tsaneva-atanasova@exeter.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Krasimira Tsaneva-Atanasova" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 206363, + "object_name": "Drosophila" + } + ] + }, + "ver_date": "2020-07-29T20:24:24.35", + "ver_number": 10 + }, + "263236": { + "citation": { + "attr_id": 391, + "value": "Di Volo, M., Romagnoni, A., Capone, C., & Destexhe, A. (2019). Biologically realistic mean-field models of conductance-based networks of spiking neurons with adaptation. Neural computation, 31(4), 653-680." + }, + "class_id": 19, + "created": "2020-03-27T11:46:10.03", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263236, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 263430, + "object_name": "di Volo, Matteo [matteo.di-volo at cyu.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263429, + "object_name": "di Volo MD, Romagnoni A, Capone C, Destexhe A (2019)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Mean-Field models of conductance-based NNs of spiking neurons with adaptation (di Volo et al 2019)", + "notes": { + "attr_id": 24, + "value": "\"Accurate population models are needed to build very large-scale neural models, but their derivation is difficult for realistic networks of neurons, in particular when nonlinear properties are involved, such as conductance-based interactions and spike-frequency adaptation. Here, we consider such models based on networks of adaptive exponential integrate-and-fire excitatory and inhibitory neurons. Using a master equation formalism, we derive a mean-field model of such networks and compare it to the full network dynamics. The mean-field model is capable of correctly predicting the average spontaneous activity levels in asynchronous irregular regimes similar to in vivo activity. It also captures the transient temporal response of the network to complex external inputs. Finally, the mean-field model is also able to quantitatively describe regimes where high- and low-activity states alternate (up-down state dynamics), leading to slow oscillations. We conclude that such mean-field models are biologically realistic in the sense that they can capture both spontaneous and evoked activity, and they naturally appear as candidates to build very large-scale models involving multiple brain areas.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263236", + "public_submitter_email": { + "attr_id": 309, + "value": "matteo.di-volo@u-cergy.fr" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Matteo di Volo" + }, + "ver_date": "2020-04-02T09:55:17.2", + "ver_number": 8 + }, + "263246": { + "citation": { + "attr_id": 391, + "value": "Asabuki, T., Fukai, T. Somatodendritic consistency check for temporal feature segmentation. Nat Commun 11, 1554 (2020). https://doi.org/10.1038/s41467-020-15367-w" + }, + "class_id": 19, + "created": "2020-03-27T20:32:09.993", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263246, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263258, + "object_name": "Asabuki T, Fukai T (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Somatodendritic consistency check for temporal feature segmentation (Asabuki & Fukai 2020)", + "notes": { + "attr_id": 24, + "value": "\"The brain identifies potentially salient features within continuous information streams to process hierarchical temporal events. This requires the compression of information streams, for which effective computational principles are yet to be explored. Backpropagating action potentials can induce synaptic plasticity in the dendrites of cortical pyramidal neurons. By analogy with this effect, we model a self-supervising process that increases the similarity between dendritic and somatic activities where the somatic activity is normalized by a running average. We further show that a family of networks composed of the two-compartment neurons performs a surprisingly wide variety of complex unsupervised learning tasks, including chunking of temporal sequences and the source separation of mixed correlated signals. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263246", + "public_submitter_email": { + "attr_id": 309, + "value": "toshitake.asabuki@oist.jp" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Toshitake Asabuki" + }, + "ver_date": "2020-07-29T20:26:59.46", + "ver_number": 7 + }, + "263259": { + "citation": { + "attr_id": 391, + "value": "Carlu, Mallory, et al. \"A mean-field approach to the dynamics of networks of complex neurons, from nonlinear Integrate-and-Fire to Hodgkin-Huxley models.\" Journal of Neurophysiology (2019)." + }, + "class_id": 19, + "created": "2020-03-28T05:34:06.377", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263259, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 263430, + "object_name": "di Volo, Matteo [matteo.di-volo at cyu.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263440, + "object_name": "Carlu M, Chehab O, Dalla Porta L, Depannemaecker D, H\u00e9ric\u00e9 C, Jedynak M, K\u00f6ksal Ers\u00f6z E, Muratore P, Souihel S, Capone C, Zerlaut Y, Destexhe A, di Volo M (2020)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Mean field model for Hodgkin Huxley networks of neurons (Carlu et al 2020)", + "notes": { + "attr_id": 24, + "value": "\"We present a mean-field formalism able to predict the collective dynamics of large networks of conductance-based interacting spiking neurons. We apply this formalism to several neuronal models, from the simplest Adaptive Exponential Integrate-and-Fire model to the more complex Hodgkin-Huxley and Morris-Lecar models. We show that the resulting mean-field models are capable of predicting the correct spontaneous activity of both excitatory and inhibitory neurons in asynchronous irregular regimes, typical of cortical dynamics. Moreover, it is possible to quantitatively predict the population response to external stimuli in the form of external spike trains. This mean-field formalism therefore provides a paradigm to bridge the scale between population dynamics and the microscopic complexity of the individual cells physiology.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263259", + "public_submitter_email": { + "attr_id": 309, + "value": "matteo.di-volo@u-cergy.fr" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Matteo di Volo" + }, + "ver_date": "2020-07-29T20:28:35.573", + "ver_number": 8 + }, + "263571": { + "citation": { + "attr_id": 391, + "value": "https://www.eneuro.org/content/eneuro/early/2019/07/03/ENEURO.0417-18.2019.full.pdf" + }, + "class_id": 19, + "created": "2020-03-30T22:08:40.45", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263571, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 114640, + "object_name": "Gunay, Cengiz [cgunay at emory.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263687, + "object_name": "G\u00fcnay C, Doloc-Mihu A, Lamb DG, Calabrese RL (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 18747, + "object_name": "GENESIS (web link to model)" + }, + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Synaptic strengths are critical in creating the proper output phasing in a CPG (Gunay et al 2019)", + "notes": { + "attr_id": 24, + "value": "\"Identified neurons and the networks they compose produce stereotypical, albeit individually unique, activity\r\nacross members of a species. We propose, for a motor circuit driven by a central pattern generator (CPG), that\r\nthe uniqueness derives mainly from differences in synaptic strength rather than from differences in intrinsic\r\nmembrane conductances. We studied a dataset of recordings from six leech (Hirudo sp.) heartbeat control\r\nnetworks, containing complete spiking activity patterns from inhibitory premotor interneurons, motor output spike\r\npatterns, and synaptic strength patterns to investigate the source of uniqueness. We used a conductance-based\r\nmulticompartmental motor neuron model to construct a bilateral motor circuit model, and controlled it by playing\r\nrecorded input spike trains from premotor interneurons to generate output inhibitory synaptic patterns similar to\r\nexperimental measurements. By generating different synaptic conductance parameter sets of this circuit model,\r\nwe found that relative premotor synaptic strengths impinging onto motor neurons must be different across\r\nindividuals to produce animal-specific output burst phasing. Obtaining unique outputs from each individual\u2019s\r\ncircuit model did not require different intrinsic ionic conductance parameters. Furthermore, changing intrinsic conductances failed to compensate for modified synaptic strength patterns. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263571", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115958, + "object_name": "Leech" + } + ] + }, + "ver_date": "2020-07-29T20:32:34.62", + "ver_number": 8 + }, + "263585": { + "citation": { + "attr_id": 391, + "value": "https://link.springer.com/chapter/10.1007/978-3-030-36683-4_61" + }, + "class_id": 19, + "created": "2020-04-01T11:37:44.573", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263585, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 116853, + "object_name": "Diwakar, Shyam [shyam at amrita.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263674, + "object_name": "Sasidharakurup H, Nair L, Bhaskar K, Diwakar S (2020)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 237011, + "object_name": "CellDesigner" + } + ] + }, + "name": "Computational Modelling of TNFalpha Pathway in Parkinson's Disease (Sasidharakurup et al 2019)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The paper aims developing a computational framework of signaling using the principles of biochemical systems theory as a model for Parkinson\u2019s disease. Several molecular interactions aided by TNFalpha, a proinflammatory cytokine play key roles in mediating glutamate excitotoxicity and neuroinflammation, resulting in neuronal cell death. In this paper, initial concentrations and rate constants were extracted from literature and simulations developed were based on systems of ordinary differential equations following first-order kinetics. In control or healthy conditions, a decrease in TNFalpha and neuronal cell death was predicted in simulations matching data from experiments, whereas in diseased condition, a drastic increase in levels of TNFalpha, glutamate, TNFR1 and ROS were observed similar to experimental data correlating diseased condition to augmented neuronal cell death. The study suggests toxic effects induced by TNFalpha in the substantia nigra may be attributed to Parkinson\u2019s disease conditions.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263585", + "other_implementer": { + "attr_id": 308, + "value": "Hemalatha Sasidharakurup (hemalathas@am.amrita.edu)" + }, + "other_type": { + "attr_id": 305, + "value": "Systems Biology" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "ver_date": "2020-04-07T10:29:04.4", + "ver_number": 8 + }, + "263637": { + "citation": { + "attr_id": 391, + "value": "\"Transfer entropy as a measure of brain connectivity: a critical analysis with the help of neural mass models\", M. Ursino, G. Ricci and E. Magosso, accepted with revision by Frontiers in Computational Neuroscience." + }, + "class_id": 19, + "created": "2020-04-06T13:07:37.537", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263637, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 239531, + "object_name": "Ursino, Mauro [mauro.ursino at unibo.it]" + }, + { + "object_id": 263678, + "object_name": "Ricci, Giulia [Giulia.Ricci at unibo.it]" + }, + { + "object_id": 261628, + "object_name": "Magosso, Elisa [elisa.magosso at unibo.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 139148, + "object_name": "Brain Rhythms" + }, + { + "object_id": 114660, + "object_name": "Connectivity matrix" + }, + { + "object_id": 97752, + "object_name": "Delay" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267635, + "object_name": "Ursino M, Ricci G, Magosso E (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + }, + { + "object_id": 83551, + "object_name": "Connectionist Network" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 263677, + "object_name": "Trentool" + } + ] + }, + "name": "A neural mass model for critical assessment of brain connectivity (Ursino et al 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 184134, + "object_name": "Neocortex layer 5 interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We use a neural mass model of interconnected regions of interest to simulate reliable neuroelectrical signals in the cortex. In particular, signals simulating mean field potentials were generated assuming two, three or four ROIs, connected via excitatory or by-synaptic inhibitory links. Then we investigated whether bivariate Transfer Entropy (TE) can be used to detect a statistically significant connection from data (as in binary 0/1 networks), and even if connection strength can be quantified (i.e., the occurrence of a linear relationship between TE and connection strength). Results suggest that TE can reliably estimate the strength of connectivity if neural populations work in their linear regions. However, nonlinear phenomena dramatically affect the assessment of connectivity, since they may significantly reduce TE estimation. Software included here allows the simulation of neural mass models with a variable number of ROIs and connections, the estimation of TE using the free package Trentool, and the realization of figures to compare true connectivity with estimated values." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263637", + "other_implementer": { + "attr_id": 308, + "value": "Giulia Ricci at unibo.it\r\nElisa Magosso at unibo.it" + }, + "other_simulator": { + "attr_id": 307, + "value": "Trentool" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "mauro.ursino@unibo.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Mauro Ursino" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2023-02-09T09:59:47.947", + "ver_number": 8 + }, + "263703": { + "citation": { + "attr_id": 391, + "value": "Nature Communications Biology (in press) Roger D. Traub*2, Karen Hawkins1, Natalie E. Adams1, Stephen P. Hall1, Anna Simon1 and Miles A Whittington1*, Layer 4 pyramidal neuron dendritic bursting underlies a post-stimulus visual cortical alpha rhythm." + }, + "class_id": 19, + "created": "2020-04-09T09:24:13.677", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263703, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33800, + "object_name": "Traub, Roger D [rtraub at us.ibm.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 139148, + "object_name": "Brain Rhythms" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263704, + "object_name": "Traub RD, Hawkins K, Adams NE, Hall SP, Simon A, Whittington MA (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 45540, + "object_name": "FORTRAN" + } + ] + }, + "name": "Alpha rhythm in vitro visual cortex (Traub et al 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + }, + { + "object_id": 263, + "object_name": "Thalamus reticular nucleus GABA cell" + }, + { + "object_id": 167345, + "object_name": "Neocortex U1 L6 pyramidal corticalthalamic GLU cell" + }, + { + "object_id": 167344, + "object_name": "Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell" + }, + { + "object_id": 184128, + "object_name": "Neocortex layer 4 pyramidal cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + }, + { + "object_id": 142232, + "object_name": "Neocortex spiking low threshold (LTS) neuron" + }, + { + "object_id": 114635, + "object_name": "Neocortex spiny stellate cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The paper describes an experimental model of the alpha rhythm generated by layer 4 pyramidal neurons in a visual cortex slice. The simulation model is derived from that of Traub et al. (2005) J Neurophysiol, developed for thalamocortical oscillations." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263703", + "other_implementer": { + "attr_id": 308, + "value": "Code was written by Roger Traub in Fortran. Runs in mpi environment. The model neurons are multicompartment (soma, dendrites, axon) and have multiple ionic conductances. There are a variety of interneurons and pyramidal cells, plus spiny stellate neurons." + }, + "public_submitter_email": { + "attr_id": 309, + "value": "rtraub@us.ibm.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Roger Traub" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + }, + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2020-04-15T13:43:08.42", + "ver_number": 11 + }, + "263705": { + "citation": { + "attr_id": 391, + "value": "Seizure initiation in infantile spasms vs. focal seizures: proposed common cellular mechanisms.\r\nTraub RD, Moeller F, Rosch R, Baldeweg T, Whittington MA, Hall SP.\r\nRev Neurosci. 2020 Jan 28;31(2):181-200. doi: 10.1515/revneuro-2019-0030.\r\n\r\nUnbalanced Peptidergic Inhibition in Superficial Neocortex Underlies Spike and Wave Seizure Activity.\r\nHall S, Hunt M, Simon A, Cunnington LG, Carracedo LM, Schofield IS, Forsyth R, Traub RD, Whittington MA.\r\n\r\nA neocortical delta rhythm facilitates reciprocal interlaminar interactions via nested theta rhythms.\r\nCarracedo LM, Kjeldsen H, Cunnington L, Jenkins A, Schofield I, Cunningham MO, Davies CH, Traub RD, Whittington MA.\r\nJ Neurosci. 2013 Jun 26;33(26):10750-61. doi: 10.1523/JNEUROSCI.0735-13.2013.\r\nJ Neurosci. 2015 Jun 24;35(25):9302-14. doi: 10.1523/JNEUROSCI.4245-14.2015." + }, + "class_id": 19, + "created": "2020-04-09T13:58:22.987", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263705, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33800, + "object_name": "Traub, Roger D [rtraub at us.ibm.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 139148, + "object_name": "Brain Rhythms" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 263707, + "object_name": "Traub RD, Moeller F, Rosch R, Baldeweg T, Whittington MA, Hall SP (2020)" + }, + { + "object_id": 235505, + "object_name": "Hall S, Hunt M, Simon A, Cunnington LG, Carracedo LM, Schofield IS, Forsyth R, Traub RD, Whittington MA (2015)" + }, + { + "object_id": 150690, + "object_name": "Carracedo LM, Kjeldsen H, Cunnington L, Jenkins A, Schofield I, Cunningham MO, Davies CH, Traub RD, Whittington MA (2013)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 45540, + "object_name": "FORTRAN" + } + ] + }, + "name": "Electrodecrements in in vitro model of infantile spasms (Traub et al 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 262, + "object_name": "Thalamus geniculate nucleus/lateral principal GLU cell" + }, + { + "object_id": 263, + "object_name": "Thalamus reticular nucleus GABA cell" + }, + { + "object_id": 167345, + "object_name": "Neocortex U1 L6 pyramidal corticalthalamic GLU cell" + }, + { + "object_id": 167344, + "object_name": "Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell" + }, + { + "object_id": 184128, + "object_name": "Neocortex layer 4 pyramidal cell" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + }, + { + "object_id": 142232, + "object_name": "Neocortex spiking low threshold (LTS) neuron" + }, + { + "object_id": 114635, + "object_name": "Neocortex spiny stellate cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The code is an extension of the thalamocortical model of Traub et al. (2005) J Neurophysiol. It is here applied to an in vitro model of the electrodecremental response seen in the EEG of children with infantile spasms (West syndrome)" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263705", + "other_implementer": { + "attr_id": 308, + "value": "Code written by RD Traub, rtraub@us.ibm.com" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "rtraub@us.ibm.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Roger Traub" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + }, + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2020-04-15T13:48:03.95", + "ver_number": 5 + }, + "263711": { + "citation": { + "attr_id": 391, + "value": "https://journals.plos.org/ploscompbiol/article%3Fid%3D10.1371/journal.pcbi.1007432" + }, + "class_id": 19, + "created": "2020-04-09T16:32:50.893", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263711, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 264552, + "object_name": "Spreizer, Sebastian " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 222956, + "object_name": "Neuromodulation" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 264553, + "object_name": "Spatial connectivity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 261827, + "object_name": "Spreizer S, Aertsen A, Kumar A (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 2832, + "object_name": "NEST" + } + ] + }, + "name": "Emergence of spatiotemporal sequences in spiking neuronal networks (Spreizer et al 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Spatio-temporal sequences of neuronal activity are observed in many brain regions in a\r\nvariety of tasks and are thought to form the basis of meaningful behavior. However, mechanisms\r\nby which a neuronal network can generate spatio-temporal activity sequences have\r\nremained obscure. Existing models are biologically untenable because they either require\r\nmanual embedding of a feedforward network within a random network or supervised learning\r\nto train the connectivity of a network to generate sequences. Here, we propose a biologically\r\nplausible, generative rule to create spatio-temporal activity sequences in a network of\r\nspiking neurons with distance-dependent connectivity. We show that the emergence of spatio-\r\ntemporal activity sequences requires: (1) individual neurons preferentially project a small\r\nfraction of their axons in a specific direction, and (2) the preferential projection direction of\r\nneighboring neurons is similar. Thus, an anisotropic but correlated connectivity of neuron\r\ngroups suffices to generate spatio-temporal activity sequences in an otherwise random neuronal\r\nnetwork model.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263711", + "other_concept": { + "attr_id": 306, + "value": "Spatial connectivity" + }, + "other_implementer": { + "attr_id": 308, + "value": "Spreizer, Sebastian" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 138633, + "object_name": "Striatum" + } + ] + }, + "ver_date": "2020-04-22T11:32:54.513", + "ver_number": 5 + }, + "263718": { + "citation": { + "attr_id": 391, + "value": "https://link.springer.com/chapter/10.1007/978-3-030-22744-9_33" + }, + "class_id": 19, + "created": "2020-04-09T16:49:18.75", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263718, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 264590, + "object_name": "Magalh\u00e3es, Bruno [brunomaga at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 264555, + "object_name": "Magalh\u00e3es BR, Sterling T, Hines M, Sch\u00fcrmann F (2019)" + }, + { + "object_id": 264557, + "object_name": "Magalh\u00e3es BRC, Sterling T, Hines M, Sch\u00fcrmann F (2019)" + }, + { + "object_id": 264562, + "object_name": "Magalh\u00e3es B, Hines M, Sterling T, Sch\u00fcermann F (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "Fully-Asynchronous Cache-Efficient Simulation of Detailed Neural Networks (Magalhaes et al 2019)", + "notes": { + "attr_id": 24, + "value": "\"Modern asynchronous runtime systems allow the re-thinking of large-scale scientific applications. With the example of a simulator of morphologically detailed neural networks, we show how detaching from the commonly used bulk-synchronous parallel (BSP) execution allows for the increase of prefetching capabilities, better cache locality, and a overlap of computation and communication, consequently leading to a lower time to solution. Our strategy removes the operation of collective synchronization of ODEs\u2019 coupling information, and takes advantage of the pairwise time dependency between equations, leading to a fully-asynchronous exhaustive yet not speculative stepping model. Combined with fully linear data structures, communication reduce at compute node level, and an earliest equation steps first scheduler, we perform an acceleration at the cache level that reduces communication and time to solution by maximizing the number of timesteps taken per neuron at each iteration.\r\n\r\nOur methods were implemented on the core kernel of the NEURON scientific application. Asynchronicity and distributed memory space are provided by the HPX runtime system for the ParalleX execution model. Benchmark results demonstrate a superlinear speed-up that leads to a reduced runtime compared to the bulk synchronous execution, yielding a speed-up between 25% to 65% across different compute architectures, and in the order of 15% to 40% for distributed executions.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263718", + "other_implementer": { + "attr_id": 308, + "value": "Magalh\u00e3es B" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "ver_date": "2023-02-09T10:09:59.93", + "ver_number": 11 + }, + "263719": { + "class_id": 19, + "created": "2020-04-10T04:09:19.89", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 88209, + "object_name": "Na/Ca exchanger" + }, + { + "object_id": 167500, + "object_name": "Kir" + }, + { + "object_id": 243506, + "object_name": "IK Skca" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 140826, + "object_name": "I Ca,p" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 244, + "object_name": "I N" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263719, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 251109, + "object_name": "Muddapu, Vignayanandam R. [vignan.0009 at gmail.com]" + }, + { + "object_id": 251110, + "object_name": "Chakravarthy, Srinivasa V. [schakra at iitm.ac.in]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 65417, + "object_name": "Parkinson's" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267360, + "object_name": "Muddapu VR, Vijayakumar K, Ramakrishnan K, Chakravarthy VS (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Levodopa-Induced Toxicity in Parkinson's Disease (Muddapu et al, 2022)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 261, + "object_name": "Substantia nigra pars compacta DA cell" + }, + { + "object_id": 260, + "object_name": "Neostriatum medium spiny direct pathway GABA cell" + }, + { + "object_id": 182591, + "object_name": "Globus pallidus principal GABA cell" + }, + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + }, + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + }, + { + "object_id": 74300, + "object_name": "Subthalamus nucleus projection neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... We present a systems-level computational model of SNc-striatum, which will help us understand the mechanism behind neurodegeneration postulated above and provide insights into developing disease-modifying therapeutics. It was observed that SNc terminals are more vulnerable to energy deficiency than SNc somas. During L-DOPA therapy, it was observed that higher L-DOPA dosage results in increased loss of terminals in SNc. It was also observed that co-administration of L-DOPA and glutathione (antioxidant) evades L-DOPA-induced toxicity in SNc neurons. Our proposed model of the SNc-striatum system is the first of its kind, where SNc neurons were modeled at a biophysical level, and striatal neurons were modeled at a spiking level. We show that our proposed model was able to capture L-DOPA-induced toxicity in SNc, caused by energy deficiency.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263719", + "other_neurotransmitter": { + "attr_id": 304, + "value": "Substance P" + }, + "other_receptors": { + "attr_id": 302, + "value": "D1-R, D2-R, NK1-R" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "vignan.0009@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Vignayanandam R Muddapu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + }, + { + "object_id": 249414, + "object_name": "Globus pallidus externa (GPe)" + }, + { + "object_id": 138633, + "object_name": "Striatum" + }, + { + "object_id": 116868, + "object_name": "Subthalamic Nucleus" + } + ] + }, + "ver_date": "2022-04-20T12:21:32.297", + "ver_number": 4 + }, + "263956": { + "citation": { + "attr_id": 391, + "value": "https://link.springer.com/article/10.1007/s10827-018-0688-6" + }, + "class_id": 19, + "created": "2020-04-14T12:05:51.823", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263956, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 264550, + "object_name": "Rodrigo Pena, [pena at njit.edu]" + }, + { + "object_id": 264551, + "object_name": "Petar Tomov, [tomov at mathematik.hu-berlin.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 183719, + "object_name": "Synaptic noise" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 93420, + "object_name": "Sleep" + }, + { + "object_id": 185463, + "object_name": "Pattern Separation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 264548, + "object_name": "Pena RFO, Zaks MA, Roque AC (2018)" + }, + { + "object_id": 264549, + "object_name": "Tomov P, Pena RF, Roque AC, Zaks MA (2016)" + }, + { + "object_id": 244287, + "object_name": "Tomov P, Pena RF, Zaks MA, Roque AC (2014)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Dynamics in random NNs with multiple neuron subtypes (Pena et al 2018, Tomov et al 2014, 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Spontaneous cortical population activity exhibits a multitude of oscillatory patterns, which often display synchrony during slow-wave sleep or under certain anesthetics and stay asynchronous during quiet wakefulness. The mechanisms behind these cortical states and transitions among them are not completely understood. Here we study spontaneous population activity patterns in random networks of spiking neurons of mixed types modeled by Izhikevich equations. Neurons are coupled by conductance-based synapses subject to synaptic noise. We localize the population activity patterns on the parameter diagram spanned by the relative inhibitory synaptic strength and the magnitude of synaptic noise. In absence of noise, networks display transient activity patterns, either oscillatory or at constant level. The effect of noise is to turn transient patterns into persistent ones: for weak noise, all activity patterns are asynchronous non-oscillatory independently of synaptic strengths; for stronger noise, patterns have oscillatory and synchrony characteristics that depend on the relative inhibitory synaptic strength. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263956", + "other_implementer": { + "attr_id": 308, + "value": "Rodrigo Pena, pena@njit.edu\r\nPetar Tomov, tomov@mathematik.hu-berlin.de " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2020-04-22T10:58:32.99", + "ver_number": 9 + }, + "263961": { + "citation": { + "attr_id": 391, + "value": "https://www.sciencedirect.com/science/article/pii/S2211124720301881" + }, + "class_id": 19, + "created": "2020-04-14T12:14:20.943", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263961, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 264172, + "object_name": "Nandi, Anirban " + }, + { + "object_id": 264170, + "object_name": "Wei, Yina " + }, + { + "object_id": 264171, + "object_name": "Anastassiou, Costas A [costasa at alleninstitute.org]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 264554, + "object_name": "Cardiac-related electrode motion" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 264169, + "object_name": "Mosher CP, Wei Y, Kaminski J, Nandi A, Mamelak AN, Anastassiou CA, Rutishauser U (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "Cellular classes revealed by heartbeat-related modulation of extracellular APs (Mosher et al 2020)", + "notes": { + "attr_id": 24, + "value": "\"Determining cell types is critical for understanding neural circuits but remains elusive in the living human brain. Current approaches discriminate units into putative cell classes using features of the extracellular action potential (EAP); in absence of ground truth data, this remains a problematic procedure. We find that EAPs in deep structures of the brain exhibit robust and systematic variability during the cardiac cycle. These cardiac-related features refine neural classification. We use these features to link bio-realistic models generated from in vitro human whole-cell recordings of morphologically classified neurons to in vivo recordings. We differentiate aspiny inhibitory and spiny excitatory human hippocampal neurons and, in a second stage, demonstrate that cardiac-motion features reveal two types of spiny neurons with distinct intrinsic electrophysiological properties and phase-locking characteristics to endogenous oscillations. This multi-modal approach markedly improves cell classification in humans, offers interpretable cell classes, and is applicable to other brain areas and species.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263961", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + }, + { + "object_id": 227987, + "object_name": "Human" + } + ] + }, + "ver_date": "2020-04-22T11:44:17.377", + "ver_number": 6 + }, + "263988": { + "citation": { + "attr_id": 391, + "value": "Capllonch-Juan, M, and Sepulveda, F. Modelling the effects of ephaptic coupling on selectivity and response patterns during artificial stimulation of peripheral nerves. PLoS Comput Biol. Forthcoming 2020. DOI 10.1371/journal.pcbi.1007826." + }, + "class_id": 19, + "created": "2020-04-15T18:11:51.663", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263988, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 183995, + "object_name": "Ephaptic coupling" + }, + { + "object_id": 230581, + "object_name": "Stimulus selectivity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266758, + "object_name": "Capllonch-Juan M, Sepulveda F (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Model of peripheral nerve with ephaptic coupling (Capllonch-Juan & Sepulveda 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52425, + "object_name": "Myelinated neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We built a computational model of a peripheral nerve trunk in which the interstitial space between the fibers and the tissues is modelled using a resistor network, thus enabling distance-dependent ephaptic coupling between myelinated axons and between fascicles as well. We used the model to simulate a) the stimulation of a nerve trunk model with a cuff electrode, and b) the propagation of action potentials along the axons. Results were used to investigate the effect of ephaptic interactions on recruitment and selectivity stemming from artificial (i.e., neural implant) stimulation and on the relative timing between action potentials during propagation." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263988", + "public_submitter_email": { + "attr_id": 309, + "value": "mcjrcmc@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Miguel Capllonch-Juan" + }, + "ver_date": "2020-04-15T19:05:45.913", + "ver_number": 2 + }, + "263992": { + "citation": { + "attr_id": 391, + "value": "Meijer G, Marchesi P, Mejias J, Montijn J, Lansink C, Pennartz C (2020) Higher-order visual cortex shows stronger neural correlates of visual and multisensory detection behavior compared to primary visual cortex. CELL-REPORTS-D-19-02481. http://dx.doi.org/10.2139/ssrn.3414701" + }, + "class_id": 19, + "created": "2020-04-17T11:46:48.083", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263992, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 249591, + "object_name": "Mejias, Jorge [j.f.mejias at uva.nl]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 264166, + "object_name": "Meijer G, Marchesi P, Mejias J, Montijn, J, Lansink, C, Pennartz C (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "V1 and AL spiking neural network for visual contrast response in mouse (Meijer et al. 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This code contains the computational model included in Meijer et al., Cell Reports 2020, which reproduces some of the main experimental findings reported --most notably, the higher sensory response of secondary visual areas compared to that of primary visual areas for moderate visual contrast levels in mice. The model is based on a two-area spiking neural network with embedded short-term synaptic plasticity mechanisms." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263992", + "public_submitter_email": { + "attr_id": 309, + "value": "j.f.mejias@uva.nl" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Jorge Mejias" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228598, + "object_name": "Mouse" + }, + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2020-04-18T11:21:34.517", + "ver_number": 5 + }, + "263995": { + "citation": { + "attr_id": 391, + "value": "https://www.eneuro.org/content/eneuro/early/2019/09/23/ENEURO.0343-19.2019.full.pdf" + }, + "class_id": 19, + "created": "2020-04-17T14:03:56.507", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 263995, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266835, + "object_name": "Hamid E, Church E, Alford S (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 2835, + "object_name": "MCell" + } + ] + }, + "name": "Action potential-evoked Ca2+ signals in CA1 pyramidal cell presynaptic terminals (Hamid et al 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The attached file contains models for 4 different MCell simulations of the same presynaptic terminal model. \r\n\r\nCalbindin22_40uM_files - model of Ca entry, buffering and extrusion from a single varicosity of a CA1 pyramidal neuron axon containing 40 \u00b5M simulated calbindin28K (binding constants from Nagerl et al 2000, Biophys J 79:3009\u20133018. doi:10.1016/S0006-3495(00)76537-4 pmid:11106608. This model represents calbindin28K with 2 low and 2 intermediate affinity Ca2+ binding sites.\r\n\r\nCalbindin31_40uM_files - similar to above but with calbindin28K with 1 low and 3 intermediate affinity Ca2+ binding sites.\r\n\r\nCalmodulin_40uM_files - model of Ca2+ using calmodulin and the principle buffer, again at 40 \u00b5M (from Faas et al 2011, Nat Neurosci 14:301\u2013304. doi:10.1038/nn.2746 pmid:21258328.\r\n\r\nPaired_Pulses - model used to simulate paired pulses of Ca2+ entry to these presynaptic terminals\r\n\r\nThese models were based on experimentally quantified action potential-evoked Calcium entry to presynaptic varicosities of CA1 pyramidal neurons. How Ca enters, diffuses, is buffered and is pumped out of the cytosol was first determined. The study used Calcium-sensitive dyes of different affinities over a range of concentrations to buffer Calcium. Following this quantification we used these data to construct Monte Carlo simulations of the Calcium transients to determine dynamics of the Calcium signal at spatiotemporal resolutions not possible with imaging methods. From these simulations we have demonstrated very high concentrations, short duration Calcium transients that are dominated by Calcium diffusion within tens of nanometers of Calcium channels. We have quantified channel densities, constrained buffer identities and determined properties of presynaptic Calcium during trains of action potentials. \r\n\r\nThis published work can be found at: \r\n\r\nEdaeni Hamid, Emily Church and Simon Alford Quantitation and Simulation of Single Action Potential-Evoked Ca2+ Signals in CA1 Pyramidal Neuron Presynaptic Terminals\r\neNeuro 24 September 2019, 6 (5) ENEURO.0343-19.2019; DOI: https://doi.org/10.1523/ENEURO.0343-19.2019" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/263995", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "ver_date": "2020-11-04T12:39:12.167", + "ver_number": 7 + }, + "264177": { + "citation": { + "attr_id": 391, + "value": "https://www.jneurosci.org/content/early/2019/09/11/JNEUROSCI.0901-19.2019.abstract" + }, + "class_id": 19, + "created": "2020-04-18T14:24:05.573", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 264177, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 128875, + "object_name": "Development" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266979, + "object_name": "Iacobucci GJ, Popescu GK (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Spatial coupling tunes NMDA receptor responses via Ca2+ diffusion (Iacobucci and Popescu 2019)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This code implements a coupled markov model for analysis of positive or negative ion channel coupling from measured unitary currents in patch clamp recordings see our paper: Spatial Coupling Tunes NMDA Receptor Responses via Ca2+ Diffusion Gary J. Iacobucci and Gabriela K. Popescu Journal of Neuroscience 6 November 2019, 39 (45) 8831-8844; DOI: https://doi.org/10.1523/JNEUROSCI.0901-19.2019" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/264177", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2021-03-24T11:09:27.797", + "ver_number": 8 + }, + "264514": { + "citation": { + "attr_id": 391, + "value": "https://www.sciencedirect.com/science/article/pii/S0006899319303592" + }, + "class_id": 19, + "created": "2020-04-20T11:48:05.44", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 264514, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 264606, + "object_name": "Spratling, MW [michael.spratling at kcl.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245691, + "object_name": "Vision" + }, + { + "object_id": 150701, + "object_name": "Posture and locomotion" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 264605, + "object_name": "Spratling MW (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 223029, + "object_name": "Predictive Coding Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Fitting predictive coding to the neurophysiological data (Spratling 2019)", + "notes": { + "attr_id": 24, + "value": "MATLAB code for simulating the response properties of V1 mismatch neurons and for testing the ability of predictive coding algorithms to scale. This code performs the experiments described in:\r\nSpratling MW (2019) Abstract:\r\n\"Recent neurophysiological data showing the effects of locomotion on neural activity in mouse primary visual cortex has been interpreted as providing strong support for the predictive coding account of cortical function. Specifically, this work has been interpreted as providing direct evidence that prediction-error, a distinguishing property of predictive coding, is encoded in cortex. This article evaluates these claims and highlights some of the discrepancies between the proposed predictive coding model and the neuro-biology. Furthermore, it is shown that the model can be modified so as to fit the empirical data more successfully.\"\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/264514", + "other_implementer": { + "attr_id": 308, + "value": "Spratling, MW" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2020-04-23T14:36:01.45", + "ver_number": 20 + }, + "264519": { + "citation": { + "attr_id": 391, + "value": "http://www.pnas.org/content/early/2018/09/12/1806486115.short" + }, + "class_id": 19, + "created": "2020-04-20T11:56:35.933", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 167500, + "object_name": "Kir" + }, + { + "object_id": 243504, + "object_name": "IK Bkca" + }, + { + "object_id": 243506, + "object_name": "IK Skca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 264519, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266499, + "object_name": "Tatsuki, Fumiya [tatsuki-tky at umin.ac.jp]" + }, + { + "object_id": 266500, + "object_name": "Yoshida, Kensuke [kensuyoshida-tky at umin.ac.jp]" + }, + { + "object_id": 266501, + "object_name": "Yamada, Tetsuya " + }, + { + "object_id": 266502, + "object_name": "Katsumata, Takahiro [alextfkd at m.u-tokyo.ac.jp]" + }, + { + "object_id": 266505, + "object_name": "Shi, Shoi [sshoi0322-tky at umin.ac.jp] " + }, + { + "object_id": 266503, + "object_name": "Ueda, R, Hiroki [hiroki.ueda at nifty.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 93420, + "object_name": "Sleep" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 185343, + "object_name": "Persistent activity" + }, + { + "object_id": 139148, + "object_name": "Brain Rhythms" + }, + { + "object_id": 266504, + "object_name": "Sleep-Wake transition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266480, + "object_name": "Tatsuki F, Sunagawa GA, Shi S, Susaki EA, Yukinaga H, Perrin D, Sumiyama K, Ukai-Tadenuma M, Fujishima H, Ohno R, Tone D, Ode KL, Matsumoto K, Ueda HR (2016)" + }, + { + "object_id": 266482, + "object_name": "Yoshida K, Shi S, Ukai-Tadenuma M, Fujishima H, Ohno RI, Ueda HR (2018)" + }, + { + "object_id": 266485, + "object_name": "Rasmussen R, Jensen MH, Heltberg ML (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Ave. neuron model for slow-wave sleep in cortex Tatsuki 2016 Yoshida 2018 Rasmussen 2017 (all et al)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 184134, + "object_name": "Neocortex layer 5 interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Averaged neuron(AN) model is a conductance-based (Hodgkin-Huxley type) neuron model which includes a mean-field approximation of a population of neurons. You can simulate previous models (AN model: Tatsuki et al., 2016 and SAN model: Yoshida et al., 2018), and various models with 'X model' based on channel and parameter modules. Also, intracellular and extracellular ion concentration can be taken into consideration using the Nernst equation (See Ramussen et al., 2017)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/264519", + "other_concept": { + "attr_id": 306, + "value": "Sleep-Wake transition" + }, + "other_currents": { + "attr_id": 303, + "value": "INMDA, IAMPA" + }, + "other_implementer": { + "attr_id": 308, + "value": "Tatsuki, Fumiya\r\nYoshida, Kensuke\r\nYamada, Tetsuya\r\nKatsumata, Takahiro\r\nUeda, R, Hiroki" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228598, + "object_name": "Mouse" + }, + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2020-05-28T11:34:32.353", + "ver_number": 16 + }, + "264539": { + "citation": { + "attr_id": 391, + "value": "https://iopscience.iop.org/article/10.1088/1741-2552/ab1685/meta" + }, + "class_id": 19, + "created": "2020-04-21T18:37:20.073", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 264539, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267091, + "object_name": "Lemaire T, Neufeld E, Kuster N, Micera S (2019)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "MultiScale Optimized Neuronal Intramembrane Cavitation (SONIC) model (Lemaire et al. 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 182735, + "object_name": "Subthalamic nucleus principal GABA cell" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + }, + { + "object_id": 97753, + "object_name": "Neocortex fast spiking (FS) interneuron" + }, + { + "object_id": 142232, + "object_name": "Neocortex spiking low threshold (LTS) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Jupyter Notebooks to reproduce data and figures of the SONIC paper (Lemaire et al. 2019) describing a computationally efficient variant to simulate ultrasound neuromodulation by intramembrane cavitation in cortical neurons." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/264539", + "other_concept": { + "attr_id": 306, + "value": "Ultrasound" + }, + "other_implementer": { + "attr_id": 308, + "value": "Th\u00e9o Lemaire (theo.lemaire@epfl.ch)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "ver_date": "2021-08-02T14:24:55.54", + "ver_number": 11 + }, + "264591": { + "biosimulations": { + "value": "modeldb-264591" + }, + "citation": { + "attr_id": 391, + "value": "Verma P, Kienle A, Flockerzi D, Ramkrishna D. Using Bifurcation Theory for Exploring Pain. Industrial & Engineering Chemistry Research. 2019.\r\n\r\nVerma P, Kienle A, Flockerzi D, Ramkrishna D. Computational analysis of a 9D model for a small DRG neuron. arXiv preprint arXiv:2001.04915. 2020 Jan 14." + }, + "class_id": 19, + "created": "2020-04-22T21:28:34.093", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92088, + "object_name": "Nav1.7 SCN9A" + }, + { + "object_id": 92089, + "object_name": "Nav1.8 SCN10A" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "icg_channel_details": { + "attr_id": 640, + "value": [ + { + "object_id": 264592, + "object_name": "ICG Detail_264591" + }, + { + "object_id": 264593, + "object_name": "ICG Detail_264591" + } + ] + }, + "id": 264591, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 264604, + "object_name": "Verma, Parul [parulv.iitb at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 264603, + "object_name": "Verma P, Kienle A, Flockerzi D, Ramkrishna D (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "A computational model of a small DRG neuron to explore pain (Verma et al. 2019, 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 136307, + "object_name": "Dorsal Root Ganglion (DRG) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a Hodgkin-Huxley type model for a small DRG neuron consisting of four voltage-gated ion channels: sodium channels 1.7 and 1.8, delayed rectifier potassium, and A-type transient potassium channels. This model was used to explore the dynamics of this neuron using bifurcation theory, with the motive to investigate pain since small DRG neuron is a pain-sensing neuron." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/264591", + "other_currents": { + "attr_id": 303, + "value": "I Leak" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "parulv.iitb@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Parul Verma" + }, + "ver_date": "2020-05-21T14:11:20.997", + "ver_number": 8 + }, + "264594": { + "class_id": 19, + "created": "2020-04-23T09:36:52.76", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 264594, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266652, + "object_name": "Sharma SK, Mondal A, Mondal A, Upadhyay RK, Hens C (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Leech Heart Interneuron model (Sharma et al 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52410, + "object_name": "Leech heart interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Fractional order Leech heart interneuron model is investigated. Different firing properties are explored. \r\nIn this article, we investigate the alternation of spiking and bursting phenomena of an uncoupled and coupled fractional Leech-Heart (L-H) neurons. We show that a complete graph of heterogeneous de-synchronized neurons in the backdrop of diverse memory settings (a mixture of integer and fractional exponents) can eventually lead to bursting with the formation of cluster\r\nsynchronization over a certain threshold of coupling\r\nstrength, however, the uncoupled L-H neurons cannot\r\nreveal bursting dynamics. Using the stability analysis\r\nin fractional domain, we demarcate the parameter\r\nspace where the quiescent or steady-state emerges\r\nin uncoupled L-H neuron. Finally, a reduced-order\r\nmodel is introduced to capture the activities of the\r\nlarge network of fractional-order model neurons." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/264594", + "public_submitter_email": { + "attr_id": 309, + "value": "arghamondalb1@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Argha Mondal" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115958, + "object_name": "Leech" + } + ] + }, + "ver_date": "2020-06-24T12:36:14.667", + "ver_number": 13 + }, + "264597": { + "citation": { + "attr_id": 391, + "value": "Zerlaut, Y., Chemla, S., Chavane, F. et al. J Comput Neurosci (2017). https://doi.org/10.1007/s10827-017-0668-2" + }, + "class_id": 19, + "created": "2020-04-23T13:30:18.77", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 264597, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 264598, + "object_name": "Soler, Amelie [amelie.soler at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 234994, + "object_name": "Zerlaut Y, Chemla S, Chavane F, Destexhe A (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 116010, + "object_name": "PyNN" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Mesoscopic dynamics from AdEx recurrent networks (Zerlaut et al JCNS 2018) (PyNN)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "PyNN simulations for Zerlaut et al 2018)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/264597", + "public_submitter_email": { + "attr_id": 309, + "value": "yann.zerlaut@iit.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Yann Zerlaut" + }, + "ver_date": "2020-04-24T11:35:26.89", + "ver_number": 9 + }, + "264628": { + "citation": { + "attr_id": 391, + "value": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5468411/" + }, + "class_id": 19, + "created": "2020-04-23T15:33:08.473", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 264628, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 265594, + "object_name": "Mirzakhalili, Ehsan " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 185343, + "object_name": "Persistent activity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 265581, + "object_name": "Mirzakhalili E, Gourgou E, Booth V, Epureanu B (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Synaptic Impairment, Robustness of Excitatory NNs w/ Different Topologies (Mirzakhalili et al 2017)", + "notes": { + "attr_id": 24, + "value": "\"Synaptic deficiencies are a known hallmark of neurodegenerative diseases, but the diagnosis of impaired synapses on the cellular level is not an easy task. Nonetheless, changes in the system-level dynamics of neuronal networks with damaged synapses can be detected using techniques that do not require high spatial resolution. This paper investigates how the structure/topology of neuronal networks influences their dynamics when they suffer from synaptic loss. We study different neuronal network structures/topologies by specifying their degree distributions. The modes of the degree distribution can be used to construct networks that consist of rich clubs and resemble small world networks, as well. We define two dynamical metrics to compare the activity of networks with different structures: persistent activity (namely, the self-sustained activity of the network upon removal of the initial stimulus) and quality of activity (namely, percentage of neurons that participate in the persistent activity of the network). Our results show that synaptic loss affects the persistent activity of networks with bimodal degree distributions less than it affects random networks. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/264628", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "ver_date": "2020-05-14T13:54:23.61", + "ver_number": 12 + }, + "264834": { + "citation": { + "attr_id": 391, + "value": "https://onlinelibrary.wiley.com/doi/abs/10.1002/ana.25438" + }, + "class_id": 19, + "created": "2020-04-24T11:22:54.19", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92082, + "object_name": "Nav1.1 SCN1A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 264834, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 265503, + "object_name": "Bryson, Alex [alexander.bryson at florey.edu.au]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 121254, + "object_name": "Depolarization block" + }, + { + "object_id": 128875, + "object_name": "Development" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 265502, + "object_name": "Berecki G, Bryson A, Terhag J, Maljevic S, Gazina EV, Hill SL, Petrou S (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 245004, + "object_name": "BluePyOpt\r\n" + } + ] + }, + "name": "SCN1A gain-of-function in early infantile encephalopathy (Berecki et al 2019)", + "notes": { + "attr_id": 24, + "value": "\"OBJECTIVE:\r\nTo elucidate the biophysical basis underlying the distinct and severe clinical presentation in patients with the recurrent missense SCN1A variant, p.Thr226Met. Patients with this variant show a well-defined genotype-phenotype correlation and present with developmental and early infantile epileptic encephalopathy that is far more severe than typical SCN1A Dravet syndrome.\r\n\r\nMETHODS:\r\nWhole cell patch clamp and dynamic action potential clamp were used to study T226M Nav 1.1 channels expressed in mammalian cells. Computational modeling was used to explore the neuronal scale mechanisms that account for altered action potential firing.\r\n\r\nRESULTS:\r\nT226M channels exhibited hyperpolarizing shifts of the activation and inactivation curves and enhanced fast inactivation. Dynamic action potential clamp hybrid simulation showed that model neurons containing T226M conductance displayed a left shift in rheobase relative to control. At current stimulation levels that produced repetitive action potential firing in control model neurons, depolarization block and cessation of action potential firing occurred in T226M model neurons. Fully computationally simulated neuron models recapitulated the findings from dynamic action potential clamp and showed that heterozygous T226M models were also more susceptible to depolarization block.\r\n\r\n...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/264834", + "other_implementer": { + "attr_id": 308, + "value": "Bryson, Alex" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "ver_date": "2020-05-02T12:18:45.497", + "ver_number": 12 + }, + "264842": { + "citation": { + "attr_id": 391, + "value": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0230327" + }, + "class_id": 19, + "created": "2020-04-24T13:16:58.933", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 88209, + "object_name": "Na/Ca exchanger" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 264842, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 262699, + "object_name": "O`Halloran DM (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "Opposing roles for Na+/Ca2+ exchange and Ca2+-activated K+ currents during STDP (O`Halloran 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Sodium Calcium exchanger (NCX) proteins utilize the electrochemical gradient of Na+ to generate Ca2+ efflux (forward mode) or influx (reverse mode). In mammals, there are three unique NCX encoding genes-NCX1, NCX2, and NCX3, that comprise the SLC8A family, and mRNA from all three exchangers is expressed in hippocampal pyramidal cells. Furthermore, mutant ncx2-/- and ncx3-/- mice have each been shown to exhibit altered long-term potentiation (LTP) in the hippocampal CA1 region due to delayed Ca2+ clearance after depolarization that alters synaptic transmission. In addition to the role of NCX at the synapse of hippocampal subfields required for LTP, the three NCX isoforms have also been shown to localize to the dendrite of hippocampal pyramidal cells. In the case of NCX1, it has been shown to localize throughout the basal and apical dendrite of CA1 neurons where it helps compartmentalize Ca2+ between dendritic shafts and spines. Given the role for NCX and calcium in synaptic plasticity, the capacity of NCX splice-forms to influence backpropagating action potentials has clear consequences for the induction of spike-timing dependent synaptic plasticity (STDP). To explore this, we examined the effect of NCX localization, density, and allosteric activation on forward and back propagating signals and, next employed a STDP paradigm to monitor the effect of NCX on plasticity using back propagating action potentials paired with EPSPs. From our simulation studies we identified a role for the sodium calcium exchange current in normalizing STDP, and demonstrate that NCX is required at the postsynaptic site for this response. We also screened other mechanisms in our model and identified a role for the Ca2+ activated K+ current at the postsynapse in producing STDP responses. Together, our data reveal opposing roles for the Na+/Ca2+ exchanger current and the Ca2+ activated K+ current in setting STDP.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/264842", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2020-06-03T12:16:28.18", + "ver_number": 4 + }, + "264844": { + "citation": { + "attr_id": 391, + "value": "https://www.mdpi.com/2313-7673/4/1/21/pdf" + }, + "class_id": 19, + "created": "2020-04-24T13:19:14.403", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 264844, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 186770, + "object_name": "Motor control" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267676, + "object_name": "Deng K, Szczecinski NS, Arnold D, Andrada E, Fischer MS, Quinn RD, Hunt AJ (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 22013, + "object_name": "Neuromuscular Junction" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 150700, + "object_name": "AnimatLab v1" + } + ] + }, + "name": "Neuromechanical Model of Rat Hindlimb Walking with Two-Layer CPGs (Deng et al, 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A neuromechanical model of the rat hindlimb and locomotor circuitry built and run in AnimatLab v1. We use separate rhythm generators and pattern formation layers to activate antagonistic muscle pairs about each joint in the sagittal plane. The model replicates the resetting and non-resetting deletions observed in the animal." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/264844", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228598, + "object_name": "Mouse" + }, + { + "object_id": 115954, + "object_name": "Spinal motoneuron" + } + ] + }, + "ver_date": "2023-02-24T09:23:29.23", + "ver_number": 5 + }, + "265511": { + "citation": { + "attr_id": 391, + "value": "Global and Multiplexed Dendritic Computations\r\nunder In Vivo-like Conditions" + }, + "class_id": 19, + "created": "2020-05-04T22:27:09.577", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 265511, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 265530, + "object_name": "Ujfalussy, Bal\u00e1zs B. 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Ujfalussy et al. use a model-based approach to show that linear integration with a single global dendritic nonlinearity can accurately predict the\r\nresponse of neurons to naturalistic synaptic input patterns.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/265511", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2020-05-06T11:05:39.107", + "ver_number": 7 + }, + "265523": { + "citation": { + "attr_id": 391, + "value": "https://academic.oup.com/cercor/advance-article-abstract/doi/10.1093/cercor/bhz334/5741374" + }, + "class_id": 19, + "created": "2020-05-05T18:09:55.743", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 247, + "object_name": "I A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 265523, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 223060, + "object_name": "Guet-McCreight, Alexandre [alexandre.guet.mccreight at mail.utoronto.ca]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 182690, + "object_name": "Spatial Navigation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 265570, + "object_name": "Luo X, Guet-McCreight A, Villette V, Francavilla R, Marino B, Chamberland S, Skinner FK, Topolnik L (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Hippocampus CA1 Interneuron Specific 3 (IS3) in vivo-like virtual NN simulations (Luo et al 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 249699, + "object_name": "Hippocampal CA1 CR/VIP cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Disinhibition is a widespread circuit mechanism for information selection and transfer. In the hippocampus, disinhibition of principal cells is provided by the interneuron-specific interneurons that express the vasoactive intestinal polypeptide (VIP-IS) and innervate selectively inhibitory interneurons. By combining optophysiological experiments with computational models, we determined the impact of synaptic inputs onto the network state-dependent recruitment of VIP-IS cells. We found that VIP-IS cells fire spikes in response to both the Schaffer collateral and the temporoammonic pathway activation. Moreover, by integrating their intrinsic and synaptic properties into computational models, we predicted recruitment of these cells between the rising phase and peak of theta oscillation and during ripples. Two-photon Ca2+-imaging in awake mice supported in part the theoretical predictions, revealing a significant speed modulation of VIP-IS cells and their preferential albeit delayed recruitment during theta-run epochs, with estimated firing at the rising phase and peak of the theta cycle. However, it also uncovered that VIP-IS cells are not activated during ripples. Thus, given the preferential theta-modulated firing of VIP-IS cells in awake hippocampus, we postulate that these cells may be important for information gating during spatial navigation and memory encoding.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/265523", + "other_currents": { + "attr_id": 303, + "value": "I Kdrf" + }, + "other_neurons": { + "attr_id": 301, + "value": "Interneuron Specific 3" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2020-05-07T18:00:08.703", + "ver_number": 8 + }, + "265528": { + "citation": { + "attr_id": 391, + "value": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007822" + }, + "class_id": 19, + "created": "2020-05-05T18:14:04.787", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 265528, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266441, + "object_name": "Cakan Caglar [cakan at ni.tu-berlin.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266438, + "object_name": "Cakan C, Obermayer K (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 266440, + "object_name": "neurolib (web link to model)" + } + ] + }, + "name": "Mean-field models of neural populations under electrical stimulation (Cakan & Obermayer 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + }, + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Weak electrical inputs to the brain in vivo using transcranial electrical stimulation or in isolated cortex in vitro can affect the dynamics of the underlying neural populations. However, it is poorly understood what the exact mechanisms are that modulate the activity of neural populations as a whole and why the responses are so diverse in stimulation experiments. Despite this, electrical stimulation techniques are being developed for the treatment of neurological diseases in humans. To better understand these interactions, it is often necessary to simulate and analyze very large networks of neurons, which can be computationally demanding. In this theoretical paper, we present a reduced model of coupled neural populations that represents a piece of cortical tissue. This efficient model retains the dynamical properties of the large network of neurons it is based on while being several orders of magnitude faster to simulate. Due to the biophysical properties of the neuron model, an electric field can be coupled to the population. We show that weak electric fields often used in stimulation experiments can lead to entrainment of neural oscillations on the population level, and argue that the responses critically depend on the dynamical state of the neural system." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/265528", + "other_simulator": { + "attr_id": 307, + "value": "neurolib" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "ver_date": "2020-05-22T16:37:19.92", + "ver_number": 8 + }, + "265540": { + "class_id": 19, + "created": "2020-05-06T13:48:20.403", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 244, + "object_name": "I N" + }, + { + "object_id": 112844, + "object_name": "I Q" + }, + { + "object_id": 112843, + "object_name": "I R" + }, + { + "object_id": 167500, + "object_name": "Kir" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122819, + "object_name": "Cav2.1 CACNA1A" + }, + { + "object_id": 122820, + "object_name": "Cav2.2 CACNA1B" + }, + { + "object_id": 122810, + "object_name": "Cav3.1 CACNA1G" + }, + { + "object_id": 122809, + "object_name": "Cav3.2 CACNA1H" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 265540, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 118393, + "object_name": "Hjorth, Johannes [hjorth at csc.kth.se]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 265539, + "object_name": "Hjorth JJJ, Kozlov A, Carannante I, Frost Nyl\u00e9n J, Lindroos R, Johansson Y, Tokarska A, Dorst MC, Suryanarayana SM, Silberberg G, Hellgren Kotaleski J, Grillner S (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 266927, + "object_name": "Snudda (web link to model)" + } + ] + }, + "name": "The microcircuits of striatum in silico (Hjorth et al 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 260, + "object_name": "Neostriatum medium spiny direct pathway GABA cell" + }, + { + "object_id": 153360, + "object_name": "Neostriatum medium spiny indirect pathway GABA cell" + }, + { + "object_id": 7021, + "object_name": "Neostriatum interneuron ACh cell" + }, + { + "object_id": 182691, + "object_name": "Neostriatum interneuron gaba/parvalbumin GABA cell" + }, + { + "object_id": 182709, + "object_name": "Neostriatum interneuron SOM/NOS GABA cell" + }, + { + "object_id": 150619, + "object_name": "Neostriatum spiny neuron" + }, + { + "object_id": 118395, + "object_name": "Neostriatum fast spiking interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Our aim is to reconstruct a full-scale mouse striatal cellular level model to provide a framework to integrate and interpret striatal data. We represent the main striatal neuronal subtypes, the two types of projection neurons (dSPNs and iSPNs) giving rise to the direct and indirect pathways, the fast-spiking interneurons, the low threshold spiking interneurons, and the cholinergic interneurons as detailed compartmental models, with properties close to their biological counterparts. Both intrastriatal and afferent synaptic inputs (cortex, thalamus, dopamine system) are optimized against existing data, including short-term plasticity. This model platform will be used to generate new hypotheses on striatal function or network dynamic phenomena.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/265540", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 138633, + "object_name": "Striatum" + }, + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2021-02-16T16:30:12.777", + "ver_number": 11 + }, + "265584": { + "citation": { + "attr_id": 391, + "value": "32385389" + }, + "class_id": 19, + "created": "2020-05-11T03:20:17.277", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 265584, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 229586, + "object_name": "Masoli, Stefano [stefano.masoli at unipv.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 265587, + "object_name": "Masoli S, Tognolina M, Laforenza U, Moccia F, D'Angelo E (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Cerebellar granule cell (Masoli et al 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 7346, + "object_name": "Cerebellum interneuron granule GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The cerebellar granule cells (GrCs) are classically described as a homogeneous neuronal population discharging \r\nregularly without adaptation. We show that GrCs in fact generate diverse response patterns to current injection \r\nand synaptic activation, ranging from adaptation to acceleration of firing. Adaptation was predicted by parameter \r\noptimization in detailed computational models based on available knowledge on GrC ionic channels. The models also \r\npredicted that acceleration required additional mechanisms. We found that yet unrecognized TRPM4 currents specifically \r\naccounted for firing acceleration and that adapting GrCs outperformed accelerating GrCs in transmitting high-frequency \r\nmossy fiber (MF) bursts over a background discharge. This implied that GrC subtypes identified by their \r\nelectroresponsiveness corresponded to specific neurotransmitter release probability values. Simulations showed \r\nthat fine-tuning of pre- and post-synaptic parameters generated effective MF-GrC transmission channels, which \r\ncould enrich the processing of input spike patterns and enhance spatio-temporal recoding at the cerebellar input stage.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/265584", + "public_submitter_email": { + "attr_id": 309, + "value": "stefano.masoli@unipv.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Stefano Masoli" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2022-11-14T10:47:03.207", + "ver_number": 14 + }, + "265591": { + "citation": { + "attr_id": 391, + "value": "Muddapu, V. R. & Chakravarthy, V. S. Influence of Energy Deficiency on the Molecular Processes of Substantia Nigra Pars Compacta Cell for Understanding Parkinsonian Neurodegeneration - A Comprehensive Biophysical Computational Model. bioRxiv 2020.02.18.950337 (2020). doi:10.1101/2020.02.18.950337" + }, + "class_id": 19, + "created": "2020-05-12T08:06:41.21", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + }, + { + "object_id": 140826, + "object_name": "I Ca,p" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 88210, + "object_name": "I_Na,Ca" + }, + { + "object_id": 88211, + "object_name": "I_SERCA" + }, + { + "object_id": 88209, + "object_name": "Na/Ca exchanger" + }, + { + "object_id": 167500, + "object_name": "Kir" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 265591, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 251109, + "object_name": "Muddapu, Vignayanandam R. 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[schakra at iitm.ac.in]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 144991, + "object_name": "Apoptosis" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + }, + { + "object_id": 245160, + "object_name": "Neurotransmitter dynamics" + }, + { + "object_id": 227131, + "object_name": "Pacemaking mechanism" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266947, + "object_name": "Muddapu VR, Chakravarthy VS (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Single-cell comprehensive biophysical model of SN pars compacta (Muddapu & Chakravarthy 2021)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 261, + "object_name": "Substantia nigra pars compacta DA cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Parkinson\u2019s disease (PD) is caused by the loss of dopaminergic cells in substantia nigra pars compacta (SNc), the decisive cause of this inexorable cell loss is not clearly elucidated. We hypothesize that \u201cEnergy deficiency at a sub-cellular/cellular/systems-level can be a common underlying cause for SNc cell loss in PD.\u201d Here, we propose a comprehensive computational model of SNc cell which helps us to understand the pathophysiology of neurodegeneration at subcellular-level in PD. We were able to show see how deficits in supply of energy substrates (glucose and oxygen) lead to a deficit in ATP, and furthermore, deficits in ATP are the common factor underlying the pathological molecular-level changes including alpha-synuclein aggregation, ROS formation, calcium elevation, and dopamine dysfunction. The model also suggests that hypoglycemia plays a more crucial role in leading to ATP deficits than hypoxia. We believe that the proposed model provides an integrated modelling framework to understand the neurodegenerative processes underlying PD." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/265591", + "public_submitter_email": { + "attr_id": 309, + "value": "vignan.0009@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Vignayanandam R Muddapu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2021-03-01T17:11:22.69", + "ver_number": 2 + }, + "265592": { + "class_id": 19, + "created": "2020-05-13T12:53:36.797", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 112841, + "object_name": "I Krp" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 265592, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245691, + "object_name": "Vision" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 265583, + "object_name": "Billeh YN, Cai B, Gratiy SL, Dai K, Iyer R, Gouwens NW, Abbasi-Asl R, Jia X, Siegle JH, Olsen SR, Koch C, Mihalas S, Arkhipov A (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 225554, + "object_name": "NEST (web link to model)" + } + ] + }, + "name": "Systematic integration of data into multi-scale models of mouse primary V1 (Billeh et al 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + }, + { + "object_id": 7387, + "object_name": "Neocortex V1 interneuron basket PV GABA cell" + }, + { + "object_id": 230185, + "object_name": "Neocortex V1 interneuron chandelier SOM GABA cell" + }, + { + "object_id": 230186, + "object_name": "Neocortex V1 L4 stellate GLU cell" + }, + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 184130, + "object_name": "Neocortex layer 2-3 interneuron" + }, + { + "object_id": 184132, + "object_name": "Neocortex layer 4 interneuron" + }, + { + "object_id": 184128, + "object_name": "Neocortex layer 4 pyramidal cell" + }, + { + "object_id": 184134, + "object_name": "Neocortex layer 5 interneuron" + }, + { + "object_id": 142232, + "object_name": "Neocortex spiking low threshold (LTS) neuron" + }, + { + "object_id": 142230, + "object_name": "Neocortex spiking regular (RS) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Highlights\r\n\u2022\r\nTwo network models of the mouse primary visual cortex are developed and released\r\n\r\n\u2022\r\nOne uses compartmental-neuron models and the other point-neuron models\r\n\r\n\u2022\r\nThe models recapitulate observations from in vivo experimental data\r\n\r\n\u2022\r\nSimulations identify experimentally testable predictions about cortex circuitry\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/265592", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - 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This makes it difficult to conserve experimental metadata and track the analysis performed. It also complicates certain experimental approaches such as online analysis. To address this, we developed NeuroMatic, an open-source software toolkit that performs data acquisition (episodic, continuous and triggered recordings), data analysis (spike rasters, spontaneous event detection, curve fitting, stationarity) and simulations (stochastic synaptic transmission, synaptic short-term plasticity, integrate-and-fire and Hodgkin-Huxley-like single-compartment models). The merging of a wide range of tools into a single package facilitates a more integrated style of research, from the development of online analysis functions during data acquisition, to the simulation of synaptic conductance trains during dynamic-clamp experiments. Moreover, NeuroMatic has the advantage of working within Igor Pro, a platform-independent environment that includes an extensive library of built-in functions, a history window for reviewing the user's workflow and the ability to produce publication-quality graphics. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266415", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2020-05-20T11:44:12.063", + "ver_number": 2 + }, + "266419": { + "class_id": 19, + "created": "2020-05-20T19:46:41.403", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266419, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206387, + "object_name": "Morozova, Ekaterina O [emorozov at indiana.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267636, + "object_name": "Morozova E, Faure P, Gutkin B, Lapish C, Kuznetsov A (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Cholinergic and nicotinic regulation of DA neuron firing (Morozova et al 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 189152, + "object_name": "Ventral tegmental area dopamine neuron" + }, + { + "object_id": 206389, + "object_name": "Ventral tegmental area GABA neuron " + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + }, + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model describes the modulation of firing properties of DA neurons by acetylcholine (ACh) and nicotine in 5 cases: knock-out of \u00df2-containing nAChRs, \u00df2-containing nAChRs only on DA neurons, the nAChRs only on GABA neurons, the nAChRs on both DA and GABA neurons and \u201cwild\u201d type (the AChRs on DA, GABA and Glu neurons). The distinct responses to ACh and nicotine could be explained by distinct temporal patterns of these inputs: pulsatile vs. continuous." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266419", + "public_submitter_email": { + "attr_id": 309, + "value": "morozova.e.o@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ekaterina O Morozova" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 218, + "object_name": "Cholinergic Receptors" + } + ] + }, + "ver_date": "2023-02-09T10:12:36.237", + "ver_number": 8 + }, + "266435": { + "citation": { + "attr_id": 391, + "value": "http://search.proquest.com/openview/1ca70c32901c3feb8636b41971d7fdd8/1?pq-origsite=gscholar" + }, + "class_id": 19, + "created": "2020-05-22T00:07:19.637", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266435, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266473, + "object_name": "Rich, Scott [sbrich at umich.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266472, + "object_name": "Rich S, Chameh HM, Rafiee M, Ferguson K, Skinner FK, Valiante TA (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Inhibitory network bistability explains increased activity prior to seizure onset (Rich et al 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... the mechanisms predisposing an inhibitory network toward increased activity, specifically prior to ictogenesis, without a permanent change to inputs to the system remain unknown. We address this question by comparing simulated inhibitory networks containing control interneurons and networks containing hyperexcitable interneurons modeled to mimic treatment with 4-Aminopyridine (4-AP), an agent commonly used to model seizures in vivo and in vitro. Our in silico study demonstrates that model inhibitory networks with 4-AP interneurons are more prone than their control counterparts to exist in a bistable state in which asynchronously firing networks can abruptly transition into synchrony driven by a brief perturbation. This transition into synchrony brings about a corresponding increase in overall firing rate. We further show that perturbations driving this transition could arise in vivo from background excitatory synaptic activity in the cortex. Thus, we propose that bistability explains the increase in interneuron activity observed experimentally prior to seizure via a transition from incoherent to coherent dynamics. Moreover, bistability explains why inhibitory networks containing hyperexcitable interneurons are more vulnerable to this change in dynamics, and how such networks can undergo a transition without a permanent change in the drive. ...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266435", + "other_implementer": { + "attr_id": 308, + "value": "Scott Rich" + }, + "other_type": { + "attr_id": 305, + "value": "Inhibitory Network" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "ver_date": "2020-05-26T08:56:49.17", + "ver_number": 5 + }, + "266488": { + "citation": { + "attr_id": 391, + "value": "Wybo et al 2019" + }, + "class_id": 19, + "created": "2020-05-27T10:08:31.953", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266488, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266489, + "object_name": "Wybo, Willem A.M. " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266487, + "object_name": "Wybo WAM, Torben-Nielsen B, Nevian T, Gewaltig MO (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + }, + { + "object_id": 53577, + "object_name": "C or C++ program (web link to model)" + } + ] + }, + "name": "Electrical compartmentalization in neurons (Wybo et al 2019)", + "notes": { + "attr_id": 24, + "value": "\"The dendritic tree of neurons plays an important role in information processing in the brain. While it is thought that dendrites require independent subunits to perform most of their computations, it is still not understood how they compartmentalize into functional subunits. Here, we show how these subunits can be deduced from the properties of dendrites. We devised a formalism that links the dendritic arborization to an impedance-based tree graph and show how the topology of this graph reveals independent subunits. This analysis reveals that cooperativity between synapses decreases slowly with increasing electrical separation and thus that few independent subunits coexist. We nevertheless find that balanced inputs or shunting inhibition can modify this topology and increase the number and size of the subunits in a context-dependent manner. We also find that this dynamic recompartmentalization can enable branch-specific learning of stimulus features. Analysis of dendritic patch-clamp recording experiments confirmed our theoretical predictions.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266488", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2020-05-27T11:16:59.12", + "ver_number": 5 + }, + "266492": { + "citation": { + "attr_id": 391, + "value": "Hokanson et al 2018" + }, + "class_id": 19, + "created": "2020-05-27T11:34:19.837", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266492, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266491, + "object_name": "Hokanson, James A. [jim.hokanson at duke.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266490, + "object_name": "Hokanson JA, Gaunt RA, Weber DJ (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Neural recruitment during synchronous multichannel microstimulation (Hokanson et al 2018)", + "notes": { + "attr_id": 24, + "value": "\" ...The effects of field interactions on neuronal recruitment depend on several factors, which have been studied extensively at the macro-scale but have been overlooked in the case of high density arrays. Here, we report that field interactions can significantly affect neural recruitment, even with low amplitude stimulation. We created a computational model of peripheral nerve axons to estimate stimulation parameters sufficient to generate neural recruitment during synchronous and asynchronous stimulation on two microelectrodes located within the peripheral nerve. Across a range of stimulus amplitudes, the model predicted that synchronous stimulation on adjacent electrodes (400 \u00b5m separation), would recruit 2-3 times more neurons than during asynchronous stimulation. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266492", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2020-05-27T11:37:49.373", + "ver_number": 3 + }, + "266497": { + "class_id": 19, + "created": "2020-05-27T12:15:47.337", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266497, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266496, + "object_name": "Lei CL, Kellard JA, Hara M, Johnson JD, Rodriguez B, Briant LJB (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Beta-cell hubs maintain Ca2+ oscillations in human and mouse islet simulations (Lei et al 2018)", + "notes": { + "attr_id": 24, + "value": "\"Islet \u00df-cells are responsible for secreting all circulating insulin in response to rising plasma glucose concentrations. These cells are a phenotypically diverse population that express great functional heterogeneity. In mice, certain \u00df-cells (termed 'hubs') have been shown to be crucial for dictating the islet response to high glucose, with inhibition of these hub cells abolishing the coordinated Ca2+ oscillations necessary for driving insulin secretion. These \u00df-cell hubs were found to be highly metabolic and susceptible to pro-inflammatory and glucolipotoxic insults. In this study, we explored the importance of hub cells in human by constructing mathematical models of Ca2+ activity in human islets. Our simulations revealed that hubs dictate the coordinated Ca2+ response in both mouse and human islets; silencing a small proportion of hubs abolished whole-islet Ca2+ activity. We also observed that if hubs are assumed to be preferentially gap junction coupled, then the simulations better adhere to the available experimental data. Our simulations of 16 size-matched mouse and human islet architectures revealed that there are species differences in the role of hubs; ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266497", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2020-05-27T13:42:52.31", + "ver_number": 4 + }, + "266498": { + "class_id": 19, + "created": "2020-05-27T21:09:18.293", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266498, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266809, + "object_name": "Pelot NA, Catherall DC, Thio BJ, Titus ND, Liang ED, Henriquez CS, Grill WM (2021)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 231240, + "object_name": "Brian 2" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Excitation Properties of Computational Models of Unmyelinated Peripheral Axons (Pelot et al., 2021)", + "notes": { + "attr_id": 24, + "value": "We implemented the single-compartment model of vagal afferents from Schild et al. 1994 and extended the model into a multi-compartment axon, presenting the first C-fiber cable model of a C-fiber vagal afferent. We also implemented the updated parameters from Schild and Kunze 1997. We compared the responses of these novel models to three published models of unmyelinated axons (Rattay and Aberham 1993; Sundt et al. 2015; Tigerholm et al. 2014)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266498", + "other_implementer": { + "attr_id": 308, + "value": "David C. Catherall\r\nBrandon J. Thio\r\nNicole A. Pelot\r\nEdward D. Liang\r\nCraig S. Henriquez" + }, + "other_neurons": { + "attr_id": 301, + "value": "Unmyelinated peripheral axons" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "nikki.pelot@duke.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Nicole A. Pelot" + }, + "ver_date": "2022-07-06T13:37:46.45", + "ver_number": 14 + }, + "266506": { + "class_id": 19, + "created": "2020-05-29T05:29:08.467", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 266506, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 262437, + "object_name": "Giacopelli, Giuseppe [giuseppe.giacopelli at unipa.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 114660, + "object_name": "Connectivity matrix" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266940, + "object_name": "Giacopelli G, Tegolo D, Spera E, Migliore M (2021)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 2832, + "object_name": "NEST" + } + ] + }, + "name": "On the structural connectivity of large-scale models of brain networks (Giacopelli et al 2021)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The brain\u2019s structural connectivity plays a fundamental role in determining how neuron networks\r\ngenerate, process, and transfer information within and between brain regions. The underlying\r\nmechanisms are extremely difficult to study experimentally and, in many cases, large-scale model\r\nnetworks are of great help. However, the implementation of these models relies on experimental\r\nfindings that are often sparse and limited. Their predicting power ultimately depends on how closely\r\na model\u2019s connectivity represents the real system. Here we argue that the data-driven probabilistic\r\nrules, widely used to build neuronal network models, may not be appropriate to represent the\r\ndynamics of the corresponding biological system. To solve this problem, we propose to use a\r\nnew mathematical framework able to use sparse and limited experimental data to quantitatively\r\nreproduce the structural connectivity of biological brain networks at cellular level." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266506", + "public_submitter_email": { + "attr_id": 309, + "value": "giuseppe.giacopelli@unipa.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Giuseppe Giacopelli" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2021-02-25T16:40:09.667", + "ver_number": 15 + }, + "266508": { + "citation": { + "attr_id": 391, + "value": "Telenczuk, B., Telenczuk, M. and Destexhe, A. A kernel-based method to calculate local field potentials from networks of spiking neurons.\r\nhttps://www.biorxiv.org/content/10.1101/2020.03.29.014654v1" + }, + "class_id": 19, + "created": "2020-05-30T12:04:00.623", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266508, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33744, + "object_name": "Destexhe, Alain [Destexhe at iaf.cnrs-gif.fr]" + }, + { + "object_id": 241795, + "object_name": "Telenczuk, Bartosz [bartosz.telenczuk at inaf.cnrs-gif.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 84598, + "object_name": "Extracellular Fields" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266509, + "object_name": "Telenczuk B, Telenczuk M, Destexhe A (2020)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Kernel method to calculate LFPs from networks of point neurons (Telenczuk et al 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 240378, + "object_name": "Abstract integrate-and-fire neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The local field potential (LFP) is usually calculated from current sources arising from transmembrane currents, in particular in asymmetric cellular morphologies such as pyramidal neurons. Here, we adopt a different point of view and relate the spiking of neurons to the LFP through efferent synaptic connections and provide a method to calculate LFPs. We show that the so-called unitary LFPs (uLFP) provide the key to such a calculation. We show experimental measurements and simulations of uLFPs in neocortex and hippocampus, for both excitatory and inhibitory neurons. We fit a \u201ckernel\u201d function to measurements of uLFPs, and we estimate its spatial and temporal spread by using simulations of morphologically detailed reconstructions of hippocampal pyramidal neurons. Assuming that LFPs are the sum of uLFPs generated by every neuron in the network, the LFP generated by excitatory and inhibitory neurons can be calculated by convolving the trains of action potentials with the kernels estimated from uLFPs. This provides a method to calculate the LFP from networks of spiking neurons, even for point neurons for which the LFP is not easily defined. We show examples of LFPs calculated from networks of point neurons.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266508", + "public_submitter_email": { + "attr_id": 309, + "value": "Destexhe@iaf.cnrs-gif.fr" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Alain Destexhe" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2023-02-09T10:14:35.71", + "ver_number": 10 + }, + "266518": { + "citation": { + "attr_id": 391, + "value": "Buccineo et al 2018" + }, + "class_id": 19, + "created": "2020-06-03T10:43:59.697", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266518, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266517, + "object_name": "Buccino AP, Kordovan M, Ness TV, Merkt B, H\u00e4fliger PD, Fyhn M, Cauwenberghs G, Rotter S, Einevoll GT (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + }, + { + "object_id": 266536, + "object_name": "LFPy (web link to model)" + } + ] + }, + "name": "Combining modeling, deep learning for MEA neuron localization, classification (Buccino et al 2018)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 167343, + "object_name": "Neocortex U1 L5B pyramidal pyramidal tract GLU cell" + }, + { + "object_id": 184134, + "object_name": "Neocortex layer 5 interneuron" + }, + { + "object_id": 93384, + "object_name": "Neocortex bitufted interneuron" + }, + { + "object_id": 150807, + "object_name": "Neocortex deep neurogliaform interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Neural circuits typically consist of many different types of neurons, and one faces a challenge in disentangling their individual contributions in measured neural activity. Classification of cells into inhibitory and excitatory neurons and localization of neurons on the basis of extracellular recordings are frequently employed procedures. Current approaches, however, need a lot of human intervention, which makes them slow, biased, and unreliable. In light of recent advances in deep learning techniques and exploiting the availability of neuron models with quasi-realistic three-dimensional morphology and physiological properties, we present a framework for automatized and objective classification and localization of cells based on the spatiotemporal profiles of the extracellular action potentials recorded by multielectrode arrays. We train convolutional neural networks on simulated signals from a large set of cell models and show that our framework can predict the position of neurons with high accuracy, more precisely than current state-of-the-art methods. Our method is also able to classify whether a neuron is excitatory or inhibitory with very high accuracy, substantially improving on commonly used clustering techniques. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266518", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2020-06-04T12:14:34.697", + "ver_number": 4 + }, + "266524": { + "citation": { + "attr_id": 391, + "value": "Liou et al 2020" + }, + "class_id": 19, + "created": "2020-06-03T11:24:17.02", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266524, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 84598, + "object_name": "Extracellular Fields" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266523, + "object_name": "Liou JY, Smith EH, Bateman LM, Bruce SL, McKhann GM, Goodman RR, Emerson RG, Schevon CA, Abbott LF (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "A model for focal seizure onset, propagation, evolution, and progression (Liou et al 2020)", + "notes": { + "attr_id": 24, + "value": "We developed a neural network model that can account for major elements common\r\nto human focal seizures. These include the tonic-clonic transition, slow advance of clinical semiology\r\nand corresponding seizure territory expansion, widespread EEG synchronization, and slowing of\r\nthe ictal rhythm as the seizure approaches termination. These were reproduced by incorporating\r\nusage-dependent exhaustion of inhibition in an adaptive neural network that receives global\r\nfeedback inhibition in addition to local recurrent projections. Our model proposes mechanisms that\r\nmay underline common EEG seizure onset patterns and status epilepticus, and postulates a role for\r\nsynaptic plasticity in the emergence of epileptic foci. Complex patterns of seizure activity and bi-\r\nstable seizure end-points arise when stochastic noise is included. With the rapid advancement of\r\nclinical and experimental tools, we believe that this model can provide a roadmap and potentially\r\nan in silico testbed for future explorations of seizure mechanisms and clinical therapies." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266524", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 212, + "object_name": "Glutamate" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "ver_date": "2020-06-03T11:24:47.193", + "ver_number": 2 + }, + "266526": { + "citation": { + "attr_id": 391, + "value": "Guet-McCray & Skinner 2020" + }, + "class_id": 19, + "created": "2020-06-03T11:49:53.05", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266526, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266525, + "object_name": "Guet-McCreight A, Skinner F (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "Dynamical assessment of ion channels during in vivo-like states (Guet-McCreight & Skinner 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 190560, + "object_name": "Hippocampus CA1 stratum oriens lacunosum-moleculare interneuron " + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\" ... Methods: We employ two morphologically-detailed multi-compartment models of a specific type of inhibitory interneuron, the oriens lacunosum moleculare (OLM) cell. The OLM cell is a well-studied cell type in CA1 hippocampus that is important in gating sensory and contextual information. We create in vivo-like states for these cellular models by including levels of synaptic bombardment that would occur in vivo. Using visualization tools and analyses we assess the ion channel current contribution profile across the different somatic and dendritic compartments of the models.\r\nResults: We identify changes in dendritic excitability, ion channel current contributions and co-activation patterns between in vitro and in vivo-like states. Primarily, we find that the relative timing between ion channel currents are mostly invariant between states, but exhibit changes in magnitudes and decreased propagation across dendritic compartments. We also find enhanced dendritic hyperpolarization-activated cyclic nucleotide-gated channel (h-channel) activation during in vivo-like states, which suggests that dendritically located h-channels are functionally important in altering signal propagation in the behaving animal. ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266526", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2020-06-03T11:57:20.663", + "ver_number": 3 + }, + "266534": { + "citation": { + "attr_id": 391, + "value": "Burke et al 2018" + }, + "class_id": 19, + "created": "2020-06-03T13:28:33.883", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266534, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 22010, + "object_name": "Facilitation" + }, + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266533, + "object_name": "Burke KJ, Keeshen CM, Bender KJ (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Two forms of synaptic depression by neuromodulation of presynaptic Ca2+ channels (Burke et al 2018)", + "notes": { + "attr_id": 24, + "value": "\"... To determine whether the different biophysical mechanisms of\r\nCaV modulation predicted by OFA (Optical Fluctuation Analysis) are sufficient to explain the\r\ndiffering effects of D1Rs and GABA B Rs on STP, we developed\r\na reduced synaptic model...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266534", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 223, + "object_name": "Dopaminergic Receptor" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "ver_date": "2020-06-03T13:29:23.23", + "ver_number": 2 + }, + "266535": { + "class_id": 19, + "created": "2020-06-03T23:29:15.14", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266535, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 222955, + "object_name": "Pedrosa, Victor [v.pedrosa15 at imperial.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98011, + "object_name": "Place cell/field" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266761, + "object_name": "Pedrosa V, Clopath C (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Interplay between somatic and dendritic inhibition promotes place fields (Pedrosa & Clopath 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 245940, + "object_name": "Abstract rate-based neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Hippocampal pyramidal neurons are thought to encode spatial information. A subset of these cells, named place cells, are active only when the animal traverses a specific region within the environment. Although vastly studied experimentally, the development and stabilization of place fields are not fully understood. Here, we propose a mechanistic model of place cell formation in the hippocampal CA1 region. Using our model, we reproduce place field dynamics observed experimentally and provide a mechanistic explanation for the stabilization of place fields. Finally, our model provides specific predictions on protocols to shift place field location." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266535", + "public_submitter_email": { + "attr_id": 309, + "value": "v.pedrosa@imperial.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Victor Pedrosa" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2020-07-21T16:14:24.067", + "ver_number": 12 + }, + "266551": { + "citation": { + "attr_id": 391, + "value": "Shusen Pu and Peter Thomas (2020). Fast and Accurate Langevin Models of Stochastic Hodgkin Huxley Dynamics. In Press at Neural Computation." + }, + "class_id": 19, + "created": "2020-06-10T16:23:22.203", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266551, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 238958, + "object_name": "Stochastic simulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266810, + "object_name": "Pu S, Thomas PJ (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Stochastic Hodgkin-Huxley Model: 14x28D Langevin Simulation (Pu and Thomas, 2020).", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model provides a natural 14-dimensional Langevin dynamics for the Hodgkin Huxley system in which each directed edge in the ion channel state transition graph acts as an independent noise source, leading to a 14 dimensional state space (1 dimension for voltage, 5 for potassium and 8 for sodium) and 14 \u00d7 28 noise coefficient matrix S. In [Pu and Thomas (2020) Neural Computation] we show that this 14 x 28 dimensional model is pathwise equivalent to the 14 x 11 dimensional Langevin model proposed in [Fox and Lu (1994) Phys Rev E], as well as an 14 x 14 model described in [Orio and Soudry (2012) PLoS One]. Unlike Fox and Lu's model, our construction does not require a matrix root extraction step, and runs significantly faster. Unlike Orio and Soudry's model, each directed edge acts as an independent noise source, which facilitates the application of stochastic shielding methods for even greater simulation speed. For comparison, we provide implementations of the following models: 1. Discrete-state Markov chain model (slow, but provides the \"gold standard\" model), adapted from [Goldwyn and Shea-Brown (2011) PLoS Comp. Biol.] 2. 14 x 11 Langevin model from [Fox and Lu (1994) Phys. Rev. E]. (We implement versions with three different boundary conditions: open boundaries, reflecting boundaries, and resampling/rejection at the boundaries.) 3. 4 x 3 Langevin model from [Fox (1997) Biophys. J.] 4. 14 x 13 Langevin model from [Goldwyn and Shea (2011) PLoS Comp. Biol.] 5. 14 x 14 Langevin model from [Dangerfield et al (2012) Phys. Rev. E] 6. 14 x 14 Langevin model from [Orio and Soudry (2012) PLoS One] 7. 14 x 28 Langevin model from [Pu and Thomas (2020) Neural Computation] implemented both with and without stochastic shielding 8. 14 x 0 deterministic HH model (also from [Pu and Thomas (2020) Neural Computation], with the full 14 dimensional state space but no noise) The Read_me.md file provides more detailed simulations.\r\n\r\nTo cite the code: Pu, Shusen, and Peter J. Thomas. \"Fast and Accurate Langevin Simulations of Stochastic Hodgkin-Huxley Dynamics.\" Neural Computation 32, 1775\u20131835 (2020)\r\n" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266551", + "other_implementer": { + "attr_id": 308, + "value": "Pu, Shusen [Case Western Reserve University]\r\nThomas, Peter [Case Western Reserve University]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "sxp600@case.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Shusen Pu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115960, + "object_name": "Generic" + } + ] + }, + "ver_date": "2020-09-16T12:35:25.903", + "ver_number": 4 + }, + "266552": { + "citation": { + "attr_id": 391, + "value": "Domanski et al 2019" + }, + "class_id": 19, + "created": "2020-06-10T16:43:15.313", + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 266550, + "object_name": "FMR1 FMRP" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266552, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 128875, + "object_name": "Development" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 182362, + "object_name": "Sensory coding" + }, + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266549, + "object_name": "Domanski APF, Booker SA, Wyllie DJA, Isaac JTR, Kind PC (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Disrupted information processing in Fmr1-KO mouse layer 4 barrel cortex (Domanski et al 2019)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 230209, + "object_name": "Neocortex U1 L4 stellate GLU cell" + }, + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Sensory hypersensitivity is a common and debilitating feature of neurodevelopmental disorders such as Fragile X Syndrome (FXS). How developmental changes in neuronal function\r\nculminate in network dysfunction that underlies sensory hypersensitivities is unknown. By\r\nsystematically studying cellular and synaptic properties of layer 4 neurons combined with\r\ncellular and network simulations, we explored how the array of phenotypes in Fmr1-knockout\r\n(KO) mice produce circuit pathology during development. We show that many of the cellular\r\nand synaptic pathologies in Fmr1-KO mice are antagonistic, mitigating circuit dysfunction, and\r\nhence may be compensatory to the primary pathology. Overall, the layer 4 network in the\r\nFmr1-KO exhibits significant alterations in spike output in response to thalamocortical input\r\nand distorted sensory encoding. This developmental loss of layer 4 sensory encoding precision would contribute to subsequent developmental alterations in layer 4-to-layer 2/3\r\nconnectivity and plasticity observed in Fmr1-KO mice, and circuit dysfunction underlying\r\nsensory hypersensitivity.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266552", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2020-06-10T16:45:10.81", + "ver_number": 2 + }, + "266555": { + "citation": { + "attr_id": 391, + "value": "https://journals.physiology.org/doi/abs/10.1152/jn.00401.2019" + }, + "class_id": 19, + "created": "2020-06-13T11:43:24.483", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266555, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266752, + "object_name": "Papasavvas, Christoforos [Christoforos.Papasavvas at newcastle.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 152026, + "object_name": "Contrast-gain control" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266751, + "object_name": "Papasavvas CA, Trevelyan AJ, Kaiser M, Wang Y (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Entrainment and divisive inhibition in a neocortical neural mass model (Papasavvas et al 2020)", + "notes": { + "attr_id": 24, + "value": "Neural mass model of a neocortical microcircuit featuring one excitatory and two inhibitory populations. The inhibitory populations represent the soma-targeting (parvalbumin) and dendrite-targeting (somatostatin) interneurons. The model uses the Wilson-Cowan formalism and differentiates between the two inhibitory populations by the way they modulate the input-output function of the excitatory population (subtractive vs divisive inhibition, based on Wilson et al., Nature, 7411, 488, 343-348, 2012). The connectivity patterns between the populations follow the patterns reported in the primary visual cortex (Pfeffer et al., Nat Neurosci 16, 1068\u20131076, 2013).\r\n\r\nThe model is used here to investigate the role of divisive inhibition during the entrainment of the microcircuit." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266555", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2020-07-16T08:04:26.787", + "ver_number": 16 + }, + "266560": { + "citation": { + "attr_id": 391, + "value": "https://link.springer.com/article/10.1007/s00359-020-01405-x" + }, + "class_id": 19, + "created": "2020-06-13T11:52:21.623", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266560, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266651, + "object_name": "Read, Jenny [jenny.read at newcastle.ac.uk]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 266650, + "object_name": "Binocular energy model/Stereopsis" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266648, + "object_name": "Rosner R, von Hadeln J, Tarawneh G, Read JCA (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Binocular energy model set for binocular neurons in optic lobe of praying mantis (Rosner et al 2019)", + "notes": { + "attr_id": 24, + "value": "This is a version of the binocular energy model with parameters chosen to reproduce individual cells in praying mantis optic lobe. The receptive fields are very coarsely sampled (6 different horizontal locations only) to match the coarse sampling of the data given very limited recording time." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266560", + "other_neurons": { + "attr_id": 301, + "value": "binocular energy model" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 266649, + "object_name": "Optic lobe/Praying Mantis" + } + ] + }, + "ver_date": "2020-06-24T11:30:22.223", + "ver_number": 8 + }, + "266577": { + "class_id": 19, + "created": "2020-06-16T12:04:23.393", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266577, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 146501, + "object_name": "Nicola, Wilten [wnicola at uwaterloo.ca]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3635, + "object_name": "Synchronization" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267402, + "object_name": "Nicola W, Campbell SA (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Wilson-Cowan Network with Homeostatic Plasticity (Nicola and Campbell 2021)", + "notes": { + "attr_id": 24, + "value": "We investigate the problem of inter-region synchronization in networks of Wilson--Cowan/neural field equations with homeostatic plasticity, each of which acts as a model for an isolated brain region. We consider arbitrary connection profiles with only one constraint: the rows of the connection matrices are all identically normalized. We found that these systems often synchronize to the solution obtained from a single, self-coupled neural region. We analyze the stability of this solution through a straightforward modification of the master stability function (MSF) approach and found that synchronized solutions lose stability for connectivity matrices when the second largest positive eigenvalue is sufficiently large for values of the global coupling parameter that are not too large. This result was numerically confirmed for ring systems and lattices and was also robust to small amounts of heterogeneity in the homeostatic set points in each node. Finally, we tested this result on connectomes obtained from 196 subjects over a broad age range (4--85 years) from the Human Connectome Project." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266577", + "public_submitter_email": { + "attr_id": 309, + "value": "wilten.nicola@ucalgary.ca" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Wilten Nicola" + }, + "ver_date": "2022-07-01T16:57:38.537", + "ver_number": 7 + }, + "266578": { + "citation": { + "attr_id": 391, + "value": "Molecular layer interneurons in the cerebellum encode for valence in associative learning\r\nMing Ma, Gregory L. Futia, Fabio M. Simoes De Souza, Baris N. Ozbay, Isabel Llano, Emily A. Gibson, Diego Restrepo\r\nhttps://doi.org/10.1101/2019.12.14.876201" + }, + "class_id": 19, + "created": "2020-06-16T23:52:18.51", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266578, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 156119, + "object_name": "Simoes-de-Souza, Fabio [fabio.souza at ufabc.edu.br]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267638, + "object_name": "Ma M, Futia GL, De Souza FM, Ozbay BN, Llano I, Gibson EA, Restrepo D (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Molecular layer interneurons in cerebellum encode valence in associative learning (Ma et al 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + }, + { + "object_id": 246494, + "object_name": "Cerebellum interneuron stellate GABA cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We used two-photon microscopy to study the role of ensembles of cerebellar molecular layer interneurons (MLIs) in a go-no go task where mice obtain a sugar water reward. In order to begin understanding the circuit basis of our findings in changes in lick behavior with chemogenetics in the go-no go associative learning olfactory discrimination task we generated a simple computational model of MLI interaction with PCs." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266578", + "public_submitter_email": { + "attr_id": 309, + "value": "fabio_mss@hotmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Fabio Simoes de Souza" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + }, + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2023-02-09T10:18:47.507", + "ver_number": 8 + }, + "266584": { + "class_id": 19, + "created": "2020-06-17T08:02:30.513", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266584, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266585, + "object_name": "Song, Jiang-Ling " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 266583, + "object_name": "Acute hepatic encephalopathy (AHE)" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266582, + "object_name": "Song JL, Paixao L, Li Q, Li SH, Zhang R, Westover MB (2019)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Model of generalized periodic discharges in acute hepatic encephalopathy (Song et al 2019)", + "notes": { + "attr_id": 24, + "value": "\"Acute hepatic encephalopathy (AHE) due to acute liver failure is a common form of delirium, a state of confusion, impaired\r\nattention, and decreased arousal. The electroencephalogram (EEG) in AHE often exhibits a striking abnormal pattern of brain\r\nactivity, which epileptiform discharges repeat in a regular repeating pattern. This pattern is known as generalized periodic\r\ndischarges, or triphasic-waves (TPWs). While much is known about the neurophysiological mechanisms underlying AHE,\r\nhow these mechanisms relate to TPWs is poorly understood. In order to develop hypotheses how TPWs arise, our work builds\r\na computational model of AHE (AHE-CM), based on three modifications of the well-studied Liley model which emulate\r\nmechanisms believed central to brain dysfunction in AHE: increased neuronal excitability, impaired synaptic transmission,\r\nand enhanced postsynaptic inhibition...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266584", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2020-06-17T08:12:14.677", + "ver_number": 3 + }, + "266609": { + "class_id": 19, + "created": "2020-06-17T10:26:34.42", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266609, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266608, + "object_name": "Muscinelli, Samuel P [samuel.muscinelli at epfl.ch]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 136098, + "object_name": "Information transfer" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266588, + "object_name": "Muscinelli SP, Gerstner W, Schwalger T (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 253623, + "object_name": "Julia (web link to model)" + } + ] + }, + "name": "Single neuron properties shape chaos and signal transmission in random NNs (Muscinelli et al 2019)", + "notes": { + "attr_id": 24, + "value": "\"While most models of randomly connected neural networks assume single-neuron models with simple dynamics, neurons in the brain exhibit complex intrinsic dynamics over multiple timescales. We analyze how the dynamical properties of single neurons and recurrent connections interact to shape the effective dynamics in large randomly connected networks. A novel dynamical mean-field theory for strongly connected networks of multi-dimensional rate neurons shows that the power spectrum of the network activity in the chaotic phase emerges from a nonlinear sharpening of the frequency response function of single neurons. For the case of two-dimensional rate neurons with strong adaptation, we find that the network exhibits a state of \u201cresonant chaos\u201d, characterized by robust, narrow-band stochastic oscillations. The coherence of stochastic oscillations is maximal at the onset of chaos and their correlation time scales with the adaptation timescale of single units. Surprisingly, the resonance frequency can be predicted from the properties of isolated neurons, even in the presence of heterogeneity in the adaptation parameters. In the presence of these internally-generated chaotic fluctuations, the transmission of weak, low-frequency signals is strongly enhanced by adaptation, whereas signal transmission is not influenced by adaptation in the non-chaotic regime. Our theoretical framework can be applied to other mechanisms at the level of single neurons, such as synaptic filtering, refractoriness or spike synchronization. These results advance our understanding of the interaction between the dynamics of single units and recurrent connectivity, which is a fundamental step toward the description of biologically realistic neural networks.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266609", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2020-06-17T10:28:21.907", + "ver_number": 2 + }, + "266636": { + "class_id": 19, + "created": "2020-06-17T17:02:40.013", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266636, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 154856, + "object_name": "Tabak J, Tomaiuolo M, Gonzalez-Iglesias AE, Milescu LS, Bertram R (2011)" + }, + { + "object_id": 266635, + "object_name": "Tenn\u00f8e S, Hodne K, Haug T, Weltzien F, Einevoll G, Halnes G (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 91894, + "object_name": "XPP (web link to model)" + }, + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + }, + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "BK Channels Promote Bursting in Pituitary Cells (Tabak et al 2011)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 62277, + "object_name": "Pituitary cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The electrical activity pattern of endocrine pituitary cells regulates their basal secretion level. Rat somatotrophs and lactotrophs exhibit spontaneous bursting and have high basal levels of hormone secretion, while gonadotrophs exhibit spontaneous spiking and have low basal hormone secretion. It has been proposed that the difference in electrical activity between bursting somatotrophs and spiking gonadotrophs is due to the presence of large conductance potassium (BK) channels on somatotrophs but not on gonadotrophs. This is one example where the role of an ion channel type may be clearly established. We demonstrate here that BK channels indeed promote bursting activity in pituitary cells. Blocking BK channels in bursting lacto-somatotroph GH4C1 cells changes their firing activity to spiking, while further adding an artificial BK conductance via dynamic clamp restores bursting. Importantly, this burst-promoting effect requires a relatively fast BK activation/deactivation, as predicted by computational models. We also show that adding a fast-activating BK conductance to spiking gonadotrophs converts the activity of these cells to bursting. Together, our results suggest that differences in BK channel expression may underlie the differences in electrical activity and basal hormone secretion levels among pituitary cell types and that the rapid rate of BK channel activation is key to its role in burst promotion.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266636", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "ver_date": "2020-06-17T17:03:51.147", + "ver_number": 2 + }, + "266637": { + "class_id": 19, + "created": "2020-06-19T10:17:16.12", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + }, + { + "object_id": 243505, + "object_name": "I Ca SOCC" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 88210, + "object_name": "I_Na,Ca" + }, + { + "object_id": 88211, + "object_name": "I_SERCA" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 88209, + "object_name": "Na/Ca exchanger" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266637, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 251109, + "object_name": "Muddapu, Vignayanandam R. [vignan.0009 at gmail.com]" + }, + { + "object_id": 251110, + "object_name": "Chakravarthy, Srinivasa V. [schakra at iitm.ac.in]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 245160, + "object_name": "Neurotransmitter dynamics" + }, + { + "object_id": 225299, + "object_name": "Multiscale" + }, + { + "object_id": 222956, + "object_name": "Neuromodulation" + }, + { + "object_id": 227131, + "object_name": "Pacemaking mechanism" + }, + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266946, + "object_name": "Muddapu VR, Chakravarthy VS (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + } + ] + }, + "name": "Multiscale model of excitotoxicity in PD (Muddapu and Chakravarthy 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 261, + "object_name": "Substantia nigra pars compacta DA cell" + }, + { + "object_id": 182591, + "object_name": "Globus pallidus principal GABA cell" + }, + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + }, + { + "object_id": 74300, + "object_name": "Subthalamus nucleus projection neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Parkinson's disease (PD) is a neurodegenerative disorder caused by loss of dopaminergic neurons in Substantia Nigra pars compacta (SNc). Although the exact cause of cell death is not clear, the hypothesis that metabolic deficiency is a key factor has been gaining attention in recent years. In the present study, we investigate this hypothesis using a multi-scale computational model of the subsystem of the basal ganglia comprising Subthalamic Nucleus (STN), Globus Pallidus externa (GPe) and SNc. The proposed model is a multiscale model in that interactions among the three nuclei are simulated using more abstract Izhikevich neuron models, while the molecular pathways involved in cell death of SNc neurons are simulated in terms of detailed chemical kinetics. Simulation results obtained from the proposed model showed that energy deficiencies occurring at cellular and network levels could precipitate the excitotoxic loss of SNc neurons in PD. At the subcellular level, the models show how calcium elevation leads to apoptosis of SNc neurons. The therapeutic effects of several neuroprotective interventions are also simulated in the model. From neuroprotective studies, it was clear that glutamate inhibition and apoptotic signal blocker therapies were able to halt the progression of SNc cell loss when compared to other therapeutic interventions, which only slows down the progression of SNc cell loss." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266637", + "public_submitter_email": { + "attr_id": 309, + "value": "vignan.0009@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Vignayanandam R Muddapu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 223, + "object_name": "Dopaminergic Receptor" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + }, + { + "object_id": 116868, + "object_name": "Subthalamic Nucleus" + } + ] + }, + "ver_date": "2021-03-01T17:05:46.287", + "ver_number": 2 + }, + "266657": { + "citation": { + "attr_id": 391, + "value": "Galloni et al 2020" + }, + "class_id": 19, + "created": "2020-06-24T11:57:42.313", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 247, + "object_name": "I A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266657, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266656, + "object_name": "Galloni AR, Laffere A, Rancz E (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 7841, + "object_name": "NEURON (web link to model)" + } + ] + }, + "name": "Apical Length Governs Computational Diversity of Layer 5 Pyramidal Neurons (Galloni et al 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Anatomical similarity across the neocortex has led to the common assumption that the circuitry is modular and performs stereotyped computations. Layer 5 pyramidal neurons (L5PNs) in particular are thought to be central to cortical computation because of their extensive arborisation and nonlinear dendritic operations. Here, we demonstrate that computations associated with dendritic Ca2+ plateaus in mouse L5PNs vary substantially between the primary and secondary visual cortices. L5PNs in the secondary visual cortex show reduced dendritic excitability and smaller propensity for burst firing. This reduced excitability is correlated with shorter apical dendrites. Using numerical modelling, we uncover a universal principle underlying the influence of apical length on dendritic backpropagation and excitability, based on a Na+ channel-dependent broadening of backpropagating action potentials. In summary, we provide new insights into the modulation of dendritic excitability by apical dendrite length and show that the operational repertoire of L5PNs is not universal throughout the brain.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266657", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2020-06-26T08:25:10.383", + "ver_number": 4 + }, + "266663": { + "class_id": 19, + "created": "2020-06-24T13:55:28.67", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266663, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266753, + "object_name": "Ausborn, Jessica [jessica.ausborn at gmail.com]" + }, + { + "object_id": 235164, + "object_name": "Danner, Simon M [simon.danner at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 150701, + "object_name": "Posture and locomotion" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266662, + "object_name": "Ausborn J, Shevtsova NA, Caggiano V, Danner SM, Rybak IA (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Brainstem circuits controlling locomotor frequency and gait (Ausborn et al 2019)", + "notes": { + "attr_id": 24, + "value": "\"A series of recent studies identified key structures in the mesencephalic locomotor region and the caudal brainstem of mice involved in the initiation and control of slow (exploratory) and fast (escape-type) locomotion and gait. However, the interactions of these brainstem centers with each other and with the spinal locomotor circuits are poorly understood. Previously we suggested that commissural and long propriospinal interneurons are the main targets for brainstem inputs adjusting gait (Danner et al., 2017). Here, by extending our previous model, we propose a connectome of the brainstem-spinal circuitry and suggest a mechanistic explanation of the operation of brainstem structures and their roles in controlling speed and gait. We suggest that brainstem control of locomotion is mediated by two pathways, one controlling locomotor speed via connections to rhythm generating circuits in the spinal cord and the other providing gait control by targeting commissural and long propriospinal interneurons.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266663", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 183076, + "object_name": "Brainstem" + } + ] + }, + "ver_date": "2020-07-15T09:41:59.15", + "ver_number": 3 + }, + "266687": { + "citation": { + "attr_id": 391, + "value": "https://europepmc.org/abstract/med/29698394" + }, + "class_id": 19, + "created": "2020-06-29T20:55:33.083", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 151550, + "object_name": "Channelrhodopsin (ChR)" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266687, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266753, + "object_name": "Ausborn, Jessica [jessica.ausborn at gmail.com]" + }, + { + "object_id": 266754, + "object_name": "Rybak,Ilya A [rybak at drexel.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 259061, + "object_name": "Ausborn J, Koizumi H, Barnett WH, John TT, Zhang R, Molkov YI, Smith JC, Rybak IA (2018)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Core respiratory network organization: Insights from optogenetics and modeling (Ausborn et al 2018)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 232103, + "object_name": "PreBotzinger complex neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"The circuit organization within the mammalian brainstem respiratory network, specifically within and between the pre-B\u00f6tzinger (pre-B\u00f6tC) and B\u00f6tzinger (B\u00f6tC) complexes, and the roles of these circuits in respiratory pattern generation are continuously debated. We address these issues with a combination of optogenetic experiments and modeling studies. We used transgenic mice expressing channelrhodopsin-2 under the VGAT-promoter to investigate perturbations of respiratory circuit activity by site-specific photostimulation of inhibitory neurons within the pre-B\u00f6tC or B\u00f6tC. The stimulation effects were dependent on the intensity and phase of the photostimulation. Specifically: (1) Low intensity (= 1.0 mW) pulses delivered to the pre-B\u00f6tC during inspiration did not terminate activity, whereas stronger stimulations (= 2.0 mW) terminated inspiration. (2) When the pre-B\u00f6tC stimulation ended in or was applied during expiration, rebound activation of inspiration occurred after a fixed latency. (3) Relatively weak sustained stimulation (20 Hz, 0.5\u20132.0 mW) of pre-B\u00f6tC inhibitory neurons increased respiratory frequency, while a further increase of stimulus intensity (> 3.0 mW) reduced frequency and finally (= 5.0 mW) terminated respiratory oscillations. (4) Single pulses (0.2\u20135.0 s) applied to the B\u00f6tC inhibited rhythmic activity for the duration of the stimulation. (5) Sustained stimulation (20 Hz, 0.5\u20133.0 mW) of the B\u00f6tC reduced respiratory frequency and finally led to apnea. We have revised our computational model of pre-B\u00f6tC and B\u00f6tC microcircuits by incorporating an additional population of post-inspiratory inhibitory neurons in the pre-B\u00f6tC that interacts with other neurons in the network. This model was able to reproduce the above experimental findings as well as previously published results of optogenetic activation of pre-B\u00f6tC or B\u00f6tC neurons obtained by other laboratories. The proposed organization of pre-B\u00f6tC and B\u00f6tC circuits leads to testable predictions about their specific roles in respiratory pattern generation and provides important insights into key circuit interactions operating within brainstem respiratory networks.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266687", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Pipeline user 82" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 183076, + "object_name": "Brainstem" + } + ] + }, + "ver_date": "2020-07-15T09:41:07.25", + "ver_number": 11 + }, + "266718": { + "citation": { + "attr_id": 391, + "value": "Mitry et al 2020" + }, + "class_id": 19, + "created": "2020-07-01T09:56:48.15", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266718, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 143724, + "object_name": "Bifurcation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266717, + "object_name": "Mitry J, Alexander RP, Farjami S, Bowie D, Khadra A (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 50657, + "object_name": "MATLAB (web link to model)" + }, + { + "object_id": 91894, + "object_name": "XPP (web link to model)" + } + ] + }, + "name": "Cerebellar stellate cells: changes in threshold, latency and frequency of firing (Mitry et al 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 246494, + "object_name": "Cerebellum interneuron stellate GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Cerebellar stellate cells are inhibitory molecular interneurons that regulate the firing properties of Purkinje cells, the sole output of cerebellar cortex. Recent evidence suggests that\r\nthese cells exhibit temporal increase in excitability during whole-cell patch-clamp configuration in a phenomenon termed runup. They also exhibit a non-monotonic first-spike\r\nlatency profile as a function of the holding potential in response to a fixed step-current.\r\nIn this study, we use modeling approaches to unravel the dynamics of runup and categorize the firing behavior of cerebellar stellate cells as either type I or type II oscillators. We\r\nthen extend this analysis to investigate how the non-monotonic latency profile manifests\r\nitself during runup. We employ a previously developed, but revised, Hodgkin\u2013Huxley type\r\nmodel to show that stellate cells are indeed type I oscillators possessing a saddle node on\r\nan invariant cycle (SNIC) bifurcation. The SNIC in the model acts as a \u201cthreshold\u201d for tonic\r\nfiring and produces a slow region in the phase space called the ghost of the SNIC. The\r\nmodel reveals that (i) the SNIC gets left-shifted during runup with respect to I app = I test\r\nin the current-step protocol, and (ii) both the distance from the stable limit cycle along\r\nwith the slow region produce the non-monotonic latency profile as a function of holding\r\npotential. Using the model, we elucidate how latency can be made arbitrarily large for a\r\nspecific range of holding potentials close to the SNIC during pre-runup (post-runup). We\r\nalso demonstrate that the model can produce transient single spikes in response to step-\r\ncurrents entirely below I SNIC , and that a pair of dynamic inhibitory and excitatory post-\r\nsynaptic inputs can robustly evoke action potentials, provided that the magnitude of the\r\ninhibition is either low or high but not intermediate. Our results show that the topology\r\nof the SNIC is the key to explaining such behaviors.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266718", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2020-07-01T09:59:40.077", + "ver_number": 3 + }, + "266720": { + "citation": { + "attr_id": 391, + "value": "Peterson & Voytek 2020" + }, + "class_id": 19, + "created": "2020-07-01T10:52:06.227", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266720, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266719, + "object_name": "Peterson EJ, Voytek B (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114647, + "object_name": "Python (web link to model)" + } + ] + }, + "name": "Homeostatic mechanisms may shape oscillatory modulations (Peterson & Voytek 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + }, + { + "object_id": 150222, + "object_name": "Abstract Morris-Lecar neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Neural oscillations are observed ubiquitously in the mammalian brain, but their stability is known to be rather variable. Some oscillations are tonic and last for seconds or even minutes. Other oscillations appear as unstable bursts. Likewise, some oscillations rely on excitatory AMPAergic synapses, but others are GABAergic and inhibitory. Why this diversity exists is not clear. We hypothesized Ca2+-dependent homeostasis could be important in finding an explanation. We tested this hypothesis in a highly simplified model of hippocampal neurons. In this model homeostasis profoundly alters the modulatory effect of neural oscillations. Under homeostasis, tonic AMPAergic oscillations actually decrease excitability and desynchronize firing. Tonic oscillations that are synaptically GABAergic-like those in real hippocampus-don't provoke a homeostatic response, however. If our simple model is correct, homeostasis can explain why the theta rhythm in the hippocampus is synaptically inhibitory: GABA has little to no intrinsic homeostatic response, and so can preserve the pyramidal cell's natural dynamic range. Based on these results we can also speculate that homeostasis may explain why AMPAergic oscillations in cortex, and hippocampus, often appear as bursts. Bursts do not interact with the slow homeostatic time constant, and so retain their normal excitatory effect.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266720", + "public_submitter_email": { + "attr_id": 309, + "value": "tom.morse@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tom Morse - MoldelDB admin" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2020-07-01T10:52:51.777", + "ver_number": 2 + }, + "266726": { + "biosimulations": { + "value": "modeldb-266726" + }, + "citation": { + "attr_id": 391, + "value": "Kuo PC, Kao ZH, Lee SW, Wu SN. Effects of sesamin, the major furofuran lignan ofsesame oil, on the amplitude and gating of voltage-gated Na+ and K+ currents. Molecules 2020;25(13):3062." + }, + "class_id": 19, + "created": "2020-07-05T00:27:16.473", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92087, + "object_name": "Nav1.6 SCN8A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266726, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 57916, + "object_name": "Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266729, + "object_name": "Kuo P, Kao Z, Lee S, Wu S (2020)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Markovian model for SCN8A-encoded channel (Kuo et al 2020)", + "notes": { + "attr_id": 24, + "value": "Sesamin (SSM) and sesamolin (SesA) are the two major furofuran lignans of sesame oil and they have been previously noticed to exert various biological actions. However, their modulatory actions on different types of ionic currents in electrically excitable cells remain largely unresolved. The present experiments were undertaken to explore the possible perturbations of SSM and SesA on different types of ionic currents, e.g., voltage-gated Na+ currents (INa), erg-mediated K+ currents (IK(erg)), M-type K+ currents (IK(M)), delayed-rectifier K+ currents (IK(DR)) and hyperpolarization-activated cation currents (Ih) identified from pituitary tumor (GH3) cells. The exposure to SSM or SesA depressed the transient and late components of INa with different potencies. The IC50 value of SSM needed to lessen the peak or sustained INa was calculated to be 7.2 or 0.6 \u00b5M, while that of SesA was 9.8 or 2.5 \u00b5M, respectively. The dissociation constant of SSM-perturbed inhibition on INa, based on the first-order reaction scheme, was measured to be 0.93 \u00b5M, a value very similar to the IC50 for its depressant action on sustained INa. The addition of SSM was also effective at suppressing the amplitude of resurgent INa. The addition of SSM could concentration-dependently inhibit the IK(M) amplitude with an IC50 value of 4.8 \u00b5M. SSM at a concentration of 30 \u00b5M could suppress the amplitude of IK(erg), while at 10 \u00b5M, it mildly decreased the IK(DR) amplitude. However, the addition of neither SSM (10 \u00b5M) nor SesA (10 \u00b5M) altered the amplitude or kinetics of Ih in response to long-lasting hyperpolarization. Additionally, in this study, a modified Markovian model designed for SCN8A-encoded (or NaV1.6) channels was implemented to evaluate the plausible modifications of SSM on the gating kinetics of NaV channels. The model demonstrated herein was well suited to predict that the SSM-mediated decrease in peak INa, followed by increased current inactivation, which could largely account for its favorable decrease in the probability of the open-blocked over open state of NaV channels. Collectively, our study provides evidence that highlights the notion that SSM or SesA could block multiple ion currents, such as INa and IK(M), and suggests that these actions are potentially important and may participate in the functional activities of various electrically excitable cells in vivo." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266726", + "public_submitter_email": { + "attr_id": 309, + "value": "snwu@mail.ncku.edu.tw" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Sheng-Nan Wu" + }, + "ver_date": "2020-07-06T10:12:28.073", + "ver_number": 6 + }, + "266732": { + "citation": { + "attr_id": 391, + "value": "PMID: 32269036" + }, + "class_id": 19, + "created": "2020-07-07T06:14:36.187", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 122812, + "object_name": "Cav1.3 CACNA1D" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266732, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 235771, + "object_name": "Kim, Hojeong [hojeong.kim03 at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 186770, + "object_name": "Motor control" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266733, + "object_name": "Kim H (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A model of closed-loop motor unit including muscle spindle feedback (Kim, 2020)", + "notes": { + "attr_id": 24, + "value": "Persistent inward current generating ion channels are located over spinal motoneurons and actively recruited during normal behaviors. Constructing a realistic computational model of closed-loop motor unit, a motoneuron and muscle fibers that it innervates including muscle spindle afferents, the study reveals functional linkage between persistent inward current location, motoneuron discharge pattern and muscle force output at various muscle lengths. This systematic analysis may provide useful insights into interplay of spinal and muscular mechanisms in control of movements." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266732", + "other_currents": { + "attr_id": 303, + "value": "I Calcium; I Potassium; I Sodium; I_AHP;" + }, + "other_gene": { + "attr_id": 477, + "value": "Cav1.3 CACNA1D;" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "hojeong.kim03@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Hojeong Kim" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115954, + "object_name": "Spinal motoneuron" + } + ] + }, + "ver_date": "2020-09-23T12:46:31.793", + "ver_number": 4 + }, + "266770": { + "class_id": 19, + "created": "2020-08-06T21:46:03.583", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266770, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266772, + "object_name": "Heitmann, Stewart [S.Heitmann at victorchang.edu.au]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 116907, + "object_name": "Direction Selectivity" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 168963, + "object_name": "Motion Detection" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266771, + "object_name": "Heitmann S, Ermentrout GB (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 266773, + "object_name": "Brain Dynamics Toolbox" + } + ] + }, + "name": "E-I-E direction-selective motion discrimination visual cortex traveling waves (Heitmann et al 2020)", + "notes": { + "attr_id": 24, + "value": "The direction-selective responses of neurons in visual cortex cannot be separated into independent spatial and temporal processes. Contemporary theories of how neurons compute non-separable responses typically rely on finely tuned transmission delays. However the existence of such delays is controversial. We propose an alternative neural mechanism for computing non-separable responses that relies on the predisposition of the cortical tissue to spontaneously generate traveling waves of neural activity. We propose that these endogenous waves resonate with the visual stimulus to elicit direction-selective neural responses without resort to time delays." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266770", + "other_implementer": { + "attr_id": 308, + "value": "Stewart Heitmann" + }, + "other_neurons": { + "attr_id": 301, + "value": "Abstract Wilson-Cowan" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "s.heitmann@victorchang.edu.au" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Stewart Heitmann" + }, + "ver_date": "2020-08-10T09:15:54.757", + "ver_number": 5 + }, + "266774": { + "citation": { + "attr_id": 391, + "value": "Learning precise spatiotemporal sequences via biophysically realistic circuits with modular structure\r\nI. Cone, H. Z. Shouval\r\nbioRxiv 2020.04.17.046862; doi: https://doi.org/10.1101/2020.04.17.046862" + }, + "class_id": 19, + "created": "2020-08-13T13:37:13.09", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266774, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266982, + "object_name": "Cone, Ian [iancone at rice dot edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 266981, + "object_name": "Sequence learning" + }, + { + "object_id": 266945, + "object_name": "Eligibility traces" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266983, + "object_name": "Cone I, Shouval HZ (2021)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Sequence learning via biophysically realistic learning rules (Cone and Shouval 2021)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This work proposes a substrate for learned sequential representations, via a network model that can robustly learn and recall discrete sequences of variable order and duration. The model consists of a network of spiking leaky-integrate-and-fire model neurons placed in a modular architecture designed to resemble cortical microcolumns. Learning is performed via a biophysically realistic learning rule based on \u201celigibility traces\u201d, which hold a history of synaptic activity before being converted into changes in synaptic strength upon neuromodulator activation. Before training, the network responds to incoming stimuli, and contains no memory of any particular sequence. After training, presentation of only the first element in that sequence is sufficient for the network to recall an entire learned representation of the sequence. An extended version of the model also demonstrates the ability to successfully learn and recall non-Markovian sequences." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266774", + "public_submitter_email": { + "attr_id": 309, + "value": "iancone@rice.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ian Cone" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 266986, + "object_name": "Visual cortex" + } + ] + }, + "ver_date": "2023-01-30T10:13:48.847", + "ver_number": 12 + }, + "266775": { + "citation": { + "attr_id": 391, + "value": "32609903" + }, + "class_id": 19, + "created": "2020-08-13T14:27:07.487", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": 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"Modeling a Nociceptive Neuro-Immune Synapse Activated by ATP and 5-HT in Meninges (Suleimanova et al., 2020)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 239, + "object_name": "Serotonin" + }, + { + "object_id": 266792, + "object_name": "ATP" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Extracellular ATP and serotonin (5-HT) are powerful triggers of nociceptive firing in the meninges, a process supporting headache and whose cellular mechanisms are incompletely understood. The current study aimed to develop, with the neurosimulator NEURON, a novel approach to explore in silico the molecular determinants of the long-lasting, pulsatile nature of migraine attacks. The present model included ATP and 5-HT release, ATP diffusion and hydrolysis, 5-HT uptake, differential activation of ATP P2X or 5-HT3 receptors, and receptor subtype-specific desensitization. The model also tested the role of branched meningeal fibers with multiple release sites. Spike generation and propagation were simulated using variable contribution by potassium and sodium channels in a multi-compartment fiber environment. Multiple factors appeared important to ensure prolonged nociceptive firing potentially relevant to long-lasting pain. Crucial roles were observed in: (i) co-expression of ATP P2X2 and P2X3 receptor subunits; (ii) intrinsic activation/inactivation properties of sodium Nav1.8 channels; and (iii) temporal and spatial distribution of ATP/5-HT release sites along the branches of trigeminal nerve fibers. Based on these factors we could obtain either persistent activation of nociceptive firing or its periodic bursting mimicking the pulsating nature of pain. In summary, our model proposes a novel tool for the exploration of peripheral nociception to test the contribution of clinically relevant factors to headache including migraine pain.\" (paper abstract)" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266782", + "public_submitter_email": { + "attr_id": 309, + "value": "sulemanovaaa@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Alina Suleimanova" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 208, + "object_name": "5-HT3" + }, + { + "object_id": 266793, + "object_name": "P2X3" + } + ] + }, + "ver_date": "2020-08-19T12:48:49.353", + "ver_number": 12 + }, + "266794": { + "class_id": 19, + "created": "2020-08-21T11:47:47.497", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266794, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267227, + "object_name": "Vuillaume, Roman" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 267152, + "object_name": "Dynamic extracellular concentrations" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267226, + "object_name": "Vuillaume R, Lorenzo J, Binczak S, Jacquir S (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 231240, + "object_name": "Brian 2" + } + ] + }, + "name": "Syn Plasticity Regulation + Information Processing in Neuron-Astrocyte Networks (Vuillaume et al 21)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 87589, + "object_name": "Astrocyte" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... we consider a model of astrocyte-regulated synapses to investigate this hypothesis at the level of layered networks of interacting neurons and astrocytes. Our simulations hint that gliotransmission sustains the transfer function across layers, although it decorrelates the neuronal activity from the signal pattern...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266794", + "public_submitter_email": { + "attr_id": 309, + "value": "roman.vuillaume@u-bourgogne.fr" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Roman Vuillaume" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2021-12-17T12:52:15.44", + "ver_number": 6 + }, + "266796": { + "class_id": 19, + "created": "2020-08-24T16:45:35.123", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266796, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 249460, + "object_name": "Aussel, Am\u00e9lie [amelie.aussel at loria.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 266504, + "object_name": "Sleep-Wake transition" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267525, + "object_name": "Aussel A, Ranta R, Aron O, Colnat-Coulbois S, Maillard L, Buhry L (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 231240, + "object_name": "Brian 2" + } + ] + }, + "name": "Healthy and Epileptic Hippocampal Circuit (Aussel et al 2022)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model aims at reproducing healthy and epileptic hippocampal oscillations, and includes modeling of the sleep-wake cycle. It was used to study theta-nested gamma oscillations, sharp-wave ripple complexes," + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266796", + "other_implementer": { + "attr_id": 308, + "value": "Laure Buhry,Radu Ranta" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "aaussel@bu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Amelie Aussel" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2022-08-23T13:38:21.803", + "ver_number": 5 + }, + "266797": { + "class_id": 19, + "created": "2020-08-25T11:16:52.43", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266797, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 253930, + "object_name": "Giacalone, Elisabetta [elisabetta.giacalone at pa.ibf.cnr.it]" + }, + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267771, + "object_name": "Topczewska A, Giacalone E, Pratt WS, Migliore M, Dolphin AC, Shah MM (2023)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Ventral medial entorhinal cortical stellate neuron model: the role of T-type Ca2+ and persistent Na+ (Topczewska et al., accepted)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 129068, + "object_name": "Entorhinal cortex stellate cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Dorsal and ventral medial entorhinal cortex (mEC) regions have distinct neural network\r\nfiring patterns to differentially support functions such as spatial memory. Correspondingly,\r\nmEC layer II stellate neuron action potential frequencies vary across the dorsal-ventral axis,\r\nwith dorsal neurons exhibiting lower firing rates than ventral neurons. This has been partly\r\nattributed to higher densities of inhibitory conductances in dorsal compared to ventral\r\nneurons. We asked whether additional conductances might also impact this dorsal-ventral\r\ngradient in spike firing. We report that T-type Ca 2+ current amplitudes increased three-fold\r\nalong the dorsal-ventral axis in mEC layer II stellate neurons. Twice as much Ca V 3.2 mRNA\r\nwas also detected in ventral mEC compared with dorsal mEC. Unusually, as T-type Ca 2+\r\ncurrents are only transiently active, long depolarizing stimuli applied to ventral, and not\r\ndorsal, stellate neurons triggered these currents to cause a sustained rise in membrane voltage\r\nand spike firing. This effect was due to T-type Ca 2+ currents acting in concert with persistent\r\nNa + currents. T-type Ca 2+ currents themselves prolonged excitatory post-synaptic potentials\r\n(EPSPs) to enhance the summation of EPSP trains and augment EPSP-spike coupling in\r\nventral neurons. In contrast, T-type Ca 2+ currents had no effect on dorsal EPSP spike-\r\ncoupling. These findings indicate that by preferentially regulating ventral neuron spike firing\r\nand synaptic potential integration, T-type Ca 2+ currents critically influence the dorsal-ventral\r\ngradient in mEC stellate neuron excitability and associated circuit activity." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266797", + "public_submitter_email": { + "attr_id": 309, + "value": "michele.migliore@pa.ibf.cnr.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Michele Migliore" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 119191, + "object_name": "Entorhinal cortex" + } + ] + }, + "ver_date": "2023-06-22T10:00:51.44", + "ver_number": 7 + }, + "266798": { + "class_id": 19, + "created": "2020-08-29T15:01:39.44", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266798, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 182362, + "object_name": "Sensory coding" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 266853, + "object_name": "Receptive field" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266852, + "object_name": "Hay E, Pruszynski JA (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3538, + "object_name": "Axon" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Human tactile FA1 neurons (Hay and Pruszynski 2020)", + "notes": { + "attr_id": 24, + "value": "\"... we show that synaptic integration across the complex signals from the first-order neuronal population could underlie human ability to accurately (< 3\u00b0) and rapidly process the orientation of edges moving across the fingertip. We first derive spiking models of human first-order tactile neurons that fit and predict responses to moving edges with high accuracy. We then use the model neurons in simulating the peripheral neuronal population that innervates a fingertip. We train classifiers performing synaptic integration across the neuronal population activity, and show that synaptic integration across first-order neurons can process edge orientations with high acuity and speed. ... our models suggest that integration of fast-decaying (AMPA-like) synaptic inputs within short timescales is critical for discriminating fine orientations, whereas integration of slow-decaying (NMDA-like) synaptic inputs supports discrimination of coarser orientations and maintains robustness over longer timescales\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266798", + "other_neurons": { + "attr_id": 301, + "value": "Tactile FA1 Neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "etay.hay@camh.ca" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Etay Hay" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 227987, + "object_name": "Human" + } + ] + }, + "ver_date": "2020-12-09T10:45:35.487", + "ver_number": 12 + }, + "266799": { + "citation": { + "attr_id": 391, + "value": "Yunliang Zang, Sungho Hong & Erik De Schutter, Firing Rate-dependent Phase Responses of Purkinje Cells Support Transient Oscillations, eLife, in press\r\nhttps://doi.org/10.7554/eLife.60692" + }, + "class_id": 19, + "created": "2020-08-29T15:14:04.467", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266799, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 244383, + "object_name": "Zang, Yunliang " + }, + { + "object_id": 153454, + "object_name": "Hong, Sungho [shhong at oist.jp]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 137847, + "object_name": "Phase Response Curves" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + }, + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266803, + "object_name": "Zang Y, Hong S, De Schutter E (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Purkinje neuron network (Zang et al. 2020)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Both spike rate and timing can transmit information in the brain. Phase response curves (PRCs) quantify how a neuron transforms input to output by spike timing. PRCs exhibit strong firing-rate adaptation, but its mechanism and relevance for network output are poorly understood. Using our Purkinje cell (PC) model we demonstrate that the rate adaptation is caused by rate-dependent subthreshold membrane potentials efficiently regulating the activation of Na+ channels. Then we use a realistic PC network model to examine how rate-dependent responses synchronize spikes in the scenario of reciprocal inhibition-caused high-frequency oscillations. The changes in PRC cause oscillations and spike correlations only at high firing rates. The causal role of the PRC is confirmed using a simpler coupled oscillator network model. This mechanism enables transient oscillations between fast-spiking neurons that thereby form PC assemblies. Our work demonstrates that rate adaptation of PRCs can spatio-temporally organize the PC input to cerebellar nuclei." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266799", + "public_submitter_email": { + "attr_id": 309, + "value": "zangyl1983@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Yunliang Zang" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2020-09-09T12:30:58.993", + "ver_number": 4 + }, + "266800": { + "citation": { + "attr_id": 391, + "value": "Imam, Nabil, and Barbara Finlay. \"Self-organization of cortical areas in the development and evolution of neocortex: a network growth model.\" bioRxiv (2020)." + }, + "class_id": 19, + "created": "2020-08-31T04:40:09.347", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266800, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 128875, + "object_name": "Development" + }, + { + "object_id": 267072, + "object_name": "Evolution" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267069, + "object_name": "Imam N, L Finlay B (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 267071, + "object_name": "Network growth" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Self-organization of cortical areas in development and evolution of neocortex (Imam & Finlay 2021)", + "notes": { + "attr_id": 24, + "value": "\"Using physical parameters representing primary and secondary visual areas as they vary from monkey to mouse, we derived a network growth model to explore if characteristic features of secondary areas could be produced from correlated activity patterns arising from V1 alone.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266800", + "public_submitter_email": { + "attr_id": 309, + "value": "ni49@cornell.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Nabil Imam" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 266986, + "object_name": "Visual cortex" + } + ] + }, + "ver_date": "2021-06-30T10:25:01.23", + "ver_number": 7 + }, + "266802": { + "class_id": 19, + "created": "2020-09-08T12:47:23.473", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266802, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266888, + "object_name": "Li C, Gulledge AT (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "NMDA receptors enhance the fidelity of synaptic integration (Li and Gulledge 2021)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + }, + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Excitatory synaptic transmission in many neurons is mediated by two co-expressed ionotropic glutamate receptor subtypes, AMPA and NMDA receptors, that differ in their kinetics, ion-selectivity, and voltage-sensitivity. AMPA receptors have fast kinetics and are voltage-insensitive, while NMDA receptors have slower kinetics and increased conductance at depolarized membrane potentials. Here we report that the voltage-dependency and kinetics of NMDA receptors act synergistically to stabilize synaptic integration of excitatory postsynaptic potentials (EPSPs) across spatial and voltage domains. Simulations of synaptic integration in simplified and morphologically realistic dendritic trees revealed that the combined presence of AMPA and NMDA conductances reduces the variability of somatic responses to spatiotemporal patterns of excitatory synaptic input presented at different initial membrane potentials and/or in different dendritic domains. This moderating effect of the NMDA conductance on synaptic integration was robust across a wide range of AMPA-to-NMDA ratios, and results from synergistic interaction of NMDA kinetics (which reduces variability across membrane potential) and voltage-dependence (which favors stabilization across dendritic location). When combined with AMPA conductance, the NMDA conductance balances voltage- and impedance-dependent changes in synaptic driving force, and distance-dependent attenuation of synaptic potentials arriving at the axon, to increase the fidelity of synaptic integration and EPSP-spike coupling across neuron state (i.e., initial membrane potential) and dendritic location of synaptic input. Thus, synaptic NMDA receptors convey advantages for synaptic integration that are independent of, but fully compatible with, their importance for coincidence detection and synaptic plasticity." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266802", + "public_submitter_email": { + "attr_id": 309, + "value": "allan@dartmouth.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Allan Gulledge" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2021-01-20T09:51:12.107", + "ver_number": 5 + }, + "266806": { + "class_id": 19, + "created": "2020-09-13T09:45:53.813", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266806, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 229586, + "object_name": "Masoli, Stefano [stefano.masoli at unipv.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 245160, + "object_name": "Neurotransmitter dynamics" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266867, + "object_name": "Masoli S, Ottaviani A, Casali S, D'Angelo E (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Cerebellar Golgi cells, dendritic processing, and synaptic plasticity (Masoli et al 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 112687, + "object_name": "Cerebellum golgi cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The Golgi cells are the main inhibitory interneurons of the cerebellar granular layer. To study the mechanisms through which these neurons integrate complex input patterns, a new set of models were developed using the latest experimental information and a genetic algorithm approach to fit the maximum ionic channel conductances. 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Conduction through the Fhf2WT model strand is resistant to these stresses." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266818", + "other_currents": { + "attr_id": 303, + "value": "I Kdr" + }, + "other_neurons": { + "attr_id": 301, + "value": "Cardiac Myocite" + }, + "other_type": { + "attr_id": 305, + "value": "Myocite" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "smgsolinas@uniss.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Sergio Solinas" + }, + "ver_date": "2023-02-06T10:48:18.18", + "ver_number": 22 + }, + "266819": { + "citation": { + "attr_id": 391, + "value": "Tiina Manninen, Ausra Saudargiene, and Marja-Leena Linne. Astrocyte-mediated spike-timing-dependent long-term depression modulates synaptic properties in the developing cortex. 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These computational experiments demonstrated that glutamatergic co-stimulation enhances GABA receptor-mediated Cl- influx at low and attenuates or reverses the Cl- efflux at high initial [Cl-]i. The size of glutamatergic influence on GABAergic Cl--fluxes depends on the conductance, decay kinetics, and localization of glutamatergic inputs. Surprisingly, the glutamatergic shift in GABAergic Cl--fluxes is invariant to latencies between GABAergic and glutamatergic inputs over a substantial interval...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266823", + "public_submitter_email": { + "attr_id": 309, + "value": "wkilb@uni-mainz.dew" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Werner Kilb" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "ver_date": "2021-01-20T10:24:26.61", + "ver_number": 5 + }, + "266836": { + "citation": { + "attr_id": 391, + "value": "Geerts, Chersi, Stachenfeld & Burgess (in press). A general model of hippocampal and dorsal striatal learning and decision making. PNAS" + }, + "class_id": 19, + "created": "2020-11-10T08:35:22.843", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266836, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83573, + "object_name": "Action Selection/Decision Making" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266847, + "object_name": "Geerts JP, Chersi F, Stachenfeld KL, Burgess N (2020)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 146899, + "object_name": "Cython" + } + ] + }, + "name": "A general model of hippocampal and dorsal striatal learning and decision making (Geerts et al 2020)", + "notes": { + "attr_id": 24, + "value": "Python and Cython implementation of a dual-systems reinforcement learning model that solves navigation and decision tasks using model-free and successor representation strategies. For questions, please contact jesse.geerts.14@ucl.ac.uk." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266836", + "public_submitter_email": { + "attr_id": 309, + "value": "jesse.geerts.14@ucl.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Jesse Geerts" + }, + "ver_date": "2020-11-29T12:05:52.187", + "ver_number": 5 + }, + "266837": { + "class_id": 19, + "created": "2020-11-10T11:26:10.6", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266837, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 234452, + "object_name": "Terman, David [terman at math.ohio-state.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 235462, + "object_name": "Spreading depolarization" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 235419, + "object_name": "Huguet G, Joglekar A, Messi LM, Buckalew R, Wong S, Terman D (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 120799, + "object_name": "Glia" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Neuroprotective Role of Gap Junctions in a Neuron Astrocyte Network Model (Huguet et al 2016)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 87589, + "object_name": "Astrocyte" + }, + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"A detailed biophysical model for a neuron/astrocyte network is developed to explore mechanisms responsible for the initiation and propagation of cortical spreading depolarizations and the role of astrocytes in maintaining ion homeostasis, thereby preventing these pathological waves... properties of spreading depolarizations, such as wave speed and duration of depolarization, depend on several factors, including the neuron and astrocyte Na+-K+ ATPase pump strengths. In particular, we consider the neuroprotective role of astrocyte gap junction coupling. The model demonstrates that a syncytium of electrically coupled astrocytes can maintain a physiological membrane potential in the presence of an elevated extracellular K+ concentration and efficiently distribute the excess K+ across the syncytium.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266837", + "public_submitter_email": { + "attr_id": 309, + "value": "Terman.l@osu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "David Terman" + }, + "ver_date": "2020-11-15T12:30:01.17", + "ver_number": 3 + }, + "266839": { + "citation": { + "attr_id": 391, + "value": "https://www.biorxiv.org/content/10.1101/2020.11.12.362491v1" + }, + "class_id": 19, + "created": "2020-11-17T18:43:38.76", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266839, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 222956, + "object_name": "Neuromodulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267249, + "object_name": "Church E, Hamid E, Zurawski Z, Pocoava M, Flores-Barrera E, Cabalero A, Tseng KY, Alford S (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 2835, + "object_name": "MCell" + } + ] + }, + "name": "Synaptic vesicle fusion model (Church et al 2021)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "These parameter files define Cell simulations of glutamate release and receptor binding at synapses. Four basic models are included that vary, the pore diameter of a fusing vesicle from full fusion (FullFusion) to a variable sized pore from a small as 0.4nm (DelayFusion), that vary the umber of fusing vesicles (Multivesicular) or that vary the position of the fusing vesicle with the post synaptic glutamate receptors (Clustered receptors). Our work demonstrates that experimental effects on release and low affinity antagonism are well-fit by reduced release rates of glutamate from a restricted pore." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266839", + "public_submitter_email": { + "attr_id": 309, + "value": "sta@uic.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Simon Alford" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2022-01-18T23:11:22.803", + "ver_number": 2 + }, + "266840": { + "citation": { + "attr_id": 391, + "value": "32293081" + }, + "class_id": 19, + "created": "2020-11-24T09:59:44.93", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266840, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 156115, + "object_name": "Gamma oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266845, + "object_name": "Lu Y, Sarter M, Zochowski M, Booth V (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Phasic ACh promotes gamma oscillations in E-I networks (Lu et al, 2020)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In a biophysically-based model, we show that a network of excitatory (E) and inhibitory (I) neurons that initially displays asynchronous firing can generate transient gamma oscillatory activity in response to simulated brief pulses of ACh. ACh effects are simulated as transient modulation of the conductance of an M-type K+ current which is blocked by activation of muscarinic receptors and has significant effects on neuronal excitability. The ACh-induced effects on the M current conductance, gks, change network dynamics to promote the emergence of network gamma rhythmicity through a Pyramidal-Interneuronal Network Gamma (PING) mechanism." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266840", + "public_submitter_email": { + "attr_id": 309, + "value": "vbooth@umich.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Victoria Booth" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 204, + "object_name": "Muscarinic" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "ver_date": "2020-11-29T11:53:29.24", + "ver_number": 3 + }, + "266841": { + "citation": { + "attr_id": 391, + "value": "28591611" + }, + "class_id": 19, + "created": "2020-11-24T10:34:11.723", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 243504, + "object_name": "IK Bkca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 266841, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266879, + "object_name": "Montefusco, Francesco [montefusco at dei.unipd.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 238957, + "object_name": "Markov-type model" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266875, + "object_name": "Montefusco F, Tagliavini A, Ferrante M, Pedersen MG (2017)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "BK - CaV coupling (Montefusco et al. 2017)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 62277, + "object_name": "Pituitary cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "An implementation of coupling between BK_Ca channels and CaV channels suitable for use in whole cell models." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266841", + "other_concept": { + "attr_id": 306, + "value": "ion channel coupling" + }, + "other_neurons": { + "attr_id": 301, + "value": "Hypothalamic neurosecretory cell" + }, + "other_type": { + "attr_id": 305, + "value": "Human beta-cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "carnevalet@protonmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ted Carnevale" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228598, + "object_name": "Mouse" + }, + { + "object_id": 227987, + "object_name": "Human" + } + ] + }, + "ver_date": "2021-02-22T23:03:07.347", + "ver_number": 16 + }, + "266842": { + "class_id": 19, + "created": "2020-11-24T11:27:12.3", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266842, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266862, + "object_name": "Schreglmann S, Wang D, Peach R, Li J, Zhang X, Latorre A, Rhodes E, Panella E, Boyden ES, Barahona M, Santaniello S, Bhatia KP, Rothwell J, Grossman N (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A cerebellar model of phase-locked tACS for essential tremor (Schreglmann et al., 2021)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + }, + { + "object_id": 136179, + "object_name": "Cerebellum deep nucleus neuron" + }, + { + "object_id": 144503, + "object_name": "Inferior olive neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model is a supplementary material for Schreglmann, Sebastian R., et al. \"Non-invasive suppression of essential tremor via phase-locked disruption of its temporal coherence\" Nature Communications (2021). The model demonstrates that phase-locked transcranial alternating current stimulation (tACS) is able to disrupt the tremor-related oscillations in the cerebellum, and its efficacy is highly dependent on the relative phase between the stimulation and tremor." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266842", + "public_submitter_email": { + "attr_id": 309, + "value": "xu.3.zhang@uconn.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Xu Zhang" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2021-02-27T13:04:26.447", + "ver_number": 24 + }, + "266844": { + "citation": { + "attr_id": 391, + "value": "Under review (Extra-large spines distort neuronal computation in synaptic disorders)" + }, + "class_id": 19, + "created": "2020-11-28T22:01:11.437", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": true + }, + "id": 266844, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267765, + "object_name": "Obi-Nagata K, Suzuki N, Miyake R, MacDonald ML, Fish KN, Ozawa K, Nagahama K, Okimura T, Tanaka S, Kano M, Fukazawa Y, Sweet RA, Hayashi-Takagi A (2023)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Neuronal computation evoked by extra-large spines (Obi-Nagata et al., 2023)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Human genetics strongly support the involvement of synaptopathy in psychiatric disorders. However, trans-scale causality linking synapse pathology to behavioral changes is lacking. To address this question, we examined the effects of synaptic inputs on dendrites, cells, and behaviors of mice with knockdown of SETD1A and DISC1, which are validated animal models of schizophrenia. Both models exhibited an overrepresentation of extra-large (XL) synapses, which evoked supralinear dendritic and somatic integration, resulting in increased neuronal firing. The probability of XL spines correlated negatively with working memory, and the optical prevention of XL spine generation restored working memory impairment. Furthermore, XL synapses were significantly more abundant in the postmortem brains of schizophrenia patients than in those of matched controls. Our findings suggest that working memory performance, a pivotal aspect of psychiatric symptoms, is shaped by distorted dendritic and somatic integration via extra-large spines." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266844", + "public_submitter_email": { + "attr_id": 309, + "value": "norimitsu.suzuki@riken.jp" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Norimitsu Suzuki" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "ver_date": "2023-06-11T10:23:32.03", + "ver_number": 7 + }, + "266848": { + "class_id": 19, + "created": "2020-11-30T05:36:25.623", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266848, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 170033, + "object_name": "Migliore, Rosanna [rosanna.migliore at cnr.it]" + }, + { + "object_id": 267061, + "object_name": "Vitale, Paola [paola.vitale at ibf.cnr.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 112047, + "object_name": "Aging/Alzheimer`s" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 267060, + "object_name": "eFEL" + } + ] + }, + "name": "Age-dependent excitability of CA1 pyramidal neurons in APPPS1 Alzheimer's model (Vitale et al 2021)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Age-dependent accumulation of amyloid-b, provoking increasing brain amyloidopathy, triggers abnormal patterns of neuron activity and circuit synchronization in Alzheimer\u2019s disease (AD) as observed in human AD patients and AD mouse models. Recent studies on AD mouse models, mimicking this age-dependent amyloidopathy, identified alterations in CA1 neuron excitability. However, these models generally also overexpress mutated amyloid precursor protein (APP) and presenilin 1 (PS1) and there is a lack of a clear correlation of neuronal excitability alterations with progressive amyloidopathy. The active development of computational models of AD points out the need of collecting such experimental data to build a reliable disease model exhibiting AD-like disease progression. We therefore used the feature extraction tool of the Human Brain Project (HBP) Brain Simulation Platform to systematically analyze the excitability profile of CA1 pyramidal neuron in the APPPS1 mouse model. We identified specific features of neuron excitability that best correlate either with over-expression of mutated APP and PS1 or increasing Ab amyloidopathy. Notably, we report strong alterations in membrane time constant and action potential width and weak alterations in firing behavior. Also, using a CA1 pyramidal neuron model, we evidence amyloidopathy-dependent alterations in Ih. Finally, cluster analysis of these recordings showed that we could reliably assign a trace to its correct group, opening the door to a more refined, less variable analysis of AD-affected neurons. This inter-disciplinary analysis, bringing together experimentalists and modelers, helps to further unravel the neuronal mechanisms most affected by AD and to build a biologically plausible computational model of the AD brain.\r\n\r\n\r\nReference: Paola Vitale, Ana Rita Salgueiro-Pereira, Carmen Alina Lupascu, Rosanna Migliore, Michele Migliore, H\u00e9l\u00e8ne Marie. \"Analysis of age-dependent alterations in excitability properties of CA1 pyramidal neurons in an APPPS1 model of Alzheimer's disease\". Frontiers in Aging Neuroscience (2021) DOI: 10.3389/fnagi.2021.668948" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266848", + "other_neurons": { + "attr_id": 301, + "value": "Hippocampus CA1 pyramidal cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "michele.migliore@pa.ibf.cnr.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Michele Migliore" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2021-06-09T10:37:00.917", + "ver_number": 19 + }, + "266849": { + "class_id": 19, + "created": "2020-12-01T02:57:23.19", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266849, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98011, + "object_name": "Place cell/field" + }, + { + "object_id": 116907, + "object_name": "Direction Selectivity" + }, + { + "object_id": 185343, + "object_name": "Persistent activity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266870, + "object_name": "Coppolino S, Giacopelli G, Migliore M (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 116010, + "object_name": "PyNN" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Single Trial Sequence learning: a spiking neurons model based on hippocampus (Coppolino et al 2021)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In contrast with our everyday experience using brain circuits, it can take a prohibitively long time to train a computational system to produce the correct sequence of outputs in the presence of a series of inputs. This suggests that something important is missing in the way in which models are trying to reproduce basic cognitive functions. \r\nIn this work, we introduce a new neuronal network architecture that is able to learn, in a single trial, an arbitrary long sequence of any known objects. The key point of the model is the explicit use of mechanisms and circuitry observed in the hippocampus. By directly following the natural system\u2019s layout and circuitry, this type of implementation has the additional advantage that the results can be more easily compared to experimental data, allowing a deeper and more direct understanding of the mechanisms underlying cognitive functions and dysfunctions." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266849", + "public_submitter_email": { + "attr_id": 309, + "value": "simone.coppolino@ibf.cnr.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Simone Coppolino" + }, + "ver_date": "2021-01-06T13:13:59.547", + "ver_number": 17 + }, + "266850": { + "citation": { + "attr_id": 391, + "value": "The input-output relation of primary nociceptive neurons is determined by the morphology of the peripheral nociceptive terminals.\r\nBarkai O, Butterman R, Katz B, Lev S, Binshtok AM.\r\nJ Neurosci. 2020. doi: 10.1523/JNEUROSCI.1546-20.2020" + }, + "class_id": 19, + "created": "2020-12-01T10:19:43.777", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266850, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 96767, + "object_name": "Nociception" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266856, + "object_name": "Barkai O, Butterman R, Katz B, Lev S, Binshtok AM (2020)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A model for a nociceptor terminal and terminal tree (Barkai et al., 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 136307, + "object_name": "Dorsal Root Ganglion (DRG) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model was used to study how the architecture of the nociceptor terminal tree affects the input-output relation of the primary nociceptive neurons. The model shows that the input-output properties of the nociceptive neurons depend on the length, the axial resistance, and location of individual terminals and that activation of multiple terminals by a capsaicin-like current allows summation of the responses from individual terminals, thus leading to increased nociceptive output." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266850", + "public_submitter_email": { + "attr_id": 309, + "value": "omer.barkai@mail.huji.ac.il" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Omer Barkai" + }, + "ver_date": "2020-12-09T11:14:30.523", + "ver_number": 5 + }, + "266851": { + "class_id": 19, + "created": "2020-12-04T09:15:39.733", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 266958, + "object_name": "TASK channel" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266851, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266959, + "object_name": "Kelley, Craig" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 266957, + "object_name": "Impedance" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266956, + "object_name": "Kelley C, Dura-Bernal S, Neymotin SA, Antic SD, Carnevale NT, Migliore M, Lytton WW (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 195884, + "object_name": "NetPyNE" + } + ] + }, + "name": "Dendritic Impedance in Neocortical L5 PT neurons (Kelley et al. 2021)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 265, + "object_name": "Neocortex L5/6 pyramidal GLU cell" + }, + { + "object_id": 227970, + "object_name": "Neocortex M1 L5B pyramidal pyramidal tract GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We simulated chirp current stimulation in the apical dendrites of 5 biophysically-detailed multi-compartment models of neocortical pyramidal tract neurons and found that a combination of HCN channels and TASK-like channels produced the best fit to experimental measurements of dendritic impedance. We then explored how HCN and TASK-like channels can shape the dendritic impedance as well as the voltage response to synaptic currents." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266851", + "public_submitter_email": { + "attr_id": 309, + "value": "craig.kelley@downstate.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Craig Kelley" + }, + "ver_date": "2021-11-05T13:38:55.813", + "ver_number": 11 + }, + "266863": { + "class_id": 19, + "created": "2020-12-16T21:00:00.23", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266863, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 186894, + "object_name": "Tikidji-Hamburyan, Ruben [ruben.tikidji.hamburyan at gmail.com] " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267011, + "object_name": "Tikidji-Hamburyan RA, Colonnese MT (2021)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 231240, + "object_name": "Brian 2" + }, + { + "object_id": 146899, + "object_name": "Cython" + } + ] + }, + "name": "PLS-framework (Tikidji-Hamburyan and Colonnese 2021)", + "notes": { + "attr_id": 24, + "value": "\"Numerical simulations become incredibly challenging when an extensive network with a detailed representation of each neuron needs to be modeled over a long time interval to study slow evolving processes, e.g. development of the thalamocortical circuits. Here we suggest a simple, powerful and flexible approach in which we approximate the right-hand sides of differential equations by combinations of functions from three families: Polynomial, piecewise-Linear, Step (PLS). To obtain a single coherent framework, we provide four core principles in which PLS functions should be combined. We show the rationale behind each of the core principles. Two examples illustrate how to build a conductance-based or phenomenological model using the PLS-framework. We use the first example as a benchmark on three different computational platforms: CPU, GPU, and mobile system-on-chip devices.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266863", + "public_submitter_email": { + "attr_id": 309, + "value": "ruben.tikidji.hamburyan@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ruben Tikidji-Hamburyan" + }, + "ver_date": "2021-03-30T17:25:25.047", + "ver_number": 9 + }, + "266864": { + "citation": { + "attr_id": 391, + "value": "Y. Zang and E. De Schutter: The Cellular Electrophysiological Properties Underlying Multiplexed Coding in Purkinje Cells. Journal of Neuroscience, in press." + }, + "class_id": 19, + "created": "2020-12-20T12:42:09.13", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 246, + "object_name": "I p,q" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266864, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 244383, + "object_name": "Zang, Yunliang " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 240376, + "object_name": "Temporal Coding" + }, + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266904, + "object_name": "Zang Y, De Schutter E (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Multiplexed coding in Purkinje neuron dendrites (Zang and De Schutter 2021)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Neuronal firing patterns are crucial to underpin circuit level behaviors. In cerebellar Purkinje cells (PCs), both spike rates and pauses are used for behavioral coding, but the cellular mechanisms causing code transitions remain unknown. We use a well-validated PC model to explore the coding strategy that individual PCs use to process parallel fiber (PF) inputs. We find increasing input intensity shifts PCs from linear rate-coders to burst-pause timing-coders by triggering localized dendritic spikes. We validate dendritic spike properties with experimental data, elucidate spiking mechanisms, and predict spiking thresholds with and without inhibition. Both linear and burst-pause computations use individual branches as computational units, which challenges the traditional view of PCs as linear point neurons. Dendritic spike thresholds can be regulated by voltage state, compartmentalized channel modulation, between-branch interaction and synaptic inhibition to expand the dynamic range of linear computation or burst-pause computation. In addition, co-activated PF inputs between branches can modify somatic maximum spike rates and pause durations to make them carry analogue signals. Our results provide new insights into the strategies used by individual neurons to expand their capacity of information processing. " + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266864", + "public_submitter_email": { + "attr_id": 309, + "value": "zangyl1983@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Yunliang Zang" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2021-02-03T13:39:52.9", + "ver_number": 12 + }, + "266868": { + "citation": { + "attr_id": 391, + "value": "Behavioral Time Scale Plasticity of Place Fields: Mathematical Analysis\r\nHarel Z. Shouval, Ian Cone\r\nbioRxiv 2020.09.11.293787; doi: https://doi.org/10.1101/2020.09.11.293787" + }, + "class_id": 19, + "created": "2021-01-05T21:06:10.35", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266868, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 98011, + "object_name": "Place cell/field" + }, + { + "object_id": 266945, + "object_name": "Eligibility traces" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266944, + "object_name": "Cone I, Shouval HZ (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Mathematical model of behavioral time scale plasticity (BTSP) of place fields (Shouval & Cone 2021)", + "notes": { + "attr_id": 24, + "value": "Inspired by experiments showing the generation of place fields in hippocampal CA1 neurons, this model describes CA3 to CA1 synaptic plasticity occurring on behavioral time-scales (order of seconds). Presynaptic activity induces LTP and LTD eligibility traces which are converted into synaptic changes upon the occurrence of an instructive signal, corresponding to the plateau potential in experiments." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266868", + "public_submitter_email": { + "attr_id": 309, + "value": "iancone@rice.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ian Cone" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2021-02-27T13:17:56.72", + "ver_number": 6 + }, + "266871": { + "citation": { + "attr_id": 391, + "value": "M. Budak, K. Grosh, A. Sasmal, G. Corfas, M. Zochowski and V. Booth. Contrasting mechanisms for hidden hearing loss: synaptopathy vs myelin defects. PLoS Comput Bio, in press, 2021." + }, + "class_id": 19, + "created": "2021-01-12T14:46:59.38", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266871, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266878, + "object_name": "Budak M, Gros K, Corfas G, Zochowski M, Booth V (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Sound-evoked activity in peripheral axons of type I spiral ganglion neurons (Budak et al. 2021)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52425, + "object_name": "Myelinated neuron" + }, + { + "object_id": 52419, + "object_name": "Auditory nerve" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Using this model, we investigated the implications of two mechanisms underlying the auditory neuropathy known as hidden hearing loss, namely synaptopathy and myelinopathy, on sound-evoked spike generation and timing in the peripheral axons of type I spiral ganglion neurons (SGNs). The model is a reduced biophysical model consisting of a population of myelinated SGN axonal fibers whose firing activity is driven by a previously developed, well accepted model for cochlear sound processing. Using the model, we investigated how synapse loss (synaptopathy) or disruption of myelin organization (myelinopathy) affected spike generation on the axons and the profile of the compound action potential (CAP) signal computed from the spike activity. Synaptopathy and myelinopathy were implemented by removing synapses and by varying the position of SGN heminodes (the nodal structures closest to the inner hair cell synapse where action potentials are generated), respectively. Model results showed that heminode disruption caused decreased amplitude and increased latency of sound-evoked CAPs. In addition, significant elongation of the initial axon segment caused spike generation failure leading to decreased spiking probability. In contrast, synaptopathy, solely decreased probability of firing, subsequently decreasing CAP peak amplitude without affecting its latency, similar to observations in noise exposed animals. Model results reveal the disruptive effect of synaptopathy or myelinopathy on neural activity in the peripheral auditory system that may contribute to perceptual deficits." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266871", + "other_neurons": { + "attr_id": 301, + "value": "spiral ganglion neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "vbooth@umich.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Victoria Booth" + }, + "ver_date": "2021-01-13T15:18:48.227", + "ver_number": 2 + }, + "266872": { + "citation": { + "attr_id": 391, + "value": "Romaro, C., Najman, F. A., Lytton, W. W., Roque, A. C., and Dura-Bernal, S. (2020).\r\nNetPyNE implementation and rescaling of the Potjans-Diesmann cortical microcircuit model.\r\narXiv preprint arXiv:2005.03764\r\n\r\nRomaro, C. (2020) STUDIES IN CORTICAL NETWORK MODELS (teses.usp.br)" + }, + "class_id": 19, + "created": "2021-01-13T07:56:42.497", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266872, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 182905, + "object_name": "Dura-Bernal, Salvador [salvadordura at gmail.com]" + }, + { + "object_id": 267282, + "object_name": "Romaro, Cecilia [ceciliaromaro at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267285, + "object_name": "Romaro C, Najman FA, Lytton WW, Roque AC, Dura-Bernal S (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 195884, + "object_name": "NetPyNE" + } + ] + }, + "name": "Potjans-Diesmann cortical microcircuit model in NetPyNE (Romaro et al 2021)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + }, + { + "object_id": 240378, + "object_name": "Abstract integrate-and-fire neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The Potjans-Diesmann cortical microcircuit model is a widely used model originally implemented in NEST. Here, we re-implemented the model using NetPyNE, a high-level Python interface to the NEURON simulator, and reproduced the findings of the original publication. We also implemented a method for rescaling the network size which preserves first and second order statistics, building on existing work on network theory. The new implementation enables using more detailed neuron models with multicompartment morphologies and multiple biophysically realistic channels. This opens the model to new research, including the study of dendritic processing, the influence of individual channel parameters, and generally multiscale interactions in the network. The rescaling method provides flexibility to increase or decrease the network size if required when running these more realistic simulations. Finally, NetPyNE facilitates modifying or extending the model using its declarative language; optimizing model parameters; running efficient large-scale parallelized simulations; and analyzing the model through built-in methods, including local field potential calculation and information flow measures." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266872", + "public_submitter_email": { + "attr_id": 309, + "value": "ceciliaromaro@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Cecilia Romaro" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 144485, + "object_name": "Auditory cortex" + }, + { + "object_id": 115949, + "object_name": "Olfactory cortex" + } + ] + }, + "ver_date": "2022-02-13T14:03:33.97", + "ver_number": 3 + }, + "266880": { + "class_id": 19, + "created": "2021-01-15T12:20:06.367", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266880, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 266891, + "object_name": "Paranoia" + }, + { + "object_id": 266892, + "object_name": "COVID-19" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267102, + "object_name": "Suthaharan P, Reed EJ, Leptourgos P, Kenney JG, Uddenberg S, Mathys CD, Litman L, Robinson J, Moss AJ, Taylor JR, Groman SM, Colett PR (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 266894, + "object_name": "Multilayer Hierarchical Gaussian filter (HGF)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 150209, + "object_name": "R" + } + ] + }, + "name": "Paranoia and belief updating during a crisis (Suthaharan et al., 2021)", + "notes": { + "attr_id": 24, + "value": "Perceptual model with three hierarchical layers defined by probability distributions: (1) reward belief, (2) contingency beliefs, (3) volatility beliefs used to investigate the relationship between real-world uncertainty, paranoia, and laboratory task behavior." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266880", + "public_submitter_email": { + "attr_id": 309, + "value": "praveen.suthaharan@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Praveen Suthaharan" + }, + "ver_date": "2021-08-10T17:15:35.167", + "ver_number": 11 + }, + "266881": { + "class_id": 19, + "created": "2021-01-15T12:21:22.953", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266881, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266748, + "object_name": "Demaegd, Maggie [mdemaeg at ilstu.edu]" + }, + { + "object_id": 267097, + "object_name": "Stein, Wolfgang [wstein@ilstu.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267096, + "object_name": "DeMaegd ML, Stein W (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A neurite to measure ePSP and AP amplitude after passive spread (DeMaegd & Stein, 2021)", + "notes": { + "attr_id": 24, + "value": "Increasing temperatures overwhelmingly shunts dendritic electrical spread in a crustacean motor neuron (LG) leading to the disruption of a vital pattern generator. LG recieves synaptic input from a descending projection neuron (MCN1) via a chemical and electrical synapse. Warmer temperatures increase leak conductances in the neurite and increase the synaptic input. Here, we modelled the conflicting influence of temperature at the MCN1-LG synapse and LG neurite to determine the resulting ePSP and AP amplitude measured at different distanced from the synaptic input after passive progation through the neurite." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266881", + "public_submitter_email": { + "attr_id": 309, + "value": "mdemaeg@ilstu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Margaret L. 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Cerebral Cortex" + }, + "class_id": 19, + "created": "2021-01-21T09:29:45.68", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266895, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 128875, + "object_name": "Development" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266897, + "object_name": "Bauer R, Clowry G, Kaiser M (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 266899, + "object_name": "Agent-based model" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 266898, + "object_name": "Cx3Dp" + } + ] + }, + "name": "An agent-based computational model for cortical layer formation (Bauer et al 2021)", + "notes": { + "attr_id": 24, + "value": "This computational model can account for layer-specific neuron numbers in various different cortical structures. It is agent-based and is initiated from a small homogeneous pool of precursor cells. The file Lamination.java includes the main function that induces the activation of multiple \"modules\" within a gene regulatory network." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266895", + "public_submitter_email": { + "attr_id": 309, + "value": "r.bauer@surrey.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Roman Bauer" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2021-01-21T21:26:39.927", + "ver_number": 4 + }, + "266900": { + "class_id": 19, + "created": "2021-01-22T06:22:06.663", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266900, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + }, + { + "object_id": 143720, + "object_name": "Bianchi, Daniela [danielabianchi12 -at- gmail.com]" + }, + { + "object_id": 170033, + "object_name": "Migliore, Rosanna [rosanna.migliore at cnr.it]" + }, + { + "object_id": 267061, + "object_name": "Vitale, Paola [paola.vitale at ibf.cnr.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 206291, + "object_name": "Membrane Properties" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267257, + "object_name": "Bianchi D, Migliore R, Vitale P, Garad M, Pousinha PA, Marie H, Lessmann V, Migliore M (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Membrane electrical properties of mouse CA1 pyramidal cells during strong inputs (Bianchi et al 22)", + "notes": { + "attr_id": 24, + "value": "ABSTRACT: In this work we highlight an electrophysiological feature, often observed in recordings from mouse CA1 pyramidal cells, which has been so far ignored by experimentalists and modelers. It consists of a large and dynamic increase in the depolarization baseline (i.e. the minimum value of the membrane potential between successive action potentials during a sustained input) in response to strong somatic current injections. Such an increase can directly affect neurotransmitter release properties and, more generally, efficacy of synaptic transmission. However, it cannot be explained by any currently available conductance-based computational model. Here we present a model addressing this issue, demonstrating that experimental recordings can be reproduced by assuming that an input current modifies, in a time-dependent manner, the electrical and permeability properties of the neuron membrane by shifting the ionic reversal potentials and channel kinetics. For this reason, we propose that any detailed model of ion channel kinetics, for neurons exhibiting this characteristic, should be adapted to correctly represent the response and the synaptic integration process during strong and sustained inputs. " + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266900", + "other_currents": { + "attr_id": 303, + "value": "I_CaT, I_CaL" + }, + "other_neurons": { + "attr_id": 301, + "value": "Hippocampus CA1 pyramidal cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "michele.migliore@pa.ibf.cnr.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Michele Migliore" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2022-01-18T22:16:28.837", + "ver_number": 18 + }, + "266901": { + "class_id": 19, + "created": "2021-01-24T07:38:13.283", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 143445, + "object_name": "I_KD" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266901, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 121254, + "object_name": "Depolarization block" + }, + { + "object_id": 3637, + "object_name": "Parameter Fitting" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267021, + "object_name": "Tomko M, Benuskova L, Jedlicka P (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Reduced-morphology model of CA1 pyramidal cells optimized + validated w/ HippoUnit (Tomko et al '21)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Here we employ the HippoUnit tests to optimize and validate our new compartmental model with reduced morphology. We show that our model is able to account for the following six well-established characteristic anatomical and physiological properties of CA1 pyramidal cells: (1) The reduced dendritic morphology contains all major dendritic branch classes. In addition to anatomy, the model reproduces also 5 key physiological features, including (2) somatic electrophysiological responses, (3) depolarization block, (4) EPSP attenuation (5) action potential (AP) backpropagation, and (6) synaptic integration at oblique dendrites." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266901", + "public_submitter_email": { + "attr_id": 309, + "value": "matus.tomko@fmph.uniba.sk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Matus Tomko" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2023-01-09T05:54:19.527", + "ver_number": 12 + }, + "266902": { + "citation": { + "attr_id": 391, + "value": "The paper describing this model is being revised for J. Neurophysiol. Acceptance is not guaranteed." + }, + "class_id": 19, + "created": "2021-01-29T14:24:05.653", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266902, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33800, + "object_name": "Traub, Roger D [rtraub at us.ibm.com]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267234, + "object_name": "Traub RD, Tu Y, Whittington MA (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 45540, + "object_name": "FORTRAN" + } + ] + }, + "name": "Piriform cortex network model with multicompartment neurons for cell assemblies (Traub et al 2021)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Model contains layer 2 and layer 3 pyr cells, semilunar cells, and multiple types of superficial and deep interneurons. Used to investigate how cell assemblies and sharp waves emerge in different parameter regimes. Correlated with experimental recording. May be useful to better understand how the structure could be used for associative memory." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266902", + "public_submitter_email": { + "attr_id": 309, + "value": "rtraub@us.ibm.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Roger Dennis Traub" + }, + "ver_date": "2021-12-17T13:28:22.973", + "ver_number": 4 + }, + "266905": { + "class_id": 19, + "created": "2021-02-10T12:37:04.26", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266905, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266908, + "object_name": "Conde, James" + }, + { + "object_id": 240367, + "object_name": "Dewell, Richard Burkett [dewell at bcm.edu]" + }, + { + "object_id": 33804, + "object_name": "Gabbiani, F" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 5232, + "object_name": "Traub RD, Wong RK, Miles R, Michelson H (1991)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "CA3 hippocampal pyramidal neuron with voltage-clamp intrinsic conductance data (Traub et al 1991)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a third-party implementation of the model from Traub et al 1991; as of 2021, Google Scholar reports about 780 citation articles. This model was one of the first biophysical models of a hippocampal pyramidal neuron with realistic conductances and the conductance equations have been used as a starting point for many models since, particularly those examining calcium dynamics and bursting." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266905", + "public_submitter_email": { + "attr_id": 309, + "value": "dewell@bcm.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Richard B. Dewell" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2021-02-22T19:46:30.927", + "ver_number": 10 + }, + "266910": { + "citation": { + "attr_id": 391, + "value": "Giacopelli et al., 2021" + }, + "class_id": 19, + "created": "2021-02-12T07:59:08.997", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266910, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 262437, + "object_name": "Giacopelli, Giuseppe [giuseppe.giacopelli at unipa.it]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267172, + "object_name": "Giacopelli G, Tegolo D, Migliore M (2021)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "The role of network connectivity on epileptiform activity (Giacopelli et al 2021)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 188553, + "object_name": "Abstract Hindmarsh-Rose neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A number of potentially important mechanisms have been identified as key players to generate epileptiform activity, such as genetic mutations, activity-dependent alteration of synaptic functions, and functional network reorganization at the macroscopic level. Here we study how network connectivity at cellular level can affect the onset of epileptiform activity, using computational model networks with different wiring properties. The model suggests that networks connected as in real brain circuits are more resistant to generate seizure-like activity. The results suggest new experimentally testable predictions on the cellular network connectivity in epileptic individuals, and highlight the importance of using the appropriate network connectivity to investigate epileptiform activity with computational models." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266910", + "public_submitter_email": { + "attr_id": 309, + "value": "michele.migliore@ibf.cnr.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Michele Migliore" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115960, + "object_name": "Generic" + } + ] + }, + "ver_date": "2021-10-21T22:19:06.677", + "ver_number": 7 + }, + "266925": { + "citation": { + "attr_id": 391, + "value": "https://www.biorxiv.org/content/10.1101/2020.10.23.352120v1" + }, + "class_id": 19, + "created": "2021-02-16T15:52:58.697", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266925, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266932, + "object_name": "Balmer TS, Borges-Merjane C, Trussell LO (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Ambient glutamate shapes AMPA receptor responses to simulated transients (Balmer et al. 2021)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "To explore how ambient glutamate contributes to the generation of ultra-slow signaling through AMPARs at the cerebellar unipolar brush cell synapse, we created this 13-state kinetic model in NEURON. A tool was also created to produce trains of glutamate concentration transients using 2D or 3D diffusion equations, a sum of up to 3 exponentials, or an alpha function that can be applied to the AMPA receptor model. \r\nAfter compiling the model using mkrndll, run 'mosinit_fast-flow.hoc' to simulate fast application of glutamate to the AMPA receptor model. 'mosinit_GluTransTrainTool_demo.hoc' opens a session where trains of synaptic glutamate transients can be created using various equations. The top panel shows the glutamate concentration transients (in mM) and the bottom panel shows the AMPA receptor mediated currents (in nA)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266925", + "other_concept": { + "attr_id": 306, + "value": "Desensitization" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "timothy.balmer@asu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tim Balmer" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2021-02-22T11:09:47.463", + "ver_number": 7 + }, + "266928": { + "class_id": 19, + "created": "2021-02-18T00:27:18.62", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266928, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 247695, + "object_name": "Denizot, Audrey [audrey.denizot at inria.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 120799, + "object_name": "Glia" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 144556, + "object_name": "STEPS" + } + ] + }, + "name": "Reaction-diffusion sims of Ca2+ signals in astrocytic branchlets at the nanoscale (Denizot et al 22)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 87589, + "object_name": "Astrocyte" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Reaction-Diffusion simulations are performed in geometries that were designed from the latest data available from super-resolution microscopy on astrocytes, published by Arizono et al., Nature Communications, 2020. The high spatial resolution of this model allows to propose plausible mechanisms by which astrocyte morphology at the nanoscale, notably shaft width, can influence local calcium dynamics and thus affect specialized neuron-astrocyte communication." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266928", + "public_submitter_email": { + "attr_id": 309, + "value": "audrey.denizot3@oist.jp" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Audrey Denizot" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 153715, + "object_name": "IP3" + } + ] + }, + "ver_date": "2022-09-29T01:22:59.547", + "ver_number": 13 + }, + "266929": { + "class_id": 19, + "created": "2021-02-19T17:38:57.62", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266929, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 146501, + "object_name": "Nicola, Wilten [wnicola at uwaterloo.ca]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267381, + "object_name": "Haidey JN, Peringod G, Institoris A, Gorzo KA, Nicola W, Vandal M, Ito K, Liu S, Fielding C, Visser F, Nguyen MD (2021)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Astrocyte and Blood Vessel Calcium Imaging Tracking code (Haidey et al 2021)", + "notes": { + "attr_id": 24, + "value": "Code for tracking Astrocytes/Blood vessels in Calcium Imaging based recordings from ...\r\n\r\nData files should be loaded as .tiff stacks with several options for processing (see matlab file). \r\n\r\nThe main-script is tracking_script.m which can be used to track multiple .tiff stacks simultaneously \r\n\r\nThe sub-function distance finder outputs various metrics computed from the blood vessel tracking, (e.g. blood vessel width measured in several ways, etc.) \r\n\r\nThese metrics, among others (e.g. cross sectional area, etc.) are then recorded into an .xls file as the final output. \r\n\r\nFor further details, please see \r\n\r\nHaidey JN, Peringod G, Institoris A, Gorzo KA, Nicola W, Vandal M, Ito K, Liu S, Fielding C, Visser F, Nguyen MD. Astrocytes regulate ultra-slow arteriole oscillations via stretch-mediated TRPV4-COX-1 feedback. Cell Reports. 2021 Aug 3;36(5):109405.\r\n\r\nhttps://doi.org/10.1016/j.celrep.2021.109405" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266929", + "other_type": { + "attr_id": 305, + "value": "Astrocyte and Bloodvessel" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "wilten.nicola@ucalgary.ca" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Wilten Nicola" + }, + "ver_date": "2022-05-17T20:04:15.853", + "ver_number": 10 + }, + "266933": { + "alternative_version": { + "attr_id": 314, + "value": [ + { + "object_id": 266977, + "object_name": "Python" + } + ] + }, + "citation": { + "attr_id": 391, + "value": "33301053" + }, + "class_id": 19, + "created": "2021-02-22T16:12:58.183", + "default_file_notes": { + "attr_id": 392, + "value": "MATLAB" + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266933, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266949, + "object_name": "Webster-Wood, Victoria A" + }, + { + "object_id": 266950, + "object_name": "Gill, Jeffrey P" + }, + { + "object_id": 266951, + "object_name": "Thomas, Peter J" + }, + { + "object_id": 266952, + "object_name": "Chiel, Hillel J" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 47316, + "object_name": "Invertebrate" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 114660, + "object_name": "Connectivity matrix" + }, + { + "object_id": 266953, + "object_name": "Behavioral switching" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266936, + "object_name": "Webster-Wood VA, Gill JP, Thomas PJ, Chiel HJ (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 266937, + "object_name": "Boolean network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Multifunctional control of feeding in Aplysia (Webster-Wood et al. 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 65418, + "object_name": "Aplysia feeding CPG neurons" + }, + { + "object_id": 52414, + "object_name": "Aplysia interneuron" + }, + { + "object_id": 52415, + "object_name": "Aplysia motor neuron" + }, + { + "object_id": 116700, + "object_name": "Aplysia B31/B32 neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Hybrid Boolean network implementation of a functional model of key feeding behaviors (biting, swallowing, rejection) and behavioral switching induced by sensory cues. Incorporates known neural connectivity and a mechanical model of the feeding apparatus." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266933", + "other_type": { + "attr_id": 305, + "value": "Hybrid Boolean network" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "carnevalet@protonmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ted Carnevale" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115956, + "object_name": "Aplysia" + } + ] + }, + "ver_date": "2021-03-16T18:12:52.87", + "ver_number": 11 + }, + "266941": { + "class_id": 19, + "created": "2021-02-26T18:17:23.633", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266941, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 261880, + "object_name": "Farokhniaee, AmirAli [aafarokh at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 185022, + "object_name": "Beta oscillations" + }, + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266973, + "object_name": "Farokhniaee A, Lowery M (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Thalamo-cortical microcircuit (TCM) (AmirAli Farokhniaee and Madeleine M. Lowery 2021)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + }, + { + "object_id": 266971, + "object_name": "Neocortex layer 5 pyramidal cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a model of exaggerated beta rhythm observed in the motor cortex, similar to animal and humans with Parkinson\u2019s disease. It is obtained by manually changing the specific cortical, thalamic and thalamocortical synaptic connections, motivated by the previous studies in the field. More importantly and in addition, it serves as a thalamocortical network model of deep brain stimulation, a therapy used for Parkinson\u2019s disease. We computationally stimulated the layer 5 pyramidal neurons of the cortex by direct injected currents to those pyramidal cells and observed well-known patterns in experimental studies, such as attenuation of the exaggerated beta rhythm, formation of excited and inhibited clusters of neurons in the motor cortex and the optimum value for the stimulation, both in amplitude and frequency domains, to obtain the most attenuated beta rhythm.\r\n " + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266941", + "public_submitter_email": { + "attr_id": 309, + "value": "aafarokh@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "AmirAli Farokhniaee" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + }, + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2021-03-15T15:06:15.467", + "ver_number": 11 + }, + "266948": { + "citation": { + "attr_id": 391, + "value": "27807272" + }, + "class_id": 19, + "created": "2021-03-01T17:11:29.8", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266948, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266969, + "object_name": "Heras, Francisco J. H." + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 64175, + "object_name": "Electrotonus" + }, + { + "object_id": 206291, + "object_name": "Membrane Properties" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + }, + { + "object_id": 266965, + "object_name": "Gain-bandwidth product (GBWP)" + }, + { + "object_id": 266966, + "object_name": "Phenomenological inductance" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266963, + "object_name": "Heras FJ, Laughlin SB, Niven JE (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 266970, + "object_name": "pHHotoreceptor" + } + ] + }, + "name": "Voltage-gated conductances can counteract filtering effect of membrane capacitance (Heras et al '16)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 266964, + "object_name": "Insect photoreceptor" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Phenomenological inductance generated by voltage-gated ionic conductances (Na or K) can increase the gain bandwidth product of subthreshold signaling (e.g. psps) in a neuron, reducing the attenuation and slowing caused by membrane capacitance." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266948", + "public_submitter_email": { + "attr_id": 309, + "value": "carnevalet@protonmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ted Carnevale" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 266968, + "object_name": "Honeybee (Apis mellifera)" + }, + { + "object_id": 266967, + "object_name": "Blowfly (Calliphora vicina)" + } + ] + }, + "ver_date": "2021-03-24T10:55:55.927", + "ver_number": 12 + }, + "266954": { + "class_id": 19, + "created": "2021-03-06T20:00:51.66", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266954, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 182782, + "object_name": "Sadeh, Sadra [s.sadeh at ucl.ac.uk]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267124, + "object_name": "Sadeh S, Clpath C (2021)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "E-I balance modulates formation and dynamics of neuronal assemblies (Sadeh and Clopath, 2021)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"Here we studied this question in large-scale cortical networks composed of excitatory (E) and inhibitory (I) neurons. We found that the dynamics of the network in which neuronal assemblies are embedded is important for their induction. In networks with strong E-E coupling at the border of E-I balance, increasing the number of perturbed neurons enhanced the potentiation of ensembles. This was, however, accompanied by off-target potentiation of connections from unperturbed neurons. When strong E-E connectivity was combined with dominant E-I interactions, formation of ensembles became specific. Counter-intuitively, increasing the number of perturbed neurons in this regime decreased the average potentiation of individual synapses, leading to an optimal assembly formation at intermediate sizes. This was due to potent lateral inhibition in this regime, which also slowed down the formation of neuronal assemblies, resulting in a speed-accuracy trade-off in the performance of the networks in pattern completion and behavioral discrimination. Our results therefore suggest that the two regimes might be suited for different cognitive tasks, with fast regimes enabling crude detections and slow but specific regimes favoring finer discriminations.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266954", + "public_submitter_email": { + "attr_id": 309, + "value": "s.sadeh@imperial.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Sadra Sadeh" + }, + "ver_date": "2023-02-13T10:53:49.21", + "ver_number": 8 + }, + "266961": { + "citation": { + "attr_id": 391, + "value": "30830905" + }, + "class_id": 19, + "created": "2021-03-11T12:25:38.287", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 88118, + "object_name": "I_KLT" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266961, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 128503, + "object_name": "Goldwyn, Joshua [jhgoldwyn at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 266997, + "object_name": "Two-port analysis of electrotonus" + }, + { + "object_id": 266998, + "object_name": "Voltage transfer ratio" + }, + { + "object_id": 266999, + "object_name": "Equivalent PI circuit" + }, + { + "object_id": 267000, + "object_name": "Excitability" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 266996, + "object_name": "Goldwyn JH, Remme MWH, Rinzel J (2019)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Coincidence detection in MSO principal cells (Goldwyn et al. 2019)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88110, + "object_name": "Medial Superior Olive (MSO) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "How a particular combination of anatomical and biophysical properties results in a short integration window (good for detection of closely-coincident inputs) while also enabling efficient axonal firing with brief interspike intervals (needed to faithfully report a series of coincidences between high frequency presynaptic spike trains)." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266961", + "public_submitter_email": { + "attr_id": 309, + "value": "carnevalet@protonmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ted Carnevale" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 143145, + "object_name": "Auditory brainstem" + } + ] + }, + "ver_date": "2021-05-19T09:23:52.717", + "ver_number": 19 + }, + "266974": { + "citation": { + "attr_id": 391, + "value": "PMID: 33285114\r\nDOI: 10.1016/j.bpj.2020.11.2264\r\nDiekman and Wei (2021) Circadian Rhythms of Early Afterdepolarizations and Ventricular Arrhythmias in a Cardiomyocyte Model. 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These neuroelectrical signals originate from the assumption that ROIs influence each other via of excitatory or by-synaptic inhibitory connections. Besides receiving long-range synapses from other ROIs, each one receives an external input and superimposed Gaussian white noise. We used the NMM to simulate different connectivity networks of four ROIs, by varying both the synaptic strengths and the inputs. The purpose of this study is to investigate how the transmission of brain rhythms behaves under linear and nonlinear conditions. To this aim, we investigated the performance of eight Functional Connectivity (FC) estimators (Correlation, Delayed Correlation, Coherence, Lagged Coherence, Temporal Granger Causality, Spectral Granger Causality, Phase Synchronization and Transfer Entropy) in detecting the connectivity network changes. 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Software included here allows the simulation of mean field potentials of four interconnected ROIs, their visualization, both in time and frequency domains, and the estimation of the related FC with eight different methods (for Transfer Entropy the Trentool package is needed)." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266980", + "public_submitter_email": { + "attr_id": 309, + "value": "giulia.ricci29@unibo.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Giulia Ricci" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2021-04-11T18:51:00.657", + "ver_number": 3 + }, + "266984": { + "citation": { + "attr_id": 391, + "value": "Rich S, Moradi Chameh H, Sekulic V, Valiante TA, Skinner FK. Modeling Reveals Human-Rodent Differences in H-Current Kinetics Influencing Resonance in Cortical Layer 5 Neurons. 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This led to an explanation for the surprising lack of subthreshold resonance seen in these cells in the human as opposed to rodent setting." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266984", + "public_submitter_email": { + "attr_id": 309, + "value": "sbrich@umich.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Scott Rich" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2021-03-23T19:10:36.967", + "ver_number": 5 + }, + "266989": { + "citation": { + "attr_id": 391, + "value": "33509950" + }, + "class_id": 19, + "created": "2021-03-24T16:01:26.62", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 266989, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52404, + "object_name": "Pathophysiology" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267038, + "object_name": "Bichler EK, Cavarretta F, Jaeger D (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Ventromedial Thalamocortical Neuron (Bichler et al 2021)", + "notes": { + "attr_id": 24, + "value": "\"Biophysical computer modeling of a thalamic neuron demonstrated that an increase in rebound spiking can also be accounted for by a decrease in the M-type potassium current. 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These findings support the hypothesis that homeostatic changes in BGMT neural properties following 6-OHDA treatment likely influence the signal processing taking place in the BG thalamocortical network in Parkinson's disease.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/266989", + "public_submitter_email": { + "attr_id": 309, + "value": "francesco.cavarretta@emory.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Francesco Cavarretta" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115951, + "object_name": "Thalamus" + } + ] + }, + "ver_date": "2021-05-05T09:31:20.853", + "ver_number": 5 + }, + "267009": { + "class_id": 19, + "created": "2021-03-25T14:53:02.337", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 143445, + "object_name": "I_KD" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 185501, + "object_name": "Kir2 leak" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267009, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 244383, + "object_name": "Zang, Yunliang " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 114307, + "object_name": "Conduction failure" + }, + { + "object_id": 128875, + "object_name": "Development" + }, + { + "object_id": 267000, + "object_name": "Excitability" + }, + { + "object_id": 267007, + "object_name": "Energy consumption" + }, + { + "object_id": 120695, + "object_name": "Sodium pump" + }, + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Diameter, Myelination and Na/K pump interactions affect axonal resilience to high frequency spiking", + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267009", + "public_submitter_email": { + "attr_id": 309, + "value": "zangyl1983@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Yunliang Zang" + }, + "ver_date": "2021-07-29T11:34:45.823", + "ver_number": 3 + }, + "267010": { + "class_id": 19, + "created": "2021-03-26T00:23:11.847", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267010, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 146501, + "object_name": "Nicola, Wilten [wnicola at uwaterloo.ca]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267616, + "object_name": "Al-Darabsah I, Chen L, Nicola W, Campbell SA (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Impact of Small Time Delays on the Onset of Oscillations and Synchrony (Al-Darabsah et al 2021)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 245940, + "object_name": "Abstract rate-based neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Code to compute Lyapunov Exponents with Delay in the Wilson-Cowan system: \r\n\r\nAl-Darabsah, I., Chen, L., Nicola, W., & Campbell, S. A. (2021). The Impact of Small Time Delays on the Onset of Oscillations and Synchrony in Brain Networks. Frontiers in Systems Neuroscience, 58." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267010", + "other_neurons": { + "attr_id": 301, + "value": "Wilson-Cowan" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "wilten.nicola@ucalgary.ca" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Wilten Nicola" + }, + "ver_date": "2023-01-20T12:45:48.78", + "ver_number": 6 + }, + "267013": { + "citation": { + "attr_id": 391, + "value": "Jones S, Zylberberg J, Schoppa N. Cellular and Synaptic Mechanisms That Differentiate Mitral Cells and Superficial Tufted Cells Into Parallel Output Channels in the Olfactory Bulb. Front Cell Neurosci. 2020;14:614377. 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By exchanging the different cell properties in our modeled MCs and sTCs, we could evaluate each property's contribution to spiking differences between these cell types. This analysis suggested that the higher sensitivity of spiking in sTCs vs. MCs reflected both their larger monosynaptic OSN signal as well as their higher input resistance, while their smaller prolonged currents had a modest opposing effect. Taken together, our results indicate that both synaptic and intrinsic cellular features contribute to the production of parallel output channels in the olfactory bulb.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267013", + "public_submitter_email": { + "attr_id": 309, + "value": "shellytessajones@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Shelly Jones" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115950, + "object_name": "Olfactory bulb" + } + ] + }, + "ver_date": "2021-04-03T14:16:53.33", + "ver_number": 7 + }, + "267018": { + "class_id": 19, + "created": "2021-04-08T14:42:26.313", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267018, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267374, + "object_name": "McDougal RA, Conte C, Eggleston L, Newton AJH, Galijasevic H (2022)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Efficient simulation of 3D reaction-diffusion in models of neurons (McDougal et al, 2022)", + "notes": { + "attr_id": 24, + "value": "Validation, visualization, and analysis scripts for NEURON's 3D reaction-diffusion support." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267018", + "public_submitter_email": { + "attr_id": 309, + "value": "robert.mcdougal@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Robert McDougal" + }, + "ver_date": "2022-05-17T14:07:01.31", + "ver_number": 11 + }, + "267023": { + "class_id": 19, + "created": "2021-04-13T05:11:07.297", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267023, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267065, + "object_name": "Ness N, Schultz SR (2021)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Grid cell-to-place cell transformation model w AD-related synaptic loss (Ness and Schultz 2021)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Simulation of grid-cell-to-place cell transformation with interneuron-mediated feedback inhibition, BCM learning and synaptic turnover. Generates place cell activity with stable place cell density over time. Excitatory synapse and/or inhibitory synapse loss can be implemented to analyse the effect of synaptic loss on place cell function." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267023", + "other_neurons": { + "attr_id": 301, + "value": "Medial entorhinal cortex grid cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "n.ness19@imperial.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Natalie Ness" + }, + "ver_date": "2021-06-21T11:21:37.207", + "ver_number": 4 + }, + "267026": { + "citation": { + "attr_id": 391, + "value": "Stinchcombe AR, Hu C, Walch OJ, Faught SD, Wong KY and Forger DB\r\n(2021) M1-Type, but Not M4-Type, Melanopsin Ganglion Cells Are Physiologically Tuned to the Central Circadian Clock. Front. Neurosci. 15:652996. doi: 10.3389/fnins.2021.652996" + }, + "class_id": 19, + "created": "2021-04-22T09:54:35.337", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267026, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 145886, + "object_name": "Circadian Rhythms" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "M1 and M4 intrinsically photosensitive retinal ganglion cells (Stinchcombe et al. 2021)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 270, + "object_name": "Retina ganglion GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Conductance-based models of the somatic membrane voltage in M1 and M4 intrinsically photosensitive retinal ganglion cells." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267026", + "public_submitter_email": { + "attr_id": 309, + "value": "stinch@math.toronto.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Adam Stinchcombe" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 118529, + "object_name": "Retina" + } + ] + }, + "ver_date": "2021-05-06T10:15:31.92", + "ver_number": 4 + }, + "267027": { + "citation": { + "attr_id": 391, + "value": "PMID: 32069073" + }, + "class_id": 19, + "created": "2021-04-22T15:39:29.323", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267027, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267032, + "object_name": "Law NC, Marinelli I, Bertram R, Corbin KL, Schildmeyer C, Nunemaker CS (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Model for pancreatic beta-cells (Law et al. 2020)", + "notes": { + "attr_id": 24, + "value": "This model is used to explain how chronic stimulation of pancreatic beta-cells can lead to compensation that restores calcium oscillations and electrical bursting." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267027", + "public_submitter_email": { + "attr_id": 309, + "value": "rbertram@fsu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Richard Bertram" + }, + "ver_date": "2021-04-25T16:35:42.85", + "ver_number": 7 + }, + "267033": { + "citation": { + "attr_id": 391, + "value": "Interneuronal dynamics facilitate the initiation of cortical spreading depression, \r\nWolfgang Stein, Allison L Harris, bioRxiv 2021.04.25.441350; doi: https://doi.org/10.1101/2021.04.25.441350" + }, + "class_id": 19, + "created": "2021-04-27T15:39:58.22", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267033, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 167716, + "object_name": "Spreading depression" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267371, + "object_name": "Stein W, Harris AL (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 45540, + "object_name": "FORTRAN" + } + ] + }, + "name": "Cortical Interneuron & Pyramidal Cell Model of Cortical Spreading Depression (Stein & Harris 2022)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This 2-cell cortical circuit model consists of a negative feedback loop between a single compartment pyramidal cell and a single compartment interneuron. Ion concentrations in the extra- and intracellular spaces are included in the model. The model is used to test the contribution of cortical inhibitory interneurons to the initiation of cortical spreading depression, as characterized by spike block in the pyramidal cell. Results show that interneuronal inhibition provides a wider dynamic range to the circuit and generally improves stability against spike block. Despite these beneficial effects, strong interneuronal firing contributed to rapidly changing extracellular ion concentrations, which facilitated hyperexcitation and led to spike block first in the interneuron and then in the pyramidal cell. The model results demonstrate that while the role of interneurons in cortical microcircuits is complex, they are critical to the initiation of pyramidal cell spike block and CSD. See reference below for more details." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267033", + "other_implementer": { + "attr_id": 308, + "value": "Allison L Harris" + }, + "other_neurons": { + "attr_id": 301, + "value": "Cortical Pyramidal Neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "alharri@ilstu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Allison L Harris" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2022-05-08T12:23:18.44", + "ver_number": 6 + }, + "267035": { + "class_id": 19, + "created": "2021-04-29T15:23:50.473", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267035, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267036, + "object_name": "Sinha, Ankur" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52404, + "object_name": "Pathophysiology" + }, + { + "object_id": 267053, + "object_name": "Structural plasticity" + }, + { + "object_id": 267051, + "object_name": "Neurite growth" + }, + { + "object_id": 267052, + "object_name": "Neurite loss" + }, + { + "object_id": 267054, + "object_name": "Homeostatic plasticity" + }, + { + "object_id": 267055, + "object_name": "Balanced networks" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267045, + "object_name": "Sinha A, Metzner C, Davey N, Adams R, Schumker M, Steuber V (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 2832, + "object_name": "NEST" + } + ] + }, + "name": "Growth Rules for Repair of Asynch Irregular Networks after Peripheral Lesions (Sinha et al 2021)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 240378, + "object_name": "Abstract integrate-and-fire neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A model of peripheral lesions and the resulting activity-dependent rewiring in a simplified balanced cortical network model that\r\nexhibits biologically realistic Asynchronous Irregular (AI) activity, used to derive activity dependent growth rules\r\nfor different synaptic elements: dendritic and axonal." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267035", + "public_submitter_email": { + "attr_id": 309, + "value": "a.sinha2@herts.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ankur Sinha" + }, + "ver_date": "2021-06-02T10:14:37.88", + "ver_number": 10 + }, + "267039": { + "citation": { + "attr_id": 391, + "value": "Susin E, Destexhe A. 2021. Integration, coincidence detection and resonance in networks of spiking neurons expressing gamma oscillations and asynchronous states. bioRxiv doi: 10.1101/2021.05.03.442436" + }, + "class_id": 19, + "created": "2021-05-05T08:11:19.213", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267039, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267042, + "object_name": "Susin, Eduarda [eduardadsusin at gmail dot com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 156115, + "object_name": "Gamma oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267041, + "object_name": "Susin E, Destexhe A (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 231240, + "object_name": "Brian 2" + } + ] + }, + "name": "PING, ING and CHING network models for Gamma oscillations in cortex (Susin and Destexhe 2021)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "These models were published at:\r\n\r\nSusin E, Destexhe A. 2021. Integration, coincidence detection and resonance in networks of spiking neurons expressing gamma oscillations and asynchronous states. bioRxiv doi: 10.1101/2021.05.03.442436 \r\n\r\n\r\nIn this article, we constructed conductance-based network models of gamma oscillations, based on different cell types found in cerebral cortex: Regular Spiking (RS), Fast Spiking (FS) and Chattering cells. The models were adjusted to extracellular unit recordings in humans, where gamma oscillations always coexist with the asynchronous firing mode. We considered three different mechanisms to generate Gamma, first a mechanism based on the interaction between pyramidal neurons and interneurons (PING), second a mechanism in which gamma is generated in interneuron networks (ING) and third, a mechanism which relies on gamma oscillations generated by pacemaker Chattering neurons (CHING). We found that in all cases, the presence of Gamma oscillations tends to diminish the responsiveness of the networks to external inputs. We tested different paradigms and found none in which Gamma oscillations would favor information flow compared to asynchronous states." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267039", + "public_submitter_email": { + "attr_id": 309, + "value": "eduardadsusin@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Eduarda Susin" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2023-02-13T11:11:44.383", + "ver_number": 8 + }, + "267046": { + "citation": { + "attr_id": 391, + "value": "Calvin, O.L. & Redish, A.D. (2021). Global disruption in excitation-inhibition balance can cause localized network dysfunction and schizophrenia-like context-integration deficits. PLoS Computational Biology. TBD" + }, + "class_id": 19, + "created": "2021-05-05T12:34:31.94", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 139268, + "object_name": "I Na, leak" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267046, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 82787, + "object_name": "Working memory" + }, + { + "object_id": 83539, + "object_name": "Attractor Neural Network" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267641, + "object_name": "Calvin OL, Redish AD (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 266899, + "object_name": "Agent-based model" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Leaky Integrate and Fire Neuron Model of Context Integration (Calvin, Redish 2021)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The maintenance of the contextual information has been shown to be sensitive to changes in excitation-inhbition (EI) balance. We constructed a multi-structure, biophysically-realistic agent that could perform context-integration as is assessed by the dot probe expectancy task. The agent included a perceptual network, a working memory network, and a decision making system and was capable of successfully performing the dot probe expectancy task. Systemic manipulation of the agent\u2019s EI balance produced localized dysfunction of the memory structure, which resulted in schizophrenia-like deficits at context integration." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267046", + "other_concept": { + "attr_id": 306, + "value": "Context Integration" + }, + "other_implementer": { + "attr_id": 308, + "value": "Calvin, Olivia [ocalvin@umn.edu]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ocalvin@umn.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Olivia Louise Calvin" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "ver_date": "2023-02-13T10:54:20.823", + "ver_number": 13 + }, + "267047": { + "class_id": 19, + "created": "2021-05-12T06:21:52.18", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 146505, + "object_name": "KCC2" + }, + { + "object_id": 146506, + "object_name": "NKCC1" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92082, + "object_name": "Nav1.1 SCN1A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267047, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 235462, + "object_name": "Spreading depolarization" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267105, + "object_name": "Lemaire L, Desroches M, Krupa M, Pizzamiglio L, Scalmani P, Mantegazza M (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Two-neuron conductance-based model with dynamic ion concentrations to study NaV1.1 channel mutations", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Gain of function mutations of SCN1A, the gene coding for the voltage-gated sodium channel NaV1.1, cause familial hemiplegic migraine type 3 (FHM-3), whereas loss of function mutations cause different types of epilepsy. \r\n\r\nTo study those mutations, we developed a two-neuron conductance-based model of interconnected GABAergic and pyramidal glutamatergic neurons, with dynamic ion concentrations. We modeled FHM-3 mutations with persistent sodium current in the GABAergic neuron and epileptogenic mutations by decreasing the fast-inactivating sodium conductance in the GABAergic neuron." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267047", + "public_submitter_email": { + "attr_id": 309, + "value": "louisiane.lemaire@inria.fr" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Louisiane Lemaire" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2021-08-17T09:35:59.813", + "ver_number": 42 + }, + "267048": { + "citation": { + "attr_id": 391, + "value": "Prediction of Neural Diameter From Morphology to Enable Accurate Simulation, J.D. Reed and K.T. Blackwell, Frontiers in Neuroinformatics, In Press" + }, + "class_id": 19, + "created": "2021-05-20T10:37:12.137", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267048, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53429, + "object_name": "Blackwell, Avrama [avrama at gmu.edu]" + }, + { + "object_id": 267077, + "object_name": "Reed, Jonathon" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267079, + "object_name": "Reed JD, Blackwell KT (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 186718, + "object_name": "MOOSE/PyMOOSE" + } + ] + }, + "name": "Reconstructed neuron (cerebellar, hippocampal, striatal) sims using predicted diameters (Reed et al)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + }, + { + "object_id": 266781, + "object_name": "Striatal projection neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Many neuron morphologies in NeuroMorpho.org do not contain accurate dendritic diameters that are needed for simulations. We used a set of archives which did have realistic morphologies to derive equations predicting dendritic diameter, and to create morphologies using the predictions.\r\nThe equations and new morphologies are derived by\r\n1. extracting morphology features from swc files (morph_feature_extract.py)\r\n2. using multiple regression to derive equations predicting diameter, (morph_feature_extract.py )\r\n3. using the equations to create the new morphology files from original swc file (shape_shifter.py).\r\nThe python programs are all available from github.com/neurord/ShapeShifter\r\nWe simulated the original morphologies and the morphologies with predicted diameter in Moose, evaluating the response to current injection and synaptic input. The code provided implements those simulations" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267048", + "other_type": { + "attr_id": 305, + "value": "reconstructed dendrites" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "kblackw1@gmu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Kim T Blackwell" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2021-07-07T17:39:41.633", + "ver_number": 4 + }, + "267049": { + "citation": { + "attr_id": 391, + "value": "PMID: 33815062" + }, + "class_id": 19, + "created": "2021-05-21T16:46:34.943", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 254, + "object_name": "I CNG" + }, + { + "object_id": 243504, + "object_name": "IK Bkca" + }, + { + "object_id": 243506, + "object_name": "IK Skca" + }, + { + "object_id": 143445, + "object_name": "I_KD" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267049, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53429, + "object_name": "Blackwell, Avrama [avrama at gmu.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267076, + "object_name": "Gorodetski L, Loewenstern Y, Faynveitz A, Bar-Gad I, Blackwell KT, Korngreen A (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 186718, + "object_name": "MOOSE/PyMOOSE" + } + ] + }, + "name": "Information trans. through Entopeduncular nucleus modified by synaptic plasticity (Gorodetsky et al)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88217, + "object_name": "Globus pallidus neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Multicompartmental model of EP neuron was created using automatic parameter optimization. We included both short term plasticity and long term plasticity. We simulated the response to inputs from globus pallidus, striatum and subthalamic nucleus. We show that dopamine long term plasticity enhances information transmission from striatum and reduces GPe and STN information transmission." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267049", + "other_neurons": { + "attr_id": 301, + "value": "entopeduncular neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "kblackw1@gmu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Kim T Blackwell" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 212, + "object_name": "Glutamate" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2021-07-07T15:56:11.503", + "ver_number": 4 + }, + "267050": { + "class_id": 19, + "created": "2021-05-28T19:38:22.463", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267050, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 247215, + "object_name": "Newton, Adam J H [adam.newton at yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 83535, + "object_name": "Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267362, + "object_name": "Huertas MA, Newton AJH, McDougal RA, Sacktor TC, Shouval HZ (2022)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Conditions for synaptic specificity in maintenance phase of synaptic plasticity (Huertas et al, '22)", + "notes": { + "attr_id": 24, + "value": "Long-lasting effects on synaptic efficacies are associated with the sustained increase in concentration of specific proteins like PKM?. Assuming that the long-term maintenance of synaptic plasticity is accomplished by a molecular switch we perform simulations using the reaction-diffusion package in NEURON and analytical calculations to determine the limits of synapse specificity during maintenance." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267050", + "public_submitter_email": { + "attr_id": 309, + "value": "adam.newton@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Adam John Hunter Newton" + }, + "ver_date": "2022-04-25T11:45:47.11", + "ver_number": 8 + }, + "267056": { + "class_id": 19, + "created": "2021-06-03T13:45:29.2", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267056, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 263070, + "object_name": "Medlock, Laura [laura.medlock at mail.utoronto.ca]" + }, + { + "object_id": 267057, + "object_name": "Sekiguchi, Kazutaka [kazutaka.sekiguchi at shionogi.co.jp]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 267059, + "object_name": "Pain processing" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267317, + "object_name": "Medlock L, Sekiguchi K, Hong S, Dura-Bernal S, Lytton WW, Prescott SA (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 195884, + "object_name": "NetPyNE" + }, + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Spinal Dorsal Horn Network Model (Medlock et al 2022)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 128582, + "object_name": "Spinal cord lamina I neuron" + }, + { + "object_id": 267058, + "object_name": "Spinal cord lamina I-III interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "To explore spinal dorsal horn (SDH) network function, we developed a computational model of the circuit that is tightly constrained by experimental data. Our model comprises conductance-based model neurons that reproduce the characteristic firing patterns of excitatory and inhibitory spinal neurons. Excitatory spinal neuron subtypes defined by calretinin, somatostatin, delta-opioid receptor, protein kinase C gamma, or vesicular glutamate transporter 3 expression or by transient/central spiking/morphology and inhibitory neuron subtypes defined by parvalbumin or dynorphin expression or by islet morphology were synaptically connected according to available qualitative data. Synaptic weights were adjusted to produce firing in projection neurons, defined by neurokinin-1 expression, matching experimentally measured responses to a range of mechanical stimulus intensities. Input to the circuit was provided by three types of afferents (A\u00df, Ad, and C-fibres) whose firing rates were also matched to experimental data." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267056", + "public_submitter_email": { + "attr_id": 309, + "value": "laura.medlock@mail.utoronto.ca" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Laura Medlock" + }, + "ver_date": "2022-03-10T12:47:50.48", + "ver_number": 15 + }, + "267063": { + "citation": { + "attr_id": 391, + "value": "Viktor J\u00e1nos Ol\u00e1h, Gergely Tarcsay and J\u00e1nos Brunner (2021) Small size of recorded neuronal structures confines the accuracy in direct axonal voltage measurements" + }, + "class_id": 19, + "created": "2021-06-17T20:05:20.583", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267063, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267084, + "object_name": "Ol\u00e1h, Viktor J\u00e1nos" + }, + { + "object_id": 267085, + "object_name": "Tarcsay, Gergely" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267083, + "object_name": "Ol\u00e1h VJ, Tarcsay G, Brunner J (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Realistic amplifier model (Ol\u00e1h et al. 2021)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... we built a model that was verified by small axonal recordings. The model accurately recreated actual action potential measurements with typical recording artefacts and predicted the native electrical behavior. The simulations verified that recording instruments substantially filter voltage recordings. Moreover, we revealed that instrumentation directly interferes with local signal generation depending on the size of the recorded structures, which complicates the interpretation of recordings from smaller structures, such as axons. However, our model offers a straightforward approach that predicts the native waveforms of fast voltage signals and the underlying conductances even from the smallest neuronal structures...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267063", + "other_concept": { + "attr_id": 306, + "value": "realistic amplifier" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "brunner.janos@koki.hu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Janos Brunner" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2021-07-15T08:27:48.39", + "ver_number": 6 + }, + "267066": { + "class_id": 19, + "created": "2021-06-23T05:12:53.95", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267066, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266547, + "object_name": "Lupascu, Carmen Alina " + }, + { + "object_id": 253930, + "object_name": "Giacalone, Elisabetta [elisabetta.giacalone at pa.ibf.cnr.it]" + }, + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 112047, + "object_name": "Aging/Alzheimer`s" + }, + { + "object_id": 121254, + "object_name": "Depolarization block" + }, + { + "object_id": 267000, + "object_name": "Excitability" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267266, + "object_name": "Spoleti E, Krashia P, La Barbera L, Nobili A, Lupascu CA, Giacalone E, Keller F, Migliore M, Renzi M, D'Amelio M (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A model of ventral Hippocampal CA1 pyramidal neurons of Tg2576 AD mice (Spoleti et al. 2021)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Gradual decline in cognitive and non-cognitive functions are considered clinical hallmarks of Alzheimer's Disease (AD). Post-mortem autoptic analysis shows the presence of amyloid \u00df deposits, neuroinflammation and severe brain atrophy. However, brain circuit alterations and cellular derailments, assessed in very early stages of AD, still remain elusive. The understanding of these early alterations is crucial to tackle defective mechanisms.\r\n\r\nIn a previous study we proved that the Tg2576 mouse model of AD displays functional deficits in the dorsal hippocampus and relevant behavioural AD-related alterations. We had shown that these deficits in Tg2576 mice correlate with the precocious degeneration of dopamine (DA) neurons in the Ventral Tegmental Area (VTA) and can be restored by L-DOPA treatment. Due to the distinct functionality and connectivity of dorsal versus ventral hippocampus, here we investigated neuronal excitability and synaptic functionality in the ventral CA1 hippocampal sub-region of Tg2576 mice. We found an age-dependent alteration of cell excitability and firing in pyramidal neurons starting at 3 months of age, that correlates with reduced levels in the ventral CA1 of tyrosine hydroxylase \u2013 the rate-limiting enzyme of DA synthesis. Additionally, at odds with the dorsal hippocampus, we found no alterations in basal glutamatergic transmission and long-term plasticity of ventral neurons in 8-month old Tg2576 mice compared to age-matched controls. Last, we used computational analysis to model the early derailments of firing properties observed and hypothesize that the neuronal alterations found could depend on dysfunctional sodium and potassium conductances, leading to anticipated depolarization-block of action potential firing. The present study depicts that impairment of cell excitability and homeostatic control of firing in ventral CA1 pyramidal neurons is a prodromal feature in Tg2576 AD mice.\r\n" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267066", + "other_neurons": { + "attr_id": 301, + "value": "Hippocampus CA1 ventral pyramidal neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "carmen.lupascu@ibf.cnr.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Carmen Alina Lupascu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2022-01-21T13:10:50.977", + "ver_number": 23 + }, + "267067": { + "class_id": 19, + "created": "2021-06-26T13:35:33.483", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92083, + "object_name": "Nav1.2 SCN2A" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267067, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 224842, + "object_name": "Ben-Shalom, Roy [rbenshalom at ucdavis.edu]" + }, + { + "object_id": 267070, + "object_name": "Kyoung, Henry [hkyoung at berkeley.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267628, + "object_name": "Spratt PWE, Alexander RPD, Ben-Shalom R, Sahagun A, Kyoung H, Keeshen CM, Sanders SJ, Bender KJ (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Hyperexcitability from Nav1.2 channel loss in neocortical pyramidal cells (Spratt et al 2021)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 266971, + "object_name": "Neocortex layer 5 pyramidal cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Based on the Layer 5 thick-tufted pyramidal cell from the Blue Brain Project, we modify the distribution of the sodium channel Nav1.2 to recapitulate an increase in excitability observed in ex vivo slice experiments." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267067", + "public_submitter_email": { + "attr_id": 309, + "value": "bens.roy@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Roy Ben-Shalom" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 147188, + "object_name": "Prefrontal cortex (PFC)" + } + ] + }, + "ver_date": "2023-04-30T16:03:58.983", + "ver_number": 12 + }, + "267073": { + "citation": { + "attr_id": 391, + "value": "bioRxiv 2020.11.04.368571; doi: https://doi.org/10.1101/2020.11.04.368571" + }, + "class_id": 19, + "created": "2021-07-02T08:31:59.89", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267073, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 53429, + "object_name": "Blackwell, Avrama [avrama at gmu.edu]" + }, + { + "object_id": 189720, + "object_name": "Jedrzejewska-Szmek, Joanna " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 238958, + "object_name": "Stochastic simulation" + }, + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267095, + "object_name": "Miningou NT, Jedrzejewska-Szmek J, Blackwell KT (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 139757, + "object_name": "NeuroRD" + } + ] + }, + "name": "Dynamics of ERK signaling pathways during L-LTP induction(Miningou et al 2021)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Biochemical model of five signaling pathways (3 activated by cAMP and 2 activated by calcium) leading to ERK activation during L-LTP induction. Simulations show that calcium and cAMP work synergistically to activate ERK and that stimuli given with large inter-trial intervals activate more ERK than shorter intervals. Epac and RasGRF pathways contribute to early dynamics and PKA and CaMKII contribute to late dynamics of ERK activation." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267073", + "other_implementer": { + "attr_id": 308, + "value": "Miningou, Nadiatou" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "kblackw1@gmu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Kim T Blackwell" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2023-02-13T10:50:27.793", + "ver_number": 3 + }, + "267086": { + "class_id": 19, + "created": "2021-07-21T13:56:52.037", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267086, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267291, + "object_name": "Zhang Y, Smolen PD, Cleary LJ, Byrne JH (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 145174, + "object_name": "Molecular Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Interactions among kinase cascades underlying LTP in Aplysia sensory neurons (Zhang et al 2021)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52413, + "object_name": "Aplysia sensory neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 239, + "object_name": "Serotonin" + }, + { + "object_id": 267292, + "object_name": "Neurotrophin" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Computational model incorporating positive and negative feedback loops of proteins in Aplysia to study \"the dynamics of kinase activity produced by different stimulus protocols and predict the critical roles of kinase interactions in the dynamics of these pathways.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267086", + "public_submitter_email": { + "attr_id": 309, + "value": "yili.zhang@uth.tmc.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Yili Zhang" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115956, + "object_name": "Aplysia" + } + ] + }, + "ver_date": "2022-02-11T11:24:11.133", + "ver_number": 7 + }, + "267093": { + "class_id": 19, + "created": "2021-08-09T17:52:14.123", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 222730, + "object_name": "I Na, slow inactivation" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267093, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267148, + "object_name": "Abdulla, Muhammad [muhammadabdulla at ufl.edu]" + }, + { + "object_id": 267149, + "object_name": "Phillips, Ryan [ryanp at pitt.edu]" + }, + { + "object_id": 125612, + "object_name": "Rubin, Jonathan E [jonrubin at pitt.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 267152, + "object_name": "Dynamic extracellular concentrations" + }, + { + "object_id": 267153, + "object_name": "Ramping" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267151, + "object_name": "Abdulla M, Phillips R, Rubin J (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Dynamics of ramping bursts in a respiratory pre-Botzinger Complex model (Abdulla et al, 2021)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 232103, + "object_name": "PreBotzinger complex neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This single-neuron model is, to the authors' knowledge, the first to capture the pre-inspiratory ramping aspects of preBotzinger Complex inspiratory neurons' activity patterns, in which relatively slow tonic spiking gradually progresses to faster spiking and a full-blown burst, with a corresponding gradual development of an underlying plateau potential. The key to this pattern is the incorporation of the dynamics of the extracellular potassium ion concentration, which is here integrated into an existing model for pre-BotC neuron bursting along with some parameter adjustments. Using fast-slow decomposition, this activity can be shown to be a form of parabolic bursting, but with burst termination at a homoclinic bifurcation rather than at a SNIC bifurcation." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267093", + "other_currents": { + "attr_id": 303, + "value": "dynamic extracellular potassium" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jonrubin@pitt.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Jonathan Rubin" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 183076, + "object_name": "Brainstem" + } + ] + }, + "ver_date": "2023-02-13T10:51:02.233", + "ver_number": 7 + }, + "267103": { + "class_id": 19, + "created": "2021-08-11T19:04:28.533", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267103, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 225299, + "object_name": "Multiscale" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267155, + "object_name": "Pham DJ, Yu GJ, Bouteiller JC, Berger TW (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Look-Up Table Synapse (LUTsyn) models for AMPA and NMDA (Pham et al., 2021)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Fast input-output synapse model of glutamatergic receptors AMPA and NMDA that can capture nonlinear interactions via look-up table abstraction. Speeds are comparable to 'linear' exponential synapses.\r\n\r\nDownload LUT files at: https://senselab.med.yale.edu/modeldb/data/267103/LUTs.zip" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267103", + "public_submitter_email": { + "attr_id": 309, + "value": "robert.mcdougal@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Duy-Tan Pham" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115947, + "object_name": "Dentate gyrus" + } + ] + }, + "ver_date": "2023-02-06T10:53:23.23", + "ver_number": 17 + }, + "267106": { + "citation": { + "attr_id": 391, + "value": "Meredith Lodge, Maria-Clemencia Hernandez, Jan M. Schulz, and Josef Bischofberger, Sparsification of AP firing in adult-born hippocampal granule cells via voltage-dependent a5-GABAA receptors\r\nCELL REPORTS 2021" + }, + "class_id": 19, + "created": "2021-08-23T20:44:26.557", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 112841, + "object_name": "I Krp" + }, + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267106, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 258947, + "object_name": "Schulz, Jan M [j.schulz at unibas.ch]" + }, + { + "object_id": 267107, + "object_name": "Bischofberger, Josef" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 128875, + "object_name": "Development" + }, + { + "object_id": 155599, + "object_name": "Neurogenesis" + }, + { + "object_id": 185463, + "object_name": "Pattern Separation" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267110, + "object_name": "Lodge M, Hernandez MC, Schulz JM, Bischofberger J (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "DG adult-born granule cell: nonlinear a5-GABAARs control AP firing (Lodge et al, 2021)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "GABA can depolarize immature neurons close to the action potential (AP) threshold in development and adult neurogenesis. Nevertheless, GABAergic synapses effectively inhibit AP firing in newborn granule cells of the adult hippocampus as early as 2 weeks post mitosis. Parvalbumin and dendrite-targeting somatostatin interneurons activate a5-subunit containing GABAA receptors (a5-GABAARs) in young neurons, which show a voltage dependent conductance profile with increasing conductance around the AP threshold. The present computational models show that the depolarized GABA reversal potential promotes NMDA receptor activation. However, the voltage-dependent conductance of a5-GABAARs in young neurons is crucial for inhibition of AP firing to generate balanced and sparse firing activity. " + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267106", + "public_submitter_email": { + "attr_id": 309, + "value": "j.schulz@unibas.ch" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Jan Michael Schulz" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115947, + "object_name": "Dentate gyrus" + } + ] + }, + "ver_date": "2023-02-13T10:10:32.403", + "ver_number": 7 + }, + "267116": { + "citation": { + "attr_id": 391, + "value": "S\u00e6tra MJ, Einevoll GT, Halnes G (2021) An electrodiffusive neuron-extracellular-glia model for exploring the genesis of slow potentials in the brain. PLoS Comput Biol 17(7): e1008143. https://doi.org/10.1371/journal.pcbi.1008143" + }, + "class_id": 19, + "created": "2021-08-26T15:01:41.64", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 146505, + "object_name": "KCC2" + }, + { + "object_id": 146506, + "object_name": "NKCC1" + }, + { + "object_id": 88209, + "object_name": "Na/Ca exchanger" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 167500, + "object_name": "Kir" + }, + { + "object_id": 144382, + "object_name": "Osmosis-driven water flux" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267116, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + }, + { + "object_id": 84598, + "object_name": "Extracellular Fields" + }, + { + "object_id": 267119, + "object_name": "Electrodiffusion" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267118, + "object_name": "S\u00e6tra MJ, Einevoll GT, Halnes G (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 120799, + "object_name": "Glia" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "name": "The electrodiffusive neuron-extracellular-glia (edNEG) model (S\u00e6tra et al. 2021)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 189094, + "object_name": "Pinsky-Rinzel CA1/3 pyramidal cell " + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... We here present the electrodiffusive neuron-extracellular-glia (edNEG) model, which we believe is the first model to combine compartmental neuron modeling with an electrodiffusive framework for intra- and extracellular ion concentration dynamics in a local piece of neuro-glial brain tissue. The edNEG model (i) keeps track of all intraneuronal, intraglial, and extracellular ion concentrations and electrical potentials, (ii) accounts for action potentials and dendritic calcium spikes in neurons, (iii) contains a neuronal and glial homeostatic machinery that gives physiologically realistic ion concentration dynamics, (iv) accounts for electrodiffusive transmembrane, intracellular, and extracellular ionic movements, and (v) accounts for glial and neuronal swelling caused by osmotic transmembrane pressure gradients. The edNEG model accounts for the concentration-dependent effects on ECS potentials that the standard models neglect. Using the edNEG model, we analyze these effects by splitting the extracellular potential into three components: one due to neural sink/source configurations, one due to glial sink/source configurations, and one due to extracellular diffusive currents ...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267116", + "public_submitter_email": { + "attr_id": 309, + "value": "martejulie@simula.no" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Marte J. S\u00e6tra" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2021-08-31T21:36:12.127", + "ver_number": 5 + }, + "267120": { + "citation": { + "attr_id": 391, + "value": "D Voina, S Recanatesi, B Hu, E Shea-Brown, S Mihalas, \u201cSingle circuit in V1 capable of switching contexts during movement using inhibitory population as a switch\u201d\r\n\r\nhttps://www.biorxiv.org/content/10.1101/2020.09.24.309500v1" + }, + "class_id": 19, + "created": "2021-08-31T01:23:20.68", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267120, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267121, + "object_name": "Voina, Doris" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 266853, + "object_name": "Receptive field" + }, + { + "object_id": 114660, + "object_name": "Connectivity matrix" + }, + { + "object_id": 116907, + "object_name": "Direction Selectivity" + }, + { + "object_id": 147931, + "object_name": "Orientation selectivity" + }, + { + "object_id": 230581, + "object_name": "Stimulus selectivity" + }, + { + "object_id": 267122, + "object_name": "Context integration" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267650, + "object_name": "Voina D, Recanatesi S, Hu B, Shea-Brown E, Mihalas S (2022)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Switching circuit for optimal context integration during static + moving contexts (Voina et al 2022)", + "notes": { + "attr_id": 24, + "value": "The brain processes information at all times and much of that information is context-dependent.The visual system presents an important example: processing is ongoing, but the context changes dramatically when an animal is still vs. running. How is context-dependent information processing achieved? We take inspiration from recent neurophysiology studies on the role of distinct cell types in primary visual cortex (V1). We find that relatively few \u201cswitching units\u201d \u2014 akin to the VIP neuron type in V1 in that they turn on and off in the running vs. still context and have connections to and from the main population \u2014 are sufficient to drive context dependent image processing. We demonstrate this in a model of feature integration and in a test of image denoising. The underlying circuit architecture illustrates a concrete computational role for the multiple cell types under increasing study across the brain, and may inspire more flexible neurally inspired computing architectures." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267120", + "other_neurons": { + "attr_id": 301, + "value": "VIP, PYR, SSM, SST, PV" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "dorisvoina@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Doris Voina" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 266986, + "object_name": "Visual cortex" + } + ] + }, + "ver_date": "2023-02-15T14:40:07.76", + "ver_number": 5 + }, + "267128": { + "class_id": 19, + "created": "2021-09-06T05:56:52.147", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267128, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267171, + "object_name": "Medalla M, Chang W, Iba\u00f1ez S, Guillamon-Vivancos T, Nittmann M, Kapitonava A, Busch SE, Moore TL, Rosene DL, Luebke JI (2021)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Layer-specific pyramidal cell props underlie diverse ACC motor + limbic networks (Medalla et al '21)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + }, + { + "object_id": 146805, + "object_name": "Abstract Wang-Buzsaki neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"A MATLAB-based model of pyramidal-interneuron network was extended from [modeldb.yale.edu/138421]... to simulate how intrinsic biophysical properties and inhibition can affect network synchrony and oscillatory frequencies in ACC L3 and L5... Four different networks were simulated depending on a subset of empirically derived biophysical, morphological and connectional properties of lamina- and target- specific ACC pyramidal neurons... our model is constrained by in vitro whole cell patch clamp recording data from the soma.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267128", + "public_submitter_email": { + "attr_id": 309, + "value": "sara.i.solas@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Sara Ibanez" + }, + "ver_date": "2021-10-20T10:29:30.533", + "ver_number": 7 + }, + "267132": { + "citation": { + "attr_id": 391, + "value": "Resurgent Na+ currents promote ultrafast spiking in projection neurons that drive fine motor control" + }, + "class_id": 19, + "created": "2021-09-13T14:05:15.937", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267132, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267133, + "object_name": "Dagostin, Andre [dagostia at ohsu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 186770, + "object_name": "Motor control" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267645, + "object_name": "Zemel BM, Nevue AA, Dagostin A, Lovell PV, Mello CV, Gersdorff HV (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 83509, + "object_name": "IGOR Pro" + } + ] + }, + "name": "Resurgent sodium transient current in zebra finch RA (Zemel et al., 2021)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 267134, + "object_name": "Zebra Finch RA projection neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "RA projection neurons in zebra finches display different levels of resurgent INa through development. This work reports that projection neurons in the adult zebra finch song nucleus RA display: 1) robust high-frequency firing, 2) ultra-short half-width spike waveforms, 3) superfast Na+ current inactivation kinetics and 4) large resurgent Na+ currents (INaR). Dynamic clamping provides evidence of INaR role in neuronal excitability. The model is composed by one gate with an activating and one inactivating particle which describe a transient inward current triggered by neuronal depolarization." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267132", + "public_submitter_email": { + "attr_id": 309, + "value": "dagostia@ohsu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Andre Dagostin" + }, + "ver_date": "2023-02-13T11:12:27.4", + "ver_number": 11 + }, + "267139": { + "citation": { + "attr_id": 391, + "value": "S\u00e6tra MJ, Einevoll GT, Halnes G (2020) An electrodiffusive, ion conserving Pinsky-Rinzel model with homeostatic mechanisms. PLoS Comput Biol 16(4): e1007661" + }, + "class_id": 19, + "created": "2021-09-17T11:30:23.003", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 146505, + "object_name": "KCC2" + }, + { + "object_id": 146506, + "object_name": "NKCC1" + }, + { + "object_id": 88209, + "object_name": "Na/Ca exchanger" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267139, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + }, + { + "object_id": 121254, + "object_name": "Depolarization block" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 267119, + "object_name": "Electrodiffusion" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267141, + "object_name": "S\u00e6tra MJ, Einevoll GT, Halnes G (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "The electrodiffusive Pinsky-Rinzel (edPR) model (S\u00e6tra et al., 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 189094, + "object_name": "Pinsky-Rinzel CA1/3 pyramidal cell " + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The edPR model is \"what we may refer to as \u201ca minimal neuronal model that\r\nhas it all\u201d. By \u201chas it all\u201d, we mean that it (1) has a spatial extension, (2) considers both extracellular- and \r\nintracellular dynamics, (3) keeps track of all ion concentrations (Na+, K+, Ca2+, and\r\nCl-) in all compartments, (4) keeps track of all electrical potentials in all compartments, \r\n(5) has differential expression of ion channels in soma versus dendrites, \r\nand can fire somatic APs and dendritic calcium spikes, \r\n(6) contains the homeostatic machinery that ensures that it maintains a realistic dynamics in the membrane potential\r\nand all ion concentrations during long-time activity, and (7) accounts for transmembrane,\r\nintracellular and extracellular ionic movements due to both diffusion and electrical migration,\r\nand thus ensures a consistent relationship between ion concentrations and electrical charge.\r\nBeing based on a unified framework for intra- and extracellular dynamics, the model\r\nthus accounts for possible ephaptic effects from extracellular dynamics, as neglected in \r\nstandard feedforward models based on volume conductor theory. By \u201cminimal\u201d\r\nwe simply mean that we reduce the number of spatial compartments to the minimal, which in\r\nthis case is four, i.e., two neuronal compartments (a soma and a dendrite), plus two extracellular \r\ncompartments (outside soma and outside dendrite). Technically, the model was \r\nconstructed by adding homeostatic mechanisms and ion concentration dynamics to an existing\r\nmodel, i.e., the two-compartment Pinsky-Rinzel (PR) model, and embedding in it a \r\nconsistent electrodiffusive framework, i.e., the previously developed Kirchhoff-Nernst-Planck framework.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267139", + "public_submitter_email": { + "attr_id": 309, + "value": "martejulie@simula.no" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Marte J. 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Ramps were applied to CA1 pyramidal neurons from male rats in vitro (slice electrophysiology) and in silico (multi-compartmental NEURON model). Under control conditions, CA1 neurons fired more action potentials at higher frequencies on the up-ramp versus the down-ramp. This effect was more pronounced for dendritic compared to somatic ramps. We incorporated a four-state Markov scheme for NaV1.6 channels into our model and calibrated the spatial dependence of long-term inactivation according to the literature; this spatial dependence was sufficient to explain the difference in dendritic versus somatic ramps. Long-term inactivation reduced the firing frequency by decreasing open-state occupancy, and reduced spike amplitude during trains by decreasing occupancy in closed states, which comprise the available pool...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267140", + "public_submitter_email": { + "attr_id": 309, + "value": "cupchu@lsuhsc.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Carol Upchurch" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2022-04-02T14:10:43.427", + "ver_number": 4 + }, + "267142": { + "class_id": 19, + "created": "2021-09-20T11:10:19.193", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267142, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266890, + "object_name": "Kilb, Werner [wkilb at uni-mainz.de]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 128875, + "object_name": "Development" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267188, + "object_name": "Lombardi A, Luhmann HJ, Kilb W (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Spatial constrains of GABAergic rheobase shift (Lombardi et al., 2021)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 259, + "object_name": "Hippocampus CA3 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In this models we investigated how the threshold eGABA, at which GABAergic inhibition switches to excitation, depends on the spatiotemporal constrains in a ball-and-stick neurons and a neurons with a topology derived from an reconstructed neuron." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267142", + "other_concept": { + "attr_id": 306, + "value": "GABA reversal potential" + }, + "other_neurons": { + "attr_id": 301, + "value": "Ball-and-stick neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "wkilb@uni-mainz.de" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Werner Kilb" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2023-02-13T10:57:35.727", + "ver_number": 4 + }, + "267144": { + "class_id": 19, + "created": "2021-09-25T05:07:08.393", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267144, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 261615, + "object_name": "Maes, Amadeus [amadeus.maes at gmail.com]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267784, + "object_name": "Maes A, Barahona M, Clopath C (2023)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Long- and short-term history effects in a spiking network model of statistical learning (Maes et al accepted)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + }, + { + "object_id": 240378, + "object_name": "Abstract integrate-and-fire neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We map inverse transform learning onto spiking networks. We show that the model manages to learn from repeated observations of a variable and samples from the target distribution during spontaneous dynamics." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267144", + "public_submitter_email": { + "attr_id": 309, + "value": "amadeus.maes@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Amadeus Maes" + }, + "ver_date": "2023-07-24T20:22:57.57", + "ver_number": 3 + }, + "267145": { + "citation": { + "attr_id": 391, + "value": "DOI : 10.1007/s10827-021-00797-2" + }, + "class_id": 19, + "created": "2021-09-27T15:01:50.98", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267145, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267164, + "object_name": "Akosy T, Shouval HZ (2021)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Active intrinsic conductances in networks, transients, activity, plasticity (Akosy and Shouval 2021)", + "notes": { + "attr_id": 24, + "value": "\"... we show that by including a small number of additional active conductances we can produce recurrent networks that are both more robust and exhibit firing-rate statistics that are more consistent with experimental results. We show that this holds both for bi-stable recurrent networks, which are thought to underlie working memory and for slowly decaying networks which might underlie the estimation of interval timing. We also show that by including these conductances, such networks can be trained to using a simple learning rule to predict temporal intervals that are an order of magnitude larger than those that can be trained in networks of leaky integrate and fire neurons.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267145", + "public_submitter_email": { + "attr_id": 309, + "value": "harel.shouval@uth.tmc.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Harel Shouval" + }, + "ver_date": "2021-10-08T15:32:12.16", + "ver_number": 4 + }, + "267146": { + "citation": { + "attr_id": 391, + "value": "Ferrario, Andrea, et al. \"Whole animal modelling reveals neuronal mechanisms of decision-making and reproduces unpredictable swimming in frog tadpoles.\" bioRxiv (2021)." + }, + "class_id": 19, + "created": "2021-09-29T06:45:11.627", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267146, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 239343, + "object_name": "Ferrario, Andrea [andrea.ferrario at plymouth.ac.uk]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267200, + "object_name": "Ferrario A, Palyanov A, Koutsikou S, Li W, Soffe S, Roberts A, Borisyuk R (2021)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Central Nervous System tadpole model in Matlab and NEURON-Python (Ferrario et al, 2021)", + "notes": { + "attr_id": 24, + "value": "This is the source code for three compuational models used for generating connectivity and swimming dynamics of spinal cord and hindbrain neurons in the Xenopus tadpoles using biological data. The model reproduces the initiation, continuation, termination and accelaration of forward swimming." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267146", + "public_submitter_email": { + "attr_id": 309, + "value": "A.A.Ferrario@exeter.ac.uk" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Andrea Ferrario" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 1361, + "object_name": "Glycine" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 239013, + "object_name": "Xenopus" + } + ] + }, + "ver_date": "2023-02-13T10:17:15.033", + "ver_number": 6 + }, + "267157": { + "citation": { + "attr_id": 391, + "value": "34491914" + }, + "class_id": 19, + "created": "2021-10-07T13:33:25.497", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 146506, + "object_name": "NKCC1" + }, + { + "object_id": 146505, + "object_name": "KCC2" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + } + ] + }, + "gene": { + "attr_id": 476, + "value": [ + { + "object_id": 92082, + "object_name": "Nav1.1 SCN1A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267157, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 235462, + "object_name": "Spreading depolarization" + }, + { + "object_id": 267152, + "object_name": "Dynamic extracellular concentrations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267162, + "object_name": "Chever O, Zerimech S, Scalmani P, Lemaire L, Pizzamiglio L, Loucif A, Ayrault M, Krupa M, Desroches M, Duprat F, L\u00e9na I, Cest\u00e8le S, Mantegazza M (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Initiation of spreading depolarization by GABAergic neuron hyperactivity & NaV 1.1 (Chever et al 21)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Experimentally, we show that acute pharmacological activation of NaV1.1 (the main Na+ channel of interneurons) or optogenetic-induced hyperactivity of GABAergic interneurons is sufficient to ignite CSD in the neocortex by spiking-generated extracellular K+ build-up. Neither GABAergic nor glutamatergic synaptic transmission were required for CSD initiation. CSD was not generated in other brain areas, suggesting that this is a neocortex-specific mechanism of CSD initiation. Gain-of-function mutations of NaV1.1 (SCN1A) cause Familial Hemiplegic Migraine type-3 (FHM3), a subtype of migraine with aura, of which CSD is the neurophysiological correlate. Our results provide the mechanism linking NaV1.1 gain-of-function to CSD generation in FHM3.\r\n\r\nThose findings are supported by the two-neuron conductance-based model with dynamic ion concentrations we developed." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267157", + "public_submitter_email": { + "attr_id": 309, + "value": "louisiane.lemaire@orange.fr" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Louisiane Lemaire" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + }, + { + "object_id": 205, + "object_name": "AMPA" + } + ] + }, + "ver_date": "2021-10-19T08:50:25.557", + "ver_number": 11 + }, + "267165": { + "citation": { + "attr_id": 391, + "value": "33616035" + }, + "class_id": 19, + "created": "2021-10-11T11:43:22.36", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267165, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267177, + "object_name": "Lazar, Aurel A." + }, + { + "object_id": 267178, + "object_name": "Liu, Tinghai" + }, + { + "object_id": 267179, + "object_name": "Turkcan, Mehmet Kerem" + }, + { + "object_id": 267180, + "object_name": "Zhou, Yiyin" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267182, + "object_name": "Lazar AA, Liu T, Turkcan MK, Zhou Y (2021)" + } + ] + }, + "name": "Accelerating with FlyBrainLab discovery of the functional logic of Drosophila brain (Lazar et al 21)", + "notes": { + "attr_id": 24, + "value": "In recent years, a wealth of Drosophila neuroscience data have become available including cell type and connectome/synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fruit fly brain, we have developed FlyBrainLab, a unique open-source computing platform that integrates 3D exploration and visualization of diverse datasets with interactive exploration of the functional logic of modeled executable brain circuits. FlyBrainLab\u2019s User Interface, Utilities Libraries and Circuit Libraries bring together neuroanatomical, neurogenetic and electrophysiological datasets with computational models of different researchers for validation and comparison within the same platform. Seeking to transcend the limitations of the connectome/synaptome, FlyBrainLab also provides libraries for molecular transduction arising in sensory coding in vision/olfaction. Together with sensory neuron activity data, these libraries serve as entry points for the exploration, analysis, comparison, and evaluation of circuit functions of the fruit fly brain." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267165", + "other_simulator": { + "attr_id": 307, + "value": "FlyBrainLab" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ted.carnevale@yale.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ted Carnevale" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 206363, + "object_name": "Drosophila" + } + ] + }, + "ver_date": "2021-10-27T21:11:22.507", + "ver_number": 20 + }, + "267173": { + "class_id": 19, + "created": "2021-10-25T03:42:53.86", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267173, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206278, + "object_name": "Kastellakis, George [gkastel at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3647, + "object_name": "Long-term Synaptic Plasticity" + }, + { + "object_id": 267410, + "object_name": "Synaptic Tagging and Capture" + }, + { + "object_id": 180821, + "object_name": "Hebbian plasticity" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 267000, + "object_name": "Excitability" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267416, + "object_name": "Chowdhury A, Luchetti A, Fernandes G, Filho DA, Kastellakis G, Tzilivaki A, Ramirez EM, Tran MY, Poirazi P, Silva AJ (2022)" + }, + { + "object_id": 206391, + "object_name": "Kastellakis G, Silva AJ, Poirazi P (2016)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 267344, + "object_name": "Spiking neural network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Locus Coeruleus blocking model (Chowdhury et al.)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 240378, + "object_name": "Abstract integrate-and-fire neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... Here, we show that Locus Coeruleus (LC) cells projecting to dCA1 have a key permissive role in contextual memory linking, without affecting contextual memory formation, and that this effect is mediated by dopamine. Additionally, we found that LC to dCA1 projecting neurons modulate the excitability of dCA1 neurons, and the extent of overlap between dCA1 memory ensembles, as well as the stability of coactivity patterns within these ensembles...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267173", + "public_submitter_email": { + "attr_id": 309, + "value": "gkastel@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "George Kastellakis" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2023-02-13T10:56:46.067", + "ver_number": 14 + }, + "267174": { + "citation": { + "attr_id": 391, + "value": "Mauro Ursino*, Giulia Ricci, Laura Astolfi, Floriana Pichiorri, Manuela Petti and Elisa Magosso, (2021)\r\nA Novel Method to Assess Motor Cortex Connectivity and Event Related Desynchronization Based on Mass Models\r\nBrain sciences\r\n(accepted with minor revision)" + }, + "class_id": 19, + "created": "2021-10-26T04:56:28.333", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267174, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 239531, + "object_name": "Ursino, Mauro [mauro.ursino at unibo.it]" + }, + { + "object_id": 263678, + "object_name": "Ricci, Giulia [Giulia.Ricci at unibo.it]" + }, + { + "object_id": 261628, + "object_name": "Magosso, Elisa [elisa.magosso at unibo.it]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267204, + "object_name": "Ursino M, Ricci G, Astolfi L, Pichiorri F, Petti M, Magosso E (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + }, + { + "object_id": 83551, + "object_name": "Connectionist Network" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Motor Cortex Connectivity & Event Related Desynchronization Based on Neural Mass Models (Ursino 21)", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Knowledge of motor cortex connectivity is of great value in cognitive neuroscience, in order to provide a better understanding of motor organization and its alterations in pathological conditions. Traditional methods provide connectivity estimations which may vary depending on the task. This work aims to propose a new method for motor connectivity assessment based on the hypothesis of a task-independent connectivity network, assuming nonlinear behavior. The model considers six cortical regions of interest (ROIs) involved in hand movement. The dynamics of each region is simulated using a neural mass model, which reproduces the oscillatory activity through the interaction among four neural populations. Parameters of the model have been assigned to simulate both power spectral densities and coherences of a patient with left-hemisphere stroke during: resting condition, movement of the affected and movement of the unaffected hand. The presented model can simulate the three conditions using a single set of connectivity parameters, assuming that only inputs to the ROIs change from one condition to the other. The proposed procedure represents an innovative method to assess a brain circuit, which does not rely on a task-dependent connectivity network, and allows brain rhythms and desynchronization to be assessed on a quantitative basis. \r\n" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267174", + "public_submitter_email": { + "attr_id": 309, + "value": "giulia.ricci29@unibo.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Giulia Ricci" + }, + "ver_date": "2021-11-26T11:47:20.013", + "ver_number": 11 + }, + "267176": { + "citation": { + "attr_id": 391, + "value": "DOI: 10.1007/s10827-020-00763-4" + }, + "class_id": 19, + "created": "2021-10-27T06:18:04.633", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267176, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267186, + "object_name": "Zhang, Qiang" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267185, + "object_name": "Zhang Q, Dai Y (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Spinal motoneuron recruitment regulated by ionic channels during fictive locomotion (Zhang & Dai 20)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 117815, + "object_name": "Spinal cord motor neuron fast fatiguing" + }, + { + "object_id": 117814, + "object_name": "Spinal cord motor neuron fatigue resistant" + }, + { + "object_id": 117813, + "object_name": "Spinal cord motor neuron slow twitch" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... we investigated the channel mechanism regulating the motoneuron recruitment. Three types of motoneuron pools including slow (S), fatigue-resistant (FR) and fast-fatigable (FF) motoneurons were constructed based on the membrane proprieties of cat lumbar motoneurons. The transient sodium (NaT), persistent sodium (NaP), delayed-rectifier potassium [K(DR)], Ca2+-dependent K+ [K(AHP)] and L-type calcium (CaL) channels were included in the models...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267176", + "public_submitter_email": { + "attr_id": 309, + "value": "2487657812@qq.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Qiang Zhang" + }, + "ver_date": "2023-02-06T11:35:52.613", + "ver_number": 11 + }, + "267183": { + "citation": { + "attr_id": 391, + "value": "Bano-Otalora & Moye et al (2021) Daily electrical activity in the master circadian clock of a diurnal mammal, eLife, https://doi.org/10.7554/eLife.68179" + }, + "class_id": 19, + "created": "2021-10-28T09:50:12.923", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267183, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 232101, + "object_name": "Diekman, Casey O. 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Model parameters were inferred from current-clamp recordings using data assimilation (DA) algorithms available at https://github.com/mattmoye/neuroDA" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267183", + "public_submitter_email": { + "attr_id": 309, + "value": "casey.o.diekman@njit.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Casey O. Diekman" + }, + "ver_date": "2022-03-31T14:47:18.2", + "ver_number": 5 + }, + "267184": { + "citation": { + "attr_id": 391, + "value": "The manuscript associated with this paper is currently under review. 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A previously developed neuromuscular model of a motor neuron was embedded into a simple FE musculoskeletal model. Input stimulation profiles from literature were simulated in the FE NMS model to verify effective integration of the software platforms. Motor unit recruitment and rate coding capabilities of the model were evaluated. The integrated model reproduced previously published output muscle forces with an average error of 0.0435 N. The integrated model effectively demonstrated motor unit recruitment and rate coding in the physiological range based upon motor unit discharge rates and muscle force output.\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267184", + "other_simulator": { + "attr_id": 307, + "value": "Abaqus" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "victoriavolk@u.boisestate.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Victoria Volk" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115954, + "object_name": "Spinal motoneuron" + } + ] + }, + "ver_date": "2023-02-06T11:40:29.877", + "ver_number": 8 + }, + "267187": { + "citation": { + "attr_id": 391, + "value": "Nicoletti M, Loppini A, Chiodo L, Folli V, Ruocco G, et al. (2019) Biophysical modeling of C. elegans neurons: Single ion currents and whole-cell dynamics of AWCon and RMD. 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Cells are stimulated both in voltage and current clamp." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267187", + "public_submitter_email": { + "attr_id": 309, + "value": "a.loppini@unicampus.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Alessandro Loppini" + }, + "ver_date": "2022-01-02T15:38:02.677", + "ver_number": 2 + }, + "267189": { + "class_id": 19, + "created": "2021-11-11T05:16:00.963", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267189, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267213, + "object_name": "Zhang X, Hancock R, Santaniello S (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Computational model of cerebellar tDCS (Zhang et al., 2021)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + }, + { + "object_id": 7346, + "object_name": "Cerebellum interneuron granule GLU cell" + }, + { + "object_id": 136179, + "object_name": "Cerebellum deep nucleus neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This archive contains models used in (Zhang et al. 2021) and simulates Purkinje cell, granule cell, and deep cerebellar neuron activities under cerebellar tDCS (transcranial direct current stimulation)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267189", + "public_submitter_email": { + "attr_id": 309, + "value": "xu.3.zhang@uconn.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Xu Zhang" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2022-01-27T14:59:32.08", + "ver_number": 12 + }, + "267201": { + "citation": { + "attr_id": 391, + "value": "@article{Fardet2020,\r\n title = {Simple Models Including Energy and Spike Constraints Reproduce Complex Activity Patterns and Metabolic Disruptions},\r\n author = {Fardet, Tanguy and Levina, Anna},\r\n year = {2020},\r\n journal = {PLOS Computational Biology},\r\n volume = {16},\r\n number = {12},\r\n pages = {e1008503},\r\n publisher = {{Public Library of Science}},\r\n issn = {1553-7358},\r\n doi = {10.1371/journal.pcbi.1008503},\r\n url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008503},\r\n abstract = {In this work, we introduce new phenomenological neuronal models (eLIF and mAdExp) that account for energy supply and demand in the cell as well as the inactivation of spike generation how these interact with subthreshold and spiking dynamics. Including these constraints, the new models reproduce a broad range of biologically-relevant behaviors that are identified to be crucial in many neurological disorders, but were not captured by commonly used phenomenological models. Because of their low dimensionality eLIF and mAdExp open the possibility of future large-scale simulations for more realistic studies of brain circuits involved in neuronal disorders. The new models enable both more accurate modeling and the possibility to study energy-associated disorders over the whole time-course of disease progression instead of only comparing the initially healthy status with the final diseased state. These models, therefore, provide new theoretical and computational methods to assess the opportunities of early diagnostics and the potential of energy-centered approaches to improve therapies.},\r\n keywords = {Action potentials,Behavior,Bioenergetics,Depolarization,Dynamical systems,Membrane potential,Neurons,Single neuron function}\r\n}" + }, + "class_id": 19, + "created": "2021-11-24T09:11:17.18", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267201, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 121254, + "object_name": "Depolarization block" + }, + { + "object_id": 139270, + "object_name": "Anoxic depolarization" + }, + { + "object_id": 267007, + "object_name": "Energy consumption" + }, + { + "object_id": 136252, + "object_name": "Rebound firing" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267205, + "object_name": "Fardet T, Levina A (2020)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 231240, + "object_name": "Brian 2" + }, + { + "object_id": 2832, + "object_name": "NEST" + }, + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "eLIF and mAdExp: energy-based integrate-and-fire neurons (Fardet and Levina 2020)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + }, + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + }, + { + "object_id": 240378, + "object_name": "Abstract integrate-and-fire neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The eLIF and mAdExp neurons respectively extend the leaky integrate-and-fire and adaptive exponential (AdExp) neuron models.\r\nThey include a new variable modelling the availability of energy substrate and model constraints that energy availability may have on the subthreshold and spiking dynamics.\r\nIn the paper, we show how these models can reproduce complex dynamics and prove especially useful to model metabolic disruption, for instance in large-scale models of epilepsy or other diseases with metabolic components, such as Alzheimer, or Parkinson.\r\nGit repository: https://git.sr.ht/~tfardet/elif-madexp" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267201", + "other_implementer": { + "attr_id": 308, + "value": "Tanguy Fardet [tanguy.fardet at tuebingen.mpg.de]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "tanguy.fardet@tuebingen.mpg.de" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tanguy Fardet" + }, + "ver_date": "2021-11-26T11:28:28.42", + "ver_number": 3 + }, + "267219": { + "citation": { + "attr_id": 391, + "value": "Parker, J.R.,Klishko, A.N., Prilutsky, B.I., Cymbalyuk,G.S. (2021). Asymmetric and transient properties of reciprocal activity of antagonists during the paw-shake response in the cat. PLoS Computational Biology. To appear." + }, + "class_id": 19, + "created": "2021-12-10T16:30:07.917", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267219, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267269, + "object_name": "Parker JR, Klishko AN, Prilutsky BI, Cymbalyuk GS (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Cat Locomotion and Paw-Shaking Central Pattern Generator Model (Parker et al 2021)", + "notes": { + "attr_id": 24, + "value": "We suggest that the cat paw-shaking response is generated as a transient response of the locomotor CPG. Our general prediction is that this CPG is multifunctional, and in addition to the locomotor rhythm, it can generate a transient, ten-times faster, paw-shake-like response to a stimulus. In our multistable half-center oscillator (HCO) CPG model, we applied perturbations to the locomotor pattern which resulted in a transient paw-shake-like pattern that eventually returned back to the locomotor pattern. We showed that the inactivation of the slow inward current that drives the locomotor rhythm produced asymmetry of the transient flexor and extensor activity in a symmetric HCO model. To test predictions from our model about the transient nature of the paw-shake response, we compared burst durations (BDs) and interburst intervals (IBIs) of the model half-centers in consecutive cycles of paw-shake-like responses with the with the BD and IBI of electromyographic (EMG) activity bursts of cat hindlimb flexors and extensors recorded during a paw-shake response. In both cases, we found similar asymmetric trends in the BD and IBI throughout a paw-shake response." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267219", + "other_concept": { + "attr_id": 306, + "value": "Multifunctional CPG" + }, + "other_neurons": { + "attr_id": 301, + "value": "Cat Locomotion CPG" + }, + "other_type": { + "attr_id": 305, + "value": "Hodgkin-Huxley model" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jessiraeparker2@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Jessica Parker" + }, + "ver_date": "2022-01-29T17:36:56.163", + "ver_number": 4 + }, + "267221": { + "class_id": 19, + "created": "2021-12-15T09:28:05.46", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 243504, + "object_name": "IK Bkca" + }, + { + "object_id": 243506, + "object_name": "IK Skca" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267221, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206377, + "object_name": "Chavlis, Spyridon [schavlis at imbb.forth.gr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Hippocampal CA1 microcircuit model including somatic and dendritic inhibition", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + }, + { + "object_id": 190560, + "object_name": "Hippocampus CA1 stratum oriens lacunosum-moleculare interneuron " + }, + { + "object_id": 227332, + "object_name": "Hippocampus CA1 basket cell - CCK/VIP" + }, + { + "object_id": 249699, + "object_name": "Hippocampal CA1 CR/VIP cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Here, we investigate the role of (dis)inhibition on the lateral entorhinal cortex (LEC) induced dendritic spikes on hippocampal CA1 pyramidal cells. The circuit model consists of pyramidal, SST+, CCK+, CR+/VIP+, and CCK+/VIP+ cells." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267221", + "other_neurons": { + "attr_id": 301, + "value": "Hippocampus CA1 CCK cell" + }, + "other_simulator": { + "attr_id": 307, + "value": "NEURON with python" + }, + "other_type": { + "attr_id": 305, + "value": "Circuit model" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "schavlis@imbb.forth.gr" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Spyridon Chavlis" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228598, + "object_name": "Mouse" + }, + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2022-12-19T13:54:09.037", + "ver_number": 7 + }, + "267222": { + "citation": { + "attr_id": 391, + "value": "Orr SA, Ahn S, Park C, Miller TH, Kassai M and Issa FA (2021) Social Experience Regulates Endocannabinoids Modulation of Zebrafish Motor Behaviors. Front. Behav. Neurosci. 15:668589. doi: 10.3389/fnbeh.2021.668589" + }, + "class_id": 19, + "created": "2021-12-15T15:25:04.997", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267222, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267233, + "object_name": "Ahn, Sungwoo" + }, + { + "object_id": 152294, + "object_name": "Park, Choongseok [cpark at ncat.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267232, + "object_name": "Orr SA, Ahn S, Park C, Miller TH, Kassai M, Issa FA (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Mauthner cell with two pre-synaptic cells, an inhibitory and an excitatory cell (Orr et al 2021)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 150222, + "object_name": "Abstract Morris-Lecar neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 184489, + "object_name": "Endocannabinoid" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "To study the role of endocannabinoids system in the modulation of social status-dependent zebrafish motor behavior, we constructed a neuronal network, which consists of Mauthner cell with two pre-synaptic cells, an inhibitory cell and an excitatory cell." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267222", + "public_submitter_email": { + "attr_id": 309, + "value": "cpark@ncat.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Choongseok Park" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 223, + "object_name": "Dopaminergic Receptor" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "ver_date": "2022-01-06T20:38:13.58", + "ver_number": 6 + }, + "267250": { + "class_id": 19, + "created": "2022-01-06T14:02:31.84", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267250, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267727, + "object_name": "Cao F, Ralph M. R., Stinchcombe A. R. (2022)." + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Phenomenological model of the mouse circadian pacemaker (Cao et al., 2022)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 145884, + "object_name": "Suprachiasmatic nucleus (SCN) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A phenomenological model of the mouse circadian pacemaker as found in the suprachiasmatic nucleus. The model is intrinsically rhythmic and responds to light input." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267250", + "public_submitter_email": { + "attr_id": 309, + "value": "stinch@math.toronto.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Adam Stinchcombe" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2023-04-26T10:52:28.673", + "ver_number": 6 + }, + "267253": { + "citation": { + "attr_id": 391, + "value": "Sharples SA, Parker J, Cruz JM, Vargas A, Lognon AP, Cheng N, Young L, Shonak A, Cymbalyuk G, Whelan PJ. Mechanisms of Episodic Rhythmicity Contributions of h-and Na+/K+ pump currents to the generation of episodic and continuous rhythmic activities. 2021. Frontiers in Cellular Neuroscience. 579. Accepted." + }, + "class_id": 19, + "created": "2022-01-11T21:07:40.647", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 222730, + "object_name": "I Na, slow inactivation" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267253, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267277, + "object_name": "Parker, Jessica" + }, + { + "object_id": 267278, + "object_name": "Varga, Alex" + }, + { + "object_id": 267279, + "object_name": "Cymbalyuk, Gennady" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267276, + "object_name": "Sharples SA, Parker J, Cruz JM, Vargas A, Lognon AP, Cheng N, Young L, Shonak A, Cymbalyuk G, Whelan PJ (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Mouse Episodic and Continuous Locomotion CPG (Sharples et al, 2022)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 267281, + "object_name": "Spinal cord Ia interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 214, + "object_name": "Glycine" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We modeled the locomotion CPG in neonatal mice, and our model produces both episodic and continuous locomotion, similar to activity seen in experimental recordings of motor neurons in the isolated spinal cords of neonatal mice. Our model is presented in Sharples SA, Parker J, Cruz JM, Vargas A, Lognon AP, Cheng N, Young L, Shonak A, Cymbalyuk G, Whelan PJ. Mechanisms of Episodic Rhythmicity Contributions of h-and Na+/K+ pump currents to the generation of episodic and continuous rhythmic activities. 2021. Frontiers in Cellular Neuroscience. 579. Accepted." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267253", + "other_currents": { + "attr_id": 303, + "value": "I Ca, slow inactivation, low voltage-activated" + }, + "other_neurons": { + "attr_id": 301, + "value": "spinal interneurons" + }, + "other_type": { + "attr_id": 305, + "value": "Hodgkin-Huxley style HCO" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jessiraeparker2@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Jessica Parker" + }, + "ver_date": "2023-02-13T11:07:03.54", + "ver_number": 8 + }, + "267259": { + "class_id": 19, + "created": "2022-01-20T11:20:44.283", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 146506, + "object_name": "NKCC1" + }, + { + "object_id": 146505, + "object_name": "KCC2" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267259, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 266959, + "object_name": "Kelley, Craig" + }, + { + "object_id": 247215, + "object_name": "Newton, Adam J H [adam.newton at yale.edu]" + }, + { + "object_id": 33738, + "object_name": "Lytton, William [bill.lytton at downstate.edu]" + }, + { + "object_id": 146951, + "object_name": "McDougal, Robert [robert.mcdougal at yale.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 235462, + "object_name": "Spreading depolarization" + }, + { + "object_id": 167716, + "object_name": "Spreading depression" + }, + { + "object_id": 183016, + "object_name": "Reaction-diffusion" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267541, + "object_name": "Kelley C, Newton AJH, Hrabetova S, McDougal RA, Lytton WW (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 120799, + "object_name": "Glia" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Spreading Depolarization in Brain Slices (Kelley et al. 2022)", + "notes": { + "attr_id": 24, + "value": "A tissue-scale model of spreading depolarization (SD) in brain slices.\r\nWe used the NEURON simulator's reaction-diffusion framework to implement embed thousands of neurons \r\n(based on the the model from Wei et al. 2014)\r\nin the extracellular space of a brain slice, which is itself embedded in an bath solution.\r\nWe initiate SD in the slice by elevating extracellular K+ in a spherical region at the center of the slice.\r\nEffects of hypoxia and propionate on the slice were modeled by appropriate changes to the volume fraction \r\nand tortuosity of the extracellular space and oxygen/chloride concentrations." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267259", + "public_submitter_email": { + "attr_id": 309, + "value": "craig.kelley@downstate.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Craig Kelley" + }, + "ver_date": "2022-08-27T17:46:04.79", + "ver_number": 6 + }, + "267260": { + "class_id": 19, + "created": "2022-01-20T17:03:52.757", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267260, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267749, + "object_name": "Lameu EL, Rasiah NP, Baimoukhametova DV, Loewen SP, Bains JS, Nicola W (2022)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Particle-Swarm Based Modelling Reveals Two Distinct Classes of CRH^{PVN} Neurons (Lameu et al 2022)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154778, + "object_name": "Abstract integrate-and-fire adaptive exponential (AdEx) neuron" + }, + { + "object_id": 267750, + "object_name": "Hypothalamus PVN CRH neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... We developed a computational modelling platform that uses particle swarm optimization to rapidly and accurately fit biophysical neuron models to patched CRHPVN neurons. A model was fitted to each patched neuron without the use of dynamic clamping, or other procedures requiring sophisticated inputs and fitting algorithms. Any neuron undergoing standard current clamp step protocols for a few minutes can be fitted by this procedure...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267260", + "public_submitter_email": { + "attr_id": 309, + "value": "ewandson.ll@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ewandson Luiz Lameu" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2023-06-01T10:44:14.267", + "ver_number": 7 + }, + "267280": { + "citation": { + "attr_id": 391, + "value": "An hypothesis concerning distinct schemes of olfactory activation evoked by perceived vs. non-perceived input. By: Roger D. Traub and the late Miles A. Whittington. Proceedings of the National Academy of Sciences USA, in press (Feb. 1, 2022)" + }, + "class_id": 19, + "created": "2022-02-01T13:21:51.42", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267280, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 33800, + "object_name": "Traub, Roger D [rtraub at us.ibm.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3635, + "object_name": "Synchronization" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 45540, + "object_name": "FORTRAN" + } + ] + }, + "name": "piriform plus endopiriform circuit model. Pyramidal cells, multipolar neurons, interneurons.", + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "An extension of the model of piriform cortex published in Reviews in the Neurosciences, by R.D. Traub, Yuhai Tu, Miles A. Whittington" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267280", + "public_submitter_email": { + "attr_id": 309, + "value": "rtraub@us.ibm.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Roger D. Traub" + }, + "ver_date": "2022-02-15T13:32:21.653", + "ver_number": 2 + }, + "267286": { + "citation": { + "attr_id": 391, + "value": "The hyperpolarization-activated current shifts the dynamic range of a voltage-dependent electrical synapse\r\nWolfgang Stein, Margaret L. DeMaegd, Lena Yolanda Braun, Andr\u00e9s Vidal-Gadea, Allison L. Harris, Carola St\u00e4dele\r\nbioRxiv 2021.12.16.472972; doi: https://doi.org/10.1101/2021.12.16.472972" + }, + "class_id": 19, + "created": "2022-02-04T14:44:39.313", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267286, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267034, + "object_name": "Harris, Allison L" + }, + { + "object_id": 267097, + "object_name": "Stein, Wolfgang [wstein@ilstu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267308, + "object_name": "Stein W, DeMaegd ML, Braun LY, Vidal-Gadea A, Harris AL, St\u00e4dele C (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 143722, + "object_name": "Mathematica" + } + ] + }, + "name": "Hyperpolarization-activated inward current and dynamic range of electrical synapse (Stein et al '22)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114276, + "object_name": "Stomatogastric Ganglion (STG) Modulatory commissural neuron 1 (MCN1)" + }, + { + "object_id": 114303, + "object_name": "Stomatogastric Ganglion (STG) Lateral Gastric (LG) cell" + }, + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Electrical synaptic transmission and voltage-gated ionic currents are often studied independently from one another. This model allows to study the interactions between the hyperpolarization-activated inward ionic current and a rectifying electrical synapse. Two single compartment nonspiking neurons are coupled through a rectifying electrical synapse. Current pulses are applied into the presynaptic neuron. The amplitude of the electrical postsynaptic potentials is measured. Ih can be added to either the pre- or postsynaptic neuron, or both. The cells represent the the MCN1 and LG neurons in the crab stomatogastric ganglion." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267286", + "public_submitter_email": { + "attr_id": 309, + "value": "wstein@ilstu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Wolfgang Stein" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115959, + "object_name": "Stomatogastric ganglion" + } + ] + }, + "ver_date": "2022-02-28T12:56:07.81", + "ver_number": 4 + }, + "267293": { + "class_id": 19, + "created": "2022-02-15T08:00:03.183", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267293, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 147118, + "object_name": "Maki-Marttunen, Tuomo [tuomomm at uio.no]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 222956, + "object_name": "Neuromodulation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267539, + "object_name": "M\u00e4ki-Marttunen T, M\u00e4ki-Marttunen V (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Simulations of modulation of HCN channels in L5PCs (M\u00e4ki-Marttunen and M\u00e4ki-Marttunen, 2022)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 266971, + "object_name": "Neocortex layer 5 pyramidal cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 209, + "object_name": "Acetylcholine" + }, + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... In this work, we build upon existing biophysically detailed models of thick-tufted layer V pyramidal cells and model the effects of over- and under-expression of Ih channels as well as their neuromodulation by dopamine (gain of Ih function) and acetylcholine (loss of Ih function). We show that Ih channels facilitate the action potentials of layer V pyramidal cells in response to proximal dendritic stimulus while they hinder the action potentials in response to distal dendritic stimulus at the apical dendrite. We also show that the inhibitory action of the Ih channels in layer V pyramidal cells is due to the interactions between Ih channels and a hot zone of low voltage-activated Ca2+ channels at the apical dendrite. Our simulations suggest that a combination of Ih-enhancing neuromodulation at the proximal apical dendrite and Ih-inhibiting modulation at the distal apical dendrite can increase the layer V pyramidal excitability more than any of the two neuromodulators alone...\"" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267293", + "public_submitter_email": { + "attr_id": 309, + "value": "tuomo.maki-marttunen@tuni.fi" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Tuomo M\u00e4ki-Marttunen" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "ver_date": "2023-02-13T11:01:57.24", + "ver_number": 9 + }, + "267295": { + "citation": { + "attr_id": 391, + "value": "Jorge F. Mejias and Xiao-Jing Wang, Mechanisms of distributed working memory in a large-scale network of macaque neocortex, eLife 11:e72136, 2022. DOI: 10.7554/eLife.72136" + }, + "class_id": 19, + "created": "2022-02-16T13:11:26.593", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267295, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 249591, + "object_name": "Mejias, Jorge [j.f.mejias at uva.nl]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 82787, + "object_name": "Working memory" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267296, + "object_name": "Mejias JF, Wang XJ (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Distributed working memory in large-scale macaque brain model (Mejias and Wang, 2022)", + "notes": { + "attr_id": 24, + "value": "This code simulates working memory in a large-scale cortical network of the macaque brain. The model is constrained by anatomical data and provides a simple framework to explain the widespread activation of cortical areas during working memory." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267295", + "public_submitter_email": { + "attr_id": 309, + "value": "j.f.mejias@uva.nl" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Jorge Mejias" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2023-02-13T10:17:45.427", + "ver_number": 4 + }, + "267297": { + "citation": { + "attr_id": 391, + "value": "A MODEL OF WORKING MEMORY FOR ENCODING MULTIPLE ITEMS AND ORDERED SEQUENCES EXPLOITING THE THETA-GAMMA CODE by Mauro Ursino, Nicole Cesaretti, Gabriele Pirazzini, in press (with minor revision) on Cognitive Neurodynamics" + }, + "class_id": 19, + "created": "2022-02-24T05:03:44.69", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267297, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 239531, + "object_name": "Ursino, Mauro [mauro.ursino at unibo.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 156115, + "object_name": "Gamma oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267301, + "object_name": "Ursino M, Cesaretti N, Pirazzini G (in press)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + }, + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A model of working memory for encoding multiple items (Ursino et al, in press)", + "notes": { + "attr_id": 24, + "value": "We present an original neural network model, based on oscillating neural masses, to investigate mechanisms at the basis of working memory in different conditions. 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Calcium and sodium conductances vary by 11 increments in the Axon bearing dendrite part to simulate a 11*11*37 models. For each model Action potential (AP) properties are measured : frequency, amplitude, Threshold, Half duration, max first and second derivative. 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They are the result of a data-driven approach aimed at optimizing the model parameters by utilizing high-resolution morphological reconstructions and patch-clamp electrophysiology data together with a multi-objective optimization algorithm.\r\n\r\nThe models quantitatively match the cell type-specific firing phenotypes and recapitulate the intrinsic population-level variability observed in the data. Additionally, the conductance values found by the optimization algorithm are consistent with differentially expressed ion channel genes in single-cell transcriptomic data for the two cell types.\r\n\r\nThe models have further been employed to investigate the cell type-specific biophysical properties involved in the generation of complex-spiking output driven by synaptic input and to show that a-thorny bursting cells are capable of encoding more information in their firing output than their counterparts, thorny regular spiking neurons.\r\n\r\nReference:\r\n\r\nLinaro D, Levy MJ, and Hunt, DL. Cell type-specific mechanisms of information transfer in data-driven biophysical models of hippocampal CA3 principal neurons. 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" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267309", + "public_submitter_email": { + "attr_id": 309, + "value": "strawcup@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Jane Yang" + }, + "ver_date": "2023-02-13T10:11:41.847", + "ver_number": 14 + }, + "267310": { + "class_id": 19, + "created": "2022-03-10T04:18:15", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267310, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267401, + "object_name": "Berry, Hughes [hughes.berry at inria.fr]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 143724, + "object_name": "Bifurcation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267400, + "object_name": "Moyse LB, Berry H (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 120799, + "object_name": "Glia" + } + ] + }, + "name": "Modulation of cortical Up-Down state switching by astrocytes (Moyse & Berry, 2022)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + }, + { + "object_id": 245940, + "object_name": "Abstract rate-based neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Recent experimental studies have suggested that the astrocytes of the local network can actually control the emergence of Up-Down regimes. 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In numerical simulations of these three-population models, the presence of astrocytes is indeed observed\r\nto promote the emergence of Up-Down regimes with realistic characteristics." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267310", + "public_submitter_email": { + "attr_id": 309, + "value": "lisa.blum-moyse@inria.fr" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Lisa Blum Moyse" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2023-02-13T11:04:08.28", + "ver_number": 4 + }, + "267311": { + "citation": { + "attr_id": 391, + "value": "Bock, T., Negrean, A., & Siegelbaum, A.S. (2022). Somatic depolarization enhances hippocampal CA1 dendritic spike propagation and distal input-driven synaptic plasticity. Journal of Neuroscience." + }, + "class_id": 19, + "created": "2022-03-10T11:18:52.527", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 112843, + "object_name": "I R" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267311, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267314, + "object_name": "Negrean, Adrian [negreanadrian at gmail.com]" + }, + { + "object_id": 267315, + "object_name": "Bock, Tobias [htb2110 at columbia.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267313, + "object_name": "Bock T, Negrean A, Siegelbaum AS (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Depolarization Enhacement of Dendritic Spike Propagation (Bock et al 2022)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model shows that small subthreshold depolarization of the soma powerfully enhances the propagation of dendritic spikes, through inactivation of dendritic A-type potassium channels." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267311", + "public_submitter_email": { + "attr_id": 309, + "value": "negreanadrian@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Adrian Negrean" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2022-03-12T13:31:07.633", + "ver_number": 9 + }, + "267318": { + "citation": { + "attr_id": 391, + "value": "Processing of cell assemblies in the lateral entorhinal cortex; by Roger D. Traub and Miles A. Whittington; in press, Reviews in the Neurosciences" + }, + "class_id": 19, + "created": "2022-03-12T14:54:29.813", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267318, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267319, + "object_name": "Traub RD, Whittington MA (in press)" + }, + { + "object_id": 267234, + "object_name": "Traub RD, Tu Y, Whittington MA (2021)" + }, + { + "object_id": 267323, + "object_name": "Traub RD, Whittington MA (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 45540, + "object_name": "FORTRAN" + } + ] + }, + "name": "Lateral entorhinal cortex network model (Traub and Whittington, in press)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 222437, + "object_name": "Entorhinal cortex pyramidal cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Circuits of multicompartment cells, including fan cells, layer 2 and layer 3 pyramidal cells, and multiple interneuron types; developed from model of piriform cortex (Traub, Tu and Whittington, Reviews in the Neurosciences), and Traub and Whittington, PNAS 2022" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267318", + "public_submitter_email": { + "attr_id": 309, + "value": "rtraub@us.ibm.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Roger D. Traub" + }, + "ver_date": "2022-03-13T22:08:02.103", + "ver_number": 4 + }, + "267324": { + "citation": { + "attr_id": 391, + "value": "Phasic dopamine changes and Hebbian mechanisms during probabilistic reversal learning in striatal circuits: a computational study, by Miriam Schirru, Florence V\u00e9ronneau-Veilleux, Fahima Nekka and Mauro Ursino,\r\nin press on International Journal of Molecular Sciences" + }, + "class_id": 19, + "created": "2022-03-18T06:36:40.693", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267324, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 239531, + "object_name": "Ursino, Mauro [mauro.ursino at unibo.it]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267328, + "object_name": "Schirru M, V\u00e9ronneau-Veilleux F, Nekka F, Ursino M (in press)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Phasic dopamine changes, Hebbian mechs during reversal learning in striatum (Schirru et al in press)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 150619, + "object_name": "Neostriatum spiny neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A model simulating probabilistic action selection in the basal ganglia and reversal learning, with the possibility to use different versions of the Hebb rule and a flexible behavior for dopamine phasic changes " + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267324", + "other_implementer": { + "attr_id": 308, + "value": "Miriam Schirru" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "mauro.ursino@unibo.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Mauro Ursino" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 196, + "object_name": "D1" + }, + { + "object_id": 197, + "object_name": "D2" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2022-03-19T14:49:20.12", + "ver_number": 7 + }, + "267329": { + "class_id": 19, + "created": "2022-03-24T11:11:03.27", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 222730, + "object_name": "I Na, slow inactivation" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267329, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 244486, + "object_name": "Booth, Victoria [vbooth at umich.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3635, + "object_name": "Synchronization" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267506, + "object_name": "Miller J, Ryu H, Wang X, Booth V, Campbell SA (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Patterns of synchronization in 2D networks of inhibitory neurons (Miller et al, 2022)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 146805, + "object_name": "Abstract Wang-Buzsaki neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We study sychronization in a 2D network of instrinsically oscillatory inhibitory neurons with connections to first nearest neighbours (horizontally, vertically and diagonally) and second nearest neighbours (horizonally and vertically)" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267329", + "public_submitter_email": { + "attr_id": 309, + "value": "sacampbell@uwaterloo.ca" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Sue Ann Campbell" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "ver_date": "2023-02-13T11:10:25.303", + "ver_number": 12 + }, + "267334": { + "citation": { + "attr_id": 391, + "value": "Kim SY, Lim W (2021) Effect of Diverse Recoding of Granule Cells on Optokinetic Response in A Cerebellar Ring Network with Synaptic Plasticity. Neural Networks 134: 173-204" + }, + "class_id": 19, + "created": "2022-03-27T21:35:31.837", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267334, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267341, + "object_name": "Kim, Sang-Yoon" + }, + { + "object_id": 267342, + "object_name": "Lim, Woochang" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 140966, + "object_name": "Sensory processing" + }, + { + "object_id": 267345, + "object_name": "Effective Optokinetic Response (OKR)" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267343, + "object_name": "Kim SY, Lim W (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 267344, + "object_name": "Spiking neural network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "Cerebellar Model for the Optokinetic Response (Kim and Lim 2021)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 7346, + "object_name": "Cerebellum interneuron granule GLU cell" + }, + { + "object_id": 246493, + "object_name": "Cerebellum interneuron Golgi GABA cell" + }, + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + }, + { + "object_id": 144503, + "object_name": "Inferior olive neuron" + }, + { + "object_id": 267346, + "object_name": "Vestibular nucleus neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We consider a cerebellar spiking neural network for the optokinetic response (OKR). Individual granule (GR) cells exhibit diverse spiking patterns which are in-phase, anti-phase, or complex out-of-phase with respect to their population-averaged firing activity. Then, these diversely-recoded signals via parallel fibers (PFs) from GR cells are effectively depressed by the error-teaching signals via climbing fibers from the inferior olive which are also in-phase ones. Synaptic weights at in-phase PF-Purkinje cell (PC) synapses of active GR cells are strongly depressed via strong long-term depression (LTD), while those at anti-phase and complex out-of-phase PF-PC synapses are weakly depressed through weak LTD. This kind of \u2018\u2018effective\u2019\u2019 depression at the PF-PC synapses causes a big modulation in firings of PCs, which then exert effective inhibitory coordination on the vestibular nucleus (VN) neuron (which evokes OKR). For the firing of the VN neuron, the learning gain degree, corresponding to the modulation gain ratio, increases with increasing the learning cycle, and it saturates." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267334", + "public_submitter_email": { + "attr_id": 309, + "value": "wclim@icn.re.kr" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Woochang Lim" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115953, + "object_name": "Cerebellum" + } + ] + }, + "ver_date": "2022-04-02T12:37:11.567", + "ver_number": 5 + }, + "267338": { + "class_id": 19, + "created": "2022-03-30T18:44:58.487", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267338, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267349, + "object_name": "Via, Guillem" + }, + { + "object_id": 267605, + "object_name": "Baravalle, Roman" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 139148, + "object_name": "Brain Rhythms" + }, + { + "object_id": 267000, + "object_name": "Excitability" + }, + { + "object_id": 156115, + "object_name": "Gamma oscillations" + }, + { + "object_id": 249458, + "object_name": "Theta oscillations" + }, + { + "object_id": 3644, + "object_name": "Short-term Synaptic Plasticity" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 231240, + "object_name": "Brian 2" + } + ] + }, + "name": "MEC PV-positive fast-spiking interneuron network generates theta-nested fast oscillations", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 222438, + "object_name": "Entorhinal cortex fast-spiking interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We use a computational model of a network of Fast-Spiking Parvalbumin-positive Basket Cells to study its synchronizing properties. The intrinsic properties of neurons, properties of chemical synapses and of gap junctions are calibrated using electrophysiological recordings in mice Medial Entorhinal Cortex slices. The neurons synchronize, generating Fast Oscillations nested in an external theta drive. We show how gap junctions are necessary for the generation of the oscillations, how hyperpolarizing chemical synapses give rise to more robust fast oscillations, compared to shunting ones, and how short-term depression in the chemical synapses confine the fast oscillation on a narrow range of phases from the external theta drive." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267338", + "other_currents": { + "attr_id": 303, + "value": "I_Kv1, I_Kv3, I_leak" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "gviarodriguez@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Guillem Via" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 119191, + "object_name": "Entorhinal cortex" + }, + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2022-11-29T12:55:21.12", + "ver_number": 20 + }, + "267339": { + "class_id": 19, + "created": "2022-03-31T04:03:59.78", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267339, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98011, + "object_name": "Place cell/field" + }, + { + "object_id": 116907, + "object_name": "Direction Selectivity" + }, + { + "object_id": 185343, + "object_name": "Persistent activity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267720, + "object_name": "Coppolino S, Migliore M (2023)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 116010, + "object_name": "PyNN" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Explainable AI for spatial navigation based on hippocampal circuitry (Coppolino + Migliore 2023)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Learning to navigate a complex environment is not a difficult task for a mammal. For example, finding\r\nthe correct way to exit a maze following a sequence of cues, does not need a long training session. Just\r\na single or a few runs through a new environment is, in most cases, sufficient to learn an exit path\r\nstarting from anywhere in the maze. This ability is in striking contrast with the well-known difficulty\r\nthat any deep learning algorithm has in learning a trajectory through a sequence of objects. Being\r\nable to learn an arbitrarily long sequence of objects to reach a specific place could take, in general,\r\nprohibitively long training sessions. This is a clear indication that current artificial intelligence methods\r\nare essentially unable to capture the way in which a real brain implements a cognitive function. In\r\nprevious work, we have proposed a proof-of-principle model demonstrating how, using hippocampal\r\ncircuitry, it is possible to learn an arbitrary sequence of known objects in a single trial. We called\r\nthis model SLT (Single Learning Trial). In the current work, we extend this model, which we will call\r\ne-STL, to introduce the capability of navigating a classic four-arms maze to learn, in a single trial,\r\nthe correct path to reach an exit ignoring dead ends. We show the conditions under which the e-\r\nSLT network, including cells coding for places, head-direction, and objects, can robustly and efficiently\r\nimplement a fundamental cognitive function. The results shed light on the possible circuit organization\r\nand operation of the hippocampus and may represent the building block of a new generation of\r\nartificial intelligence algorithms for spatial navigation." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267339", + "public_submitter_email": { + "attr_id": 309, + "value": "simone.coppolino@ibf.cnr.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Simone Coppolino" + }, + "ver_date": "2023-04-07T11:41:11.963", + "ver_number": 11 + }, + "267355": { + "citation": { + "attr_id": 391, + "value": "Nav1.2 and BK channels interaction shapes the action potential in the axon initial segment" + }, + "class_id": 19, + "created": "2022-04-11T10:13:14.67", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267355, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 244680, + "object_name": "Filipis, Luiza [luizafilipu at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3541, + "object_name": "Action Potential Initiation" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267701, + "object_name": "Filipis L, Bl\u00f6mer LA, Montnach J, Loussouarn G, De Waard M, Canepari M (2023)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "AIS model of L5 cortical pyramidal neuron (Filipis et al., 2023)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 266971, + "object_name": "Neocortex layer 5 pyramidal cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "In neocortical layer-5 pyramidal neurons, the action potential (AP) is generated in the axon initial segment\r\n(AIS) when the membrane potential (Vm) reaches the threshold for activation of NaV1.6 and Nav1.2 voltage-\r\ngated Na+ channels, that differ in spatial distribution and biophysical properties. Here, we used ultrafast Na+, Vm\r\nand Ca2+ imaging in combination with pharmacological blocks of different channels to demonstrate the exclusive\r\nrole of each of them in shaping the generating AP. We mimicked the experimental results with this NEURON model where the role of the different ion channels tested reproduced the experimental evidence.\r\n\r\nNav1.2 and BK channels interaction shapes the action potential in the axon initial segment\r\nDOI: 10.1113/JP283801" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267355", + "public_submitter_email": { + "attr_id": 309, + "value": "luizafilipu@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Filipis Luiza" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2023-03-23T15:16:01.1", + "ver_number": 6 + }, + "267357": { + "class_id": 19, + "created": "2022-04-18T17:54:18.023", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267357, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267724, + "object_name": "Knowlton C, Canavier CC (2022)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Pacemaking, Bursting, and Depolarization Block in Midbrain Dopamine Neurons (Knowlton et al. 2022)", + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267357", + "public_submitter_email": { + "attr_id": 309, + "value": "cknow1@lsuhsc.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Christopher Knowlton" + }, + "ver_date": "2023-04-26T11:29:48.343", + "ver_number": 5 + }, + "267363": { + "citation": { + "attr_id": 391, + "value": "Accepted for publication in PNAS. \r\nTitle: TRPC4 and GIRK channels underlie neuronal coding\r\nof firing patterns that reflect Gq/11\u2013Gi/o coincidence signals\r\nof variable strengths\r\nAuthors: Jinbin Tian, Jane Yang, William C. Joslin, Veit Flockerzi, Steven A. Prescott, Lutz Birnbaumer, Michael X. Zhu," + }, + "class_id": 19, + "created": "2022-04-27T16:48:08.3", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267363, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267320, + "object_name": "Yang, Jane" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 55277, + "object_name": "Signaling pathways" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267366, + "object_name": "Tian J, Yang J, Joslin WC, Flockerzi V, Prescott SA, Birnbaumer L, Zhu MX (accepted)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A modified Morris-Lecar with TRPC4 & GIRK (Tian et al. 2022)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 150222, + "object_name": "Abstract Morris-Lecar neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Simulates differential activation of TRPC4 and GIRK channel to reproduce various spiking patterns underlying Gq/11\u2013Gi/o coincidence signals.\r\nThe attached code reproduces Fig.5B-F in Tian et al. 2022. Please see readme.txt to get started." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267363", + "public_submitter_email": { + "attr_id": 309, + "value": "strawcup@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Jane Yang" + }, + "ver_date": "2022-05-03T19:58:52.517", + "ver_number": 3 + }, + "267382": { + "citation": { + "attr_id": 391, + "value": "Liang Chen, Sue Ann Campbell, Exact mean-field models for spiking neural networks with adaptation, https://arxiv.org/abs/2203.08341" + }, + "class_id": 19, + "created": "2022-05-24T15:35:36.273", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267382, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267383, + "object_name": "Chen, Liang" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267498, + "object_name": "Chen L, Campbell SA (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + }, + { + "object_id": 267344, + "object_name": "Spiking neural network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Exact mean-field models for Izhikevich networks (Chen and Campbell 2022)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154741, + "object_name": "Abstract Izhikevich neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Main code on time series and bifurcation diagrams from the paper L. Chen and S. A. Campbell, Exact mean-field models for spiking neural networks with adaptation (preprint: https://arxiv.org/abs/2203.08341)" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267382", + "public_submitter_email": { + "attr_id": 309, + "value": "L477chen@uwaterloo.ca" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Liang Chen" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115962, + "object_name": "Unknown" + } + ] + }, + "ver_date": "2022-07-23T12:02:12.657", + "ver_number": 18 + }, + "267384": { + "citation": { + "attr_id": 391, + "value": "Ben-Shalom, Roy, et al. \"NeuroGPU: Accelerating multi-compartment, biophysically detailed neuron simulations on GPUs.\" Journal of neuroscience methods 366 (2022): 109400." + }, + "class_id": 19, + "created": "2022-05-27T20:05:30.44", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267384, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 224842, + "object_name": "Ben-Shalom, Roy [rbenshalom at ucdavis.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267390, + "object_name": "Ben-Shalom R, Ladd A, Artherya NS, Cross C, Kim KG, Sanghevi H, Korngreen A, Bouchard KE, Bender KJ (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 267385, + "object_name": "NeuroGPU" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "NeuroGPU example on L5_TTPC1_cADpyr232_1 (Ben-Shalom 2022)(Ramaswamy et al., 2015)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 266971, + "object_name": "Neocortex layer 5 pyramidal cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This shows an example use case of building NeuroGPU simulation around a model pyramidal cell from the BBP portal. While the simulation can be run without python, we show how to update the parameters and run the simulation in python." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267384", + "other_simulator": { + "attr_id": 307, + "value": "NeuroGPU" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "zladd@berkeley.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Alexander Ladd" + }, + "ver_date": "2023-05-01T18:58:30.4", + "ver_number": 15 + }, + "267391": { + "citation": { + "attr_id": 391, + "value": "Michael Lewis Italiano et al 2022 J. Neural Eng. in press https://doi.org/10.1088/1741-2552/ac72c2" + }, + "class_id": 19, + "created": "2022-05-30T03:23:41.17", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267391, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267662, + "object_name": "Italiano, Michael L" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267394, + "object_name": "Italiano ML, Guo T, Lovell NH, Tsai D (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A 3D population model of midget retinal ganglion cells at the human fovea (Italiano et al, 2022)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A robust means of generating eccentricity-dependent and morphologically realistic and three-dimensional populations of midget retinal ganglion cells at the central human retina (specifically, at the (para-)foveal region)." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267391", + "other_implementer": { + "attr_id": 308, + "value": "Michael L Italiano [m.italiano@unsw.edu.au]" + }, + "other_type": { + "attr_id": 305, + "value": "Population" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "michael.italiano39@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Michael Lewis Italiano" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 118529, + "object_name": "Retina" + } + ] + }, + "ver_date": "2023-02-21T09:51:47.117", + "ver_number": 20 + }, + "267395": { + "citation": { + "attr_id": 391, + "value": "PMID: 35695984" + }, + "class_id": 19, + "created": "2022-06-14T19:55:58.84", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267395, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267396, + "object_name": "Hoshino O, Zheng M, Fukuoka Y (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 267344, + "object_name": "Spiking neural network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "A sensorimotor-spinal cord model (Hoshino et al. 2022)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "To elucidate how the flattening of sensory tuning due to a deficit in tonic inhibition slows motor responses, we simulated a neural network model in which a sensory cortical network (NS) and a motor cortical network (NM) are reciprocally connected, and the NM projects to spinal motoneurons (Mns). The NS was presented with a feature stimulus and the reaction time of Mns was measured. The flattening of sensory tuning in NS caused by decreasing the centration of GABA in extracellular space resulted in a decrease in the stimulus-sensitive NM pyramidal cell activity while increasing the stimulus-insensitive NM pyramidal cell activity, thereby prolonging the reaction time of Mns to the applied feature stimulus. We suggest that a reduction in extracellular GABA concentration in sensory cortex may interfere with selective activation in motor cortex, leading to slowing the activation of spinal motoneurons and therefore to slowing motor responses." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267395", + "public_submitter_email": { + "attr_id": 309, + "value": "ysohoshino@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Osamu Hoshino" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 202, + "object_name": "GabaA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115945, + "object_name": "Neocortex" + }, + { + "object_id": 115954, + "object_name": "Spinal motoneuron" + } + ] + }, + "ver_date": "2022-06-17T22:22:39.193", + "ver_number": 6 + }, + "267499": { + "citation": { + "attr_id": 391, + "value": "Gentiletti D, de Curtis M, Gnatkovsky V and Suffczynski P (2022), Focal seizures are organized by feedback between neural activity and ion concentration changes. eLife" + }, + "class_id": 19, + "created": "2022-07-25T08:22:08.723", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 148257, + "object_name": "I_HCO3" + }, + { + "object_id": 146505, + "object_name": "KCC2" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 144382, + "object_name": "Osmosis-driven water flux" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267499, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267502, + "object_name": "Suffczynski, Piotr [Piotr.Suffczynski at fuw.edu.pl]" + }, + { + "object_id": 222320, + "object_name": "Gentiletti, Damiano [gentiletti.damiano at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 267152, + "object_name": "Dynamic extracellular concentrations" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267505, + "object_name": "Gentiletti D, de Curtis M, Gnatkovsky V, Suffczynski P (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A focal seizure model with ion concentration changes (Gentiletti et al., 2022)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 222437, + "object_name": "Entorhinal cortex pyramidal cell" + }, + { + "object_id": 222438, + "object_name": "Entorhinal cortex fast-spiking interneuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Computer model was used to investigate the possible mechanisms of seizure initiation, progression and termination. The model was developed by complementing the Hodgkin-Huxley equations with activity-dependent changes in intra- and extracellular ion concentrations. 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Dopamine modulates the biophysical behavior of MSNs. In ~40% of MSNs, inward rectifying potassium (KIR) currents inactivate significantly, imparting greater excitability. Employing a 189-compartment computational model of the MSN and using spatiotemporally distributed synaptic inputs, the regulation of excitability by KIR inactivation and dopaminergic modulation was investigated and quantitatively characterized. It was shown that by forming different combinations, these regulating agents could fine tune MSN excitability across a wide range. With existing evidence indicating MSNs with and without KIR inactivation to be the likely targets for D2- and D1-receptor mediated modulations, respectively, the present findings suggest that dopaminergic channel modulation may intensify the existing excitability difference between them by suppressing the excitability of MSNs without KIR inactivation while further enhancing the excitability of the more excitable MSNs with KIR inactivation. On the other hand, the combined modulation of channels and synapses by dopamine may reverse the relative excitability of one cell type with respect to the other.\r\n\r\nThis model contains a complete biophysical model of MSN cell. The application allows the user to vary the cell properties by choosing the type of KIR channels included (inKIR or non-inKIR), the type of Dopamine receptors (D1R or D2R) and the modulation mechanism (Intrinsic modulation , Intrinsic-synaptic modulation, or No modulation). The user can also choose between the single pulse current clamp stimulation or a physiologically realistic synaptic stimulation scheme. More details are available in the Help provided with the application." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267508", + "public_submitter_email": { + "attr_id": 309, + "value": "mithun.padmakumar@duk.ac.in" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Mithun Padmakumar" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 138633, + "object_name": "Striatum" + }, + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2022-08-23T14:47:15.227", + "ver_number": 6 + }, + "267509": { + "citation": { + "attr_id": 391, + "value": "EGGER, R., SCHMITT, A. C., WALLACE, D. J., SAKMANN, B., OBERLAENDER, M. & KERR, \r\nJ. N. 2015. 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Bertram, \"Canards Underlie Both Electrical and Calcium-Induced Early Afterdepolarizations in a Model for Cardiac Myocytes\", SIAM Journal on Applied Dynamical Systems, 21:1059-1091, 2022." + }, + "class_id": 19, + "created": "2022-09-22T15:42:56.18", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267563, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267571, + "object_name": "Bertram, Richard [rbertram at fsu.edu]" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267579, + "object_name": "Kimrey J, Vo T, Bertram R (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "A model for early afterdepolarizations in the cardiomyocyte action potential (Kimrey et al., 2022)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 88205, + "object_name": "Cardiac ventricular cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "The model demonstrates a common dynamic mechanism for calcium-dependent and calcium-independent early afterdepolarizations in cardiomyocytes." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267563", + "public_submitter_email": { + "attr_id": 309, + "value": "rbertram@fsu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Richard Bertram" + }, + "ver_date": "2022-09-23T12:39:31.653", + "ver_number": 4 + }, + "267564": { + "citation": { + "attr_id": 391, + "value": "I. Marinelli, B. M. Thompson, V. S. Parekh, P. A. Fletcher, L. Gerardo-Giorda, A. S. Sherman, R. Bertram, \"Oscillations in K(ATP) Conductance Drive Slow Calcium Oscillations in Pancreatic Beta-Cells\", Biophysical Journal, 121:1449-1464, 2022. 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Duncan, M. Fazli, N.. Romano, P. Le Tissier, R. Bertram, M.J. Shipston, \"Chronic Stress Facilitates Bursting Electrical Activity in Pituitary Corticotrophs\", Journal of Physiology, 600:313-332, 2022. PMID: 34855218" + }, + "class_id": 19, + "created": "2022-09-22T16:55:40.603", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267566, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267571, + "object_name": "Bertram, Richard [rbertram at fsu.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267570, + "object_name": "Duncan PJ, Fazli M, Roman\u00f2 N, Le Tissier P, Bertram R, Shipston MJ (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Stochastic model for pituitary corticotrophs (Duncan et al., 2022)", + "notes": { + "attr_id": 24, + "value": "This model describes the electrical activity of pituitary corticotrophs, in which bursting occurs due to the stochastic opening of BK-type potassium channels." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267566", + "public_submitter_email": { + "attr_id": 309, + "value": "rbertram@fsu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Richard Bertram" + }, + "ver_date": "2022-09-23T11:05:06.84", + "ver_number": 3 + }, + "267583": { + "class_id": 19, + "created": "2022-09-27T20:51:32.073", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 116966, + "object_name": "I_K,Na" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267583, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267604, + "object_name": "Wang YC, Rudi J, Velaso J, Sinha N, Idumah G, Powers RK, Heckman CJ, Chardon MK (2022)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Parallel Tempering MCMC on Liu et al 1998 (Wang et al 2022)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... we propose using a particular Markov chain Monte Carlo (MCMC) algorithm, which has the advantage of inferring parameters in a Bayesian framework. The Bayesian approach is designed to be suitable for multimodal solutions to inverse problems. We introduce and demonstrate the method using a three-channel HH model. We then focus on the inference of nine parameters in an eight-channel HH model, which we analyze in detail. We explore how the MCMC algorithm can uncover complex relationships between inferred parameters using five injected current levels. The MCMC method provides as a result a nine-dimensional posterior distribution, which we analyze visually with solution maps or landscapes of the possible parameter sets...\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267583", + "public_submitter_email": { + "attr_id": 309, + "value": "ywang11@calstatela.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Y. Curtis Wang" + }, + "ver_date": "2022-11-22T11:22:59.827", + "ver_number": 2 + }, + "267586": { + "citation": { + "attr_id": 391, + "value": "Separability of Human Motor Memories during Reaching Adaptation with Force Cues. Crevecoeur et al., 2022, PLoS Computational Biology, 10.1371/journal.pcbi.1009966" + }, + "class_id": 19, + "created": "2022-10-06T08:23:27.82", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267586, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 259370, + "object_name": "Crevecoeur, Fr\u00e9d\u00e9ric " + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Simulations of Reaching Adaptation and Control (Crevecoeur et al., 2022)", + "notes": { + "attr_id": 24, + "value": "The code provided is the one that was used to produce the simulations in Fig. 6. The main script is script_AC.m, which calls several subroutines. The routine script_AC.m defines the online learning rates and simulate a series of trials with either only offline learning, or both offline and online learning. The offline component of learning is identical for the two series. It calls adaptiveReaching.m and adaptiveLQG.m. The former defines the model matrices and the latter runs the simulation of a movement trajectory. Importantly, these two subroutines are the same as those published with our previous study (Crevecoeur et al., eNeuro, 7(1), 2020) and can be accessed at this link: http://modeldb.yale.edu/261466 . Two other functions are added to the model files: the functions expfit.m and expfitdual.m, which are called to extract the time constants associated with exponential models including one or two decay rates, respectively. The routines also use the function nlinfit.m and nlparci.m of the Statistics and Machine Learning Toolbox (Mathworks, Matlab R2017b)." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267586", + "other_type": { + "attr_id": 305, + "value": "Motor control" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "frederic.crevecoeur@uclouvain.be" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Fr\u00e9d\u00e9ric Crevecoeur" + }, + "ver_date": "2022-10-22T07:57:22.633", + "ver_number": 2 + }, + "267587": { + "citation": { + "attr_id": 391, + "value": "36124673" + }, + "class_id": 19, + "created": "2022-10-06T13:28:21.803", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 243506, + "object_name": "IK Skca" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267587, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 112047, + "object_name": "Aging/Alzheimer`s" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 144398, + "object_name": "LFPy" + } + ] + }, + "name": "Human L5 Cortical Circuit (Guet-McCreight)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 230198, + "object_name": "Neocortex U1 interneuron basket PV GABA cell" + }, + { + "object_id": 230211, + "object_name": "Neocortex U1 interneuron bipolar VIP/CR GABA cell" + }, + { + "object_id": 266971, + "object_name": "Neocortex layer 5 pyramidal cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We used L5 Pyr neuron models fit to electrophysiology data from younger and older individuals to simulate detailed human layer 5 microcircuits. These circuits also included detailed parvalbumin+ (PV), somatostatin+ (SST), and vasoactivate intestinal polypeptide+ (VIP) inhibitory interneuron models." + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267587", + "other_currents": { + "attr_id": 303, + "value": "I Kv3.1, IKp, IKt, ICa_HVA, ICa_LVA" + }, + "other_implementer": { + "attr_id": 308, + "value": "Guet-McCreight, Alexandre [agmccrei@ at gmail.com]\r\nHay, Etay [etay.hay at camh.ca]" + }, + "other_neurons": { + "attr_id": 301, + "value": "Neocortex U1 interneuron martinotti SOM GABA cell" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "agmccrei@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Alexandre Guet-McCreight" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 227987, + "object_name": "Human" + }, + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2022-10-18T11:12:27.863", + "ver_number": 4 + }, + "267589": { + "class_id": 19, + "created": "2022-10-15T21:36:38.907", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267589, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 186894, + "object_name": "Tikidji-Hamburyan, Ruben [ruben.tikidji.hamburyan at gmail.com] " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 128875, + "object_name": "Development" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267734, + "object_name": "Tikidji-Hamburyan RA, Govindaiah G, Guido W, Colonnese MT (accepted)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Decorrelation in the developing visual thalamus (Tikidji-Hamburyan et al, accepted)", + "notes": { + "attr_id": 24, + "value": "The developing visual thalamus and cortex extract positional information encoded in\r\nthe correlated activity of retinal ganglion cells by synaptic plasticity, allowing for the refinement of\r\nconnectivity. Here, we use a biophysical model of the visual thalamus during the initial visual circuit\r\nrefinement period to explore the role of synaptic and circuit properties in the regulation of such\r\nneural correlations. We find that the NMDA receptor dominance, combined with weak recurrent\r\nexcitation and inhibition characteristic of this age, prevents the emergence of spike-\u00adcorrelations\r\nbetween thalamocortical neurons on the millisecond timescale. Such precise correlations, which\r\nwould emerge due to the broad, unrefined connections from the retina to the thalamus, reduce the\r\nspatial information contained by thalamic spikes, and therefore we term them \"parasitic\" correlations.\r\nOur results suggest that developing synapses and circuits evolved mechanisms to compensate for\r\nsuch detrimental parasitic correlations arising from the unrefined and immature circuit." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267589", + "other_neurons": { + "attr_id": 301, + "value": "Dorsal lateral geniculate nucleus, thalamocortical" + }, + "other_type": { + "attr_id": 305, + "value": "phenomenological" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ruben.tikidji.hamburyan@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ruben Tikidji-Hamburyan" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2023-05-10T13:11:56.053", + "ver_number": 13 + }, + "267591": { + "class_id": 19, + "created": "2022-10-17T21:03:04.72", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267591, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267779, + "object_name": "Kato M, De Schutter E (2023)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 267776, + "object_name": "NeuroDevSim" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Purkinje cell dendritic tree selection in early cerebellar development (Kato + De Schutter)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 271, + "object_name": "Cerebellum Purkinje GABA cell" + }, + { + "object_id": 7346, + "object_name": "Cerebellum interneuron granule GLU cell" + } + ] + }, + "notes": { + "value": "\"The study presents the first computational model that simultaneously simulates Purkinje cell growth and the dynamics of granule cell migrations during the first two postnatal weeks, allowing exploration of the role of physical and synaptic interactions upon dendritic selection. The model suggests that interaction with parallel fibers is important to establish the distinct planar morphology of Purkinje cell dendrites. Specific rules to select which dendritic trees to keep or retract result in larger winner trees with more synaptic contacts than using random selection. A rule based on afferent synaptic activity was less effective than rules based on dendritic size or numbers of synapses.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267591", + "public_submitter_email": { + "attr_id": 309, + "value": "mizuki.kato@oist.jp" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Mizuki Kato" + }, + "ver_date": "2023-06-28T21:18:13.21", + "ver_number": 9 + }, + "267594": { + "class_id": 19, + "created": "2022-10-19T12:19:16.967", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 143445, + "object_name": "I_KD" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267594, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 240367, + "object_name": "Dewell, Richard Burkett [dewell at bcm.edu]" + }, + { + "object_id": 33804, + "object_name": "Gabbiani, F" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3636, + "object_name": "Spatio-temporal Activity Patterns" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + }, + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 8114, + "object_name": "Dendrite" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "LGMD - ON excitation to dendritic field C", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 116948, + "object_name": "Locust Lobula Giant Movement Detector (LGMD) neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Neuron model code used in \"Contrast-polarity specific mapping improves efficiency of neuronal computation for collision detection\". This model adapts previous LGMD model to investigate the effects of newly discovered ON excitation impinging on dendritic field C" + }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267594", + "other_implementer": { + "attr_id": 308, + "value": "Ricky Morse" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "dewell@bcm.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Richard B. 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under review." + }, + "class_id": 19, + "created": "2022-10-26T17:03:25.157", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 116966, + "object_name": "I_K,Na" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267596, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 260748, + "object_name": "Muller, Salomon Z [szm2106 at columbia.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3629, + "object_name": "Dendritic Action Potentials" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + }, + { + "object_id": 3645, + "object_name": "Axonal Action Potentials" + }, + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267737, + "object_name": "Muller SZ, Abbott LF, Sawtell NB (accepted)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "ELL Medium Ganglion Cell (Mormyrid fish) (Muller et al, 2023)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 260746, + "object_name": "ELL Medium Ganglion cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"In addition to the action potentials used for axonal signaling, many neurons generate dendritic 'spikes' associated with synaptic plasticity. However, in order to control both plasticity and signaling, synaptic inputs must be able to differentially modulate the firing of these two spike types. Here we investigate this issue in the electrosensory lobe (ELL) of weakly electric mormyrid fish, where separate control over axonal and dendritic spikes is essential for the transmission of learned predictive signals from inhibitory interneurons to the output stage of the circuit. Through a combination of experimental and modeling studies, we uncover a novel mechanism by which sensory input selectively modulates the rate of dendritic spiking by adjusting the amplitude of backpropagating axonal action potentials. Interestingly, this mechanism does not require spatially segregated synaptic inputs or dendritic compartmentalization, but relies instead on an electrotonically distant spike initiation site in the axon\u2014a common biophysical feature of neurons. \"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267596", + "public_submitter_email": { + "attr_id": 309, + "value": "szm2106@columbia.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Salomon Z Muller" + }, + "ver_date": "2023-05-17T18:17:41.47", + "ver_number": 17 + }, + "267598": { + "citation": { + "attr_id": 391, + "value": "An Adaptive GLIF model for hippocampal CA1 pyramidal neurons and interneurons" + }, + "class_id": 19, + "created": "2022-11-08T11:26:15.597", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267598, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 2015407, + "object_name": "Marasco A et al. 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Rapid inactivation is a determinant of millisecond scale phenomena, such as spike shape and refractory period. Na+ channels also inactivate orders of magnitude more slowly, and this slow inactivation has impacts on excitability over much longer time scales than those of a single spike or a single inter-spike interval. Here, we focus on the contribution of slow inactivation to the resilience of axonal excitability when ion channels are unevenly distributed along the axon. We study models in which the voltage-gated Na+ and K+ channels are unevenly distributed along axons with different variances, capturing the heterogeneity that biological axons display. In the absence of slow inactivation, many conductance distributions result in spontaneous tonic activity. Faithful axonal propagation is achieved with the introduction of Na+ channel slow inactivation. This \u201cnormalization\u201d effect depends on relations between the kinetics of slow inactivation and the firing frequency. Consequently, neurons with characteristically different firing frequencies will need to implement different sets of channel properties to achieve resilience. The results of this study demonstrate the importance of the intrinsic biophysical properties of ion channels in normalizing axonal function." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267610", + "other_neurons": { + "attr_id": 301, + "value": "axon" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ylzang@brandeis.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Yunliang Zang" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115962, + "object_name": "Unknown" + } + ] + }, + "ver_date": "2023-04-05T10:47:03.1", + "ver_number": 15 + }, + "267611": { + "citation": { + "attr_id": 391, + "value": "On the long time behaviour of single stochastic Hodgkin-Huxley neurons with constant signal, and a construction of circuits of interacting neurons showing self-organized rhythmic oscillations. \r\narXiv:2203.16160\r\nMathematical Neuroscience and Applications (MNA), to appear" + }, + "class_id": 19, + "created": "2023-01-02T13:17:06.367", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267611, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 267612, + "object_name": "H\u00f6pfner, Reinhard" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 238958, + "object_name": "Stochastic simulation" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 150209, + "object_name": "R" + } + ] + }, + "name": "On the long time behaviour of single stochastic Hodgkin-Huxley neurons (H\u00f6pfner 2023)", + "notes": { + "attr_id": 24, + "value": "This is the R program associated to the paper \r\nR. Hoepfner \r\nOn the long time behaviour of \r\nsingle stochastic Hodgkin-Huxley neurons with constant signal, \r\nand a construction of circuits of interacting neurons \r\nshowing self-organized rhythmic interactions \r\nMathematical Neuroscience and Applications, to appear \r\narXiv:2203:16160 " + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267611", + "public_submitter_email": { + "attr_id": 309, + "value": "hoepfner@mathematik.uni-mainz.de" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Reinhard Hoepfner" + }, + "ver_date": "2023-01-10T15:08:51.493", + "ver_number": 10 + }, + "267613": { + "citation": { + "attr_id": 391, + "value": "not available" + }, + "class_id": 19, + "created": "2023-01-14T14:25:26.523", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 242, + "object_name": "I Na,t" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267613, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 98011, + "object_name": "Place cell/field" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Dendritic mechanisms underlying the formation of a Place Cell (Mazzara et al. 2023)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267613", + "other_implementer": { + "attr_id": 308, + "value": "Camille Mazzara camille.mazzara@ibf.cnr.it" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "camille.mazzara@ibf.cnr.it" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Camille Mazzara" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2023-12-01T05:15:30.42", + "ver_number": 7 + }, + "267614": { + "class_id": 19, + "created": "2023-01-18T16:51:51.91", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267614, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 143724, + "object_name": "Bifurcation" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 267000, + "object_name": "Excitability" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 267059, + "object_name": "Pain processing" + }, + { + "object_id": 238958, + "object_name": "Stochastic simulation" + }, + { + "object_id": 116951, + "object_name": "Spike Frequency Adaptation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267740, + "object_name": "Ma X, Miraucourt L, Qiu H, Sharif-Naeini R, Khadra A (2023)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "HH-type model of fast-spiking parvalbumin interneurons in spinal dorsal horn (Ma et al, 2023)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 267058, + "object_name": "Spinal cord lamina I-III interneuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This code is a Hodgkin-Huxley type model of parvalbumin-expressing interneurons (PVINs) in the dorsal horn of the spinal cord. This model is adopted from Bischop et al. 2012 and reparametrized to fit the electrical activity of spinal dorsal horn PVINs from naive mice. It reproduces the firing behavior change from tonic to transient observed in PVINs following nerve injury, which is achieved by a reduction in cytosolic calcium buffer concentration. The bifurcation analysis of PVIN model further explains how the firing pattern transits as the injection current increases, in a manner similar to that seen in our transient firing PVIN recordings. The code also includes an \u201cin vivo-like\u201d neural circuit model of A\u00df fiber-mediate nociceptive neural circuit. The circuit model is stimulated by Poisson-distributed excitatory synaptic currents representing the presynaptic inputs from the A\u00df fibers. It includes the PVIN model above and another HH type model describing the excitability of a PVIN post-synaptic target: the excitatory interneuron expressing protein kinase C gamma (PKCgIN). 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The model resembling inactivating axonal K current caused a broadening of action potentials, while replacement with a non-inactivating model abolished the use-dependent changes.\"\r\nReference:\r\n1. Zheng F, Kamiya H (2023) Simulation test for impartment of use-dependent plasticity by inactivation of axonal potassium channels on hippocampal mossy fibers. Front Cell Neurosci doi: 10.3389/fncel.2023.1154910" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267617", + "public_submitter_email": { + "attr_id": 309, + "value": "kamiya@med.hokudai.ac.jp" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Haruyuki Kamiya" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2023-04-05T10:57:50.75", + "ver_number": 34 + }, + "267618": { + "class_id": 19, + "created": "2023-01-29T19:36:06.673", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267618, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 84598, + "object_name": "Extracellular Fields" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267788, + "object_name": "Xie Y, Qin P, Guo T, Al Abed A, Lovell NH, Tsai D (2023)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 152965, + "object_name": "COMSOL" + }, + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Hybrid NEURON-COMSOL sciatic nerve model with extracellular TIME stimulation (Xie et al, accepted)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 52425, + "object_name": "Myelinated neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A toolkit that automates the NEURON-to-COMSOL and COMSOL-to-NEURON pipeline. It converts a NEURON nerve model to a COMSOL nerve model. It uses TIME electrode to extracellularly stimulate the nerve. It meshes and studies the COMSOL model, and exports the generated extracellular voltage values to the NEURON model. The generated COMSOL model consists of a cubic simulation box. Inside the simulation box is a cylindrical nerve defined by users. The nerve consists of cylindrical fascicles, which consist of cylindrical fibres. A TIME electrode is inserted transversally into the nerve. The TIME electrode consists of a cuboid substrate of type P25N Polyimide and a cylindrical stimulating electrode recessed at its centre. Orientation is towards x-axis. Unit of length is in micrometre. " + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267618", + "other_neurons": { + "attr_id": 301, + "value": "unmyelinated neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "yuyang.xie@outlook.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Yuyang Xie" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2023-07-27T15:52:15.39", + "ver_number": 6 + }, + "267619": { + "class_id": 19, + "created": "2023-01-30T06:55:22.837", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 251, + "object_name": "I h" + } + ] + }, + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + 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"object_name": "Prinz, Astrid [astrid.prinz at emory.edu]" + }, + { + "object_id": 114640, + "object_name": "Gunay, Cengiz [cgunay at emory.edu]" + }, + { + "object_id": 267780, + "object_name": "Megwa, Obinna [omegwa@emory.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 225300, + "object_name": "Electrical-chemical" + }, + { + "object_id": 188516, + "object_name": "Memory" + }, + { + "object_id": 206291, + "object_name": "Membrane Properties" + }, + { + "object_id": 3638, + "object_name": "Simplified Models" + }, + { + "object_id": 120695, + "object_name": "Sodium pump" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 136098, + "object_name": "Information transfer" + }, + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "A single compartment model of Drosophila motor neuron (Megwa et al 2023)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + }, + { + "object_id": 156124, + "object_name": "Abstract single compartment conductance based cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This is a single compartment model aparted from Drosophilia larval crawl motor neuron data as described in\r\nMegwa, Pascual, Gunay, Pulver, Prinz (2023). The model contains Fast and Slow Potassium currents, Transient and Persistent Sodium Currents, Sodium and Potassium Leak Currents, and a NA/K Pump. No Calcium and Potassium concentrations aren't tracked." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267620", + "other_neurons": { + "attr_id": 301, + "value": "Drosophila Motor Neuron" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "omegwa00@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Obinna Fitzgerald Megwa" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 206363, + "object_name": "Drosophila" + } + ] + }, + "ver_date": "2023-06-29T11:31:25.127", + "ver_number": 2 + }, + "267621": { + "citation": { + "attr_id": 391, + "value": "Y. Zang, E. Marder, Neuronal morphology enhances robustness to perturbations of channel densities. Proc. Natl. Acad. Sci. U.S.A. 120, e2219049120 (2023)." + }, + "class_id": 19, + "created": "2023-01-31T12:02:52.357", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267621, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 244383, + "object_name": "Zang, Yunliang " + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267679, + "object_name": "Zang Yunliang, Marder Eve" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "LP neuron model database (Zang and Marder 2023)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 116963, + "object_name": "Stomatogastric Ganglion (STG) Lateral Pyloric (LP) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Biological neurons show significant cell-to-cell variability but have the striking ability to maintain their key firing properties in the face of unpredictable perturbations and stochas- tic noise. Using a population of multi-compartment models consisting of soma, neurites, and axon for the lateral pyloric neuron in the crab stomatogastric ganglion, we explored how rebound bursting is preserved when the 14 channel conductances in each model are all randomly varied. The coupling between the axon and other compartments is critical for the ability of the axon to spike during bursts and consequently determines the set of successful solutions. When the coupling deviates from a biologically realistic range, the neuronal tolerance of conductance variations is lessened. Thus, the gross morphological features of these neurons enhance their robustness to perturbations of channel densities and expand the space of individual variability that can maintain a desired output pattern." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267621", + "other_concept": { + "attr_id": 306, + "value": "robustness" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ylzang@brandeis.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Yunliang Zang" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115959, + "object_name": "Stomatogastric ganglion" + } + ] + }, + "ver_date": "2023-03-03T11:48:31.283", + "ver_number": 8 + }, + "267646": { + "citation": { + "attr_id": 391, + "value": "K. Ly, T. Guo, D. Tsai, M. Muralidharan, M. N. Shivdasani et al., \"Simulating the impact of photoreceptor loss and inner retinal network changes on electrical activity of the retina,\" J Neural Eng, vol. 19, 2022" + }, + "class_id": 19, + "created": "2023-02-12T22:40:13.57", + "gap_junctions": { + "attr_id": 469, + "value": [ + { + "object_id": 114107, + "object_name": "Gap junctions" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267646, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 245691, + "object_name": "Vision" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267654, + "object_name": "Ly K, Guo T, Tsai D, Muralidharan M, Shivdasani MN, Lovell NH, Dokos S (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Biophysically Realistic Network Model of the Wild-Type and Degenerate Retina (Ly et al 2022)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 153361, + "object_name": "Retina photoreceptor rod GLU cell" + }, + { + "object_id": 1966, + "object_name": "Retina photoreceptor cone GLU cell" + }, + { + "object_id": 245428, + "object_name": "Retina horizontal GABA cell" + }, + { + "object_id": 7020, + "object_name": "Retina bipolar GLU cell" + }, + { + "object_id": 270, + "object_name": "Retina ganglion GLU cell" + }, + { + "object_id": 223953, + "object_name": "Retina amacrine cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + }, + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 214, + "object_name": "Glycine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Please read the readme.txt file before running any code.\r\n\r\nObjective: A major reason for poor visual outcomes provided by existing retinal prostheses is the limited knowledge of the impact of photoreceptor loss on retinal remodelling and its subsequent impact on neural responses to electrical stimulation. Computational network models of the neural retina assist in the understanding of normal retinal function but can be also useful for investigating diseased retinal responses to electrical stimulation. \r\nApproach: We developed and validated a biophysically detailed discrete neuronal network model of the retina in the software package NEURON. The model includes rod and cone photoreceptors, ON and OFF bipolar cell pathways, amacrine and horizontal cells and finally, ON and OFF retinal ganglion cells with detailed network connectivity and neural intrinsic properties. By accurately controlling the network parameters, we simulated the impact of varying levels of degeneration on retinal electrical function.\r\nMain results: Our model was able to reproduce characteristic monophasic and biphasic oscillatory patterns seen in ON and OFF neurons during retinal degeneration. Oscillatory activity occurred at 3 Hz with partial photoreceptor loss and at 6 Hz when all photoreceptor input to the retina was removed. Oscillations were found to gradually weaken, then disappear when synapses and gap junctions were destroyed in the inner retina. Without requiring any changes to intrinsic cellular properties of individual inner retinal neurons, our results suggest that changes in connectivity alone were sufficient to give rise to neural oscillations during photoreceptor degeneration, and significant network connectivity destruction in the inner retina terminated the oscillations.\r\nSignificance: Our results provide a platform for further understanding physiological retinal changes with progressive photoreceptor and inner retinal degeneration. Furthermore, our model can be used to guide future stimulation strategies for retinal prostheses to benefit patients at different stages of disease progression, particularly in the early and mid-stages of retinal degeneration." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267646", + "public_submitter_email": { + "attr_id": 309, + "value": "keith.ly@unsw.edu.au" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Keith Ly" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 207, + "object_name": "mGluR" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 118529, + "object_name": "Retina" + } + ] + }, + "ver_date": "2023-02-16T11:44:54.367", + "ver_number": 4 + }, + "267647": { + "citation": { + "attr_id": 391, + "value": "Susi G, Garc\u00e9s P, Paracone E, Cristini A, Salerno M, Maest\u00fa F, Pereda E. FNS allows efficient event-driven spiking neural network simulations based on a neuron model supporting spike latency. Sci Rep. 2021 Jun 9;11(1):12160. doi: 10.1038/s41598-021-91513-8. PMID: 34108523; PMCID: PMC8190312." + }, + "class_id": 19, + "created": "2023-02-15T14:00:07.637", + "gitrepo": true, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267647, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 114660, + "object_name": "Connectivity matrix" + }, + { + "object_id": 139148, + "object_name": "Brain Rhythms" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 87578, + "object_name": "STDP" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267660, + "object_name": "Susi G, Garces P, Paracone E, Cristini A, Salerno M, Maestu F, Pereda E (2021)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + }, + { + "object_id": 267344, + "object_name": "Spiking neural network" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 53895, + "object_name": "Java" + }, + { + "object_id": 267677, + "object_name": "FNS Neural Simulator" + } + ] + }, + "name": "FNS spiking neural simulator; LIFL neuron model, event-driven simulation (Susi et al 2021)", + "notes": { + "attr_id": 24, + "value": "FNS is an event-driven Spiking Neural Network simulator, oriented to data-driven simulations.\r\nFNS combines spiking/synaptic level description with the event-driven approach, allowing the user to define heterogeneous modules and multi-scale connectivity with delayed connections and plastic synapses, providing fast simulations at the same time. A novel parallelization strategy is also implemented in order to further speed up simulations.\r\nFNS is based on the Leaky-Integrate and Fire with Latency (LIFL) spiking neuron model, that combines some realistic neurocomputational features to low computational complexity.\r\nFNS is written in Java, distributed as open source and protected by the GPL license." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267647", + "other_concept": { + "attr_id": 306, + "value": "Overthreshold Latency" + }, + "other_implementer": { + "attr_id": 308, + "value": "https://www.fnsneuralsimulator.org/" + }, + "other_neurons": { + "attr_id": 301, + "value": "Leaky integrate-and-fire with latency" + }, + "other_type": { + "attr_id": 305, + "value": "event-driven" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "gianluca.susi82@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Gianluca Susi" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 227987, + "object_name": "Human" + } + ] + }, + "ver_date": "2023-02-24T09:29:24.14", + "ver_number": 23 + }, + "267661": { + "citation": { + "attr_id": 391, + "value": "PMID: 36607284; DOI: 10.1097/j.pain.0000000000002822\r\nC Marra, TV Hartke, M Ringkamp, M Goldfarb (2023) Enhanced sodium channel inactivation by temperature and FHF2 deficiency blocks heat nociception. Pain, in press." + }, + "class_id": 19, + "created": "2023-02-19T14:53:57.137", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267661, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 206273, + "object_name": "Goldfarb, Mitchell goldfarb at genectr.hunter.cuny.edu]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 96767, + "object_name": "Nociception" + }, + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 152639, + "object_name": "Temperature" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267668, + "object_name": "Marra C, Hartke TV, Ringkamp M, Goldfarb M (in press)" + } + ] + }, + "name": "DRG nociceptors from wild-type and Fhf2-KO mice. Fhf2 gene knockout blocks heat nociception. (Marra et al., 2023)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 136307, + "object_name": "Dorsal Root Ganglion (DRG) cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We employ electrophysiological and computational methods to show that the heat nociception deficit in Fhf2 knockout mice can be explained by the combined effects of elevated temperature and FHF2 deficiency on the fast inactivation gating of Na v 1.7 and tetrodotoxin-resistant sodium channels expressed in dorsal root ganglion C-fibers." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267661", + "public_submitter_email": { + "attr_id": 309, + "value": "goldfarb@genectr.hunter.cuny.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Mitchell Goldfarb" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2023-06-23T12:01:54.043", + "ver_number": 5 + }, + "267666": { + "class_id": 19, + "created": "2023-02-21T12:25:12.907", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267666, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 22010, + "object_name": "Facilitation" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 267059, + "object_name": "Pain processing" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3540, + "object_name": "Channel/Receptor" + }, + { + "object_id": 3539, + "object_name": "Synapse" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "A model of ASIC1a and synaptic cleft pH modulating wind-up in wide dynamic range neurons (Delrocq)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 128565, + "object_name": "Wide dynamic range neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We introduce a model of ASIC1a homomeric (and heteromeric) ion channel inserted into a pre-existing model of wide dynamic range (WDR) neuron of the spinal cord together with a novel synaptic cleft acidification mechanism. This computational model shows a dual contribution of the ASIC1a channels to wind-up, a facilitation mechanism of WDR neurons, which has been verified experimentally: inhibiting or maximally activating ASICs reduce wind-up. The wind-up inhibition by activation of ASICs is likely mediated by calcium influx and calcium-activated potassium channels." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267666", + "other_concept": { + "attr_id": 306, + "value": "wind-up; acidification" + }, + "other_implementer": { + "attr_id": 308, + "value": "Ariane Delrocq\r\nRomain Veltz" + }, + "other_receptors": { + "attr_id": 302, + "value": "ASIC" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ariane-delrocq@laposte.net" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ariane Delrocq" + }, + "ver_date": "2023-03-28T16:12:05.643", + "ver_number": 5 + }, + "267669": { + "citation": { + "attr_id": 391, + "value": "36802196" + }, + "class_id": 19, + "created": "2023-02-22T23:47:36.907", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 3277, + "object_name": "I A, slow" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 88212, + "object_name": "KCNQ1" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267669, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 143112, + "object_name": "Kitano, Katsunori [kkt23219 at is.ritsumei.ac.jp]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 65417, + "object_name": "Parkinson's" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267683, + "object_name": "Kitano K (2023)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "The STN-GPe network; subthalamic nucleus, prototypic GPe, and arkypallidal GPe neurons (Kitano 2023)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 74300, + "object_name": "Subthalamus nucleus projection neuron" + }, + { + "object_id": 88217, + "object_name": "Globus pallidus neuron" + } + ] + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267669", + "other_implementer": { + "attr_id": 308, + "value": "Kitano, Katsunori [kkt23219 at is.ritsumei.ac.jp] (updated email address)" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "kkt23219@is.ritsumei.ac.jp" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Katsunori Kitano" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] + }, + "ver_date": "2023-03-03T13:40:21.147", + "ver_number": 6 + }, + "267680": { + "citation": { + "attr_id": 391, + "value": "Dainauskas JJ, Marie H, Migliore M and Saudargiene A. GluN2B-NMDAR subunit contribution on synaptic plasticity: a phenomenological model for CA3-CA1 synapses. Frontiers in Synaptic Neuroscience 2023. DOI: 10.3389/fnsyn.2023.1113957" + }, + "class_id": 19, + "created": "2023-02-24T14:25:27.52", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267680, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 87578, + "object_name": "STDP" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267688, + "object_name": "Dainauskas JJ, Marie H, Migliore M, Saudargiene A" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "CA1 pyr cell: phenomenological NMDAR-based model of synaptic plasticity (Dainauskas et al 2023)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This Python code implements a phenomenological NMDA receptor-based voltage-dependent model of synaptic plasticity for CA3-CA1 synapse and shows weight changes of a synapse placed on a two-compartmental model of a hippocampal CA1 pyramidal neuron for spike-timing-dependent synaptic plasticity (STDP) and frequency-dependent synaptic plasticity stimulation protocols. The developed model predicts altered learning rules in synapses formed on the apical dendrites of the detailed compartmental model of CA1 pyramidal neuron in the presence of the GluN2B-NMDA receptor hypofunction." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267680", + "other_implementer": { + "attr_id": 308, + "value": "Dainauskas, Justinas [justinas.dainauskas at gmail.com]\r\nSaudargiene, Ausra [ausra.saudargiene at lsmu.lt]" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "ausra.saudargiene@lsmu.lt" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Ausra Saudargiene" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2023-03-06T15:06:37.833", + "ver_number": 10 + }, + "267682": { + "citation": { + "attr_id": 391, + "value": "https://www.biorxiv.org/content/10.1101/2022.08.23.505010v2.abstract" + }, + "class_id": 19, + "created": "2023-02-28T09:16:43.033", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267682, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267685, + "object_name": "Meng JH, Schuman B, Rudy B, Wang XJ" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 231240, + "object_name": "Brian 2" + } + ] + }, + "name": "Single neuron models of four types of L1 mouse Interneurons: Canpy, NGFC, alpha7 and VIP cells", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Neocortical Layer 1 (L1) consists of the distal dendrites of pyramidal cells and GABAergic interneurons (INs) and receives extensive long-range \u201ctop-down\u201d projections, but L1 INs remain poorly understood. In this work, we systematically examined the distinct dominant electrophysiological features for four unique IN subtypes in L1 that were previously identified from mice of either gender: Canopy cells\r\nshow an irregular firing pattern near rheobase; Neurogliaform cells (NGFCs) are\r\nlate-spiking, and their firing rate accelerates during current injections; cells with strong\r\nexpression of the a7 nicotinic receptor (a7 cells), display onset (rebound) bursting;\r\nvasoactive intestinal peptide (VIP) expressing cells exhibit high input resistance, strong\r\nadaptation, and irregular firing. Computational modeling revealed that these diverse\r\nneurophysiological features could be explained by an extended\r\nexponential-integrate-and-fire neuron model with varying contributions of a slowly\r\ninactivating K+ channel (SIK), a T-type Ca2+ channel, and a spike-triggered\r\nCa2+-dependent K+ channel. In particular, we show that irregular firing results from\r\nsquare-wave bursting through a fast-slow analysis. Furthermore, we demonstrate that\r\nirregular firing is frequently observed in VIP cells due to the interaction between strong\r\nadaptation and a SIK channel. At last, we reveal that the VIP and a7 cell models\r\nresonant with Alpha/Theta band input through a dynamic gain analysis." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267682", + "other_currents": { + "attr_id": 303, + "value": "slow-inactivation Potassium channel" + }, + "other_implementer": { + "attr_id": 308, + "value": "John Hongyu Meng" + }, + "other_neurons": { + "attr_id": 301, + "value": "Neurocortex S1 L1 Interneurons;" + }, + "other_simulator": { + "attr_id": 307, + "value": "Python" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "john.meng@nyu.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "John Hongyu Meng" + }, + "ver_date": "2023-04-03T09:45:30.753", + "ver_number": 13 + }, + "267686": { + "citation": { + "attr_id": 391, + "value": "Theta-gamma phase amplitude coupling in a hippocampal CA1 microcircuit, Adam Ponzi, Salvador Dura-Bernal, Michele Migliore. 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The experimental data for initial conditions for this model and validation of the model was based on data reported in literature. In simulations, elevations in the aggregations of major proteins involved in the pathology of AD and PD including amyloid beta, alpha synuclein, tau have been modelled. 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The biochemical systems theory model shows autocrine loops with positive feedback enabling excessive immune response, cytokines, transcription factors, and interferons, which can imbalance homeostasis of the system.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267736", + "public_submitter_email": { + "attr_id": 309, + "value": "shyam@amrita.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Shyam Diwakar" + }, + "ver_date": "2023-05-31T23:07:18.897", + "ver_number": 4 + }, + "267738": { + "citation": { + "attr_id": 391, + "value": "The paper associated with this model will be published." + }, + "class_id": 19, + "created": "2023-05-19T02:02:43.043", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267738, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 235771, + "object_name": "Kim, Hojeong [hojeong.kim03 at gmail.com]" + } + ] + }, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 186770, + "object_name": "Motor control" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267751, + "object_name": "Kim H, Heckman CJ (2023)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "A computational model for fast skeletal muscle (Kim and Heckman, accepted)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 105531, + "object_name": "Skeletal muscle cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "A computational framework for investigating the dynamic variation in the calcium-force relationship during force generation under physiological conditions of neural excitation and muscle length in intact fast skeletal muscles" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267738", + "public_submitter_email": { + "attr_id": 309, + "value": "hojeong.kim03@gmail.com" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Hojeong Kim" + }, + "ver_date": "2023-06-02T15:38:15.95", + "ver_number": 6 + }, + "267742": { + "class_id": 19, + "created": "2023-05-30T19:03:16.943", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 250, + "object_name": "I M" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267742, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 49304, + "object_name": "Methods" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267775, + "object_name": "Chapman DP, Vicini S, Burns MP, Evans R (2023)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + }, + { + "object_id": 266893, + "object_name": "Statistical learning model" + }, + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 245004, + "object_name": "BluePyOpt\r\n" + }, + { + "object_id": 267060, + "object_name": "eFEL" + } + ] + }, + "name": "CA1 pyramidal populations after high frequency head impacts (Chapman, et al., 2023)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "\"... we generated in silico models of CA1 pyramidal neurons from current clamp data of control mice and mice that sustained HFHI. 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We provide an open access set of CA1 pyramidal neuron models for both control and HFHI conditions that can be used to predict the effects of pharmacological interventions in TBI models.\"" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267742", + "other_concept": { + "attr_id": 306, + "value": "Traumatic brain injury" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "dpc53@georgetown.edu" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Daniel Patrick Chapman" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + }, + { + "object_id": 228598, + "object_name": "Mouse" + } + ] + }, + "ver_date": "2023-06-27T01:12:04.62", + "ver_number": 4 + }, + "267754": { + "citation": { + "attr_id": 391, + "value": "Sun, Z., Crompton, D., Lankarany, M., & Skinner, F. K. (2023). Reduced oriens-lacunosum/moleculare cell model identifies biophysical current balances for in vivo theta frequency spiking resonance. Frontiers in Neural Circuits, 17, 1076761\u20131076761. https://doi.org/10.3389/fncir.2023.1076761" + }, + "class_id": 19, + "created": "2023-06-07T13:02:58.43", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 143445, + "object_name": "I_KD" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 247, + "object_name": "I A" + } + ] + }, + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267754, + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 143636, + "object_name": "Conductance distributions" + }, + { + "object_id": 243515, + "object_name": "Conductances estimation" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267767, + "object_name": "Sun Z, Crompton D, Lankarany M, Skinner FK (2023)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Hippocampus CA1 OLM cell multicompartment conductance-based model (Sun et al. 2023)", + "neurons": { + "attr_id": 414, + "value": [ + { + "object_id": 190560, + "object_name": "Hippocampus CA1 stratum oriens lacunosum-moleculare interneuron " + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Referred to as FULL in the reference paper. 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Through experiments and computational simulations, we explained the basis for and consequences of desynchronization, showing that asynchronous spikes are sufficient to mediate pain relief whereas synchronous spikes are necessary for paresthesia." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267768", + "other_implementer": { + "attr_id": 308, + "value": "Tianhe.Zhang@bsci.com, nooshin.abdollahi@mail.utoronto.ca, laura.medlock at mail.utoronto.ca" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "nooshin.abdollahi@mail.utoronto.ca" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Nooshin Abdollahi" + }, + "ver_date": "2023-10-19T04:42:13.059145", + "ver_number": 17 + }, + "2001735": { + "_citation_text": "Mousa, M. H., & Elbasiouny, S. M. (2021). Estimating the effects of slicing on the electrophysiological properties of spinal motoneurons under normal and disease conditions. 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Using high-fidelity cell models of neonatal WT and SOD cells, we examined the effects of slice thickness, soma position within the slice, and slice orientation. Our results offer information that enhances the rigor of MN electrophysiological data measured from the slice preparation under normal and disease conditions." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2001735", + "public_submitter_email": { + "attr_id": 309, + "value": "mohamed.mousa@wright.edu" + }, + "receptors": { + "attr_id": 26, + "value": [] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115954, + "object_name": "Spinal motoneuron" + } + ] + }, + "ver_date": "2023-06-15T19:11:48.660506+00:00", + "ver_number": 1 + }, + "2014814": { + "_citation_text": "Fietkiewicz, C., Corrales Marco, D., Chiel, H. J. and Thomas, P. J. (2023). Tutorial: Using NEURON for Neuromechanical Simulations. Frontiers in Computational Neuroscience 17: 1143323.", + "_implementers_text": "C Fietkiewicz, RA McDougal", + "class_id": 19, + "created": "2023-06-27T14:48:27.465876+00:00", + "currents": { + "attr_id": 27, + "value": [] + }, + "expmotivation": "", + "gene": { + "attr_id": 476, + "value": [] + }, + "id": 2014814, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 189948, + "object_name": "Fietkiewicz, Chris" + }, + { + "object_id": 146951, + "object_name": "McDougal, Robert [robert.mcdougal at yale.edu]" + } + ] + }, + "license": "MIT", + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + }, + { + "object_id": 49304, + "object_name": "Methods" + }, + { + "object_id": 232102, + "object_name": "Respiratory control" + }, + { + "object_id": 3642, + "object_name": "Tutorial/Teaching" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267778, + "object_name": "Fietkiewicz C, McDougal RA, Marco DC, Chiel HJ, Thomas PJ (2023)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 111952, + "object_name": "Neural mass" + }, + { + "object_id": 22013, + "object_name": "Neuromuscular Junction" + }, + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Tutorial: Using NEURON for Neuromechanical Simulations (Fietkiewicz et al., 2023)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 232103, + "object_name": "PreBotzinger complex neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [] + }, + "notes": { + "attr_id": 24, + "value": "Multiple models demonstrate concepts for neuromechanical simulations using pointers. 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Neurosci. 17:1281932. doi: 10.3389/fncel.2023.1281932", + "_implementers_text": "Paola Vitale, Rosanna Migliore, Fabio Librizzi", + "class_id": 19, + "created": "2023-06-30T13:45:21.959562+00:00", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 143445, + "object_name": "I_KD" + } + ] + }, + "expmotivation": "The fundamental role of any neuron within a network is to transform complex spatiotemporal synaptic input patterns into individual output spikes. These spikes, in turn, act as inputs for other neurons in the network. Neurons must execute this function across a diverse range of physiological conditions, often based on species-specific traits. Therefore, it is crucial to determine the extent to which findings can be extrapolated between species and, ultimately, to humans. In this study, we employed a multidisciplinary approach to pinpoint the factors accounting for the observed electrophysiological differences between mice and rats, the two species most used in experimental and computational research. After analyzing the morphological properties of their hippocampal CA1 pyramidal cells, we conducted a statistical comparison of rat and mouse electrophysiological\r\nfeatures in response to somatic current injections. This analysis aimed to uncover the parameters underlying these distinctions. Using a well-established computational workflow, we created ten distinct single-cell computational models of mouse CA1 pyramidal neurons, ready to be used in a full-scale hippocampal circuit. By comparing their responses to a variety of somatic and\r\nsynaptic inputs with those of rat models, we generated experimentally testable hypotheses regarding species-specific differences in ion channel distribution, kinetics, and the electrophysiological mechanisms underlying their distinct responses to synaptic inputs during the behaviorally relevant Gamma and Sharp-Wave rhythms.", + "gene": { + "attr_id": 476, + "value": [] + }, + "id": 2014816, + "license": "other", + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 143636, + "object_name": "Conductance distributions" + }, + { + "object_id": 243515, + "object_name": "Conductances estimation" + }, + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + }, + { + "object_id": 267000, + "object_name": "Excitability" + }, + { + "object_id": 3631, + "object_name": "Ion Channel Kinetics" + }, + { + "object_id": 64174, + "object_name": "Synaptic Integration" + } + ] + }, + "model_paper": { + "value": [] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Different responses of mice and rats hippocampus CA1 pyramidal neurons to in vitro and in vivo-like inputs (vitale et al., 2023)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [] + }, + "notes": { + "attr_id": 24, + "value": "" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2014816", + "public_submitter_email": { + "attr_id": 309, + "value": "michele.migliore@cnr.it" + }, + "receptors": { + "attr_id": 26, + "value": [] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2023-06-30T13:45:21.959562+00:00", + "ver_number": 1 + }, + "2014817": { + "_citation_text": "Bueno-Orovio A, Cherry EM, Fenton FH. Minimal model for human ventricular action potentials in tissue. J Theor Biol 2008;253(3):544-560.", + "_implementers_text": "Dr. Shou-Hei Tseng, Dr. Edmund C. So, and Dr. Sheng-Nan Wu", + "class_id": 19, + "created": "2023-07-03T07:40:14.205442+00:00", + "currents": { + "attr_id": 27, + "value": [] + }, + "expmotivation": "To better understand the electrical behaivor of human ventricular action potentials", + "gene": { + "attr_id": 476, + "value": [] + }, + "id": 2014817, + "license": "other", + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 2014820, + "object_name": "Bueno-Orovio A, Cherry EM, Fenton FH. (2008)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Minimal model for human ventricular action potentials (Bueno-Orovio et al 2008)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 88205, + "object_name": "Cardiac ventricular cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [] + }, + "notes": { + "attr_id": 24, + "value": "Modeling the dynamics of wave propagation in human ventricular tissue and studying wave stability require models that reproduce realistic characteristics in tissue. We present a minimal ventricular (MV) human model that is designed to reproduce important tissue-level characteristics of epicardial, endocardial and midmyocardial cells, including action potential (AP) amplitudes and morphologies, upstroke velocities, steady-state action potential duration (APD) and conduction velocity (CV) restitution curves, minimum APD, and minimum diastolic interval. The model is then compared with three previously published human ventricular cell models, the Priebe and Beuckelmann (PB), the Ten Tusscher-Noble-Noble-Panfilov (TNNP), and the Iyer-Mazhari-Winslow (IMW). For the first time, the stability of reentrant waves for all four models is analyzed, and quantitative comparisons are made among the models in single cells and in tissue. The PB, TNNP, and IMW models exhibit quantitative differences in APD and CV rate adaptation, as well as completely different reentrant wave dynamics of quasi-breakup, stability, and breakup, respectively. All the models have dominant frequencies comparable to clinical values except for the IMW model, which has a large range of frequencies extending beyond the clinical range for both ventricular tachycardia (VT) and ventricular fibrillation (VF). The TNNP and IMW models possess a large degree of short-term memory and we show for the first time the existence of memory in CV restitution. The MV model also can be fitted to reproduce the dynamics of other models and is computationally more efficient: the times required to simulate the MV, TNNP, PB and IMW models follow the ratio 1:31:50:8084." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2014817", + "public_submitter_email": { + "attr_id": 309, + "value": "071320@tool.caaumed.org.tw" + }, + "receptors": { + "attr_id": 26, + "value": [] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 227987, + "object_name": "Human" + } + ] + }, + "ver_date": "2023-07-03T07:40:14.205442+00:00", + "ver_number": 1 + }, + "2014822": { + "_citation_text": "Justinas J Dainauskas, Paola Vitale, Sebastien Moreno, Helene Marie, Michele Migliore and Ausra Saudargiene. Altered synaptic plasticity at hippocampal CA1\u2013CA3 synapses in Alzheimer's disease: integration of amyloid precursor protein intracellular domain and amyloid beta effects into computational models. Frontiers in Computational Neuroscience 2023 doi.org/10.3389/fncom.2023.1305169", + "_implementers_text": "Justinas Dainauskas", + "class_id": 19, + "created": "2023-07-17T09:39:28.624325+00:00", + "currents": { + "attr_id": 27, + "value": [] + }, + "expmotivation": "Accumulating experimental evidence suggests that AICD and amyloid beta play a role in modifying hippocampus function in the early stages of the disease. AICD has a strong impact on synapse function (Pousinha et al., 2017) and on intrinsic excitability (Pousinha et al., 2019). Increased levels of AICD enhance GluN2B-NMDAR contribution, overactivate SK channels, and strongly perturb long-term potentiation (LTP), but spare long-term depression (LTD) in CA1 pyramidal neurons. Partial antagonism of GluN2B-NMDAR rescues LTP in early AD. Oligomeric forms of amyloid beta prevented induction of LTP in hippocampal cultured neurons (Opazo et al., 2018). CaMKII inhibitors KN93, tatCN21, and specific GluN2B-NMDAR antagonist ifenprodil completely rescued A\u03b2-induced inhibition of LTP by preventing CaMKII overactivation and dendritic spine loss (Opazo et al., 2018). \r\n\r\nPousinha, P. A., Mouska, X., Raymond, E. F., Gwizdek, C., Dhib, G., Poupon, G., et al. (2017). Physiological and pathophysiological control of synaptic GluN2B-NMDA receptors by the C-terminal domain of amyloid precursor protein. eLife 6:e25659. doi: 10.7554/eLife.25659\r\n\r\nPousinha, P. A., Mouska, X., Bianchi, D., Temido-Ferreira, M., Raj ao-Saraiva, J., Gomes, R., et al. (2019). The amyloid precursor protein C-terminal domain alters CA1 neuron firing, modifying hippocampus oscillations and impairing spatial memory encoding. Cell Rep. 29, 317\u2013331.e5. doi: 10.1016/j.celrep.2019.08.103\r\n\r\nOpazo, P., Viana da Silva, S., Carta, M., Breillat, C., Coultrap, S. J., Grillo-Bosch, D., et al. (2018). CaMKII metaplasticity drives A\u03b2 oligomer-mediated synaptotoxicity. Cell Rep. 23, 3137\u20133145.", + "gene": { + "attr_id": 476, + "value": [] + }, + "id": 2014822, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 2015568, + "object_name": "Dainauskas, Justinas J" + } + ] + }, + "license": "other", + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "value": [] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "Hippocampal synaptic plasticity in Alzheimer's disease (Dainauskas et al., 2023)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "This model analyses altered hippocampal synaptic plasticity and its rescue under the Alzheimer's disease (AD) conditions, when the concentrations of AD-related peptides, such as the amyloid precursor protein intracellular domain (AICD) and amyloid beta (A\u03b2), are increased. The phenomenological NMDA receptor-based voltage-dependent model is used to model synaptic modifications at the CA3-CA1 synapses onto the multicompartmental CA1 pyramidal neuron. The modeling results show that partial blockade of Glu2NB-NMDAR-gated channel restores intrinsic excitability of a CA1 pyramidal neuron and rescues long-term potentiation in AICD and A\u03b2 conditions. The model is implemented in Python and NEURON." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2014822", + "public_submitter_email": { + "attr_id": 309, + "value": "ausra.saudargiene@lsmu.lt" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 205, + "object_name": "AMPA" + }, + { + "object_id": 206, + "object_name": "NMDA" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2023-12-08T17:53:02.385486", + "ver_number": 2 + }, + "2014825": { + "_citation_text": "Doya and Blackwell, PLoS Computational Biology, 2023 In Press", + "_implementers_text": "", + "class_id": 19, + "created": "2023-07-25T14:42:15.499288+00:00", + "currents": { + "attr_id": 27, + "value": [] + }, + "expmotivation": "", + "gene": { + "attr_id": 476, + "value": [] + }, + "id": 2014825, + "license": "GPL", + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 83535, + "object_name": "Learning" + }, + { + "object_id": 83536, + "object_name": "Reinforcement Learning" + }, + { + "object_id": 3643, + "object_name": "Synaptic Plasticity" + } + ] + }, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 267785, + "object_name": "Blackwell K, Doya K (2023)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "TD2Q reinforcement learning (Blackwell and Doya, in press)", + "neurons": { + "attr_id": 25, + "value": [] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "TD2Q is a Q learning type of reinforcement learning algorithm that corresponds to the basal ganglia with two Q matrices, one representing direct pathway neurons (G) and another representing indirect pathway neurons (N). Unlike previous two-Q architectures, a novel and critical aspect of TD2Q is to update the G and N matrices utilizing the temporal difference reward prediction error. A best action is selected for N and G using a softmax with a reward-dependent adaptive exploration parameter, and then differences are resolved using a second selection step applied to the two action probabilities. The model is tested on a range of multi-step tasks including extinction, renewal, discrimination; switching reward probability learning; and sequence learning. Simulations show that TD2Q produces behaviors similar to rodents in choice and sequence learning tasks, and that use of the temporal difference reward prediction error is required to learn multi-step tasks. Performance in the sequence learning task is dramatically improved with two matrices. " + }, + "public_submitter_email": { + "attr_id": 309, + "value": "kim-blackwell@uiowa.edu" + }, + "receptors": { + "attr_id": 26, + "value": [] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + }, + { + "object_id": 138633, + "object_name": "Striatum" + } + ] + }, + "ver_date": "2023-07-25T14:42:15.499288+00:00", + "ver_number": 1 + }, + "2014830": { + "_citation_text": "S\u00e6tra MJ, Ellingsrud AJ, Rognes ME (2023) Neural activity induces strongly coupled electro-chemo-mechanical interactions and fluid flow in astrocyte networks and extracellular space\u2014A computational study. PLoS Comput Biol 19(7): e1010996. https://doi.org/10.1371/journal.pcbi.1010996", + "_implementers_text": "", + "class_id": 19, + "created": "2023-08-28T15:20:34.096895+00:00", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 167500, + "object_name": "Kir" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + } + ] + }, + "expmotivation": "", + "id": 2014830, + "license": "MIT", + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 144383, + "object_name": "Cellular volume dynamics" + }, + { + "object_id": 151948, + "object_name": "Potassium buffering" + }, + { + "object_id": 267119, + "object_name": "Electrodiffusion" + }, + { + "object_id": 144409, + "object_name": "Homeostasis" + }, + { + "object_id": 225300, + "object_name": "Electrical-chemical" + } + ] + }, + "model_paper": { + "value": [ + { + "object_id": 2015400, + "object_name": "S\u00e6tra MJ, Ellingsrud AJ, Rognes ME (2023)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 120799, + "object_name": "Glia" + }, + { + "object_id": 183720, + "object_name": "Extracellular" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "ffian: Fluid Flow In Astrocyte Networks (S\u00e6tra et al. 2023)", + "notes": { + "attr_id": 24, + "value": "ffian is an implementation of the KNP continuity equations for a one-dimensional system containing two compartments: one representing an astrocyte network (ICS) and one representing the extracellular space (ECS). ffian.The model takes both transmembrane- and compartmental fluid flow into account and predicts the evolution in time and distribution in space of the volume fractions, ion concentrations (Na+, K+, Cl-), electrical potentials, and hydrostatic pressures in the ICS and ECS.\r\n\r\nExample simulations and installation guidelines can be found here: https://github.com/martejulie/fluid-flow-in-astrocyte-networks" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2014830", + "public_submitter_email": { + "attr_id": 309, + "value": "martejulie@simula.no" + }, + "ver_date": "2023-08-28T15:20:34.096895+00:00", + "ver_number": 1 + }, + "2014833": { + "_citation_text": "", + "_implementers_text": "", + "class_id": 19, + "created": "2023-09-06T09:55:34.348355+00:00", + "currents": { + "attr_id": 27, + "value": [] + }, + "expmotivation": "", + "gene": { + "attr_id": 476, + "value": [] + }, + "id": 2014833, + "license": "other", + "model_concept": { + "attr_id": 113, + "value": [] + }, + "model_paper": { + "value": [ + { + "object_id": 2015677, + "object_name": "Rose AC, Alashwal H, Moustafa AA, Weidemann G (2023)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 83551, + "object_name": "Connectionist Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "A neural network model of mathematics anxiety: The role of attention (Rose et al., 2023)", + "neurons": { + "attr_id": 25, + "value": [] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [] + }, + "notes": { + "attr_id": 24, + "value": "Anxiety about performing numerical calculations is becoming an increasingly important issue. Termed mathematics anxiety, this condition negatively impacts performance in numerical tasks which can affect education outcomes and future employment. The disruption account proposes poor performance is due to anxiety disrupting limited attentional and inhibitory resources leaving fewer cognitive resources for the current task. This study provides the first neural network model of math anxiety. The model simulates performance in two commonly-used tasks related to math anxiety: the numerical Stroop and symbolic number comparison. Different model modifications were used to simulate high and low math-anxious conditions by modifying attentional processes and learning; these model modifications address different theories of math anxiety. The model simulations suggest that math anxiety is associated with reduced attention to numerical stimuli. These results are consistent with the disruption account and the attentional control theory where anxiety decreases goal-directed attention and increases stimulus-driven attention. " + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2014833", + "public_submitter_email": { + "attr_id": 309, + "value": "a.rose@westernsydney.edu.au" + }, + "receptors": { + "attr_id": 26, + "value": [] + }, + "region": { + "attr_id": 471, + "value": [] + }, + "ver_date": "2023-09-06T09:55:34.348355+00:00", + "ver_number": 1 + }, + "2014996": { + "_citation_text": "John SR, Barnett WH, Abdala AP, Zoccal DB, Rubin JE & Molkov YI. (2023). Exploring the role of K\u04e7lliker-Fuse nucleus in breathing variability via mathematical modeling. Journal of Physiology", + "_implementers_text": "S. R. John, W. H. Barnett, A. P. Abdala, D. B. Zoccal, J. E. Rubin, and Y. I. Molkov", + "class_id": 19, + "created": "2023-09-17T01:10:04.499935+00:00", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 241, + "object_name": "I Na,p" + } + ] + }, + "expmotivation": "", + "gene": { + "attr_id": 476, + "value": [] + }, + "id": 2014996, + "license": "MIT", + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 232102, + "object_name": "Respiratory control" + } + ] + }, + "model_paper": { + "value": [] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + }, + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Exploring the role of K\u04e7lliker-Fuse nucleus in breathing variability via mathematical modeling (John et al., 2023)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 232103, + "object_name": "PreBotzinger complex neuron" + }, + { + "object_id": 125611, + "object_name": "Respiratory column neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 239, + "object_name": "Serotonin" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We explore the dynamics of K\u04e7lliker-Fuse nucleus (KF), which is involved in regulating normal breathing, controlling active abdominal expiration during increased ventilation, and is also known to play a role in the development of breathing abnormalities associated with Rett syndrome (RTT). We present reduced computational models of the respiratory core neurons along with the KF unit that simulate both normal and RTT-like breathing patterns. These models provide a general framework for understanding KF dynamics and potential network interactions." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2014996", + "public_submitter_email": { + "attr_id": 309, + "value": "srj35@pitt.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 198, + "object_name": "5-HT1" + }, + { + "object_id": 213, + "object_name": "Gaba" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 183076, + "object_name": "Brainstem" + } + ] + }, + "ver_date": "2023-09-17T01:10:04.499935+00:00", + "ver_number": 1 + }, + "2014998": { + "_citation_text": "", + "_implementers_text": "Selena Singh, Abraham Nunes", + "class_id": 19, + "created": "2023-09-27T19:29:09.970268+00:00", + "currents": { + "attr_id": 27, + "value": [] + }, + "expmotivation": "", + "gene": { + "attr_id": 476, + "value": [] + }, + "id": 2014998, + "license": "MIT", + "model_concept": { + "attr_id": 113, + "value": [] + }, + "model_paper": { + "value": [] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Frequency-dependent pattern separation in a biophysical model of the dentate gyrus, (Singh et al., 2023)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 207692, + "object_name": "Dentate gyrus HIPP cell" + }, + { + "object_id": 52417, + "object_name": "Dentate gyrus basket cell" + }, + { + "object_id": 257, + "object_name": "Dentate gyrus granule GLU cell" + }, + { + "object_id": 52416, + "object_name": "Dentate gyrus mossy cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [] + }, + "notes": { + "attr_id": 24, + "value": "Background and Objectives: Mnemonic discrimination (MD) may be dependent on oscillatory perforant path input frequencies to the hippocampus in a \u201cU\u201d shaped fashion, where some studies show that slow and fast input frequencies support MD, while other studies show that intermediate frequencies disrupt MD. We hypothesise that pattern separation (PS) underlies frequency-dependent MD performance. We aim to study, in a computational model of the hippocampal dentate gyrus (DG), the network and cellular mechanisms governing this putative \u201cU\u201d shaped PS relationship. \r\nMethods: We implemented a biophysical model of the DG that produces the hypothesised \u201cU\u201d-shaped input frequency-PS relationship, and its associated oscillatory electrophysiological signatures. We subsequently evaluated the network\u2019s PS ability using an adapted spatiotemporal task. We undertook systematic lesion studies to identify the network-level mechanisms driving the \u201cU\u201d shaped input frequency-PS relationship. A minimal circuit of a single granule cell (GC) stimulated with oscillatory inputs was also used to study potential cellular-level mechanisms.\r\nResults: Lesioning synapses onto GCs did not impact the \u201cU\u201d-shaped input frequency-PS relationship. Furthermore, GC inhibition limits PS performance for fast frequency inputs, while enhancing PS for slow frequency inputs. GC interspike interval was found to be input frequency dependent in a \u201cU\u201d-shaped fashion, paralleling frequency-dependent PS observed at the network level. Additionally, GCs showed an attenuated firing response for fast frequency inputs.\r\nConclusions: Independent of network-level inhibition, GCs may intrinsically be capable of producing a \u201cU\u201d shaped input frequency-PS relationship. GCs may preferentially decorrelate slow and fast inputs via spike timing reorganisation and high frequency filtering. " + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2014998", + "public_submitter_email": { + "attr_id": 309, + "value": "singhs11@mcmaster.ca" + }, + "receptors": { + "attr_id": 26, + "value": [] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115947, + "object_name": "Dentate gyrus" + } + ] + }, + "ver_date": "2023-09-27T19:29:09.970268+00:00", + "ver_number": 1 + }, + "2014999": { + "_citation_text": "PMID: 37528537 \r\nPMCID: PMC10528740 ", + "_implementers_text": "Pei-Chi Yang, Mao-Tsuen Jeng, Adam Rose, Timothy J Lewis, Colleen Clancy", + "class_id": 19, + "created": "2023-09-29T19:29:05.102345+00:00", + "currents": { + "attr_id": 27, + "value": [] + }, + "expmotivation": "A multi-layered model representation of the autonomic nervous system that includes sympathetic and parasympathetic branches, each with sparse random intralayer connectivity, synaptic dynamics and conductance based integrate-and-fire neurons, generates firing patterns in agreement with experiment. ", + "gene": { + "attr_id": 476, + "value": [] + }, + "id": 2014999, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 2015396, + "object_name": "Yang, Pei-Chi" + }, + { + "object_id": 2015397, + "object_name": "Jeng, Mao-Tsuen" + }, + { + "object_id": 2015398, + "object_name": "Rose, Adam" + }, + { + "object_id": 2015399, + "object_name": "Lewis, Timothy J" + }, + { + "object_id": 87283, + "object_name": "Clancy, Colleen E [ceclancy at ucdavis.edu]" + } + ] + }, + "license": "MIT", + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 128875, + "object_name": "Development" + } + ] + }, + "model_paper": { + "value": [ + { + "object_id": 2015395, + "object_name": "Yang PC et al. (2023)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3539, + "object_name": "Synapse" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + } + ] + }, + "name": "A multiscale predictive digital twin for neurocardiac modulation (Yang et al., 2023)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + }, + { + "object_id": 88205, + "object_name": "Cardiac ventricular cell" + }, + { + "object_id": 52423, + "object_name": "Heart cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [] + }, + "notes": { + "attr_id": 24, + "value": "Cardiac function is tightly regulated by the autonomic nervous system (ANS). Activation of the sympathetic nervous system increases cardiac output by increasing heart rate and stroke volume, while parasympathetic nerve stimulation instantly slows heart rate. Importantly, imbalance in autonomic control of the heart has been implicated in the development of arrhythmias and heart failure. Understanding of the mechanisms and effects of autonomic stimulation is a major challenge because synapses in different regions of the heart result in multiple changes to heart function. For example, nerve synapses on the sinoatrial node (SAN) impact pacemaking, while synapses on contractile cells alter contraction and arrhythmia vulnerability. Here, we present a multiscale neurocardiac modelling and simulator tool that predicts the effect of efferent stimulation of the sympathetic and parasympathetic branches of the ANS on the cardiac SAN and ventricular myocardium. The model includes a layered representation of the ANS and reproduces firing properties measured experimentally. Model parameters are derived from experiments and atomistic simulations. The model is a first prototype of a digital twin that is applied to make predictions across all system scales, from subcellular signalling to pacemaker frequency to tissue level responses. We predict conditions under which autonomic imbalance induces proarrhythmia and can be modified to prevent or inhibit arrhythmia. In summary, the multiscale model constitutes a predictive digital twin framework to test and guide high-throughput prediction of novel neuromodulatory therapy. KEY POINTS: A multi-layered model representation of the autonomic nervous system that includes sympathetic and parasympathetic branches, each with sparse random intralayer connectivity, synaptic dynamics and conductance based integrate-and-fire neurons generates firing patterns in close agreement with experiment. A key feature of the neurocardiac computational model is the connection between the autonomic nervous system and both pacemaker and contractile cells, where modification to pacemaker frequency drives initiation of electrical signals in the contractile cells. We utilized atomic-scale molecular dynamics simulations to predict the association and dissociation rates of noradrenaline with the \u03b2-adrenergic receptor. Multiscale predictions demonstrate how autonomic imbalance may increase proclivity to arrhythmias or be used to terminate arrhythmias. The model serves as a first step towards a digital twin for predicting neuromodulation to prevent or reduce disease. " + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2014999", + "public_submitter_email": { + "attr_id": 309, + "value": "pcyang@ucdavis.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 215, + "object_name": "Adrenergic" + } + ] + }, + "region": { + "attr_id": 471, + "value": [] + }, + "ver_date": "2023-10-19T03:13:01.329329", + "ver_number": 6 + }, + "2015412": { + "_citation_text": "Maksymchuk, Natalia, Akira Sakurai, Daniel N. Cox, and Gennady S. Cymbalyuk. 2023. \"Cold-Temperature Coding with Bursting and Spiking Based on TRP Channel Dynamics in Drosophila Larva Sensory Neurons\" International Journal of Molecular Sciences 24, no. 19: 14638. https://doi.org/10.3390/ijms241914638 ", + "_implementers_text": "Natalia Maksymchuk", + "class_id": 19, + "created": "2023-10-21T22:59:24.437151+00:00", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 137264, + "object_name": "I trp" + } + ] + }, + "expmotivation": "How CIII neurons code temperature remains unclear. We combined computational and electrophysiological methods to address this question. In electrophysiological experiments, we identified two basic cold-evoked patterns of CIII neurons: bursting and spiking. In response to a fast temperature drop to noxious cold, CIII neurons distinctly mark different phases of the stimulus. Bursts frequently occurred along with the fast temperature drop, forming a peak in the spiking rate and likely coding the high rate of the temperature change. Single spikes dominated at a steady temperature and exhibited frequency adaptation following the peak. When temperature decreased slowly to the same value, mainly spiking activity was observed, with bursts occurring sporadically throughout the stimulation. The spike and the burst frequencies positively correlated with the rate of the temperature drop. ", + "gene": { + "attr_id": 476, + "value": [] + }, + "id": 2015412, + "license": "GPL", + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 47316, + "object_name": "Invertebrate" + }, + { + "object_id": 96767, + "object_name": "Nociception" + }, + { + "object_id": 182362, + "object_name": "Sensory coding" + }, + { + "object_id": 152639, + "object_name": "Temperature" + }, + { + "object_id": 240376, + "object_name": "Temporal Coding" + }, + { + "object_id": 3633, + "object_name": "Temporal Pattern Generation" + } + ] + }, + "model_paper": { + "value": [] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Cold-Temperature Coding with Bursting and Spiking Based on TRP Channel Dynamics in Drosophila Larva Sensory Neurons (Maksymchuk, N., A. Sakurai, D.N. Cox, and G.S. Cymbalyuk, 2023)", + "neurons": { + "attr_id": 25, + "value": [] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [] + }, + "notes": { + "attr_id": 24, + "value": "Temperature sensation involves thermosensitive TRP (thermoTRP) and non-TRP channels. Drosophila larval Class III (CIII) neurons serve as the primary cold nociceptors and express a suite of thermoTRP channels implicated in noxious cold sensation. Using this computational model, we explain the distinction in the occurrence of the two CIII cold-evoked patterns bursting and spiking using the dynamics of a thermoTRP current. A two-parameter activity map (Temperature, constant TRP current conductance) marks parameters that support silent, spiking, and bursting regimes. Projecting on the map the instantaneous TRP conductance, governed by activation and inactivation processes, reflects temperature coding responses as a path across silent, spiking, or bursting domains on the map. The map sheds light on how various parameter sets for TRP kinetics represent various types of cold-evoked responses. Our results indicate that bursting detects the high rate of temperature change, whereas tonic spiking could reflect both the rate of change and magnitude of steady cold temperature." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2015412", + "public_submitter_email": { + "attr_id": 309, + "value": "gcymbalyuk@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 206363, + "object_name": "Drosophila" + } + ] + }, + "ver_date": "2023-10-21T22:59:24.437151+00:00", + "ver_number": 1 + }, + "2015413": { + "_citation_text": "Maksymchuk, N., Sakurai, A., Cox, D. N., & Cymbalyuk, G. (2022). Transient and Steady-State Properties of Drosophila Sensory Neurons Coding Noxious Cold Temperature. Frontiers in cellular neuroscience, 16, 831803. https://doi.org/10.3389/fncel.2022.831803", + "_implementers_text": "Natalia Maksymchuk", + "class_id": 19, + "created": "2023-10-22T17:15:32.219952+00:00", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 137264, + "object_name": "I trp" + } + ] + }, + "expmotivation": "In response to a fast temperature change (-2 to -6\u00b0C/s) from room temperature to noxious cold, the Drosophila larva class III (CIII) neurons exhibited a pronounced peak of a spiking rate with subsequent relaxation to a steady-state spiking. The magnitude of the peak was higher for a higher rate of temperature decrease, while slow temperature decrease (-0.1\u00b0C/s) evoked no distinct peak of the spiking rate. The rate of the steady-state spiking depended on the magnitude of the final temperature and was higher at lower temperatures. For each neuron, we characterized this dependence by estimating the temperature of the half activation of the spiking rate by curve fitting neuron's spiking rate responses to a Boltzmann function. We found that neurons had a temperature of the half activation distributed over a wide temperature range. We also found that CIII neurons responded to decrease rather than increase in temperature. There was a significant difference in spiking activity between fast and slow returns from noxious cold to room temperature: The CIII neurons usually stopped activity abruptly in the case of the fast return and continued spiking for some time in the case of the slow return.", + "id": 2015413, + "license": "GPL", + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 47316, + "object_name": "Invertebrate" + }, + { + "object_id": 96767, + "object_name": "Nociception" + }, + { + "object_id": 182362, + "object_name": "Sensory coding" + }, + { + "object_id": 152639, + "object_name": "Temperature" + } + ] + }, + "model_paper": { + "value": [] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Transient and Steady-State Properties of Drosophila Sensory Neurons Coding Noxious Cold Temperature; (Maksymchuk, N., Sakurai, A., Cox, D. N., & Cymbalyuk, G. 2022) ", + "notes": { + "attr_id": 24, + "value": "Coding noxious cold signals, such as the magnitude and rate of temperature change, play essential roles in the survival of organisms. We combined electrophysiological and computational neuroscience methods to investigate the neural dynamics of Drosophila larva cold-sensing Class III (CIII) neurons.\r\nWe developed a biophysical model of CIII neurons using a generalized description of transient receptor potential (TRP) current kinetics with temperature-dependent activation and Ca2+-dependent inactivation. This model recapitulated the key features of the spiking rate responses found in experiments and suggested mechanisms explaining the transient and steady-state activity of the CIII neurons at different cold temperatures and rates of their decrease and increase. We conclude that CIII neurons encode at least three types of cold sensory information: the rate of temperature decrease by a peak of the firing rate, the magnitude of cold temperature by the rate of steady spiking activity, and direction of temperature change by spiking activity augmentation or suppression corresponding to temperature decrease and increase, respectively. " + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2015413", + "public_submitter_email": { + "attr_id": 309, + "value": "gcymbalyuk@gmail.com" + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 206363, + "object_name": "Drosophila" + } + ] + }, + "ver_date": "2023-10-22T17:15:32.219952+00:00", + "ver_number": 1 + }, + "2015414": { + "_citation_text": "Katie N. Clements, Sungwoo Ahn, Choongseok Park, Faith K. Heagy, Thomas H. Miller, Miki Kassai, Fadi A. Issa (2023). Socially mediated shift in neural circuits activation regulated by synergistic neuromodulatory signaling. eNeuro (accepted). ", + "_implementers_text": "Ahn, Sungwoo; Park, Choongseok", + "class_id": 19, + "created": "2023-10-24T16:43:08.470034+00:00", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + } + ] + }, + "expmotivation": "Animals exhibit context-dependent behavioral decisions that are mediated by specific motor circuits. In social species these decisions are often influenced by social status. Although social status-dependent neural plasticity of motor circuits has been investigated in vertebrates, little is known of how cellular plasticity translates into differences in motor activity. Here, we used zebrafish (Danio rerio) as a model organism to examine how social dominance influences the activation of swimming and the Mauthner mediated startle escape behaviors. ", + "gene": { + "attr_id": 476, + "value": [] + }, + "id": 2015414, + "license": "other", + "model_concept": { + "attr_id": 113, + "value": [] + }, + "model_paper": { + "value": [] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 33977, + "object_name": "XPPAUT" + } + ] + }, + "name": "Zebrafish Mauthner escape circuit with dopamine, gaba, and glycine (Clements et al., accepted)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 150222, + "object_name": "Abstract Morris-Lecar neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 224, + "object_name": "Dopamine" + }, + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 214, + "object_name": "Glycine" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "We used zebrafish (Danio rerio) as a model organism to examine how social dominance influences the activation of swimming and the Mauthner mediated startle escape behaviors. We show that the status-dependent shift in behavior patterns whereby dominants increase swimming and reduce sensitivity of startle escape while subordinates reduce their swimming and increase startle sensitivity is regulated by the synergistic interactions of dopaminergic, glycinergic, and GABAergic inputs to shift the balance of activation of the underlying motor circuits. Neurocomputational modeling of the empirical results shows that drd1b functions as the molecular regulator to facilitate the shift between excitatory and inhibitory pathways. The results illustrate how reconfiguration in network dynamics serves as an adaptive strategy to cope with changes in the social environment and are likely conserved and applicable to other social species. \r\n\r\n" + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2015414", + "public_submitter_email": { + "attr_id": 309, + "value": "ahns15@ecu.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 196, + "object_name": "D1" + } + ] + }, + "region": { + "attr_id": 471, + "value": [] + }, + "ver_date": "2023-10-24T16:43:08.470034+00:00", + "ver_number": 1 + }, + "2015420": { + "_citation_text": "F\u00fczesi, T., Rasiah, N.P., Rosenegger, D.G. et al. Hypothalamic CRH neurons represent physiological memory of positive and negative experience. Nat Commun 14, 8522 (2023). https://doi.org/10.1038/s41467-023-44163-5", + "_implementers_text": "Wilten Nicola", + "class_id": 19, + "created": "2023-11-14T17:54:34.421658+00:00", + "currents": { + "attr_id": 27, + "value": [] + }, + "expmotivation": "", + "gene": { + "attr_id": 476, + "value": [] + }, + "id": 2015420, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 146501, + "object_name": "Nicola, Wilten [wnicola at uwaterloo.ca]" + } + ] + }, + "license": "MIT", + "model_concept": { + "attr_id": 113, + "value": [] + }, + "model_paper": { + "value": [ + { + "object_id": 2015950, + "object_name": "F\u00fczesi T et al. (2023)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 267344, + "object_name": "Spiking neural network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Hypothalamic CRH neurons represent physiological memory of positive and negative experience (F\u00fczesi et al., 2023)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 154740, + "object_name": "Abstract integrate-and-fire leaky neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [] + }, + "notes": { + "attr_id": 24, + "value": "Recalling a salient experience provokes specific behaviors and changes in the physiology or internal state. Relatively little is known about how physiological memories are encoded. Here we examined the neural substrates of physiological memory by probing CRHPVN neurons of mice, which control the endocrine response to stress. We demonstrate these cells show contextual memory following exposure to a stimulus with negative or positive valence. Specifically, a negative stimulus invokes a two-factor learning rule that favors an increase in the activity of weak cells during recall. In contrast, the contextual memory of positive valence relies on a one-factor rule to decrease the activity of all neurons. Finally, the aversive memory in CRHPVN neurons outlasts the behavioral response. These observations provide new information about how specific physiological memories of aversive and appetitive experiences are encoded and demonstrate that behavioral readouts may not accurately reflect physiological changes invoked by the memory of salient experiences." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2015420", + "public_submitter_email": { + "attr_id": 309, + "value": "wilten.nicola@ucalgary.ca" + }, + "receptors": { + "attr_id": 26, + "value": [] + }, + "region": { + "attr_id": 471, + "value": [] + }, + "ver_date": "2023-12-23T01:03:57.025112", + "ver_number": 3 + }, + "2015421": { + "_citation_text": "O'Neill KM, Anderson ED, Mukherjee S, Gandu S, McEwan SA, Omelchenko A, Rodriguez AR, Losert W, Meaney DF, Babadi B, Firestein BL. Time-dependent homeostatic mechanisms underlie Brain-Derived Neurotrophic Factor action on neural circuitry. Comms Bio, 2023.", + "_implementers_text": "", + "class_id": 19, + "created": "2023-11-14T18:25:07.265928+00:00", + "currents": { + "attr_id": 27, + "value": [] + }, + "expmotivation": "Plasticity and homeostatic mechanisms allow neural networks to maintain proper function while responding to physiological challenges. Despite previous work investigating morphological and synaptic effects of brain-derived neurotrophic factor (BDNF), the most prevalent growth factor in the central nervous system, how exposure to BDNF manifests at the network level remains unknown. Here we report that BDNF treatment affects rodent hippocampal network dynamics during development and recovery from glutamate-induced excitotoxicity in culture. Importantly, these effects are not obvious when traditional activity metrics are used, so we delve more deeply into network organization, functional analyses, and in silico simulations. We demonstrate that BDNF partially restores homeostasis by promoting recovery of weak and medium connections after injury. Imaging and computational analyses suggest these effects are caused by changes to inhibitory neurons and connections. Based on our in silico simulations, we find that BDNF remodels the network by indirectly strengthening weak excitatory synapses after injury. Ultimately, our findings may explain the difficulties encountered in preclinical and clinical trials with BDNF and also offer information for future trials to consider. 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J Physiol 552(3):673-689", + "id": 137, + "identifier": "ca1-oriens-lacunosum-moleculare-saraga-et-al-2003", + "main_branch": "master", + "name": "CA1 Oriens Lacunosum Moleculare - Saraga et al. 2003" + }, + "ca1-pv-fast-firing-cell-ferguson-et-al-2013": { + "Brain region": "Hippocampus", + "Brian support": "0", + "Category": "Project", + "Cell type": "CA1 PV+ fast-firing cell", + "Curation level": "3", + "Endorsement": "2", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/FergusonEtAl2013-PVFastFiringCell.git", + "MOOSE support": "-1", + "ModelDB reference": "182843", + "NEST support": "-1", + "NEURON support": "3", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "Brian", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Mouse", + "Spine classification": "Vertebrate", + "Status info": "Connverted to LEMS/NeuroML2", + "Tags": "Izhikevich neuron model,Hippocampal formation,Interneuron", + "description": "\r\n\r\ngithub:README.md", + "id": 132, + "identifier": "ca1-pv-fast-firing-cell-ferguson-et-al-2013", + "main_branch": "master", + "name": "CA1 PV+ fast firing cell - Ferguson et al. 2013" + }, + "ca1-pyr-cell-ferguson-et-al-2014": { + "Brain region": "Hippocampus", + "Brian support": "-1", + "Category": "Project", + "Cell type": "CA1 Pyramidal cell", + "Curation level": "3", + "Endorsement": "2", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/FergusonEtAl2014-CA1PyrCell.git", + "MOOSE support": "-1", + "ModelDB reference": "182515", + "NEST support": "-1", + "NEURON support": "3", + "NeuroLex Ids: Cells": "sao830368389", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "Brain", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Has been converted to NeuroML 2/LEMS.", + "Tags": "Hippocampal formation,CA1,Izhikevich neuron model", + "description": "\r\n\r\ngithub:README.md", + "id": 129, + "identifier": "ca1-pyr-cell-ferguson-et-al-2014", + "main_branch": "master", + "name": "CA1 pyramidal cell - Ferguson et al. 2014" + }, + "ca1pyramidalcell": { + "Brain region": "Hippocampus", + "Brian support": "-1", + "Category": "Project", + "Cell type": "CA1 Pyramidal cell", + "Curation level": "2", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "3", + "GitHub repository": "https://github.com/OpenSourceBrain/CA1PyramidalCell", + "MOOSE support": "3", + "ModelDB reference": "55035", + "NEST support": "-1", + "NEURON support": "3", + "NeuroLex Ids: Cells": "sao830368389", + "NeuroML v1.x support": "3", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "NEURON", + "PSICS support": "3", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "This model has been fully converted to *NeuroML version 1.8.1*. This process was described in Gleeson et al. 2010 (http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000815).\r\n\r\nThis model can currently be executed in *NEURON*, *GENESIS*, *MOOSE* and *PSICS*. It can also be exported to pure NeuroML2 and from there converted to NEURON using jNeuroML.", + "Tags": "Hippocampal formation,CA1,Detailed cell model", + "description": "![](/attachments/download/1/CA1-spike.PNG)\r\nConversion of [hippocampal CA1 pyramidal cell](http://neurolex.org/wiki/Category:Hippocampus_CA1_pyramidal_cell) from [Migliore et al 2005](http://senselab.med.yale.edu/ModelDB/ShowModel.asp?model=55035).\r\n\r\n[![Build Status](https://travis-ci.org/OpenSourceBrain/CA1PyramidalCell.svg?branch=master)](https://travis-ci.org/OpenSourceBrain/CA1PyramidalCell)\r\n", + "id": 15, + "identifier": "ca1pyramidalcell", + "main_branch": "master", + "name": "CA1 Pyramidal Cell - Migliore et al. 2005" + }, + "catmaidshowcase": { + "Brain region": "Other", + "Brian support": "-1", + "Category": "Showcase", + "Cell type": "Multiple", + "Curation level": "1", + "Endorsement": "1", + "Family": "Insect", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/CATMAIDShowcase.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "-1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "2", + "NeuroML version": "v2.x", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Drosophila", + "Spine classification": "Invertebrate", + "Status info": "Only contains some example exported morphologies so far ", + "Tags": "Connectomics,Neuronal reconstruction", + "description": "Project for example NeuroML files generated by [CATMAID](http://www.catmaid.org).\n", + "id": 67, + "identifier": "catmaidshowcase", + "main_branch": "master", + "name": "CATMAID Showcase" + }, + "celegans": { + "Brain region": "Nervous system", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "1", + "Endorsement": "1", + "Family": "Nematode", + "GENESIS 2 support": "0", + "GitHub repository": "https://github.com/openworm/CElegansNeuroML", + "MOOSE support": "0", + "NEST support": "-1", + "NEURON support": "1", + "NeuroLex Ids: Cells": "birnlex_420", + "NeuroML v1.x support": "2", + "NeuroML v2.x support": "2", + "NeuroML version": "v1.x", + "Original format": "Blender", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "C. elegans", + "Spine classification": "Invertebrate", + "Status info": "*This model is still very much in the early stages of development.* \r\n\r\nWhile the individual cell morphologies are in NeuroML, and the connectivity can be generated in neuroConstruct using real connection data, a full spiking model requires much more information about the electrical properties of neurons in C. elegans", + "Tags": "OpenWorm,Whole brain model", + "description": "![](http://www.opensourcebrain.org/attachments/download/22/medium.png)\r\n\r\n**The latest version of this model can be found at http://www.opensourcebrain.org/projects/c302**\r\nNote: the development of the OpenWorm model of [C. elegans](http://en.wikipedia.org/wiki/Caenorhabditis_elegans) is taking place at [http://www.openworm.org](http://www.openworm.org).\r\n\r\nA full list of those involved in that project can be found [here](http://www.openworm.org/people.html).\r\n\r\nThe C. elegans 3D model this was derived from was produced by Dr. Christian Grove and Dr. Paul Sternberg at the VirtualWorm project (WormBase, CalTech) and released into the public domain. You can visit the VirtualWorm home page at http://caltech.wormbase.org/virtualworm/ .\r\n\r\nFor details on running this neuroConstruct project see: https://github.com/openworm/OpenWorm/wiki/Running-the-C.-elegans-model-in-neuroConstruct.\r\n\r\nThis is a **work in progress**. Please [get in contact](http://www.openworm.org/contacts.html) for more information.\r\n", + "id": 19, + "identifier": "celegans", + "main_branch": "master", + "name": "OpenWorm C. elegans network model" + }, + "cerebellarnucleusneuron": { + "Brain region": "Cerebellum", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Cerebellar Nucleus Neuron", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "1", + "GitHub repository": "https://github.com/OpenSourceBrain/CerebellarNucleusNeuron", + "MOOSE support": "1", + "ModelDB reference": "136175", + "NEST support": "-1", + "NEURON support": "1", + "NeuroLex Ids: Cells": "BAMSC1042", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "1", + "Original format": "GENESIS", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "The original source code for this model in *GENESIS* is available on the OSB [here](http://www.opensourcebrain.org/projects/cerebellarnucleusneuron/repository/revisions/master/show/GENESIS). A slightly modified version of this model has also been created in *NEURON* by the original developers [here](http://www.opensourcebrain.org/projects/cerebellarnucleusneuron/repository/revisions/master/show/NEURON).\r\n\r\nConversion of this model to NeuroML is underway. A subset of the channels have been converted (see [here](http://www.opensourcebrain.org/projects/cerebellarnucleusneuron/repository/revisions/master/show/neuroConstruct/cellMechanisms)). Most channels in the original model can be expressed in *NeuroML version 1.x*, but the GHK based current will require *NeuroML v2.0*.", + "Tags": "Detailed cell model,Goldman-Hodgkin-Katz current,neuroConstruct", + "description": "![](http://www.opensourcebrain.org/attachments/download/34/medium.png)\r\n\r\n[Deep cerebellar nucleus neuron](http://neurolex.org/wiki/Category:Cerebellum_nucleus_reciprocal_projections_neuron) model from: Determinants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cells. Steuber V, Schultheiss NW, Silver RA, De Schutter E, Jaeger D. J Comput Neurosci. 2011 Jun;30(3):633-58\r\n", + "id": 16, + "identifier": "cerebellarnucleusneuron", + "main_branch": "master", + "name": "Cerebellar Nucleus Neuron - Steuber et al. 2011" + }, + "cerebellum--cerebellar-golgi-cell--solinasetal-golgicell": { + "Brain region": "Cerebellum", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Golgi cell", + "Curation level": "3", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/SolinasEtAl-GolgiCell", + "MOOSE support": "-1", + "ModelDB reference": "112685", + "NEST support": "-1", + "NEURON support": "2", + "NeuroLex Ids: Cells": "sao1415726815", + "NeuroML v1.x support": "2", + "NeuroML v2.x support": "0", + "NeuroML version": "v1.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Well tested NeuroML v1.8.1 -> NEURON. KAHP channel with kinetic scheme formalism precludes use on GENESIS/MOOSE for now.", + "Tags": "Detailed cell model,neuroConstruct", + "description": "Multicompartmental model of cerebellar Golgi cell from: Solinas S, Forti L, Cesana E, Mapelli J, De Schutter E, D\u2019Angelo E. **Computational reconstruction of pacemaking and intrinsic electroresponsiveness in cerebellar Golgi cells**. [Front Cell Neurosci. 2007;1:2](http://journal.frontiersin.org/article/10.3389/neuro.03.002.2007/abstract). \r\n\r\nBased on implementation in NEURON taken from: http://senselab.med.yale.edu/modeldb/ShowModel.asp?model=112685.\r\n", + "id": 4, + "identifier": "cerebellum--cerebellar-golgi-cell--solinasetal-golgicell", + "main_branch": "master", + "name": "Cerebellar Golgi Cell - Solinas et al. 2007" + }, + "channelworm": { + "Brian support": "-1", + "Category": "Showcase", + "Curation level": "2", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/openworm/ChannelWorm.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "-1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "1", + "NeuroML version": "v1.x", + "PSICS support": "-1", + "PyNN support": "-1", + "Spine classification": "Vertebrate", + "Tags": "C. elegans,OpenWorm,Ion channels", + "description": "An OpenWorm repository to integrate data, information, scripts, and models of ion channels in C. elegans", + "id": 142, + "identifier": "channelworm", + "main_branch": "master", + "name": "ChannelWorm" + }, + "cnoshowcase": { + "Brian support": "-1", + "Category": "Showcase", + "Curation level": "2", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/CNOShowcase.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "-1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "-1", + "NeuroML version": "v1.x", + "Original format": "Python", + "PSICS support": "-1", + "PyNN support": "-1", + "Spine classification": "Vertebrate", + "Status info": "Simple script written. Yann Le Franc will manually add CNO Ids to text file which will be updated on each OSB project.", + "Tags": "Ontology,Model annotation,Metadata", + "description": "![](http://www.opensourcebrain.org/attachments/download/103/CNO_image.jpg)\r\nScripts for interacting with the Computational Neuroscience Ontology:\r\nhttp://www.incf.org/programs/modeling/cno\r\n\r\nSee the [[Wiki]] for more details.\r\n", + "id": 75, + "identifier": "cnoshowcase", + "main_branch": "master", + "name": "Computational Neuroscience Ontology Showcase" + }, + "connectivityshowcase": { + "Brian support": "-1", + "Category": "Showcase", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/ConnectivityShowcase.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "-1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "1", + "NeuroML version": "v2.x", + "Original format": "Multiple", + "PSICS support": "-1", + "PyNN support": "-1", + "Spine classification": "Vertebrate", + "Status info": "Work in progress...", + "description": "github:README.md", + "id": 206, + "identifier": "connectivityshowcase", + "main_branch": "master", + "name": "ConnectivityShowcase" + }, + "csashowcase": { + "Brain region": "Other", + "Brian support": "2", + "Category": "Showcase", + "Cell type": "None", + "Curation level": "1", + "Endorsement": "1", + "Family": "Other", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/CSAShowcase", + "MOOSE support": "0", + "NEST support": "2", + "NEURON support": "2", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "2", + "NeuroML version": "v1.x", + "Original format": "Python", + "PSICS support": "-1", + "PyNN support": "2", + "Specie": "None", + "Spine classification": "None", + "Status info": "A number of example scripts are available showing basic CSA functionality and its use in PyNN scripts (which can be exported to NeuroML 2).", + "Tags": "Python tools for computational neuroscience", + "description": "![](http://www.opensourcebrain.org/projects/csashowcase/repository/revisions/master/entry/images/gaussian_sm.png)\n\nA project highlighting some of the features of the [Connection Set Algebra](http://software.incf.org/software/csa) library, and how it can interact with NeuroML & PyNN.\n\nFor more details see the [[Wiki]].\n", + "id": 44, + "identifier": "csashowcase", + "main_branch": "master", + "name": "Connection Set Algebra Showcase" + }, + "dentate": { + "Bitbucket repository": "https://bitbucket.org/mbezaire/dentate", + "Brain region": "Dentate gyrus", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "0", + "MOOSE support": "0", + "ModelDB reference": "124513", + "NEST support": "-1", + "NEURON support": "1", + "NeuroLex Ids: Cells": "nifext_153; nlx_cell_091201;\tnlx_cell_100201;\tnlx_22799", + "NeuroML v1.x support": "1", + "NeuroML v2.x support": "0", + "NeuroML version": "v1.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "NEURON scripts are based on Schneider et al 2012. In parallel to the ongoing development of this, the neuroConstruct project contains the cell models in NeuroML format (not yet complete).", + "Tags": "Hippocampal formation,Detailed cell model", + "description": "![](http://www.opensourcebrain.org/attachments/download/109/DG_500.jpg)\r\n\r\nDentate Gyrus network model developed by the [Soltesz lab](http://www.ivansolteszlab.org). This is an ongoing update to the large scale dentate gyrus model presented in Schneider CJ, Bezaire M, Soltesz I.\r\nToward a full-scale computational model of the rat dentate gyrus. Front Neural Circuits. (2012) 6:83.\r\n\r\n\r\nSee also [CA1 Local Network, Bezaire and Soltesz 2013](http://www.opensourcebrain.org/projects/nc_ca1) and [CA1 Pyramidal Sublayer Microcircuit, Lee et al 2014](http://www.opensourcebrain.org/projects/nc_superdeep) from the [Soltesz lab](http://www.opensourcebrain.org/groups/201).\r\n", + "id": 79, + "identifier": "dentate", + "name": "Dentate Gyrus network model - Soltesz lab" + }, + "dentategyrus2005": { + "Brain region": "Dentate gyrus", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "1", + "GitHub repository": "https://github.com/OpenSourceBrain/DentateGyrus2005.git", + "MOOSE support": "1", + "ModelDB reference": "51781", + "NEST support": "-1", + "NEURON support": "2", + "NeuroLex Ids: Cells": "nifext_153; nlx_cell_091201;\tnlx_cell_100201;\tnlx_22799", + "NeuroML v1.x support": "1", + "NeuroML v2.x support": "0", + "NeuroML version": "v1.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Model mostly converted to neuroConstruct. Some channels have been converted to NeuroML format. \r\n\r\nNOTE Work on this project will halt in favour of: http://www.opensourcebrain.org/projects/dentate", + "Tags": "Hippocampal formation", + "description": "github:README.md", + "id": 76, + "identifier": "dentategyrus2005", + "main_branch": "master", + "name": "Dentate Gyrus - Santhakumar et al 2005" + }, + "destexhe_jcns_2009": { + "Bitbucket repository": "https://bitbucket.org/apdavison/destexhe_jcns_2009", + "Brain region": "Neocortex", + "Brian support": "1", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "2", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/destexhe_jcns_2009", + "MOOSE support": "-1", + "ModelDB reference": "126466", + "NEST support": "2", + "NEURON support": "2", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "1", + "NeuroML version": "v1.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "3", + "Specie": "Generic", + "Spine classification": "Vertebrate", + "Status info": "**In development**", + "Tags": "Point neuron network,Adaptive exponential integrate and fire neuron", + "description": "Network simulations of self-sustained activity in networks of adaptive exponential integrate and fire neurons.\r\n\r\nFrom: Self-sustained asynchronous irregular states and Up\u2013Down states in thalamic, cortical and thalamocortical networks of nonlinear integrate-and-fire neurons, Alain Destexhe, [J Comp Neuroscience 2009](http://link.springer.com/article/10.1007%2Fs10827-009-0164-4)\r\n", + "id": 31, + "identifier": "destexhe_jcns_2009", + "main_branch": "master", + "name": "Self Sustained Network Activity - Destexhe 2009" + }, + "dlgninterneuronhalnesetal2011": { + "Brain region": "Thalamus", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Dorsal geniculate interneuron", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GitHub repository": "https://github.com/OpenSourceBrain/dLGNinterneuronHalnesEtAl2011.git", + "ModelDB reference": "140249", + "NEST support": "-1", + "NEURON support": "1", + "NeuroML v1.x support": "0", + "NeuroML version": "v1.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "In progress...", + "description": "A multi-compartment model for interneurons in the dLGN", + "id": 125, + "identifier": "dlgninterneuronhalnesetal2011", + "main_branch": "master", + "name": "dLGN Interneuron - Halnes et al 2011" + }, + "drosophila-acc-l3-motoneuron-gunay-et-al-2014": { + "Brain region": "Neuromuscular system", + "Brian support": "-1", + "Category": "Project", + "Cell type": "aCC motoneuron", + "Curation level": "1", + "Endorsement": "1", + "Family": "Insect", + "GENESIS 2 support": "0", + "GitHub repository": "https://github.com/cengique/drosophila-aCC-L3-motoneuron-model.git", + "MOOSE support": "0", + "ModelDB reference": "152028", + "NEST support": "-1", + "NEURON support": "1", + "NeuroML v1.x support": "1", + "NeuroML v2.x support": "1", + "NeuroML version": "v2.x", + "Original format": "XPP; NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Drosophila", + "Spine classification": "Invertebrate", + "Status info": "Original XPP & NEURON versions on GitHub; conversion to ", + "Tags": "Motoneuron,Detailed cell model", + "description": "github:README.md", + "id": 97, + "identifier": "drosophila-acc-l3-motoneuron-gunay-et-al-2014", + "main_branch": "master", + "name": "Drosophila 3rd instar larval aCC motoneuron - Gunay et al 2014" + }, + "drosophila_projection_neuron": { + "Brain region": "Antennal lobe", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Projection neuron", + "Curation level": "1", + "Endorsement": "1", + "Family": "Insect", + "GENESIS 2 support": "1", + "GitHub repository": "https://github.com/OpenSourceBrain/Drosophila_Projection_Neuron", + "MOOSE support": "1", + "Metadata": "\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n", + "ModelDB reference": "118662", + "NEST support": "-1", + "NEURON support": "2", + "NeuroML v1.x support": "2", + "NeuroML v2.x support": "2", + "NeuroML version": "v1.x", + "Original format": "NEURON", + "PSICS support": "1", + "PyNN support": "-1", + "Specie": "Drosophila", + "Spine classification": "Invertebrate", + "Status info": "In early stages of development!\r\n\r\nThis version basically represents an export of the model from NEURON into NeuroML and import into neuroConstruct.", + "Tags": "Detailed cell model,neuroConstruct", + "description": "![](/attachments/download/50/Selection_140.png)\r\n\r\nIn early stages of development!\r\n\r\nCell model based on pubmed:19439602. \r\n", + "id": 38, + "identifier": "drosophila_projection_neuron", + "main_branch": "master", + "name": "Drosophila Projection Neuron - Gouwens and Wilson, 2009" + }, + "ebneretal2019": { + "Brain region": "Neocortex", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Synapse model", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/EbnerEtAl2019.git", + "MOOSE support": "-1", + "ModelDB reference": "251493", + "NEST support": "-1", + "NeuroML v1.x support": "0", + "NeuroML v2.x support": "1", + "NeuroML version": "v2.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "In development", + "description": "github:README.md", + "id": 228, + "identifier": "ebneretal2019", + "main_branch": "master", + "name": "Ebner et al. 2019 - Unified synaptic plasticity model" + }, + "farinellaetal_nmdaspikes": { + "Brain region": "Neocortex", + "Brian support": "-1", + "Category": "Project", + "Cell type": "L5 pyramidal cell", + "Curation level": "2", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/FarinellaEtAl_NMDAspikes.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "2", + "NeuroML v1.x support": "2", + "NeuroML v2.x support": "-1", + "NeuroML version": "v1.x", + "Original format": "neuroConstruct", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "In the process of being cleaned up/annotated. The cell model can be found in: [Synaptic integration in L5 Pyramidal cell, Larkum et al. 2009](http://www.opensourcebrain.org/projects/larkumetal2009).", + "Tags": "Pyramidal cell,Detailed cell model,NMDAR synapse,neuroConstruct", + "description": "Model from: **Glutamate-bound NMDARs arising from in vivo-like network activity extend spatio-temporal integration in a L5 cortical pyramidal cell model**\r\nMatteo Farinella, Daniel T. Ruedt, Padraig Gleeson, Frederic Lanore and\r\nR. Angus Silver\r\n\r\nThis paper has just [been published](http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003590).\r\n\r\nThe cell model used is based on: [Synaptic integration in L5 Pyramidal cell, Larkum et al. 2009](http://www.opensourcebrain.org/projects/larkumetal2009)\r\n", + "id": 108, + "identifier": "farinellaetal_nmdaspikes", + "main_branch": "master", + "name": "NMDA spikes in L5 Pyramidal cells - Farinella et al 2014" + }, + "fast-genesis-vogels-abbott-model": { + "Brain region": "Neocortex", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "0", + "GitHub repository": "https://github.com/dbeeman/VAnet2-GENESIS.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "-1", + "NeuroML v1.x support": "0", + "NeuroML v2.x support": "-1", + "NeuroML version": "v1.x", + "Original format": "GENESIS", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Generic", + "Spine classification": "Vertebrate", + "Tags": "Single compartment conductance based neuron model,Synaptic plasticity", + "description": "This is an improved and much more efficient GENESIS implementation of\nthe dual exponential conductance version of the Vogels-Abbott (2005)\nnetwork model with Hodgkin-Huxley neurons and conductance-based\nsynaptic activation (COBA). Details are given in Brette et al. (2007).\nIt uses hsolve and runs 16 times faster than the original. The\n\u2018VAnet2-batch.g\u2019 script is intended to be extended for testing GENESIS\nspike timing dependent plasticity (STDP) implementations with hsolve.\n", + "id": 82, + "identifier": "fast-genesis-vogels-abbott-model", + "main_branch": "master", + "name": "VAnet2-GENESIS" + }, + "fitzhugh-nagumo-fitzhugh-1969": { + "Brain region": "Other", + "Brian support": "3", + "Category": "Project", + "Cell type": "None", + "Curation level": "3", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/FitzHugh-Nagumo", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "3", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v1.x", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Generic", + "Spine classification": "Vertebrate", + "Status info": "Stable. This model has been incorporated into the core of NeuroML 2 for the v2beta4 release.", + "Tags": "Abstract neuron model,", + "description": "github:README.md", + "id": 63, + "identifier": "fitzhugh-nagumo-fitzhugh-1969", + "main_branch": "master", + "name": "FitzHugh Nagumo - FitzHugh 1969" + }, + "fpgashowcase": { + "Brian support": "-1", + "Category": "Showcase", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/FPGAShowcase.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "-1", + "NeuroML v1.x support": "0", + "NeuroML v2.x support": "0", + "NeuroML version": "v1.x", + "PSICS support": "-1", + "PyNN support": "-1", + "Spine classification": "Vertebrate", + "Status info": "In development", + "Tags": "FPGA,Hardware based simulation", + "description": "Information on various packages available for executing neuronal simulations on FPGA boards.\r\n\r\nFor more information, see the [[Wiki]].\r\n", + "id": 94, + "identifier": "fpgashowcase", + "main_branch": "master", + "name": "FPGA Based Simulations Showcase" + }, + "functional-balanced-network": { + "Brain region": "Neocortex", + "Brian support": "0", + "Category": "Project", + "Cell type": "Network", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/sdrsd/SadehRotter_2015_PLOS_ComputBiol.git", + "MOOSE support": "-1", + "ModelDB reference": "http://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=182759", + "NEST support": "1", + "NEURON support": "-1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "0", + "NeuroML version": "v1.x", + "Original format": "NEST", + "PSICS support": "-1", + "PyNN support": "0", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Tags": "Visual system", + "description": "Orientation selectivity in inhibition-dominated networks of spiking neurons", + "id": 140, + "identifier": "functional-balanced-network", + "main_branch": "master", + "name": "Functional Balanced Network" + }, + "geppettoshowcase": { + "Brian support": "-1", + "Category": "Showcase", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/openworm/org.geppetto.samples.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "-1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "1", + "NeuroML version": "v2.x", + "Original format": "NeuroML", + "PSICS support": "-1", + "PyNN support": "-1", + "Spine classification": "Vertebrate", + "Status info": "Early stages of testing", + "Tags": "Simulator showcase", + "description": "![](/attachments/download/210/geppettologo.png)\r\n\r\nGeppetto is a web-based multi-algorithm, multi-scale simulation platform engineered to support the simulation of complex biological systems and their surrounding environment.\r\n\r\nExamples of NeuroML 2 files along with scripts for visualising/executing them in Geppetto (http://www.geppetto.org).\r\n", + "id": 101, + "identifier": "geppettoshowcase", + "main_branch": "master", + "name": "Geppetto Showcase" + }, + "ghk-nernst": { + "Brian support": "-1", + "Category": "Showcase", + "Curation level": "2", + "Endorsement": "1", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/ghk-nernst.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "3", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Spine classification": "Vertebrate", + "Status info": "NeuroML 2 version stable", + "Tags": "Goldman-Hodgkin-Katz current", + "description": "Examples of cell models with currents using the [GHK flux equation](http://en.wikipedia.org/wiki/GHK_flux_equation), or using\n [Nernst\u2019s Equation](http://en.wikipedia.org/wiki/Nernst_equation) for the reversal potential in NeuroML 2 and a number of other formats.\n\nSee the [[Wiki]] for more details.\n", + "id": 102, + "identifier": "ghk-nernst", + "main_branch": "master", + "name": "GHK & Nernst examples" + }, + "golgicelldendgapjunctions": { + "Brain region": "Cerebellum", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Golgi cell", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/GolgiCellDendGapJunctions.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "0", + "NeuroLex Ids: Cells": "sao1415726815", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "1", + "NeuroML version": "v2.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Original scripts added. Some morphologies exported to NeuroML 2", + "description": "github:README.md", + "id": 152, + "identifier": "golgicelldendgapjunctions", + "main_branch": "master", + "name": "Golgi Cell Dendritic Gap Junctions - Szoboszlay et al. 2016" + }, + "gpushowcase": { + "Brian support": "0", + "Category": "Showcase", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/GPUShowcase.git", + "MOOSE support": "-1", + "NEST support": "0", + "NEURON support": "3", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v1.x", + "PSICS support": "-1", + "PyNN support": "0", + "Spine classification": "Vertebrate", + "Status info": "In development. Contributors welcome!", + "Tags": "GPU,Hardware based simulation", + "description": "github:README.md\r\n", + "id": 93, + "identifier": "gpushowcase", + "main_branch": "master", + "name": "GPU Based Simulation Showcase" + }, + "grancelllayer": { + "Brain region": "Cerebellum", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "3", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "3", + "GitHub repository": "https://github.com/OpenSourceBrain/GranCellLayer", + "MOOSE support": "1", + "NEST support": "-1", + "NEURON support": "3", + "NeuroML v1.x support": "3", + "NeuroML v2.x support": "2", + "NeuroML version": "v1.x", + "Original format": "GENESIS", + "PSICS support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Stable implementation on NEURON and GENESIS", + "Tags": "NMDAR synapse,Single compartment conductance based neuron model,neuroConstruct", + "description": "github:README.md", + "id": 10, + "identifier": "grancelllayer", + "main_branch": "master", + "name": "Granule Cell Layer - Maex and De Schutter 1998" + }, + "grancellrothmanif": { + "Brain region": "Cerebellum", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Granule cell", + "Curation level": "2", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/GranCellRothmanIf", + "NEST support": "-1", + "NEURON support": "2", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "Igor Pro", + "PSICS support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "The model is conceptually stable, but less so as far as the implementation is concerned. Currently, development is focused on ensuring that most of the simulation configurations defined in this neuroConstruct project run as intended in both NEURON and jLEMS (via NeuroMLv2 export).", + "Tags": "Integrate and fire neuron,neuroConstruct", + "description": "This project contains an integrate and fire model of the cerebellar granule cell and a simple model of the mossy fibre to granule cell synapse. The cell model (IaF\\_GrC.nml) is the average (ie the one whose parameters have the average value) of the model population developed by Jason Rothman and published in Schwartz et, J Neurosci (2012). The synaptic model is based on the one used in that same paper, but it has been developed further to improve the fit to the experimental data and to ensure LEMS/NeuroMLv2 compatibility.\n", + "id": 5, + "identifier": "grancellrothmanif", + "main_branch": "master", + "name": "I&F granule cell model - Rothman & Piasini" + }, + "grancellsolinasetal10": { + "Brain region": "Cerebellum", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Granule cell", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/GranCellSolinasEtAl10", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "1", + "NeuroLex Ids: Cells": "nifext_128", + "NeuroML v1.x support": "2", + "NeuroML v2.x support": "1", + "NeuroML version": "v1.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "In the process of conversion from original NEURON to NeuroML. Temperature dependence of channel conductances (see [here](http://www.opensourcebrain.org/projects/grancellsolinasetal10/repository/revisions/master/entry/NEURON/GRANULE_NA.mod#L82)) will require implementation in NeuroML 2, but initial impl in NeuroML v1.8.1, fixed at 30 deg C is present in [neuroConstruct folder](http://www.opensourcebrain.org/projects/grancellsolinasetal10/repository/revisions/master/show/neuroConstruct).", + "Tags": "NMDAR synapse,Single compartment conductance based neuron model,neuroConstruct", + "description": "![](http://www.opensourcebrain.org/attachments/download/24/solinasal10.png)\r\nInitial version of Granule cell from: Solinas S., Nieus T, d\u2019Angelo E. (2010) **A Realistic Large-Scale Model of the Cerebellum Granular Layer Predicts Circuit Spatio-Temporal Filtering Properties**. Front Cell Neurosci. 2010;4:12.\r\n\r\nFor more information, see the [[Wiki]]\r\n\r\n[![Build Status](https://travis-ci.com/OpenSourceBrain/GranCellSolinasEtAl10.svg?branch=master)](https://travis-ci.com/OpenSourceBrain/GranCellSolinasEtAl10)\r\n", + "id": 6, + "identifier": "grancellsolinasetal10", + "main_branch": "master", + "name": "Cerebellar Granule Cell - Solinas et al. 2010" + }, + "granularlayersolinasnieusdangelo2010": { + "Brain region": "Cerebellum", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/GranularLayerSolinasNieusDAngelo2010.git", + "MOOSE support": "-1", + "ModelDB reference": "Submitted...", + "NEST support": "-1", + "NEURON support": "0", + "NeuroLex Ids: Cells": "nifext_128, sao1415726815", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "0", + "NeuroML version": "v2.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Currently being converted to Python scripts based on NeuroML v2", + "description": "Cerebellar granular layer network model from: Solinas S., Nieus T, d'Angelo E. (2010) [A Realistic Large-Scale Model of the Cerebellum Granular Layer Predicts Circuit Spatio-Temporal Filtering Properties](http://journal.frontiersin.org/article/10.3389/fncel.2010.00012/abstract). Front Cell Neurosci. 2010;4:12.", + "id": 126, + "identifier": "granularlayersolinasnieusdangelo2010", + "main_branch": "master", + "name": "Granular Layer Network Model - Solinas, Nieus & D'Angelo 2010" + }, + "granule-cell-layer-piasini-2014": { + "Brain region": "Cerebellum", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "3", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/epiasini/BillingsEtAl2014_GCL_Models.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "3", + "NeuroLex Ids: Cells": "nifext_128", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "neuroConstruct", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Published in pubmed:25123311", + "Tags": "Point neuron network,NMDAR synapse,AMPAR synapse,neuroConstruct,Integrate and fire neuron,Synaptic plasticity", + "description": "github:README.md\r\n\r\nThis model has been used in pubmed:25123311.", + "id": 112, + "identifier": "granule-cell-layer-piasini-2014", + "main_branch": "master", + "name": "Granule Cell Layer - Piasini et al. " + }, + "granulecell": { + "Brain region": "Cerebellum", + "Brian support": "0", + "Category": "Project", + "Cell type": "Granule cell", + "Curation level": "3", + "Endorsement": "2", + "Family": "Mammalian", + "GENESIS 2 support": "3", + "GitHub repository": "https://github.com/OpenSourceBrain/GranuleCell", + "MOOSE support": "3", + "NEST support": "-1", + "NEURON support": "3", + "NeuroLex Ids: Cells": "nifext_128", + "NeuroML v1.x support": "3", + "NeuroML v2.x support": "3", + "NeuroML version": "v1.x", + "Original format": "GENESIS", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Stable implementation in NeuroML mapping to NEURON, GENESIS and MOOSE.\r\n\r\nThis model is being used to test mapping of single compartment conductance based models to other simulators using [jNeuroML](https://github.com/NeuroML/jNeuroML).", + "Tags": "Single compartment conductance based neuron model,neuroConstruct", + "description": "![](http://www.opensourcebrain.org/attachments/download/25/shutter98.png)\r\nA project illustrating the behaviour of the cerebellar granule cell model from: Maex, R and De Schutter, E. [Synchronization of Golgi and Granule Cell Firing in a Detailed Network Model of the Cerebellar Granule Cell Layer](http://www.ncbi.nlm.nih.gov/pubmed/9819260) J Neurophysiol, Nov 1998; 80: 2521 - 2537. \r\n\r\nBased on scripts obtained from: http://www.tnb.ua.ac.be/models/network.shtml.\r\n\r\nFor more details see the [[Wiki]].\r\n\r\n[![Build Status](https://travis-ci.org/OpenSourceBrain/GranuleCell.svg?branch=master)](https://travis-ci.org/OpenSourceBrain/GranuleCell)\r\n", + "id": 7, + "identifier": "granulecell", + "main_branch": "master", + "name": "Cerebellar Granule Cell - Maex De Schutter 1998" + }, + "granulecellvscs": { + "Brain region": "Cerebellum", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Granule cell", + "Curation level": "2", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "2", + "GitHub repository": "https://github.com/OpenSourceBrain/GranuleCellVSCS", + "MOOSE support": "0", + "NEST support": "-1", + "NEURON support": "2", + "NeuroML v1.x support": "2", + "NeuroML v2.x support": "0", + "NeuroML version": "v1.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Originally developed in NEURON. Converted to NeuroML v1.8.1.", + "Tags": "Single compartment conductance based neuron model,neuroConstruct", + "description": "![](http://www.opensourcebrain.org/attachments/download/26/steubersaviane.png)\nConversion to NeuroML of a model granule cell developed by Volker Steuber and Chiara Saviane (Silver lab).\n\nBased on a Granule cell model from Michiel Berends, Neural Comp 15, 2531-47 (2005) originally modified by Chiara Saviane and further modified by VS, July 2006.\n", + "id": 8, + "identifier": "granulecellvscs", + "main_branch": "master", + "name": "Granule Cell - Steuber, Saviane & Berends" + }, + "grclayer-caycogajic2017": { + "Brain region": "Cerebellum", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "3", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/SilverLabUCL/MF-GC-network-backprop-public.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "3", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "NeuroML2", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Stable", + "description": "github:README.md", + "id": 187, + "identifier": "grclayer-caycogajic2017", + "main_branch": "master", + "name": "Granule Cell Layer - CaycoGajicClopathSilver2017" + }, + "hindmarshrose1984": { + "Brain region": "Other", + "Brian support": "2", + "CNO Ids": "cno_0000074", + "Category": "Project", + "Cell type": "None", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/HindmarshRose1984.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "2", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "2", + "NeuroML version": "v2.x", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Generic", + "Spine classification": "Vertebrate", + "Status info": "In development...", + "Tags": "Abstract neuron model", + "description": "The Hindmarsh Rose model consists of a fast spiking subsystem, which is a generalization of the Fitzhugh-Nagumo (aka Bonhoeffer-van der Pol) oscillator, coupled to a slower subsystem which allows the system to fire bursts of spikes.\n\nImplemented according to:\nHindmarsh J. L., and Rose R. M. (1984) A model of neuronal bursting using three coupled first order differential equations. Proc. R. Soc. London, Ser. B 221:87\u2013102.\n", + "id": 62, + "identifier": "hindmarshrose1984", + "main_branch": "master", + "name": "Hindmarsh and Rose 1984" + }, + "hippocampus3ddemo": { + "Brain region": "Hippocampus", + "Brian support": "-1", + "Category": "Showcase", + "Cell type": "Network", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/Hippocampus3DDemo.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "3", + "NeuroML v1.x support": "1", + "NeuroML v2.x support": "2", + "NeuroML version": "v1.x", + "Original format": "neuroConstruct", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Tags": "Demo,Hippocampal formation", + "description": "github:README.md", + "id": 145, + "identifier": "hippocampus3ddemo", + "main_branch": "master", + "name": "Hippocampus 3D Demo" + }, + "hnn": { + "Brain region": "Neocortex", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Network", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/HNN.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "1", + "NeuroML version": "v2.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Human", + "Spine classification": "Vertebrate", + "Status info": "Work in progress...", + "description": "github:README.md", + "id": 262, + "identifier": "hnn", + "main_branch": "main", + "name": "HNN" + }, + "hodgkin-huxley-tutorial": { + "Brain region": "Other", + "Brian support": "-1", + "Category": "Showcase", + "Cell type": "Giant axon", + "Curation level": "3", + "Endorsement": "1", + "Family": "Other", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/openworm/hodgkin_huxley_tutorial.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "3", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Squid", + "Spine classification": "Invertebrate", + "Status info": "Stable. Developed initially by members of the OpenWorm community.", + "Tags": "Tutorial,Hodgkin Huxley model", + "description": "github:README.md", + "id": 134, + "identifier": "hodgkin-huxley-tutorial", + "main_branch": "master", + "name": "Hodgkin Huxley Tutorial" + }, + "izhikevichmodel": { + "Brain region": "Neocortex", + "Brian support": "2", + "CNO Ids": "cno_0000072", + "Category": "Project", + "Cell type": "Multiple", + "Curation level": "3", + "Endorsement": "2", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/IzhikevichModel", + "MOOSE support": "0", + "ModelDB reference": "39948", + "NEST support": "2", + "NEURON support": "2", + "NeuroLex Ids: Cells": "sao2128417084", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "MATLAB", + "PSICS support": "-1", + "PyNN support": "2", + "Specie": "Generic", + "Spine classification": "Vertebrate", + "Status info": "The Izhikevich model is supported by NeuroML v2.0 and PyNN 0.8. This project will demonstrate all of the main [firing behaviours](http://izhikevich.org/publications/whichmod.htm) of this cell model.", + "Tags": "Google Summer of Code,Izhikevich neuron model,Abstract neuron model", + "description": "github:README.md\r\n", + "id": 18, + "identifier": "izhikevichmodel", + "main_branch": "master", + "name": "Izhikevich Spiking Neuron Model" + }, + "joglekaretal18": { + "Brain region": "Neocortex", + "Brian support": "1", + "Category": "Project", + "Cell type": "network", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/JoglekarEtAl18.git", + "MOOSE support": "-1", + "NEST support": "1", + "NEURON support": "1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "1", + "NeuroML version": "v2.x", + "Original format": "MATLAB", + "PSICS support": "-1", + "PyNN support": "1", + "Specie": "Macaque", + "Spine classification": "Vertebrate", + "Status info": "In progress", + "description": "# JoglekarEtAl18\r\n\r\nInter-areal Balanced Amplification Enhances Signal Propagation in a Large-Scale Circuit Model of the Primate Cortex\r\nMadhura R. Joglekar, Jorge F. Mejia\r\ns, Guangyu Robert Yang, Xiao-Jing Wang\r\n\r\nhttps://www.cell.com/neuron/fulltext/S0896-6273(18)30152-1", + "id": 252, + "identifier": "joglekaretal18", + "main_branch": "master", + "name": "Large-Scale Circuit Model of the Primate Cortex - Joglekar et al. 2018" + }, + "l23dendriticspikes": { + "Brain region": "Neocortex", + "Brian support": "-1", + "Category": "Project", + "Cell type": "L2/3 pyramidal cell", + "Curation level": "3", + "Endorsement": "2", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/SmithEtAl2013-L23DendriticSpikes.git", + "MOOSE support": "-1", + "ModelDB reference": "245805", + "NEST support": "-1", + "NEURON support": "1", + "NeuroLex Ids: Cells": "nifext_49", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Converted to NeuroML v2", + "Tags": "Detailed cell model,Pyramidal cell", + "description": "github:README.md", + "id": 122, + "identifier": "l23dendriticspikes", + "main_branch": "master", + "name": "Dendritic spikes in L2/3 pyramidal cells - Smith et al. 2013" + }, + "l23pyramidalcelltutorial": { + "Brain region": "Neocortex", + "Brian support": "-1", + "Category": "Project", + "Cell type": "L2/3 pyramidal cell", + "Curation level": "3", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/L23PyramidalCellTutorial.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "3", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Stable NeuroML v2.0 model. ", + "Tags": "Tutorial,Detailed cell model,Pyramidal cell", + "description": "github:README.md", + "id": 204, + "identifier": "l23pyramidalcelltutorial", + "main_branch": "master", + "name": "L2/3 Pyramidal Cell Tutorial" + }, + "l5bpyrcellhayetal2011": { + "Brain region": "Neocortex", + "Brian support": "-1", + "CNO Ids": "cno_0000020", + "Category": "Project", + "Cell type": "L5 pyramidal cell", + "Curation level": "3", + "Endorsement": "2", + "Family": "Mammalian", + "GENESIS 2 support": "0", + "GitHub repository": "https://github.com/OpenSourceBrain/L5bPyrCellHayEtAl2011.git", + "MOOSE support": "0", + "ModelDB reference": "139653", + "NEST support": "-1", + "NEURON support": "3", + "NeuroLex Ids: Cells": "nifext_50", + "NeuroML v1.x support": "3", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Converted to NeuroMLv2.\r\n\r\n", + "Tags": "Pyramidal cell,Detailed cell model,neuroConstruct", + "description": "![](https://raw.github.com/OpenSourceBrain/L5bPyrCellHayEtAl2011/master/neuroConstruct/images/large.png)\r\nLayer 5b Pyramidal cell constrained by experimental data on perisomatic firing properties as well as dendritic activity during backpropagation of the action potential.\r\n\r\n\r\nFrom: **Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties**, Etay Hay, Sean Hill, Felix Sch\u00fcrmann, Henry Markram and Idan Segev, [PLoS Comp Biol 2011](http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002107)\r\n\r\n[![Build Status](https://travis-ci.org/OpenSourceBrain/L5bPyrCellHayEtAl2011.svg?branch=master)](https://travis-ci.org/OpenSourceBrain/L5bPyrCellHayEtAl2011)\r\n", + "id": 45, + "identifier": "l5bpyrcellhayetal2011", + "main_branch": "master", + "name": "Layer 5b Pyramidal cell - Hay et al. 2011" + }, + "larkumetal2009": { + "Brain region": "Neocortex", + "Brian support": "-1", + "CNO Ids": "cno_0000020", + "Category": "Project", + "Cell type": "L5 pyramidal cell", + "Curation level": "2", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/LarkumEtAl2009.git", + "MOOSE support": "-1", + "ModelDB reference": "124043", + "NEST support": "-1", + "NEURON support": "2", + "NeuroLex Ids: Cells": "nifext_50", + "NeuroML v1.x support": "2", + "NeuroML v2.x support": "1", + "NeuroML version": "v1.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Almost completely converted to NeuroML v1.x", + "Tags": "Pyramidal cell,neuroConstruct,Detailed cell model", + "description": "![](http://www.opensourcebrain.org/attachments/download/172/L5Spike.png)\n\nConversion to neuroConstruct/NeuroML of Layer 5 Pyramidal cell model from:\n\nLarkum ME, Nevian T, Sandler M, Polsky A, Schiller J (2009) Synaptic\nintegration in tuft dendrites of layer 5 pyramidal neurons: a new\nunifying principle. Science 325:756-60\n", + "id": 98, + "identifier": "larkumetal2009", + "main_branch": "master", + "name": "Synaptic integration in L5 Pyramidal cell - Larkum et al. 2009" + }, + "m1networkmodel": { + "Brain region": "Neocortex", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Network", + "Curation level": "1", + "Endorsement": "2", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/M1NetworkModel.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "1", + "NeuroML version": "v2.x", + "Original format": "NetPyNE", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Working on conversion of model to NeuroML 2 via export from NetPyNE", + "Tags": "Large scale network simulation", + "description": "github:README.md", + "id": 155, + "identifier": "m1networkmodel", + "main_branch": "master", + "name": "M1 Network Model" + }, + "mainenetalpyramidalcell": { + "Brain region": "Neocortex", + "Brian support": "-1", + "CNO Ids": "cno_0000020", + "Category": "Project", + "Cell type": "L5 pyramidal cell", + "Curation level": "2", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "2", + "GitHub repository": "https://github.com/OpenSourceBrain/MainenEtAl_PyramidalCell", + "MOOSE support": "1", + "ModelDB reference": "8210", + "NEST support": "-1", + "NEURON support": "3", + "NeuroLex Ids: Cells": "nifext_50", + "NeuroML v1.x support": "3", + "NeuroML v2.x support": "1", + "NeuroML version": "v1.x", + "Original format": "NEURON", + "PSICS support": "1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Converted to NeuroML v1.8.1. Close agreement between NEURON & GENESIS mappings", + "Tags": "Pyramidal cell,Detailed cell model,neuroConstruct", + "description": "![](http://www.opensourcebrain.org/attachments/download/27/mainen95.png)\r\n\r\nImplementation of the Mainen et al. pyramidal cell model from: Mainen ZF, Joerges J, Huguenard JR, Sejnowski TJ (1995) A model of spike initiation in neocortical pyramidal neurons. [Neuron 15:1427-39](http://www.ncbi.nlm.nih.gov/pubmed/8845165). This project is based on scripts obtained from: http://senselab.med.yale.edu/senselab/modeldb/ShowModel.asp?model=8210\r\n", + "id": 12, + "identifier": "mainenetalpyramidalcell", + "main_branch": "master", + "name": "L5 Pyramidal Cell - Mainen et al. 1995" + }, + "mejiasetal2016": { + "Brain region": "Neocortex", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Whole brain", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/MejiasEtAl2016.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "2", + "NeuroML version": "v2.x", + "Original format": "MATLAB", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Macaque", + "Spine classification": "Vertebrate", + "Status info": "In progress.", + "Tags": "Rate based model", + "description": "github:README.md", + "id": 205, + "identifier": "mejiasetal2016", + "main_branch": "master", + "name": "Large scale laminar cortical network - Mejias et al. 2016" + }, + "miglioreetal14_olfactorybulb3d": { + "Brain region": "Olfactory bulb", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "1", + "Endorsement": "2", + "Family": "Mammalian", + "GENESIS 2 support": "0", + "GitHub repository": "https://github.com/OpenSourceBrain/MiglioreEtAl14_OlfactoryBulb3D.git", + "MOOSE support": "0", + "ModelDB reference": "151681", + "NEST support": "-1", + "NEURON support": "1", + "NeuroLex Ids: Cells": "nifext_120", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "1", + "NeuroML version": "v2.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Conversion of the model to NeuroML2 has begun", + "Tags": "Olfaction,Olfactory system", + "description": "github:README.md", + "id": 123, + "identifier": "miglioreetal14_olfactorybulb3d", + "main_branch": "master", + "name": "3D Olfactory Bulb - Migliore et al. 2014" + }, + "modeldbshowcase": { + "Brian support": "-1", + "Category": "Showcase", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/ModelDBShowcase.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "-1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "-1", + "NeuroML version": "v1.x", + "PSICS support": "-1", + "PyNN support": "-1", + "Spine classification": "Vertebrate", + "Tags": "Database,Neuroinformatics,Model sharing", + "description": "OSB Showcase project for investigating options for interactions between [ModelDB](http://senselab.med.yale.edu/modeldb/) and OSB", + "id": 110, + "identifier": "modeldbshowcase", + "main_branch": "master", + "name": "ModelDB Showcase" + }, + "morrislecarmodel": { + "Brain region": "Neuromuscular system", + "Brian support": "2", + "CNO Ids": "cno_0000093", + "Category": "Project", + "Cell type": "Muscle cell", + "Curation level": "1", + "Endorsement": "1", + "Family": "Crustacean", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/MorrisLecarModel", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "2", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "2", + "NeuroML version": "v2.x", + "Original format": "MLAB", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Barnacle", + "Spine classification": "Invertebrate", + "Status info": "In development", + "Tags": "Abstract neuron model", + "description": "Definition in LEMS/NeuroML 2 of the Morris Lecar Model. Code originally contributed by Daniel Salles Chevitarese and Yiyin Zhou.\r\n\r\nFor more information & latest status, see the [[Wiki]]\r\n", + "id": 30, + "identifier": "morrislecarmodel", + "main_branch": "master", + "name": "Morris Lecar Model" + }, + "mouselightshowcase": { + "Brain region": "Nervous system", + "Brian support": "-1", + "Category": "Showcase", + "Cell type": "Multiple", + "Curation level": "3", + "Endorsement": "2", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/MouseLightShowcase.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "-1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "SWC", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Mouse", + "Spine classification": "Vertebrate", + "Status info": "A number of reconstructed morphologies have been converted to NeuroML v2", + "Tags": "Large scale brain initiative,Neuronal reconstruction,Database", + "description": "github:README.md", + "id": 184, + "identifier": "mouselightshowcase", + "main_branch": "master", + "name": "MouseLight Showcase" + }, + "multicompgrc": { + "Brain region": "Cerebellum", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Granule cell", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "0", + "GitHub repository": "https://github.com/rizland/cereb_grc_mc.git", + "MOOSE support": "0", + "ModelDB reference": "116835", + "NEST support": "-1", + "NEURON support": "1", + "NeuroLex Ids: Cells": "nifext_128", + "NeuroML v1.x support": "1", + "NeuroML v2.x support": "1", + "NeuroML version": "v1.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Currently being converted to NeuroML/neuroConstruct", + "Tags": "Detailed cell model", + "description": "Multicompartmental cerebellar granule cell model.\n\nBased on: Diwakar S, Magistretti J, Goldfarb M, Naldi G, D\\`Angelo E (2009) Axonal Na+ channels ensure fast spike activation and back-propagation in cerebellar granule cells J Neurophysiol 101(2):519-32\n", + "id": 72, + "identifier": "multicompgrc", + "main_branch": "master", + "name": "Multicompartmental granule cell - Diwakar et al. 2009" + }, + "multiscale": { + "Brain region": "Neocortex", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Network", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/pgleeson/multi.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "NeuroML2", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "In progress...", + "description": "Testing networks at multiple scales", + "id": 181, + "identifier": "multiscale", + "main_branch": "master", + "name": "MultiTest" + }, + "multiscaleisn": { + "Brain region": "Neocortex", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Network", + "Curation level": "3", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/MultiscaleISN.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "3", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "NeuroML2", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Stable", + "description": "Inhibition Stabilized Networks at multiple scales based on Sadeh et al. 2017 ", + "id": 195, + "identifier": "multiscaleisn", + "main_branch": "master", + "name": "MultiscaleISN" + }, + "muscle_model": { + "Brain region": "Neuromuscular system", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Muscle cell model", + "Curation level": "2", + "Endorsement": "1", + "Family": "Nematode", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/openworm/muscle_model", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "-1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "2", + "NeuroML version": "v2.x", + "Original format": "C++", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "C. elegans", + "Spine classification": "Invertebrate", + "Status info": "In development - multiple approaches being taken, pure NEURON, NeuroML 2, libNeuroML/Pyramidal. See commit messages for individual directories for more information.", + "Tags": "OpenWorm,Muscle cell model,Neuromusculature system", + "description": "![](http://www.openworm.org/img/OpenWormLogo.png)\r\n\r\nThis project is being developed as part of the [OpenWorm](http://www.openworm.org) project. This is an attempt to convert the model of [Boyle & Cohen, 2008](http://www.sciencedirect.com/science/article/pii/S0303264708001408) into NeuroML format for use on NEURON and other simulators.\r\n\r\nSee [here](/projects/muscle_model/wiki) for more details.\r\n", + "id": 32, + "identifier": "muscle_model", + "main_branch": "master", + "name": "Muscle cell model - Boyle & Cohen 2008" + }, + "musicshowcase": { + "Brian support": "0", + "Category": "Showcase", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/MUSICShowcase.git", + "MOOSE support": "0", + "NEST support": "0", + "NEURON support": "0", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "0", + "NeuroML version": "v1.x", + "Original format": "MUSIC", + "PSICS support": "-1", + "PyNN support": "0", + "Spine classification": "Vertebrate", + "Status info": "In development", + "description": "[MUlti-SImulation Coordinator, MUSIC, ](http://incf.org/core/programs/modeling/projects/music/music-multi-simulation-coordinator/) is a software that allows large scale neuron simulators to communicate during runtime.\n\nThis OSB Showcase project will highlight some of the examples in development to illustrate its functionality.\n", + "id": 56, + "identifier": "musicshowcase", + "main_branch": "master", + "name": "MUSIC Showcase" + }, + "nc_ca1": { + "Brain region": "Hippocampus", + "Brian support": "-1", + "CNO Ids": "sao830368389", + "Category": "Project", + "Cell type": "Network", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/mbezaire/ca1.git", + "MOOSE support": "-1", + "ModelDB reference": "187604", + "NEST support": "-1", + "NEURON support": "2", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "2", + "NeuroML version": "v2.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "In development", + "Tags": "Hippocampal formation,Interneuron,Detailed cell model,Pyramidal cell,Large scale network simulation,Google Summer of Code,CA1,High Performance Computing,Network oscillations", + "description": "CA1 Network Model - Bezaire et al 2016\r\n", + "id": 178, + "identifier": "nc_ca1", + "main_branch": "master", + "name": "CA1 Network Model - Bezaire et al 2016" + }, + "nc_superdeep": { + "Bitbucket repository": "https://bitbucket.org/mbezaire/nc_superdeep", + "Brain region": "Hippocampus", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "1", + "MOOSE support": "1", + "ModelDB reference": "153280", + "NEST support": "-1", + "NEURON support": "1", + "NeuroML v1.x support": "1", + "NeuroML v2.x support": "1", + "NeuroML version": "v1.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "A number of elements of the model are available in the repository. More will be added in time. The original NEURON code will be used as the basis of the conversion to NeuroML/neuroConstruct.", + "Tags": "Hippocampal formation,CA1,Detailed cell model,Interneuron", + "description": "![](http://www.opensourcebrain.org/attachments/download/204/Figure1B.jpg)\r\n\r\nThis model examines the interactions between the pyramidal cells of the superficial and deep layers of CA1. It is featured in: pubmed:24836505. Specifically, this model shows how the experimentally observed differences in synapse strength and number between PV+ basket cells and either superficial and deep pyramidal cells could have significant effects at the network level.\r\n", + "id": 107, + "identifier": "nc_superdeep", + "name": "CA1 Pyramidal Sublayer Microcircuit - Lee et al 2014 " + }, + "nengoneuroml": { + "Brain region": "Other", + "Brian support": "0", + "Category": "Showcase", + "Cell type": "Network model", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/NengoNeuroML", + "MOOSE support": "-1", + "NEST support": "0", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "1", + "NeuroML version": "v2.x", + "Original format": "Nengo", + "PSICS support": "0", + "PyNN support": "0", + "Specie": "Generic", + "Spine classification": "Vertebrate", + "Status info": "In early stages of development", + "Tags": "Simulator showcase,Python tools for computational neuroscience,Large scale network simulation", + "description": "![](http://nengo.ca/sites/nengo.ca/files/logo_ctn.png)\n\nProject to test scenarios for NeuroML & [Nengo](http://nengo.ca) interoperability\n", + "id": 39, + "identifier": "nengoneuroml", + "main_branch": "master", + "name": "Nengo - NeuroML interoperability" + }, + "nestshowcase": { + "Brain region": "Neocortex", + "Brian support": "-1", + "Category": "Showcase", + "Cell type": "Network", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/NESTShowcase.git", + "MOOSE support": "-1", + "NEST support": "1", + "NEURON support": "-1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "1", + "NeuroML version": "v2.x", + "Original format": "NEST", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Generic", + "Spine classification": "Vertebrate", + "Status info": "In progress...", + "description": "github:README.md", + "id": 171, + "identifier": "nestshowcase", + "main_branch": "master", + "name": "NEST Showcase" + }, + "netpyneshowcase": { + "Brian support": "-1", + "Category": "Showcase", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/NetPyNEShowcase.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "2", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "2", + "NeuroML version": "v2.x", + "Original format": "Python", + "PSICS support": "-1", + "PyNN support": "-1", + "Spine classification": "Vertebrate", + "Status info": "In active development", + "description": "github:README.md", + "id": 153, + "identifier": "netpyneshowcase", + "main_branch": "master", + "name": "NetPyNE Showcase" + }, + "neuroconstructshowcase": { + "Brian support": "2", + "Category": "Showcase", + "Curation level": "2", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "3", + "GitHub repository": "https://github.com/OpenSourceBrain/neuroConstructShowcase.git", + "MOOSE support": "3", + "NEST support": "1", + "NEURON support": "3", + "NeuroML v1.x support": "3", + "NeuroML v2.x support": "2", + "NeuroML version": "v1.x", + "Original format": "neuroconstruct", + "PSICS support": "2", + "PyNN support": "2", + "Spine classification": "Vertebrate", + "Status info": "Stable, tested projects which run on \"the latest version of neuroConstruct\":https://github.com/NeuralEnsemble/neuroConstruct.", + "Tags": "neuroConstruct", + "description": "![](/attachments/download/111/logoMain.png)\n\nExample projects illustrating the functionality of [neuroConstruct](http://www.neuroconstruct.org/)\n", + "id": 81, + "identifier": "neuroconstructshowcase", + "main_branch": "master", + "name": "neuroConstruct Showcase" + }, + "neuroelectrosciunit": { + "Brain region": "Other", + "Brian support": "-1", + "Category": "Showcase", + "Cell type": "None", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/NeuroElectroSciUnit", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "-1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "1", + "NeuroML version": "v1.x", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Generic", + "Spine classification": "Vertebrate", + "Status info": "In early stages of development.\r\n\r\nSee \"here\":https://github.com/cyrus-/sciunit/tree/master/sciunit/test/neurounit for more details.", + "Tags": "Neuroinformatics,Database,Python tools for computational neuroscience", + "description": "![](/images/neuroelectro_logo.png)\r\n\r\nA project highlighting some of the possible interactions between OSB and [NeuroElectro](http://neuroelectro.org/) and [SciUnit](https://github.com/rgerkin/sciunit).\r\n\r\nNeuroElectro is one of the key external resources OSB will interact with, see [Interactions with other Neuroinformatics resources](http://www.opensourcebrain.org/projects/neuroinformatics/wiki/Wiki)\r\n", + "id": 40, + "identifier": "neuroelectrosciunit", + "main_branch": "master", + "name": "NeuroElectro & SciUnit Showcase" + }, + "neuroml2": { + "Brain region": "Other", + "Brian support": "-1", + "Category": "Showcase", + "Cell type": "---", + "Curation level": "1", + "Endorsement": "1", + "Family": "Other", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/NeuroML/NeuroML2.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "3", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "NeuroML 2", + "PSICS support": "-1", + "PyNN support": "1", + "Specie": "--", + "Spine classification": "None", + "Status info": "Stable", + "description": "The standard examples for NeuroML 2 from the repository for the specification.", + "id": 165, + "identifier": "neuroml2", + "main_branch": "master", + "name": "NeuroML2 Showcase " + }, + "neuromllite-showcase": { + "Brain region": "Neocortex", + "Brian support": "0", + "Category": "Showcase", + "Cell type": "Network", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GitHub repository": "https://github.com/NeuroML/NeuroMLlite.git", + "NEST support": "0", + "NeuroML v1.x support": "-1", + "NeuroML version": "v2.x", + "Original format": "NeuroML2", + "PSICS support": "0", + "PyNN support": "0", + "Specie": "Generic", + "Spine classification": "Vertebrate", + "description": "Work in progress...", + "id": 193, + "identifier": "neuromllite-showcase", + "main_branch": "master", + "name": "NeuroMLlite Showcase" + }, + "neuromorpho": { + "Brain region": "Other", + "Brian support": "-1", + "Category": "Showcase", + "Cell type": "Multiple", + "Curation level": "3", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/NeuroMorpho", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "-1", + "NeuroML v1.x support": "3", + "NeuroML v2.x support": "3", + "NeuroML version": "v1.x", + "Original format": "SWC", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Multiple", + "Spine classification": "Vertebrate", + "Status info": "Some morphologies added already. More will be added over time.", + "Tags": "Neuronal reconstruction,Neuroinformatics,Database", + "description": "github:README.md\r\n", + "id": 42, + "identifier": "neuromorpho", + "main_branch": "master", + "name": "NeuroMorpho.Org Showcase" + }, + "neurosciences-repository": { + "Brain region": "Basal ganglia", + "Brian support": "0", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/rougier/Neurosciences/tree/master/basal-ganglia/guthrie-et-al-2013", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "0", + "NeuroML v1.x support": "0", + "NeuroML v2.x support": "0", + "NeuroML version": "v1.x", + "Original format": "Python", + "PSICS support": "-1", + "PyNN support": "0", + "Specie": "Generic", + "Spine classification": "Vertebrate", + "Status info": "Reproduction of a model", + "description": "Reproduction of a model of action selection in the basal ganglia (Guthrie et al., 2013)", + "id": 121, + "identifier": "neurosciences-repository", + "main_branch": "master", + "name": "Action Selection in the Basal Ganglia - Guthrie et al, 2013" + }, + "nifshowcase": { + "Brian support": "-1", + "Category": "Showcase", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/NIFShowcase.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "-1", + "NeuroML v1.x support": "0", + "NeuroML v2.x support": "-1", + "NeuroML version": "v1.x", + "PSICS support": "-1", + "PyNN support": "-1", + "Spine classification": "Vertebrate", + "Tags": "Ontology,Model annotation,Model sharing,Neuroinformatics,Database", + "description": "![](http://www.opensourcebrain.org/attachments/download/85/nifneurolex.png)\n\nA repository for information, scripts and configuration files which demonstrate the interactions possible between [NIF](https://www.neuinfo.org), [NeuroLex](http://neurolex.org/wiki/Main_Page) and OSB.\n\nSee the [[Wiki]] for more details.\n", + "id": 66, + "identifier": "nifshowcase", + "main_branch": "master", + "name": "NIF and NeuroLex Showcase" + }, + "ninemlshowcase": { + "Brain region": "Other", + "Brian support": "-1", + "Category": "Showcase", + "Cell type": "None", + "Curation level": "1", + "Endorsement": "1", + "Family": "Other", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/NineMLShowcase.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "-1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "1", + "NeuroML version": "v1.x", + "Original format": "XML", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "None", + "Spine classification": "None", + "Status info": "Currently testing mapping of NeuroML/LEMS to NineML and SpineML.", + "Tags": "NineML,SpineML,Model sharing,Standardization", + "description": "![](/attachments/download/53/image_mini.png)\n\nExamples of models in [NineML](http://software.incf.org/software/nineml) (and the related language [SpineML](http://bimpa.group.shef.ac.uk/SpineML/index.php/Home)) and conversion to/from NeuroML/LEMS.\n\nSee the [[Wiki]] for more details.\n", + "id": 54, + "identifier": "ninemlshowcase", + "main_branch": "master", + "name": "NineML Showcase" + }, + "norenbergetal2010_dgbasketcell": { + "Brain region": "Dentate gyrus", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Basket cell", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/NorenbergEtAl2010_DGBasketCell.git", + "MOOSE support": "-1", + "ModelDB reference": "140789", + "NEST support": "-1", + "NEURON support": "1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "1", + "NeuroML version": "v2.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "In development...", + "Tags": "Hippocampal formation,Detailed cell model,Passive model,Interneuron,Dentate gyrus", + "description": "github:README.md", + "id": 172, + "identifier": "norenbergetal2010_dgbasketcell", + "main_branch": "master", + "name": "DG Basket Cell - Norenberg et al. 2010" + }, + "nwbshowcase": { + "Brian support": "-1", + "Category": "Showcase", + "Curation level": "2", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/NWBShowcase.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "-1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "-1", + "NeuroML version": "v2.x", + "Original format": "NWB", + "PSICS support": "-1", + "PyNN support": "-1", + "Spine classification": "Vertebrate", + "Status info": "Under active development", + "Tags": "NWB", + "description": "Showcasing the interaction with [Neurodata Without Borders (NWB)](https://www.nwb.org) format experimental data on OSB.", + "id": 218, + "identifier": "nwbshowcase", + "main_branch": "master", + "name": "NWB Showcase" + }, + "olfactory-bulb": { + "Brain region": "Olfactory bulb", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "0", + "GitHub repository": "https://github.com/adityagilra/olfactory-bulb.git", + "MOOSE support": "1", + "NEST support": "-1", + "NEURON support": "0", + "NeuroML v1.x support": "1", + "NeuroML v2.x support": "0", + "NeuroML version": "v1.x", + "Original format": "MOOSE", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Tags": "Olfactory system", + "description": "A detailed network model of part of the rat olfactory bulb comprising compartmental mitral, granule and PG cells developed by Aditya Gilra and Upinder S. Bhalla (manuscript in preparation, 16 Apr 2013). The cell morphologies and network connections are in NeuroML v1.8.\n", + "id": 57, + "identifier": "olfactory-bulb", + "main_branch": "master", + "name": "Olfactory Bulb" + }, + "olfactory-bulb-network-model-o-connor-angelo-and-jacob-2012": { + "Brain region": "Olfactory bulb", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Mitral cell network", + "Curation level": "2", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "2", + "GitHub repository": "https://github.com/Simon-at-Ely/OlfactoryBulbMitralCell.git", + "MOOSE support": "1", + "ModelDB reference": "146030", + "NEST support": "-1", + "NEURON support": "2", + "NeuroLex Ids: Cells": "nifext_120", + "NeuroML v1.x support": "2", + "NeuroML v2.x support": "1", + "NeuroML version": "v1.x", + "Original format": "neuroConstruct", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "neuroConstruct version of model being updated to allow simulation on NEURON & GENESIS", + "Tags": "Olfactory system,neuroConstruct", + "description": "![](http://www.opensourcebrain.org/attachments/download/92/Mitral_Cell_Model.png)\r\nA model of olfactory bulb mitral cells connected by apical dendrite gap junctions in a glomerular network\r\n", + "id": 74, + "identifier": "olfactory-bulb-network-model-o-connor-angelo-and-jacob-2012", + "main_branch": "master", + "name": "Olfactory Bulb Network Model - O'Connor, Angelo and Jacob 2012" + }, + "opencortex": { + "Brain region": "Neocortex", + "Brian support": "0", + "Category": "Project", + "Cell type": "Network", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/OpenCortex.git", + "MOOSE support": "-1", + "NEST support": "0", + "NEURON support": "2", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "2", + "NeuroML version": "v2.x", + "Original format": "NeuroML2", + "PSICS support": "-1", + "PyNN support": "1", + "Specie": "Generic", + "Spine classification": "Vertebrate", + "Status info": "In development", + "description": "github:README.md", + "id": 149, + "identifier": "opencortex", + "main_branch": "master", + "name": "OpenCortex" + }, + "pinskyrinzelmodel": { + "Brain region": "Hippocampus", + "Brian support": "0", + "Category": "Project", + "Cell type": "CA3 Pyramidal cell", + "Curation level": "3", + "Endorsement": "2", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/PinskyRinzelModel", + "MOOSE support": "-1", + "ModelDB reference": "35358", + "NEST support": "-1", + "NEURON support": "1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "Fortran", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Converted to LEMS/NeuroML2. ", + "Tags": "Hippocampal formation,CA3,Two compartment model,Google Summer of Code", + "description": "Reduced CA3 cell model from [Pinsky & Rinzel, Intrinsic and network rhythmogenesis in a reduced Traub model for CA3 neurons, Journal of Computational Neuroscience, 1994, Volume 1, Issue 1-2, pp 39-60](http://dx.doi.org/10.1007/BF00962717).\r\n\r\nFor more details, see the [Wiki](http://www.opensourcebrain.org/projects/pinskyrinzelmodel/wiki).\r\n\r\n \r\n", + "id": 49, + "identifier": "pinskyrinzelmodel", + "main_branch": "master", + "name": "Reduced CA3 model - Pinsky and Rinzel 1994" + }, + "piriformcortexvanier": { + "Brain region": "Piriform cortex", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Pyramidal cell", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "1", + "GitHub repository": "https://github.com/Simon-at-Ely/PiriformCortexPyramidalNeuron", + "MOOSE support": "1", + "NEST support": "-1", + "NEURON support": "1", + "NeuroML v1.x support": "2", + "NeuroML v2.x support": "1", + "NeuroML version": "v1.x", + "Original format": "GENESIS", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "In the process of being converted based on information in Mike Vanier's PhD thesis", + "Tags": "Pyramidal cell,Olfactory system,neuroConstruct", + "description": "A conversion to NeuroML of the Piriform Cortex pyramidal cell model developed by Mike Vanier on GENESIS, see [here](http://users.cms.caltech.edu/~mvanier/science/parampaper/param.html).\r\n", + "id": 48, + "identifier": "piriformcortexvanier", + "main_branch": "master", + "name": "Piriform Cortex Pyramidal Neuron" + }, + "pospischiletal2008": { + "Brain region": "Neocortex", + "Brian support": "-1", + "CNO Ids": "cno_0000020", + "Category": "Project", + "Cell type": "Multiple", + "Curation level": "1", + "Endorsement": "2", + "Family": "Mammalian", + "GENESIS 2 support": "1", + "GitHub repository": "https://github.com/OpenSourceBrain/PospischilEtAl2008", + "MOOSE support": "1", + "ModelDB reference": "123623", + "NEST support": "-1", + "NEURON support": "2", + "NeuroLex Ids: Cells": "sao2128417084", + "NeuroML v1.x support": "2", + "NeuroML v2.x support": "3", + "NeuroML version": "v1.x", + "Original format": "NEURON", + "PSICS support": "0", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Follow progress on this here: https://github.com/OpenSourceBrain/Contribute/issues/6", + "Tags": "neuroConstruct,Single compartment conductance based neuron model,Pyramidal cell,Google Summer of Code", + "description": "Conversion to NeuroML of cell models from: [Minimal Hodgkin\u2013Huxley type models for different classes of cortical and thalamic neurons](http://link.springer.com/article/10.1007/s00422-008-0263-8), Martin Pospischil, Maria Toledo-Rodriguez, Cyril Monier, Zuzanna Piwkowska, Thierry Bal, Yves Fr\u00e9gnac, Henry Markram and Alain Destexhe, Biological Cybernetics, 2008.\r\n", + "id": 37, + "identifier": "pospischiletal2008", + "main_branch": "master", + "name": "Minimal HH models - Pospischil et al. 2008" + }, + "potjansdiesmann2014": { + "Brain region": "Neocortex", + "Brian support": "0", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "2", + "Endorsement": "2", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/PotjansDiesmann2014.git", + "MOOSE support": "-1", + "NEST support": "1", + "NEURON support": "0", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "2", + "Original format": "NEST SLI", + "PSICS support": "-1", + "PyNN support": "3", + "Specie": "Generic", + "Spine classification": "Vertebrate", + "Status info": "NEST SLI and PyNEST versions are available, matching those in the main NEST distribution version 2.12. \r\n\r\nA version compatible with PyNN 0.8 is available here:\r\nhttps://github.com/NeuralEnsemble/PyNN/tree/master/examples/Potjans2014", + "Tags": "Large scale network simulation,Point neuron network,Integrate and fire neuron", + "description": "github:README.md", + "id": 113, + "identifier": "potjansdiesmann2014", + "main_branch": "master", + "name": "Spiking cortical network model - Potjans and Diesmann 2014" + }, + "purkinjecell": { + "Brain region": "Cerebellum", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Purkinje cell", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "2", + "GitHub repository": "https://github.com/OpenSourceBrain/PurkinjeCell", + "MOOSE support": "2", + "ModelDB reference": "7176", + "NEST support": "-1", + "NEURON support": "2", + "NeuroML v1.x support": "2", + "NeuroML v2.x support": "1", + "NeuroML version": "v1.x", + "Original format": "GENESIS", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Converted to NeuroML based on a conversion of the original GENESIS model to NEURON by Arnd Roth and colleagues. Close agreement between GENESIS & NEURON but not perfect. Conversion process has revealed a number of implementation issues with original model (e.g. table discretisation for Ca dependence in KC channel). Many channels in NeuroML implementation still in earlier version of ChannelML. ", + "Tags": "Detailed cell model,neuroConstruct", + "description": "![](http://www.opensourcebrain.org/attachments/download/28/shutterbower94.png)\nAn initial implementation in NeuroML of the Purkinje Cell model from De Schutter, E. and Bower, J. M. (1994). Based on Arnd Roth el al\u2019s conversion of the original GENESIS code to NEURON. Note: conversion not fully complete.\n", + "id": 9, + "identifier": "purkinjecell", + "main_branch": "master", + "name": "Purkinje Cell - De Schutter and Bower 1994" + }, + "pyloricnetwork": { + "Brain region": "Stomatogastric ganglion", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Multiple cells", + "Curation level": "2", + "Endorsement": "1", + "Family": "Crustacean", + "GENESIS 2 support": "1", + "GitHub repository": "https://github.com/OpenSourceBrain/PyloricNetwork.git", + "MOOSE support": "1", + "NEST support": "-1", + "NEURON support": "1", + "NeuroML v1.x support": "1", + "NeuroML v2.x support": "2", + "NeuroML version": "v1.x", + "Original format": "C", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Lobster", + "Spine classification": "Invertebrate", + "Status info": "In development. Cell models converted to NeuroML. Network converted, but used chemical synapses as opposed to graded synapses.", + "Tags": "neuroConstruct,Point neuron network", + "description": "Implementation of the pyloric network of the lobster stomatogastric ganglion system of Prinz, Marder, et al.\r\n\r\nWork in progress! For more details see the [[Wiki]].\r\n", + "id": 69, + "identifier": "pyloricnetwork", + "main_branch": "master", + "name": "Pyloric pacemaker network - Prinz et al. 2003/04" + }, + "pynnshowcase": { + "Brian support": "3", + "Category": "Showcase", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/PyNNShowcase.git", + "MOOSE support": "-1", + "NEST support": "3", + "NEURON support": "3", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v1.x", + "Original format": "PyNN/NeuroML2", + "PSICS support": "-1", + "PyNN support": "3", + "Spine classification": "Vertebrate", + "Status info": "See https://github.com/NeuroML/NeuroML2/issues/73", + "description": "github:README.md", + "id": 154, + "identifier": "pynnshowcase", + "main_branch": "master", + "name": "PyNN Showcase" + }, + "rothmanetalkoleetalpyrcell": { + "Brain region": "Neocortex", + "Brian support": "-1", + "CNO Ids": "cno_0000020", + "Category": "Project", + "Cell type": "L5 pyramidal cell", + "Curation level": "2", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "1", + "GitHub repository": "https://github.com/OpenSourceBrain/RothmanEtAl_KoleEtAl_PyrCell", + "MOOSE support": "1", + "ModelDB reference": "114394", + "NEST support": "-1", + "NEURON support": "2", + "NeuroLex Ids: Cells": "nifext_50", + "NeuroML v1.x support": "2", + "NeuroML v2.x support": "0", + "NeuroML version": "v1.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Rothman et al 2009 used cell in neuroConstruct with original mod files (File based cell mechanisms). Channels converted to ChannelML, but full cell needs further testing in NeuroML/GENESIS/MOOSE.", + "Tags": "Pyramidal cell,Detailed cell model,neuroConstruct", + "description": "![](http://www.opensourcebrain.org/attachments/download/29/rothman2009.png)\nA project which was used in Rothman et al. \u201cSynaptic depression enables neuronal gain control\u201d Nature 2009 to demonstrate gain control in realistic cell models. Based on cell model from Kole et al. 2008 (obtained from http://senselab.med.yale.edu/modeldb/ShowModel.asp?model=114394).\n", + "id": 13, + "identifier": "rothmanetalkoleetalpyrcell", + "main_branch": "master", + "name": "L5 Pyramidal cell - Rothman et al. 2009" + }, + "sadehetal2017-inhibitionstabilizednetworks": { + "Brain region": "Neocortex", + "Brian support": "1", + "Category": "Project", + "Cell type": "Network", + "Curation level": "3", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/SadehEtAl2017-InhibitionStabilizedNetworks.git", + "MOOSE support": "-1", + "NEST support": "2", + "NEURON support": "1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "2", + "NeuroML version": "v2.x", + "Original format": "NEST", + "PSICS support": "-1", + "PyNN support": "3", + "Specie": "Generic", + "Spine classification": "Vertebrate", + "Status info": "Being converted to PyNN/NeuroML2", + "description": "github:README.md", + "id": 194, + "identifier": "sadehetal2017-inhibitionstabilizednetworks", + "main_branch": "master", + "name": "SadehEtAl2017-InhibitionStabilizedNetworks" + }, + "sbmlshowcase": { + "Brian support": "2", + "Category": "Showcase", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/SBMLShowcase.git", + "MOOSE support": "0", + "NEST support": "-1", + "NEURON support": "2", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "2", + "NeuroML version": "v1.x", + "Original format": "SBML", + "PSICS support": "-1", + "PyNN support": "-1", + "Spine classification": "Vertebrate", + "Status info": "In development in line with org.neuroml.import/export packages.", + "Tags": "Standardization,Systems biology", + "description": "![](/attachments/download/55/Official-sbml-supported-70.jpg)\r\n\r\nOSB showcase of interactions between SBML and NeuroML/LEMS.\r\n\r\nSee the [[Wiki]] for more details.\r\n", + "id": 59, + "identifier": "sbmlshowcase", + "main_branch": "master", + "name": "SBML Showcase" + }, + "spineshowcase": { + "Brian support": "0", + "Category": "Showcase", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/SpineShowcase.git", + "MOOSE support": "-1", + "NEST support": "0", + "NEURON support": "0", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "NeuroML2", + "PSICS support": "0", + "PyNN support": "0", + "Spine classification": "Vertebrate", + "Status info": "Work in progress...", + "description": "github:README.md", + "id": 189, + "identifier": "spineshowcase", + "main_branch": "master", + "name": "SpineShowcase" + }, + "striatalspinyprojectionneuron": { + "Brain region": "Basal ganglia", + "Brian support": "0", + "Category": "Project", + "Cell type": "Striatal Spiny Projection Neuron", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/StriatalSpinyProjectionNeuron", + "MOOSE support": "0", + "NEST support": "-1", + "NEURON support": "0", + "NeuroLex Ids: Cells": "nlx_149135", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "2", + "NeuroML version": "v2.x", + "Original format": "GENESIS", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "In development. Avrama Blackwell's lab is building a new SSPN model in GENESIS and various elements of the model are being converted to NeuroML 2 here to test the scope of the model description language.", + "Tags": "Single compartment conductance based neuron model,Goldman-Hodgkin-Katz current", + "description": "[Striatal spiny projection neuron](http://neurolex.org/wiki/Category:Neostriatum_direct_pathway_spiny_neuron) model being developed by Avrama Blackwell. A version of this model in \u201cpure\u201d NeuroML v2.0 is being developed here.\n\nFor more information, see the [[Wiki]]\n", + "id": 17, + "identifier": "striatalspinyprojectionneuron", + "main_branch": "master", + "name": "Striatal Spiny Projection Neuron - Blackwell" + }, + "synapticintegrationdemo": { + "Brain region": "Neocortex", + "Brian support": "-1", + "Category": "Project", + "Cell type": "L23 Pyramidal cell", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/SynapticIntegration.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "3", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v1.x", + "Original format": "NeuroML 2", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Generic", + "Spine classification": "Vertebrate", + "Status info": "Work in progress", + "Tags": "Tutorial", + "description": "github:README.md", + "id": 163, + "identifier": "synapticintegrationdemo", + "main_branch": "master", + "name": "Synaptic Integration Demo" + }, + "tcrelay-neuron-in-high-conductance-state-zeldenrust-et-al-2018": { + "Brain region": "Thalamus", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Thalamocortical relay cell", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GitHub repository": "https://github.com/fleurzeldenrust/TCrelay-Neuron-model", + "ModelDB reference": "https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=232876#tabs-1", + "NEST support": "-1", + "NEURON support": "1", + "NeuroML v1.x support": "-1", + "NeuroML version": "v1.x", + "Original format": "Neuron", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Mouse", + "Spine classification": "Vertebrate", + "Tags": "Thalamus", + "description": "github:README.md", + "id": 222, + "identifier": "tcrelay-neuron-in-high-conductance-state-zeldenrust-et-al-2018", + "main_branch": "master", + "name": "TCrelay neuron in high conductance state - Zeldenrust et al. 2018" + }, + "thalamocortical": { + "Brain region": "Neocortex", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "2", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "3", + "GitHub repository": "https://github.com/OpenSourceBrain/Thalamocortical", + "MOOSE support": "3", + "ModelDB reference": "45539", + "NEST support": "-1", + "NEURON support": "3", + "NeuroLex Ids: Cells": "nifext_52; nifext_56; nifext_50; nifext_49; nifext_45;nlx_cell_20081203", + "NeuroML v1.x support": "3", + "NeuroML v2.x support": "1", + "NeuroML version": "v1.x", + "Original format": "FORTRAN", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Single cell models are stable in NeuroML v1.8.1, NEURON, GENESIS, MOOSE. [Mappings to NeuroML v2.0](https://github.com/OpenSourceBrain/Thalamocortical/tree/master/NeuroML2) are in an advanced state of development.\r\n\r\nThe large scale networks from Traub et al 2005 are in the process of being ported, see the wiki for more details.", + "Tags": "Detailed cell model,Pyramidal cell,Large scale network simulation,High Performance Computing,neuroConstruct,Gap junctions,Interneuron", + "description": "![](http://www.opensourcebrain.org/attachments/download/125/Column3_S.png)\r\nThis is a project implementing cells from the thalamocortical network model of Traub et al 2005 in NeuroML. Based on the NEURON implementation from: http://senselab.med.yale.edu/ModelDB/ShowModel.asp?model=45539.\r\n\r\nThe [[Wiki]] gives details of running this project with neuroConstruct.\r\n", + "id": 14, + "identifier": "thalamocortical", + "main_branch": "master", + "name": "Thalamocortical network - Traub et al. 2005" + }, + "thevirtualbrainshowcase": { + "Brian support": "-1", + "Category": "Showcase", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/TheVirtualBrainShowcase.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "-1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "2", + "NeuroML version": "v2.x", + "Original format": "TheVirtualBrain", + "PSICS support": "-1", + "PyNN support": "-1", + "Spine classification": "Vertebrate", + "Status info": "In development", + "Tags": "Whole brain model,Neural population model", + "description": "![](http://www.opensourcebrain.org/attachments/download/177/Logo_VB.png)\r\n\r\nRepository for an OSB project to show the interaction between neural mass models implemented in [The Virtual Brain](http://www.thevirtualbrain.org) and how they can make use of model based description languages like NeuroML\r\n", + "id": 104, + "identifier": "thevirtualbrainshowcase", + "main_branch": "master", + "name": "The Virtual Brain Showcase" + }, + "tobinetal2017": { + "Brain region": "Other", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Multiple", + "Curation level": "2", + "Endorsement": "1", + "Family": "Insect", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/TobinEtAl2017.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "3", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "neuroConstruct", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Drosophila", + "Spine classification": "Invertebrate", + "Status info": "Stable", + "description": "github:README.md", + "id": 207, + "identifier": "tobinetal2017", + "main_branch": "master", + "name": "Tobin et al. 2017" + }, + "tvb_neuroml": { + "Brain region": "Neocortex", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "-1", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "-1", + "NeuroML version": "v2.x", + "Original format": "TheVirtualBrain", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Human", + "Spine classification": "Vertebrate", + "Tags": "Neural population model", + "description": "Project to test scenarios for [TVB](http://www.thevirtualbrain.org) & NeuroML interoperability\r\n", + "id": 103, + "identifier": "tvb_neuroml", + "name": "TVB NeuroML" + }, + "v1networkmodels": { + "Brain region": "Neocortex", + "Brian support": "1", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "2", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/V1NetworkModels.git", + "MOOSE support": "-1", + "NEST support": "2", + "NEURON support": "2", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "0", + "NeuroML version": "v2.x", + "Original format": "PyNN", + "PSICS support": "-1", + "PyNN support": "2", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Implements a significant part of pubmed:9671678. See the [Wiki](http://www.opensourcebrain.org/projects/v1networkmodels/wiki) for full details.", + "Tags": "Point neuron network,Google Summer of Code,Visual system", + "description": "\r\ngithub:README.md", + "id": 111, + "identifier": "v1networkmodels", + "main_branch": "master", + "name": "Network models of V1" + }, + "vertexshowcase": { + "Brain region": "Neocortex", + "Brian support": "-1", + "Category": "Showcase", + "Cell type": "Network model", + "Curation level": "2", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/VERTEXShowcase.git", + "MOOSE support": "-1", + "NEST support": "-1", + "NEURON support": "0", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "0", + "NeuroML version": "v2.x", + "Original format": "MATLAB", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "In early stages of development...", + "description": "github:README.md", + "id": 130, + "identifier": "vertexshowcase", + "main_branch": "master", + "name": "VERTEX Showcase" + }, + "vervaekeetalgolgicellnetwork": { + "Brain region": "Cerebellum", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "3", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/VervaekeEtAl-GolgiCellNetwork", + "MOOSE support": "-1", + "ModelDB reference": "127996", + "NEST support": "-1", + "NEURON support": "3", + "NeuroLex Ids: Cells": "sao1415726815", + "NeuroML v1.x support": "3", + "NeuroML v2.x support": "1", + "NeuroML version": "v1.x", + "Original format": "neuroConstruct", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Well tested mapping from NeuroML v1.8.1 -> NEURON. KAHP channel with kinetic scheme formalism precludes use on GENESIS/MOOSE for now. Calcium dynamics prevent using this model on PSICS.", + "Tags": "Detailed cell model,neuroConstruct,Gap junctions,Interneuron", + "description": "github:README.md\r\n", + "id": 11, + "identifier": "vervaekeetalgolgicellnetwork", + "main_branch": "master", + "name": "Golgi Cell Network - Vervaeke et al 2010" + }, + "vfb_showcase": { + "Brain region": "Other", + "Brian support": "-1", + "Category": "Showcase", + "Cell type": "Multiple", + "Curation level": "1", + "Endorsement": "1", + "Family": "Insect", + "GENESIS 2 support": "0", + "GitHub repository": "https://github.com/jefferis/osb_vfb_showcase", + "MOOSE support": "0", + "NEST support": "-1", + "NEURON support": "0", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "1", + "NeuroML version": "v1.x", + "PSICS support": "0", + "PyNN support": "-1", + "Specie": "Drosophila", + "Spine classification": "Invertebrate", + "Status info": "Mapping of VFB morphologies to NeuroML being tested.", + "Tags": "Neuronal reconstruction", + "description": "![](/attachments/download/46/flyBrain.gif)\r\n\r\nShowcase of some *Drosophila melanogaster* neuronal morphologies processed by [Virtual Fly Brain](http://www.virtualflybrain.org) team. These will likely include examples based on raw data available from http://flybrain.stanford.edu and http://flycircuit.tw.\r\n", + "id": 43, + "identifier": "vfb_showcase", + "main_branch": "master", + "name": "Virtual Fly Brain Showcase" + }, + "vierlingclaassenetal2010": { + "Brain region": "Neocortex", + "Brian support": "-1", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "0", + "GitHub repository": "https://github.com/OpenSourceBrain/VierlingClaassenEtAl2010.git", + "MOOSE support": "0", + "ModelDB reference": "141273", + "NEST support": "-1", + "NEURON support": "0", + "NeuroLex Ids: Cells": "Category:Neocortex_pyramidal_layer_2-3_cell; nlx_cell_1003113 ", + "NeuroML v1.x support": "0", + "NeuroML v2.x support": "0", + "NeuroML version": "v1.x", + "Original format": "NEURON", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "In early stages of conversion to NeuroML", + "Tags": "Network oscillations", + "description": "github:README.md", + "id": 118, + "identifier": "vierlingclaassenetal2010", + "main_branch": "master", + "name": "VierlingClaassenEtAl2010" + }, + "vogelsetal2011": { + "Brain region": "Neocortex", + "Brian support": "1", + "CNO Ids": "cno_0000015; cno_0000066; cno_0000185", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "2", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/VogelsEtAl2011.git", + "MOOSE support": "-1", + "ModelDB reference": "143751", + "NEST support": "1", + "NEURON support": "2", + "NeuroLex Ids: Cells": "sao2128417084", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "0", + "NeuroML version": "v2.x", + "Original format": "C/Matlab", + "PSICS support": "-1", + "PyNN support": "2", + "Specie": "Generic", + "Spine classification": "Vertebrate", + "Status info": "In development. Tested with PyNN 0.8.", + "Tags": "Google Summer of Code,Point neuron network,Inhibition", + "description": "![](/attachments/download/107/test_full.png)\n\nNetwork model from: Vogels TP, Sprekeler H, Zenke F, Clopath C, Gerstner W (2011) [Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks](http://www.sciencemag.org/content/334/6062/1569.abstract). Science 334:1569-73.\n\nSee the [[Wiki]] for more details.\n", + "id": 68, + "identifier": "vogelsetal2011", + "main_branch": "master", + "name": "Balanced network with inhibitory plasticity - Vogels et al. 2011" + }, + "wangbuzsaki1996": { + "Brain region": "Hippocampus", + "Brian support": "0", + "Category": "Project", + "Cell type": "Network", + "Curation level": "2", + "Endorsement": "2", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/WangBuzsaki1996.git", + "MOOSE support": "-1", + "ModelDB reference": "26997", + "NEST support": "-1", + "NEURON support": "2", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "?", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Tags": "Hippocampal formation,Interneuron", + "description": "github:README.md", + "id": 148, + "identifier": "wangbuzsaki1996", + "main_branch": "master", + "name": "Wang & Buzsaki 1996" + }, + "weileretal08-laminarcortex": { + "Brain region": "Neocortex", + "Brian support": "0", + "Category": "Project", + "Cell type": "Network model", + "Curation level": "1", + "Endorsement": "1", + "Family": "Mammalian", + "GENESIS 2 support": "-1", + "GitHub repository": "https://github.com/OpenSourceBrain/WeilerEtAl08-LaminarCortex.git", + "MOOSE support": "-1", + "ModelDB reference": "114655", + "NEST support": "-1", + "NEURON support": "0", + "NeuroML v1.x support": "-1", + "NeuroML v2.x support": "0", + "NeuroML version": "v1.x", + "Original format": "MATLAB", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "Rodent", + "Spine classification": "Vertebrate", + "Status info": "Currently converting model from Matlab to Python", + "description": "github:README.md", + "id": 127, + "identifier": "weileretal08-laminarcortex", + "main_branch": "master", + "name": "Laminar organization of motor cortex - 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Human" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2022-12-06 20:14:07.737640+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000293/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "59": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "master", + "id": 59, + "name": "Test nwb_conversion", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2022-12-08 09:03:13.631168+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/pgleeson/nwb_conversion", + "user": { + "email": "p.gleeson@gmail.com", + "first_name": "Padraig", + "id": "0103eaaf-6a34-4509-a025-14367a52aa2b", + "last_name": "Gleeson", + "username": "padraig" + }, + "user_id": "0103eaaf-6a34-4509-a025-14367a52aa2b" + }, + "60": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 60, + "name": "Multiscale model of primary motor cortex (M1) circuits developed in NetPyNE", + "repository_type": "github", + "summary": "", + "tags": [], + "timestamp_created": "2022-12-29 21:39:05.441256+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/suny-downstate-medical-center/M1_NetPyNE_CellReports_2023", + "user": { + "email": "salvadordura@gmail.com", + "first_name": "Salvador", + "id": "aab08e79-1830-4b2c-8116-7565205fd9d2", + "last_name": "Dura-Bernal", + "username": "salvadordura@gmail.com" + }, + "user_id": "aab08e79-1830-4b2c-8116-7565205fd9d2" + }, + "61": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.221219.1506", + "id": 61, + "name": "Glia Accumulate Evidence that Actions Are Futile and Suppress Unsuccessful Behavior", + "repository_type": "dandi", + "summary": "When a behavior repeatedly fails to achieve its goal, animals often give up and become passive, which can be strategic for preserving energy or regrouping between attempts. It is unknown how the brain identifies behavioral failures and mediates this behavioral-state switch. In larval zebrafish swimming in virtual reality, visual feedback can be withheld so that swim attempts fail to trigger expected visual flow. After tens of seconds of such motor futility, animals became passive for similar durations. Whole-brain calcium imaging revealed noradrenergic neurons that responded specifically to failed swim attempts and radial astrocytes whose calcium levels accumulated with increasing numbers of failed attempts. Using cell ablation and optogenetic or chemogenetic activation, we found that noradrenergic neurons progressively activated brainstem radial astrocytes, which then suppressed swimming. Thus, radial astrocytes perform a computation critical for behavior: they accumulate evidence that current actions are ineffective and consequently drive changes in behavioral states.", + "tags": [ + { + "id": 28, + "tag": "neuroscience" + }, + { + "id": 100, + "tag": "glia" + }, + { + "id": 101, + "tag": "astrocytes" + }, + { + "id": 102, + "tag": "norepinephrine" + }, + { + "id": 103, + "tag": "noradrenaline" + }, + { + "id": 104, + "tag": "learned helplessness" + }, + { + "id": 105, + "tag": "neuromodulation" + }, + { + "id": 106, + "tag": "behavioral states" + }, + { + "id": 107, + "tag": "evidence accumulation" + }, + { + "id": 108, + "tag": "zebrafish" + }, + { + "id": 286, + "tag": "DANDI:000350" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 287, + "tag": "Danio rerio - Zebra fish" + } + ], + "timestamp_created": "2023-01-18 09:37:51.569908+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000350/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "63": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 63, + "name": "SBML Showcase", + "repository_type": "github", + "summary": "![](/attachments/download/55/Official-sbml-supported-70.jpg)\r\n\r\nOSB showcase of interactions between SBML and NeuroML/LEMS.\r\n\r\nSee the [[Wiki]] for more details.\r\n", + "tags": [ + { + "id": 92, + "tag": "SBML" + }, + { + "id": 91, + "tag": "Systems biology" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 734, + "tag": "Standardization" + } + ], + "timestamp_created": "2023-01-30 10:35:05.424215+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/SBMLShowcase", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "64": { + "auto_sync": true, + "content_types": "experimental,modeling", + "content_types_list": [ + "experimental", + "modeling" + ], + "default_context": "test_keras", + "id": 64, + "name": "MDF", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 109, + "tag": "machine-learning" + }, + { + "id": 28, + "tag": "neuroscience" + }, + { + "id": 110, + "tag": "onnx" + } + ], + "timestamp_created": "2023-01-30 20:39:48.430510+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/ModECI/MDF", + "user": { + "email": "mpofukusanele@gmail.com", + "first_name": "Marble Kusanele", + "id": "9ae126d1-1608-4a75-83cd-0ad52f0d70c8", + "last_name": "Mpofu", + "username": "kusanele" + }, + "user_id": "9ae126d1-1608-4a75-83cd-0ad52f0d70c8" + }, + "65": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 65, + "name": "Thalamocortical network - Traub et al. 2005", + "repository_type": "github", + "summary": "![](http://www.opensourcebrain.org/attachments/download/125/Column3_S.png)\r\nThis is a project implementing cells from the thalamocortical network model of Traub et al 2005 in NeuroML. Based on the NEURON implementation from: http://senselab.med.yale.edu/ModelDB/ShowModel.asp?model=45539.\r\n\r\nThe [[Wiki]] gives details of running this project with neuroConstruct.\r\n", + "tags": [ + { + "id": 321, + "tag": "Detailed cell model" + }, + { + "id": 756, + "tag": "FORTRAN" + }, + { + "id": 757, + "tag": "Gap junctions" + }, + { + "id": 724, + "tag": "High Performance Computing" + }, + { + "id": 329, + "tag": "Interneuron" + }, + { + "id": 708, + "tag": "Large scale network simulation" + }, + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 651, + "tag": "Network model" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 318, + "tag": "Pyramidal cell" + }, + { + "id": 652, + "tag": "Rodent" + }, + { + "id": 319, + "tag": "neuroConstruct" + } + ], + "timestamp_created": "2023-02-02 16:55:15.278149+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/Thalamocortical", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "66": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "main", + "id": 66, + "name": "DeepLabCut source", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 112, + "tag": "behavior-analysis" + }, + { + "id": 113, + "tag": "deep-learning" + }, + { + "id": 114, + "tag": "pose-estimation" + }, + { + "id": 115, + "tag": "feature-detectors" + }, + { + "id": 116, + "tag": "toolbox" + }, + { + "id": 117, + "tag": "deeplabcut" + }, + { + "id": 118, + "tag": "animal-pose-estimation" + }, + { + "id": 119, + "tag": "labeling-tool" + }, + { + "id": 120, + "tag": "keypoint-tracking" + }, + { + "id": 121, + "tag": "keypoint-detection" + } + ], + "timestamp_created": "2023-02-07 11:16:35.495938+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/DeepLabCut/DeepLabCut", + "user": { + "email": "p.gleeson@gmail.com", + "first_name": "Padraig", + "id": "0103eaaf-6a34-4509-a025-14367a52aa2b", + "last_name": "Gleeson", + "username": "padraig" + }, + "user_id": "0103eaaf-6a34-4509-a025-14367a52aa2b" + }, + "67": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220126.1852", + "id": 67, + "name": "A NWB-based dataset and processing pipeline of human single-neuron activity during a declarative memory task", + "repository_type": "dandi", + "summary": "A challenge for data sharing in systems neuroscience is the multitude of different data formats used. Neurodata Without Borders: Neurophysiology 2.0 (NWB:N) has emerged as a standardized data format for the storage of cellular-level data together with meta-data, stimulus information, and behavior. A key next step to facilitate NWB:N adoption is to provide easy to use processing pipelines to import/export data from/to NWB:N. Here, we present a NWB-formatted dataset of 1863 single neurons recorded from the medial temporal lobes of 59 human subjects undergoing intracranial monitoring while they performed a recognition memory task. We provide code to analyze and export/import stimuli, behavior, and electrophysiological recordings to/from NWB in both MATLAB and Python. The data files are NWB:N compliant, which affords interoperability between programming languages and operating systems. This combined data and code release is a case study for how to utilize NWB:N for human single-neuron recordings and enables easy re-use of this hard-to-obtain data for both teaching and research on the mechanisms of human memory.", + "tags": [ + { + "id": 122, + "tag": "cognitive neuroscience" + }, + { + "id": 123, + "tag": "data standardization" + }, + { + "id": 124, + "tag": "decision making" + }, + { + "id": 125, + "tag": "declarative memory" + }, + { + "id": 27, + "tag": "neurophysiology" + }, + { + "id": 126, + "tag": "neurosurgery" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 127, + "tag": "open source" + }, + { + "id": 128, + "tag": "single-neurons" + }, + { + "id": 296, + "tag": "DANDI:000004" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 94, + "tag": "Human" + } + ], + "timestamp_created": "2023-02-17 08:42:12.632540+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000004/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "68": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220126.1855", + "id": 68, + "name": "Mouse anterior lateral motor cortex (ALM) in delay response task", + "repository_type": "dandi", + "summary": "Extracellular electrophysiology recordings performed on mouse anterior lateral motor cortex (ALM) in delay response task. Neural activity from two neuron populations, pyramidal track upper and lower, were characterized, in relation to movement execution. Some files, as originally (re)distributed from e.g. http://datasets.datalad.org/?dir=/labs/svoboda/Economo_2018 were found to be broken and would not be able available among reorganized files under sub-* directories.", + "tags": [ + { + "id": 283, + "tag": "DANDI:000006" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 282, + "tag": "House mouse" + } + ], + "timestamp_created": "2023-02-17 08:42:27.863281+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000006/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "69": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220126.1903", + "id": 69, + "name": "A cortico-cerebellar loop for motor planning", + "repository_type": "dandi", + "summary": "Extracellular recording in ALM", + "tags": [ + { + "id": 297, + "tag": "DANDI:000007" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 282, + "tag": "House mouse" + } + ], + "timestamp_created": "2023-02-17 08:42:29.075043+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000007/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "70": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.211014.0809", + "id": 70, + "name": "Phenotypic variation within and across transcriptomic cell types in mouse motor cortex", + "repository_type": "dandi", + "summary": "We used Patch-seq to combine patch-clamp recording, biocytin staining, and single-cell RNA sequencing of over 1300 neurons in adult mouse motor cortex, providing a comprehensive morpho-electric annotation of almost all transcriptomically defined neural cell types. Contained in this dandiset are the intracellular electrophysiological recordings. See Dandiset #35 for an additional dataset, recorded under the physiological temperature. ", + "tags": [ + { + "id": 129, + "tag": "Patch-seq" + }, + { + "id": 96, + "tag": "cortex" + }, + { + "id": 130, + "tag": "motor cortex" + }, + { + "id": 97, + "tag": "mouse" + }, + { + "id": 298, + "tag": "DANDI:000008" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + } + ], + "timestamp_created": "2023-02-17 08:42:30.856436+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000008/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "71": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220126.1903", + "id": 71, + "name": "Maintenance of persistent activity in a frontal thalamocortical loop", + "repository_type": "dandi", + "summary": "We recorded spikes from the ALM and thalamus during tactile discrimination with a delayed directional response. Here we show that, similar to ALM neurons, thalamic neurons exhibited selective persistent delay activity that predicted movement direction. Unilateral photoinhibition of delay activity in the ALM or thalamus produced contralesional neglect. Photoinhibition of the thalamus caused a short-latency and near-complete collapse of ALM activity. Similarly, photoinhibition of the ALM diminished thalamic activity. Our results show that the thalamus is a circuit hub in motor preparation and suggest that persistent activity requires reciprocal excitation across multiple brain areas.", + "tags": [ + { + "id": 300, + "tag": "DANDI:000009" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 282, + "tag": "House mouse" + } + ], + "timestamp_created": "2023-02-17 08:42:32.103846+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000009/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "72": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220126.1905", + "id": 72, + "name": "A motor cortex circuit for motor planning and movement", + "repository_type": "dandi", + "summary": "Data from \"A motor cortex circuit for motor planning and movement\" Li et al. Nature 2015", + "tags": [ + { + "id": 301, + "tag": "DANDI:000010" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 282, + "tag": "House mouse" + } + ], + "timestamp_created": "2023-02-17 08:42:33.310029+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000010/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "73": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220126.1907", + "id": 73, + "name": "Robust neuronal dynamics in premotor cortex during motor planning", + "repository_type": "dandi", + "summary": "Data from \"Robust neuronal dynamics in premotor cortex during motor planning\" Nature 2016", + "tags": [ + { + "id": 302, + "tag": "DANDI:000011" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 282, + "tag": "House mouse" + } + ], + "timestamp_created": "2023-02-17 08:42:34.461291+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000011/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "74": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 74, + "name": "Kriegstein2020", + "repository_type": "dandi", + "summary": "Data from the Kriegstein Lab as part of the BICCN", + "tags": [ + { + "id": 303, + "tag": "DANDI:000012" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 94, + "tag": "Human" + } + ], + "timestamp_created": "2023-02-17 08:42:35.602778+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000012/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "75": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220126.2143", + "id": 75, + "name": "Low-noise encoding of active touch by layer 4 in the somatosensory cortex", + "repository_type": "dandi", + "summary": "Data from \"Low-noise encoding of active touch by layer 4 in the somatosensory cortex\" Hires, Gutnisky et al. Elife 2015", + "tags": [ + { + "id": 304, + "tag": "DANDI:000013" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 282, + "tag": "House mouse" + } + ], + "timestamp_created": "2023-02-17 08:42:36.755612+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000013/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "76": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220126.1914", + "id": 76, + "name": "A Map of Anticipatory Activity in Mouse Motor Cortex", + "repository_type": "dandi", + "summary": "Data from \"A Map of Anticipatory Activity in Mouse Motor Cortex\" Chen et al. Neuron 2017", + "tags": [ + { + "id": 305, + "tag": "DANDI:000015" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 282, + "tag": "House mouse" + } + ], + "timestamp_created": "2023-02-17 08:42:37.917910+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000015/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "77": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 77, + "name": "Excitatory and inhibitory subnetworks are equally selective during decision-making and emerge simultaneously during learning", + "repository_type": "dandi", + "summary": "This package contains data, in NWB (Neurodata Without Borders) format, from the 4 mice included in \"Najafi, Farzaneh, Gamaleldin F. Elsayed, Robin Cao, Eftychios Pnevmatikakis, Peter E. Latham, John P. Cunningham, and Anne K. Churchland. \"Excitatory and inhibitory subnetworks are equally selective during decision-making and emerge simultaneously during learning.\" Neuron 105, no. 1 (2020): 165-179.\u201d Each NWB file represents the data and metadata associated with one recording session. In each NWB file, the metadata related to the session (mouse name, session date/time, lab/institution name, etc.) can be found under \"general\". Information related to ROI-segmentation such as ROI mask, ROI type (excitatory or inhibitory), poor or good quality, etc. can be found under \"modules/Image-Segmentation/pln-seg\". Trial information (e.g. start, end times, trial types, trial outcomes, etc.) can be found under \"trials\". Recorded trial-segmented neuronal responses aligned to different time event (e.g. stimulus start, animal choice, etc.) can be found under \"modules/ Trial-based-Segmentation\". A jupyter notebook presenting in detail how to work with NWB files is provided at https://github.com/ttngu207/najafi-2018-nwb/blob/master/notebooks/Najafi-2018_example.ipynb.", + "tags": [ + { + "id": 306, + "tag": "DANDI:000016" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:42:39.061106+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000016/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "78": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 78, + "name": "Distributed coding of choice, action and engagement across the mouse brain", + "repository_type": "dandi", + "summary": "Data from \"Distributed coding of choice, action and engagement across the mouse brain\" Steinmetz et. al Nature 2019", + "tags": [ + { + "id": 131, + "tag": "neuropixels" + }, + { + "id": 307, + "tag": "DANDI:000017" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 282, + "tag": "House mouse" + } + ], + "timestamp_created": "2023-02-17 08:42:40.201705+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000017/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "79": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220126.2148", + "id": 79, + "name": "Human ECoG speaking consonant-vowel syllables", + "repository_type": "dandi", + "summary": "The enclosed data is collected using a high-density 256-channel electrocorticography (ECoG) array implanted in human patients during treatment for epilepsy. The subjects are reading aloud consonant-vowel syllables from a list. The data was collected by Dr. Edward Chang and Dr. Kristofer Bouchard at the University of California, San Francisco, and curated by Dr. Kristofer Bouchard and Dr. Benjamin Dichter.\n\n\nData is organized by subject ID, and each file is a continuous recording session in Neurodata Without Borders (NWB) 2.0 format. Voltage traces are included for each of the recorded 256 channels. Microphone signal was recorded at the time but is removed for HIPAA compliance. Detailed hand-marked annotations are provided which mark what syllable was said, and the times of the start, consonant-vowel transition, and end of each syllable. A rest-period time is also included when the subject was silent, which can be used as a baseline. Hand-marked anatomical labels are included for electrodes in the relevant brain regions. All dates have been removed for HIPAA compliance and replaced with Jan 1, 1900.", + "tags": [ + { + "id": 132, + "tag": "electrocorticography (ECoG)" + }, + { + "id": 133, + "tag": "speech production" + }, + { + "id": 308, + "tag": "DANDI:000019" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 94, + "tag": "Human" + } + ], + "timestamp_created": "2023-02-17 08:42:41.361544+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000019/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "80": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.210913.1639", + "id": 80, + "name": "Patch-seq recordings from mouse visual cortex", + "repository_type": "dandi", + "summary": "Whole-cell Patch-seq recordings from neurons of the mouse visual cortex from the Allen Institute for Brain Science, released in June 2020. The majority of cells in this dataset are GABAergic interneurons, but there are also a small number of glutamatergic neurons from layer 2/3 of the mouse visual cortex.", + "tags": [ + { + "id": 129, + "tag": "Patch-seq" + }, + { + "id": 97, + "tag": "mouse" + }, + { + "id": 134, + "tag": "visual cortex" + }, + { + "id": 135, + "tag": "interneuron" + }, + { + "id": 309, + "tag": "DANDI:000020" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 282, + "tag": "House mouse" + } + ], + "timestamp_created": "2023-02-17 08:42:43.238231+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000020/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "81": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 81, + "name": "20191003_AIBS_mouse_ecephys_functional_connectivity", + "repository_type": "dandi", + "summary": "Allen Institute October 2019 Mouse extracellular electrophysiology data, collected under analogous conditions to the two-photon Brain Observatory imaging experiments, with mice shown a subset of stimuli with a higher number of repeats. \n\nFor more information, see https://portal.brain-map.org/explore/circuits/visual-coding-neuropixels\n\nData are subject to Allen Institute Terms of Use policy, available at: http://www.alleninstitute.org/legal/terms-use/\n", + "tags": [ + { + "id": 17, + "tag": "electrophysiology" + }, + { + "id": 18, + "tag": "life sciences" + }, + { + "id": 19, + "tag": "machine learning" + }, + { + "id": 20, + "tag": "neurobiology" + }, + { + "id": 21, + "tag": "signal processing" + }, + { + "id": 311, + "tag": "DANDI:000022" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 282, + "tag": "House mouse" + } + ], + "timestamp_created": "2023-02-17 08:42:45.208884+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000022/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "82": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.210914.1900", + "id": 82, + "name": "Patch-seq recordings from human cortex (June 2020)", + "repository_type": "dandi", + "summary": "Whole-cell Patch-seq recordings from neurons of the human neocortex from the Allen Institute for Brain Science, released in June 2020.", + "tags": [ + { + "id": 129, + "tag": "Patch-seq" + }, + { + "id": 98, + "tag": "human" + }, + { + "id": 136, + "tag": "neocortex" + }, + { + "id": 312, + "tag": "layer 2/3" + }, + { + "id": 313, + "tag": "DANDI:000023" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 94, + "tag": "Human" + } + ], + "timestamp_created": "2023-02-17 08:42:46.392132+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000023/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "83": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 83, + "name": "Human brain cell census for BA 44/45", + "repository_type": "dandi", + "summary": "Magnetic resonance imaging (MRI) is used to establish a macroscopic reference coordinate system of laminar and cytoarchitectural boundaries. Cell counting is obtained with both traditional immunohistochemistry, to provide a stereological gold standard, and with a custom-made inverted confocal light-sheet fluorescence microscope (LSM) for 3D imaging at cellular resolution. Finally, polarization-sensitive optical coherence tomography (PSOCT) enables registration of the distorted histological cell typing obtained with LSM to the MRI-based atlas coordinate system.", + "tags": [ + { + "id": 138, + "tag": "multi-modal imaging" + }, + { + "id": 139, + "tag": "MRI" + }, + { + "id": 140, + "tag": "OCT" + }, + { + "id": 141, + "tag": "SPIM" + }, + { + "id": 142, + "tag": "human cortex" + }, + { + "id": 143, + "tag": "Broca's area" + }, + { + "id": 144, + "tag": "Motor cortex" + }, + { + "id": 145, + "tag": "Stereology" + }, + { + "id": 330, + "tag": "DANDI:000026" + }, + { + "id": 11, + "tag": "DANDI" + } + ], + "timestamp_created": "2023-02-17 08:42:50.716843+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000026/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "84": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.210831.2033", + "id": 84, + "name": "Test dataset for testing dandi-cli.", + "repository_type": "dandi", + "summary": "Should be ignored by regular mortals.\n\nATM contains only a few files from http://github.com/dandi-datasets/nwb_test_data which more or less appropriate (do not lack critical metadata) for testing", + "tags": [ + { + "id": 146, + "tag": "development" + }, + { + "id": 331, + "tag": "DANDI:000027" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + } + ], + "timestamp_created": "2023-02-17 08:42:51.878012+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000027/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "85": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 85, + "name": "Simulated cortical Neuropixels recording with ground truth", + "repository_type": "dandi", + "summary": "A 10 minute Neuropixels recording simulated using the MEArec Python package (Buccino et al., 2020). The recording contains the spiking activity of 250 biophysically detailed neurons (200 excitatory and 50 inhibitory cells from the Neocortical Micro Circuit Portal with independent Poisson firing patterns, and additive Gaussian noise with 10uV standard deviation.", + "tags": [ + { + "id": 332, + "tag": "DANDI:000028" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 282, + "tag": "House mouse" + } + ], + "timestamp_created": "2023-02-17 08:42:53.033182+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000028/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "86": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.231017.2004", + "id": 86, + "name": "Test dataset for development purposes", + "repository_type": "dandi", + "summary": "Should be ignored by regular mojgjhhj. fjrtalddgdfgdfgs, and not relied upon being static or ever correct.\n\u0414\u043e\u043b\u0436\u043d\u043e \u0431\u044b\u0442\u044c \u0432\u0432\u0435\u0434\u0435\u043d\u043e \u043d\u0430 \u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u043e\u043c, \u043d\u043e \u043f\u043e\u0447\u0435\u043c\u0443 \u0431\u044b \u043d\u0430\u043c \u043d\u0435 \u043f\u0440\u043e\u0432\u0435\u0440\u0438\u0442\u044c \u0432\u0441\u044e \u044d\u0442\u0443 \u043a\u0443\u0445\u043d\u044e \n\u0394\u0419\u05e7\u0645\u0e57\u3042\nabcdefghi", + "tags": [ + { + "id": 146, + "tag": "development" + }, + { + "id": 333, + "tag": "DANDI:000029" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 334, + "tag": "Macaca mulatta - Rhesus monkey" + } + ], + "timestamp_created": "2023-02-17 08:42:54.240971+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000029/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "87": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.211030.0713", + "id": 87, + "name": "SpikeInterface, a unified framework for spike sorting", + "repository_type": "dandi", + "summary": "SpikeInterface is a Python framework designed to make the analysis of extracellular electrophysiology data more accessible and standardized. The SpikeInterface documentation can be found at https://spikeinterface.readthedocs.io/en/latest/.\n\nThis dataset contains all recordings analysed in the paper: \"SpikeInterface, a unified framework for spike sorting.\" Alessio P. Buccino, Cole L. Hurwitz, Samuel Garcia, Jeremy Magland, Joshua H. Siegle, Roger Hurwitz, Matthias H. Hennig, eLife - doi: https://doi.org/10.7554/eLife.61834; ", + "tags": [ + { + "id": 147, + "tag": "Spike Sorting" + }, + { + "id": 148, + "tag": "extracellular electrophysiology" + }, + { + "id": 335, + "tag": "DANDI:000034" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 282, + "tag": "House mouse" + } + ], + "timestamp_created": "2023-02-17 08:42:55.664784+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000034/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "88": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.211014.0808", + "id": 88, + "name": "Temperature-controlled intracellular Patch-seq recordings in mouse motor cortex", + "repository_type": "dandi", + "summary": " We used Patch-seq to combine patch-clamp recording, biocytin staining, and single-cell RNA sequencing of over 1300 neurons in adult mouse motor cortex, providing a comprehensive morpho-electric annotation of almost all transcriptomically defined neural cell types. This dataset contains rectangular stimulation of cells under temperature-controlled conditions (34 \u2103). See Dandiset #8 for the main dataset, recorded under the room temperature.", + "tags": [ + { + "id": 129, + "tag": "Patch-seq" + }, + { + "id": 97, + "tag": "mouse" + }, + { + "id": 96, + "tag": "cortex" + }, + { + "id": 130, + "tag": "motor cortex" + }, + { + "id": 336, + "tag": "DANDI:000035" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 282, + "tag": "House mouse" + } + ], + "timestamp_created": "2023-02-17 08:42:56.881625+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000035/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "89": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230515.1917", + "id": 89, + "name": "Allen Institute Openscope - Measuring Stimulus-Evoked Neurophysiological Differentiation in Distinct Populations of Neurons in Mouse Visual Cortex", + "repository_type": "dandi", + "summary": "Despite significant progress in understanding neural coding, it remains unclear how the coordinated activity of large populations of neurons relates to what an observer actually perceives. Since neurophysiological differences must underlie differences among percepts, differentiation analysis-quantifying distinct patterns of neurophysiological activity-has been proposed as an \"inside-out\" approach that addresses this question. This methodology contrasts with \"outside-in\" approaches such as feature tuning and decoding analyses, which are defined in terms of extrinsic experimental variables. Here, we used two-photon calcium imaging in mice of both sexes to systematically survey stimulus-evoked neurophysiological differentiation (ND) in excitatory neuronal populations in layers (L)2/3, L4, and L5 across five visual cortical areas (primary, lateromedial, anterolateral, posteromedial, and anteromedial) in response to naturalistic and phase-scrambled movie stimuli. We find that unscrambled stimuli evoke greater ND than scrambled stimuli specifically in L2/3 of the anterolateral and anteromedial areas, and that this effect is modulated by arousal state and locomotion. By contrast, decoding performance was far above chance and did not vary substantially across areas and layers. Differentiation also differed within the unscrambled stimulus set, suggesting that differentiation analysis may be used to probe the ethological relevance of individual stimuli.\n\nFor more details, consult the associated publication : https://doi.org/10.1523/eneuro.0280-21.2021", + "tags": [ + { + "id": 149, + "tag": "two photon imaging" + }, + { + "id": 150, + "tag": "visual stimuli" + }, + { + "id": 151, + "tag": "mice" + }, + { + "id": 152, + "tag": "openscope" + }, + { + "id": 337, + "tag": "DANDI:000036" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:42:58.022918+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000036/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "90": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230426.0054", + "id": 90, + "name": "Allen Institute Openscope - Responses to inconsistent stimuli in somata and distal apical dendrites in primary visual cortex", + "repository_type": "dandi", + "summary": "This dataset was collected for the Credit Assignment project, as part of the Allen Institute for Brain Science's OpenScope project. The most up-to-date links to the publications associated with this dataset (a dataset descriptor paper and an analysis paper) can be found by going to the metadata Github repository, linked below.\n\nBriefly, mice were habituated to head-fixation on a running disc over a period of 11 days. Over the last 6 days, mice were presented with repeating stimulus sequences, with consistent features. These sessions are not included in the dataset, as imaging was not performed. Following habituation, sequences with inconsistent features were introduced over three sessions, while two-photon calcium imaging was performed in pyramidal neurons of primary visual cortex (VisP). Specifically, recordings for each mouse were made in one of four planes: L2/3 somata, L5 somata, L2/3 distal apical dendrites or L5 distal apical dendrites. As mice gained experience with the inconsistent sequences, responses to these events were found to evolve in opposite ways in the dendritic and somatic compartments.\n\nThe dataset includes 50 sessions total, recorded in 13 subjects, with at least 3 sessions per subject. Each session file includes: **(1)** ROI dF/F traces, **(2)** ROI masks, **(3)** ROI tracking information (where applicable), **(4)** running velocity traces, **(5)** pupil diameter traces, **(6)** pupil centroid position traces in x and y, **(7)** stimulus parameters. Note that gaze was not computed for this dataset. What is provided is the position of the pupil centroid in the pupil recording videos. In addition, a second, slightly larger version of each file was created, also including: **(8)** stimulus frame images (identifiable by `+image` in the file name). Lastly, a third, much larger version of each file was created, also including: **(9)** the motion corrected imaging stack (identifiable by `_obj-raw` in the file name).\n\nCertain sessions in this dataset were excluded from our analyses for quality control reasons. They are provided here, as the reasons we excluded them from our analyses may not pose a problem for all use cases. The metadata Github repository, linked below, provides detailed information about each session. ", + "tags": [ + { + "id": 153, + "tag": "learning" + }, + { + "id": 136, + "tag": "neocortex" + }, + { + "id": 154, + "tag": "pyramidal neurons" + }, + { + "id": 155, + "tag": "distal apical dendrites" + }, + { + "id": 156, + "tag": "somata" + }, + { + "id": 157, + "tag": "L2/3" + }, + { + "id": 158, + "tag": "L5" + }, + { + "id": 159, + "tag": "two-photon calcium imaging" + }, + { + "id": 160, + "tag": "mouse VisP" + }, + { + "id": 161, + "tag": "prediction" + }, + { + "id": 162, + "tag": "credit assignment" + }, + { + "id": 338, + "tag": "DANDI:000037" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + } + ], + "timestamp_created": "2023-02-17 08:42:59.193357+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000037/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "91": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230223.1216", + "id": 91, + "name": "Allen Institute \u2013 Contrast tuning in mouse visual cortex with calcium imaging", + "repository_type": "dandi", + "summary": "A two photon calcium imaging dataset from Allen Institute measuring responses to full-field drifting gratings (approx. 120x90 degrees of visual space) of 8 directions and 6 contrasts (5%, 10%, 20%, 40%, 60%, 80%). Mouse Cre lines expressing GCaMP6f were imaged to record responses of pyramidal neurons across cortical layers (Cux2: layer 2/3; Rorb: layer 4; Rbp4: layer 5; Ntsr1: layer 6) as well as inhibitory interneurons (Vip and Sst). All experimental sessions took place on the same data collection pipeline as the Allen Brain Observatory (see http://observatory.brain-map.org/visualcoding) and have the same visual stimulus monitor calibration and positioning, two photon imaging systems and image processing pipeline, and running wheel to track locomotion.\n\n Data are subject to Allen Institute Terms of Use policy, available at: http://www.alleninstitute.org/legal/terms-use/", + "tags": [ + { + "id": 163, + "tag": "vision" + }, + { + "id": 134, + "tag": "visual cortex" + }, + { + "id": 164, + "tag": "inhibition" + }, + { + "id": 165, + "tag": "inhibitory circuits" + }, + { + "id": 166, + "tag": "circuit dynamics" + }, + { + "id": 167, + "tag": "gain control" + }, + { + "id": 339, + "tag": "DANDI:000039" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 282, + "tag": "House mouse" + } + ], + "timestamp_created": "2023-02-17 08:43:00.367528+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000039/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "92": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.210812.1515", + "id": 92, + "name": "Network Homeostasis and State Dynamics of Neocortical Sleep", + "repository_type": "dandi", + "summary": "Data was recorded using silicon probe electrodes in the frontal cortices of male Long Evans rats between 4-7 months of age. The design was to have no specific behavior, task or stimulus, rather the animal was left alone in it\u2019s home cage (which it lives in at all\ntimes). Data includes both local field potentials (LFP) and spikes. 11 total animals, 27 recording sessions, 1360 total units recorded, 1121 units considered stable, 995 putative excitatory units and 126 putative inhibitory units. Only recordings including a \u201cWAKE-SLEEP\u201d episode wherein at least 7 minutes of wake are followed by 20 minutes of sleep. On average 2 such WAKE-SLEEP episodes per recording session. ", + "tags": [ + { + "id": 168, + "tag": "Firing patterns" + }, + { + "id": 169, + "tag": "Sleep/awake states" + }, + { + "id": 170, + "tag": "Sleep stages" + }, + { + "id": 171, + "tag": "Homeostasis" + }, + { + "id": 340, + "tag": "DANDI:000041" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 341, + "tag": "Brown rat" + } + ], + "timestamp_created": "2023-02-17 08:43:01.611818+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000041/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "93": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 93, + "name": "Human, macaque, and mouse L5 pyramidal neuron physiology", + "repository_type": "dandi", + "summary": "Patch clamp recordings from human premotor cortex, macaque M1 and mouse MOp.", + "tags": [ + { + "id": 129, + "tag": "Patch-seq" + }, + { + "id": 144, + "tag": "Motor cortex" + }, + { + "id": 172, + "tag": "Betz cell" + }, + { + "id": 94, + "tag": "Human" + }, + { + "id": 173, + "tag": "Macaque" + }, + { + "id": 16, + "tag": "Mouse" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 342, + "tag": "DANDI:000043" + }, + { + "id": 282, + "tag": "House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:02.790691+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000043/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "94": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.210812.1516", + "id": 94, + "name": "Diversity in neural firing dynamics supports both rigid and learned hippocampal sequences", + "repository_type": "dandi", + "summary": "This data set is composed of eight bilateral silicon-probe multi-cellular electrophysiological recordings performed on four male Long-Evans rats in the Buzs\u00e1ki lab at NYU. These recordings were performed to assess the effect of novel spatial learning on hippocampal CA1 neural firing and LFP patterns in na\u00efve animals. Each session consisted of a long (~4 hour) PRE rest/sleep epoch home-cage recordings performed in a familiar room, followed by a Novel MAZE running epoch (~45 minutes) in which the animals were transferred to a novel room, and water-rewarded to run on a novel maze. These mazes were either A) a wooden 1.6m linear platform, B) a wooden 1m diameter circular platform or C) a 2m metal linear platform. Animals were rewarded either at both ends of the linear platform, or at a predetermined location on the circular platform. The animal was gently encouraged to run unidirectionally on the circular platform. After the MAZE epochs the animals were transferred back to their home-cage in the familiar room where a long (~4 hour) POST rest/sleep was recorded. All eight sessions were concatenated from the PRE, MAZE, and POST recording epochs. In addition to hippocampal electrophysiological recordings, neck EMG and head-mounted accelerometer signals were recorded, and the animal\u2019s position during MAZE running epochs was tracked via head-mounted LEDs.", + "tags": [ + { + "id": 341, + "tag": "Brown rat" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 343, + "tag": "DANDI:000044" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:03.952334+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000044/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "95": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.211209.1413", + "id": 95, + "name": "IBL behavioral data", + "repository_type": "dandi", + "summary": "This dataset is a result of a multi-institution, cross country collaboration of labs, called International Brain Laboratory conducting standardized experiments on decision-making in mice. In the experiment, mice are shown a visual grating on screen with varying levels of contrast, and they are trained to rotate a wheel to move the on-screen stimulus from either side of their visual field to the center. These NWB files contain datasets corresponding to this wheel movement, camera footage of mice and information about the visual stimulus at every trial. \nPaper: \nAguillon, V., Angelaki, D., Bayer, H. M., Bonacchi, N., Carandini, M., Cazettes, F., Churchland, A. K., Chapuis, G., Dan, Y., Dewitt, E., Faulkner, M., Hamish, F., Haetzel, L., Hausser, M., Hofer, S., Hu, F., Khanal, A., Krasniak, C., Laranjeira, I., \u2026 Zador, A. (2020). A standardized and reproducible method to measure decision-making in mice. BioRxiv, 2020.01.17.909838. https://doi.org/10.1101/2020.01.17.909838", + "tags": [ + { + "id": 174, + "tag": "International Brain Laboratory" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 344, + "tag": "DANDI:000045" + }, + { + "id": 282, + "tag": "House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:05.790818+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000045/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "96": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 96, + "name": "Electrical and optical physiology in in vivo population-scale two-photon calcium imaging", + "repository_type": "dandi", + "summary": "Spiking activity and simultaneously recorded fluorescence signals in transgenic mice expressing GCaMP6\n\nWe present a dataset consisting of simultaneously measured fluorescence and spiking activity of pyramidal neurons in layer 2/3 of primary visual cortex in transgenic mouse lines expressing genetically-encoded calcium indicators (GECIs) GCaMP6s or GCaMP6f.\n\nReference: https://portal.brain-map.org/explore/circuits/oephys", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 345, + "tag": "DANDI:000048" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:06.929567+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000048/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "97": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230223.1424", + "id": 97, + "name": "Allen Institute \u2013 TF x SF tuning in mouse visual cortex with calcium imaging", + "repository_type": "dandi", + "summary": "A two photon calcium imaging dataset from Allen Institute measuring responses to full-field drifting gratings (approx. 120x90 degrees of visual space) moving in 4 different directions, at 5 spatial frequencies (0.02, 0.04, 0.08, 0.16, 0.32 cpd), and 5 temporal frequencies (0.5, 1, 2, 4, 8 Hz). The ratio of TF/SF is speed (deg/sec) and the extent to which visual neurons exhibit speed tuning has been shown to vary across some cortical areas (Andermann et al. 2011).\nMouse Cre lines expressing GCaMPf were imaged to record responses of pyramidal neurons across cortical layers (Cux2: layer 2/3; Rorb: layer 4; Rbp4: layer 5; Ntsr1: layer 6) as well as somatostatin inhibitory interneurons (Sst). All Cre lines were imaged in VISp, and some (Cux2 and Sst) were also imaged in VISl, VISal, VISpm, VISam, and VISrl. All experimental sessions took place on the same data collection pipeline as the Allen Brain Observatory (see de Vries, Lecoq, Buice et al. 2020) and have the same visual stimulus monitor calibration and positioning, two photon imaging systems and image processing pipeline, and running wheel to track locomotion. Data are subject to Allen Institute Terms of Use policy, available at: http://www.alleninstitute.org/legal/terms-use/", + "tags": [ + { + "id": 16, + "tag": "Mouse" + }, + { + "id": 346, + "tag": "2-photon calcium imaging" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 347, + "tag": "DANDI:000049" + }, + { + "id": 282, + "tag": "House mouse" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 134, + "tag": "visual cortex" + } + ], + "timestamp_created": "2023-02-17 08:43:08.061716+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000049/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "98": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 98, + "name": "Allen Institute - Run Tuning in the Mouse Visual Cortex", + "repository_type": "dandi", + "summary": "Allen Institute for Brain Science, MindScope Project.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 348, + "tag": "DANDI:000050" + }, + { + "id": 282, + "tag": "House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:09.537806+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000050/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "99": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 99, + "name": "pons8-yo_16xdownsampled", + "repository_type": "dandi", + "summary": "Downsampled image of pons slice #8 stained with a nuclear dye. For testing only.\n", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 349, + "tag": "DANDI:000051" + }, + { + "id": 94, + "tag": "Human" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:10.677600+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000051/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "100": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 100, + "name": "Pons8-BIDS-16xdownsampled", + "repository_type": "dandi", + "summary": "A test upload of Pons8 YO and Lectin, downsampled 16x using the BIDS schema", + "tags": [ + { + "id": 72, + "tag": "BIDS" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 350, + "tag": "DANDI:000052" + } + ], + "timestamp_created": "2023-02-17 08:43:11.847435+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000052/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "101": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.210819.0345", + "id": 101, + "name": "Recordings from medial entorhinal cortex during linear track and open exploration", + "repository_type": "dandi", + "summary": "This dataset contains two task types. The first is tetrode recordings from medial entorhinal cortex during open field navigation with simultaneous inertial measurements of the head, and the second is Neuropixel recordings from medial entorhinal cortex during navigation down a virtual linear track with simultaneous eye measurements.", + "tags": [ + { + "id": 175, + "tag": "neuropixel" + }, + { + "id": 176, + "tag": "entorhinal cortex" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 351, + "tag": "DANDI:000053" + }, + { + "id": 282, + "tag": "House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:13.057388+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000053/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "102": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.210819.1547", + "id": 102, + "name": "Plitt & Giocomo (2021) Experience Dependent Contextual Codes in the Hippocampus. Nat Neuro", + "repository_type": "dandi", + "summary": "Data included in Plitt & Giocomo (2021) Nature Neuroscience", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 352, + "tag": "DANDI:000054" + }, + { + "id": 282, + "tag": "House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:14.349502+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000054/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "103": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220127.0436", + "id": 103, + "name": "AJILE12: Long-term naturalistic human intracranial neural recordings and pose", + "repository_type": "dandi", + "summary": "Understanding the neural basis of human movement in naturalistic scenarios is critical for expanding neuroscience research beyond constrained laboratory paradigms. The neural correlates of unstructured, spontaneous movements in completely naturalistic settings have rarely been studied, due in large part to a lack of available data. Here, we present our Annotated Joints in Long-term Electrocorticography for 12 human participants (AJILE12) dataset, the largest human neurobehavioral dataset that is publicly available; the dataset was recorded opportunistically during passive clinical epilepsy monitoring. AJILE12 includes synchronized intracranial neural recordings and upper body pose trajectories across 55 semi-continuous days of naturalistic movements, along with relevant metadata, including thousands of wrist movement events and annotated behavioral states. Neural recordings are available at 500 Hz from at least 64 electrodes per participant, for a total of 1280 hours. Pose trajectories at 9 upper-body keypoints, including wrist, elbow, and shoulder joints, were sampled at 30 frames per second and estimated from 118 million video frames. In adherence with the FAIR data principles, we have shared AJILE12 on The Dandi Archive in the Neurodata Without Borders (NWB) data standard and developed a browser-based dashboard to facilitate data exploration and reuse.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 353, + "tag": "DANDI:000055" + }, + { + "id": 94, + "tag": "Human" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:15.504036+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000055/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "105": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 105, + "name": "MITU01 Dataset", + "repository_type": "dandi", + "summary": "7T MR structural images. B0 and B1+ code corrected parameter maps. ", + "tags": [ + { + "id": 72, + "tag": "BIDS" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 355, + "tag": "DANDI:000058" + } + ], + "timestamp_created": "2023-02-17 08:43:17.886886+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000058/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "106": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230907.2101", + "id": 106, + "name": "Cooling of Medial Septum Reveals Theta Phase Lag Coordination of Hippocampal Cell Assemblies", + "repository_type": "dandi", + "summary": "Hippocampal theta oscillations coordinate neuronal firing to support memory and spatial navigation. The medial septum (MS) is critical in theta generation by two possible mechanisms: either a unitary \u201cpacemaker\u201d timing signal is imposed on the hippocampal system, or it may assist in organizing target subcircuits within the phase space of theta oscillations. We used temperature manipulation of the MS to test these models. Cooling of the MS reduced both theta frequency and power and was associated with an enhanced incidence of errors in a spatial navigation task, but it did not affect spatial correlates of neurons. MS cooling decreased theta frequency oscillations of place cells and reduced distance-time compression but preserved distance-phase compression of place field sequences within the theta cycle. Thus, the septum is critical for sustaining precise theta phase coordination of cell assemblies in the hippocampal system, a mechanism needed for spatial memory.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 356, + "tag": "DANDI:000059" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + } + ], + "timestamp_created": "2023-02-17 08:43:19.015692+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000059/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "107": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 107, + "name": "Dataset for Finkelstein, Fontolan et al. \"Attractor dynamics gate cortical information flow during decision-making\"", + "repository_type": "dandi", + "summary": "Extracellular electrophysiology recordings in anterior lateral motor cortex and in vibrissal sensory cortex in mice trained to detect optogenetic stimulation of the vibrissal sensory cortex.\n\nThe data analysis code for this dataset is available here: \n https://github.com/arsenyf/FinkelsteinFontolan_2021NN", + "tags": [ + { + "id": 130, + "tag": "motor cortex" + }, + { + "id": 148, + "tag": "extracellular electrophysiology" + }, + { + "id": 178, + "tag": "decision-making" + }, + { + "id": 179, + "tag": "attractor" + }, + { + "id": 180, + "tag": "optogenetic stimulation" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 357, + "tag": "DANDI:000060" + }, + { + "id": 282, + "tag": "House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:20.165181+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000060/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "108": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.210812.1517", + "id": 108, + "name": "Reactivations of emotional memory in the hippocampus\u2013amygdala system during sleep", + "repository_type": "dandi", + "summary": "The consolidation of context-dependent emotional memory requires communication between the hippocampus and the basolateral amygdala (BLA), but the mechanisms of this process are unknown. We recorded neuronal ensembles in the hippocampus and BLA while rats learned the location of an aversive air puff on a linear track, as well as during sleep before and after training. We found coordinated reactivations between the hippocampus and the BLA during non-REM sleep following training. These reactivations peaked during hippocampal sharp wave\u2013ripples (SPW-Rs) and involved a subgroup of BLA cells positively modulated during hippocampal SPW-Rs. Notably, reactivation was stronger for the hippocampus\u2013BLA correlation patterns representing the run direction that involved the air puff than for the 'safe' direction. These findings suggest that consolidation of contextual emotional memory occurs during ripple-reactivation of hippocampus\u2013amygdala circuits.", + "tags": [ + { + "id": 341, + "tag": "Brown rat" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 358, + "tag": "DANDI:000061" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:21.310530+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000061/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "109": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.221025.1735", + "id": 109, + "name": "Simulation extension example", + "repository_type": "dandi", + "summary": "This is data produced by the Soltesz Lab NeuroH5 software (https://github.com/iraikov/neuroh5). The data has been converted to NWB using the ndx-simulation-output extension (https://github.com/catalystneuro/ndx-simulation-output).", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 359, + "tag": "DANDI:000064" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:22.539305+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000064/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "110": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 110, + "name": "Polymer probe recordings from hippocampus (LFP), OFC, NAc, and mPFC", + "repository_type": "dandi", + "summary": "Recordings made by Jason Chung in the Frank laboratory at UCSF using 64 channel polymer probes across 4 regions in an animal resting in a box. Note that the position data is duplicated, one copy for each epoch.", + "tags": [ + { + "id": 182, + "tag": "polymer probe" + }, + { + "id": 17, + "tag": "electrophysiology" + }, + { + "id": 183, + "tag": "nucleus accumbens" + }, + { + "id": 184, + "tag": "medial prefrontal cortex" + }, + { + "id": 185, + "tag": "orbitofrontal cortex" + }, + { + "id": 186, + "tag": "hippocampus" + }, + { + "id": 187, + "tag": "sleep" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 360, + "tag": "DANDI:000065" + }, + { + "id": 361, + "tag": "rat" + } + ], + "timestamp_created": "2023-02-17 08:43:23.761747+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000065/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "111": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 111, + "name": "Allen Mouse Common Coordinate Framework - Average Brain Template", + "repository_type": "dandi", + "summary": "The reference space or brain template was constructed as a population average of 1,675 young adult C57BL/6J mice brains imaged using serial two photon tomography (STPT) for the Allen Mouse Brain Connectivity Atlas. The average template was created from tissue autofluorescence detected in the red channel. To maximize input data and create a symmetrical atlas, each dataset was reflected across the midline, for a total of 3,350 (2 x 1,675) hemisphere datasets. Creation of the template followed a two-step iterative process: (1) We deformably registered each specimen to the current iteration of the template and computed an intensity average. (2) We then computed the average deformation field, inverted it, and applied it to the intensity average created in (1). This resulted in a volume with an average unbiased shape and intensity to be used as the template in the next iteration until convergence.\n\nThe axes the average template volume is a +X=Posterior, +Y=Inferior(Ventral) and +Z=Right frame with the origin at the corner of the volume.", + "tags": [ + { + "id": 72, + "tag": "BIDS" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 362, + "tag": "DANDI:000066" + } + ], + "timestamp_created": "2023-02-17 08:43:24.915859+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000066/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "112": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.210812.1457", + "id": 112, + "name": "Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex", + "repository_type": "dandi", + "summary": "Although short-term plasticity is believed to play a fundamental role in cortical computation, empirical evidence bearing on its role during behavior is scarce. Here we looked for the signature of short-term plasticity in the fine-timescale spiking relationships of a simultaneously recorded population of physiologically identified pyramidal cells and interneurons, in the medial prefrontal cortex of the rat, in a working memory task. On broader timescales, sequentially organized and transiently active neurons reliably differentiated between different trajectories of the rat in the maze. On finer timescales, putative monosynaptic interactions reflected short-term plasticity in their dynamic and predictable modulation across various aspects of the task, beyond a statistical accounting for the effect of the neurons' co-varying firing rates. Seeking potential mechanisms for such effects, we found evidence for both firing pattern\u2013dependent facilitation and depression, as well as for a supralinear effect of presynaptic coincidence on the firing of postsynaptic targets.", + "tags": [ + { + "id": 341, + "tag": "Brown rat" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 363, + "tag": "DANDI:000067" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:26.097696+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000067/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "113": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 113, + "name": "Testing", + "repository_type": "dandi", + "summary": "Nothing to see", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 364, + "tag": "DANDI:000068" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:27.241424+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000068/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "114": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 114, + "name": "Neural population dynamics during reaching", + "repository_type": "dandi", + "summary": "Monkeys recordings of Motor Cortex (M1) and dorsal Premotor Cortex (PMd) using two 96 channel high density Utah Arrays (Blackrock Microsystems) while performing reaching tasks with right hand.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 365, + "tag": "DANDI:000070" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 366, + "tag": "Rhesus monkey" + } + ], + "timestamp_created": "2023-02-17 08:43:28.759972+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000070/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "115": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 115, + "name": "MGH19-1-021520", + "repository_type": "dandi", + "summary": "Pons section from acccession MGH 19-1_021520 stained with YOYO1", + "tags": [ + { + "id": 72, + "tag": "BIDS" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 367, + "tag": "DANDI:000105" + } + ], + "timestamp_created": "2023-02-17 08:43:29.879920+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000105/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "116": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 116, + "name": "IVSCC stimulus sets", + "repository_type": "dandi", + "summary": "Allen Institute for Brain Science IVSCC (In-vitro Single Cell Characterization) project stimulus sets stored in NWB format", + "tags": [ + { + "id": 17, + "tag": "electrophysiology" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 368, + "tag": "DANDI:000107" + }, + { + "id": 369, + "tag": "MIES" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:31.115877+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000107/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "117": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.210914.1904", + "id": 117, + "name": "Patch-seq recordings from human cortex (June 2021)", + "repository_type": "dandi", + "summary": "Whole-cell Patch-seq recordings from neurons of the human neocortex from the Allen Institute for Brain Science, released in June 2021.", + "tags": [ + { + "id": 129, + "tag": "Patch-seq" + }, + { + "id": 98, + "tag": "human" + }, + { + "id": 136, + "tag": "neocortex" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 371, + "tag": "DANDI:000109" + }, + { + "id": 94, + "tag": "Human" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:32.408141+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000109/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "118": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.210914.1732", + "id": 118, + "name": "Gillespie et al (2021) Hippocampal replay reflects specific past experiences rather than a plan for subsequent choice", + "repository_type": "dandi", + "summary": "This dataset includes the electrophysiological (dorsal CA1 tetrodes) and behavioral (port triggers, reward delivery, and position tracking) data from Gillespie et al, Neuron 2021: \"Hippocampal replay reflects specific past experiences rather than a plan for subsequent choice\". For more information about this data, please contact Anna Gillespie or Loren Frank. ", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 375, + "tag": "DANDI:000115" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 376, + "tag": "Rat; norway rat; rats; brown rat" + } + ], + "timestamp_created": "2023-02-17 08:43:33.618099+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000115/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "119": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 119, + "name": "1U01MH116990-01_July_2021", + "repository_type": "dandi", + "summary": "Patch-clamp recording data from spinal cord neurons. Data was generated at Huizhong Tao's lab at USC(htao@usc.edu). The recording was performed by Can Tao(cantao@usc.edu) and Bo Peng(pengb@usc.edu).", + "tags": [ + { + "id": 189, + "tag": "spinal cord" + }, + { + "id": 190, + "tag": "patch-clamp" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 377, + "tag": "DANDI:000117" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:34.766790+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000117/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "120": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 120, + "name": "Human fNIRS recordings of motor cortex during finger-tapping task", + "repository_type": "dandi", + "summary": "This experiment examines how the motor cortex is activated during a finger-tapping task. Participants are asked to either tap their left thumb to fingers, tap their right thumb to fingers, or no cue is given (control). Tapping lasts for 5 seconds and is prompted by an auditory cue. Sensors are placed over the motor cortex as described in the montage section in the link below, short channels are attached to the scalp too. Further details about the experiment (including presentation code) can be found at https://github.com/rob-luke/experiment-fNIRS-tapping.", + "tags": [ + { + "id": 191, + "tag": "fNIRS" + }, + { + "id": 192, + "tag": "Haemodynamics" + }, + { + "id": 193, + "tag": "Motor Cortex" + }, + { + "id": 194, + "tag": "Finger Tapping Task" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 378, + "tag": "DANDI:000122" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:35.906428+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000122/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "121": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.210813.0327", + "id": 121, + "name": "NWB API Test Data", + "repository_type": "dandi", + "summary": "This dandiset consists of NWB files used for testing the NWB APIs (PyNWB, MatNWB).", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 379, + "tag": "DANDI:000126" + }, + { + "id": 282, + "tag": "House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:37.091508+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000126/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "122": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220113.0359", + "id": 122, + "name": "Area2_Bump: macaque somatosensory area 2 spiking activity during reaching with perturbations", + "repository_type": "dandi", + "summary": "This dataset contains sorted unit spiking times and behavioral data from a macaque performing a reaching task with perturbations. In the experimental task, the subject performed delayed center-out reaches using a manipulandum to control a cursor. On a portion of the trials, the manipulandum applied a bump during the center hold prior to the reach. Neural activity was recorded from an electrode array implanted in somatosensory area 2. Hand position, cursor position, force applied to the manipulandum, length and velocity of various arm muscles, and angle and velocity of various arm joints were all recorded during the experiment. Provided as part of the Neural Latents Benchmark: https://neurallatents.github.io.", + "tags": [ + { + "id": 195, + "tag": "Neural Latents Benchmark" + }, + { + "id": 196, + "tag": "NLB" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 380, + "tag": "DANDI:000127" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 366, + "tag": "Rhesus monkey" + } + ], + "timestamp_created": "2023-02-17 08:43:38.257581+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000127/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "123": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220113.0400", + "id": 123, + "name": "MC_Maze: macaque primary motor and dorsal premotor cortex spiking activity during delayed reaching", + "repository_type": "dandi", + "summary": "This dataset contains sorted unit spiking times and behavioral data from a macaque performing a delayed reaching task. The experimental task was a center-out reaching task with obstructing barriers forming a maze, resulting in a variety of straight and curved reaches. Neural activity was recorded from electrode arrays implanted in the motor cortex (M1) and dorsal premotor cortex (PMd). Cursor position, hand position, and eye position were also recorded during the experiment, and hand velocity was calculated offline from hand position. Provided as part of the Neural Latents Benchmark: https://neurallatents.github.io.", + "tags": [ + { + "id": 195, + "tag": "Neural Latents Benchmark" + }, + { + "id": 196, + "tag": "NLB" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 381, + "tag": "DANDI:000128" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 366, + "tag": "Rhesus monkey" + } + ], + "timestamp_created": "2023-02-17 08:43:39.392679+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000128/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "124": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 124, + "name": "MC_RTT: macaque motor cortex spiking activity during self-paced reaching", + "repository_type": "dandi", + "summary": "This dataset contains sorted unit spiking times and behavioral data from a macaque performing a self-paced reaching task. In the experimental task, the subject reached between targets randomly selected from an 8x8 grid without gaps or pre-movement delay intervals. Neural activity was recorded from an electrode array implanted in the primary motor cortex. Finger position, cursor position, and target position were also recorded during the experiment. Provided as part of the Neural Latents Benchmark: https://neurallatents.github.io.", + "tags": [ + { + "id": 195, + "tag": "Neural Latents Benchmark" + }, + { + "id": 196, + "tag": "NLB" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 382, + "tag": "DANDI:000129" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 366, + "tag": "Rhesus monkey" + } + ], + "timestamp_created": "2023-02-17 08:43:40.530797+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000129/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "125": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220113.0407", + "id": 125, + "name": "DMFC_RSG: macaque dorsomedial frontal cortex spiking activity during time interval reproduction task", + "repository_type": "dandi", + "summary": "This dataset contains sorted unit spiking times from a macaque performing a time-interval reproduction task. In the experimental task, the monkey was presented with two stimuli separated by a specific interval of time. The monkey then attempted to time their response such that the interval between the second stimulus and their response matched the interval separating the two stimuli. Neural activity was recorded from neural probes implanted in the dorsomedial frontal cortex. Provided as part of the Neural Latents Benchmark: https://neurallatents.github.io.", + "tags": [ + { + "id": 195, + "tag": "Neural Latents Benchmark" + }, + { + "id": 196, + "tag": "NLB" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 383, + "tag": "DANDI:000130" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 366, + "tag": "Rhesus monkey" + } + ], + "timestamp_created": "2023-02-17 08:43:41.669488+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000130/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "126": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220113.0407", + "id": 126, + "name": "MC_Maze_Large: macaque primary motor and dorsal premotor cortex spiking activity during delayed reaching", + "repository_type": "dandi", + "summary": "This dataset contains sorted unit spiking times and behavioral data from a macaque performing a delayed reaching task. The experimental task was a center-out reaching task with obstructing barriers forming a maze, resulting in a variety of straight and curved reaches. Neural activity was recorded from electrode arrays implanted in the motor cortex (M1) and dorsal premotor cortex (PMd). Cursor position, hand position, and eye position were also recorded during the experiment, and hand velocity was calculated offline from hand position. The provided data has been limited to 500 train trials and 100 test trials. Provided as part of the Neural Latents Benchmark: https://neurallatents.github.io.", + "tags": [ + { + "id": 195, + "tag": "Neural Latents Benchmark" + }, + { + "id": 196, + "tag": "NLB" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 384, + "tag": "DANDI:000138" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 366, + "tag": "Rhesus monkey" + } + ], + "timestamp_created": "2023-02-17 08:43:42.825684+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000138/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "127": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220113.0408", + "id": 127, + "name": "MC_Maze_Medium: macaque primary motor and dorsal premotor cortex spiking activity during delayed reaching", + "repository_type": "dandi", + "summary": "This dataset contains sorted unit spiking times and behavioral data from a macaque performing a delayed reaching task. The experimental task was a center-out reaching task with obstructing barriers forming a maze, resulting in a variety of straight and curved reaches. Neural activity was recorded from electrode arrays implanted in the motor cortex (M1) and dorsal premotor cortex (PMd). Cursor position, hand position, and eye position were also recorded during the experiment, and hand velocity was calculated offline from hand position. The provided data has been limited to 250 train trials and 100 test trials. Provided as part of the Neural Latents Benchmark: https://neurallatents.github.io.", + "tags": [ + { + "id": 195, + "tag": "Neural Latents Benchmark" + }, + { + "id": 196, + "tag": "NLB" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 385, + "tag": "DANDI:000139" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 366, + "tag": "Rhesus monkey" + } + ], + "timestamp_created": "2023-02-17 08:43:44.031033+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000139/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "128": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220113.0408", + "id": 128, + "name": "MC_Maze_Small: macaque primary motor and dorsal premotor cortex spiking activity during delayed reaching", + "repository_type": "dandi", + "summary": "This dataset contains sorted unit spiking times and behavioral data from a macaque performing a delayed reaching task. The experimental task was a center-out reaching task with obstructing barriers forming a maze, resulting in a variety of straight and curved reaches. Neural activity was recorded from electrode arrays implanted in the motor cortex (M1) and dorsal premotor cortex (PMd). Cursor position, hand position, and eye position were also recorded during the experiment, and hand velocity was calculated offline from hand position. The provided data has been limited to 100 train trials and 100 test trials. Provided as part of the Neural Latents Benchmark: https://neurallatents.github.io.", + "tags": [ + { + "id": 195, + "tag": "Neural Latents Benchmark" + }, + { + "id": 196, + "tag": "NLB" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 386, + "tag": "DANDI:000140" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 366, + "tag": "Rhesus monkey" + } + ], + "timestamp_created": "2023-02-17 08:43:45.181113+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000140/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "129": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.211007.1926", + "id": 129, + "name": "20210923_AIBS_Patchseq_human", + "repository_type": "dandi", + "summary": "U01 Lein PatchSeq upload September 2021", + "tags": [ + { + "id": 129, + "tag": "Patch-seq" + }, + { + "id": 98, + "tag": "human" + }, + { + "id": 197, + "tag": "neocortical" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 387, + "tag": "DANDI:000142" + }, + { + "id": 94, + "tag": "Human" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:43:46.446700+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000142/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "130": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 130, + "name": "croat-test", + "repository_type": "dandi", + "summary": "Testing", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 389, + "tag": "DANDI:000144" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:06.692699+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000144/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "131": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.221122.2256", + "id": 131, + "name": "PPC_Finger: human posterior parietal cortex recordings during attempted finger movements", + "repository_type": "dandi", + "summary": "This dataset contains neural activity and trial information from a tetraplegic human participant performing a brain-computer interface (BCI) finger press task. Neural activity was recorded from a 96-channel Utah array (Blackrock) implanted in the left posterior parietal cortex (PPC) at the junction of the postcentral and intraparietal sulci (PC-IP). On each trial, a finger cue, at a pseudorandom location on a screen, was selected. The participant immediately looked at the cue and pressed the cued finger until decoder feedback was shown (1.5 seconds after the cue).\n\nThis dataset includes sorted unit spiking times, finger cue, in-session classified finger, cue location, and trial timing.", + "tags": [ + { + "id": 200, + "tag": "PPC" + }, + { + "id": 98, + "tag": "human" + }, + { + "id": 201, + "tag": "finger" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 390, + "tag": "DANDI:000147" + }, + { + "id": 391, + "tag": "Homo sapiens - Human" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:07.865725+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000147/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "132": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 132, + "name": "Electrophysiological properties of adult mouse spinal cord neurons - 01_Oct_2021", + "repository_type": "dandi", + "summary": "Patch-clamp recording data from spinal cord neurons. Data was generated at Huizhong Tao's lab at USC(htao@usc.edu). The recording was performed by Can Tao(cantao@usc.edu) and Bo Peng(pengb@usc.edu).", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 392, + "tag": "DANDI:000148" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:08.991900+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000148/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "133": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 133, + "name": "IBL ephys data", + "repository_type": "dandi", + "summary": "This dataset is a result of a multi-institution, cross country collaboration of labs, called International Brain Laboratory conducting standardized experiments on decision-making in mice. This dataset contains contains data similar to the dandiset https://dandiarchive.org/dandiset/000045 with additional ephys data.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 393, + "tag": "DANDI:000149" + }, + { + "id": 282, + "tag": "House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:10.141476+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000149/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "134": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.211118.1526", + "id": 134, + "name": "Aery Jones et al (2021) Dentate Gyrus and CA3 GABAergic Interneurons Bidirectionally Modulate Signatures of Internal and External Drive to CA1", + "repository_type": "dandi", + "summary": "The experiment consisted of three cohorts of PVCre, SSTCre, and PVCre/SSTCre mice injected with either AAV5-hSyn-DIO-hM4D(Gi)-mCherry (hM4D) or AAV5-hSyn-DIO-mCherry (empty vector) in DG and CA3, then injected with vehicle or CNO prior to recordings. Recordings from all layers of CA1, CA3, and DG were taken during home cage rest and linear track movement. Data are described in Aery Jones et al, 2021, bioRxiv: \"Dentate Gyrus and CA3 GABAergic Interneurons Bidirectionally Modulate Signatures of Internal and External Drive to CA1\". For more information about this data, please contact Emily Aery Jones, Yadong Huang, or Loren Frank.", + "tags": [ + { + "id": 186, + "tag": "hippocampus" + }, + { + "id": 97, + "tag": "mouse" + }, + { + "id": 40, + "tag": "LFP" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 394, + "tag": "DANDI:000165" + }, + { + "id": 282, + "tag": "House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:11.344206+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000165/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "135": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220116.2037", + "id": 135, + "name": "Layer-Specific Physiological Features and Interlaminar Interactions in the Primary Visual Cortex of the Mouse", + "repository_type": "dandi", + "summary": "The relationship between mesoscopic local field potentials (LFPs) and single-neuron firing in the multi-layered neocortex is poorly understood. Simultaneous recordings from all layers in the primary visual cortex (V1) of the behaving mouse revealed functionally defined layers in V1. The depth of maximum spike power and sink-source distributions of LFPs provided consistent laminar landmarks across animals. Coherence of gamma oscillations (30-100 Hz) and spike-LFP coupling identified six physiological layers and further sublayers. Firing rates, burstiness, and other electrophysiological features of neurons displayed unique layer and brain state dependence. Spike transmission strength from layer 2/3 cells to layer 5 pyramidal cells and interneurons was stronger during waking compared with non-REM sleep but stronger during non-REM sleep among deep-layer excitatory neurons. A subset of deep-layer neurons was active exclusively in the DOWN state of non-REM sleep. These results bridge mesoscopic LFPs and single-neuron interactions with laminar structure in V1.", + "tags": [ + { + "id": 96, + "tag": "cortex" + }, + { + "id": 17, + "tag": "electrophysiology" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 395, + "tag": "DANDI:000166" + }, + { + "id": 282, + "tag": "House mouse" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 289, + "tag": "current source density" + }, + { + "id": 290, + "tag": "laminar recordings" + } + ], + "timestamp_created": "2023-02-17 08:53:12.518840+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000166/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "136": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230720.2001", + "id": 136, + "name": "Two photon calcium imaging of mice piriform cortex under passive odor presentation", + "repository_type": "dandi", + "summary": "Two-photon calcium imaging of mouse piriform cortex pyramidal population, under head-fixed condition and passive odor stimulus delivery. There were 10 different odors used (8 trials each), delivered at 10-second for each of the 30-second trial. Imaging was preprocessed with Suite2p. The data was collected by Simon Daste in the Fleischmann lab at Brown University. Showcase notebook available at: https://gitlab.com/fleischmann-lab/datasets/daste-odor-set-2021-11", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 396, + "tag": "DANDI:000167" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:13.774589+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000167/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "137": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 137, + "name": "Simultaneous loose seal cell-attached recordings and two-photon imaging of GCaMP8 expressing mouse V1 neurons with drifting gratings visual stimuli", + "repository_type": "dandi", + "summary": "We tested the jGCaMP8 sensors in L2/3 pyramidal neurons of mouse primary visual cortex. We made a craniotomy over V1 and infected neurons with adeno-associated virus (AAV2/1-hSynapsin-1) encoding jGCaMP8 variants (s/m/f), jGCaMP7f, or XCaMP-Gf. 18-80 days after the virus injection, the mouse was anesthetized, and we surgically removed the cranial window and performed durotomy. The craniotomy was filled with 10-15 \u03bcL of 1.5% agarose, and a D-shaped coverslip was secured on top to suppress brain motion and leave access to the brain on the lateral side of the craniotomy. Then mice were lightly anesthetized and mounted under a custom two-photon microscope. Full-field, high-contrast drifting gratings were presented in each of eight directions to the contralateral eye. Two-photon imaging (122 Hz) was performed of L2/3 somata and neuropil combined with loose-seal, cell-attached electrophysiological recording of a single neuron in the field of view.", + "tags": [ + { + "id": 204, + "tag": "2-photon" + }, + { + "id": 134, + "tag": "visual cortex" + }, + { + "id": 205, + "tag": "calcium" + }, + { + "id": 206, + "tag": "spike" + }, + { + "id": 207, + "tag": "action potential" + }, + { + "id": 208, + "tag": "layer 2" + }, + { + "id": 209, + "tag": "AAV" + }, + { + "id": 210, + "tag": "adeno-associated virus" + }, + { + "id": 211, + "tag": "jGCaMP8s" + }, + { + "id": 212, + "tag": "jGCaMP8m" + }, + { + "id": 213, + "tag": "jGCaMP8f" + }, + { + "id": 214, + "tag": "jGCaMP7f" + }, + { + "id": 215, + "tag": "XCaMP-Gf" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 397, + "tag": "DANDI:000168" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:14.975052+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000168/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "138": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220927.0404", + "id": 138, + "name": "Neural Spiking Data in Primary Somatosensory Cortex before and after Transcranial Focused Ultrasound Stimulation in Rats", + "repository_type": "dandi", + "summary": "In this study, we investigate how transcranial focused ultrasound (tFUS) modulates neural interaction and response to peripheral electrical limb stimulation through intracranial multi-electrode recordings in the rat somatosensory cortex. Rats were anesthetized using isoflurane anesthesia at 2%. Recordings were taken while delivering peripheral electrical stimulation once every 5 seconds to analyze the neural response in the rat S1HL region, before and after ultrasound. We delivered ultrasound for 5 minutes in a pulsed pattern at 5 different stimulation repetition frequencies (10 Hz, 50 Hz, 75 Hz, 100 Hz, and 125 Hz) to induce frequency dependent plasticity in a manner similar to that found following tetanic electrical stimulation. The fundamental frequency of ultrasound used was 0.5 MHz, the pulse repetition frequency (PRF) was 3 kHz, and the total duty cycle was 36%. This dataset includes spike timing data from the recordings including time before and after ultrasound stimulation, for both true ultrasound conditions and sham ultrasound conditions in which ultrasound is directed off target. Details of the study are in the following publication: S. Ramachandran, X. Niu, K. Yu, B. He, \"Transcranial ultrasound neuromodulation induces neuronal correlation change in the rat somatosensory cortex\", 2022 J. Neural Eng. 19 056002 https://pubmed.ncbi.nlm.nih.gov/35947970/. Please cite this publication if you would use a portion of the data. ", + "tags": [ + { + "id": 216, + "tag": "Ultrasound" + }, + { + "id": 217, + "tag": "Plasticity" + }, + { + "id": 218, + "tag": "Rat" + }, + { + "id": 219, + "tag": "tFUS" + }, + { + "id": 220, + "tag": "Somatosensory Cortex" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 398, + "tag": "DANDI:000173" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + } + ], + "timestamp_created": "2023-02-17 08:53:16.224285+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000173/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "139": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220103.2119", + "id": 139, + "name": "Visual cortical activity in mice performing naturalistic virtual foraging task", + "repository_type": "dandi", + "summary": "Large FOV two-photon calcium imaging dataset recorded from V1 L2/3 neurons from mouse performing a naturalistic foraging task in virtual reality.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 399, + "tag": "DANDI:000206" + }, + { + "id": 282, + "tag": "House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:17.379366+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000206/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "140": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230530.1822", + "id": 140, + "name": "Data for: Neurons detect cognitive boundaries to structure episodic memories in humans (Zheng et al., 2022, Nat Neuro in press)", + "repository_type": "dandi", + "summary": "This dataset contains human single-neuron data recorded from the medial temporal lobe (MTL) during a set of experiments to explore the role of neurons that respond to cognitive boundaries. This dataset accompanies the paper cited below. Example code on how to plot this data can be found at https://github.com/rutishauserlab/cogboundary-zheng .\n\nReference (to be updated upon publication): Cognitive boundary signals in the human medial temporal lobe shape episodic memory representation. Jie Zheng, Andrea G\u00f3mez Palacio Schjetnan, Mar Yebra, Clayton Mosher, Suneil Kalia, Taufik A. Valiante, Adam N. Mamelak, Gabriel Kreiman, Ueli Rutishauser. bioRxiv 2021.01.16.426538. [Nat Neuro, in press, 2022]", + "tags": [ + { + "id": 221, + "tag": "human single neuron" + }, + { + "id": 186, + "tag": "hippocampus" + }, + { + "id": 222, + "tag": "episodic memory" + }, + { + "id": 223, + "tag": "event segmentation" + }, + { + "id": 224, + "tag": "amygdala" + }, + { + "id": 225, + "tag": "parahippocampal gyrus" + }, + { + "id": 226, + "tag": "cognitive boundaries" + }, + { + "id": 227, + "tag": "continuous experience" + }, + { + "id": 228, + "tag": "ROH consortium" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 400, + "tag": "DANDI:000207" + }, + { + "id": 391, + "tag": "Homo sapiens - Human" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:18.563912+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000207/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "141": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230522.1918", + "id": 141, + "name": "20211223_AIBS_Patchseq_human", + "repository_type": "dandi", + "summary": "U01 Lein PatchSeq upload December 2021", + "tags": [ + { + "id": 129, + "tag": "Patch-seq" + }, + { + "id": 98, + "tag": "human" + }, + { + "id": 197, + "tag": "neocortical" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 401, + "tag": "DANDI:000209" + }, + { + "id": 94, + "tag": "Human" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:19.707961+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000209/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "142": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 142, + "name": "Tracking of Drosophila during egg-laying decisions", + "repository_type": "dandi", + "summary": "Dataset on the egg-laying behavior of flies used to understand how flies make egg-laying decisions. Each NWB file contains time series data from an individual fly: the x-y position of the fly, egg-deposition moments of the fly, and sucrose concentration underneath the fly. A variety of fly genotypes and a variety of environments (egg-laying chambers) were used. Data and methods are described in \"A rise-to-threshold signal for a relative value deliberation\" (https://www.biorxiv.org/content/10.1101/2021.09.23.461548v1) and \u201cAn internal expectation guides Drosophila egg-laying decisions\u201d (https://doi.org/10.1126/sciadv.abn3852). Please contact Vikram Vijayan and/or Gaby Maimon for more information including different download options and different raw/processed data formats.", + "tags": [ + { + "id": 229, + "tag": "Drosophila" + }, + { + "id": 230, + "tag": "egg laying" + }, + { + "id": 231, + "tag": "flies" + }, + { + "id": 124, + "tag": "decision making" + }, + { + "id": 232, + "tag": "internal expectation" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 402, + "tag": "DANDI:000212" + }, + { + "id": 403, + "tag": "Drosophila melanogaster - Fruit fly" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:20.944919+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000212/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "143": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220127.1738", + "id": 143, + "name": "Transformation of a Spatial Map across the Hippocampal-Lateral Septal Circuit", + "repository_type": "dandi", + "summary": "The hippocampus constructs a map of the environment. How this \u201ccognitive map\u201d is utilized by other brain regions to guide behavior remains unexplored. To examine how neuronal firing patterns in the hippocampus are transmitted and transformed, we recorded neurons in its principal subcortical target, the lateral septum (LS). We observed that LS neurons carry reliable spatial information in the phase of action potentials, relative to hippocampal theta oscillations, while the firing rates of LS neurons remained uninformative. Furthermore, this spatial phase code had an anatomical microstructure within the LS and was bound to the hippocampal spatial code by synchronous gamma frequency cell assemblies. Using a data-driven model, we show that rate-independent spatial tuning arises through the dynamic weighting of CA1 and CA3 cell assemblies. Our findings demonstrate that transformation of the hippocampal spatial map depends on higher-order theta-dependent neuronal sequences.", + "tags": [ + { + "id": 186, + "tag": "hippocampus" + }, + { + "id": 233, + "tag": "lateral septum" + }, + { + "id": 17, + "tag": "electrophysiology" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 404, + "tag": "DANDI:000213" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + } + ], + "timestamp_created": "2023-02-17 08:53:22.091341+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000213/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "144": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220125.2004", + "id": 144, + "name": "Sniff-synchronized, gradient-guided olfactory search by freely moving mice -- Behavioral Dataset", + "repository_type": "dandi", + "summary": "This dataset contains the movement tracking, sniff recording, and trial statistics for the dataset used in the publication: Sniff-synchronized, gradient-guided olfactory search by freely moving mice in eLife (Findley et al. 2021)", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 405, + "tag": "DANDI:000217" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:23.659481+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000217/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "145": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220131.1608", + "id": 145, + "name": "Routing of Hippocampal Ripples to Subcortical Structures via the Lateral Septum", + "repository_type": "dandi", + "summary": "The mnemonic functions of hippocampal sharp wave ripples (SPW-Rs) have been studied extensively. Because hippocampal outputs affect not only cortical but also subcortical targets, we examined the impact of SPW-Rs on the firing patterns of lateral septal (LS) neurons in behaving rats. A large fraction of SPW-Rs were temporally locked to high-frequency oscillations (HFOs) (120\u2013180 Hz) in LS, with strongest coupling during non-rapid eye movement (NREM) sleep, followed by waking immobility. However, coherence and spike-local field potential (LFP) coupling between the two structures were low, suggesting that HFOs are generated locally within the LS GABAergic population. This hypothesis was supported by optogenetic induction of HFOs in LS. Spiking of LS neurons was largely independent of the sequential order of spiking in SPW-Rs but instead correlated with the magnitude of excitatory synchrony of the hippocampal output. Thus, LS is strongly activated by SPW-Rs and may convey hippocampal population events to its hypothalamic and brainstem targets.", + "tags": [ + { + "id": 186, + "tag": "hippocampus" + }, + { + "id": 233, + "tag": "lateral septum" + }, + { + "id": 234, + "tag": "electrophyisology" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 406, + "tag": "DANDI:000218" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + } + ], + "timestamp_created": "2023-02-17 08:53:24.925774+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000218/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "146": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 146, + "name": "Two photon calcium imaging in the CA1 region of the hippocampus in neonatal mice.", + "repository_type": "dandi", + "summary": "We performed in vivo 2-photon calcium imaging in the CA1 region of the hippocampus in awake mouse pups aged between 5 and 12 days postnatal. We used GCaMP6s calcium indicator in WT mice or GCaMP6s and flex-tdTomato in GadCre mice to record calcium dynamics from both pyramidal cells and interneurons. The imaging data set was acquired at 8 Hz, in field of view of 400x400 \u00b5m. Simultaneously with imaging, we record the spontaneous motor behavior of the mouse pups.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 407, + "tag": "DANDI:000219" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:26.080707+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000219/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "147": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 147, + "name": "Electrophysiological properties of adult mouse spinal cord neurons - 25_Jan_2022", + "repository_type": "dandi", + "summary": "Patch-clamp recording data from spinal cord neurons. Data was generated at Huizhong Tao's lab at USC(htao@usc.edu). The recording was performed by Can Tao(cantao@usc.edu) and Bo Peng(pengb@usc.edu).", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 408, + "tag": "DANDI:000220" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:27.224114+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000220/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "148": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220307.1320", + "id": 148, + "name": "A midbrain-thalamus-cortex circuit reorganizes cortical dynamics to initiate movement", + "repository_type": "dandi", + "summary": "Data from \"A midbrain-thalamus-cortex circuit reorganizes cortical dynamics to initiate movement\" Inagaki & Chen et al\nExample codes to plot data is at https://github.com/hidehikoinagaki/InagakiAndChenEtAl2022", + "tags": [ + { + "id": 235, + "tag": "Midbrain" + }, + { + "id": 236, + "tag": "ALM" + }, + { + "id": 237, + "tag": "motor planning" + }, + { + "id": 238, + "tag": "movement initiation" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 409, + "tag": "DANDI:000221" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:28.409579+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000221/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "149": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220823.0826", + "id": 149, + "name": "Inferring monosynaptic connections from paired spine calcium imaging and large-scale recording of extracellular spiking", + "repository_type": "dandi", + "summary": "This dataset contains paired calcium imaging and high-density microelectrode array (HD-MEA) recordings from cortical embryonic cell cultures. \nThe data is used to infer monosynaptic connections using dendritic spine calcium traces and extracellular spiking.\nEach file includes:\n- raw extracellular recordings \n- spike-sorted units\n- imaging series\n- segmentation ROIs (of the target spines and adjacent dendritic shaft)", + "tags": [ + { + "id": 239, + "tag": "calcium imaging; extracellular recordings; HD-MEA; spike sorting; dendritic spines" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 410, + "tag": "DANDI:000223" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + } + ], + "timestamp_created": "2023-02-17 08:53:29.581734+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000223/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "150": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230607.1747", + "id": 150, + "name": "Active Touch and Self-Motion Encoding by Merkel Cell-Associated Afferents", + "repository_type": "dandi", + "summary": "Touch perception depends on integrating signals from multiple types of peripheral mechanoreceptors. Merkel-cell associated afferents are thought to play a major role in form perception by encoding surface features of touched objects. However, activity of Merkel afferents during active touch has not been directly measured. Here, we show that Merkel and unidentified slowly adapting afferents in the whisker system of behaving mice respond to both self-motion and active touch. Touch responses were dominated by sensitivity to bending moment (torque) at the base of the whisker and its rate of change and largely explained by a simple mechanical model. Self-motion responses encoded whisker position within a whisk cycle (phase), not absolute whisker angle, and arose from stresses reflecting whisker inertia and activity of specific muscles. Thus, Merkel afferents send to the brain multiplexed information about whisker position and surface features, suggesting that proprioception and touch converge at the earliest neural level.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 411, + "tag": "DANDI:000226" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:30.714265+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000226/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "151": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 151, + "name": "20220330_AIBS_Patchseq_human", + "repository_type": "dandi", + "summary": "U01 Lein PatchSeq upload March 2022", + "tags": [ + { + "id": 129, + "tag": "Patch-seq" + }, + { + "id": 98, + "tag": "human" + }, + { + "id": 197, + "tag": "neocortical" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 412, + "tag": "DANDI:000228" + }, + { + "id": 391, + "tag": "Homo sapiens - Human" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:31.919441+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000228/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "152": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220506.1516", + "id": 152, + "name": "Jacobsen 2022", + "repository_type": "dandi", + "summary": "Data for \"All-viral tracing of monosynaptic inputs to single birthdate-defined neurons in the intact brain\", Jacobsen et al 2022. \n\nPhotostimulation laser power: sessions are labelled with the set point power. Actual power can be derived as follows: (set [mW] | actual [mW]), (20 | 7.5), (40 | 15.1)\n", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 413, + "tag": "DANDI:000230" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:33.078957+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000230/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "153": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220904.1554", + "id": 153, + "name": "A detailed behavioral, videographic, and neural dataset on object recognition in mice", + "repository_type": "dandi", + "summary": "Mice adeptly use their whiskers to touch, recognize, and learn about objects in their environment. This behavior is enabled by computations performed by populations of neurons in the somatosensory cortex. To understand these computations, we trained mice to use their whiskers to recognize different shapes while we recorded activity in the barrel cortex, which processes whisker input. Here, we present a large dataset of high-speed video of the whiskers, along with rigorous tracking of the entire extent of multiple whiskers and every contact they made on the shape. We used spike sorting to identify individual neurons, which responded with precise timing to whisker contacts and motion. These data will be useful for understanding the behavioral strategies mice use to explore objects, as well as the neuronal dynamics that mediate those strategies. In addition, our carefully curated labeled data could be used to develop new computer vision algorithms for tracking body posture, or for extracting responses of individual neurons from large-scale neural recordings. For further description, see https://www.biorxiv.org/content/10.1101/2022.05.10.491259v1.", + "tags": [ + { + "id": 240, + "tag": "mouse behavior" + }, + { + "id": 241, + "tag": "whisker system" + }, + { + "id": 242, + "tag": "somatosensory cortex" + }, + { + "id": 243, + "tag": "barrel cortex" + }, + { + "id": 244, + "tag": "object recognition" + }, + { + "id": 245, + "tag": "shape discrimination" + }, + { + "id": 17, + "tag": "electrophysiology" + }, + { + "id": 246, + "tag": "pose tracking" + }, + { + "id": 247, + "tag": "population recordings" + }, + { + "id": 248, + "tag": "single unit recordings" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 414, + "tag": "DANDI:000231" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:34.732261+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000231/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "154": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 154, + "name": "Rule-based modulation of a sensorimotor transformation across cortical areas", + "repository_type": "dandi", + "summary": "Flexibly responding to sensory stimuli depending on rules is critical for adapting in a constantly changing environment. However, it remains unclear how the brain encodes rule information and uses this information to guide behavioral responses to sensory stimuli. Here, we made single-unit recordings while head-fixed mice performed a cross-modal sensory selection task in which they switched between two rules in different blocks of trials: licking in response to tactile stimuli while rejecting visual stimuli, or licking to visual stimuli while rejecting tactile stimuli. Along the cortical processing stream, touch-evoked responses in primary (S1) and secondary (S2) somatosensory cortical areas, and in the medial (MM) and anterolateral (ALM) motor cortical areas were modulated by rules at a single-neuron level. In addition, the activity of individual cortical neurons prior to stimulus delivery also changed in a rule-dependent manner. We proposed that rule-dependent pre-stimulus activity may contribute to setting the cortical areas in different initial conditions, which prepares them to process sensory information according to the appropriate rule. This hypothesis was supported by our findings that (1) the task rules could be decoded from the pre-stimulus population states of the sensory and motor cortical areas, (2) cortical areas with a larger difference in pre-stimulus state between rules used better separated subspaces to process subsequent tactile signals, and (3) optogenetic disruption of pre-stimulus states within the motor cortical areas impaired rule-dependent tactile detection. Taken together, our findings suggest that rule-guided sensorimotor flexibility may occur via appropriate configuration of pre-stimulus states within motor cortical networks, enabling distinct actions in response to the same sensory input.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 415, + "tag": "DANDI:000232" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:35.890939+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000232/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "155": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230223.0815", + "id": 155, + "name": "A metabolic function of the hippocampal sharp wave-ripple", + "repository_type": "dandi", + "summary": "The hippocampus has previously been implicated in both cognitive and endocrine functions. We simultaneously measured electrophysiological activity from the hippocampus and interstitial glucose concentrations in the body of freely behaving rats to identify an activity pattern that may link these disparate functions of the hippocampus. Here we report that clusters of sharp wave-ripples recorded from the hippocampus reliably predicted a decrease in peripheral glucose concentrations within about 10 min. This correlation was not dependent on circadian, ultradian or meal-triggered fluctuations, could be mimicked with optogenetically induced ripples in the hippocampus (but not in the parietal cortex) and was attenuated to chance levels by pharmacogenetically suppressing activity of the lateral septum, which is the major conduit between the hippocampus and the hypothalamus. Our findings demonstrate that a function of the sharp wave-ripple is to modulate peripheral glucose homeostasis, and offer a mechanism for the link between sleep disruption and blood glucose dysregulation in type 2 diabetes.", + "tags": [ + { + "id": 249, + "tag": "glucose" + }, + { + "id": 251, + "tag": "pharmacology" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 416, + "tag": "DANDI:000233" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + }, + { + "id": 417, + "tag": "ecephys" + } + ], + "timestamp_created": "2023-02-17 08:53:37.084149+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000233/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "156": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230607.1752", + "id": 156, + "name": "Cortical processing of flexible and context-dependent sensorimotor sequences", + "repository_type": "dandi", + "summary": "The brain generates complex sequences of movements that can be flexibly configured based on behavioural context or real-time sensory feedback, but how this occurs is not fully understood. Here we developed a \u2018sequence licking\u2019 task in which mice directed their tongue to a target that moved through a series of locations. Mice could rapidly branch the sequence online based on tactile feedback. Closed-loop optogenetics and electrophysiology revealed that the tongue and jaw regions of the primary somatosensory (S1TJ) and motor (M1TJ) cortices encoded and controlled tongue kinematics at the level of individual licks. By contrast, the tongue \u2018premotor\u2019 (anterolateral motor) cortex encoded latent variables including intended lick angle, sequence identity and progress towards the reward that marked successful sequence execution. Movement-nonspecific sequence branching signals occurred in the anterolateral motor cortex and M1TJ. Our results reveal a set of key cortical areas for flexible and context-informed sequence generation.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 422, + "tag": "DANDI:000239" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:38.272873+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000239/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "157": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 157, + "name": "MRI of human ex vivo brainstem", + "repository_type": "dandi", + "summary": "This dataset contains MRI images associated with an ex vivo specimen of a human brainstem.", + "tags": [ + { + "id": 72, + "tag": "BIDS" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 423, + "tag": "DANDI:000243" + } + ], + "timestamp_created": "2023-02-17 08:53:39.386677+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000243/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "158": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 158, + "name": "One photon mesoscale calcium imaging of multiple cell types", + "repository_type": "dandi", + "summary": "One photon dual wavelength mesoscale calcium imaging of mouse isocortex. Includes data from awake and anesthetized subjects with excitatory neuron sensitive GECIs, and data from awake subjects with GECIs sensitive to three different inhibitory interneuron cell types (PV, SOM and VIP).", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 424, + "tag": "DANDI:000244" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:40.547721+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000244/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "159": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 159, + "name": "developing CaMPARI3", + "repository_type": "dandi", + "summary": "This dataset contains in vivo and in vitro data for the development of CaMPARI3.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 426, + "tag": "DANDI:000246" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:42.697799+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000246/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "160": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230423.1416", + "id": 160, + "name": "Innate and plastic mechanisms for maternal behaviour in auditory cortex", + "repository_type": "dandi", + "summary": "Infant cries evoke powerful responses in parents. Whether parental animals are intrinsically sensitive to neonatal vocalizations, or instead learn about vocal cues for parenting responses is unclear. In mice, pup-naive virgin females do not recognize the meaning of pup distress calls, but retrieve isolated pups to the nest after having been co-housed with a mother and litte. Distress calls are variable, and require co-caring virgin mice to generalize across calls for reliable retrieval. Here we show that the onset of maternal behaviour in mice results from interactions between intrinsic mechanisms and experience-dependent plasticity in the auditory cortex. In maternal females, calls with inter-syllable intervals (ISIs) from 75 to 375 milliseconds elicited pup retrieval, and cortical responses were generalized across these ISIs. By contrast, naive virgins were neuronally and behaviourally sensitized to the most common (\u2018prototypical\u2019) ISIs. Inhibitory and excitatory neural responses were initially mismatched in the cortex of naive mice, with untuned inhibition and overly narrow excitation. During co-housing experiments, excitatory responses broadened to represent a wider range of ISIs, whereas inhibitory tuning sharpened to form a perceptual boundary. We presented synthetic calls during co-housing and observed that neurobehavioural responses adjusted to match these statistics, a process that required cortical activity and the hypothalamic oxytocin system. Neuroplastic mechanisms therefore build on an intrinsic sensitivity in the mouse auditory cortex, and enable rapid plasticity for reliable parenting behaviour.", + "tags": [ + { + "id": 252, + "tag": "oxytocin" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 429, + "tag": "DANDI:000249" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:43.898751+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000249/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "161": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 161, + "name": "A Unified Framework for Dopamine Signals across Timescales", + "repository_type": "dandi", + "summary": "This dandiset contains the data associated with \"A Unified Framework for Dopamine Signals across Timescales\" (Kim et al. 2020). It is comprised of fiber photometry data, single-unit recordings, stimulus variables, and behavioral measurements across a wide variety of experimental manipulations. ", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 432, + "tag": "DANDI:000251" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:45.413271+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000251/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "162": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 162, + "name": "20220630_AIBS_Patchseq_human", + "repository_type": "dandi", + "summary": "U01 Lein PatchSeq upload June 2022", + "tags": [ + { + "id": 129, + "tag": "Patch-seq" + }, + { + "id": 98, + "tag": "human" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 437, + "tag": "DANDI:000288" + }, + { + "id": 391, + "tag": "Homo sapiens - Human" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:46.568947+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000288/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "163": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 163, + "name": "A multi-modal fitting approach to construct single-neuron models with patch-clamp and high-density microelectrode arrays", + "repository_type": "dandi", + "summary": "This dataset contains simultaneous intracellular whole-cell patch clamp and extracellular high-density microelectrode array (HD-MEA) recordings.\nIt includes data from two files, corresponding to two recorded neurons. Each NWB file contains several runs of 8 eCode protocols, with syncronized intracellular voltages, stimulus currents, and extracellular voltages.", + "tags": [ + { + "id": 253, + "tag": "HD-MEA, patch-clamp, multimodal" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 440, + "tag": "DANDI:000294" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + } + ], + "timestamp_created": "2023-02-17 08:53:49.832683+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000294/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "164": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 164, + "name": "Electrophysiological properties of adult mouse spinal cord neurons - 22Q2_Ephys_DANDI", + "repository_type": "dandi", + "summary": "Whole-cell patch clamp recording of specific cell types in the adult mouse spinal cord. Neurons are either input defined, output defined or expressing specific molecular markers.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 441, + "tag": "DANDI:000295" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:50.955129+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000295/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "165": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.220805.1724", + "id": 165, + "name": "Drosophila visual neural responses to stochastic stimuli", + "repository_type": "dandi", + "summary": "Associated Reference Publication: Excitatory and inhibitory neural dynamics jointly tune motion detection\nNeurons integrate excitatory and inhibitory signals to produce their outputs, but the role of input timing in this integration remains poorly understood. Motion detection is a paradigmatic example of this integration, since theories of motion detection rely on different delays in visual signals. These delays allow circuits to compare scenes at different times to calculate the direction and speed of motion. Different motion detection circuits have different velocity sensitivity, but it remains untested how the response dynamics of individual cell types drive this tuning. Here, we sped up or slowed down specific neuron types in Drosophila\u2019s motion detection circuit by manipulating ion channel expression. Altering the dynamics of individual neuron types upstream of motion detectors increased their sensitivity to fast or slow visual motion, exposing distinct roles for excitatory and inhibitory dynamics in tuning directional signals, including a role for the amacrine cell CT1. A circuit model constrained by functional data and anatomy qualitatively reproduced the observed tuning changes. Overall, these results reveal how excitatory and inhibitory dynamics together tune a canonical circuit computation.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 442, + "tag": "DANDI:000296" + }, + { + "id": 403, + "tag": "Drosophila melanogaster - Fruit fly" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:52.758036+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000296/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "166": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 166, + "name": "UHN whole-cell patch-clamp excitability recordings from human cortical neurons", + "repository_type": "dandi", + "summary": "Whole-cell current clamp recordings from surgically resected human cortical tissue ", + "tags": [ + { + "id": 95, + "tag": "excitability" + }, + { + "id": 98, + "tag": "human" + }, + { + "id": 96, + "tag": "cortex" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 443, + "tag": "DANDI:000297" + }, + { + "id": 391, + "tag": "Homo sapiens - Human" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-17 08:53:53.939397+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000297/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "167": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 167, + "name": "Stephen Test Set", + "repository_type": "dandi", + "summary": "asdfalsdfswadbfnk", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 444, + "tag": "DANDI:000299" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + } + ], + "timestamp_created": "2023-02-17 08:53:55.080608+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000299/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "168": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "main", + "id": 168, + "name": "IBL NeuroDataShare2023", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 254, + "tag": "IBL" + }, + { + "id": 255, + "tag": "Neuropixels" + } + ], + "timestamp_created": "2023-02-22 11:53:58.441562+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/int-brain-lab/UCL_NeuroDataShare2023", + "user": { + "email": "p.gleeson@gmail.com", + "first_name": "Padraig", + "id": "0103eaaf-6a34-4509-a025-14367a52aa2b", + "last_name": "Gleeson", + "username": "padraig" + }, + "user_id": "0103eaaf-6a34-4509-a025-14367a52aa2b" + }, + "169": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 169, + "name": "IBL - Brain Wide Map", + "repository_type": "dandi", + "summary": "The International Brain lab (IBL) aims to understand the neural basis of decision-making in the mouse by gathering a whole-brain activity map composed of electrophysiological recordings pooled from multiple laboratories. We have systematically recorded from nearly all major brain areas with Neuropixels probes, using a grid system for unbiased sampling and replicating each recording site in at least two laboratories. These data have been used to construct a brain-wide map of activity at single-spike cellular resolution during a decision-making task. In addition to the map, this data set contains other information gathered during the task: sensory stimuli presented to the mouse; mouse decisions and response times; and mouse pose information from video recordings and DeepLabCut analysis.", + "tags": [ + { + "id": 174, + "tag": "International Brain Laboratory" + }, + { + "id": 256, + "tag": "multi-probe" + }, + { + "id": 255, + "tag": "Neuropixels" + }, + { + "id": 257, + "tag": "Allen Mouse Brain CCFv3" + }, + { + "id": 258, + "tag": "Allen Mouse Brain Atlas" + }, + { + "id": 259, + "tag": "head-fixed" + }, + { + "id": 178, + "tag": "decision-making" + }, + { + "id": 260, + "tag": "face-tracking" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 477, + "tag": "DANDI:000409" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2023-02-22 14:33:13.668337+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000409/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "170": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 170, + "name": "Downstate Computational Neuroscience Python Tutorials", + "repository_type": "github", + "summary": "", + "tags": [], + "timestamp_created": "2023-02-24 18:26:59.825288+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/jchen6727/tutorials", + "user": { + "email": "jchen.6727@gmail.com", + "first_name": "James", + "id": "128cd68d-dfbf-4355-9919-59cef4957e4e", + "last_name": "Chen", + "username": "jchen6727" + }, + "user_id": "128cd68d-dfbf-4355-9919-59cef4957e4e" + }, + "171": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + 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This guide can be used for learning about/testing the Octave support on OSBv2's JupyerLab application", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 274, + "tag": "Octave" + } + ], + "timestamp_created": "2023-11-02 11:30:07.834513+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/bushlab-ucl/intro_to_matlab", + "user": { + "email": "p.gleeson@gmail.com", + "first_name": "Padraig", + "id": "0103eaaf-6a34-4509-a025-14367a52aa2b", + "last_name": "Gleeson", + "username": "padraig" + }, + "user_id": "0103eaaf-6a34-4509-a025-14367a52aa2b" + }, + "182": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 182, + "name": "Biochemically detailed model of LTP and LTD in a cortical spine (Maki-Marttunen et al 2020)", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 92, + "tag": "SBML" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 276, + "tag": "Synapse" + } + ], + "timestamp_created": "2023-11-23 11:35:28.239387+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/Maki-MarttunenEtAl2020", + "user": { + "email": "p.gleeson@gmail.com", + "first_name": "Padraig", + "id": "0103eaaf-6a34-4509-a025-14367a52aa2b", + "last_name": "Gleeson", + "username": "padraig" + }, + "user_id": "0103eaaf-6a34-4509-a025-14367a52aa2b" + }, + "183": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 183, + "name": "model-db-test", + "repository_type": "github", + "summary": "", + "tags": [], + "timestamp_created": "2023-11-24 10:38:07.935493+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/ModelDBRepository/267184", + "user": { + "email": "vbragin19@gmail.com", + "first_name": "Valery", + "id": "4158408e-7784-4397-be06-45523e3e105f", + "last_name": "Bragin", + "username": "vbragin" + }, + "user_id": "4158408e-7784-4397-be06-45523e3e105f" + }, + "184": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "development", + "id": 184, + "name": "NetPyNE Interactive Tutorials", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 3, + "tag": "netpyne" + }, + { + "id": 5, + "tag": "Tutorial" + }, + { + "id": 279, + "tag": "microcircuits" + }, + { + "id": 280, + "tag": "multi-scale modeling" + } + ], + "timestamp_created": "2023-12-01 16:42:36.356589+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/suny-downstate-medical-center/netpyne", + "user": { + "email": "vbragin19@gmail.com", + "first_name": "Valery", + "id": "4158408e-7784-4397-be06-45523e3e105f", + "last_name": "Bragin", + "username": "vbragin" + }, + "user_id": "4158408e-7784-4397-be06-45523e3e105f" + }, + "185": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "new_file_format", + "id": 185, + "name": "M1 NetPyNE CellReports 2023", + "repository_type": "github", + "summary": "", + "tags": [], + "timestamp_created": "2023-12-13 16:10:37.790156+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/suny-downstate-medical-center/M1_NetPyNE_CellReports_2023", + "user": { + "email": "vbragin19@gmail.com", + "first_name": "Valery", + "id": "4158408e-7784-4397-be06-45523e3e105f", + "last_name": "Bragin", + "username": "vbragin" + }, + "user_id": "4158408e-7784-4397-be06-45523e3e105f" + }, + "186": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 186, + "name": "Multiphoton imaging in macaque visual cortex (preliminary data)", + "repository_type": "dandi", + "summary": "Two- and three-photon imaging from V1/V2 labeled with GCaMP6s. Grating stimuli (sf x dir).", + "tags": [ + { + "id": 284, + "tag": "DANDI:000347" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 285, + "tag": "Macaca nemestrina - Pig-tailed macaque" + } + ], + "timestamp_created": "2023-12-19 10:12:45.528315+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000347/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "187": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230629.1955", + "id": 187, + "name": "Physiological Properties and Behavioral Correlates of Hippocampal Granule Cells and Mossy Cells", + "repository_type": "dandi", + "summary": "Data from \"Physiological Properties and Behavioral Correlates of Hippocampal Granule Cells and Mossy Cells\" Senzai, Buzsaki, Neuron 2017. Electrophysiology recordings of hippocampus during theta maze exploration.", + "tags": [ + { + "id": 288, + "tag": "cell types" + }, + { + "id": 289, + "tag": "current source density" + }, + { + "id": 290, + "tag": "laminar recordings" + }, + { + "id": 291, + "tag": "oscillations" + }, + { + "id": 292, + "tag": "mossy cells" + }, + { + "id": 293, + "tag": "granule cells" + }, + { + "id": 294, + "tag": "optogenetics" + }, + { + "id": 295, + "tag": "DANDI:000003" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 282, + "tag": "House mouse" + } + ], + "timestamp_created": "2023-12-19 10:15:50.694640+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000003/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "188": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 188, + "name": "Primary Auditory Cortex network", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 318, + "tag": "Pyramidal cell" + }, + { + "id": 319, + "tag": "neuroConstruct" + }, + { + "id": 320, + "tag": "Auditory system" + }, + { + "id": 321, + "tag": "Detailed cell model" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 651, + "tag": "Network model" + }, + { + "id": 652, + "tag": "Rodent" + } + ], + "timestamp_created": "2023-12-20 16:08:19.848056+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/ACnet2", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "189": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 189, + "name": "L5 Pyramidal Neuron - Almog and Korngreen 2014", + "repository_type": "github", + "summary": "Conversion to neuroConstruct and NeuroML of the L5 Pyramidal cell model described in:\n\nAlmog M, Korngreen A (2014) [A Quantitative Description of Dendritic Conductances and Its Application to Dendritic Excitation in Layer 5 Pyramidal Neurons](http://www.jneurosci.org/content/34/1/182) J Neurosci 34(1):182-196\n", + "tags": [ + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 318, + "tag": "Pyramidal cell" + }, + { + "id": 321, + "tag": "Detailed cell model" + }, + { + "id": 319, + "tag": "neuroConstruct" + }, + { + "id": 322, + "tag": "Goldman-Hodgkin-Katz current" + }, + { + "id": 655, + "tag": "L5 pyramidal cell" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 652, + "tag": "Rodent" + } + ], + "timestamp_created": "2023-12-20 16:09:37.235236+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/korngreen-pyramidal", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "190": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 190, + "name": "Arbor Showcase", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 323, + "tag": "Arbor" + }, + { + "id": 32, + "tag": "Showcase" + } + ], + "timestamp_created": "2023-12-20 16:09:37.768398+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/ArborShowcase", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "191": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 191, + "name": "Reduced L5 Pyramidal Cell - Bahl et al. 2012 ", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 657, + "tag": "Layer 5 Pyramidal cell" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 652, + "tag": "Rodent" + } + ], + "timestamp_created": "2023-12-20 16:09:38.333332+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/BahlEtAl2012_ReducedL5PyrCell", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "192": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 192, + "name": "Blender to NeuroML for C elegans connectome", + "repository_type": "github", + "summary": "Test of Blender to NeuroML conversion\n", + "tags": [ + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 38, + "tag": "OpenWorm" + }, + { + "id": 658, + "tag": "Blender" + }, + { + "id": 79, + "tag": "C. elegans" + }, + { + "id": 659, + "tag": "Nervous system" + }, + { + "id": 651, + "tag": "Network model" + } + ], + "timestamp_created": "2023-12-20 16:09:38.965040+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/openworm/Blender2NeuroML", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "193": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 193, + "name": "Blue Brain Project Showcase", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 321, + "tag": "Detailed cell model" + }, + { + "id": 318, + "tag": "Pyramidal cell" + }, + { + "id": 324, + "tag": "Neuronal reconstruction" + }, + { + "id": 317, + "tag": "Large scale brain initiative" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 653, + "tag": "Network" + }, + { + "id": 652, + "tag": "Rodent" + } + ], + "timestamp_created": "2023-12-20 16:09:39.418884+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/BlueBrainProjectShowcase", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "194": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 194, + "name": "Sparsely connected spiking neuron network - Brunel 2000", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 325, + "tag": "Point neuron network" + }, + { + "id": 326, + "tag": "Integrate and fire neuron" + }, + { + "id": 660, + "tag": "Generic" + }, + { + "id": 661, + "tag": "NEST" + }, + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 651, + "tag": "Network model" + } + ], + "timestamp_created": "2023-12-20 16:09:39.860805+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/Brunel2000", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "195": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 195, + "name": "CA1 Oriens Lacunosum Moleculare - Lawrence et al. 2006", + "repository_type": "github", + "summary": "CA1 Oriens Lacunosum Moleculare multi-compartment model:\r\nLawrence JJ, Saraga F, Churchill JF, Statland JM, Travis KE, Skinner FK, McBain CJ (2006) Somatodendritic Kv7-KCNQ-M channels control interspike interval in hippocampal interneurons. J Neurosci 26:12325-38", + "tags": [ + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 327, + "tag": "Hippocampal formation" + }, + { + "id": 328, + "tag": "CA1" + }, + { + "id": 329, + "tag": "Interneuron" + }, + { + "id": 550, + "tag": "Hippocampus" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 663, + "tag": "Oriens Lacunosum Moleculare" + }, + { + "id": 652, + "tag": "Rodent" + } + ], + "timestamp_created": "2023-12-20 16:09:40.597357+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/agmccrei/CA1-Oriens-Lacunosum-Moleculare---Lawrence-et-al.-2006", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "196": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.210812.1518", + "id": 196, + "name": "Internally organized mechanisms of the head direction sense", + "repository_type": "dandi", + "summary": "The head-direction (HD) system functions as a compass, with member neurons robustly increasing their firing rates when the animal's head points in a specific direction. HD neurons may be driven by peripheral sensors or, as computational models postulate, internally generated (attractor) mechanisms. We addressed the contributions of stimulus-driven and internally generated activity by recording ensembles of HD neurons in the antero-dorsal thalamic nucleus and the post-subiculum of mice by comparing their activity in various brain states. The temporal correlation structure of HD neurons was preserved during sleep, characterized by a 60\u00b0-wide correlated neuronal firing (activity packet), both within and across these two brain structures. During rapid eye movement sleep, the spontaneous drift of the activity packet was similar to that observed during waking and accelerated tenfold during slow-wave sleep. These findings demonstrate that peripheral inputs impinge on an internally organized network, which provides amplification and enhanced precision of the HD signal.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 354, + "tag": "DANDI:000056" + }, + { + "id": 282, + "tag": "House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:12:42.162867+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000056/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "197": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 197, + "name": "Light sheet imaging of the human brain", + "repository_type": "dandi", + "summary": "This dataset contains images of 1 mm sections of human brain tissue showing nuclei, NeuN+ cells and blood vessels. Each tissue section was first SHIELD-processed for protein protection and delipidated to clear the tissue. The tissues were stained with YOYO1 (nuclei), anti-NeuN antibody (with Rhodamine Red-X secondary antibody) and Lectin (blood vessel) for 8 days in total and then optically cleared using ExPROTOS (a refractive matching solution). The sample was imaged using light sheet microscopy at a resolution of ~2.5 um x 3.6 um x 2.5 um. Each slab was imaged using multiple stacks. Offset transforms are included with the dataset to enable reconstruction of each slab.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 370, + "tag": "DANDI:000108" + } + ], + "timestamp_created": "2024-01-03 17:18:34.723542+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000108/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "198": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230602.1643", + "id": 198, + "name": "Oxytocin neurons enable social transmission of maternal behaviour", + "repository_type": "dandi", + "summary": "Maternal care, including by non-biological parents, is important for offspring survival. Oxytocin which is released by the hypothalamic paraventricular nucleus (PVN), is a critical maternal hormone. In mice, oxytocin enables neuroplasticity in the auditory cortex for maternal recognition of pup distress. However, it is unclear how initial parental experience promotes hypothalamic signalling and cortical plasticity for reliable maternal care. Here we continuously monitored the behaviour of female virgin mice co-housed with an experienced mother and litter. This documentary approach was synchronized with neural recordings from the virgin PVN, including oxytocin neurons. These cells were activated as virgins were enlisted in maternal care by experienced mothers, who shepherded virgins into the nest and demonstrated pup retrieval. Virgins visually observed maternal retrieval, which activated PVN oxytocin neurons and promoted alloparenting. Thus rodents can acquire maternal behaviour by social transmission, providing a mechanism for adapting the brains of adult caregivers to infant needs via endogenous oxytocin.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 372, + "tag": "DANDI:000114" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 373, + "tag": "alloparenting" + }, + { + "id": 374, + "tag": "maternal behavior" + }, + { + "id": 252, + "tag": "oxytocin" + } + ], + "timestamp_created": "2024-01-03 17:18:37.170654+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000114/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "199": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230316.1600", + "id": 199, + "name": "Thermoregulatory Responses Forebrain", + "repository_type": "dandi", + "summary": "Imaging in the forebrain of nac -/-; Elavl3-H2B:GCaMP6s +/-; vglut2a-mCherry +/- larval zebrafish during random wave temperature stimulus presentation", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 418, + "tag": "DANDI:000235" + }, + { + "id": 287, + "tag": "Danio rerio - Zebra fish" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:22:06.758787+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000235/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "200": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230316.2031", + "id": 200, + "name": "Thermoregulatory Responses Midbrain", + "repository_type": "dandi", + "summary": "Imaging in the midbrain of nac -/-; Elavl3-H2B:GCaMP6s +/-; vglut2a-mCherry +/- larval zebrafish during random wave temperature stimulus presentation", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 419, + "tag": "DANDI:000236" + }, + { + "id": 287, + "tag": "Danio rerio - Zebra fish" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:22:07.910016+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000236/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "201": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230316.1655", + "id": 201, + "name": "Thermoregulatory Responses Hindbrain", + "repository_type": "dandi", + "summary": "Imaging in the hindbrain of nac -/-; Elavl3-H2B:GCaMP6s +/-; vglut2a-mCherry +/- larval zebrafish during random wave temperature stimulus presentation", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 420, + "tag": "DANDI:000237" + }, + { + "id": 287, + "tag": "Danio rerio - Zebra fish" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:25:23.260873+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000237/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "202": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230316.1519", + "id": 202, + "name": "Thermoregulatory Responses Reticulospinal system", + "repository_type": "dandi", + "summary": "Imaging in the hindbrain and midbrain of nac -/-; Elavl3-H2B:GCaMP6s +/- larval zebrafish after reticulospinal backfills with Texas-Red Dextran during random wave temperature stimulus presentation", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 421, + "tag": "DANDI:000238" + }, + { + "id": 287, + "tag": "Danio rerio - Zebra fish" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:25:24.521983+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000238/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "203": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 203, + "name": "Calcium imaging of egg-laying related neurons in head-fixed Drosophila", + "repository_type": "dandi", + "summary": "Datasets on 2-photon calcium imaging of oviposition descending neurons (oviDNs) as head-fixed flies walk and lay eggs on an agarose-laden wheel. Each NWB file contains time series data from an individual recording session from an individual fly: imaging data, behavior data, and stimulation data (if applicable). A variety of fly genotypes and a variety of environments (egg-laying wheels) were used. Data and methods are described in \"A rise-to-threshold signal for a relative value deliberation\" (https://www.biorxiv.org/content/10.1101/2021.09.23.461548v1). Please contact Vikram Vijayan and/or Gaby Maimon for more information including different download options and different raw/processed data formats.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 427, + "tag": "DANDI:000247" + }, + { + "id": 229, + "tag": "Drosophila" + }, + { + "id": 403, + "tag": "Drosophila melanogaster - Fruit fly" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 124, + "tag": "decision making" + }, + { + "id": 230, + "tag": "egg laying" + }, + { + "id": 231, + "tag": "flies" + }, + { + "id": 428, + "tag": "rise-to-threshold" + } + ], + "timestamp_created": "2024-01-03 17:29:28.213883+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000247/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "204": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 204, + "name": "High-resolution tracking of Drosophila during egg-laying", + "repository_type": "dandi", + "summary": "Dataset on the egg-laying behavior of flies used to understand the egg-laying behavioral sequence. Each NWB file contains time series data from an individual fly: the x-y position of the fly, the body length of the fly, egg-deposition moments of the fly, and other behavioral annotations related to egg laying. Data and methods are described in \"A rise-to-threshold signal for a relative value deliberation\" (https://www.biorxiv.org/content/10.1101/2021.09.23.461548v1). Please contact Vikram Vijayan and/or Gaby Maimon for more information including different download options and different raw/processed data formats.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 430, + "tag": "DANDI:000250" + }, + { + "id": 229, + "tag": "Drosophila" + }, + { + "id": 403, + "tag": "Drosophila melanogaster - Fruit fly" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 431, + "tag": "behavioral sequence" + }, + { + "id": 124, + "tag": "decision making" + }, + { + "id": 230, + "tag": "egg laying" + }, + { + "id": 231, + "tag": "flies" + } + ], + "timestamp_created": "2024-01-03 17:29:30.351425+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000250/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "205": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230408.2207", + "id": 205, + "name": "Finger_RL: human intracortical recordings during attempted finger movements of right and left hands", + "repository_type": "dandi", + "summary": "This dataset contains single-neuron recordings from two tetraplegic human participants as they attempted individual finger movements. One participant had an electrode array implanted in the left posterior parietal cortex (PPC) at the junction of the postcentral and intraparietal sulci (PC-IP). The other participant had one electrode array implanted in the hand knob of the left motor cortex (MC) and one electrode array implanted in the superior parietal lobule (SPL) of the left PPC.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 433, + "tag": "DANDI:000252" + }, + { + "id": 391, + "tag": "Homo sapiens - Human" + }, + { + "id": 434, + "tag": "MC" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 200, + "tag": "PPC" + }, + { + "id": 201, + "tag": "finger" + }, + { + "id": 98, + "tag": "human" + }, + { + "id": 435, + "tag": "ipsilateral" + }, + { + "id": 130, + "tag": "motor cortex" + }, + { + "id": 436, + "tag": "posterior parietal cortex" + } + ], + "timestamp_created": "2024-01-03 17:29:32.879017+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000252/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "206": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230806.0034", + "id": 206, + "name": "Extracellular electrophysiology unit data from mouse Superior Colliculus during whisker guided virtual navigation", + "repository_type": "dandi", + "summary": "Spatial awareness is often intentional, arising from deliberate actions towards a target, while at other times it emerges from unexpected motion in the scene. To navigate the environment, animals must differentiate the spatial cues generated by self-motion from object movements that originate externally. To reveal the neural basis of this ability, we examined the midbrain superior colliculus (SC), which contains multiple egocentric maps of sensorimotor space. By simulating whisker-guided navigation through a dynamic landscape, we discovered a transient neural response that selectively emerged for unexpected, externally generated tactile motion. This transient response only emerged when external motion either gained or lost contact with a whisker, arguing that sensorimotor expectations are specific to a somatotopic location. When external motion sustained contact with the same whiskers, neurons shifted their spike timing to follow the dynamics of self-generated tactile features. Thus, representations based on the timing of self-generated cues may surpass the spatial acuity of the whisker array. In conclusion, the SC contains complementary rate and temporal codes to differentiate external from self-generated tactile features.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 445, + "tag": "DANDI:000301" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:29:45.023769+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000301/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "207": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 207, + "name": "Habenular neurophysiology", + "repository_type": "dandi", + "summary": "Habenular neurophysiology data associated with Sylwestrak*, Jo*, Vesuna* et al. Cell (2022).", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 446, + "tag": "DANDI:000302" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:29:46.781758+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000302/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "208": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 208, + "name": "20220917_AIBS_Patchseq_human", + "repository_type": "dandi", + "summary": "U01 Lein PatchSeq upload March 2022 - Mansvelder lab data", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 447, + "tag": "DANDI:000337" + }, + { + "id": 391, + "tag": "Homo sapiens - Human" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:29:47.900990+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000337/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "209": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 209, + "name": "groupweight BMI", + "repository_type": "dandi", + "summary": "This dataset is the group-weight BMI experiment data. ", + "tags": [ + { + "id": 448, + "tag": "BCI BMI" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 449, + "tag": "DANDI:000338" + }, + { + "id": 334, + "tag": "Macaca mulatta - Rhesus monkey" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:29:49.106416+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000338/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "210": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 210, + "name": "Local Field Potential Recordings in the Primary Somatosensory Cortex before and after Transcranial Focused Ultrasound Stimulation in Rats", + "repository_type": "dandi", + "summary": "In this study, we investigate how transcranial focused ultrasound (tFUS) modulates neural interaction and response to peripheral electrical stimulation at hindlimb through intracranial multi-electrode recordings in the rat somatosensory cortex. Recordings were performed using a 32-channel NeuroNexus\u00ae multi-electrode array. Rats were anesthetized using isoflurane at 2%. Recordings were taken while delivering peripheral electrical stimulation once every 5 seconds to analyze the neural response in the rat S1HL region, before ultrasound for 30 minutes and then afterwards for an hour, in order to determine how the ultrasound modulated the electrical stimulation induced local field potential (LFP) waveforms. We delivered ultrasound for 5 minutes in a pulsed pattern at 5 levels of sonication repetition frequencies (SRF) (10 Hz, 50 Hz, 75 Hz, 100 Hz, and 125 Hz) to induce frequency dependent plasticity in a manner similar to that is found following tetanic electrical stimulation. We investigated whether delivering tFUS that alters connectivity/correlation between the targeted neurons may alter collective responses manifested in the modulated LFP waveforms. The applied fundamental frequency of ultrasound was 0.5 MHz, the pulse repetition frequency (PRF) was 3 kHz, and the total duty cycle was 36%. This dataset contains LFP recordings, with 32 channels for each datafile.\nIn each subject, recordings are provided with \"pre\" and \"post\" tFUS for two different frequencies (SRF), and most contain recordings during the delivery of ultrasound and during rest periods between sessions of different ultrasound parameters. For example, a single subject may include \"100Hz_pre\", \"100Hz_post\", \"50Hz_pre\", \"50Hz_post\", which are the pre and post tFUS recordings for the two used parameters, and then \"50Hz\" and \"100Hz\", which are the recordings taken during stimulation, and \"rest1\" and \"rest2\", which are the rest periods between sessions. These names are preceded by a label such as \"BH280\", which is the animal subject label. Ultrasound is delivered in a continuous paradigm, with pulses delivered at the stimulation repetition frequency continuously throughout the 5 minutes of stimulation. During the \"pre\" and \"post\" recordings, the peripheral electrical stimulation is delivered once every 5 seconds approximately. Electrical stimulation event trigger is not included in the dataset as it was not recorded due to using a separate system to deliver the electrical stimulation. We were using threshold detection on a channel of LFP recordings in order to detect the electrical stimulation events, in which events can be recognized by the quick rising of voltage. One could also approximate it by detecting the first event, and then add periods of 5 seconds to generate the rest of the trigger times.\nMore details about the experimental details can be found in our paper published by Journal of Neural Engineering (DOI: 10.1088/1741-2552/ac889f).", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 450, + "tag": "DANDI:000339" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 217, + "tag": "Plasticity" + }, + { + "id": 218, + "tag": "Rat" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + }, + { + "id": 451, + "tag": "Somatosensory" + }, + { + "id": 220, + "tag": "Somatosensory Cortex" + }, + { + "id": 216, + "tag": "Ultrasound" + }, + { + "id": 219, + "tag": "tFUS" + } + ], + "timestamp_created": "2024-01-03 17:29:50.262505+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000339/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "211": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 211, + "name": "Temporal disparity of action potentials triggered in axon initial segments and distal axons in the neocortex", + "repository_type": "dandi", + "summary": "Patch-clamp recordings of Layer 1 interneurons in the human and rodent neocortex. Data comes from Gabor Tamas Lab (ELKH-SZTE Research Group for Cortical Microcircuits; University of Szeged, Hungary).", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 452, + "tag": "DANDI:000341" + }, + { + "id": 453, + "tag": "Layer 1 interneurons" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 454, + "tag": "Persistent firing" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + }, + { + "id": 455, + "tag": "Retoaxonal action potentials" + }, + { + "id": 456, + "tag": "Retroaxonal firing" + }, + { + "id": 98, + "tag": "human" + }, + { + "id": 457, + "tag": "in vitro" + }, + { + "id": 458, + "tag": "in vivo" + }, + { + "id": 459, + "tag": "rodent" + } + ], + "timestamp_created": "2024-01-03 17:29:51.368009+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000341/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "212": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 212, + "name": "Jeong et al (2022) Mesolimbic dopamine release conveys causal associations", + "repository_type": "dandi", + "summary": "This dataset includes fiber photometry (NAcc) and behavioral data from Jeong et al., 2022: \"Mesolimbic dopamine release conveys causal associations\". Animals names and session numbers used for each figure can be found from 'Subject and session information' in Related resource.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 460, + "tag": "DANDI:000351" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:29:54.461891+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000351/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "213": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 213, + "name": "Simultaneous loose seal cell-attached recordings and two-photon imaging of GCaMP8 expressing mouse V1 neurons with drifting gratings visual stimuli - RAW movies", + "repository_type": "dandi", + "summary": "We tested the jGCaMP8 sensors in L2/3 pyramidal neurons of mouse primary visual cortex. We made a craniotomy over V1 and infected neurons with adeno-associated virus (AAV2/1-hSynapsin-1) encoding jGCaMP8 variants (s/m/f), jGCaMP7f, or XCaMP-Gf. 18-80 days after the virus injection, the mouse was anesthetized, and we surgically removed the cranial window and performed durotomy. The craniotomy was filled with 10-15 \u03bcL of 1.5% agarose, and a D-shaped coverslip was secured on top to suppress brain motion and leave access to the brain on the lateral side of the craniotomy. Then mice were lightly anesthetized and mounted under a custom two-photon microscope. Full-field, high-contrast drifting gratings were presented in each of eight directions to the contralateral eye. Two-photon imaging (122 Hz) was performed of L2/3 somata and neuropil combined with loose-seal, cell-attached electrophysiological recording of a single neuron in the field of view. \nThis dataset contains the raw 2-photon videos, for registered movies see: https://dandiarchive.org/dandiset/000168/", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 461, + "tag": "DANDI:000362" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:29:55.554190+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000362/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "214": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.231012.2129", + "id": 214, + "name": "Mesoscale Activity Map Dataset", + "repository_type": "dandi", + "summary": "Mesoscale Activity Map Project. Map behavior-related activity in a multi-regional network supporting memory-guided movement in mice. Anatomy-guided recordings from multiple connected brain regions, from anterior lateral motor cortex to the medulla.\n\nSupported by Simons Collaboration on the Global Brain, Janelia Visitor Project, NIH U19NS123714-01, R01NS112312, R01EB028171, McKnight foundation", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 462, + "tag": "DANDI:000363" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:29:56.705262+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000363/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "215": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 215, + "name": "Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex", + "repository_type": "dandi", + "summary": "Recent advances in multi-electrode array technology have made it possible to monitor large neuronal ensembles at cellular resolution in animal models. In humans, however, current approaches restrict recordings to few neurons per penetrating electrode or combine the signals of thousands of neurons in local field potential (LFP) recordings. Here, we describe a new probe variant and set of techniques which enable simultaneous recording from over 200 well-isolated cortical single units in human participants during intraoperative neurosurgical procedures using silicon Neuropixels probes. We characterized a diversity of extracellular waveforms with eight separable single unit classes, with differing firing rates, locations along the length of the electrode array, waveform spatial spread, and modulation by LFP events such as inter-ictal discharges and burst suppression. While some challenges remain in creating a turn-key recording system, high-density silicon arrays provide a path for studying human-specific cognitive processes and their dysfunction at unprecedented spatiotemporal resolution. ", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 463, + "tag": "DANDI:000397" + }, + { + "id": 391, + "tag": "Homo sapiens - Human" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:29:58.030337+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000397/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "216": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.221208.1803", + "id": 216, + "name": "Scalable Thousand Channel Penetrating Microneedle Arrays on Flex for Multimodal and Large Area Coverage BrainMachine Interfaces", + "repository_type": "dandi", + "summary": "The Utah array powers cutting-edge projects for restoration of neurological function, such as BrainGate, but the underlying electrode technology has itself advanced little in the last three decades. Here, advanced dual-side lithographic microfabrication processes is exploited to demonstrate a 1024-channel penetrating silicon microneedle array (SiMNA) that is scalable in its recording capabilities and cortical coverage and is suitable for clinical translation. The SiMNA is the first penetrating microneedle array with a flexible backing that affords compliancy to brain movements. In addition, the SiMNA is optically transparent permitting simultaneous optical and electrophysiological interrogation of neuronal activity. The SiMNA is used to demonstrate reliable recordings of spontaneous and evoked field potentials and of single unit activity in chronically implanted mice for up to 196 days in response to optogenetic and to whisker air-puff stimuli. Significantly, the 1024-channel SiMNA establishes detailed spatiotemporal mapping of broadband brain activity in rats. This novel scalable and biocompatible SiMNA with its multimodal capability and sensitivity to broadband brain activity will accelerate the progress in fundamental neurophysiological investigations and establishes a new milestone for penetrating and large area coverage microelectrode arrays for brain\u2013machine interfaces.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 464, + "tag": "DANDI:000398" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:29:59.267056+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000398/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "217": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 217, + "name": "All-optical physiology resolves a synaptic basis for behavioral time scale plasticity", + "repository_type": "dandi", + "summary": "Data included in Fan (2022) Cell", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 465, + "tag": "DANDI:000399" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:00.700122+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000399/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "218": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230307.2132", + "id": 218, + "name": "MICrONS Two Photon Functional Imaging", + "repository_type": "dandi", + "summary": "The light microscopic images were acquired from a cubic millimeter volume that spanned portions of primary visual cortex and three higher visual cortical areas. The volume was imaged in vivo by two-photon random access mesoscope (2P-RAM) from postnatal days P75 to P81 in a male mouse expressing a genetically encoded calcium indicator in excitatory cells, while the mouse viewed natural movies and parametric stimuli. The calcium imaging data includes the single-cell responses of an estimated 75,000 pyramidal cells imaged over a volume of approximately 1200 x 1100 x 500 \u03bcm3 (anteroposterior x mediolateral x radial depth). The center of the volume was placed at the junction of primary visual cortex (VISp) and three higher visual areas, lateromedial area (VISlm), rostrolateral area (VISrl) and anterolateral area (VISal). During imaging, the animal was head-restrained, and the stimulus was presented to the left visual field. Treadmill rotation (single axis) and video of the animal's left eye were captured throughout the scan, yielding the locomotion velocity, eye movements, and pupil diameter data included here.\n\nThe functional data were co-registered with electron microscopy (EM) data. The structural identifiers of the matched cells are added as plane segmentation columns extracted from the CAVE database. To access the latest revision see the notebook that is linked to this dandiset. The structural ids might not be present for all plane segmentations.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 466, + "tag": "DANDI:000402" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:01.901222+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000402/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "219": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230605.2024", + "id": 219, + "name": "Monkey 2D cursor BMI", + "repository_type": "dandi", + "summary": "This dataset includes binned spike count data (chronic microwire arrays in PMd/M1) and brain-machine-interface behavioral data (2D cursor kinematics, target locations, trials) from Athalye, V*, Khanna, P*, Gowda S, Orsborn, AL, Costa RM**, Carmena, JC**, (2023) \"Invariant neural dynamics drive commands to control different movements\": https://www.biorxiv.org/content/10.1101/2021.08.27.457931v2. \n\nFor more information about this data, please contact Vivek Athalye and/or Preeya Khanna. \n\nCode for analyzing this data and re-creating manuscript figures is located: https://github.com/pkhanna104/bmi_dynamics_code and archived at https://zenodo.org/record/8006653", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 467, + "tag": "DANDI:000404" + }, + { + "id": 334, + "tag": "Macaca mulatta - Rhesus monkey" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 468, + "tag": "brain-machine interface" + }, + { + "id": 469, + "tag": "dynamical systems" + }, + { + "id": 470, + "tag": "motor commands" + }, + { + "id": 471, + "tag": "motor control" + }, + { + "id": 130, + "tag": "motor cortex" + }, + { + "id": 472, + "tag": "movement representations" + }, + { + "id": 473, + "tag": "neural population dynamics" + }, + { + "id": 474, + "tag": "neuroprosthetics" + }, + { + "id": 475, + "tag": "optimal feedback control" + } + ], + "timestamp_created": "2024-01-03 17:30:03.074122+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000404/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "220": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 220, + "name": "Gonzalez & Giocomo (2022) Parahippocampal neurons encode task-relevant information for goal-directed navigation", + "repository_type": "dandi", + "summary": "Data used in the Gonzalez & Giocomo (2022) manuscript. This contains session electrophysiological data and accompanying event metadata/time-series necessary to generate figures in the manuscript. Github link contains metadata files that contain information for each session, including if it is an open-field foraging session or a Tree-Maze session, and how many units were collected in it. Additional metadata includes unit matching across session and a detailed behavioral performance table.\n\nPre-print DOI: \nhttps://doi.org/10.1101/2022.12.15.520660\n\nGithub:\nhttps://github.com/alexgonzl/TMA\n", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 476, + "tag": "DANDI:000405" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + } + ], + "timestamp_created": "2024-01-03 17:30:04.144533+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000405/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "221": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 221, + "name": "Joshi et al (2023) Dynamic Synchronization between Hippocampal Spatial Representations and the Stepping Rhythm", + "repository_type": "dandi", + "summary": "This dateset includes electrophysiological data from from dorsal CA1 of rats running on a linear or w-shaped track. Please contact Abhilasha Joshi or Loren Frank for more information about this data. ", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 478, + "tag": "DANDI:000410" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + } + ], + "timestamp_created": "2024-01-03 17:30:06.294349+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000410/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "222": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 222, + "name": "test", + "repository_type": "dandi", + "summary": "It is a test to use DANDI archive, like hello world. I will not upload any neural data in this set.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 479, + "tag": "DANDI:000411" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:07.414153+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000411/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "223": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230316.2133", + "id": 223, + "name": "Novel-familiar-novel WTrack (CA1-PFC)", + "repository_type": "dandi", + "summary": "Hippocampal-prefrontal recordings in rats (dorsal CA1 region of the hippocampus and primarily the prelimbic region of prefrontal cortex) during exposure to novel and familiar WTrack environments. Data includes spikes from single units, local field potential recordings, position (x, y, velocity), and trajectory information. Each file contains data from 3 behavioral epochs - Novel Wtrack (first exposure) - Familiar WTrack - Novel WTrack (second exposure)", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 480, + "tag": "DANDI:000447" + }, + { + "id": 481, + "tag": "Hippocampus, Prefrontal cortex, Learning, Memory, Decision making" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + } + ], + "timestamp_created": "2024-01-03 17:30:08.675782+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000447/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "224": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230421.1844", + "id": 224, + "name": "Time kinetics of the membrane potential at the cathode- and anode-facing poles of a cell induced by a train of 5 pulses at 833 kHz", + "repository_type": "dandi", + "summary": "Time-lapse recordings for the investigation of the relationship between the electroporation efficiency of nsEP and changes in the transmembrane potential (TMP). A strobe imaging synchronized with nsEP exposure was used. This allowed studying the nanosecond kinetics of TMP charging and relaxation, which is the primary effect of nsEP that rapidly charges the plasma membrane. The methodology involved pulsed laser fluorescence microscopy with voltage-sensitive FluoVolt dye-loaded cells that respond to TMP changes within nanoseconds. FluoVolt has a high sensitivity of about 10 % deltaF/F per 100 mV. During the imaging, a single short pulse laser flash is delivered at a precise time interval (addition of 25ns in each frame) before, during, or after nsEP exposure. The camera shutter opens in advance of and closes after the laser flash, capturing one TMP image per nsEP exposure. Supported by NIH 1R21EY034258", + "tags": [ + { + "id": 482, + "tag": "Cricetulus griseus - Cricetulus aureus" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 483, + "tag": "DANDI:000448" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:09.945727+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000448/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "225": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230302.2331", + "id": 225, + "name": "Guide to the construction and use of an adaptive optics two-photon microscope with direct wavefront sensing", + "repository_type": "dandi", + "summary": "Two-photon microscopy, combined with appropriate optical labeling, has enabled the study of structure and function throughout animals and their organ systems, especially nervous systems. This methodology enables, for example, the measurement and tracking of sub-micrometer structures within brain cells, the spatio-temporal mapping of spikes in individual neurons, and the spatio-temporal mapping of transmitter release in individual synapses. Yet the spatial resolution of two-photon microscopy rapidly degrades as imaging is attempted at depths more than a few scattering lengths into tissue, i.e., below the superficial layers that constitute the top 300 to 400\u00a0\u00b5m of neocortex. To obviate this limitation, we measure the wavefront of the guide star at the focus of the excitation beam and utilize adaptive optics that alters the incident wavefront to achieve an improved focal volume. We describe the construction, calibration, and operation of a two-photon microscope that incorporates adaptive optics to restore diffraction-limited resolution throughout the nearly 900\u00a0\u00b5m depth of mouse cortex. Our realization utilizes a guide star formed by excitation of red-shifted dye within the blood serum to directly measure the wavefront. We incorporate predominantly commercial optical, optomechanical, mechanical, and electronic components; computer aided design models of the exceptional custom components are supplied. The resultant adaptive-optics two-photon microscope is modular and allows for expanded imaging and optical excitation capabilities. We demonstrate our methodology in mouse neocortex by imaging the morphology of somatostatin-expressing neurons that lie 700 \u00b5m beneath the pia, calcium dynamics of layer 5b projection neurons, and thalamocortical glutamate transmission to L4 neurons.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 484, + "tag": "DANDI:000454" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:11.075590+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000454/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "226": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230317.0039", + "id": 226, + "name": "Simultaneous electroencephalography, extracellular electrophysiology, and cortical electrical stimulation in head-fixed mice", + "repository_type": "dandi", + "summary": "In this set of experiments, we recorded neural signals using an electroencephalography (EEG) array and Neuropixels probes while directly electrically stimulating the cortex in awake and anesthetized head-fixed mice. Details of the experiments can be found in the associated manuscript (Claar, Rembado et al., 2023). Briefly, the recordings and stimulation commenced while the mouse was awake, free to rest or move on a rotating disc. After delivering a series of stimuli, anesthesia was induced with isoflurane (5% via inhalation). Once a surgical level of anesthesia was reached, the mouse was maintained unconscious (1-1.5% isoflurane) during the delivery of another series of stimuli. In some subjects another series of stimuli was delivered after the isoflurane was turned off, this epoch/state is referred to as \u201crecovery\u201d in the dataset.\n\nEach session file includes the following data: raw EEG signals from all 30 surface electrodes and the associated electrode information; raw LFP signals from all electrodes on all Neuropixels probes (if used) and the associated electrode information; spike times for all units (spike sorted with Kilosort 2.0) that passed our quality threshold with associated information (brain region, spike waveform duration, etc.); subject\u2019s speed computed from the rotation of the disc for the entire session; epochs marking when isoflurane was induced and when it was maintained; and a trial table containing information about every stimulus delivered throughout the session.\n\nAll sessions included an awake and an isoflurane epoch, but not all sessions included a recovery epoch. Some subjects received visual stimulation trials (visual stimuli presented on a screen in the subject\u2019s right field of view) interleaved with the electrical stimulation trials.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 485, + "tag": "DANDI:000458" + }, + { + "id": 44, + "tag": "EEG" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 255, + "tag": "Neuropixels" + }, + { + "id": 486, + "tag": "brain states" + }, + { + "id": 487, + "tag": "cortico-thalamic interactions" + }, + { + "id": 488, + "tag": "electrical stimulation" + }, + { + "id": 148, + "tag": "extracellular electrophysiology" + } + ], + "timestamp_created": "2024-01-03 17:30:12.252493+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000458/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "227": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 227, + "name": "Cohen Tickertapes Exploratory Data 1", + "repository_type": "dandi", + "summary": "Exploratory dataset for the second generation of molecular tickertapes in the Adam Cohen Lab.", + "tags": [ + { + "id": 489, + "tag": "Canis lupus familiaris - Dog" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 490, + "tag": "DANDI:000461" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:13.397875+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000461/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "228": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230416.2132", + "id": 228, + "name": "HippocampusRewardDataset", + "repository_type": "dandi", + "summary": "Dataset that applies to the paper.\nKrishnan, S., Heer, C., Cherian, C. et al. Nat Commun 13, 6662 (2022). https://doi.org/10.1038/s41467-022-34465-5\n\nUploaded datasets include data from WT mice where mice underwent a reward expectation extinction task as described in Figures 1-3. Four datasets with DREADD manipulation of VTA dopaminergic neurons and controls are also included as described in Figure 4. All datasets are processed raw fluorescence obtained from suite2p. Behavior data is also included.\n\nScripts used for analysis can be found on https://github.com/seethakris/HPCrewardpaper", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 491, + "tag": "DANDI:000462" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 492, + "tag": "hippocampus; dopamine; mice; reward; calcium imaging; VR based navigation; DREADD" + } + ], + "timestamp_created": "2024-01-03 17:30:14.617331+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000462/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "229": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 229, + "name": "Electrophysiological Recordings in Anesthetized Rats in the Primary Somatosensory Cortex with Phased Ultrasound Array Stimulation", + "repository_type": "dandi", + "summary": "In these recordings, we test different intensities and PRFs (pulse repetition frequencies) of ultrasound stimulation using a phased ultrasound array to stimulate the somatosensory cortex of rats anesthetized with 2% isoflurane through inhalation. Recordings are taken using 32 channel Neuronexus electrodes. Ultrasound stimulation is delivered every 2.5 seconds, and each recording has either 200 or 500 trials. ", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 493, + "tag": "DANDI:000463" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + } + ], + "timestamp_created": "2024-01-03 17:30:15.782700+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000463/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "230": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230530.2349", + "id": 230, + "name": "Human brain mapping with multithousand-channel PtNRGrids resolves spatiotemporal dynamics", + "repository_type": "dandi", + "summary": "This dataset contains recordings from PtNRGrids, which are devices designed to record electrocorticography (ECoG) activities from the brain surface at high-spatial resolution. PtNRGrids used here were a square-shaped array with 1024 channels and a 150-\u00b5m pitch, which was implanted to record neural activity from the entire right primary somatosensory barrel cortex. The sensory response on the brain was evoked by delivering air puffs through a microcapillary tube to individually stimulate individual whiskers on the contralateral side. This dataset demonstrates the high-spatial resolution recording capability of PtNRGrids isolate functional cortical columns in sub-mm resolution from the surface of the brain.\n\n[Publication corresponding to this dataset] Tchoe, Youngbin, et al. \"Human brain mapping with multithousand-channel PtNRGrids resolves spatiotemporal dynamics.\" Science translational medicine 14.628 (2022): eabj1441.\n[Electrode mapping information & Basic analysis codes] Github: https://ytchoe.github.io/", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 494, + "tag": "DANDI:000465" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + }, + { + "id": 495, + "tag": "micro-ECoG, barrel cortex, high gamma activity" + } + ], + "timestamp_created": "2024-01-03 17:30:17.131944+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000465/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "231": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 231, + "name": "Sparse and stereotyped encoding implicates a core glomerulus for ant alarm behavior", + "repository_type": "dandi", + "summary": "In these experiments, we recorded odor-evoked calcium responses in the antennal lobe of the clonal raider ant (Ooceraea biroi). Transgenic ants expressed GCaMP6s in olfactory sensory neurons, and the right antennal lobe was imaged using a two-photon microscope. A piezo device and resonant scanning galvanometer were used to image whole-antennal lobe volumes at a rate of 0.83 volumes per second. 33 z-planes were imaged at 5 micrometer intervals. In each recording, odor stimulus commenced after 3s and lasted for 5s. Negative control: paraffin oil. General odorant stimuli: isopropanol, ethylpyrazine, ethanol, 3-hexanone, propionic acid. Ant alarm pheromones: 4-methyl-3-heptanone, 4-methyl-3-heptanol, 4-methyl-3-hexanol, 6-methyl-5-hepten-2-one.\n\nExperiment key:\nGeneral odorant experiment subjects:\nsub-ant1-m10-d6-y2022\nsub-ant3-m10-d6-y2022\nsub-ant1-m10-d11-y2022\nsub-ant2-m10-d11-y2022\nsub-ant3-m10-d11-y2022\nsub-ant5-m10-d11-y2022\n\nBilateral antennal lobe imaging experiment subjects:\nsub-ant2-m2-d10-y2023\nsub-ant3-m2-d10-y2023\nsub-ant4-m2-d10-y2023\n\nUnilateral alarm pheromone imaging experiment subjects:\nsub-ant2-m4-d20-y2022\nsub-ant4-m4-d20-y2022\nsub-ant1-m4-d21-y2022\nsub-ant2-m4-d21-y2022\nsub-ant3-m4-d21-y2022\nsub-ant1-m4-d26-y2022\nsub-ant2-m4-d26-y2022\nsub-ant3-m4-d26-y2022\nsub-ant4-m4-d26-y2022\nsub-ant5-m4-d26-y2022\nsub-ant1-m4-d27-y2022\nsub-ant2-m4-d27-y2022\nsub-ant3-m4-d27-y2022", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 496, + "tag": "DANDI:000467" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 497, + "tag": "Ooceraea biroi - Clonal raider ant" + }, + { + "id": 498, + "tag": "antennal lobe; calcium imaging; chemosensation; clonal raider ant; communication; GCaMP; odor coding; olfaction; Ooceraea biroi; pheromone" + } + ], + "timestamp_created": "2024-01-03 17:30:18.492347+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000467/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "232": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.231213.2047", + "id": 232, + "name": "Dataset of human-single neuron activity during a Sternberg working memory task.", + "repository_type": "dandi", + "summary": "We present a dataset of 1809 single neurons recorded from the human medial temporal lobe (amygdala and hippocampus) and medial frontal lobe (anterior cingulate cortex, pre-supplementary motor area, ventral medial prefrontal cortex) across 41 sessions from 21 patients that underwent intracranial monitoring for epileptic activity. Subjects first performed a screening task (907 neurons), based on which we identified images for which highly selective cells were present in the medial temporal lobe. Subjects then performed a working memory task (902 neurons), in which they were sequentially presented with 1-3 images, and following a maintenance period, were asked if a probe was identical to one of the currently maintained images. This Neural data without borders (NWB) formatted dataset includes spike times, extracellular spike waveforms, stimuli presented, behavior, electrode locations, and subject demographics. As validation, we replicate previous findings on the existence of concept cells and their persistent activity during working memory maintenance. This dataset provides a substantial amount of rare human single neuron recordings together with behavior, thereby enabling investigation of the neural mechanisms of working memory at the single-neuron level.\n\nSample code to access and analyze this dataset has been provided: \nhttps://github.com/rutishauserlab/workingmem-release-NWB\n", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 499, + "tag": "DANDI:000469" + }, + { + "id": 391, + "tag": "Homo sapiens - Human" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 122, + "tag": "cognitive neuroscience" + }, + { + "id": 123, + "tag": "data standardization" + }, + { + "id": 27, + "tag": "neurophysiology" + }, + { + "id": 126, + "tag": "neurosurgery" + }, + { + "id": 127, + "tag": "open source" + }, + { + "id": 128, + "tag": "single-neurons" + }, + { + "id": 500, + "tag": "working memory" + } + ], + "timestamp_created": "2024-01-03 17:30:19.765218+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000469/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "233": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 233, + "name": "Test", + "repository_type": "dandi", + "summary": "Test data", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 501, + "tag": "DANDI:000470" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:20.961223+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000470/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "234": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 234, + "name": "NeuroPAL volumetric images", + "repository_type": "dandi", + "summary": "Partially labeled NeuroPAL volumetric images produced by the Kato lab at UCSF as part of an effort to add to the corpus of existing labeled data to facilitate the development of automatic cell identification algorithms. We hope that making these datasets more easily accessible in a centralized location like DANDI will significantly speed up development by allowing new researchers to start working on algorithms without the need to build up their own full dataset of training data beforehand. \n\nAs part of this effort, we also propose a novel extension to the NWB data format to allow for multichannel volumetric data.\n\nContents:\n\nSubject (C. elegans subject) - contains relevant metadata about the experimental subject\n\nAcquisition, \"NeuroPALImageRaw\" (MultiChannelVolume) - contains the raw image and associated metadata with dimensions (X, Y, Z, C)\n\nProcessing, \"NeuroPAL\", \"VolumeSegmentation\" (VolumeSegmentation) - contains a list of neuron centers and associated IDs (X, Y, Z, weight, ID)\n\nProcessing, \"NeuroPAL\", \"ImagingVolume\" (ImagingVolume) - contains the metadata associated with the imaging acquisition\n\nProcessing, \"NeuroPAL\", \"OpticalChannelRefs\" (OpticalChannelReferences) - contains the order of the optical channels in the image\n\nProcessing, \"ProcessedImage\", \"Hist_match_med_filt\" - contains the processed NeuroPAL image with dimensions (X,Y,Z,C)", + "tags": [ + { + "id": 502, + "tag": "Caenorhabditis elegans" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 503, + "tag": "DANDI:000472" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:22.180445+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000472/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "235": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230417.1502", + "id": 235, + "name": "Esr1+ hypothalamic-habenula neurons shape aversive states.", + "repository_type": "dandi", + "summary": "Prefrontal cortex (PFC) high-density extracellular recordings (Neuropixels) in head-fixed mice during an aversive-conditioning task. Neuronal responses to internally generated (i.e. pathway-specific optogenetic activation, see the main article for details) and externally derived (i.e. air puffs) aversive signals have been recorded.", + "tags": [ + { + "id": 504, + "tag": "Aversion" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 505, + "tag": "DANDI:000473" + }, + { + "id": 506, + "tag": "Head-fixed" + }, + { + "id": 507, + "tag": "Lateral Habenula" + }, + { + "id": 508, + "tag": "Lateral Hypothalamus" + }, + { + "id": 16, + "tag": "Mouse" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 255, + "tag": "Neuropixels" + }, + { + "id": 509, + "tag": "Prefrontal cortex" + } + ], + "timestamp_created": "2024-01-03 17:30:23.239390+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000473/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "236": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230417.2148", + "id": 236, + "name": "State-dependent processing in visual cortex", + "repository_type": "dandi", + "summary": "Extracellular recordings from rabbit visual cortex neurons. Orientation testing with drifting grating stimuli. Hippocampal EEG for the state scoring procedure. ", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 510, + "tag": "DANDI:000481" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 511, + "tag": "Oryctolagus cuniculus - Rabbits" + }, + { + "id": 512, + "tag": "electrophysiology, signal processing" + } + ], + "timestamp_created": "2024-01-03 17:30:24.733934+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000481/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "237": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 237, + "name": "State-dependent processing in visual cortex", + "repository_type": "dandi", + "summary": "Test upload. Extracellular recordings from rabbit visual cortex neurons. Orientation testing with drifting grating stimuli. Hippocampal EEG for the state scoring procedure.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 513, + "tag": "DANDI:000482" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 511, + "tag": "Oryctolagus cuniculus - Rabbits" + } + ], + "timestamp_created": "2024-01-03 17:30:26.229230+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000482/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "238": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230421.2321", + "id": 238, + "name": "Dataset for \"Coregistration of heading to visual cues in retrosplenial cortex\"", + "repository_type": "dandi", + "summary": "These are all the accompanying data for the following publication:\nSit, K.K., Goard, M.J. Coregistration of heading to visual cues in retrosplenial cortex. Nat Commun 14, 1992 (2023). https://doi.org/10.1038/s41467-023-37704-5", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 514, + "tag": "DANDI:000483" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:28.335863+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000483/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "239": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230602.2022", + "id": 239, + "name": "Allen Institute Openscope - Differential encoding of temporal context and expectation", + "repository_type": "dandi", + "summary": "This dataset was collected for the Predictive Coding project, as part of the Allen Institute for Brain Science's OpenScope project. \n\nThe experimental design involved visual stimulation with sequences of 4 natural scene images (ABCD) that are repeated many times, with an occasional rare oddball image in the 4th place in the sequence (ABCX). There are 10 unique oddball images which are each shown 10 times during the recording session. The main sequence (ABCD) is shown thousands of times. In addition to the oddball blocks there are control conditions where the 4 main sequence images and 10 oddball images are shown either entirely randomly, or where pairwise image transitions are maintained, but the overall sequence is shuffled (example pairs: AB, BC, CD, CX, XA, etc). \n\nDuring this stimulus paradigm, 2-photon calcium imaging was used to record neural activity in three cortical areas (one area per recording session): the primary visual cortex, higher order visual area PM, and the retrosplenial cortex, across multiple cortical depths. During the recordings, mice were free to run on a circular disk and running speed was measured, along with pupil diameter and eye position. In each mouse, two of the three areas were imaged, and the third had a retrograde tracer (rAAV-mRuby2) injected to label inputs to that region. No differences in physiology were identified between retrogradely labeled and non-labeled neurons, thus this information was not included in the primary study of this dataset. \n\nResults are provided in the form of normalized calcium traces (dF/F) for all recorded neurons, along with running speed, pupil measurements, and the timing of all stimulus events. ", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 515, + "tag": "DANDI:000488" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 160, + "tag": "mouse VisP" + }, + { + "id": 136, + "tag": "neocortex" + }, + { + "id": 161, + "tag": "prediction" + }, + { + "id": 516, + "tag": "predictive coding" + }, + { + "id": 154, + "tag": "pyramidal neurons" + }, + { + "id": 159, + "tag": "two-photon calcium imaging" + } + ], + "timestamp_created": "2024-01-03 17:30:29.563722+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000488/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "240": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230518.1811", + "id": 240, + "name": "The impact of the second phase amplitude (% to the first phase) on the electroporation efficiency (measured as YP emission)", + "repository_type": "dandi", + "summary": "Cells were electroporated by a train of 5 unipolar 600 ns pulses (0 percent) or bipolar 600 + 600 ns pulses (25, 50, 75, 100 percent) at 1 Hz. 25, 50, 75, and 100% represent the second pulse's amplitude in relation to the first pulse's amplitude. 0% represents a unipolar pulse, where no second pulse was applied. The electrodes were two parallel stainless-steel cylinders, 1.5 mm in diameter and 3 mm apart. Supported by NIH 1R21EY034258", + "tags": [ + { + "id": 517, + "tag": "Bos taurus - Cattle" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 518, + "tag": "DANDI:000489" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:30.669503+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000489/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "241": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230602.1307", + "id": 241, + "name": "BrainFlowZZZ", + "repository_type": "dandi", + "summary": "Dataset from the 2023 manuscript titled **_Sizes and Shapes of Perivascular Spaces Surrounding Murine Pial Arteries_** by Raicevic et al. DOI: 10.21203/rs.3.rs-2587250/v1. \n\n## Overview\nThe **14 datasets** from **9 subjects** include the original 3D two photon microscopy data from three channels which show tracer in the vessel, PVSs, and microspheres. Additionally, each dataset also includes the final binary segmentation of the PVS and vessel used to generate the model and statistics in the manuscript. Additional details regarding the subjects, tracer injection, image acquisition, and segmentation can be found in the manuscript at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9949243/.\n\n## Content\nFor easier navigation, below is a mapping between the NWB file names and the datasets referenced in the manuscript.\n1. sub-21-07-19-b-act (Mouse 6, dataset K)\n2. sub-21-09-01-b-act (Mouse 8, dataset M)\n3. sub-21-09-20-b-act (Mouse 9, dataset N)\n4. sub-21-10-08-b-act (Mouse 7, dataset L)\n5. sub-BPN-M4 (Mouse 3, dataset E and F)\n6. sub-BPN-M6 (Mouse 4, dataset G and H)\n7. sub-BPN-M7 (Mouse 5, dataset I and J)\n8. sub-BPN-OLD-M2 (Mouse 1, dataset A and B)\n9. sub-BPN-OLD-M3 (Mouse 2, dataset C and D)\n\n## Data Reading Instructions\nThe .nwb files can be viewed using PyNWB or MatNWB. To install and set up, please visit . Below, we show how to open and view an .nwb file using MatNWB. \n### Loading the image data\n```matlab\nnwb = nwbRead(PATH_TO_NWB_FILE);\n\n% first check what color channels are present\n>> nwb.acquisition\nans = \n 3\u00d71 Set array with properties:\n TwoPhotonSeriesChanA: [types.core.TwoPhotonSeries]\n TwoPhotonSeriesChanB: [types.core.TwoPhotonSeries]\n TwoPhotonSeriesChanC: [types.core.TwoPhotonSeries]\n```\nThe above code load the .nwb file and the output tells us that there are three channels present in the nwb file, which are ChanA, ChanB, and ChanC. Then, to load the actual data from a channel,\n```matlab\n% load the image data from ChanA\n>> chanAdata = nwb.acquisition.get('TwoPhotonSeriesChanA').data.load();\n\n% check its shape\n>> size(chanAdata)\nans =\n 1 512 512 181\n```\n### Loading the segmentation masks\nFor an overview of the mask for ChanA, for example,\n```matlab\n>> nwb.processing.get('ophys').nwbdatainterface.get('ImageSegmentation').planesegmentation.get('PlaneSegmentationChanA').image_mask.data\nans = \n DataStub with properties:\n filename: '.\\sub-BPN-M4_ses-20210524-m1_obj-1c8nyxo_ophys.nwb'\n path: '/processing/ophys/ImageSegmentation/PlaneSegmentationChanA/image_mask'\n dims: [512 512 181]\n ndims: 3\n dataType: 'logical'\n```\nTo load the actual mask data into array (may take several seconds to load),\n```matlab\n% load mask from ChanA\n>> mask = nwb.processing.get('ophys').nwbdatainterface.get('ImageSegmentation').planesegmentation.get('PlaneSegmentationChanA').image_mask.data.load();\n\n% check its shape\n>> size(mask)\nans =\n 512 512 181\n```", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 519, + "tag": "DANDI:000491" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:31.820483+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000491/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "242": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 242, + "name": "Test 2", + "repository_type": "dandi", + "summary": "try to upload EIS", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 520, + "tag": "DANDI:000529" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + } + ], + "timestamp_created": "2024-01-03 17:30:33.233824+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000529/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "243": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230524.0416", + "id": 243, + "name": "Allen Institute Openscope - Effects of Periodic Visual Stimulation on Neural Activity in Mouse Visual Cortex", + "repository_type": "dandi", + "summary": "This study investigates the effects of periodic sensory stimulation (PSS) on the activity of neurons of different types in the mouse visual cortex. The PSS modality used is the flickering light visual stimulation. The dataset provides calcium imaging data from VIP (vasoactive intestinal peptide), SST (somatostatin), and PV (parvalbumin) expressing inhibitory neuron, as well as excitatory neurons in V1, in mice exposed to different visual stimulation conditions.\n \nThe visual stimulus is provided by an LED strip that is programmed for 4 conditions: 40 Hz flicker (gamma frequency), 8 Hz flicker (theta frequency), random frequency (light delivered with a random interval determined by a Poisson process with an average frequency of 40 Hz), and constant light on. Those conditions are reported in NWB file trial table. The LED strip was positioned in front of the mouse right eye in place of the visual stimulation screen used in (De Vries, Lecoq, Buice et al, Nature Neuroscience 2020). Light illumination started approximately 485 seconds into the experiment. The exact timing can be found in the stimulus table. Each mouse was recorded multiple times with individual cells matched. Each recording session only used one of the above stimulus conditions.\n \nThe data includes images from layers 2/3 and 5 (layer 4 instead of 5 for VIP) of the left visual cortex, capturing the contributions of superficial and deep cortical neurons to the network dynamics under visual PSS. The dataset was collected from 5 mice for SST, 6 mice for VIP, 2 mice for PV, and 2 mice for excitatory neurons. Mice were awake but not trained to perform any tasks. They were passively viewing the LED strip. All animal procedures were approved by the Institutional Animal Care and Use Committee (IACUC) at the Allen Institute for Brain Science in compliance with NIH guidelines.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 521, + "tag": "DANDI:000535" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 522, + "tag": "cortical recording" + }, + { + "id": 523, + "tag": "gamma" + }, + { + "id": 524, + "tag": "neuroprotection" + }, + { + "id": 291, + "tag": "oscillations" + }, + { + "id": 525, + "tag": "two-photon" + }, + { + "id": 150, + "tag": "visual stimuli" + } + ], + "timestamp_created": "2024-01-03 17:30:34.324001+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000535/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "244": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 244, + "name": "Scaling of GEVI Fluorescence with 1P and 2P Illumination", + "repository_type": "dandi", + "summary": "This dataset contains videos demonstrating the fluorescence of Human Embryonic Kidney (HEK) cells transfected with common genetically encoded voltage indicators (GEVIs). ", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 526, + "tag": "DANDI:000537" + }, + { + "id": 391, + "tag": "Homo sapiens - Human" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:35.481892+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000537/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "245": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 245, + "name": "Comparing the 1P and 2P Voltage Contrast of JEDI2P and Voltron2_JF525", + "repository_type": "dandi", + "summary": "This dataset demonstrates the voltage contrast of JEDI2P and Voltron2_JF525 under 1P and 2P illumination.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 527, + "tag": "DANDI:000538" + }, + { + "id": 391, + "tag": "Homo sapiens - Human" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:36.927127+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000538/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "246": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230515.0530", + "id": 246, + "name": "Dataset for: A change in behavioral state switches the pattern of motor output that underlies rhythmic head and orofacial movements", + "repository_type": "dandi", + "summary": "Recorded multi-modal data (videography, respiration, electromyogram, wearable sensor signals, and human annotation) from rats performing naturalistic foraging and rearing behaviors in an open arena. Dataset for S.-M. Liao and D. Kleinfeld, Current Biology (2023). A change in behavioral state switches the pattern of motor output that underlies rhythmic head and orofacial movements. Dataset uploaded by the Kleinfeld Laboratory at University of California San Diego. Code can be found on https://rhythm-n-rodents.github.io/software/.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 528, + "tag": "DANDI:000540" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + }, + { + "id": 529, + "tag": "breathing" + }, + { + "id": 530, + "tag": "coupled oscillators" + }, + { + "id": 531, + "tag": "electromyogram" + }, + { + "id": 532, + "tag": "foraging" + }, + { + "id": 533, + "tag": "muscles" + }, + { + "id": 534, + "tag": "neck" + }, + { + "id": 535, + "tag": "nose" + }, + { + "id": 536, + "tag": "preBotzinger complex" + }, + { + "id": 537, + "tag": "rearing" + }, + { + "id": 538, + "tag": "vibrissae" + }, + { + "id": 539, + "tag": "whiskers" + } + ], + "timestamp_created": "2024-01-03 17:30:39.113474+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000540/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "247": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 247, + "name": "NeuroPAL Microfluidic Chip Images and GCaMP activity", + "repository_type": "dandi", + "summary": "NeuroPAL Microfluidic chip images and GCaMP activity from \"NeuroPAL: A Multicolor Atlas for Whole-Brain Neuronal Identification in C. elegans\" and \"Extracting neural signals from semi-immobilized animals with deformable non-negative matrix factorization.\" ", + "tags": [ + { + "id": 502, + "tag": "Caenorhabditis elegans" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 540, + "tag": "DANDI:000541" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:40.322494+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000541/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "248": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230514.1148", + "id": 248, + "name": "Test Dataset", + "repository_type": "dandi", + "summary": "Test dataset for DataJoint uploads to DANDI", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 541, + "tag": "DANDI:000544" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:41.420031+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000544/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "249": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 249, + "name": "Test set", + "repository_type": "dandi", + "summary": "Another test upload for DataJoint", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 542, + "tag": "DANDI:000545" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:42.746817+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000545/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "250": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 250, + "name": "vStr_phase_stim", + "repository_type": "dandi", + "summary": "Extracellular ephys recording in the ventral Striatum", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 543, + "tag": "DANDI:000546" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:43.877776+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000546/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "251": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230822.1616", + "id": 251, + "name": "Perivascular Pumping of Cerebrospinal Fluid in the Brain with a Valve Mechanism", + "repository_type": "dandi", + "summary": "This dataset contains the 2-photon recordings of the penetrating arteries of mice, included in the project \"Perivascular Pumping of Cerebrospinal Fluid in the Brain with a Valve Mechanism\".", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 544, + "tag": "DANDI:000547" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:45.012494+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000547/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "252": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230519.1825", + "id": 252, + "name": "Effect of the number of pulses on electroporation by unipolar and 50 % bipolar pulses", + "repository_type": "dandi", + "summary": "Trains of 5, 10, or 15 pulses all at 1 Hz. Cells were electroporated by a train of 5, 10, or 15 unipolar 600 ns pulses (0 percent) or bipolar 600 + 600 ns pulses (50 percent) at 1 Hz. 50% represents the second pulse's amplitude in relation to the first pulse's amplitude. 0% represents a unipolar pulse, where no second pulse was applied. The electrodes were two parallel stainless-steel cylinders, 1.5 mm in diameter and 3 mm apart. Supported by NIH 1R21EY034258", + "tags": [ + { + "id": 517, + "tag": "Bos taurus - Cattle" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 545, + "tag": "DANDI:000548" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:46.089892+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000548/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "253": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230519.2043", + "id": 253, + "name": "Effect of the pulse duration on electroporation by unipolar and 50% bipolar pulses", + "repository_type": "dandi", + "summary": "Trains of 5 different duration pulses, all exposures were at 1 Hz. Cells were electroporated by a train of 5 unipolar 300ns, 1200ns, 50us, 100us, 500us or bipolar 300+300ns, 1200+1200ns, 50+50us, 100+100us, 500+500us pulses (second pulse's amplitude was 50% in relation to the first pulse's amplitude). The electrodes were two parallel stainless-steel cylinders, 1.5 mm in diameter and 3 mm apart. Supported by NIH 1R21EY034258", + "tags": [ + { + "id": 517, + "tag": "Bos taurus - Cattle" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 546, + "tag": "DANDI:000549" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:47.304615+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000549/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "254": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230520.1609", + "id": 254, + "name": "Pulse repetition rate", + "repository_type": "dandi", + "summary": "Effect of pulse repetition rate on electroporation by 5 600ns unipolar or 600+600ns bipolar pulses (second phase's amplitude was 50% in relation to the first pulse's amplitude), at 833kHz, 500kHz, 100kHz, 10kHz. The electrodes were two parallel stainless-steel cylinders, 1.5 mm in diameter and 3 mm apart. Supported by NIH 1R21EY034258\n", + "tags": [ + { + "id": 517, + "tag": "Bos taurus - Cattle" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 547, + "tag": "DANDI:000550" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:48.764168+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000550/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "255": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230630.2304", + "id": 255, + "name": "Preconfigured dynamics in the hippocampus are guided by embryonic birthdate and rate of neurogenesis", + "repository_type": "dandi", + "summary": "The incorporation of novel information into the hippocampal network is likely be constrained by its innate architecture and internally generated activity patterns. However, the origin, organization, and consequences of such patterns remain poorly understood. Here, we show that hippocampal network dynamics are affected by sequential neurogenesis. We birthdated CA1 pyramidal neurons with in-utero electroporation over 4 embryonic days encompassing the peak of hippocampal neurogenesis, and compared their functional features in freely moving, adult mice. Neurons of the same birthdate displayed distinct connectivity, coactivity across brain states, and assembly dynamics. Same birthdate hippocampal neurons were topographically organized, in that anatomically clustered (<500\u00b5m) neurons exhibited overlapping spatial representations. Overall, the wiring and functional features of CA1 pyramidal neurons reflected a combination of birthdate and the rate of neurogenesis. These observations demonstrate that sequential neurogenesis in embryonic development shapes the preconfigured forms of adult network dynamics.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 548, + "tag": "DANDI:000552" + }, + { + "id": 549, + "tag": "Development of the nervous system" + }, + { + "id": 550, + "tag": "Hippocampus" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 551, + "tag": "Neural circuits" + } + ], + "timestamp_created": "2024-01-03 17:30:50.679877+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000552/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "256": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230531.1237", + "id": 256, + "name": "Human brain mapping with multithousand-channel PtNRGrids resolves spatiotemporal dynamics", + "repository_type": "dandi", + "summary": "This dataset contains recordings from PtNRGrids, which are devices designed to record electrocorticography (ECoG) activities from the brain surface at high-spatial resolution. PtNRGrids used here were a square-shaped array with 1024 channels and a 150-\u00b5m pitch, which was implanted to record neural activity from the entire right primary somatosensory barrel cortex. The sensory response on the brain was evoked by delivering air puffs through a microcapillary tube to individually stimulate individual whiskers on the contralateral side. This dataset demonstrates the high-spatial resolution recording capability of PtNRGrids isolate functional cortical columns in sub-mm resolution from the surface of the brain.\n\n[Publication corresponding to this dataset] Tchoe, Youngbin, et al. \"Human brain mapping with multithousand-channel PtNRGrids resolves spatiotemporal dynamics.\" Science translational medicine 14.628 (2022): eabj1441.\n[Electrode mapping information & Basic analysis codes] Github: https://ytchoe.github.io/", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 552, + "tag": "DANDI:000554" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + } + ], + "timestamp_created": "2024-01-03 17:30:52.621311+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000554/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "257": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 257, + "name": "Spontaneous behaviour is structured by reinforcement without explicit reward", + "repository_type": "dandi", + "summary": "Spontaneous animal behaviour is built from action modules that are concatenated by the brain into sequences. However, the neural mechanisms that guide the composition of naturalistic, self-motivated behaviour remain unknown. Here we show that dopamine systematically fluctuates in the dorsolateral striatum (DLS) as mice spontaneously express sub-second behavioural modules, despite the absence of task structure, sensory cues or exogenous reward. Photometric recordings and calibrated closed-loop optogenetic manipulations during open field behaviour demonstrate that DLS dopamine fluctuations increase sequence variation over seconds, reinforce the use of associated behavioural modules over minutes, and modulate the vigour with which modules are expressed, without directly influencing movement initiation or moment-to-moment kinematics. Although the reinforcing effects of optogenetic DLS dopamine manipulations vary across behavioural modules and individual mice, these differences are well predicted by observed variation in the relationships between endogenous dopamine and module use. Consistent with the possibility that DLS dopamine fluctuations act as a teaching signal, mice build sequences during exploration as if to maximize dopamine. Together, these findings suggest a model in which the same circuits and computations that govern action choices in structured tasks have a key role in sculpting the content of unconstrained, high-dimensional, spontaneous behavior.", + "tags": [ + { + "id": 553, + "tag": "Basal Ganglia" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 554, + "tag": "DANDI:000559" + }, + { + "id": 391, + "tag": "Homo sapiens - Human" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 551, + "tag": "Neural circuits" + }, + { + "id": 555, + "tag": "Reward" + } + ], + "timestamp_created": "2024-01-03 17:30:54.006007+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000559/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "258": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 258, + "name": "C. elegans whole-brain neuroPAL and immobilized calcium imaging", + "repository_type": "dandi", + "summary": "All of the datasets here contain neuroPAL whole-brain structural images. A subset of these files also contain unstimulated calcium imaging data from the worm immobilized in a micro fluidic chip. Each dataset has ~30-50% of neuron centers labeled with cell IDs.", + "tags": [ + { + "id": 502, + "tag": "Caenorhabditis elegans" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 556, + "tag": "DANDI:000565" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:55.373655+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000565/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "259": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 259, + "name": "ASAP4 data", + "repository_type": "dandi", + "summary": "Raw data for \"A positively tuned voltage indicator for extended electrical recordings in the brain\", Nature Methods 2023, https://doi.org/10.1038/s41592-023-01913-z", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 557, + "tag": "DANDI:000566" + }, + { + "id": 403, + "tag": "Drosophila melanogaster - Fruit fly" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:56.501501+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000566/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "260": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230705.1633", + "id": 260, + "name": "Probing subthreshold dynamics of hippocampal neurons by pulsed optogenetics", + "repository_type": "dandi", + "summary": "Understanding how excitatory (E) and inhibitory (I) inputs are integrated by neurons requires monitoring their subthreshold behavior. We probed the subthreshold dynamics using optogenetic depolarizing pulses in hippocampal neuronal assemblies in freely moving mice. Excitability decreased during sharp- wave ripples coupled with increased I. In contrast to this \u201cnegative gain,\u201d optogenetic probing showed increased within-field excitability in place cells by weakening I and unmasked stable place fields in initially non\u2013place cells. Neuronal assemblies active during sharp-wave ripples in the home cage predicted spatial overlap and sequences of place fields of both place cells and unmasked preexisting place fields of non\u2013place cells during track running. Thus, indirect probing of subthreshold dynamics in neuronal populations permits the disclosing of preexisting assemblies and modes of neuronal operations.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 558, + "tag": "DANDI:000568" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:30:57.714639+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000568/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "261": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230706.1630", + "id": 261, + "name": "20230630_AIBS_Patchseq_nonhuman_primate", + "repository_type": "dandi", + "summary": "HMBA Lein PatchSeq upload (Q2 2023)", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 559, + "tag": "DANDI:000569" + }, + { + "id": 560, + "tag": "Macaca nemestrina - Pigtail macaque" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 129, + "tag": "Patch-seq" + }, + { + "id": 561, + "tag": "multimodal" + }, + { + "id": 562, + "tag": "non-human primate" + } + ], + "timestamp_created": "2024-01-03 17:30:59.285004+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000569/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "262": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230706.1630", + "id": 262, + "name": "20230630_AIBS_Patchseq_human", + "repository_type": "dandi", + "summary": "U01 Lein Patch-seq data upload (Q2 2023)", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 563, + "tag": "DANDI:000570" + }, + { + "id": 391, + "tag": "Homo sapiens - Human" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 129, + "tag": "Patch-seq" + }, + { + "id": 98, + "tag": "human" + }, + { + "id": 561, + "tag": "multimodal" + } + ], + "timestamp_created": "2024-01-03 17:31:00.839289+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000570/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "263": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 263, + "name": "Intracranial recordings using BCI2000 and the CorTec BrainInterchange", + "repository_type": "dandi", + "summary": "An Ecosystem of Technology and Protocols for Adaptive Neuromodulation Research in Humans\n\nThis study aims to develop an ecosystem for the purpose of neurmodulation using the Cortec BCI device and BCI2000 software.\n\nContact: For questions regarding this dataset, please contact mivalt.filip@mayo.edu or Miller.Kai@mayo.edu", + "tags": [ + { + "id": 72, + "tag": "BIDS" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 564, + "tag": "DANDI:000571" + } + ], + "timestamp_created": "2024-01-03 17:31:02.040691+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000571/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "264": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230826.0140", + "id": 264, + "name": "Activity map of a cortico-cerebellar loop underlying motor planning", + "repository_type": "dandi", + "summary": "Data from \"Activity map of a cortico-cerebellar loop underlying motor planning\" Jia Zhu, Hana Hasanbegovic, Liu D. Liu, Zhenyu Gao, Nuo Li. Nat Neurosci 2023\n\nAn activity map of the cerebellar cortex during a delayed response task in which mice used short-term memory to plan directional licking.\n\nSupported by Robert and Janice McNair Foundation, Whitehall Foundation, Alfred P. Sloan Foundation, Searle Scholars Program, Pew Scholars Program, NIH NS104781, NS112312, NS113110, McKnight Foundation, Simons Collaboration on the Global Brain.\n", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 565, + "tag": "DANDI:000572" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:31:03.503700+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000572/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "265": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.231010.1809", + "id": 265, + "name": "Dataset of human medial temporal lobe neurons, scalp and intracranial EEG during a verbal working memory task", + "repository_type": "dandi", + "summary": "We present an electrophysiological dataset recorded from nine subjects during a verbal working memory task. Subjects were epilepsy patients undergoing intracranial monitoring for localization of epileptic seizures. Subjects performed a modified Sternberg task in which the encoding of memory items, maintenance, and recall were temporally separated. The dataset includes simultaneously recorded scalp EEG with the 10-20 system, intracranial EEG (iEEG) recorded with depth electrodes, waveforms and spike times of 1526 units recorded in the medial temporal lobe, and the MNI coordinates and anatomical labels of all intracranial electrodes. Subject characteristics and information on sessions (set size, match/mismatch, correct/incorrect, response, response time for each trial) are also provided. This dataset enables the investigation of working memory by providing simultaneous scalp EEG and iEEG recordings, which can be used for connectivity analysis, alongside hard to obtain unit recordings from humans.", + "tags": [ + { + "id": 566, + "tag": "Amygdala" + }, + { + "id": 567, + "tag": "Awake" + }, + { + "id": 568, + "tag": "Cognitive task" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 569, + "tag": "DANDI:000574" + }, + { + "id": 570, + "tag": "Electrophysiology" + }, + { + "id": 571, + "tag": "Entorhinal cortex" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 550, + "tag": "Hippocampus" + }, + { + "id": 391, + "tag": "Homo sapiens - Human" + }, + { + "id": 94, + "tag": "Human" + }, + { + "id": 573, + "tag": "Intracranial EEG" + }, + { + "id": 574, + "tag": "Local field potential" + }, + { + "id": 575, + "tag": "Medial temporal lobe" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 576, + "tag": "Neuronal action potential" + }, + { + "id": 577, + "tag": "Scalp EEG" + }, + { + "id": 578, + "tag": "Spikes" + }, + { + "id": 579, + "tag": "Verbal working memory" + } + ], + "timestamp_created": "2024-01-03 17:31:04.753022+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000574/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "266": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.231010.1811", + "id": 266, + "name": "Dataset of human medial temporal lobe neurons during a visual working memory task", + "repository_type": "dandi", + "summary": "We present an electrophysiological dataset recorded from thirteen subjects during a visual working memory task. Subjects were epilepsy patients undergoing intracranial monitoring for localization of epileptic seizures. Here, we recorded single neuron firing in 13 epilepsy patients (7 male) while they performed a visual working memory task. The task is a change detection task designed to examine the visual working memory of subjects. In each trial, arrays of colored squares were presented and had to be memorized. The number of squares determined the set size: 1, 2, 4 or 6. There was a total 192 trials per session. Each trial started with a warning signal (0.4 s) that was a red fixation dot. The fixation dot was then changed to black (0.4 \u2013 0.5 s, jittered). A memory array (encoding period, 0.8 s) was followed by a delay (retention interval, 0.9 s). After the delay, a test array was shown (2 s) followed by a jittered inter-trial interval of 1.3 to 2.3 s. The participants indicated by button press (\u201cSame\u201d or \u201cDifferent\u201d, forced choice) whether the test array differed from the memory array. If the arrays differed, only one square changed in colour, but all squares remained on the same location. The fixation dot was visible on the screen during the whole trial period. Eight different colours were used for the memory and test array (yellow, red, green, blue, magenta, cyan, grey, black). Before starting the sessions, participants conducted trial runs in a practice session to learn the task. In this session we verified if participants were colour-blind and could discriminate all colours. Practice sessions were repeated until the participant understood the task and was able to follow the pace of the trials.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 580, + "tag": "DANDI:000575" + }, + { + "id": 391, + "tag": "Homo sapiens - Human" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:31:06.221455+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000575/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "267": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.231010.1811", + "id": 267, + "name": "Dataset of neurons and intracranial EEG from human amygdala during aversive dynamic visual stimulation", + "repository_type": "dandi", + "summary": "We present an electrophysiological dataset collected from the amygdalae of nine subjects attending a visual dynamic stimulation of emotional aversive content. The subjects were patients affected by epilepsy who underwent preoperative invasive monitoring in the mesial temporal lobe. Subjects were presented with dynamic visual sequences of fearful faces (aversive condition), interleaved with sequences of neutral landscapes (neutral condition).\n\nWe provide simultaneous recordings of intracranial EEG (iEEG) and neuronal spike times and waveforms, and metadata related to the task, subjects, sessions and electrodes in the NIX standard. We technically validated this dataset and provide here the spike sorting quality metrics and the spectra of iEEG signals.\nThis dataset allows the investigation of amygdalar response to dynamic aversive stimuli at multiple spatial scales, from the macroscopic EEG to the neuronal firing in the human brain.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 581, + "tag": "DANDI:000576" + }, + { + "id": 391, + "tag": "Homo sapiens - Human" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:31:07.641161+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000576/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "268": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230728.1727", + "id": 268, + "name": "Two-photon calcium imaging of mouse posterior cortical areas during dynamic navigation decisions (Tseng et al., 2022, Neuron)", + "repository_type": "dandi", + "summary": "This is the dataset for Tseng et al, \"Shared and specialized coding across posterior cortical areas for dynamic navigation decisions\" in Neuron (2022). The dataset contains calcium activity of >200,000 neurons recorded from 6 different cortical areas in mouse posterior cortex L2/3 and L5 using two-photon imaging, including V1 and secondary visual areas, retrosplenial cortex (RSC) and posterior parietal cortex (PPC), while the mice were performing a flexible decision-making task based on rule-switching during virtual navigation. There are total 300 behavior + imaging sessions collected from 8 mice. The neurons in each experiment have been registered into the Allen Institute Mouse Common Coordinate Framework (CCF) based on widefield retinotopy. In addition, these neurons contain fluorescent labels of retroAAV injected in one of the two sets of projection targets: an anterior part of anterior cingulate cortex/secondary motor cortex (ACC/M2) and striatum, or a posterior part of ACC/M2 and orbitofrontal cortex (OFC). \n\nPlease see the related paper for more details: https://doi.org/10.1016/j.neuron.2022.05.012\n\nAdditional resources:\n\n- Jupyter notebook for a tutorial to read and extract information from these NWB files: https://github.com/sytseng/Notebook_for_Dandiset_000579\n\n- NWB extension code for custom lab meta data (required for reading NWB files): https://github.com/sytseng/ndx-harvey-swac \n\n- Code and tutorials for fitting GLM to neural activity in Tensorflow 2: https://github.com/sytseng/GLM_Tensorflow_2", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 582, + "tag": "DANDI:000579" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 96, + "tag": "cortex" + }, + { + "id": 178, + "tag": "decision-making" + }, + { + "id": 583, + "tag": "flexible decisions" + }, + { + "id": 97, + "tag": "mouse" + }, + { + "id": 584, + "tag": "navigation" + }, + { + "id": 585, + "tag": "posterior cortex" + }, + { + "id": 436, + "tag": "posterior parietal cortex" + }, + { + "id": 586, + "tag": "retrograde labeling" + }, + { + "id": 587, + "tag": "retrosplenial cortex" + }, + { + "id": 588, + "tag": "rule-switching" + }, + { + "id": 589, + "tag": "two-photon imaging" + }, + { + "id": 590, + "tag": "virtual reality" + }, + { + "id": 134, + "tag": "visual cortex" + } + ], + "timestamp_created": "2024-01-03 17:31:08.873430+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000579/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "269": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 269, + "name": "Conjunctive Representation of Position, Direction, and Velocity in Entorhinal Cortex", + "repository_type": "dandi", + "summary": "The dataset includes spike times for recorded grid cells from the medial entorhinal cortex (MEC) in rats that explored two-dimensional environments. The behavioral data includes position from the tracking LEDs. \n\nThis sample was published in Sargolini et al. (Science, 2006).\n\nGrid cells in the medial entorhinal cortex (MEC) are part of an environment-independent spatial coordinate system. To determine how information about location, direction, and distance is integrated in the grid-cell network, we recorded from each principal cell layer of MEC in rats that explored two-dimensional environments. Whereas layer II was predominated by grid cells, grid cells colocalized with head-direction cells and conjunctive grid x head-direction cells in the deeper layers. All cell types were modulated by running speed. The conjunction of positional, directional, and translational information in a single MEC cell type may enable grid coordinates to be updated during self-motion-based navigation.\n", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 591, + "tag": "DANDI:000582" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + } + ], + "timestamp_created": "2024-01-03 17:31:10.071465+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000582/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "270": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 270, + "name": "A Novel Neuropathic Pain Treatment: Achieving Neuronal Inhibition with a Splti Ring Resonator", + "repository_type": "dandi", + "summary": "Thesis defense data set consisting of extracellular action potentials and postsynaptic potentials captured in Procambarus clarkii.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 592, + "tag": "DANDI:000615" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 593, + "tag": "Procambarus clarkii - Red swamp crayfish" + } + ], + "timestamp_created": "2024-01-03 17:31:11.491953+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000615/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "271": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 271, + "name": "SpikeForest ground truth datasets", + "repository_type": "dandi", + "summary": "The SpikeForest project contains a collection of electrophysiological recordings together with ground truth spiking information for the purpose of benchmarking the performance of spike sorting algorithms. In this dandiset we provide a subset of these recordings together with ground truth.\n\nThis dataset was prepared using the following script: https://github.com/flatironinstitute/spikeforest/blob/main/devel/dandiset/prepare_dandiset.py\n\nFor more information, see https://elifesciences.org/articles/55167", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 594, + "tag": "DANDI:000618" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:31:12.793458+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000618/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "272": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 272, + "name": "Multimodal brain responses during movie watching: single-neuron, intracranial EEG, and fMRI in human patients", + "repository_type": "dandi", + "summary": "We present a multimodal dataset of intracranial recordings, fMRI, and eye tracking in 20 human participants. Intracranial recordings consist of single neurons, local field potential, and intracranial EEG activity recorded concurrently from depth electrodes targeting the amygdala, hippocampus, and medial frontal cortex while participants underwent intracranial monitoring for localization of epileptic seizures. Participants watched an 8-min long movie (\"Bang You're Dead\") and performed a recognition memory test for movie content. 3T fMRI activity was recorded prior to surgery in 11 of the same participants while performing the same task. This NWB- and BIDS-formatted dataset includes the spike times of all neurons, field potential activity, behavior, eye tracking, electrode locations, demographics, and functional and structural MRI scans. For technical validation, we provide signal quality metrics, assess eye tracking quality, behavior, the tuning of cells and high-frequency broadband power field potentials to familiarity and event boundaries, and show brain-wide inter-subject correlations. This dataset will facilitate the investigation of brain activity during movie watching, recognition memory, and the neural basis of the fMRI-BOLD signal.\n\nGit Link: https://github.com/rutishauserlab/bmovie-release-NWB-BIDS", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 595, + "tag": "DANDI:000623" + }, + { + "id": 391, + "tag": "Homo sapiens - Human" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:31:14.046671+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000623/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "273": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 273, + "name": "A brainstem circuit for the expression of defensive facial reactions in rat", + "repository_type": "dandi", + "summary": "Chemoreceptors in the nasal epithelium can trigger an apneic reaction and a grimace in response to airborne irritants. Callado Perez et al. find that the underlying circuit does not involve olfaction. Rather, activation of neurons in the muralis subnucleus of the spinal trigeminal complex will inhibit the Pre-B\u00f6tzinger inhalation oscillator.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 596, + "tag": "DANDI:000624" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + } + ], + "timestamp_created": "2024-01-03 17:31:15.116024+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000624/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "274": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.231114.0019", + "id": 274, + "name": "Molecularly Identified CA1 Interneuron Dynamics", + "repository_type": "dandi", + "summary": "This dataset was collected to examine the dynamics of mouse interneurons whose subtypes were molecularly identified. 231 ROIs were observed over 3 separate imaging sessions. For each session you will find the following:\n- Raw images (individual cropped ROIs)\n- DfOverF traces\n- Fluorescence traces\n- Subtype of each ROI (some are undetermined)\n- Timing of water rewards\n- Time-stamped position of subject\n\n\nData was collected by Tristan Geiller at the Losonczy Lab using two photon calcium imaging and head-fixed mice running on a voluntary treadmill.", + "tags": [ + { + "id": 346, + "tag": "2-photon calcium imaging" + }, + { + "id": 597, + "tag": "Columbia University" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 598, + "tag": "DANDI:000625" + }, + { + "id": 599, + "tag": "Losonczy Lab" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 600, + "tag": "ca1" + }, + { + "id": 259, + "tag": "head-fixed" + }, + { + "id": 186, + "tag": "hippocampus" + }, + { + "id": 135, + "tag": "interneuron" + }, + { + "id": 97, + "tag": "mouse" + }, + { + "id": 601, + "tag": "treadmill" + } + ], + "timestamp_created": "2024-01-03 17:31:16.340079+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000625/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "275": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230922.1642", + "id": 275, + "name": "Extracellular recording along macaque ventral stream during natural image free viewing", + "repository_type": "dandi", + "summary": "Recordings spanning six areas in the macaque ventral stream (V1, V2 ,V4, PIT, CIT, AIT; primarily in CIT and AIT) including 679 experimental sessions, 883 hours, 13 monkeys, 4.6 million fixations, and thousands of natural images. For more information, see the preprint at https://doi.org/10.1101/2023.02.08.527666", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 602, + "tag": "DANDI:000628" + }, + { + "id": 603, + "tag": "Extracellular electrophysiology" + }, + { + "id": 604, + "tag": "Free viewing" + }, + { + "id": 334, + "tag": "Macaca mulatta - Rhesus monkey" + }, + { + "id": 173, + "tag": "Macaque" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 605, + "tag": "Natural images" + }, + { + "id": 606, + "tag": "Ventral stream" + } + ], + "timestamp_created": "2024-01-03 17:31:17.507096+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000628/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "276": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 276, + "name": "Gillespie et al (2024) Neurofeedback training can modulate task-relevant memory replay rate in rats", + "repository_type": "dandi", + "summary": "This dataset includes the electrophysiological (dorsal CA1 tetrodes) and behavioral (port triggers, reward delivery, and position tracking) data from the 4 subjects in the neurofeedback cohort described in Gillespie et al, eLife 2024: \"Neurofeedback training can modulate task-relevant memory replay rate in rats\". The data for the 4 control cohort subjects can be found in Dandiset 000115 (https://dandiarchive.org/dandiset/000115). For more information about this data, please contact Anna Gillespie.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 607, + "tag": "DANDI:000629" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + } + ], + "timestamp_created": "2024-01-03 17:31:18.776940+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000629/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "277": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230915.2257", + "id": 277, + "name": "Human L1 patch-seq electrophysiology", + "repository_type": "dandi", + "summary": "Patch-seq electrophysiology data from human neocortical L1 neurons, accompanying the manuscript \"Morpho-electric and transcriptomic divergence of the layer 1 interneuron repertoire in human versus mouse neocortex\" (https://www.biorxiv.org/content/10.1101/2022.10.24.511199).\n\nAnalysis code and extracted features available at https://github.com/AllenInstitute/patchseq_human_L1. Feature extraction package available at https://github.com/AllenInstitute/ipfx.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 608, + "tag": "DANDI:000630" + }, + { + "id": 94, + "tag": "Human" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 98, + "tag": "human" + }, + { + "id": 561, + "tag": "multimodal" + }, + { + "id": 136, + "tag": "neocortex" + }, + { + "id": 609, + "tag": "patch-seq" + } + ], + "timestamp_created": "2024-01-03 17:31:20.712693+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000630/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "278": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.231012.1508", + "id": 278, + "name": "Effect of the electric field vector change on the electroporation efficiency of paired-pulse trains compared to single-pulse trains", + "repository_type": "dandi", + "summary": "In the study, the impact of varying the electric field vector on the effectiveness of electroporation was examined, utilizing both paired-pulse trains, consisting of 5 pulses at 600 + 600 ns, and single-pulse trains with 5 pulses at 600 ns. These were applied to a BPAE cell monolayer at frequencies of either 1 Hz or 833 kHz. The focus was on assessing the alterations in electroporation efficiency, gauged through the changes in YoPro-1 emission when compared to the baseline established by a single-pulse train in the identical region of interest, which was set at 100%. The evaluation criteria included the angle at which the electric field vector direction alternated between two pulses within a pair. Introduction of pulses in pairs amplified the electroporation efficiency at smaller angles but resulted in an inhibition at larger angles, indicating a frequency-dependent relationship. The images captured during the experiments provided a comprehensive visualization of this effect, highlighting the nuanced impact of electric field vector modifications on electroporation efficiency. Supported by NIH 1R21EY034258", + "tags": [ + { + "id": 517, + "tag": "Bos taurus - Cattle" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 610, + "tag": "DANDI:000631" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:31:21.871418+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000631/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "279": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.231012.1930", + "id": 279, + "name": "Electroporation efficiency of co-directional and cross-directional paired pulses", + "repository_type": "dandi", + "summary": "The efficiency of electroporation was evaluated in an experiment involving co-directional and cross-directional paired pulses using a triangular electrode array. BPAE cell monolayers were exposed to trains of four paired pulses, each consisting of 600 + 600 ns, at two distinct frequencies: 1 Hz and 770 kHz, with an amplitude of 10 kV/cm. In one scenario, the electric field direction remained consistent throughout each pair (co-directional). In another, the field direction was altered by 90\u00b0 (cross-directional). The electroporation efficiency was quantified through the cells\u2019 uptake of YoPro-1 dye, serving as a measure of the permeabilization of the cell membranes under varying electric field orientations and frequencies. Supported by NIH 1R21EY034258\n", + "tags": [ + { + "id": 517, + "tag": "Bos taurus - Cattle" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 611, + "tag": "DANDI:000632" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:31:23.131576+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000632/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "280": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.231013.2226", + "id": 280, + "name": "The difference in electroporation patterns produced by a train of single pulses and a train of paired pulses", + "repository_type": "dandi", + "summary": "The difference in electroporation patterns produced by a train of single pulses and a train of paired pulses. Supported by NIH 1R21EY034258", + "tags": [ + { + "id": 517, + "tag": "Bos taurus - Cattle" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 612, + "tag": "DANDI:000633" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:31:24.290689+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000633/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "281": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.231005.1820", + "id": 281, + "name": "Cell Membrane Charging by Co- and Counter-Directional ns electrical pulses (nsEP)", + "repository_type": "dandi", + "summary": "Strobe photography, used at nanosecond resolution, captures the dynamic process of cell membrane charging and relaxation kinetics in cells treated with FluoVolt dye and exposed to electric pulses. This study uses a triangular electrode array where 300 ns pulses are applied alternately at a frequency of 1.67 MHz. The precise coordination of electric pulses and laser flashes, adjusted in 50 ns steps, allows for the detailed imaging of the cell's response. The electric field induces enhanced fluorescence at the cathode-facing side of the FluoVolt-loaded CHO cells and suppressed fluorescence at the anode-facing side. This research method provides a comprehensive and reliable examination of the cell membrane's behavior under electrical stimulation without causing damage, ensuring consistent observations to understand cellular responses to varying electrical stimuli. Supported by NIH 1R21EY034258", + "tags": [ + { + "id": 482, + "tag": "Cricetulus griseus - Cricetulus aureus" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 613, + "tag": "DANDI:000634" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:31:25.458233+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000634/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "282": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.230921.1734", + "id": 282, + "name": "20230930_AIBS_Patchseq_nonhuman_primate", + "repository_type": "dandi", + "summary": "HMBA Lein PatchSeq upload (Q3 2023)", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 614, + "tag": "DANDI:000635" + }, + { + "id": 615, + "tag": "Macaca nemestrina" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 129, + "tag": "Patch-seq" + }, + { + "id": 561, + "tag": "multimodal" + }, + { + "id": 562, + "tag": "non-human primate" + } + ], + "timestamp_created": "2024-01-03 17:31:26.582254+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000635/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "283": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 283, + "name": "Human interneuron patch-seq electrophysiology", + "repository_type": "dandi", + "summary": "Patch-seq electrophysiology data from human neocortical interneurons from acute and slice culture preparations, accompanying the manuscript \"Signature morpho-electric properties of diverse GABAergic interneurons in the human neocortex\" (https://www.biorxiv.org/content/10.1101/2022.11.08.515739).", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 616, + "tag": "DANDI:000636" + }, + { + "id": 94, + "tag": "Human" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 98, + "tag": "human" + }, + { + "id": 561, + "tag": "multimodal" + }, + { + "id": 136, + "tag": "neocortex" + }, + { + "id": 609, + "tag": "patch-seq" + } + ], + "timestamp_created": "2024-01-03 17:31:27.741017+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000636/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "284": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 284, + "name": "Neural Spike Time Response Data in Anesthetized Rats in the Primary Somatosensory Cortex with Phased Ultrasound Array Stimulation", + "repository_type": "dandi", + "summary": "In this study, we investigate the neuronal response to transcranial focused ultrasound (tFUS) stimulation on somatosensory cortex by using a 128-element array transducer. Intracranial recordings were performed using a 32-channel Neuronexus multi-electrode array from somatosensory cortex. Wistar rats were anesthetized using isoflurane at 2%. tFUS with different parameters was applied every 2.5s (\u00b110%). Data was recorded using a TDT system and MUA recordings were sorted using the Plexon Offline Sorter to determine the spike times.\nThis dataset contains spike times of the sorted neural signals and event time for each tFUS trial. In these recordings, we test different pulse repetition frequencies (PRFs) (30Hz, 300Hz, 1500Hz, 3000Hz, and 4500Hz), ultrasound durations (UDs) (100\u00b5s, 200\u00b5s, and 400\u00b5s) of ultrasound stimulation to explore the neuronal response to tFUS. The pulse duration was kept at 67ms. Each recording has 500 trials.\n", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 617, + "tag": "DANDI:000637" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + } + ], + "timestamp_created": "2024-01-03 17:31:28.864008+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000637/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "285": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.231108.1843", + "id": 285, + "name": "32-CH Local Field Potential Data During Probabilistic Reversal Learning Task", + "repository_type": "dandi", + "summary": "Male Long Evans Rats 7-12 months of age. 24 total animals. 256 sessions. 13 animals received a frontal traumatic brain injury (controlled cortical impact). 11 animals received Sham (no craniotomy no TBI) conditions. One week later, rats underwent a second surgery to implant 32 electrodes (Francoeur et al., Front. Psychiatry, 2021). Rats were trained to perform a self-paced probabilistic reversal learning task in a custom operant box with five noseports, stepper-motors to deliver water reward, houselight, and auditory tones. Local field potential (LFP) streams recorded simultaneously from 32 Brain Regions - recorded using a 32-Channel RHD headstage coupled to a RHD USB interface board (Intan Technologies). Open Ephys software at 1KHz band-pass filter set at 0.3-999Hz during acquisition. Lab-streaming-layer (LSL) was used to integrate physiology data with behavior. Physiology can be time-locked to trial onset (rat responds to start trial), response (rat choice between noseports), reward (time of reward delivery)", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 618, + "tag": "DANDI:000640" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 315, + "tag": "Rattus norvegicus - Norway rat" + }, + { + "id": 17, + "tag": "electrophysiology" + }, + { + "id": 619, + "tag": "reversal learning" + }, + { + "id": 620, + "tag": "rodent behavior" + }, + { + "id": 621, + "tag": "traumatic brain injury" + } + ], + "timestamp_created": "2024-01-03 17:31:29.938214+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000640/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "286": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 286, + "name": "Volumetric multiplex imaging of whole human and non-human primate brains", + "repository_type": "dandi", + "summary": "4mm SHIELD-processed human, macaque, and marmoset brain coronal sections, multi-round stained with DNA-labeled antibodies and Fluorescence In Situ Hybridization probes, and imaged with oblique fluorescence light sheet microscopes.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 622, + "tag": "DANDI:000674" + } + ], + "timestamp_created": "2024-01-03 17:31:31.163829+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000674/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "287": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.231004.2146", + "id": 287, + "name": "Pupil and movement measurements during mouse auditory and visual discrimination", + "repository_type": "dandi", + "summary": "Pupil diameter, face motion energy, and locomotion speed measures recorded during sensory discrimination behavior of head-fixed mice. Subjects classify the frequency of an auditory tone cloud stimulus, or the angle of a drifting Gabor patch, at right or left lick ports. Data include 391 behavioral sessions from 13 subjects. For more information see the preprint at https://www.biorxiv.org/content/10.1101/2023.03.02.530651v2.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 623, + "tag": "DANDI:000678" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:31:32.624150+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000678/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "288": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 288, + "name": "similarity-weighted interleaved learning", + "repository_type": "dandi", + "summary": "High-density silicon probe recordings from dorsal and intermediate CA1, primary visual cortex, and antero-lateral visual cortex in mice while they are exploring VR environments with different levels of similarity. The experiment was aimed at studying the content of replay across superficial and deep layers of visual cortex. ", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 624, + "tag": "DANDI:000687" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 625, + "tag": "cortex layers" + }, + { + "id": 186, + "tag": "hippocampus" + }, + { + "id": 153, + "tag": "learning" + }, + { + "id": 626, + "tag": "memory" + }, + { + "id": 627, + "tag": "memory consolidation" + }, + { + "id": 628, + "tag": "memory replay" + } + ], + "timestamp_created": "2024-01-03 17:31:33.721465+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000687/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "289": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 289, + "name": "An optical design enabling lightweight and large field-of-view head-mounted microscopes", + "repository_type": "dandi", + "summary": "Here we present a fluorescence microscope light path that enables imaging, during free behavior, of thousands of neurons in mice and hundreds of neurons in juvenile songbirds. The light path eliminates traditional illumination optics, allowing for head-mounted microscopes that have both a lower weight and a larger field of view (FOV) than previously possible. Using this light path, we designed two microscopes: one optimized for FOV (~4 mm FOV; 1.4 g), and the other optimized for weight (1.0 mm FOV; 1.0 g). \nThis dataset includes the calcium imaging data from our microscope optimized for weight. For this experiment, we stereotactically located a region of the mouse brain at coordinates associatied with primary visual and somatosensory regions, performed approximately 20 viral injections of GCaMP7f across a 4-mm-diameter region of cortex, and then implanted a 4-mm-diameter glass window in the skull. After letting each mouse recover, we installed our head-mounted microscope and allowed it to freely explore a circular maze while recording calcium activity for several minutes.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 629, + "tag": "DANDI:000691" + } + ], + "timestamp_created": "2024-01-03 17:31:34.982874+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000691/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "290": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 290, + "name": "Whole-brain spontaneous GCaMP activity with NeuroPAL cell ID information of semi-restricted worms", + "repository_type": "dandi", + "summary": "Spontaneous neuronal activities were extracted from GCaMP and tagRFP images, covering nearly all neurons in the nematode C. elegans brain. Cell segmentation and tracking were performed using 3DeeCellTracker (https://github.com/WenChentao/3DeeCellTracker). Neuronal identity information was derived from NeuroPAL signals with the NeuroPAL Auto-ID software (https://www.yeminilab.com/neuropal) and manual corrections. During the data acquisition, worms were semi-restricted in a microfluidic device (https://www.nature.com/articles/nature06292). Note: There are occasional transient failures in neuronal tracking. We recommend researchers to use a moving median filter (e.g., \u00b13) on the time series data to address this issue.", + "tags": [ + { + "id": 502, + "tag": "Caenorhabditis elegans" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 630, + "tag": "DANDI:000692" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:31:36.218656+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000692/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "291": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 291, + "name": "The organization of context versus content coding in the hippocampus and neocortex", + "repository_type": "dandi", + "summary": "High-density silicon probe recordings from primary somatosensory cortex, posterior parietal cortex, and dorsal hippocampus (CA1) in head-fixed mice while running on a treadmill virtual reality task with different tactile cues.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 631, + "tag": "DANDI:000696" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:31:37.585934+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000696/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "292": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 292, + "name": "Laminar coding properties of visual object representations in the mouse neocortex across multiple contexts", + "repository_type": "dandi", + "summary": "High-density silicon probe recordings in the visual and parietal cortices during visual object recognition in the mouse.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 632, + "tag": "DANDI:000710" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:31:38.797795+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000710/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "293": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.231121.1730", + "id": 293, + "name": "Allen Institute - Visual Behavior - Optical Physiology", + "repository_type": "dandi", + "summary": "The Visual Behavior Ophys dataset was generated using in vivo 2-photon calcium imaging (also called optical physiology, or \u201cophys\u201d) to measure the activity of genetically identified neurons in the visual cortex of mice performing a go/no-go visual change detection task. Population of neurons were recorded over multiple days with varying sensory and behavioral contexts, including familiar and novel stimuli, and passive exposure sessions. This dataset can be used to evaluate the influence of experience, expectation, and task engagement on neural coding and dynamics in excitatory, Vip inhibitory, and Sst inhibitory cell populations. \n\nThe full ophys dataset includes neural and behavioral measurements from 107 well-trained mice during 703 in vivo 2-photon imaging sessions from 326 unique fields of view, resulting in a total of 50,476 cortical neurons recorded. The full behavioral training history of all imaged mice is also provided as part of the dataset, allowing investigation into task learning, behavioral strategy, and inter-animal variability. There are a total of 4,782 behavior sessions available for analysis.\n\nFull documentation of the Visual Behavior Ophys dataset and tutorials can be found here: https://allensdk.readthedocs.io/en/latest/visual_behavior_optical_physiology.html\n\nThe recommended route for interacting with these data is through the AllenSDK (https://github.com/AllenInstitute/AllenSDK/) which provides methods for downloading and processing data. Documentation of the AllenSDK can be found here: https://allensdk.readthedocs.io/en/latest/. \n\nThere are several metadata summary tables that are available as \"related resources\" associated with this DANDI-set. You can also use the AllenSDK to download these tables. See this notebook (https://allensdk.readthedocs.io/en/latest/_static/examples/nb/visual_behavior_ophys_data_access.html) for information on downloading the metadata and the associated NWB files.\n\nPublicly available pre-prints using this dataset include: \n\nStimulus novelty uncovers coding diversity in visual cortical circuits\nhttps://doi.org/10.1101/2023.02.14.528085 \n\nBehavioral strategy shapes activation of the Vip-Sst disinhibitory circuit in visual cortex\nhttps://doi.org/10.1101/2023.04.28.538575 \n\nFamiliarity modulated synapses model visual cortical circuit novelty responses\nhttps://doi.org/10.1101/2023.08.16.553635 ", + "tags": [ + { + "id": 633, + "tag": "2-photon microscopy" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 634, + "tag": "DANDI:000711" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + }, + { + "id": 635, + "tag": "behavior" + }, + { + "id": 636, + "tag": "calcium imaging" + }, + { + "id": 637, + "tag": "change detection" + }, + { + "id": 638, + "tag": "disinhibition" + }, + { + "id": 639, + "tag": "excitatory neurons" + }, + { + "id": 640, + "tag": "inhibitory neurons" + }, + { + "id": 153, + "tag": "learning" + }, + { + "id": 97, + "tag": "mouse" + }, + { + "id": 641, + "tag": "novelty" + }, + { + "id": 642, + "tag": "task engagement" + }, + { + "id": 134, + "tag": "visual cortex" + } + ], + "timestamp_created": "2024-01-03 17:31:39.946566+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000711/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "294": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 294, + "name": "Allen Institute - Visual Behavior - Neuropixels", + "repository_type": "dandi", + "summary": "Data released here is for archival purposes. The recommended route for interacting with these data is through the AllenSDK (https://github.com/AllenInstitute/AllenSDK/) which provides methods for downloading and processing data. Documentation of the AllenSDK can be found here: https://allensdk.readthedocs.io/en/latest/. Full documentation of the Visual Behavior Neuropixels dataset and tutorials can be found here: https://allensdk.readthedocs.io/en/latest/visual_behavior_neuropixels.html ", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 643, + "tag": "DANDI:000713" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:31:41.078006+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000713/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "295": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 295, + "name": "Segmented and labeled NeuroPAL structural images", + "repository_type": "dandi", + "summary": "Segmented and labeled NeuroPAL datasets from \"Graphical-model framework for automated annotation of cell identities in dense cellular images\" by Shivesh Chaudhary, et al.", + "tags": [ + { + "id": 502, + "tag": "Caenorhabditis elegans" + }, + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 644, + "tag": "DANDI:000714" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:31:42.181891+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000714/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "296": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 296, + "name": "CatalystNeuro Placeholder 1", + "repository_type": "dandi", + "summary": "This dandiset is reserved by CatalystNeuro and will be published at a future point in time.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 645, + "tag": "DANDI:000717" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:31:43.393939+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000717/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "297": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 297, + "name": "CatalystNeuro Placeholder 2", + "repository_type": "dandi", + "summary": "This dandiset is reserved by CatalystNeuro and will be published at a future point in time.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 646, + "tag": "DANDI:000718" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:31:44.469619+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000718/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "298": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 298, + "name": "CatalystNeuro Placeholder 3", + "repository_type": "dandi", + "summary": "This dandiset is reserved by CatalystNeuro and will be published at a future point in time.", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 647, + "tag": "DANDI:000719" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:31:45.810386+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000719/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "299": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 299, + "name": "Allen Institute - Visual Coding - Optical Physiology", + "repository_type": "dandi", + "summary": "From the Allen Institute Brain Observatory, the Visual Coding (optical physiology) dataset is a large-scale, standardized survey of physiological activity across the mouse visual cortex, hippocampus, and thalamus. This two-photon imaging dataset features visually evoked calcium responses from GCaMP6-expressing neurons in a range of cortical layers, visual areas, and Cre lines. We hope that experimentalists and modelers will use these comprehensive, open datasets as a testbed for theories of visual information processing.\n\nFull documentation of the Visual Coding Ophys dataset and tutorials can be found here: https://observatory.brain-map.org/visualcoding/\n\n", + "tags": [ + { + "id": 11, + "tag": "DANDI" + }, + { + "id": 648, + "tag": "DANDI:000728" + }, + { + "id": 299, + "tag": "Mus musculus - House mouse" + }, + { + "id": 10, + "tag": "NWB" + } + ], + "timestamp_created": "2024-01-03 17:31:46.983261+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000728/draft", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "300": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 300, + "name": "CA1 Oriens Lacunosum Moleculare - Saraga et al. 2003", + "repository_type": "github", + "summary": "CA1 Oriens Lacunosum Moleculare multi-compartment model:\r\nSaraga F, Wu CP, Zhang L, Skinner FK (2003) Active Dendrites and Spike Propagation in Multi-compartment Models of Oriens-Lacunosum/Moleculare Hippocampal Interneurons. J Physiol 552(3):673-689", + "tags": [ + { + "id": 328, + "tag": "CA1" + }, + { + "id": 321, + "tag": "Detailed cell model" + }, + { + "id": 327, + "tag": "Hippocampal formation" + }, + { + "id": 550, + "tag": "Hippocampus" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 663, + "tag": "Oriens Lacunosum Moleculare" + }, + { + "id": 652, + "tag": "Rodent" + } + ], + "timestamp_created": "2024-01-04 16:08:25.891479+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/agmccrei/CA1-Oriens-Lacunosum-Moleculare---Saraga-et-al.-2003", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "301": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 301, + "name": "CA1 pyramidal cell - Ferguson et al. 2014", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 664, + "tag": "Brain" + }, + { + "id": 328, + "tag": "CA1" + }, + { + "id": 665, + "tag": "CA1 Pyramidal cell" + }, + { + "id": 327, + "tag": "Hippocampal formation" + }, + { + "id": 550, + "tag": "Hippocampus" + }, + { + "id": 666, + "tag": "Izhikevich neuron model" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 652, + "tag": "Rodent" + } + ], + "timestamp_created": "2024-01-04 16:10:39.955561+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/FergusonEtAl2014-CA1PyrCell", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "302": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 302, + "name": "CA1 Pyramidal Cell - Migliore et al. 2005", + "repository_type": "github", + "summary": "![](/attachments/download/1/CA1-spike.PNG)\r\nConversion of [hippocampal CA1 pyramidal cell](http://neurolex.org/wiki/Category:Hippocampus_CA1_pyramidal_cell) from [Migliore et al 2005](http://senselab.med.yale.edu/ModelDB/ShowModel.asp?model=55035).\r\n\r\n[![Build Status](https://travis-ci.org/OpenSourceBrain/CA1PyramidalCell.svg?branch=master)](https://travis-ci.org/OpenSourceBrain/CA1PyramidalCell)\r\n", + "tags": [ + { + "id": 328, + "tag": "CA1" + }, + { + "id": 665, + "tag": "CA1 Pyramidal cell" + }, + { + "id": 321, + "tag": "Detailed cell model" + }, + { + "id": 327, + "tag": "Hippocampal formation" + }, + { + "id": 550, + "tag": "Hippocampus" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 652, + "tag": "Rodent" + } + ], + "timestamp_created": "2024-01-04 16:10:40.492999+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/CA1PyramidalCell", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "303": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 303, + "name": "CATMAID Showcase", + "repository_type": "github", + "summary": "Project for example NeuroML files generated by [CATMAID](http://www.catmaid.org).\n", + "tags": [ + { + "id": 667, + "tag": "Connectomics" + }, + { + "id": 229, + "tag": "Drosophila" + }, + { + "id": 668, + "tag": "Multiple" + }, + { + "id": 324, + "tag": "Neuronal reconstruction" + }, + { + "id": 316, + "tag": "OSBv1" + } + ], + "timestamp_created": "2024-01-04 16:10:40.982711+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/CATMAIDShowcase", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "304": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 304, + "name": "OpenWorm C. elegans network model", + "repository_type": "github", + "summary": "![](http://www.opensourcebrain.org/attachments/download/22/medium.png)\r\n\r\n**The latest version of this model can be found at http://www.opensourcebrain.org/projects/c302**\r\nNote: the development of the OpenWorm model of [C. elegans](http://en.wikipedia.org/wiki/Caenorhabditis_elegans) is taking place at [http://www.openworm.org](http://www.openworm.org).\r\n\r\nA full list of those involved in that project can be found [here](http://www.openworm.org/people.html).\r\n\r\nThe C. elegans 3D model this was derived from was produced by Dr. Christian Grove and Dr. Paul Sternberg at the VirtualWorm project (WormBase, CalTech) and released into the public domain. You can visit the VirtualWorm home page at http://caltech.wormbase.org/virtualworm/ .\r\n\r\nFor details on running this neuroConstruct project see: https://github.com/openworm/OpenWorm/wiki/Running-the-C.-elegans-model-in-neuroConstruct.\r\n\r\nThis is a **work in progress**. Please [get in contact](http://www.openworm.org/contacts.html) for more information.\r\n", + "tags": [ + { + "id": 658, + "tag": "Blender" + }, + { + "id": 79, + "tag": "C. elegans" + }, + { + "id": 659, + "tag": "Nervous system" + }, + { + "id": 651, + "tag": "Network model" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 38, + "tag": "OpenWorm" + }, + { + "id": 669, + "tag": "Whole brain model" + } + ], + "timestamp_created": "2024-01-04 16:10:41.443512+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/openworm/CElegansNeuroML", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "305": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 305, + "name": "Cerebellar Nucleus Neuron - Steuber et al. 2011", + "repository_type": "github", + "summary": "![](http://www.opensourcebrain.org/attachments/download/34/medium.png)\r\n\r\n[Deep cerebellar nucleus neuron](http://neurolex.org/wiki/Category:Cerebellum_nucleus_reciprocal_projections_neuron) model from: Determinants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cells. 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Rothman & Piasini", + "repository_type": "github", + "summary": "This project contains an integrate and fire model of the cerebellar granule cell and a simple model of the mossy fibre to granule cell synapse. The cell model (IaF\\_GrC.nml) is the average (ie the one whose parameters have the average value) of the model population developed by Jason Rothman and published in Schwartz et, J Neurosci (2012). The synaptic model is based on the one used in that same paper, but it has been developed further to improve the fit to the experimental data and to ensure LEMS/NeuroMLv2 compatibility.\n", + "tags": [ + { + "id": 671, + "tag": "Cerebellum" + }, + { + "id": 698, + "tag": "Granule cell" + }, + { + "id": 699, + "tag": "Igor Pro" + }, + { + "id": 326, + "tag": "Integrate and fire neuron" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 652, + "tag": "Rodent" + }, + { + "id": 319, + "tag": "neuroConstruct" + } + ], + "timestamp_created": "2024-01-04 16:30:57.321873+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/GranCellRothmanIf", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "328": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 328, + "name": "Cerebellar Granule Cell - Solinas et al. 2010", + "repository_type": "github", + "summary": "![](http://www.opensourcebrain.org/attachments/download/24/solinasal10.png)\r\nInitial version of Granule cell from: Solinas S., Nieus T, d\u2019Angelo E. 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Front Cell Neurosci. 2010;4:12.\r\n\r\nFor more information, see the [[Wiki]]\r\n\r\n[![Build Status](https://travis-ci.com/OpenSourceBrain/GranCellSolinasEtAl10.svg?branch=master)](https://travis-ci.com/OpenSourceBrain/GranCellSolinasEtAl10)\r\n", + "tags": [ + { + "id": 671, + "tag": "Cerebellum" + }, + { + "id": 698, + "tag": "Granule cell" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 688, + "tag": "NMDAR synapse" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 652, + "tag": "Rodent" + }, + { + "id": 690, + "tag": "Single compartment conductance based neuron model" + }, + { + "id": 319, + "tag": "neuroConstruct" + } + ], + "timestamp_created": "2024-01-05 12:04:58.412185+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/GranCellSolinasEtAl10", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "329": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 329, + "name": "Granular Layer Network Model - Solinas, Nieus & D'Angelo 2010", + "repository_type": "github", + "summary": "Cerebellar granular layer network model from: Solinas S., Nieus T, d'Angelo E. (2010) [A Realistic Large-Scale Model of the Cerebellum Granular Layer Predicts Circuit Spatio-Temporal Filtering Properties](http://journal.frontiersin.org/article/10.3389/fncel.2010.00012/abstract). Front Cell Neurosci. 2010;4:12.", + "tags": [ + { + "id": 671, + "tag": "Cerebellum" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 651, + "tag": "Network model" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 652, + "tag": "Rodent" + } + ], + "timestamp_created": "2024-01-05 12:04:59.155946+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/GranularLayerSolinasNieusDAngelo2010", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "330": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 330, + "name": "Granule Cell Layer - Piasini et al. 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[Synchronization of Golgi and Granule Cell Firing in a Detailed Network Model of the Cerebellar Granule Cell Layer](http://www.ncbi.nlm.nih.gov/pubmed/9819260) J Neurophysiol, Nov 1998; 80: 2521 - 2537. \r\n\r\nBased on scripts obtained from: http://www.tnb.ua.ac.be/models/network.shtml.\r\n\r\nFor more details see the [[Wiki]].\r\n\r\n[![Build Status](https://travis-ci.org/OpenSourceBrain/GranuleCell.svg?branch=master)](https://travis-ci.org/OpenSourceBrain/GranuleCell)\r\n", + "tags": [ + { + "id": 671, + "tag": "Cerebellum" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 698, + "tag": "Granule cell" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 652, + "tag": "Rodent" + }, + { + "id": 690, + "tag": "Single compartment conductance based neuron model" + }, + { + "id": 319, + "tag": "neuroConstruct" + } + ], + "timestamp_created": "2024-01-05 12:05:00.294619+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/GranuleCell", + "user": { + "email": "p.gleeson@gmail.com", + "first_name": "Padraig", + "id": "0103eaaf-6a34-4509-a025-14367a52aa2b", + "last_name": "Gleeson", + "username": "padraig" + }, + "user_id": "0103eaaf-6a34-4509-a025-14367a52aa2b" + }, + "333": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 333, + "name": "Cerebellar Granule Cell - Maex De Schutter 1998", + "repository_type": "github", + "summary": "![](http://www.opensourcebrain.org/attachments/download/25/shutter98.png)\r\nA project illustrating the behaviour of the cerebellar granule cell model from: Maex, R and De Schutter, E. [Synchronization of Golgi and Granule Cell Firing in a Detailed Network Model of the Cerebellar Granule Cell Layer](http://www.ncbi.nlm.nih.gov/pubmed/9819260) J Neurophysiol, Nov 1998; 80: 2521 - 2537. \r\n\r\nBased on scripts obtained from: http://www.tnb.ua.ac.be/models/network.shtml.\r\n\r\nFor more details see the [[Wiki]].\r\n\r\n[![Build Status](https://travis-ci.org/OpenSourceBrain/GranuleCell.svg?branch=master)](https://travis-ci.org/OpenSourceBrain/GranuleCell)\r\n", + "tags": [ + { + "id": 671, + "tag": "Cerebellum" + }, + { + "id": 698, + "tag": "Granule cell" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 652, + "tag": "Rodent" + }, + { + "id": 690, + "tag": "Single compartment conductance based neuron model" + }, + { + "id": 319, + "tag": "neuroConstruct" + }, + { + "id": 649, + "tag": "GENESIS" + } + ], + "timestamp_created": "2024-01-05 12:05:26.100143+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/GranuleCell", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "334": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 334, + "name": "Granule Cell Layer - CaycoGajicClopathSilver2017", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 671, + "tag": "Cerebellum" + }, + { + "id": 651, + "tag": "Network model" + }, + { + "id": 662, + "tag": "NeuroML2" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 652, + "tag": "Rodent" + } + ], + "timestamp_created": "2024-01-05 12:06:31.758583+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/SilverLabUCL/MF-GC-network-backprop-public", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "335": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 335, + "name": "Hindmarsh and Rose 1984", + "repository_type": "github", + "summary": "The Hindmarsh Rose model consists of a fast spiking subsystem, which is a generalization of the Fitzhugh-Nagumo (aka Bonhoeffer-van der Pol) oscillator, coupled to a slower subsystem which allows the system to fire bursts of spikes.\n\nImplemented according to:\nHindmarsh J. L., and Rose R. M. (1984) A model of neuronal bursting using three coupled first order differential equations. Proc. R. Soc. London, Ser. B 221:87\u2013102.\n", + "tags": [ + { + "id": 692, + "tag": "Abstract neuron model" + }, + { + "id": 660, + "tag": "Generic" + }, + { + "id": 316, + "tag": "OSBv1" + } + ], + "timestamp_created": "2024-01-05 12:06:32.275050+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/HindmarshRose1984", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "336": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 336, + "name": "Hippocampus 3D Demo", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 701, + "tag": "Demo" + }, + { + "id": 327, + "tag": "Hippocampal formation" + }, + { + "id": 550, + "tag": "Hippocampus" + }, + { + "id": 653, + "tag": "Network" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 652, + "tag": "Rodent" + }, + { + "id": 319, + "tag": "neuroConstruct" + } + ], + "timestamp_created": "2024-01-05 12:06:32.830419+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/Hippocampus3DDemo", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "337": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 337, + "name": "HNN", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 94, + "tag": "Human" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 653, + "tag": "Network" + }, + { + "id": 316, + "tag": "OSBv1" + } + ], + "timestamp_created": "2024-01-05 12:06:33.474832+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/HNN", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "338": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 338, + "name": "Izhikevich Spiking Neuron Model", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 692, + "tag": "Abstract neuron model" + }, + { + "id": 660, + "tag": "Generic" + }, + { + "id": 705, + "tag": "Google Summer of Code" + }, + { + "id": 666, + "tag": "Izhikevich neuron model" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 668, + "tag": "Multiple" + }, + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 316, + "tag": "OSBv1" + } + ], + "timestamp_created": "2024-01-05 12:06:34.308184+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/IzhikevichModel", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "339": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 339, + "name": "Large-Scale Circuit Model of the Primate Cortex - Joglekar et al. 2018", + "repository_type": "github", + "summary": "# JoglekarEtAl18\r\n\r\nInter-areal Balanced Amplification Enhances Signal Propagation in a Large-Scale Circuit Model of the Primate Cortex\r\nMadhura R. Joglekar, Jorge F. 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Science 325:756-60\n", + "tags": [ + { + "id": 321, + "tag": "Detailed cell model" + }, + { + "id": 655, + "tag": "L5 pyramidal cell" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 318, + "tag": "Pyramidal cell" + }, + { + "id": 652, + "tag": "Rodent" + }, + { + "id": 319, + "tag": "neuroConstruct" + } + ], + "timestamp_created": "2024-01-05 12:06:37.060548+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/LarkumEtAl2009", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "343": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 343, + "name": "M1 Network Model", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 708, + "tag": "Large scale network simulation" + }, + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 709, + "tag": "NetPyNE" + }, + { + "id": 653, + "tag": "Network" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 652, + "tag": "Rodent" + } + ], + "timestamp_created": "2024-01-05 12:06:37.606741+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/M1NetworkModel", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "344": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 344, + "name": "L5 Pyramidal Cell - Mainen et al. 1995", + "repository_type": "github", + "summary": "![](http://www.opensourcebrain.org/attachments/download/27/mainen95.png)\r\n\r\nImplementation of the Mainen et al. pyramidal cell model from: Mainen ZF, Joerges J, Huguenard JR, Sejnowski TJ (1995) A model of spike initiation in neocortical pyramidal neurons. [Neuron 15:1427-39](http://www.ncbi.nlm.nih.gov/pubmed/8845165). This project is based on scripts obtained from: http://senselab.med.yale.edu/senselab/modeldb/ShowModel.asp?model=8210\r\n", + "tags": [ + { + "id": 321, + "tag": "Detailed cell model" + }, + { + "id": 655, + "tag": "L5 pyramidal cell" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 318, + "tag": "Pyramidal cell" + }, + { + "id": 652, + "tag": "Rodent" + }, + { + "id": 319, + "tag": "neuroConstruct" + } + ], + "timestamp_created": "2024-01-05 12:06:38.199790+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/MainenEtAl_PyramidalCell", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "345": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 345, + "name": "Large scale laminar cortical network - Mejias et al. 2016", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 173, + "tag": "Macaque" + }, + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 710, + "tag": "Rate based model" + }, + { + "id": 711, + "tag": "Whole brain" + } + ], + "timestamp_created": "2024-01-05 12:06:38.695947+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/MejiasEtAl2016", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "346": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 346, + "name": "ModelDB Showcase", + "repository_type": "github", + "summary": "OSB Showcase project for investigating options for interactions between [ModelDB](http://senselab.med.yale.edu/modeldb/) and OSB", + "tags": [ + { + "id": 714, + "tag": "Database" + }, + { + "id": 715, + "tag": "Model sharing" + }, + { + "id": 716, + "tag": "Neuroinformatics" + }, + { + "id": 316, + "tag": "OSBv1" + } + ], + "timestamp_created": "2024-01-05 12:06:39.577187+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/ModelDBShowcase", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "347": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 347, + "name": "Morris Lecar Model", + "repository_type": "github", + "summary": "Definition in LEMS/NeuroML 2 of the Morris Lecar Model. Code originally contributed by Daniel Salles Chevitarese and Yiyin Zhou.\r\n\r\nFor more information & latest status, see the [[Wiki]]\r\n", + "tags": [ + { + "id": 692, + "tag": "Abstract neuron model" + }, + { + "id": 717, + "tag": "Barnacle" + }, + { + "id": 718, + "tag": "MLAB" + }, + { + "id": 719, + "tag": "Muscle cell" + }, + { + "id": 682, + "tag": "Neuromuscular system" + }, + { + "id": 316, + "tag": "OSBv1" + } + ], + "timestamp_created": "2024-01-05 12:06:40.042712+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/MorrisLecarModel", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "348": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 348, + "name": "Multicompartmental granule cell - Diwakar et al. 2009", + "repository_type": "github", + "summary": "Multicompartmental cerebellar granule cell model.\n\nBased on: Diwakar S, Magistretti J, Goldfarb M, Naldi G, D\\`Angelo E (2009) Axonal Na+ channels ensure fast spike activation and back-propagation in cerebellar granule cells J Neurophysiol 101(2):519-32\n", + "tags": [ + { + "id": 671, + "tag": "Cerebellum" + }, + { + "id": 321, + "tag": "Detailed cell model" + }, + { + "id": 698, + "tag": "Granule cell" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 652, + "tag": "Rodent" + } + ], + "timestamp_created": "2024-01-05 12:06:40.918581+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/rizland/cereb_grc_mc", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "349": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 349, + "name": "MultiTest", + "repository_type": "github", + "summary": "Testing networks at multiple scales", + "tags": [ + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 653, + "tag": "Network" + }, + { + "id": 662, + "tag": "NeuroML2" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 652, + "tag": "Rodent" + } + ], + "timestamp_created": "2024-01-05 12:06:41.496660+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/pgleeson/multi", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "350": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 350, + "name": "MultiscaleISN", + "repository_type": "github", + "summary": "Inhibition Stabilized Networks at multiple scales based on Sadeh et al. 2017 ", + "tags": [ + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 653, + "tag": "Network" + }, + { + "id": 662, + "tag": "NeuroML2" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 652, + "tag": "Rodent" + } + ], + "timestamp_created": "2024-01-05 12:06:41.964250+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/MultiscaleISN", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "351": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 351, + "name": "Muscle cell model - Boyle & Cohen 2008", + "repository_type": "github", + "summary": "![](http://www.openworm.org/img/OpenWormLogo.png)\r\n\r\nThis project is being developed as part of the [OpenWorm](http://www.openworm.org) project. This is an attempt to convert the model of [Boyle & Cohen, 2008](http://www.sciencedirect.com/science/article/pii/S0303264708001408) into NeuroML format for use on NEURON and other simulators.\r\n\r\nSee [here](/projects/muscle_model/wiki) for more details.\r\n", + "tags": [ + { + "id": 79, + "tag": "C. elegans" + }, + { + "id": 721, + "tag": "Muscle cell model" + }, + { + "id": 682, + "tag": "Neuromuscular system" + }, + { + "id": 722, + "tag": "Neuromusculature system" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 38, + "tag": "OpenWorm" + } + ], + "timestamp_created": "2024-01-05 12:06:42.599648+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/openworm/muscle_model", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "352": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 352, + "name": "MUSIC Showcase", + "repository_type": "github", + "summary": "[MUlti-SImulation Coordinator, MUSIC, ](http://incf.org/core/programs/modeling/projects/music/music-multi-simulation-coordinator/) is a software that allows large scale neuron simulators to communicate during runtime.\n\nThis OSB Showcase project will highlight some of the examples in development to illustrate its functionality.\n", + "tags": [ + { + "id": 723, + "tag": "MUSIC" + }, + { + "id": 316, + "tag": "OSBv1" + } + ], + "timestamp_created": "2024-01-05 12:06:43.101111+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/MUSICShowcase", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "353": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 353, + "name": "CA1 Network Model - Bezaire et al 2016", + "repository_type": "github", + "summary": "CA1 Network Model - Bezaire et al 2016\r\n", + "tags": [ + { + "id": 328, + "tag": "CA1" + }, + { + "id": 321, + "tag": "Detailed cell model" + }, + { + "id": 705, + "tag": "Google Summer of Code" + }, + { + "id": 724, + "tag": "High Performance Computing" + }, + { + "id": 327, + "tag": "Hippocampal formation" + }, + { + "id": 550, + "tag": "Hippocampus" + }, + { + "id": 329, + "tag": "Interneuron" + }, + { + "id": 708, + "tag": "Large scale network simulation" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 653, + "tag": "Network" + }, + { + "id": 725, + "tag": "Network oscillations" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 318, + "tag": "Pyramidal cell" + }, + { + "id": 652, + "tag": "Rodent" + } + ], + "timestamp_created": "2024-01-05 12:06:43.611547+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/mbezaire/ca1", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "354": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 354, + "name": "Nengo - NeuroML interoperability", + "repository_type": "github", + "summary": "![](http://nengo.ca/sites/nengo.ca/files/logo_ctn.png)\n\nProject to test scenarios for NeuroML & [Nengo](http://nengo.ca) interoperability\n", + "tags": [ + { + "id": 660, + "tag": "Generic" + }, + { + "id": 708, + "tag": "Large scale network simulation" + }, + { + "id": 726, + "tag": "Nengo" + }, + { + "id": 651, + "tag": "Network model" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 676, + "tag": "Python tools for computational neuroscience" + }, + { + "id": 696, + "tag": "Simulator showcase" + } + ], + "timestamp_created": "2024-01-05 12:08:32.970953+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/NengoNeuroML", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "355": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 355, + "name": "NEST Showcase", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 660, + "tag": "Generic" + }, + { + "id": 661, + "tag": "NEST" + }, + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 653, + "tag": "Network" + }, + { + "id": 316, + "tag": "OSBv1" + } + ], + "timestamp_created": "2024-01-05 12:08:33.577862+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/NESTShowcase", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "356": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 356, + "name": "neuroConstruct Showcase", + "repository_type": "github", + "summary": "![](/attachments/download/111/logoMain.png)\n\nExample projects illustrating the functionality of [neuroConstruct](http://www.neuroconstruct.org/)\n", + "tags": [ + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 319, + "tag": "neuroConstruct" + }, + { + "id": 727, + "tag": "neuroconstruct" + } + ], + "timestamp_created": "2024-01-05 12:08:34.412715+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/neuroConstructShowcase", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "357": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 357, + "name": "NeuroElectro & SciUnit Showcase", + "repository_type": "github", + "summary": "![](/images/neuroelectro_logo.png)\r\n\r\nA project highlighting some of the possible interactions between OSB and [NeuroElectro](http://neuroelectro.org/) and [SciUnit](https://github.com/rgerkin/sciunit).\r\n\r\nNeuroElectro is one of the key external resources OSB will interact with, see [Interactions with other Neuroinformatics resources](http://www.opensourcebrain.org/projects/neuroinformatics/wiki/Wiki)\r\n", + "tags": [ + { + "id": 714, + "tag": "Database" + }, + { + "id": 660, + "tag": "Generic" + }, + { + "id": 716, + "tag": "Neuroinformatics" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 676, + "tag": "Python tools for computational neuroscience" + } + ], + "timestamp_created": "2024-01-05 12:08:34.897009+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/NeuroElectroSciUnit", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "358": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 358, + "name": "NeuroMorpho.Org Showcase", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 714, + "tag": "Database" + }, + { + "id": 668, + "tag": "Multiple" + }, + { + "id": 716, + "tag": "Neuroinformatics" + }, + { + "id": 324, + "tag": "Neuronal reconstruction" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 720, + "tag": "SWC" + } + ], + "timestamp_created": "2024-01-05 12:08:36.080222+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/NeuroMorpho", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "359": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 359, + "name": "Action Selection in the Basal Ganglia - Guthrie et al, 2013", + "repository_type": "github", + "summary": "Reproduction of a model of action selection in the basal ganglia (Guthrie et al., 2013)", + "tags": [ + { + "id": 731, + "tag": "Basal ganglia" + }, + { + "id": 660, + "tag": "Generic" + }, + { + "id": 651, + "tag": "Network model" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 89, + "tag": "Python" + } + ], + "timestamp_created": "2024-01-05 12:08:36.656079+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/rougier/Neurosciences/tree/master/basal-ganglia/guthrie-et-al-2013", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "360": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 360, + "name": "NIF and NeuroLex Showcase", + "repository_type": "github", + "summary": "![](http://www.opensourcebrain.org/attachments/download/85/nifneurolex.png)\n\nA repository for information, scripts and configuration files which demonstrate the interactions possible between [NIF](https://www.neuinfo.org), [NeuroLex](http://neurolex.org/wiki/Main_Page) and OSB.\n\nSee the [[Wiki]] for more details.\n", + "tags": [ + { + "id": 714, + "tag": "Database" + }, + { + "id": 674, + "tag": "Model annotation" + }, + { + "id": 715, + "tag": "Model sharing" + }, + { + "id": 716, + "tag": "Neuroinformatics" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 675, + "tag": "Ontology" + } + ], + "timestamp_created": "2024-01-05 12:08:37.291892+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/NIFShowcase", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "361": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 361, + "name": "NineML Showcase", + "repository_type": "github", + "summary": "![](/attachments/download/53/image_mini.png)\n\nExamples of models in [NineML](http://software.incf.org/software/nineml) (and the related language [SpineML](http://bimpa.group.shef.ac.uk/SpineML/index.php/Home)) and conversion to/from NeuroML/LEMS.\n\nSee the [[Wiki]] for more details.\n", + "tags": [ + { + "id": 715, + "tag": "Model sharing" + }, + { + "id": 732, + "tag": "NineML" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 733, + "tag": "SpineML" + }, + { + "id": 734, + "tag": "Standardization" + }, + { + "id": 735, + "tag": "XML" + } + ], + "timestamp_created": "2024-01-05 12:08:37.782588+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/NineMLShowcase", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "362": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": 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The cell morphologies and network connections are in NeuroML v1.8.\n", + "tags": [ + { + "id": 738, + "tag": "MOOSE" + }, + { + "id": 651, + "tag": "Network model" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 712, + "tag": "Olfactory bulb" + }, + { + "id": 713, + "tag": "Olfactory system" + }, + { + "id": 652, + "tag": "Rodent" + } + ], + "timestamp_created": "2024-01-05 12:08:39.180186+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/adityagilra/olfactory-bulb", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "364": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 364, + "name": "Olfactory Bulb Network Model - O'Connor, Angelo and Jacob 2012", + "repository_type": "github", + "summary": "![](http://www.opensourcebrain.org/attachments/download/92/Mitral_Cell_Model.png)\r\nA model of olfactory bulb mitral cells connected by apical dendrite gap junctions in a glomerular network\r\n", + "tags": [ + { + "id": 739, + "tag": "Mitral cell network" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 712, + "tag": "Olfactory bulb" + }, + { + "id": 713, + "tag": "Olfactory system" + }, + { + "id": 652, + "tag": "Rodent" + }, + { + "id": 319, + "tag": "neuroConstruct" + } + ], + "timestamp_created": "2024-01-05 12:08:39.651164+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/Simon-at-Ely/OlfactoryBulbMitralCell", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "365": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 365, + "name": "Reduced CA3 model - 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M. (1994). Based on Arnd Roth el al\u2019s conversion of the original GENESIS code to NEURON. Note: conversion not fully complete.\n", + "tags": [ + { + "id": 671, + "tag": "Cerebellum" + }, + { + "id": 321, + "tag": "Detailed cell model" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 746, + "tag": "Purkinje cell" + }, + { + "id": 652, + "tag": "Rodent" + }, + { + "id": 319, + "tag": "neuroConstruct" + } + ], + "timestamp_created": "2024-01-05 12:08:42.713643+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/PurkinjeCell", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "370": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 370, + "name": "Pyloric pacemaker network - Prinz et al. 2003/04", + "repository_type": "github", + "summary": "Implementation of the pyloric network of the lobster stomatogastric ganglion system of Prinz, Marder, et al.\r\n\r\nWork in progress! For more details see the [[Wiki]].\r\n", + "tags": [ + { + "id": 747, + "tag": "C" + }, + { + "id": 748, + "tag": "Lobster" + }, + { + "id": 749, + "tag": "Multiple cells" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 325, + "tag": "Point neuron network" + }, + { + "id": 750, + "tag": "Stomatogastric ganglion" + }, + { + "id": 319, + "tag": "neuroConstruct" + } + ], + "timestamp_created": "2024-01-05 12:08:43.312693+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/PyloricNetwork", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "371": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 371, + "name": "PyNN Showcase", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 751, + "tag": "PyNN/NeuroML2" + } + ], + "timestamp_created": "2024-01-05 12:08:43.830126+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/PyNNShowcase", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "372": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 372, + "name": "L5 Pyramidal cell - Rothman et al. 2009", + "repository_type": "github", + "summary": "![](http://www.opensourcebrain.org/attachments/download/29/rothman2009.png)\nA project which was used in Rothman et al. \u201cSynaptic depression enables neuronal gain control\u201d Nature 2009 to demonstrate gain control in realistic cell models. 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A version of this model in \u201cpure\u201d NeuroML v2.0 is being developed here.\n\nFor more information, see the [[Wiki]]\n", + "tags": [ + { + "id": 731, + "tag": "Basal ganglia" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 322, + "tag": "Goldman-Hodgkin-Katz current" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 652, + "tag": "Rodent" + }, + { + "id": 690, + "tag": "Single compartment conductance based neuron model" + }, + { + "id": 752, + "tag": "Striatal Spiny Projection Neuron" + } + ], + "timestamp_created": "2024-01-05 12:08:46.900703+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/StriatalSpinyProjectionNeuron", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "376": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 376, + "name": "Synaptic Integration Demo", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 660, + "tag": "Generic" + }, + { + "id": 753, + "tag": "L23 Pyramidal cell" + }, + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 730, + "tag": "NeuroML 2" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 5, + "tag": "Tutorial" + } + ], + "timestamp_created": "2024-01-05 12:08:47.430713+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/SynapticIntegration", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "377": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 377, + "name": "TCrelay neuron in high conductance state - 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These will likely include examples based on raw data available from http://flybrain.stanford.edu and http://flycircuit.tw.\r\n", + "tags": [ + { + "id": 229, + "tag": "Drosophila" + }, + { + "id": 668, + "tag": "Multiple" + }, + { + "id": 324, + "tag": "Neuronal reconstruction" + }, + { + "id": 316, + "tag": "OSBv1" + } + ], + "timestamp_created": "2024-01-05 12:09:37.961650+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/jefferis/osb_vfb_showcase", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "384": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 384, + "name": "VierlingClaassenEtAl2010", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 651, + "tag": "Network model" + }, + { + "id": 725, + "tag": "Network oscillations" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 652, + "tag": "Rodent" + } + ], + "timestamp_created": "2024-01-05 12:09:38.431152+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/VierlingClaassenEtAl2010", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "385": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 385, + "name": "Balanced network with inhibitory plasticity - Vogels et al. 2011", + "repository_type": "github", + "summary": "![](/attachments/download/107/test_full.png)\n\nNetwork model from: Vogels TP, Sprekeler H, Zenke F, Clopath C, Gerstner W (2011) [Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks](http://www.sciencemag.org/content/334/6062/1569.abstract). Science 334:1569-73.\n\nSee the [[Wiki]] for more details.\n", + "tags": [ + { + "id": 761, + "tag": "C/Matlab" + }, + { + "id": 660, + "tag": "Generic" + }, + { + "id": 705, + "tag": "Google Summer of Code" + }, + { + "id": 762, + "tag": "Inhibition" + }, + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 651, + "tag": "Network model" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 325, + "tag": "Point neuron network" + } + ], + "timestamp_created": "2024-01-05 12:09:38.949051+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/VogelsEtAl2011", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "386": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 386, + "name": "Wang & Buzsaki 1996", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 763, + "tag": "?" + }, + { + "id": 327, + "tag": "Hippocampal formation" + }, + { + "id": 550, + "tag": "Hippocampus" + }, + { + "id": 329, + "tag": "Interneuron" + }, + { + "id": 653, + "tag": "Network" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 652, + "tag": "Rodent" + } + ], + "timestamp_created": "2024-01-05 12:09:39.448642+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/WangBuzsaki1996", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "387": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 387, + "name": "Laminar organization of motor cortex - Weiler et al 2008", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 651, + "tag": "Network model" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 652, + "tag": "Rodent" + } + ], + "timestamp_created": "2024-01-05 12:09:39.971329+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/WeilerEtAl08-LaminarCortex", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "388": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 388, + "name": "Wilson and Cowan model", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 660, + "tag": "Generic" + }, + { + "id": 650, + "tag": "Neocortex" + }, + { + "id": 653, + "tag": "Network" + }, + { + "id": 754, + "tag": "Neuron" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 710, + "tag": "Rate based model" + } + ], + "timestamp_created": "2024-01-05 12:09:40.487287+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/WilsonCowan", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "389": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 389, + "name": "Low Threshold Calcium Currents in TC cells (Destexhe et al 1998)", + "repository_type": "github", + "summary": "In Destexhe, Neubig, Ulrich, and Huguenard (1998) experiments and models examine low threshold calcium current's (IT, or T-current) distribution in thalamocortical (TC) cells. Multicompartmental modeling supports the hypothesis that IT currents have a density at least several fold higher in the dendrites than the soma. The IT current contributes significantly to rebound bursts and is thought to have important network behavior consequences. See the paper for details. See also http://cns.iaf.cnrs-gif.fr Correspondance may be addressed to Alain Destexhe: Destexhe@iaf.cnrs-gif.fr", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 775, + "tag": "Rebound firing" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 953, + "tag": "ModelDB:279" + } + ], + "timestamp_created": "2024-01-05 15:00:31.353254+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/279", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "390": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 390, + "name": "Olfactory Mitral Cell (Davison et al 2000)", + "repository_type": "github", + "summary": "A four-compartment model of a mammalian olfactory bulb mitral cell, reduced \r\nfrom the complex 286-compartment model described by Bhalla and Bower (1993). \r\nThe compartments are soma/axon, secondary dendrites, primary dendrite shaft \r\nand primary dendrite tuft. The reduced model runs 75 or more times faster \r\nthan the full model, making its use in large, realistic network models of the \r\nolfactory bulb practical.", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 954, + "tag": "ModelDB:2487" + } + ], + "timestamp_created": "2024-01-05 15:00:32.249864+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2487", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "391": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 391, + "name": "Influence of dendritic structure on neocortical neuron firing patterns (Mainen and Sejnowski 1996)", + "repository_type": "github", + "summary": "This package contains compartmental models of four reconstructed neocortical neurons (layer 3 Aspiny, layer 4 Stellate, layer 3 and layer 5 Pyramidal neurons) with active dendritic currents using NEURON. Running this simulation demonstrates that an entire spectrum of firing patterns can be reproduced in this set of model neurons which share a common distribution of ion channels and differ only in their dendritic geometry. The reference paper is: Z. F. Mainen and T. J. Sejnowski (1996) Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382: 363-366. See also http://www.cnl.salk.edu/~zach/methods.html and http://www.cnl.salk.edu/~zach/ More info in readme.txt file below made visible by clicking on the patdemo folder and then on the readme.txt file.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 955, + "tag": "ModelDB:2488" + } + ], + "timestamp_created": "2024-01-05 15:00:32.995687+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2488", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "392": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 392, + "name": "Olfactory Bulb Network (Davison et al 2003)", + "repository_type": "github", + "summary": "A biologically-detailed model of the mammalian olfactory bulb, incorporating \r\nthe mitral and granule cells and the dendrodendritic synapses between them. \r\nThe results of simulation experiments with electrical stimulation agree \r\nclosely in most details with published experimental data. The model predicts \r\nthat the time course of dendrodendritic inhibition is dependent on the \r\nnetwork connectivity as well as on the intrinsic parameters of the synapses. \r\nIn response to simulated odor stimulation, strongly activated mitral cells \r\ntend to suppress neighboring cells, the mitral cells readily synchronize \r\ntheir firing, and increasing the stimulus intensity increases the degree of \r\nsynchronization. For more details, see the reference below.", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 956, + "tag": "ModelDB:2730" + } + ], + "timestamp_created": "2024-01-05 15:00:33.613204+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2730", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "393": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 393, + "name": "Olfactory Mitral Cell (Bhalla, Bower 1993)", + "repository_type": "github", + "summary": "This is a conversion to NEURON of the mitral cell model described in Bhalla \r\nand Bower (1993). \r\nThe original model was written in GENESIS and is available by joining BABEL, the GENESIS users' group here http://www.genesis-sim.org/GENESIS/babel.html", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 957, + "tag": "ModelDB:2733" + } + ], + "timestamp_created": "2024-01-08 12:20:38.332269+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2733", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "394": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 394, + "name": "Granule Cell - Steuber, Saviane & Berends", + "repository_type": "github", + "summary": "![](http://www.opensourcebrain.org/attachments/download/26/steubersaviane.png)\nConversion to NeuroML of a model granule cell developed by Volker Steuber and Chiara Saviane (Silver lab).\n\nBased on a Granule cell model from Michiel Berends, Neural Comp 15, 2531-47 (2005) originally modified by Chiara Saviane and further modified by VS, July 2006.\n", + "tags": [ + { + "id": 671, + "tag": "Cerebellum" + }, + { + "id": 698, + "tag": "Granule cell" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 652, + "tag": "Rodent" + }, + { + "id": 690, + "tag": "Single compartment conductance based neuron model" + }, + { + "id": 319, + "tag": "neuroConstruct" + } + ], + "timestamp_created": "2024-01-08 12:30:34.654590+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/GranuleCellVSCS", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "395": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 395, + "name": "CA1 pyramidal neuron (Migliore et al 1999)", + "repository_type": "github", + "summary": "Hippocampal CA1 pyramidal neuron model from the paper \r\nM.Migliore, D.A Hoffman, J.C. Magee and D. Johnston (1999) Role of an A-type K+ conductance in the back-propagation of action potentials in the dendrites of hippocampal pyramidal neurons,\r\nJ. Comput. Neurosci. 7, 5-15. Instructions are provided in the below README file.Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 958, + "tag": "ModelDB:2796" + } + ], + "timestamp_created": "2024-01-09 11:20:43.105826+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2796", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "396": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 396, + "name": "Hopfield and Brody model (Hopfield, Brody 2000)", + "repository_type": "github", + "summary": "NEURON implementation of the Hopfield and Brody model from the papers:\r\nJJ Hopfield and CD Brody (2000)\r\nJJ Hopfield and CD Brody (2001). Instructions are provided in the below readme.txt file.", + "tags": [ + { + "id": 790, + "tag": "Attractor Neural Network" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 959, + "tag": "ModelDB:2798" + } + ], + "timestamp_created": "2024-01-09 11:22:07.304893+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2798", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "397": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 397, + "name": "CA1 Pyramidal Neuron: slow Na+ inactivation (Migliore 1996)", + "repository_type": "github", + "summary": "Model files from the paper: \r\nM. Migliore, Modeling the attenuation and failure of action potentials in \r\nthe dendrites of hippocampal neurons, Biophys. J. 71:2394-403 (1996). Please see the below readme file for installation and use instructions. Contact michele.migliore@pa.ibf.cnr.it\r\n if you have any questions about the implementation of the model.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 960, + "tag": "ModelDB:2937" + } + ], + "timestamp_created": "2024-01-09 11:22:07.959126+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2937", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "398": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 398, + "name": "Estimation and Production of Time Intervals (Migliore et al 2001)", + "repository_type": "github", + "summary": "NEURON model files from the paper \r\nM. Migliore, L. Messineo, M. Cardaci, G.F. Ayala, \r\nQuantitative modeling of perception and production of time intervals, J.Neurophysiol. 86, 2754-2760 (2001). Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model.", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 961, + "tag": "ModelDB:3167" + } + ], + "timestamp_created": "2024-01-09 11:22:08.552967+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3167", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "399": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 399, + "name": "CA3 Pyramidal Neuron (Migliore et al 1995)", + "repository_type": "github", + "summary": "Model files from the paper:\r\nM. Migliore, E. Cook, D.B. Jaffe, D.A. Turner and D. Johnston, Computer\r\nsimulations of morphologically reconstructed CA3 hippocampal neurons, J.\r\nNeurophysiol. 73, 1157-1168 (1995).\r\n\r\nDemonstrates how the same cell could be bursting or non bursting according to the Ca-independent conductance densities. Includes calculation of intracellular Calcium. Instructions are provided in the below README file. Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model.", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 962, + "tag": "ModelDB:3263" + } + ], + "timestamp_created": "2024-01-09 11:22:09.299883+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3263", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "400": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 400, + "name": "Short term plasticity of synapses onto V1 layer 2/3 pyramidal neuron (Varela et al 1997)", + "repository_type": "github", + "summary": "This archive contains 3 mod files for NEURON that implement the short term \r\nsynaptic plasticity model described in\r\n Varela, J.A., Sen, K., Gibson, J., Fost, J., Abbott, L.R., \r\n and Nelson, S.B.. \r\n A quantitative description of short-term plasticity at \r\n excitatory synapses in layer 2/3 of rat primary visual cortex.\r\n Journal of Neuroscience 17:7926-7940, 1997.\r\n\r\nContact ted.carnevale@yale.edu if you have questions \r\nabout this implementation of the model.", + "tags": [ + { + "id": 797, + "tag": "Depression" + }, + { + "id": 798, + "tag": "Facilitation" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 800, + "tag": "Vision" + }, + { + "id": 963, + "tag": "ModelDB:3264" + } + ], + "timestamp_created": "2024-01-09 11:22:09.821248+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3264", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "401": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 401, + "name": "Pyramidal Neuron Deep: K+ kinetics (Korngreen, Sakmann 2000)", + "repository_type": "github", + "summary": "NEURON mod files for the slow and fast K+ currents from the paper:\r\nVoltage-gated K+ channels in layer 5 neocortical pyramidal neurones from young rats: subtypes and gradients\r\nA. Korngreen and B. Sakmann, J.Physiol. 525.3, 621-639 (2000).", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 964, + "tag": "ModelDB:3289" + } + ], + "timestamp_created": "2024-01-09 11:22:10.290488+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3289", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "402": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 402, + "name": "CN Octopus Cell: Ih current (Bal, Oertel 2000)", + "repository_type": "github", + "summary": "NEURON mod files for the Ih current from the paper \r\nR. Bal and D. Oertel\r\nHyperpolarization-Activated, Mixed-Cation Current (Ih) in Octopus Cells of the Mammalian Cochlear Nucleus, J. Neurophysiol. 84, 806-817 (2000).\r\nContact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model.", + "tags": [ + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 965, + "tag": "ModelDB:3332" + } + ], + "timestamp_created": "2024-01-09 11:22:10.733203+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3332", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "403": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 403, + "name": "Action potential initiation in the olfactory mitral cell (Shen et al 1999)", + "repository_type": "github", + "summary": "Mitral cell model with standard parameters for the paper:\r\nShen, G.Y., Chen, W. R., Midtgaard, J., Shepherd, G.M., and Hines, M.L.\r\n(1999)\r\nComputational Analysis of Action Potential Initiation in Mitral\r\nCell Soma and Dendrites Based on Dual Patch Recordings.\r\nJournal of Neurophysiology 82:3006. Contact Michael.Hines@yale.edu if you have any questions about the implementation of the model.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 966, + "tag": "ModelDB:3342" + } + ], + "timestamp_created": "2024-01-09 11:22:11.190945+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3342", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "404": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 404, + "name": "Thalamocortical and Thalamic Reticular Network (Destexhe et al 1996)", + "repository_type": "github", + "summary": "NEURON model of oscillations in networks of thalamocortical and thalamic reticular neurons in the ferret. (more applications for a model quantitatively identical to previous DLGN model; updated for NEURON v4 and above)", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 802, + "tag": "Sleep" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 803, + "tag": "Spindles" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 967, + "tag": "ModelDB:3343" + } + ], + "timestamp_created": "2024-01-09 11:22:11.652053+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3343", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "405": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 405, + "name": "DG granule cell: I-A model (Beck et al 1992)", + "repository_type": "github", + "summary": "NEURON mod files for the I-A current from the paper:\r\nBeck H, Ficker E, Heinemann U.\r\nProperties of two voltage-activated potassium currents in \r\nacutely isolated juvenile rat dentate gyrus granule cells.\r\nJ. Neurophysiol. 68, 2086-2099 (1992) Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model.", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 968, + "tag": "ModelDB:3344" + } + ], + "timestamp_created": "2024-01-09 11:22:12.098578+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3344", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "406": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 406, + "name": "Coincidence detection in avian brainstem (Simon et al 1999)", + "repository_type": "github", + "summary": "A detailed biophysical model of coincidence\r\ndetector neurons in the nucleus laminaris (auditory brainstem) which are\r\npurported to detect interaural time differences (ITDs) from Simon et al 1999.", + "tags": [ + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 969, + "tag": "ModelDB:3434" + } + ], + "timestamp_created": "2024-01-09 11:22:12.565162+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3434", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "407": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 407, + "name": "MNTB Neuron: Kv3.1 currents (Wang et al 1998)", + "repository_type": "github", + "summary": "Model of Medial Nucleus of the Trapezoid Body (MNTB) neurons described in Lu-Yang Wang, Li Gan, Ian D. Forsythe and Leonard K. Kaczmarek. Contribution of the Kv3.1 potassium channel to high-frequency firing in mouse auditory neurones. J. Physiol (1998) 509.1 183-194. Created by David Kornfeld, Byram Hills High School, Armonk NY. Please email dbk1@mindspring.com for questions about the model. See Readme.txt below for more info.", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 970, + "tag": "ModelDB:3454" + } + ], + "timestamp_created": "2024-01-09 11:22:13.070522+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3454", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "408": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 408, + "name": "Retinal Ganglion Cell: I-CaN and I-CaL (Benison et al. 2001)", + "repository_type": "github", + "summary": "NEURON mod files for the CaN and CaL currents from the papers:\r\nHuang, S.-J. & Robinson, D.W. (1998). Activation and Inactivation properties of voltage-gated calcium currents in developing cat retinal ganglion cells. Neuroscience 85:239-247 (experimental) and\r\nBenison G. Keizer J., Chalupa L.M., Robinson D.W., (2001) J. theor. Biol. 210:187-199 (theoretical).", + "tags": [ + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 971, + "tag": "ModelDB:3457" + } + ], + "timestamp_created": "2024-01-09 11:22:13.517511+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3457", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "409": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 409, + "name": "Retinal Ganglion Cell: I-Na,t (Benison et al 2001)", + "repository_type": "github", + "summary": "NEURON mod files for the Na current from the papers:\r\n(model)\r\nBenison G, Keizer J, Chalupa LM, Robinson DW. Modeling temporal behavior of postnatal cat retinal ganglion cells. J Theor Biol. 2001 210:187-99 and a reference from this paper: (experimental)\r\nSkaliora I, Scobey RP, Chalupa LM. Prenatal development of excitability in cat retinal ganglion cells: action potentials and sodium currents. J Neurosci 1993 13:313-23. See the readme.txt file below for more information.", + "tags": [ + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 972, + "tag": "ModelDB:3483" + } + ], + "timestamp_created": "2024-01-09 11:22:13.958342+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3483", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "410": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 410, + "name": "Retinal Ganglion Cell: I-A (Benison et al 2001)", + "repository_type": "github", + "summary": "NEURON mod files for the K-A current from the papers: (model) Benison G, Keizer J, Chalupa LM, Robinson DW. Modeling temporal behavior of postnatal cat retinal ganglion cells. J.Theor.Biol. 210:187-199 (2001) and (experiment) Skaliora I, Robinson DW, Scobey RP, Chalupa LM., Properties of K+ conductances in cat retinal ganglion cells during the period of activity-mediated refinements in retinofugal pathways. Eur.J.Neurosci. 7:1558-1568 (1995).", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 973, + "tag": "ModelDB:3488" + } + ], + "timestamp_created": "2024-01-09 11:22:14.393535+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3488", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "411": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 411, + "name": "Retinal Ganglion Cell: I-K (Skaliora et al 1995)", + "repository_type": "github", + "summary": "NEURON mod files for the K-DR current from the paper: \r\nSkaliora I, Robinson DW, Scobey RP, Chalupa LM. Properties of K+ conductances in cat retinal ganglion cells during the period of activity-mediated refinements in retinofugal pathways.\r\nEur J Neurosci 1995 7(7):1558-1568. See the readme.txt file below for more information.", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 974, + "tag": "ModelDB:3491" + } + ], + "timestamp_created": "2024-01-09 12:01:20.820086+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3491", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "412": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 412, + "name": "Spinal Motor Neuron: Na, K_A, and K_DR currents (Safronov, Vogel 1995)", + "repository_type": "github", + "summary": "NEURON mod files for the Na, K-A, and K-DR currents from the paper: \r\nSafronov, B.V. and Vogel,W. Single voltage-activated Na+ and K+ channels in the somata of rat motorneurons. Journal of Physiology 487.1:91-106 (1995). See the readme.txt file for more information.", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 975, + "tag": "ModelDB:3493" + } + ], + "timestamp_created": "2024-01-09 12:01:21.454191+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3493", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "413": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 413, + "name": "Xenopus Myelinated Neuron (Frankenhaeuser, Huxley 1964)", + "repository_type": "github", + "summary": "Frankenhaeuser, B. and Huxley, A. F. (1964),\r\nThe action potential in the myelinated nerve fiber of Xenopus Laevis as computed on the basis of voltage clamp data. J. Physiol. 171: 302-315. See README file for more information.", + "tags": [ + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 976, + "tag": "ModelDB:3507" + } + ], + "timestamp_created": "2024-01-09 12:01:22.030482+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3507", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "414": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 414, + "name": "Ca-dependent K Channel: kinetics from rat muscle (Moczydlowski, Latorre 1983) NEURON", + "repository_type": "github", + "summary": "Macroscopic channel model based on\r\nMoczydlowski, E. and Latorre, R. (1983).\r\nGating kinetics of Ca++ activated K+ channels from rat muscle incorporated into planar lipid bilayers. \r\nJ. Gen. Physiol. 82: 511-542 \r\nSee README file for more information.", + "tags": [ + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 977, + "tag": "ModelDB:3509" + } + ], + "timestamp_created": "2024-01-09 12:01:22.461951+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3509", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "415": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 415, + "name": "Regulation of a slow STG rhythm (Nadim et al 1998)", + "repository_type": "github", + "summary": "Frequency regulation of a slow rhythm by a fast periodic input. Nadim, F., Manor, Y., Nusbaum, M. P., Marder, E. (1998) J. Neurosci. 18: 5053-5067", + "tags": [ + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 978, + "tag": "ModelDB:3511" + } + ], + "timestamp_created": "2024-01-09 12:01:22.901277+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3511", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "416": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 416, + "name": "Thalamic Relay Neuron: I-T current (Williams, Stuart 2000)", + "repository_type": "github", + "summary": "NEURON mod files for the Ca-T current from the paper:\r\nWilliams SR, Stuart GJ, Action potential backpropagation and \r\nsomato-dendritic distribution of ion channels in thalamocortical neurons.\r\nJ Neurosci. 2000 20:1307-17.\r\nContact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model.", + "tags": [ + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 979, + "tag": "ModelDB:3533" + } + ], + "timestamp_created": "2024-01-09 12:01:23.344009+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3533", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "417": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 417, + "name": "Olfactory Mitral Cell: I-A and I-K currents (Wang et al 1996)", + "repository_type": "github", + "summary": "NEURON mod files for the I-A and I-K currents from the paper:\r\nX.Y. Wang, J.S. McKenzie and R.E. Kemm, Whole-cell K+ currents in identified olfactory \r\nbulb output neurones of rats. J Physiol. 1996 490.1:63-77. Please see the readme.txt included in the model file for more information.", + "tags": [ + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 980, + "tag": "ModelDB:3648" + } + ], + "timestamp_created": "2024-01-09 12:01:23.775736+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3648", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "418": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 418, + "name": "Glutamate diffusion and AMPA receptor activation in the cerebellar glomerulus (Saftenku 2005)", + "repository_type": "github", + "summary": "Synaptic conductances are influenced markedly by the geometry of the space surrounding the synapse since the transient glutamate concentration in the synaptic cleft is determined by this geometry. Our paper is an attempt to understand the reasons for slow glutamate diffusion in the cerebellar glomerulus, a structure situated around the enlarged mossy fiber terminal in the cerebellum and surrounded by a glial sheath. \r\n...\r\nOur results suggest at least a 7- to 10-fold lower apparent diffusion coefficient of glutamate in the porous medium of the glomerulus than in water. \r\n... See paper for details and more.", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 981, + "tag": "ModelDB:3658" + } + ], + "timestamp_created": "2024-01-09 12:01:24.209448+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3658", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "419": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 419, + "name": "Neocortical Layer I: I-A and I-K (Zhou, Hablitz 1996)", + "repository_type": "github", + "summary": "NEURON mod files for the I-A and I-K currents from the paper:\r\nZhou FM, Hablitz JJ.\r\nLayer I neurons of the rat neocortex. II. Voltage-dependent outward currents.\r\nJ Neurophysiol 1996 76:668-82.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 982, + "tag": "ModelDB:3660" + } + ], + "timestamp_created": "2024-01-09 12:01:24.654969+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3660", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "420": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 420, + "name": "Olfactory Periglomerular Cells: I-h kinetics (Cadetti, Belluzzi 2001)", + "repository_type": "github", + "summary": "NEURON mod files for the Ih current from the paper:\r\nCadetti L, Belluzzi O.\r\nHyperpolarisation-activated current in glomerular cells \r\nof the rat olfactory bulb.\r\nNeuroreport 12:3117-20 (2001).", + "tags": [ + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 983, + "tag": "ModelDB:3665" + } + ], + "timestamp_created": "2024-01-09 12:01:25.108848+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3665", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "421": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 421, + "name": "Thalamic Reticular Network (Destexhe et al 1994)", + "repository_type": "github", + "summary": "Demo for simulating networks of thalamic reticular neurons (reproduces figures from Destexhe A et al 1994)", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 802, + "tag": "Sleep" + }, + { + "id": 803, + "tag": "Spindles" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + }, + { + "id": 984, + "tag": "ModelDB:3670" + } + ], + "timestamp_created": "2024-01-09 12:01:25.553709+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3670", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "422": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 422, + "name": "Salamander retinal ganglion cell: ion channels (Fohlmeister, Miller 1997)", + "repository_type": "github", + "summary": "A realistic five (5) channel spiking model reproduces \r\nthe bursting behavior of tiger salamander\r\nganglion cells in the retina.\r\nPlease see the readme for more information.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 985, + "tag": "ModelDB:3673" + } + ], + "timestamp_created": "2024-01-09 12:01:26.008832+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3673", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "423": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 423, + "name": "Ephaptic interactions in olfactory nerve (Bokil et al 2001)", + "repository_type": "github", + "summary": "Bokil, H., Laaris, N., Blinder, K., Ennis, M., and Keller, A. (2001) Ephaptic interactions in the mammalian olfactory system. J. Neurosci. 21:RC173(1-5)", + "tags": [ + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 809, + "tag": "Ephaptic coupling" + }, + { + "id": 810, + "tag": "Extracellular Fields" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 986, + "tag": "ModelDB:3676" + } + ], + "timestamp_created": "2024-01-09 12:01:26.550819+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3676", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "424": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 424, + "name": "Pyramidal Neuron Deep: attenuation in dendrites (Stuart, Spruston 1998)", + "repository_type": "github", + "summary": "Stuart, G. and Spruston, N. Determinants of voltage attenuation in neocortical pyramidal neuron dendrites. Journal of Neuroscience 18:3501-3510, 1998.", + "tags": [ + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 987, + "tag": "ModelDB:3677" + } + ], + "timestamp_created": "2024-01-09 12:01:26.982197+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3677", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "425": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 425, + "name": "Visual Cortex Neurons: Dendritic computations (Archie, Mel 2000)", + "repository_type": "github", + "summary": "Neuron and C program files from Archie, K.A. and Mel, B.W. A model of intradendritic computation of binocular disparity. Nature Neuroscience 3:54-63, 2000\r\nThe original files for this model are located at \r\nthe web site http://www-lnc.usc.edu/~karchie/synmap", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 800, + "tag": "Vision" + }, + { + "id": 988, + "tag": "ModelDB:3682" + } + ], + "timestamp_created": "2024-01-09 12:01:27.420251+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3682", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "426": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 426, + "name": "Thalamic Relay Neuron: I-h (McCormick, Pape 1990)", + "repository_type": "github", + "summary": "NEURON mod files for the Ih current from the paper:\r\nMcCormick DA, Pape HC.\r\nProperties of a hyperpolarization-activated cation current \r\nand its role in rhythmic oscillation in thalamic relay neurones.\r\nJ. Physiol. 1990 431:291-318.", + "tags": [ + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 989, + "tag": "ModelDB:3684" + } + ], + "timestamp_created": "2024-01-09 12:01:28.207216+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3684", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "427": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 427, + "name": "Arteriolar networks: Spread of potential (Crane et al 2001)", + "repository_type": "github", + "summary": "Crane, G.J., Hines, M.L., and Neild, T.O. (2001)\r\nSimulating the spread of membrane potential changes in arteriolar networks.\r\nMicrocirculation 8:33-43.\r\n\r\nThis model uses a gap junction density mechanism\r\nto couple arteriolar smooth muscle and endothelium\r\nin microvascular trees.", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 990, + "tag": "ModelDB:3785" + } + ], + "timestamp_created": "2024-01-09 12:01:28.642601+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3785", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "428": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 428, + "name": "Olfactory Mitral cell: AP initiation modes (Chen et al 2002)", + "repository_type": "github", + "summary": "The mitral cell primary dendrite plays an important role in transmitting distal olfactory nerve input from olfactory glomerulus to the soma-axon initial segment. To understand how dendritic active properties are involved in this transmission, we have combined dual soma and dendritic patch recordings with computational modeling to analyze action-potential initiation and propagation in the primary dendrite.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 991, + "tag": "ModelDB:3793" + } + ], + "timestamp_created": "2024-01-09 12:01:29.084598+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3793", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "429": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 429, + "name": "Demyelinated and remyelinating axon conductances (Hines, Shrager 1991)", + "repository_type": "github", + "summary": "Hines, Michael and Peter Shrager (1991). A computational test of\r\nthe requirements for conduction in demyelinated axons.\r\nJ. Restorative Neurology and Neuroscience. 3 81--93.", + "tags": [ + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 811, + "tag": "Multiple sclerosis" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 992, + "tag": "ModelDB:3798" + } + ], + "timestamp_created": "2024-01-09 12:01:29.540737+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3798", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "430": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 430, + "name": "Cardiac Atrial Cell (Courtemanche et al 1998)", + "repository_type": "github", + "summary": "Marc Courtemanche, Rafael J. Ramirez, and Stanley Nattel.\r\nIonic mechanisms underlying human atrial action potential properties\r\ninsights from a mathematical model\r\nAm J Physiol Heart Circ Physiol 1998 275: H301-H321.\r\n\r\nThe implementation of this model in NEURON\r\nwas contributed by Ingemar Jacobson.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 814, + "tag": "Na/Ca exchanger" + }, + { + "id": 993, + "tag": "ModelDB:3800" + } + ], + "timestamp_created": "2024-01-09 12:01:30.020188+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3800", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "431": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 431, + "name": "Dentate Basket Cell: spatial summation of inhibitory synaptic inputs (Bartos et al 2001)", + "repository_type": "github", + "summary": "Spatial summation of inhibitory synaptic input in a passive model of a basket cell from the dentate gyrus of rat hippocampus. Reproduces Figs. 5Ac and d in Bartos, M., Vida, I., Frotscher, M., Geiger, J.R.P, and Jonas, P.. Rapid signaling at inhibitory synapses in a dentate gyrus interneuron network. Journal of Neuroscience 21:2687-2698, 2001.", + "tags": [ + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 994, + "tag": "ModelDB:3801" + } + ], + "timestamp_created": "2024-01-09 12:01:30.478941+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3801", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "432": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 432, + "name": "Retinal Photoreceptor: I Potassium (Beech, Barnes 1989)", + "repository_type": "github", + "summary": "NEURON mod files for a Potassium current from the paper:\r\nBeech DJ, Barnes S.\r\nCharacterization of a voltage-gated K+ channel that accelerates \r\nthe rod response to dim light.\r\nNeuron 3:573-81 (1989).", + "tags": [ + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 995, + "tag": "ModelDB:3802" + } + ], + "timestamp_created": "2024-01-09 12:01:30.910485+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3802", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "433": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 433, + "name": "Spinal Motor Neuron (Dodge, Cooley 1973)", + "repository_type": "github", + "summary": "\"The excitability of various regions of the spinal motorneuron can be specified by solving the partial differential equation of a nerve fiber whose diameter and membrane properties vary with distance. For our model geometrical factors for the myelinated axon, initial segment and cell body were derived from anatomical measurements, the dendritic tree was represented by its equivalent cylinder, and the current-voltage relations of the membrane were described by a modification of the Hodgkin-Huxley model that fits voltage-clamp data from the motorneuron. ...\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + }, + { + "id": 996, + "tag": "ModelDB:3805" + } + ], + "timestamp_created": "2024-01-09 12:01:31.584252+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3805", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "434": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 434, + "name": "Leech Mechanosensory Neurons: Synaptic Facilitation by Reflected APs (Baccus 1998)", + "repository_type": "github", + "summary": "This model by Stephen Baccus explores the phenomena of action potential (AP) propagation at branch boints in axons. APs are sometimes transmitted down the efferent processes and sometimes are reflected back to the axon of AP origin or neither. See the paper for details. The model zip file contains a readme.txt which list introductory steps to follow to run the simulation. Stephen Baccus's email address: baccus@fas.harvard.edu", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 798, + "tag": "Facilitation" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 997, + "tag": "ModelDB:3807" + } + ], + "timestamp_created": "2024-01-09 12:01:32.094634+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3807", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "435": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 435, + "name": "MyFirstNEURON (Houweling, Sejnowski 1997)", + "repository_type": "github", + "summary": "MyFirstNEURON is a NEURON demo by Arthur Houweling and Terry Sejnowski. Perform experiments from the book 'Electrophysiology of the Neuron, A Companion to Shepherd's Neurobiology, An Interactive Tutorial' by John Huguenard & David McCormick, Oxford University Press 1997, or design your own one or two cell simulation.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 802, + "tag": "Sleep" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + }, + { + "id": 998, + "tag": "ModelDB:3808" + } + ], + "timestamp_created": "2024-01-09 12:01:32.605474+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3808", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "436": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 436, + "name": "Spinal Motor Neuron (McIntyre et al 2002)", + "repository_type": "github", + "summary": "Simulation of peripheral nervous system (PNS) mylelinated axon.\r\n\r\nThis model is described in detail in:\r\n\r\nMcIntyre CC, Richardson AG, and Grill WM.(2002)", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 810, + "tag": "Extracellular Fields" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 999, + "tag": "ModelDB:3810" + } + ], + "timestamp_created": "2024-01-09 12:01:33.107788+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3810", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "437": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 437, + "name": "Visual Cortex Neurons: Dendritic study (Anderson et al 1999)", + "repository_type": "github", + "summary": "Neuron mod and hoc files for the paper: Anderson, J.C. Binzegger, T., Kahana, O., Segev, I., and Martin, K.A.C Dendritic asymmetry cannot account for directional responses in visual cortex. Nature Neuroscience 2:820:824, 1999", + "tags": [ + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 1000, + "tag": "ModelDB:3812" + } + ], + "timestamp_created": "2024-01-09 12:01:33.622535+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3812", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "438": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 438, + "name": "Synaptic plasticity: pyramid->pyr and pyr->interneuron (Tsodyks et al 1998)", + "repository_type": "github", + "summary": "An implementation of a model of short-term synaptic plasticity with NEURON. The model was originally described by Tsodyks et al., who assumed that the synapse acted as a current source, but this implementation treats it as a conductance change.\r\n\r\nTsodyks, M., Pawelzik, K., Markram, H.\r\n Neural networks with dynamic synapses.\r\n Neural Computation 10:821-835, 1998.\r\nTsodyks, M., Uziel, A., Markram, H.\r\n Synchrony generation in recurrent networks with\r\n frequency-dependent synapses.\r\n J. Neurosci. 2000 RC50.", + "tags": [ + { + "id": 797, + "tag": "Depression" + }, + { + "id": 798, + "tag": "Facilitation" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + }, + { + "id": 1001, + "tag": "ModelDB:3815" + } + ], + "timestamp_created": "2024-01-09 12:01:34.076856+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3815", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "439": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 439, + "name": "Pyramidal Neuron: Deep, Thalamic Relay and Reticular, Interneuron (Destexhe et al 1998, 2001)", + "repository_type": "github", + "summary": "This package shows single-compartment models of different classes of cortical neurons, such as the \"regular-spiking\", \"fast-spiking\" and \"bursting\" (LTS) neurons. The mechanisms included are the Na+ and K+ currents for generating action potentials (INa, IKd), the T-type calcium current (ICaT), and a slow voltage-dependent K+ current (IM). See http://cns.fmed.ulaval.ca/alain_demos.html", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1002, + "tag": "ModelDB:3817" + } + ], + "timestamp_created": "2024-01-09 12:01:34.523868+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/3817", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "440": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 440, + "name": "Squid axon (Hodgkin, Huxley 1952) (NEURON)", + "repository_type": "github", + "summary": "The classic HH model of squid axon membrane\r\nimplemented in NEURON.\r\nHodgkin, A.L., Huxley, A.F. (1952)", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1003, + "tag": "ModelDB:5426" + } + ], + "timestamp_created": "2024-01-09 12:01:35.185726+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/5426", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "441": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 441, + "name": "Midbrain dopamine neuron: firing patterns (Canavier 1999)", + "repository_type": "github", + "summary": "Sodium dynamics drives the generation of\r\nslow oscillations postulated to underly\r\nNMDA-evoked bursting activity.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 816, + "tag": "Na/K pump" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 817, + "tag": "Sodium pump" + }, + { + "id": 1004, + "tag": "ModelDB:6763" + } + ], + "timestamp_created": "2024-01-09 12:01:35.635118+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/6763", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "442": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 442, + "name": "Cerebellar purkinje cell (De Schutter and Bower 1994)", + "repository_type": "github", + "summary": "Tutorial simulation of a cerebellar Purkinje cell.\r\n\r\nThis tutorial is based upon a GENESIS simulation of a cerebellar Purkinje cell, modeled and fine-tuned by Erik de Schutter. The tutorial assumes that you have a basic knowledge of the Purkinje cell and its synaptic inputs. It gives visual insight in how different properties as concentrations and channel conductances vary and interact within a real Purkinje cell.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 818, + "tag": "I p,q" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + }, + { + "id": 1005, + "tag": "ModelDB:7176" + } + ], + "timestamp_created": "2024-01-09 12:01:36.121126+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/7176", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "443": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 443, + "name": "CA1 pyramidal neuron: conditional boosting of dendritic APs (Watanabe et al 2002)", + "repository_type": "github", + "summary": "Model files from the paper Watanabe S, Hoffman DA, Migliore M,\r\nJohnston D (2002). The experimental and modeling results support the\r\nhypothesis that\r\ndendritic K-A channels and the boosting of back-propagating action\r\npotentials\r\ncontribute to the induction of LTP in CA1 neurons.\r\n See the paper for details.\r\nQuestions about the model may be addressed to Michele Migliore:\r\n michele.migliore@pa.ibf.cnr.it", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1006, + "tag": "ModelDB:7386" + } + ], + "timestamp_created": "2024-01-09 12:01:36.583654+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/7386", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "444": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 444, + "name": "Feedforward heteroassociative network with HH dynamics (Lytton 1998)", + "repository_type": "github", + "summary": "Using the original McCulloch-Pitts notion of simple on and off spike coding in lieu of rate coding, an Anderson-Kohonen artificial neural network (ANN) associative memory model was ported to a neuronal network with Hodgkin-Huxley dynamics.", + "tags": [ + { + "id": 790, + "tag": "Attractor Neural Network" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1007, + "tag": "ModelDB:7399" + } + ], + "timestamp_created": "2024-01-09 12:01:37.050436+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/7399", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "445": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 445, + "name": "Hippocampus temporo-septal engram shift model (Lytton 1999)", + "repository_type": "github", + "summary": "Temporo-septal engram shift model of hippocampal memory. The model posits that memories gradually move along the hippocampus from a temporal encoding site to ever more septal sites from which they are recalled. We propose that the sense of time is encoded by the location of the engram along the temporo-septal axis.", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1008, + "tag": "ModelDB:7400" + } + ], + "timestamp_created": "2024-01-09 12:01:37.504335+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/7400", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "446": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 446, + "name": "CA1 Pyramidal Neuron: Synaptic Scaling (London, Segev 2001)", + "repository_type": "github", + "summary": "London and Segev (2001) discuss location dependent and location independent synaptic scaling in a model CA1 neuron with passive dendrites. The freely available text is followed by a critique by Maggee and Cook who comment that\r\nthe London and Segev model is accurate and informative and however needs to be augmented by\r\nactive channels in dendrites. Note: the\r\nzip files for this model are stored at the nature neuroscience website - Click above Supplementary Source Code in the readme.html in the model files", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 819, + "tag": "NEURON (web link to model)" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1009, + "tag": "ModelDB:7485" + } + ], + "timestamp_created": "2024-01-09 12:01:37.949405+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/7485", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "447": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 447, + "name": "CA1 pyramidal neuron: Synaptic Scaling (Magee, Cook 2000)", + "repository_type": "github", + "summary": "Jeffrey Magee and Erik Cook found evidence in experiments and modeling that support the hypothesis that an increase in synaptic conductance for\r\nsynapses at larger distances from the soma is\r\nresponsible for reducing the location dependence (relative to the soma) of synapses.", + "tags": [ + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1010, + "tag": "ModelDB:7509" + } + ], + "timestamp_created": "2024-01-09 12:01:38.391737+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/7509", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "448": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 448, + "name": "Modulation of temporal integration window (Migliore, Shepherd 2002)", + "repository_type": "github", + "summary": "Model simulation file from the paper \r\nM.Migliore and Gordon M. Shepherd\r\nEmerging rules for distributions of active dendritic properties underlying specific neuronal functions. Nature Rev. Neurosci. 3, 362-370 (2002).", + "tags": [ + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1011, + "tag": "ModelDB:7659" + } + ], + "timestamp_created": "2024-01-09 12:01:38.821503+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/7659", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "449": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 449, + "name": "Dendritica (Vetter et al 2001)", + "repository_type": "github", + "summary": "Dendritica is a collection of programs for relating dendritic geometry and signal propagation. The programs are based on those used for the simulations described in: Vetter, P., Roth, A. & Hausser, M. (2001) For reprint requests\r\nand additional information please contact Dr. M. Hausser, email address: m.hausser@ucl.ac.uk", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 818, + "tag": "I p,q" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1012, + "tag": "ModelDB:7907" + } + ], + "timestamp_created": "2024-01-09 12:01:39.291500+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/7907", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "450": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 450, + "name": "NMDA receptor saturation (Chen et al 2001)", + "repository_type": "github", + "summary": "Experiments and modeling reported in the paper Chen N, Ren J, Raymond LA, and Murphy T (2001) support the hypothesis that glutamate has a relatively lower potency at NMDARs than previously thought from agonist application under equilibrium conditions. Further information and reprint requests are available from Dr T.H. Murphy thmurphy at interchange.ubc.ca", + "tags": [ + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1013, + "tag": "ModelDB:7988" + } + ], + "timestamp_created": "2024-01-09 12:01:39.783311+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/7988", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "451": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 451, + "name": "Fluctuating synaptic conductances recreate in-vivo-like activity (Destexhe et al 2001)", + "repository_type": "github", + "summary": "This model (and experiments) reported in Destexhe, Rudolh, Fellous, and Sejnowski (2001) support the hypothesis that many of the\r\ncharacteristics of cortical neurons in vivo can be explained by fast glutamatergic and GABAergic conductances varying stochastically.\r\n\r\nSome of these cortical neuron characteristics of fluctuating synaptic origin are a depolarized membrane potential, the\r\npresence of high-amplitude membrane potential fluctuations, a low input resistance and irregular spontaneous firing activity. In addition, the\r\npoint-conductance model could simulate the enhancement of responsiveness due to background activity.\r\nFor more information please contact Alain Destexhe. email: Destexhe@iaf.cnrs-gif.fr", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 820, + "tag": "Synaptic noise" + }, + { + "id": 1014, + "tag": "ModelDB:8115" + } + ], + "timestamp_created": "2024-01-09 12:01:40.250463+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/8115", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "452": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 452, + "name": "Spike Initiation in Neocortical Pyramidal Neurons (Mainen et al 1995)", + "repository_type": "github", + "summary": "This model reproduces figure 3A from the paper\r\nMainen ZF, Joerges J, Huguenard JR, Sejnowski TJ (1995). Please see the paper for detail whose full text is available at http://www.cnl.salk.edu/~zach/methods.html\r\nEmail Zach Mainen for questions: mainen@cshl.org", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1015, + "tag": "ModelDB:8210" + } + ], + "timestamp_created": "2024-01-09 12:01:40.709265+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/8210", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "453": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 453, + "name": "Febrile seizure-induced modifications to Ih (Chen et al 2001)", + "repository_type": "github", + "summary": "Modeling and experiments in the paper Chen K,Aradi I, Thom N,Eghbal-Ahmadi M, Baram TZ, and Soltesz I (2001) support the hypothesis that modified Ih currents strongly influence inhibitory inputs in CA1 cells and that the depolarizing shift in Ih activation plays a primary role in this process.\r\nPlease see the paper for details. Some modeling details are available at http://www.ucihs.uci.edu/anatomy/soltesz/supp.htm Correspondance should be addressed to isoltesz@uci.edu (modeling was done by Ildiko Aradi, iaradi@uci.edu)", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 775, + "tag": "Rebound firing" + }, + { + "id": 1016, + "tag": "ModelDB:8284" + } + ], + "timestamp_created": "2024-01-09 12:01:41.156543+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/8284", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "454": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 454, + "name": "CA1 pyramidal neuron: effects of Lamotrigine on dendritic excitability (Poolos et al 2002)", + "repository_type": "github", + "summary": "NEURON mod files from N. Poolos, M. Migliore, and D. Johnston, Nature Neuroscience (2002).\r\nThe experimental and modeling results in this paper demonstrate for the first time that neuronal excitability can be altered by pharmaceuticals acting selectively on dendrites, and suggest an important role for Ih in controlling dendritic excitability and epileptogenesis.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1017, + "tag": "ModelDB:9769" + } + ], + "timestamp_created": "2024-01-09 12:01:41.620136+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/9769", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "455": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 455, + "name": "Myelinated axon conduction velocity (Brill et al 1977)", + "repository_type": "github", + "summary": "Examines conduction velocity as function of\r\ninternodal length.", + "tags": [ + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1018, + "tag": "ModelDB:9848" + } + ], + "timestamp_created": "2024-01-09 12:01:42.075466+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/9848", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "456": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 456, + "name": "Temperature-Sensitive conduction at axon branch points (Westerfield et al 1978)", + "repository_type": "github", + "summary": "Propagation of impulses through branching regions of squid axons was examined experimentally and with computer simulations. The ratio of postbranch/prebranch diameters at which propagation failed was very sensitive to temperature.", + "tags": [ + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 821, + "tag": "Conduction failure" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1019, + "tag": "ModelDB:9849" + } + ], + "timestamp_created": "2024-01-09 12:01:42.522625+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/9849", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "457": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 457, + "name": "Conduction in uniform myelinated axons (Moore et al 1978)", + "repository_type": "github", + "summary": "Examines the relative sensitivity of the velocity of impulse propagation to changes in nodal and internodal parameters.", + "tags": [ + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 811, + "tag": "Multiple sclerosis" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1020, + "tag": "ModelDB:9851" + } + ], + "timestamp_created": "2024-01-09 12:01:42.968264+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/9851", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "458": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 458, + "name": "Site of impulse initiation in a neuron (Moore et al 1983)", + "repository_type": "github", + "summary": "Examines the effect of temperature, the taper of the axon hillock, and HH channel density on antidromic spike invasion into the soma and spike initiation under dendritic stimulation.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1021, + "tag": "ModelDB:9852" + } + ], + "timestamp_created": "2024-01-09 12:01:43.420015+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/9852", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "459": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 459, + "name": "Current flow during PAP in squid axon at diameter change (Joyner et al 1980)", + "repository_type": "github", + "summary": "From the paper abstract: An impulse ... sees an increased electrical load at regions of increasing diameter or at branch points with certain morphologies. We present here theoretical and experimental studies on the changes in membrane current and axial current associated with diameter changes. The theoretical studies were done with numerical solutions for cable equations that were generalized to include a varying diameter; the Hodgkin-Huxley equations were used to represent the membrane properties. ... As an action potential approaches a region of increased electrical load, the action potential amplitude and rate of rise decrease, but there is a marked increase in the magnitude of the inward sodium current. ... (See paper for more details.)", + "tags": [ + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1022, + "tag": "ModelDB:9853" + } + ], + "timestamp_created": "2024-01-09 12:01:43.863653+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/9853", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "460": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 460, + "name": "Presynaptic calcium dynamics at neuromuscular junction (Stockbridge, Moore 1984)", + "repository_type": "github", + "summary": "The diffusion of calcium is effectively reduced\r\nby the ratio of bound to free calcium. Treating\r\nthe release magnitude as proportional to the\r\nfourth power of calcium concentration next to\r\nthe membrane gives reasonable facilitation\r\nwith very little release between spikes.", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 798, + "tag": "Facilitation" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1023, + "tag": "ModelDB:9888" + } + ], + "timestamp_created": "2024-01-09 12:01:44.319602+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/9888", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "461": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 461, + "name": "Thalamic quiescence of spike and wave seizures (Lytton et al 1997)", + "repository_type": "github", + "summary": "A phase plane analysis of a two cell interaction between a thalamocortical neuron (TC) and a thalamic reticularis neuron (RE).", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1024, + "tag": "ModelDB:9889" + } + ], + "timestamp_created": "2024-01-09 12:01:44.765252+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/9889", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "462": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 462, + "name": "Nerve terminal currents at lizard neuromuscular junction (Lindgren, Moore 1989)", + "repository_type": "github", + "summary": "Loose patch clamp measurement of presynaptic ionic currents at lizard neuromuscular junction compared with computer simulations.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1025, + "tag": "ModelDB:10360" + } + ], + "timestamp_created": "2024-01-09 12:01:45.310401+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/10360", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "463": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 463, + "name": "Computer model of clonazepam's effect in thalamic slice (Lytton 1997)", + "repository_type": "github", + "summary": "Demonstration of the effect of a minor pharmacological synaptic\r\nchange at the network level. Clonazepam, a benzodiazepine, enhances\r\ninhibition but is paradoxically useful for certain types of\r\nseizures. This simulation shows how inhibition of\r\ninhibitory cells (the RE cells) produces this counter-intuitive\r\neffect.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 822, + "tag": "Therapeutics" + }, + { + "id": 1026, + "tag": "ModelDB:12631" + } + ], + "timestamp_created": "2024-01-09 12:01:45.810573+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/12631", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "464": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 464, + "name": "Thalamic reticular neurons: the role of Ca currents (Destexhe et al 1996)", + "repository_type": "github", + "summary": "The experiments and modeling reported in this paper show how intrinsic bursting properties of RE cells may be explained by dendritic calcium currents.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1027, + "tag": "ModelDB:17663" + } + ], + "timestamp_created": "2024-01-09 12:01:46.284369+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/17663", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "465": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 465, + "name": "Cerebellar purkinje cell: K and Ca channels regulate APs (Miyasho et al 2001)", + "repository_type": "github", + "summary": "We adopted De Schutter and Bower's model as the starting point, then modified the descriptions of\r\nseveral ion channels, such as the P-type Ca channel and the delayed rectifier K channel, and added class-E Ca channels and D-type K channels to the model. Our new model reproduces most of our experimental results and supports the conclusions of our experimental study that class-E Ca channels and D-type K channels are present and functioning in the dendrites of Purkinje neurons.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 823, + "tag": "I_KD" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1028, + "tag": "ModelDB:17664" + } + ], + "timestamp_created": "2024-01-09 12:01:46.875190+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/17664", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "466": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 466, + "name": "Efficient Method for Computing Synaptic Conductance (Destexhe et al 1994)", + "repository_type": "github", + "summary": "A simple model of transmitter release is used to solve first order kinetic equations of neurotransmiter/receptor binding. This method is applied to a glutamate and gabaa receptor. See reference for more details. The method is extended to more complex kinetic schemes in a seperate paper (Destexhe et al J Comp Neuro 1:195-231, 1994). Application to AMPA, NMDA, GABAA, and GABAB receptors is given in a book chapter (Destexhe et al In: The Neurobiology of Computation, Edited by Bower, J., Kluwer Academic Press, Norwell MA, 1995, pp. 9-14.) More information and papers at\r\nhttp://cns.iaf.cnrs-gif.fr/Main.html\r\nand through email: Destexhe@iaf.cnrs-gif.fr", + "tags": [ + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1029, + "tag": "ModelDB:18197" + } + ], + "timestamp_created": "2024-01-09 12:01:47.441920+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/18197", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "467": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 467, + "name": "Application of a common kinetic formalism for synaptic models (Destexhe et al 1994)", + "repository_type": "github", + "summary": "Application to AMPA, NMDA, GABAA, and GABAB receptors is given in a book chapter. The reference paper synthesizes a comprehensive general description of synaptic transmission with Markov kinetic models. This framework is applicable to modeling ion channels, synaptic release, and all receptors. Please see the references for more details. A simple introduction to this method is given in a seperate paper Destexhe et al Neural Comput 6:14-18 , 1994). More information and papers at http://cns.iaf.cnrs-gif.fr/Main.html and through email: Destexhe@iaf.cnrs-gif.fr", + "tags": [ + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1030, + "tag": "ModelDB:18198" + } + ], + "timestamp_created": "2024-01-09 12:01:47.891426+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/18198", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "468": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 468, + "name": "Kinetic synaptic models applicable to building networks (Destexhe et al 1998)", + "repository_type": "github", + "summary": "Simplified AMPA, NMDA, GABAA, and GABAB receptor models useful for building networks are described in a book chapter. One reference paper synthesizes a comprehensive general description of synaptic transmission with Markov kinetic models which is applicable to modeling ion channels, synaptic release, and all receptors. Also a simple introduction to this method is given in a seperate paper Destexhe et al Neural Comput 6:14-18 , 1994). More information and papers at http://cns.iaf.cnrs-gif.fr/Main.html and through email: Destexhe@iaf.cnrs-gif.fr", + "tags": [ + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1031, + "tag": "ModelDB:18500" + } + ], + "timestamp_created": "2024-01-09 12:01:48.327782+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/18500", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "469": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 469, + "name": "Salamander retinal ganglian cells: morphology influences firing (Sheasby, Fohlmeister 1999)", + "repository_type": "github", + "summary": "Nerve impulse entrainment and other\r\nexcitation and passive phenomena are analyzed for a morphologically\r\ndiverse and exhaustive data set (n=57) of realistic (3-dimensional\r\ncomputer traced) soma-dendritic tree structures of ganglion cells in\r\nthe tiger salamander (Ambystoma tigrinum) retina.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1032, + "tag": "ModelDB:18501" + } + ], + "timestamp_created": "2024-01-09 12:01:48.806962+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/18501", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "470": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 470, + "name": "Spine fusion and branching affects synaptic response (Rusakov et al 1996, 1997)", + "repository_type": "github", + "summary": "This compartmental model of a hippocampal granule cell has spinous synapses\r\nplaced on the second-order dendrites. Changes in shape and connectivity of\r\nthe spines usually does not effect the synaptic response of the cell unless\r\nactive conductances are incorporated into the spine membrane (e.g. voltage-dependent\r\nCa2+ channels). With active conductances, spines can generate spike-like events.\r\nWe showed that changes like fusion and branching, or in fact any increase in the \r\nequivalent spine neck resistance, could trigger a dramatic increase in the spine's\r\ninfluence on the dendritic shaft potential.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1033, + "tag": "ModelDB:18502" + } + ], + "timestamp_created": "2024-01-09 12:01:49.322858+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/18502", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "471": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 471, + "name": "Dendritic tip geometry effects electrical properties (Tsutsui, Oka 2001)", + "repository_type": "github", + "summary": "In their teleost thalamic neuron models the authors demonstrate a dramatic increase in the passive propagation of synaptic inputs through the dendritic stalk to the soma in cells with larger tips.", + "tags": [ + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1034, + "tag": "ModelDB:18738" + } + ], + "timestamp_created": "2024-01-09 12:01:49.835497+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/18738", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "472": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 472, + "name": "Novel Na current with slow de-inactivation (Tsutsui, Oka 2002)", + "repository_type": "github", + "summary": "The authors found a novel Na current in teleost thalamic nuclei was well described by the m^3 h Hodgkin-Huxley model. The kinetic parameters derived from their experiments (see the reference for details) revealed that the h gate had a large time constant\r\n(~100ms at -80 to -50mV). This explains the thalamic neurons long refractory period and the gradual recovery of AP amplitude as the inter spike interval grows.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1035, + "tag": "ModelDB:18742" + } + ], + "timestamp_created": "2024-01-09 12:01:50.423146+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/18742", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "473": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 473, + "name": "Emergent properties of networks of biological signaling pathways (Bhalla, Iyengar 1999)", + "repository_type": "github", + "summary": "Biochemical signaling networks were constructed with experimentally obtained constants and analyzed by computational methods to understand their role in complex biological processes. These networks exhibit emergent properties such as integration of signals across multiple time scales, generation of distinct outputs depending on input strength and duration, and self-sustaining feedback loops. Properties of signaling networks raise the possibility that information for \"learned behavior\" of biological systems may be stored within intracellular biochemical reactions that comprise signaling pathways.", + "tags": [ + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 826, + "tag": "GENESIS (web link to model)" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1036, + "tag": "ModelDB:18871" + } + ], + "timestamp_created": "2024-01-09 12:01:50.875943+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/18871", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "474": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 474, + "name": "Fast AMPA receptor signaling (Geiger et al 1997)", + "repository_type": "github", + "summary": "Glutamatergic transmission at a principal neuron-interneuron synapse was investigated by dual whole-cell patch-clamp recording in rat hippocampal slices combined with morphological analysis and modeling. Simulations based on a compartmental model of the interneuron indicated that the rapid postsynaptic conductance change determines the shape and the somatodendritic integration of EPSPs, thus enabling interneurons to detect synchronous principal neuron activity.", + "tags": [ + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1037, + "tag": "ModelDB:19022" + } + ], + "timestamp_created": "2024-01-09 12:01:51.332345+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/19022", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "475": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 475, + "name": "Activity dependent regulation of pacemaker channels by cAMP (Wang et al 2002)", + "repository_type": "github", + "summary": "Demonstration of the physiological consequences of the cyclic allosteric gating scheme for Ih mediated by HCN2 in thalamocortical relay cells.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1038, + "tag": "ModelDB:19176" + } + ], + "timestamp_created": "2024-01-09 12:01:51.780361+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/19176", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "476": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 476, + "name": "Geometry-induced features of current transfer in neuronal dendrites (Korogod, Kulagina 1998)", + "repository_type": "github", + "summary": "The impact of dendritic geometry on somatopetal transfer of the current generated by steady uniform activation of excitatory synaptic conductance distributed over passive, or active (Hodgkin-Huxley type), dendrites was studied in simulated neurons.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1039, + "tag": "ModelDB:19214" + } + ], + "timestamp_created": "2024-01-09 12:01:52.340645+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/19214", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "477": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 477, + "name": "Conditions of dominant effectiveness of distal dendrites (Korogod, Kulagina 1998)", + "repository_type": "github", + "summary": "The model illustrates and explains bistable spatial patterns of the current transfer effectiveness \r\nin the active dendrite with distributed (multiple) tonic excitatory, NMDA type, synaptic input.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1040, + "tag": "ModelDB:19366" + } + ], + "timestamp_created": "2024-01-09 12:01:52.787276+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/19366", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "478": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 478, + "name": "Dendritic L-type Ca currents in motoneurons (Carlin et al 2000)", + "repository_type": "github", + "summary": "A component of recorded currents demonstrated kinetics consistent with a current originating at a site spatially segregated from the soma. In response to step commands this component was seen as a late-onset, low amplitude persistent current whilst in response to depolarizing-repolarizing ramp commands a low voltage clockwise current hysteresis was recorded. Simulations using a neuromorphic motoneuron model could reproduce these currents only if a noninactivating calcium conductance was placed in the dendritic compartments.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 828, + "tag": "Dendritic Bistability" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1041, + "tag": "ModelDB:19491" + } + ], + "timestamp_created": "2024-01-09 12:01:53.263076+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/19491", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "479": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 479, + "name": "Synaptic integration in a model of granule cells (Gabbiani et al 1994)", + "repository_type": "github", + "summary": "We have developed a compartmental model of a turtle cerebellar granule cell consisting of 13 compartments that represent the soma and 4 dendrites. We used this model to investigate the synaptic integration of mossy fiber inputs in granule cells. See reference or abstract at PubMed link below for more information.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1042, + "tag": "ModelDB:19591" + } + ], + "timestamp_created": "2024-01-09 12:01:53.798942+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/19591", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "480": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 480, + "name": "CA1 pyramidal neuron: integration of subthreshold inputs from PP and SC (Migliore 2003)", + "repository_type": "github", + "summary": "The model shows how the experimentally observed increase in the dendritic density of Ih and IA could have a major role in constraining the temporal integration window for the main CA1 \r\nsynaptic inputs.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1043, + "tag": "ModelDB:19696" + } + ], + "timestamp_created": "2024-01-09 12:01:54.256253+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/19696", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "481": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 481, + "name": "Leech heart interneuron network model (Hill et al 2001, 2002)", + "repository_type": "github", + "summary": "We have created a computational model of the timing network that paces the heartbeat of the medicinal leech, Hirudo medicinalis. In the intact nerve cord, segmental oscillators are mutually entrained to the same cycle period. Although experiments have shown that the segmental oscillators are coupled by inhibitory coordinating interneurons, the underlying mechanisms of intersegmental coordination have not yet been elucidated. To help understand this coordination, we have created a simple computational model with two variants: symmetric and asymmetric. See references for more details. Biologically realistic network models with two, six, and eight cells and a tutorial are available at the links to Calabrese's web site below.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + }, + { + "id": 1044, + "tag": "ModelDB:19698" + } + ], + "timestamp_created": "2024-01-09 12:01:54.725399+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/19698", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "482": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 482, + "name": "Signal integration in a CA1 pyramidal cell (Graham 2001)", + "repository_type": "github", + "summary": "This model investigates signal integration in the dendritic tree of a hippocampal CA1 pyramidal cell when different combinations of active channels are present in the tree (Graham, 2001)", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1045, + "tag": "ModelDB:19746" + } + ], + "timestamp_created": "2024-01-09 12:01:55.188390+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/19746", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "483": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 483, + "name": "Synaptic transmission at the calyx of Held (Graham et al 2001)", + "repository_type": "github", + "summary": "This model allows the user to investigate faciliation and depression in a complex Monte Carlo model of the calyx of Held, a giant synapse in the mammalian auditory system (Graham et al, 2001)", + "tags": [ + { + "id": 797, + "tag": "Depression" + }, + { + "id": 798, + "tag": "Facilitation" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 829, + "tag": "Vestibular" + }, + { + "id": 1046, + "tag": "ModelDB:19747" + } + ], + "timestamp_created": "2024-01-09 12:01:55.646702+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/19747", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "484": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 484, + "name": "T-type Calcium currents (McRory et al 2001)", + "repository_type": "github", + "summary": "NEURON mod files for CaT currents from the paper\r\nMcRory et al., J.Biol.Chem. 276:3999 (2001).\r\nIn this paper, three members (alpha-1G, -1H, and -1I) of the LVA calcium channels family were studied. Kinetic parameters were derived from functional expression in transfected cells.", + "tags": [ + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1047, + "tag": "ModelDB:19920" + } + ], + "timestamp_created": "2024-01-09 12:01:56.081432+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/19920", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "485": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 485, + "name": "CA3 pyramidal neuron (Lazarewicz et al 2002)", + "repository_type": "github", + "summary": "The model shows how using a CA1-like distribution of active dendritic conductances in a CA3 morphology results in dendritic initiation of spikes during a burst.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1048, + "tag": "ModelDB:20007" + } + ], + "timestamp_created": "2024-01-09 12:01:56.575641+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/20007", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "486": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 486, + "name": "Transfer properties of Neuronal Dendrites (Korogod et al 1998)", + "repository_type": "github", + "summary": "The somatopetal current transfer was studied in mathematical models of a reconstructed brainstem motoneuron with tonically activated excitatory synaptic inputs uniformly distributed over the dendritic arborization. See paper and below readme.txt for more information.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1049, + "tag": "ModelDB:20014" + } + ], + "timestamp_created": "2024-01-09 12:01:57.049286+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/20014", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "487": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 487, + "name": "CA1 interneuron: K currents (Lien et al 2002)", + "repository_type": "github", + "summary": "NEURON mod files for slow and fast K-DR, and K-A potassium currents in inhibitory interneurones of stratum oriens-alveus of the hippocampal CA1 region.", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1050, + "tag": "ModelDB:20015" + } + ], + "timestamp_created": "2024-01-09 12:01:57.497416+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/20015", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "488": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 488, + "name": "CA1 pyramidal neuron: as a 2-layer NN and subthreshold synaptic summation (Poirazi et al 2003)", + "repository_type": "github", + "summary": "We developed a CA1\r\npyramidal cell model calibrated with a broad spectrum of in vitro data. Using simultaneous\r\ndendritic and somatic recordings, and combining results for two different response measures\r\n(peak vs. mean EPSP), two different stimulus formats (single shock vs. 50 Hz trains),\r\nand two different spatial integration conditions (within vs. between-branch summation),\r\nwe found the cell's subthreshold responses to paired inputs are best described as a sum of\r\nnonlinear subunit responses, where the subunits correspond to different dendritic branches.\r\nIn addition to suggesting a new type of experiment and providing testable predictions, our\r\nmodel shows how conclusions regarding synaptic arithmetic can be influenced by an array\r\nof seemingly innocuous experimental design choices.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 830, + "tag": "Delay" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 797, + "tag": "Depression" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1051, + "tag": "ModelDB:20212" + } + ], + "timestamp_created": "2024-01-09 12:01:57.975505+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/20212", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "489": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 489, + "name": "Mechanisms of fast rhythmic bursting in a layer 2/3 cortical neuron (Traub et al 2003)", + "repository_type": "github", + "summary": "This simulation is based on the reference paper listed below.\r\nThis port was made by Roger D Traub and Maciej T Lazarewicz (mlazarew at seas.upenn.edu)\r\n\r\nThanks to Ashlen P Reid for help with porting a morphology of the cell.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1052, + "tag": "ModelDB:20756" + } + ], + "timestamp_created": "2024-01-09 12:01:58.505739+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/20756", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "490": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 490, + "name": "Stable propagation of synchronous spiking in cortical neural networks (Diesmann et al 1999)", + "repository_type": "github", + "summary": "\"...\r\nHere we show that precisely synchronized action\r\npotentials can propagate within a model of cortical network activity\r\nthat recapitulates many of the features of biological systems. An\r\nattractor, yielding a stable spiking precision in the (sub)millisecond\r\nrange, governs the dynamics of synchronization. Our results indicate\r\nthat a combinatorial neural code, based on rapid associations of\r\ngroups of neurons co-ordinating their activity at the single spike\r\nlevel, is possible within a cortical-like network.\"", + "tags": [ + { + "id": 790, + "tag": "Attractor Neural Network" + }, + { + "id": 831, + "tag": "Brian (web link to method)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 832, + "tag": "Python (web link to model)" + }, + { + "id": 1922, + "tag": "ModelDB:153988" + } + ], + "timestamp_created": "2024-01-11 15:46:48.682098+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/153988", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "491": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 491, + "name": "Impact of fast Na channel inact. on AP threshold & synaptic integration (Platkiewicz & Brette 2011)", + "repository_type": "github", + "summary": "Slope-threshold relationship with noisy inputs, in the adaptive threshold model.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 831, + "tag": "Brian (web link to method)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 832, + "tag": "Python (web link to model)" + }, + { + "id": 1923, + "tag": "ModelDB:153998" + } + ], + "timestamp_created": "2024-01-11 15:46:49.447396+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/153998", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "492": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 492, + "name": "Electrostimulation to reduce synaptic scaling driven progression of Alzheimers (Rowan et al. 2014)", + "repository_type": "github", + "summary": "\"...\r\n\r\nAs cells die and synapses lose their drive, remaining cells suffer an initial decrease in activity.\r\n\r\nNeuronal homeostatic synaptic scaling then provides a feedback mechanism to restore activity.\r\n...\r\nThe scaling mechanism increases the firing rates of remaining cells in the network to compensate for decreases in network activity.\r\n\r\nHowever, this effect can itself become a pathology, ...\r\n\r\nHere, we present a mechanistic explanation of how directed brain stimulation might be expected to slow AD progression based on computational simulations in a 470-neuron biomimetic model of a neocortical column.\r\n...\r\n\"", + "tags": [ + { + "id": 833, + "tag": "Aging/Alzheimer`s" + }, + { + "id": 834, + "tag": "Deep brain stimulation" + }, + { + "id": 171, + "tag": "Homeostasis" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 1924, + "tag": "ModelDB:154096" + } + ], + "timestamp_created": "2024-01-11 15:46:50.183487+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/154096", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "493": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 493, + "name": "Nonlinear neuronal computation based on physiologically plausible inputs (McFarland et al. 2013)", + "repository_type": "github", + "summary": "\"...\r\nHere we present an approach for modeling sensory processing, termed the Nonlinear Input\r\nModel (NIM), which is based on the hypothesis that the dominant nonlinearities imposed by physiological mechanisms arise\r\nfrom rectification of a neuron\u2019s inputs. Incorporating such \u2018upstream nonlinearities\u2019 within the standard linear-nonlinear (LN)\r\ncascade modeling structure implicitly allows for the identification of multiple stimulus features driving a neuron\u2019s response,\r\nwhich become directly interpretable as either excitatory or inhibitory.\r\n\r\nBecause its form is analogous to an integrate-and-fire\r\nneuron receiving excitatory and inhibitory inputs, model fitting can be guided by prior knowledge about the inputs to a\r\ngiven neuron, and elements of the resulting model can often result in specific physiological predictions.\r\n\r\nFurthermore, by\r\nproviding an explicit probabilistic model with a relatively simple nonlinear structure, its parameters can be efficiently\r\noptimized and appropriately regularized. ... \u201d", + "tags": [ + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 1925, + "tag": "ModelDB:154192" + } + ], + "timestamp_created": "2024-01-11 15:46:50.715169+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/154192", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "494": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 494, + "name": "Theory of arachnid prey localization (Sturzl et al. 2000)", + "repository_type": "github", + "summary": "\"Sand scorpions and many other arachnids locate their prey through highly sensitive slit sensilla at the tips (tarsi) of their eight legs. This sensor array responds to vibrations with stimulus-locked action potentials encoding the target direction. We present a neuronal model to account for stimulus angle determination using a population of second-order neurons, each receiving excitatory input from one tarsus and inhibition from a triad opposite to it. ...\"", + "tags": [ + { + "id": 831, + "tag": "Brian (web link to method)" + }, + { + "id": 837, + "tag": "Direction Selectivity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 832, + "tag": "Python (web link to model)" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1926, + "tag": "ModelDB:154198" + } + ], + "timestamp_created": "2024-01-11 15:46:51.180172+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/154198", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "495": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 495, + "name": "Time-warp-invariant neuronal processing (Gutig & Sompolinsky 2009)", + "repository_type": "github", + "summary": "\" ... Here, we report that time-warp-invariant neuronal processing can be subserved by the shunting action of synaptic conductances that automatically rescales the effective integration time of postsynaptic neurons.\r\n\r\nWe propose a novel spike-based learning rule for synaptic conductances that adjusts the degree of synaptic shunting to the temporal processing requirements of a given task.\r\n\r\nApplying this general biophysical mechanism to the example of speech processing, we propose a neuronal network model for time-warp-invariant word discrimination and demonstrate its excellent performance on a standard benchmark speech-recognition task.\r\n\r\n...\"\r\n", + "tags": [ + { + "id": 831, + "tag": "Brian (web link to method)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 832, + "tag": "Python (web link to model)" + }, + { + "id": 1927, + "tag": "ModelDB:154288" + } + ], + "timestamp_created": "2024-01-11 15:46:51.672122+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/154288", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "496": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 496, + "name": "Reliability of spike timing is a general property of spiking model neurons (Brette & Guigon 2003)", + "repository_type": "github", + "summary": "\"... Here we show, through simulations and theoretical\r\nconsiderations, that for a general class of spiking neuron models,\r\nwhich includes, in particular, the leaky integrate-and-fire model as\r\nwell as nonlinear spiking models, aperiodic currents, contrary to\r\nperiodic currents, induce reproducible responses, which are stable\r\nunder noise, change in initial conditions and deterministic\r\nperturbations of the input. We provide a theoretical explanation for\r\naperiodic currents that cross the threshold.\"", + "tags": [ + { + "id": 831, + "tag": "Brian (web link to method)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 832, + "tag": "Python (web link to model)" + }, + { + "id": 838, + "tag": "Reliability" + }, + { + "id": 1928, + "tag": "ModelDB:154293" + } + ], + "timestamp_created": "2024-01-11 15:46:52.165223+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/154293", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "497": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 497, + "name": "Late emergence of the whisker direction selectivity map in rat barrel cortex (Kremer et al. 2011)", + "repository_type": "github", + "summary": "\"... We discovered that the emergence of a direction map in rat barrel cortex occurs long after all known critical periods in the somatosensory system.\r\n\r\nThis map is remarkably specific, taking a pinwheel-like form centered near the barrel center and aligned to the barrel cortex somatotopy.\r\n\r\nWe suggest that this map may arise from intracortical mechanisms and demonstrate by simulation that the combination of spike-timing-dependent plasticity at synapses between layer 4 and layer 2/3 and realistic pad stimulation is sufficient to produce such a map.\r\n...\"", + "tags": [ + { + "id": 831, + "tag": "Brian (web link to method)" + }, + { + "id": 839, + "tag": "Development" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 832, + "tag": "Python (web link to model)" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 840, + "tag": "Whisking" + }, + { + "id": 1929, + "tag": "ModelDB:154348" + } + ], + "timestamp_created": "2024-01-11 15:46:52.617633+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/154348", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "498": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 498, + "name": "Spine head calcium in a CA1 pyramidal cell model (Graham et al. 2014)", + "repository_type": "github", + "summary": "\"We use a computational model of a hippocampal CA1 pyramidal cell to demonstrate that spine\r\nhead calcium provides an instantaneous readout at each synapse of the postsynaptic weighted\r\nsum of all presynaptic activity impinging on the cell. The form of the readout is equivalent\r\nto the functions of weighted, summed inputs used in neural network learning rules. Within a\r\ndendritic layer, peak spine head calcium levels are either a linear or sigmoidal function of\r\nthe number of coactive synapses, with nonlinearity depending on the ability of voltage spread\r\nin the dendrites to reach calcium spike threshold. ...\"", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 1930, + "tag": "ModelDB:154732" + } + ], + "timestamp_created": "2024-01-11 15:46:53.079075+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/154732", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "499": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 499, + "name": "Norns - Neural Network Studio (Visser & Van Gils 2014)", + "repository_type": "github", + "summary": "The Norns - Neural Network Studio is a software package for designing, simulation and analyzing networks of spiking neurons. It consists of three parts:\r\n\r\n1. \"Urd\": a Matlab frontend with high-level functions for quickly defining networks\r\n2. \"Verdandi\": an optimized C++ simulation environment which runs the simulation defined by Urd\r\n3. \"Skuld\": an advanced Matlab graphical user interface (GUI) for visual inspection of simulated data.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 830, + "tag": "Delay" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 843, + "tag": "Norns - Neural Net Studio" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 775, + "tag": "Rebound firing" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + }, + { + "id": 1931, + "tag": "ModelDB:154739" + } + ], + "timestamp_created": "2024-01-11 15:46:53.553246+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/154739", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "500": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 500, + "name": "Gamma oscillations in hippocampal interneuron networks (Bartos et al 2002)", + "repository_type": "github", + "summary": "To examine whether an interneuron network with fast inhibitory synapses can act as a gamma frequency oscillator, we developed an interneuron network model based on experimentally determined properties. In comparison to previous interneuron network models, our model was able to generate oscillatory activity with higher coherence over a broad range of frequencies (20-110 Hz). In this model, high coherence and flexibility in frequency control emerge from the combination of synaptic properties, network structure, and electrical coupling.\r\n", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 1053, + "tag": "ModelDB:21329" + } + ], + "timestamp_created": "2024-01-11 15:49:20.110997+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/21329", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "501": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 501, + "name": "Facilitation by residual calcium (Stockbridge, Hines 1982)", + "repository_type": "github", + "summary": "The residual calcium hypothesis is compatible\r\nwith facilitation of transmitter release from\r\nthe neuromuscular junction.", + "tags": [ + { + "id": 798, + "tag": "Facilitation" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1054, + "tag": "ModelDB:21984" + } + ], + "timestamp_created": "2024-01-11 15:49:20.624615+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/21984", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "502": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 502, + "name": "Correcting space clamp in dendrites (Schaefer et al. 2003 and 2007)", + "repository_type": "github", + "summary": "In voltage-clamp experiments, incomplete space clamp distorts the recorded currents, rendering accurate analysis impossible. Here, we present\r\na simple numerical algorithm that corrects such distortions. The method enabled accurate\r\nretrieval of the local densities, kinetics, and density gradients of somatic and dendritic channels. The correction method was applied to two-electrode voltage-clamp recordings of K currents from the apical dendrite of layer 5 neocortical pyramidal\r\nneurons. The generality and robustness of the algorithm make it a useful tool for voltage-clamp analysis of voltage-gated\r\ncurrents in structures of any morphology that is amenable to the voltage-clamp technique.", + "tags": [ + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 1055, + "tag": "ModelDB:22203" + } + ], + "timestamp_created": "2024-01-11 15:49:21.107883+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/22203", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "503": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 503, + "name": "Gamma oscillations in hippocampal interneuron networks (Wang, Buzsaki 1996)", + "repository_type": "github", + "summary": "The authors investigated the hypothesis that 20-80Hz neuronal (gamma) oscillations can emerge in sparsely connected network models of GABAergic fast-spiking interneurons. They explore model NN synchronization and compare their results to anatomical and electrophysiological data from hippocampal fast spiking interneurons.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 1056, + "tag": "ModelDB:26997" + } + ], + "timestamp_created": "2024-01-11 15:49:21.561523+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/26997", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "504": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 504, + "name": "Sleep-wake transitions in corticothalamic system (Bazhenov et al 2002)", + "repository_type": "github", + "summary": "The authors investigate the transition between sleep and awake states with intracellular recordings in cats and computational models. The model describes many essential features of slow wave sleep and activated states as well as the transition between them.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 797, + "tag": "Depression" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + }, + { + "id": 802, + "tag": "Sleep" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 1057, + "tag": "ModelDB:28189" + } + ], + "timestamp_created": "2024-01-11 15:49:22.021108+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/28189", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "505": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 505, + "name": "Active dendrites and spike propagation in a hippocampal interneuron (Saraga et al 2003)", + "repository_type": "github", + "summary": "We create multi-compartment models of an Oriens-Lacunosum/Moleculare (O-LM) hippocampal interneuron using passive properties, channel kinetics, densities and distributions specific to this cell type, and explore its signaling characteristics. We find that spike initiation depends on both location and amount of input, as well as the intrinsic properties of the interneuron. Distal synaptic input always produces strong back-propagating spikes whereas proximal input could produce both forward and back-propagating spikes depending on the input strength. Please see paper for more details.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1058, + "tag": "ModelDB:28316" + } + ], + "timestamp_created": "2024-01-11 15:49:22.514835+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/28316", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "506": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 506, + "name": "Signal integration in LGN cells (Briska et al 2003)", + "repository_type": "github", + "summary": "Computer models were used to investigate passive properties of lateral geniculate nucleus thalamocortical cells and thalamic\r\ninterneurons based on in vitro whole-cell study. Two neurons of each type were characterized physiologically and morphologically. Differences in the attenuation of propagated signals depend on both cell morphology and signal frequency. See the paper for details.", + "tags": [ + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 800, + "tag": "Vision" + }, + { + "id": 1059, + "tag": "ModelDB:29942" + } + ], + "timestamp_created": "2024-01-11 15:49:22.971097+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/29942", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "507": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 507, + "name": "CA1 pyramidal neuron: effects of Ih on distal inputs (Migliore et al 2004)", + "repository_type": "github", + "summary": "NEURON mod files from the paper:\r\nM. Migliore, L. Messineo, M. Ferrante\r\nDendritic Ih selectively blocks temporal summation of unsynchronized distal inputs in CA1 pyramidal neurons, J.Comput. Neurosci. 16:5-13 (2004).\r\nThe model demonstrates how the dendritic Ih in pyramidal neurons could selectively suppress AP generation for a volley of excitatory afferents \r\nwhen they are asynchronously and distally activated.\r\n", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 1060, + "tag": "ModelDB:32992" + } + ], + "timestamp_created": "2024-01-11 15:49:23.766954+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/32992", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "508": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 508, + "name": "Irregular oscillations produced by cyclic recurrent inhibition (Friesen, Friesen 1994)", + "repository_type": "github", + "summary": "Model of recurrent cyclic inhibition as described on p.119 of Friesen and Friesen (1994), which was slightly modified from Szekely's model (1965) of a network for producing alternating limb movements.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1061, + "tag": "ModelDB:33728" + } + ], + "timestamp_created": "2024-01-11 15:49:24.247104+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/33728", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "509": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 509, + "name": "Local variable time step method (Lytton, Hines 2005)", + "repository_type": "github", + "summary": "The local variable time-step method utilizes separate variable step integrators for individual neurons in the network. It is most suitable for medium size networks in which average synaptic input intervals to a single cell are much greater than a fixed step dt.", + "tags": [ + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1062, + "tag": "ModelDB:33975" + } + ], + "timestamp_created": "2024-01-11 15:49:24.698289+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/33975", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "510": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 510, + "name": "Serotonergic modulation of Aplysia sensory neurons (Baxter et al 1999)", + "repository_type": "github", + "summary": "The present study investigated how the modulation of these currents altered the spike duration\r\nand excitability of sensory neurons and examined the relative contributions\r\nof PKA- and PKC-mediated effects to the actions of 5-HT. A\r\nHodgkin-Huxley type model was developed that described the ionic\r\nconductances in the somata of sensory neurons. The descriptions of\r\nthese currents and their modulation were based largely on voltageclamp\r\ndata from sensory neurons. Simulations were preformed with\r\nthe program SNNAP (Simulator for Neural Networks and Action\r\nPotentials). The model was sufficient to replicate empirical data that\r\ndescribes the membrane currents, action potential waveform and\r\nexcitability as well as their modulation by application of 5-HT,\r\nincreased levels of adenosine cyclic monophosphate or application of\r\nactive phorbol esters. The results provide\r\nseveral predictions that warrant additional experimental investigation\r\nand illustrate the importance of considering indirect as well as direct\r\neffects of modulatory agents on the modulation of membrane currents. See paper for more details.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 1063, + "tag": "ModelDB:33986" + } + ], + "timestamp_created": "2024-01-11 15:49:25.149940+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/33986", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "511": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 511, + "name": "Enhanced Excitability in Hermissenda: modulation by 5-HT (Cai et al 2003)", + "repository_type": "github", + "summary": "Serotonin (5-HT) applied to the exposed but otherwise intact nervous system results in enhanced excitability\r\nof Hermissenda type-B photoreceptors. Several ion currents in the type-B photoreceptors are modulated\r\nby 5-HT, including the A-type K+ current (IK,A), sustained Ca2+ current (ICa,S), Ca-dependent K+ current (IK,Ca),\r\nand a hyperpolarization-activated inward rectifier current (Ih). In this study,we developed a computational model that\r\nreproduces physiological characteristics of type B photoreceptors, e.g. resting membrane potential, dark-adapted\r\nspike activity, spike width, and the amplitude difference between somatic and axonal spikes. We then used the\r\nmodel to investigate the contribution of different ion currents modulated by 5-HT to the magnitudes of enhanced\r\nexcitability produced by 5-HT. See paper for results and more details.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 1064, + "tag": "ModelDB:34163" + } + ], + "timestamp_created": "2024-01-11 15:49:25.618199+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/34163", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "512": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 512, + "name": "Burst induced synaptic plasticity in Apysia sensorimotor neurons (Phares et al 2003)", + "repository_type": "github", + "summary": "The Aplysia sensorimotor synapse is a key site of plasticity for several simple forms of learning. Intracellular stimulation of sensory neurons to fire a burst of action potentials at 10 Hz for 1 sec led to significant\r\nhomosynaptic depression of postsynaptic responses. During the burst, the steady-state depressed phase of the postsynaptic response, which was only 20% of the initial EPSP of the burst, still contributed to firing the motor neuron. To explore the functional contribution of transient homosynaptic depression to the response of the motor neuron, computer simulations of the sensorimotor synapse with and without depression were compared. Depression allowed the motor\r\nneuron to produce graded responses over a wide range of presynaptic input strength. \r\nThus, synaptic depression increased the dynamic range of the sensorimotor synapse and can, in principle, have a profound effect on\r\ninformation processing. Please see paper for results and details.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 797, + "tag": "Depression" + }, + { + "id": 798, + "tag": "Facilitation" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 845, + "tag": "Sensory processing" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1065, + "tag": "ModelDB:34168" + } + ], + "timestamp_created": "2024-01-11 15:49:26.095348+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/34168", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "513": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 513, + "name": "Caffeine-induced electrical oscillations in Aplysia neurons (Komendantov, Kononenko 2000)", + "repository_type": "github", + "summary": "It has been found that in cultured Aplysia neurons bath applications of 40 mM cafffeine evokes oscillations of the membrane potential with about a 40 mV amplitude with a frequency of 0.2 to 0.5 Hz. The most probable mechanism of these caffeine-induced oscillations is inhibition of voltage-activated outward potassium current and, as can be seen from our mathematical modeling, slowdown of inactivation of inward sodium current. It seems likely that these oscillations have a purely membrane origin. Please see paper for results and details.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 1066, + "tag": "ModelDB:34558" + } + ], + "timestamp_created": "2024-01-11 15:49:26.588212+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/34558", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "514": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 514, + "name": "I A in Kenyon cells resemble Shaker currents (Pelz et al 1999)", + "repository_type": "github", + "summary": "Cultured Kenyon cells from the mushroom body of the honeybee,\r\nApis mellifera, show a voltage-gated, fast transient K1 current that is\r\nsensitive to 4-aminopyridine, an A current. The kinetic properties of\r\nthis A current and its modulation by extracellular K1 ions were\r\ninvestigated in vitro with the whole cell patch-clamp technique. The\r\nA current was isolated from other voltage-gated currents either pharmacologically\r\nor with suitable voltage-clamp protocols. Hodgkin- and\r\nHuxley-style mathematical equations were used for the description of\r\nthis current and for the simulation of action potentials in a Kenyon cell\r\nmodel. The data of the A\r\ncurrent were incorporated into a reduced computational model of the\r\nvoltage-gated currents of Kenyon cells. In addition, the model contained\r\na delayed rectifier K current, a Na current, and a leakage\r\ncurrent. The model reproduces several experimental features and makes \r\npredictions. See paper for details and results.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 1067, + "tag": "ModelDB:34560" + } + ], + "timestamp_created": "2024-01-11 15:49:27.049564+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/34560", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "515": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 515, + "name": "A network model of tail withdrawal in Aplysia (White et al 1993)", + "repository_type": "github", + "summary": "The contributions of monosynaptic and polysynaptic circuitry to the tail-withdrawal reflex in the marine mollusk Aplysia californica were assessed by the use of physiologically based neural network models. Effects of monosynaptic circuitry were examined by the use of a two-layer network model with four sensory neurons in the input layer and one motor neuron in the output layer. Results of these simulations indicated that the monosynaptic circuit could not account fully for long-duration responses of tail motor neurons elicited by tail stimulation.\r\nA three-layer network model was constructed by interposing a layer of two excitatory interneurons between the input and output layers of the two-layer network model. The three-layer model could account for long-duration responses in motor neurons. Sensory neurons are a known site of plasticity in Aplysia. Synaptic plasticity at more than one locus modified dramatically the input-output relationship of the three-layer network model. This feature gave the model redundancy in its plastic properties and points to the possibility of distributed memory in the circuitry mediating withdrawal reflexes in Aplysia.\r\nPlease see paper for more results and details.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 798, + "tag": "Facilitation" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1068, + "tag": "ModelDB:34606" + } + ], + "timestamp_created": "2024-01-11 15:49:27.533519+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/34606", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "516": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 516, + "name": "CA3 pyramidal cell: rhythmogenesis in a reduced Traub model (Pinsky, Rinzel 1994)", + "repository_type": "github", + "summary": "Fig. 2A and 3 are reproduced in this simulation of Pinsky PF, Rinzel J (1994).", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1069, + "tag": "ModelDB:35358" + } + ], + "timestamp_created": "2024-01-11 15:49:28.027459+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/35358", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "517": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 517, + "name": "Stochastic LTP/LTD conditioning of a synapse (Migliore and Lansky 1999)", + "repository_type": "github", + "summary": "Protracted presynaptic activity can induce long-term potentiation\r\n(LTP) or long-term depression (LTD) of the synaptic strength. However,\r\nvirtually all the experiments testing how LTP and LTD depend on the\r\nconditioning input are carried out with trains of stimuli at constant\r\nfrequencies, whereas neurons in vivo most likely experience a stochastic\r\nvariation of interstimulus intervals. We used a computational model of\r\nsynaptic transmission to test if and to what extent the stochastic\r\nfluctuations of an input signal could alter the probability to change the\r\nstate of a synapse. See paper for conclusions.", + "tags": [ + { + "id": 797, + "tag": "Depression" + }, + { + "id": 798, + "tag": "Facilitation" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 847, + "tag": "Post-Tetanic Potentiation" + }, + { + "id": 848, + "tag": "QBasic/QuickBasic/Turbo Basic/VBA" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1070, + "tag": "ModelDB:35781" + } + ], + "timestamp_created": "2024-01-11 15:49:28.655990+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/35781", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "518": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 518, + "name": "Auditory nerve model for predicting performance limits (Heinz et al 2001)", + "repository_type": "github", + "summary": "A computational auditory nerve (AN) model was developed for\r\nuse in modeling psychophysical experiments with normal and impaired\r\nhuman listeners. In this phenomenological model, many physiologically\r\nvulnerable response properties associated with the cochlear amplifier are\r\nrepresented by a single nonlinear control mechanism, see paper for details. Several model versions are described that\r\ncan be used to evaluate the relative effects of these nonlinear properties.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 849, + "tag": "Audition" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1071, + "tag": "ModelDB:36834" + } + ], + "timestamp_created": "2024-01-11 15:49:29.113439+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/36834", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "519": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 519, + "name": "Model of cochlear membrane adapted (Peterson, Bogert 1950)", + "repository_type": "github", + "summary": "This model, adapted from Peterson and Bogert (1950), simulates the response of the gerbil basilar membrane to a pure tone stimulus. This model does not attempt to simulate the effect of outer hair cell motility. The program prompts the user for the stimulus frequency and the Q (quality factor) for the basilar membrane impedance. It then plots cochlear partition volume velocity, the pressure difference across the partition and the cochlear partition impedance as a function of cochlear location. More information on the actual computations are contained in comments within the m-file.", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1072, + "tag": "ModelDB:36861" + } + ], + "timestamp_created": "2024-01-11 15:49:29.564938+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/36861", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "520": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 520, + "name": "Inner hair cell auditory nerve synapse model (Deligeorges, Mountain 1997)", + "repository_type": "github", + "summary": "This model simulates the response of the synapse between the inner hair cell and an auditory nerve fiber to a square voltage pulse applied to the IHC membrane. The model output is average firing rate. More details of this model can be found in: Deligeorges and Mountain.", + "tags": [ + { + "id": 849, + "tag": "Audition" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1073, + "tag": "ModelDB:36869" + } + ], + "timestamp_created": "2024-01-11 15:49:59.987580+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/36869", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "521": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 521, + "name": "Multiple modes of inner hair cell stimulation (Mountain, Cody 1999)", + "repository_type": "github", + "summary": "This model simulates the membrane potential of an inner hair cell for a sinusoidal stimulus to the hair bundle. It uses a 2-state Boltzmann model for the tension-gated conductance in the stereocilia and a linear model for the basolateral membrane. This model is based on the IHC model used in Mountain and Cody (1999).", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1074, + "tag": "ModelDB:36956" + } + ], + "timestamp_created": "2024-01-11 15:50:00.493531+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/36956", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "522": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 522, + "name": "Auditory nerve response model (Zhang et al 2001)", + "repository_type": "github", + "summary": "A phenomenological model was developed to describe responses of high-spontaneous-rate auditory-nerve (AN) fibers, including several nonlinear response properties. The implementation of this model represents a relatively simple phenomenological description of a single mechanism that underlies several important nonlinear response properties of AN fibers. The model provides a tool for studying the roles of these nonlinearities in the encoding of simple and complex sounds in the responses of populations of AN fibers.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1075, + "tag": "ModelDB:37103" + } + ], + "timestamp_created": "2024-01-11 15:50:00.970758+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/37103", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "523": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 523, + "name": "Auditory nerve response model (Tan, Carney 2003)", + "repository_type": "github", + "summary": "A computational model was developed to simulate the responses of auditory-nerve (AN) fibers in cat. The incorporation of both the level-independent frequency glide and the level-dependent compressive nonlinearity into a phenomenological model for the AN was the primary focus of this work. The ability of this model to process arbitrary sound inputs makes it a useful tool for studying peripheral auditory processing.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1076, + "tag": "ModelDB:37129" + } + ], + "timestamp_created": "2024-01-11 15:50:01.430122+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/37129", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "524": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 524, + "name": "Auditory nerve model with linear tuning (Heinz et al 2001)", + "repository_type": "github", + "summary": "A method for calculating psychophysical performance limits based on stochastic \r\nneural responses is introduced and compared to previous analytical methods for \r\nevaluating auditory discrimination of tone frequency and level. The method uses \r\nsignal detection theory and a computational model for a population of auditory \r\nnerve (AN) fiber responses. Please see paper for details.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1077, + "tag": "ModelDB:37436" + } + ], + "timestamp_created": "2024-01-11 15:50:01.878572+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/37436", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "525": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 525, + "name": "Thalamocortical augmenting response (Bazhenov et al 1998)", + "repository_type": "github", + "summary": "In the cortical model, augmenting responses were more powerful in the \"input\" layer compared with those in the \"output\" layer. Cortical stimulation of the network model produced augmenting responses in cortical neurons in distant cortical areas through corticothalamocortical loops and low-threshold intrathalamic augmentation. ... The predictions of the model were compared with in vivo recordings from neurons in cortical area 4 and thalamic ventrolateral nucleus of anesthetized cats. The known intrinsic properties of thalamic cells and thalamocortical interconnections can account for the basic properties of cortical augmenting responses. See reference for details. NEURON implementation note: cortical SU cells are getting slightly too little stimulation - reason unknown.", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 1078, + "tag": "ModelDB:37819" + } + ], + "timestamp_created": "2024-01-11 15:50:02.334789+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/37819", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "526": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 526, + "name": "CN pyramidal fusiform cell (Kanold, Manis 2001)", + "repository_type": "github", + "summary": "Pyramidal cells in the dorsal cochlear nucleus (DCN) show three characteristic discharge patterns in response tones: pauser, buildup, and regular firing. Experimental evidence suggests that a rapidly inactivating K+ current (I(KIF)) plays a critical role in generating these discharge patterns. To explore the role of I(KIF), we used a computational model based on the biophysical data. The model replicated the dependence of the discharge pattern on the magnitude and duration of hyperpolarizing prepulses, and I(KIF) was necessary to convey this dependence. Experimentally, half-inactivation voltage and kinetics of I(KIF) show wide variability. Varying these parameters in the model ... suggests that pyramidal cells can adjust their sensitivity to different temporal patterns of inhibition and excitation by modulating the kinetics of I(KIF). Overall, I(KIF) is a critical conductance controlling the excitability of DCN pyramidal cells. (See readme.txt and paper for details).\r\n\r\nAny questions regarding these implementations should be directed to:\r\npmanis@med.unc.edu\r\n\r\n2 April 2004\r\nPaul B Manis, Ph.D.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1079, + "tag": "ModelDB:37856" + } + ], + "timestamp_created": "2024-01-11 15:50:02.811614+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/37856", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "527": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 527, + "name": "CN bushy, stellate neurons (Rothman, Manis 2003)", + "repository_type": "github", + "summary": "Using kinetic data from three different K+ currents in acutely isolated neurons, a single electrical compartment model representing the soma of a ventral cochlear nucleus (VCN) neuron was created. The K+ currents include a fast transient current (IA), a slow-inactivating low-threshold current (ILT), and a noninactivating high-threshold current (IHT). The model also includes a fast-inactivating Na+ current, a hyperpolarization-activated cation current (Ih), and 1-50 auditory nerve synapses. With this model, the role IA, ILT, and IHT play in shaping the discharge patterns of VCN cells is explored. Simulation results indicate these currents have specific roles in shaping the firing patterns of stellate and bushy CN cells. (see readme.txt and the papers, esp 2003c, for details). Any questions regarding these implementations should be directed to: pmanis@med.unc.edu 2 April 2004 Paul B Manis, Ph.D.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 849, + "tag": "Audition" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1080, + "tag": "ModelDB:37857" + } + ], + "timestamp_created": "2024-01-11 15:50:03.349339+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/37857", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "528": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 528, + "name": "Artificial neuron model (Izhikevich 2003, 2004, 2007)", + "repository_type": "github", + "summary": "A set of models is presented based on 2 related\r\nparameterizations to reproduce spiking and bursting behavior of multiple\r\ntypes of cortical neurons and thalamic neurons. These models combine the\r\nbiologically plausibility of Hodgkin Huxley-type dynamics and the\r\ncomputational efficiency of integrate-and-fire neurons. Using these\r\nmodel, one can simulate tens of thousands of spiking cortical neurons in\r\nreal time (1 ms resolution) using a desktop PC.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + }, + { + "id": 1081, + "tag": "ModelDB:39948" + } + ], + "timestamp_created": "2024-01-11 15:50:03.862389+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/39948", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "529": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 529, + "name": "Spiny neuron model with dopamine-induced bistability (Gruber et al 2003)", + "repository_type": "github", + "summary": "These files implement a model of dopaminergic modulation of voltage-gated currents (called kir2 and caL in the original paper). See spinycell.html for details of usage and implementation. For questions about this implementation, contact Ted Carnevale (ted.carnevale@yale.edu)", + "tags": [ + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 850, + "tag": "Intrinsic plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1082, + "tag": "ModelDB:39949" + } + ], + "timestamp_created": "2024-01-11 15:50:04.355315+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/39949", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "530": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 530, + "name": "Sparsely connected networks of spiking neurons (Brunel 2000)", + "repository_type": "github", + "summary": "The dynamics of networks of sparsely connected excitatory and inhibitory integrate-and-fire neurons\r\nare studied analytically (and with simulations). The analysis reveals a rich repertoire of states, including synchronous states in which\r\nneurons fire regularly; asynchronous states with stationary global activity and very irregular individual cell activity;\r\nand states in which the global activity oscillates but individual cells fire irregularly, typically at rates lower than\r\nthe global oscillation frequency. See paper for more and details.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 661, + "tag": "NEST" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 1083, + "tag": "ModelDB:42020" + } + ], + "timestamp_created": "2024-01-11 15:50:04.803020+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/42020", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "531": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 531, + "name": "Kenyon cells in the honeybee (Wustenberg et al 2004)", + "repository_type": "github", + "summary": "The mushroom body of the insect brain is an important locus for olfactory information\r\nprocessing and associative learning. ... Current- and voltage-clamp analyses were\r\nperformed on cultured Kenyon cells from honeybees. ... Voltage-clamp analyses characterized a\r\nfast transient Na+ current (INa), a delayed rectifier K+ current (IK,V) and a fast transient K+ current\r\n(IK,A). Using the neurosimulator SNNAP, a Hodgkin-Huxley type model was developed and\r\nused to investigate the roles of the different currents during spiking. The model led to the\r\nprediction of a slow transient outward current (IK,ST) that was subsequently identified by reevaluating\r\nthe voltage-clamp data. Simulations indicated that the primary currents that underlie\r\nspiking are INa and IK,V, whereas IK,A and IK,ST primarily determined the responsiveness of the\r\nmodel to stimuli such constant or oscillatory injections of current. See paper for more details.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 1084, + "tag": "ModelDB:42022" + } + ], + "timestamp_created": "2024-01-11 15:50:05.277984+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/42022", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "532": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 532, + "name": "Touch Sensory Cells (T Cells) of the Leech (Cataldo et al. 2004) (Scuri et al. 2007)", + "repository_type": "github", + "summary": "Bursts of spikes in leech T cells produce an AHP, which results from activation of a Na+/K+ pump and \r\na Ca2+-dependent K+ current. Activity-dependent increases in the AHP are believed to induce conduction \r\nblock of spikes in several regions of the neuron, which in turn, may decrease presynaptic invasion of spikes and \r\nthereby decrease transmitter release. To explore this possibility, we used the neurosimulator SNNAP to develop \r\na multi-compartmental model of the T cell. Each compartment was modeled as an equivalent electrical circuit, \r\nin which some currents were regulated by intracellular Ca2+ and Na+. The membrane model consisted of \r\na membrane capacitance (Cm), for which we used the value 1 uF/cm2, in parallel with \r\ntwo inward currents (Na+ and Ca2+), two K+ currents, a leak current and pump current. \r\nThe model incorporated empirical data that describe the geometry of the cell and activity-dependent changes of the\r\nAHP (see paper for details). \r\nSimulations indicated that at some branching points, activity-dependent increases of the AHP reduced the number \r\nof spikes transmitted from the minor receptive field to the soma and beyond. \r\nThese results suggest that the AHP can regulate spike conduction within the presynaptic arborizations of the cell and \r\ncould in principle contribute to the synaptic depression that is correlated with increases in the AHP.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 821, + "tag": "Conduction failure" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 816, + "tag": "Na/K pump" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 817, + "tag": "Sodium pump" + }, + { + "id": 851, + "tag": "Touch" + }, + { + "id": 1085, + "tag": "ModelDB:42036" + } + ], + "timestamp_created": "2024-01-11 15:50:05.747873+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/42036", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "533": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 533, + "name": "Effects of Acetyl-L-carnitine on neural transmission (Lombardo et al 2004)", + "repository_type": "github", + "summary": "Acetyl-L-carnitine is known to improve many aspects of the neural activity even if its exact role in neurotransmission is \r\nstill unknown. This study investigates the effects of acetyl-L-carnitine in T segmental sensory neurons of the leech Hirudo \r\nmedicinalis. These neurons are involved in some forms of neural plasticity associated with learning processes. \r\nTheir physiological firing is accompanied by a large afterhyperpolarization that is mainly due to the Na+/K+ ATPase \r\nactivity and partially to a Ca2+-dependent K+ current. A clear-cut hyperpolarization and a significant increase of the \r\nafterhyperpolarization have been recorded in T neurons of leeches injected with 2 mM acetyl-L-carnitine some days \r\nbefore. Acute treatments of 50 mM acetyl-L-carnitine induced similar effects in T cells of naive animals. \r\nMoreover, in these cells, widely arborized, the afterhyperpolarization seems to play an important role in determining \r\nthe action potential transmission at neuritic bifurcations. \r\nA computational model of a T cell has been previously developed considering detailed data for geometry and the \r\nmodulation of the pump current. Herein, we showed that to a larger afterhyperpolarization, due to the \r\nacetyl-L-carnitine-induced effects, corresponds a decrement in the number of action potentials \r\nreaching synaptic terminals.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 816, + "tag": "Na/K pump" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 817, + "tag": "Sodium pump" + }, + { + "id": 1086, + "tag": "ModelDB:42037" + } + ], + "timestamp_created": "2024-01-11 15:50:06.230497+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/42037", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "534": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 534, + "name": "Squid axon (Hodgkin, Huxley 1952) (SNNAP)", + "repository_type": "github", + "summary": "The classic HH model of squid axon membrane\r\nimplemented in SNNAP.\r\nHodgkin, A.L., Huxley, A.F. (1952)", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 1087, + "tag": "ModelDB:42044" + } + ], + "timestamp_created": "2024-01-11 15:50:06.697932+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/42044", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "535": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 535, + "name": "Morris-Lecar model of the barnacle giant muscle fiber (Morris, Lecar 1981)", + "repository_type": "github", + "summary": "... This paper presents an analysis of the possible modes of behavior available to a system of two noninactivating conductance mechanisms, and indicates a good correspondence to the types of behavior exhibited by barnacle fiber. The differential equations of a simple equivalent circuit for the fiber are dealt with by means of some of the mathematical techniques of nonlinear mechanics. General features of the system are (a) a propensity to produce damped or sustained oscillations over a rather broad parameter range, and (b) considerable latitude in the shape of the oscillatory potentials. It is concluded that for cells subject to changeable parameters (either from cell to cell or with time during cellular activity), a system dominated by two noninactivating conductances can exhibit varied oscillatory and bistable behavior. See paper for details.", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1088, + "tag": "ModelDB:42046" + } + ], + "timestamp_created": "2024-01-11 15:50:07.182431+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/42046", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "536": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 536, + "name": "Minimal cell model (Av-Ron et al 1991)", + "repository_type": "github", + "summary": "The minimal cell model (MCM) is a reduced Hodgkin-Huxley model that can\r\nexhibit excitable and oscillatory behavior. It consists of two\r\nordinary differential equations, dV/dt for membrane voltage and dW/dt\r\nfor potassium activation and sodium inactivation. The MCM has a stable\r\nmembrane potential of -60mV. With constant input current of 10uA/cm2, it\r\nexhibits oscillations of 150Hz. It is based on the work by FitzHugh and\r\nRinzel.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1089, + "tag": "ModelDB:42047" + } + ], + "timestamp_created": "2024-01-11 15:50:07.656694+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/42047", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "537": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 537, + "name": "Multiple modes of a conditional neural oscillator (Epstein, Marder 1990)", + "repository_type": "github", + "summary": "We present a model for a conditional bursting neuron consisting of five conductances: Hodgkin-Huxley type time- and voltage-dependent Na+ and K+ conductances, a calcium activated voltage-dependent K+ conductance, a calcium-inhibited time- and voltage-dependent Ca++ conductance, and a leakage Cl- conductance.\r\nDifferent bursting and silent modes and transitions between them are analyzed in the model and compared to bursting modes in experiment. See the paper for details.", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 852, + "tag": "I Chloride" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 1090, + "tag": "ModelDB:42321" + } + ], + "timestamp_created": "2024-01-11 15:50:08.125124+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/42321", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "538": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 538, + "name": "Spike propagation and bouton activation in terminal arborizations (Luscher, Shiner 1990)", + "repository_type": "github", + "summary": "Action potential propagation in axons with bifurcations involving short collaterals with synaptic boutons has been simulated ... The architecture of the terminal arborizations has a profound effect on the activation pattern of synapses, suggesting that terminal arborizations not only distribute neural information to postsynaptic cells but may also be able to process neural information presynaptically. Please see paper for details.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 1091, + "tag": "ModelDB:42322" + } + ], + "timestamp_created": "2024-01-11 15:50:08.598124+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/42322", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "539": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 539, + "name": "Bursting activity of neuron R15 in Aplysia (Canavier et al 1991, Butera et al 1995)", + "repository_type": "github", + "summary": "An equivalent circuit model of the R15 bursting neuron in Aplysia has been combined with a fluid compartment model, resulting in a model that incorporates descriptions of most of the membrane ion channels that are known to exist in the somata of R15, as well as providing a Ca2+ balance on the cell. ... (from the second paper) we have implemented proposed mechanisms for the modulation of two ionic currents (IR and ISI) that play key roles in regulating its spontaneous electrical activity. The model was sufficient to simulate a wide range of endogenous activity in the presence of various concentrations of 5-HT or DA. See papers for more and details.", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 1092, + "tag": "ModelDB:42323" + } + ], + "timestamp_created": "2024-01-11 15:50:09.064134+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/42323", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "540": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 540, + "name": "Olfactory bulb mitral cell: synchronization by gap junctions (Migliore et al 2005)", + "repository_type": "github", + "summary": "In a realistic model of two electrically connected mitral cells,\r\nthe paper shows that the somatically-measured experimental properties\r\nof Gap Junctions (GJs) may correspond to a variety of different local coupling strengths\r\nand dendritic distributions of GJs in the tuft. The model suggests\r\nthat the propagation of the GJ-induced local tuft depolarization\r\nis a major mechanim for intraglomerular synchronization of mitral cells.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 1093, + "tag": "ModelDB:43039" + } + ], + "timestamp_created": "2024-01-11 15:50:09.543452+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/43039", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "541": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 541, + "name": "CA1 pyramidal neuron: dendritic spike initiation (Gasparini et al 2004)", + "repository_type": "github", + "summary": "NEURON mod files from the paper:\r\nSonia Gasparini, Michele Migliore, and Jeffrey C. Magee\r\nOn the initiation and propagation of dendritic spikes in CA1 pyramidal neurons,\r\nJ. Neurosci., J. Neurosci. 24:11046-11056 (2004).", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1094, + "tag": "ModelDB:44050" + } + ], + "timestamp_created": "2024-01-11 15:50:10.055278+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/44050", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "542": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 542, + "name": "Sympathetic neuron (Wheeler et al 2004)", + "repository_type": "github", + "summary": "This study shows how synaptic convergence and plasticity can interact to generate synaptic gain in autonomic ganglia and thereby enhance homeostatic control. Using a conductance-based computational model of an idealized sympathetic neuron, we simulated the postganglionic response to noisy patterns of presynaptic activity and found that a threefold amplification in postsynaptic spike output can arise in ganglia, depending on the number and strength of nicotinic synapses, the presynaptic firing rate, the extent of presynaptic facilitation, and the expression of muscarinic and peptidergic excitation. See references for details.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 853, + "tag": "I CNG" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 854, + "tag": "Synaptic Convergence" + }, + { + "id": 1095, + "tag": "ModelDB:44972" + } + ], + "timestamp_created": "2024-01-11 15:50:10.573139+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/44972", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "543": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 543, + "name": "The activity phase of postsynaptic neurons (Bose et al 2004)", + "repository_type": "github", + "summary": "We show, in a simplified network consisting of an oscillator \r\ninhibiting a follower neuron, how the interaction between synaptic depression\r\nand a transient potassium current in the follower neuron determines the \r\nactivity phase of this neuron. We derive a mathematical expression to \r\ndetermine at what phase of the oscillation the follower neuron becomes \r\nactive. This expression can be used to understand which parameters determine\r\nthe phase of activity of the follower as the frequency of the oscillator is \r\nchanged. See paper for more.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1096, + "tag": "ModelDB:45513" + } + ], + "timestamp_created": "2024-01-11 15:50:11.036790+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/45513", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "544": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 544, + "name": "Large cortex model with map-based neurons (Rulkov et al 2004)", + "repository_type": "github", + "summary": "We develop a new computationally efficient approach for the analysis of complex large-scale neurobiological networks. Its key element is the use of a new phenomenological model of a neuron capable of replicating important spike pattern characteristics and designed in the form of a system of difference equations (a map). ... Interconnected with synaptic currents these model neurons demonstrated responses very similar to those found with Hodgkin-Huxley models and in experiments. We illustrate the efficacy of this approach in simulations of one- and two-dimensional cortical network models consisting of regular spiking neurons and fast spiking interneurons to model sleep and activated states of the thalamocortical system. See paper for more.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 802, + "tag": "Sleep" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 1097, + "tag": "ModelDB:45525" + } + ], + "timestamp_created": "2024-01-11 15:50:11.520442+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/45525", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "545": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 545, + "name": "A single column thalamocortical network model (Traub et al 2005)", + "repository_type": "github", + "summary": "To better understand population phenomena in thalamocortical neuronal ensembles,\r\nwe have constructed a preliminary network model with 3,560 multicompartment neurons\r\n(containing soma, branching dendrites, and a portion of axon). Types of neurons included\r\nsuperficial pyramids (with regular spiking [RS] and fast rhythmic bursting [FRB] firing\r\nbehaviors); RS spiny stellates; fast spiking (FS) interneurons, with basket-type and axoaxonic\r\ntypes of connectivity, and located in superficial and deep cortical layers; low threshold spiking\r\n(LTS) interneurons, that contacted principal cell dendrites; deep pyramids, that could have RS or\r\nintrinsic bursting (IB) firing behaviors, and endowed either with non-tufted apical dendrites or\r\nwith long tufted apical dendrites; thalamocortical relay (TCR) cells; and nucleus reticularis\r\n(nRT) cells. To the extent possible, both electrophysiology and synaptic connectivity were\r\nbased on published data, although many arbitrary choices were necessary.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 756, + "tag": "FORTRAN" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 802, + "tag": "Sleep" + }, + { + "id": 803, + "tag": "Spindles" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1098, + "tag": "ModelDB:45539" + } + ], + "timestamp_created": "2024-01-11 15:50:12.005136+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/45539", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "546": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 546, + "name": "Bursting and resonance in cerebellar granule cells (D'Angelo et al. 2001)", + "repository_type": "github", + "summary": "In this study we report theta-frequency (3-12 Hz)\r\nbursting and resonance in rat cerebellar granule cells and show that these neurons express a previously unidentified slow repolarizing K1 current (IK-slow ). Our experimental and modeling results indicate that IK-slow was necessary for both bursting and resonance. See paper for more.", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1099, + "tag": "ModelDB:46839" + } + ], + "timestamp_created": "2024-01-11 15:50:12.486621+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/46839", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "547": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 547, + "name": "Cerebellar Purkinje Cell: resurgent Na current and high frequency firing (Khaliq et al 2003)", + "repository_type": "github", + "summary": "These mod files supplied by Dr Raman are for the below two references. ... we modeled action potential firing by simulating eight currents directly recorded from Purkinje cells in both wild-type and (mutant) med mice.\r\nRegular, high-frequency firing was slowed in med Purkinje neurons. In addition to disrupted sodium currents, med neurons had small\r\nbut significant changes in potassium and leak currents. Simulations indicated that these modified non-sodium currents could not\r\naccount for the reduced excitability of med cells but instead slightly facilitated spiking. The loss of NaV1.6-specific kinetics, however,\r\nslowed simulated spontaneous activity. Together, the data suggest that across a range of conditions, sodium currents with a resurgent\r\ncomponent promote and accelerate firing. See papers for more and details.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1100, + "tag": "ModelDB:48332" + } + ], + "timestamp_created": "2024-01-11 15:50:13.050156+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/48332", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "548": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 548, + "name": "Pyramidal neurons: IKHT offsets activation of IKLT to increase gain (Fernandez et al 2005)", + "repository_type": "github", + "summary": "This matlab model was supplied by Dr Fernandez. It provides the model specification for the below paper. The influence of a high threshold K current on low threshold K and Na currents (especially frequency-current relationships) are studied in the paper with both experiments and modeling. Please see the reference for more and details.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 855, + "tag": "I_KHT" + }, + { + "id": 856, + "tag": "I_KLT" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1101, + "tag": "ModelDB:48506" + } + ], + "timestamp_created": "2024-01-11 15:50:13.520702+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/48506", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "549": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 549, + "name": "Spike timing detection in different forms of LTD (Doi et al 2005)", + "repository_type": "github", + "summary": "To understand the spike-timing detection mechanisms in cerebellar long-term depression (LTD), we developed a kinetic model of Ca dynamics within a Purkinje dendritic spine. In our kinetic simulation, IP3 was first produced via the metabotropic pathway of parallel fiber (PF) inputs, and the Ca influx in response to the climbing fiber (CF) input triggered regenerative Ca-induced Ca release from the internal stores via the IP3\r\nreceptors activated by the increased IP3. The delay in IP3 increase caused by the PF metabotropic pathway generated the optimal PF\u2013CF\r\ninterval. The Ca dynamics revealed a threshold for large Ca2 release that decreased as IP3 increased, and it coherently explained the\r\ndifferent forms of LTD. See paper for more and details.", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 797, + "tag": "Depression" + }, + { + "id": 826, + "tag": "GENESIS (web link to model)" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1102, + "tag": "ModelDB:49305" + } + ], + "timestamp_created": "2024-01-11 15:50:13.983573+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/49305", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "550": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 550, + "name": "Kinetic NMDA receptor model (Kampa et al 2004)", + "repository_type": "github", + "summary": "This kinetic NMDA receptor model is based on voltage-clamp recordings of NMDA receptor-mediated currents in nucleated patches of rat neocortical layer 5 pyramidal neurons (Kampa et al 2004 J Physiol), this model was fit with AxoGraph directly to experimental recordings in order to obtain the optimal values for the parameters. The demo shows the behaviour of a kinetic NMDA receptor model reproducing the data in figure 2.\r\nThe NMDA receptor model uses realistic rates of magnesium block and its effects on channel desensitisation. Presynaptic transmitter release is necessary for glutamate binding to the receptor. This model was written by Bjoern Kampa, Canberra, 2004.", + "tags": [ + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1103, + "tag": "ModelDB:50207" + } + ], + "timestamp_created": "2024-01-11 15:50:14.659640+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/50207", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "551": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 551, + "name": "Olfactory bulb granule cell: effects of odor deprivation (Saghatelyan et al 2005)", + "repository_type": "github", + "summary": "The model supports the experimental findings on the effects of postnatal odor deprivation, and shows that a -10mV shift in the\r\nNa activation or a reduction in the dendritic length of newborn GC\r\ncould independently explain the observed increase in excitability. \r\n", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 1104, + "tag": "ModelDB:50210" + } + ], + "timestamp_created": "2024-01-11 15:50:15.221212+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/50210", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "552": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 552, + "name": "Spontaneous firing caused by stochastic channel gating (Chow, White 1996)", + "repository_type": "github", + "summary": "NEURON implementation of model of stochastic channel gating, resulting in spontaneous firing. Qualitatively reproduces the phenomena described in \r\nthe reference.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1105, + "tag": "ModelDB:50391" + } + ], + "timestamp_created": "2024-01-11 15:50:15.692625+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/50391", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "553": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 553, + "name": "Fast oscillations in inhibitory networks (Maex, De Schutter 2003)", + "repository_type": "github", + "summary": "We observed a new phenomenon of resonant synchronization in computer-simulated networks of inhibitory neurons in which the synaptic current has a delayed onset, reflecting finite spike propagation and synaptic transmission times. At the resonant level of network excitation, all neurons fire synchronously and rhythmically with a period approximately four times the mean delay of the onset of the inhibitory synaptic current. ... By varying the axonal delay of the inhibitory connections, networks with a realistic synaptic kinetics can be tuned to frequencies from 40 to >200 Hz. ... We conclude that the delay of the synaptic current is the primary parameter controlling the oscillation frequency of inhibitory networks and propose that delay-induced synchronization is a mechanism for fast brain rhythms that depend on intact inhibitory synaptic transmission.", + "tags": [ + { + "id": 826, + "tag": "GENESIS (web link to model)" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 1106, + "tag": "ModelDB:50392" + } + ], + "timestamp_created": "2024-01-11 15:50:16.153166+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/50392", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "554": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 554, + "name": "Neural model of two-interval discrimination (Machens et al 2005)", + "repository_type": "github", + "summary": "Two-interval discrimination involves comparison of two stimuli that are presented at different times. It has three phases: loading, in which the first stimulus is perceived and stored in working memory; maintenance of working memory; decision making, in which the second stimulus is perceived and compared with the first. In behaving monkeys, each phase is associated with characteristic firing activity of neurons in the prefrontal cortex. This model implements both working memory and decision making with a mutual inhibition network that reproduces all three phases of two-interval discrimination.\r\nMachens, C.K., Romo, R., and Brody, C.D.\r\nFlexible control of mutual inhibition: a neural model of two-interval discrimination.\r\nScience 307:1121-1124, 2005.", + "tags": [ + { + "id": 857, + "tag": "Action Selection/Decision Making" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 858, + "tag": "Maintenance" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1107, + "tag": "ModelDB:50656" + } + ], + "timestamp_created": "2024-01-11 15:50:16.618371+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/50656", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "555": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 555, + "name": "Ribbon Synapse (Sikora et al 2005)", + "repository_type": "github", + "summary": "A model of the ribbon synapse was developed to replicate both pre- and postsynaptic functions of this glutamatergic juncture. The presynaptic portion of the model is rich in anatomical and physiological detail and includes multiple release sites for each ribbon based on anatomical studies of presynaptic terminals, presynaptic voltage at the terminal, the activation of voltage-gated calcium channels and a calcium-dependent release mechanism whose rate varies as a function of the calcium concentration that is monitored at two different sites which control both an ultrafast, docked pool of vesicles and a release ready pool of tethered vesicles. See paper for more and details.", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 850, + "tag": "Intrinsic plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1108, + "tag": "ModelDB:50997" + } + ], + "timestamp_created": "2024-01-11 15:50:17.075478+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/50997", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "556": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 556, + "name": "Dorsal root ganglion (DRG) neuronal model (Amir, Devor 2003)", + "repository_type": "github", + "summary": "The model shows that an electrically excitable soma is not necessary for spike through-conduction in the t-shaped geometry of a dorsal root ganglion neuron axon. Electrical excitability of the soma is required, however, for soma spike invasion. See papers for details and more.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1109, + "tag": "ModelDB:51022" + } + ], + "timestamp_created": "2024-01-11 15:50:17.654612+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/51022", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "557": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 557, + "name": "LTP in cerebellar mossy fiber-granule cell synapses (Saftenku 2002)", + "repository_type": "github", + "summary": "We simulated synaptic transmission and modified a simple model of long-term potentiation (LTP) and long-term depression (LTD) in order to describe long-term plasticity related changes in cerebellar mossy fiber-granule cell synapses. In our model, protein autophosphorylation, leading to the maintenance of long-term plasticity, is controlled by Ca2+ entry through the NMDA receptor channels. The observed nonlinearity in the development of long-term changes of EPSP in granule cells is explained by the difference in the rate constants of two independent autocatalytic processes.\r\n", + "tags": [ + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 858, + "tag": "Maintenance" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1110, + "tag": "ModelDB:51196" + } + ], + "timestamp_created": "2024-01-11 15:50:18.182466+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/51196", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "558": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 558, + "name": "Dentate gyrus network model (Santhakumar et al 2005)", + "repository_type": "github", + "summary": "Mossy cell loss and mossy fiber sprouting are two characteristic \r\n\r\nconsequences of repeated seizures and head trauma. However, their \r\n\r\nprecise contributions to the hyperexcitable state are not well \r\n\r\nunderstood. Because it is difficult, and frequently impossible, to \r\n\r\nindependently examine using experimental techniques whether it is the\r\n\r\nloss of mossy cells or the sprouting of mossy fibers that leads to \r\n\r\ndentate hyperexcitability, we built a biophysically realistic and \r\n\r\nanatomically representative computational model of the dentate gyrus\r\n\r\nto examine this question. The 527-cell model, containing granule, \r\n\r\nmossy, basket, and hilar cells with axonal projections to the \r\n\r\nperforant-path termination zone, showed that even weak mossy fiber \r\n\r\nsprouting (10-15% of the strong sprouting observed in the pilocarpine\r\n\r\nmodel of epilepsy) resulted in the spread of seizure-like activity to\r\n\r\nthe adjacent model hippocampal laminae after focal stimulation of the\r\n\r\nperforant path. See reference for more and details.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 859, + "tag": "neuroConstruct (web link to model)" + }, + { + "id": 1111, + "tag": "ModelDB:51781" + } + ], + "timestamp_created": "2024-01-11 15:50:18.650845+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/51781", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "559": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 559, + "name": "Cortical network model of posttraumatic epileptogenesis (Bush et al 1999)", + "repository_type": "github", + "summary": "This simulation from Bush, Prince, and Miller 1999 shows the epileptiform response (Fig. 6C) to a brief single stimulation in a 500 cell\r\nnetwork of multicompartment models, some of which have active dendrites. The results which I obtained under Redhat Linux is shown in result.gif.\r\n\r\nOriginal 1997 code from Paul Bush modified slightly by Bill Lytton to make it work with\r\ncurrent version of NEURON (5.7.139). Thanks to Paul Bush and Ken Miller for\r\nmaking the code available.\r\n", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 1112, + "tag": "ModelDB:52034" + } + ], + "timestamp_created": "2024-01-11 15:50:19.131660+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/52034", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "560": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 560, + "name": "Motoneuron simulations for counting motor units (Major and Jones 2005)", + "repository_type": "github", + "summary": "Simulations of clinical methods to count the number of motoneurons/motor units in human patients. Models include stimulation of motor axons or voluntary activation and responses are measured as muscle tension or EMG.", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1113, + "tag": "ModelDB:53425" + } + ], + "timestamp_created": "2024-01-11 15:50:19.609639+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/53425", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "561": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 561, + "name": "Paired turbulence and light effect on calcium increase in Hermissenda (Blackwell 2004)", + "repository_type": "github", + "summary": "The sea slug Hermissenda learns to associate light and hair cell stimulation, but not when the stimuli are temporally uncorrelated...These issues were addressed using a multi-compartmental computer model of phototransduction, calcium dynamics, and ionic currents of the Hermissenda photoreceptor...simulations show that a potassium leak channel, which closes with an increase in calcium, is required to produce both the untrained LLD and the enhanced LLD due to the decrease in voltage dependent potassium currents.", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 860, + "tag": "Chemesis" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1114, + "tag": "ModelDB:53427" + } + ], + "timestamp_created": "2024-01-11 15:50:20.134608+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/53427", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "562": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 562, + "name": "Compartmental model of a mitral cell (Popovic et al. 2005)", + "repository_type": "github", + "summary": "Usage of a morphologically realistic compartmental model of a mitral cell and data obtained from whole-cell patch-clamp and voltage-imaging experiments in order to explore passive parameter space in which reported low EPSP attenuation is observed.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 1115, + "tag": "ModelDB:53435" + } + ], + "timestamp_created": "2024-01-11 15:50:20.639782+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/53435", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "563": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 563, + "name": "Discrete event simulation in the NEURON environment (Hines and Carnevale 2004)", + "repository_type": "github", + "summary": "A short introduction to how \"integrate and fire\" cells are implemented in NEURON. Network simulations that use only artificial spiking cells are extremely efficient, with runtimes proportional to the total number of synaptic inputs received and independent of the number of cells or problem time.", + "tags": [ + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + }, + { + "id": 1116, + "tag": "ModelDB:53437" + } + ], + "timestamp_created": "2024-01-11 15:50:21.088746+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/53437", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "564": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 564, + "name": "Spatial gridding and temporal accuracy in NEURON (Hines and Carnevale 2001)", + "repository_type": "github", + "summary": "A heuristic for compartmentalization based on\r\nthe space constant at 100 Hz is proposed.\r\nThe paper also discusses spatio/temporal accuracy\r\nand the use of CVODE.", + "tags": [ + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + }, + { + "id": 1117, + "tag": "ModelDB:53451" + } + ], + "timestamp_created": "2024-01-11 15:50:21.570446+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/53451", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "565": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 565, + "name": "Method of probabilistic principle surfaces (PPS) (Chang and Ghosh 2001)", + "repository_type": "github", + "summary": "Principal curves and surfaces are nonlinear generalizations of principal\r\ncomponents and subspaces, respectively. They can provide insightful summary\r\nof high-dimensional data not typically attainable by classical linear\r\nmethods. See paper for more and details. The matlab code supplied at the authors website calculates probabilistic principle surfaces on benchmark data sets.", + "tags": [ + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1118, + "tag": "ModelDB:53457" + } + ], + "timestamp_created": "2024-01-11 15:50:22.037825+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/53457", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "566": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 566, + "name": "S cell network (Moss et al 2005)", + "repository_type": "github", + "summary": "Excerpts from the abstract:\r\nS cells form a chain of electrically coupled neurons that extends the length of the leech CNS and plays a critical role in sensitization during whole-body shortening. ...\r\nSerotonin ... increasedAP latency across the electrical synapse, suggesting that serotonin reduced coupling between S cells. ...\r\nSerotonin modulated \r\ninstantaneous AP frequency when APs were initiated in separate S \r\ncells and in a computational model of S cell activity following \r\nmechanosensory input. Thus, serotonergic modulation of S cell \r\nelectrical synapses may contribute to changes in the pattern of\r\nactivity in the S cell network. See paper for more.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 1119, + "tag": "ModelDB:53559" + } + ], + "timestamp_created": "2024-01-11 15:50:22.480022+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/53559", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "567": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 567, + "name": "Tonic neuron in spinal lamina I: prolongation of subthreshold depol. (Prescott and De Koninck 2005)", + "repository_type": "github", + "summary": "Model demonstrates mechanism whereby two kinetically distinct inward currents act synergistically to prolong subthreshold depolarization. The important currents are a persistent Na current (with fast kinetics) and a persistent Ca current (with slower kinetics). Model also includes a slow K current and transient Ca current, in addition to standard HH currents. Model parameters are set to values used in Fig. 8A. Simulation shows prolonged depolarizations in response to two brief stimuli.\r\n", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 830, + "tag": "Delay" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 861, + "tag": "Nociception" + }, + { + "id": 1120, + "tag": "ModelDB:53569" + } + ], + "timestamp_created": "2024-01-11 15:50:22.949094+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/53569", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "568": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 568, + "name": "Rat phrenic motor neuron (Amini et al 2004)", + "repository_type": "github", + "summary": "We have developed a model for the rat phrenic motor neuron (PMN) that robustly replicates many experimentally observed behaviors of PMNs in response to pharmacological, ionic, and electrical perturbations using a single set of parameters.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 863, + "tag": "Spike Frequency Adaptation" + }, + { + "id": 1121, + "tag": "ModelDB:53572" + } + ], + "timestamp_created": "2024-01-11 15:50:23.413242+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/53572", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "569": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 569, + "name": "A model for interaural time difference sensitivity in the medial superior olive (Zhou et al 2005)", + "repository_type": "github", + "summary": "This model simulates responses of neurons to interaural time difference (ITD) in the medial superior olive (MSO) of the mammalian brainstem. The model has a bipolar cell structure and incorporates two anatomic observations in the MSO: (1) the axon arises from the dendrite that receives ipsilateral inputs and (2) inhibitory synapses are located primarily on the soma in adult animals. Fine adjustment of the best ITD is achieved by the interplay of somatic sodium currents and synaptic inhibitory currents. The model suggests a mechanism for dynamically \"fine-tuning\" the ITD sensitivity of MSO cells by the opponency between depolarizing sodium currents and hyperpolarizing inhibitory currents.", + "tags": [ + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1122, + "tag": "ModelDB:53869" + } + ], + "timestamp_created": "2024-01-11 15:50:23.876356+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/53869", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "570": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 570, + "name": "Medial vestibular neuron models (Quadroni and Knopfel 1994)", + "repository_type": "github", + "summary": "The structure and the parameters of the model cells were chosen to reproduce the responses of type A and type B MVNns as described in electrophysiological recordings. The emergence of oscillatory firing under these two specific experimental conditions is consistent with electrophysiological recordings not used during construction of the model. We, therefore, suggest that these models have a high predictive value.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1123, + "tag": "ModelDB:53876" + } + ], + "timestamp_created": "2024-01-11 15:50:24.353239+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/53876", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "571": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 571, + "name": "T-type Ca current in thalamic neurons (Wang et al 1991)", + "repository_type": "github", + "summary": "A model of the transient, low-threshold voltage-dependent (T-type)\r\nCa2+ current is constructed using whole-cell voltage-clamp \r\ndata from enzymatically isolated rat thalamocortical relay neurons. \r\nThe T-type Ca2+ current is described according to the Hodgkin-Huxley \r\nscheme, using the m3h format, with rate constants determined from the experimental data.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1124, + "tag": "ModelDB:53893" + } + ], + "timestamp_created": "2024-01-11 15:50:24.837660+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/53893", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "572": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 572, + "name": "C elegans pharynx simulation (Avery and Shtonda 2003)", + "repository_type": "github", + "summary": "Experimental obervations, measurements, and theoretical analysis of C. elegans pharynx feeding behavior function are reported in the paper. See the paper and the model files for more.", + "tags": [ + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 864, + "tag": "Java" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1125, + "tag": "ModelDB:53894" + } + ], + "timestamp_created": "2024-01-11 15:50:25.300296+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/53894", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "573": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 573, + "name": "T channel currents (Vitko et al 2005)", + "repository_type": "github", + "summary": "Computer simulations predict that seven of the SNPs would increase firing of neurons, with three of them inducing oscillations at similar frequencises. 3 representative models from the paper have been submited: a wild-type (WT) recombinant Cav3.2 T-channel, and two of the\r\nmutants described in the Vitko et al., 2005 paper (C456S and R788C). See the paper for more and details.\r\n", + "tags": [ + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1126, + "tag": "ModelDB:53965" + } + ], + "timestamp_created": "2024-01-11 15:50:25.885369+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/53965", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "574": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 574, + "name": "NEUROFIT: fitting HH models to voltage clamp data (Willms 2002)", + "repository_type": "github", + "summary": "Publicly available software for accurate fitting of Hodgkin-Huxley models to voltage-clamp data... The set of parameter values for the model determined by this software yield current traces that are substantially closer to the observed data than those determined from the usual fitting method. This improvement is due to the fact that the software fits all of the parameters simultaneously utilizing all of the data rather than fitting steady-state and time constant parameters disjointly using peak currents and portions of the rising and falling phases... The software also incorporates a linear pre-estimation procedure to help in determining reasonable initial values for the full non-linear algorithm. See references for details and more.", + "tags": [ + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 756, + "tag": "FORTRAN" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 1127, + "tag": "ModelDB:54141" + } + ], + "timestamp_created": "2024-01-11 15:50:26.337493+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/54141", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "575": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 575, + "name": "Channel parameter estimation from current clamp and neuronal properties (Toth, Crunelli 2001)", + "repository_type": "github", + "summary": "In this paper, we present a method by which the activation and kinetic properties of INa, IK can be estimated from current-clamp data, more precisely from the time course of the action potential, provided some additional electrophysiological properties of the neurone are a priori known. See reference for details and more.", + "tags": [ + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 274, + "tag": "Octave" + }, + { + "id": 1128, + "tag": "ModelDB:54154" + } + ], + "timestamp_created": "2024-01-11 15:50:26.801780+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/54154", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "576": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 576, + "name": "Olfactory receptor neuron model (Dougherty et al 2005)", + "repository_type": "github", + "summary": "Demonstration of ORN model by Dougherty, Wright and Yew (2005) PNAS 102: 10415-10420. This program, dwy_pnas_demo2, simulates the transduction current response of a single olfactory receptor neuron being stimulated by an odorant plume. The program is interactive in that a user can tweak parameter values and stimulus conditions. Also, users can save a configuration in a mat-file or export all aspects to a directory of text files. These text files can be read by other programs. There is also an import facility for importing text files from a directory that allows the user to specify their own data, pulses and parameters.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 853, + "tag": "I CNG" + }, + { + "id": 865, + "tag": "I Cl,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1129, + "tag": "ModelDB:54896" + } + ], + "timestamp_created": "2024-01-11 15:50:27.287206+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/54896", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "577": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 577, + "name": "AP initiation and propagation in type II cochlear ganglion cell (Hossain et al 2005)", + "repository_type": "github", + "summary": "The model of type II cochlear ganglion cell was based on the\r\nimmunostaining of the mouse auditory pathway. Specific antibodies were\r\nused to map the distribution of voltage-dependent sodium channels along\r\nthe two unmyelinated axon-like processes of the bipolar ganglion cells.\r\nThree distinct hot spots were detected. A high density of sodium\r\nchannels was present over the entire trajectory of sensory endings\r\nbeneath the outer hair cells (the most distal portion of the peripheral\r\naxon). The other two hot spots were localized in the initial segments of\r\nboth of the axons that flank the unmyelinated bipolar ganglion cell bodies.\r\n\r\nA biophysical model indicates that all three hot spots might play\r\nimportant roles in action potential initiation and propagation. For\r\ninstance, the hot spot in the receptor segment is important for\r\ntransforming the receptor potentials into a full blown action potential\r\n(Supplemental Fig. 1). The hot spots in the two paraganglionic axon\r\ninitial segments are there to ensure the successful propagation of\r\naction potentials from the peripheral to the central axon through the\r\ncell body.\r\n\r\nThe Readme.txt file provides step by step instructions on how to\r\nrecreate Figures 6 and 7 of Hossain et al., 2005 paper.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 849, + "tag": "Audition" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1130, + "tag": "ModelDB:54903" + } + ], + "timestamp_created": "2024-01-11 15:50:27.768671+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/54903", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "578": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 578, + "name": "CA1 pyramidal neuron: signal propagation in oblique dendrites (Migliore et al 2005)", + "repository_type": "github", + "summary": "NEURON mod files from the paper:\r\nM. Migliore, M. Ferrante, GA Ascoli (2005).\r\nThe model shows how the back- and forward propagation of action potentials in the oblique dendrites of CA1 neurons could be modulated by local properties such as morphology or active conductances.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 859, + "tag": "neuroConstruct (web link to model)" + }, + { + "id": 1131, + "tag": "ModelDB:55035" + } + ], + "timestamp_created": "2024-01-11 15:50:28.248833+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/55035", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "579": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 579, + "name": "Regulation of KCNQ2/KCNQ3 current by G protein cycling (Suh et al 2004)", + "repository_type": "github", + "summary": "Receptor-mediated modulation of KCNQ channels regulates neuronal excitability. This study concerns\r\nthe kinetics and mechanism of M1 muscarinic receptor-mediated regulation of the cloned neuronal M channel, KCNQ2/KCNQ3 (Kv7.2/Kv7.3). ... observations were successfully described by a kinetic model representing biochemical steps of the signaling\r\ncascade using published rate constants where available. The model supports the following sequence of events for\r\nthis Gq-coupled signaling: A classical G-protein cycle, including competition for nucleotide-free G-protein by all\r\nnucleotide forms and an activation step requiring Mg2, followed by G-protein-stimulated phospholipase C and\r\nhydrolysis of PIP2, and finally PIP2 dissociation from binding sites for inositol lipid on the channels so that KCNQ\r\ncurrent was suppressed. See paper for details and more.", + "tags": [ + { + "id": 866, + "tag": "G-protein coupled" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 867, + "tag": "Virtual Cell (web link to model)" + }, + { + "id": 1132, + "tag": "ModelDB:55273" + } + ], + "timestamp_created": "2024-01-11 15:50:28.779889+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/55273", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "580": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 580, + "name": "Role of KCNQ1 and IKs in cardiac repolarization (Silva, Rudy 2005)", + "repository_type": "github", + "summary": "Detailed Markov models of IKs (the slow delayed rectifier K+ current) and its alpha-subunit KCNQ1 were developed. The model is compared to experiment in the paper. The role of IKs in disease and drug treatments is elucidated (the prevention of excessive action potential prolongation and development of arrhythmogenic early afterdepolarizations). See paper for more and details.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 868, + "tag": "I_Ks" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 869, + "tag": "KCNQ1" + }, + { + "id": 870, + "tag": "Long-QT" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1133, + "tag": "ModelDB:55748" + } + ], + "timestamp_created": "2024-01-11 15:50:29.246502+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/55748", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "581": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 581, + "name": "Breakdown of accmmodation in nerve: a possible role for INAp (Hennings et al 2005)", + "repository_type": "github", + "summary": "The present modeling study suggests that persistent, low-threshold, rapidly activating\r\nsodium currents have a key role in breakdown of accommodation, and that breakdown of\r\naccommodation can be used as a tool for studying persistent sodium current under normal and\r\npathological conditions. See paper for more and details.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 871, + "tag": "Electrotonus" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 872, + "tag": "Pathophysiology" + }, + { + "id": 1134, + "tag": "ModelDB:55749" + } + ], + "timestamp_created": "2024-01-11 15:50:29.710116+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/55749", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "582": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 582, + "name": "A dynamic model of the canine ventricular myocyte (Hund, Rudy 2004)", + "repository_type": "github", + "summary": "The Hund-Rudy dynamic (HRd) model is based on data from the canine epicardial ventricular myocyte. Rate-dependent phenomena associated with ion channel kinetics, action potential properties and Ca2+ handling are simulated by the model. See paper for more and details.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 852, + "tag": "I Chloride" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 816, + "tag": "Na/K pump" + }, + { + "id": 817, + "tag": "Sodium pump" + }, + { + "id": 1135, + "tag": "ModelDB:55756" + } + ], + "timestamp_created": "2024-01-11 15:50:30.202139+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/55756", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "583": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 583, + "name": "Ventricular cell model (Guinea-pig-type) (Luo, Rudy 1991, +11 other papers!) (C++)", + "repository_type": "github", + "summary": "A mathematical model of the membrane action potential of the mammalian ventricular cell is introduced. The model is based, whenever possible, on recent single-cell and single-channel data and incorporates the possibility of changing extracellular potassium concentration [K]o. ... The results are consistent with recent experimental observations, and the model simulations relate these phenomena to the underlying ionic channel kinetics. See paper for more and details.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 868, + "tag": "I_Ks" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1136, + "tag": "ModelDB:55859" + } + ], + "timestamp_created": "2024-01-11 15:50:30.691900+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/55859", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "584": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 584, + "name": "Boundary effects influence velocity in transverse propagation of cardiac APs (Sperelakis et al 2005)", + "repository_type": "github", + "summary": "... earlier experiments were carried out with 2-dimensional\r\nsheets of cells: 2 \u00c3\u2014 3, 3 \u00c3\u2014 4, and 5 \u00c3\u2014 5 models (where the first number is the number of parallel\r\nchains and the second is the number of cells in each chain). The purpose of the present study was\r\nto enlarge the model size to 7 \u00c3\u2014 7, thus enabling the transverse velocities to be compared in models\r\nof different sizes (where all circuit parameters are identical in all models). This procedure should\r\nenable the significance of the role of edge (boundary) effects in transverse propagation to be\r\ndetermined. See paper for more and details.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 873, + "tag": "PSpice" + }, + { + "id": 1137, + "tag": "ModelDB:56012" + } + ], + "timestamp_created": "2024-01-11 15:50:31.159572+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/56012", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "585": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 585, + "name": "Persistent synchronized bursting activity in cortical tissues (Golomb et al 2005)", + "repository_type": "github", + "summary": "The program simulates a one-dimensional model of a cortical tissue with excitatory and inhibitory populations.\r\n", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 1138, + "tag": "ModelDB:57905" + } + ], + "timestamp_created": "2024-01-11 15:50:31.629310+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/57905", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "586": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 586, + "name": "HERG K+ channels spike-frequency adaptation (Chiesa et al 1997)", + "repository_type": "github", + "summary": "Spike frequency adaptation has contributions from the IHERG current (encoded by the human eag-related gene\r\n(HERG); Warmke & Ganetzky, 1994), which develops with\r\nslow kinetics during depolarization and contributes to the\r\nrepolarization of the long action potentials typically present\r\nin the heart. IHERG is one of the delayed rectifier currents\r\n(IK(r)) of the heart, and HERG mutations are associated\r\nwith one of the cardiac arrhythmia LQT syndromes (LQT2).\r\nSee paper for more and details.\r\n", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 874, + "tag": "I_HERG" + }, + { + "id": 870, + "tag": "Long-QT" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 863, + "tag": "Spike Frequency Adaptation" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1139, + "tag": "ModelDB:57910" + } + ], + "timestamp_created": "2024-01-11 15:50:32.103994+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/57910", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "587": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 587, + "name": "Consequences of HERG mutations in the long QT syndrome (Clancy, Rudy 2001)", + "repository_type": "github", + "summary": "This study demonstrates which mutations can prolong APD sufficiently to generate early \r\nafterdepolarizations (EADs), which may trigger life-threatening arrhythmias. The severity of the phenotype is shown to \r\ndepend on the specific kinetic changes and how they affect I(Kr) during the time course of the action potential. See paper for more and details. \r\n\r\n", + "tags": [ + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 874, + "tag": "I_HERG" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 870, + "tag": "Long-QT" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1140, + "tag": "ModelDB:58172" + } + ], + "timestamp_created": "2024-01-11 15:50:32.626911+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/58172", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "588": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 588, + "name": "Allosteric gating of K channels (Horrigan et al 1999)", + "repository_type": "github", + "summary": "Calcium sensitive large-conductance K channel conductance is controlled by both cytoplasmic calcium and membrane potential.\r\nExperimental data obtained by the inside out patch method can be understood in terms of a gating scheme where a central transition between a closed and an open conformation is allosterically regulated by the state of four independent and identical voltage sensors. See paper for more and details.", + "tags": [ + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1141, + "tag": "ModelDB:58195" + } + ], + "timestamp_created": "2024-01-11 15:50:33.099711+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/58195", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "589": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 589, + "name": "Ca(2+) oscillations based on Ca-induced Ca-release (Dupont et al 1991)", + "repository_type": "github", + "summary": "We consider a simple, minimal model for signal-induced Ca2+ oscillations based on Ca(2+)-induced Ca2+ release. The model takes into account the existence of two pools of intracellular Ca2+, namely, one sensitive to inositol 1,4,5 trisphosphate (InsP3) whose synthesis is elicited by the stimulus, and one insensitive to InsP3. See paper for more and details.", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 875, + "tag": "Calcium waves" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1142, + "tag": "ModelDB:58199" + } + ], + "timestamp_created": "2024-01-11 15:50:33.678595+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/58199", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "590": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 590, + "name": "Role of KCNQ1 and IKs in cardiac repolarization (Silva, Rudy 2005) (XPP)", + "repository_type": "github", + "summary": "Detailed Markov model of IKs (the slow delayed rectifier K+ current) is supplied here in XPP. The model is compared to experiment in the paper. The role of IKs in disease and drug treatments is elucidated (the prevention of excessive action potential prolongation and development of arrhythmogenic early afterdepolarizations). See also modeldb accession number 55748 code and reference for more and details. This XPP version of the model reproduces Figure 3C in the paper by default.\r\nThese model files were submitted by: Dr. Sheng-Nan Wu, Han-Dong Chang, Jiun-Shian Wu\r\nDepartment of Physiology\r\nNational Cheng Kung University Medical College\r\n", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 868, + "tag": "I_Ks" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 869, + "tag": "KCNQ1" + }, + { + "id": 870, + "tag": "Long-QT" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1143, + "tag": "ModelDB:58581" + } + ], + "timestamp_created": "2024-01-11 15:50:34.255665+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/58581", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "591": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 591, + "name": "Coding of stimulus frequency by latency in thalamic networks (Golomb et al 2005)", + "repository_type": "github", + "summary": "The paper presents models of the rat vibrissa processing system including the\r\nposterior medial (POm) thalamus, ventroposterior medial (VPm) thalamus, and GABAB-\r\nmediated feedback inhibition from the reticular thalamic (Rt) nucleus.\r\nA clear match between the experimentally measured spike-rates and the\r\nnumerically calculated rates for the full model occurs when VPm thalamus receives stronger\r\nbrainstem input and weaker GABAB-mediated inhibition than POm thalamus.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1144, + "tag": "ModelDB:58582" + } + ], + "timestamp_created": "2024-01-11 15:50:34.720144+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/58582", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "592": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 592, + "name": "Proximal inhibition of Renshaw cells (Bui et al 2005)", + "repository_type": "github", + "summary": "Inhibitory synaptic inputs to Renshaw cells are concentrated on the\r\nsoma and the juxtasomatic dendrites. In the present study, we \r\ninvestigated whether this proximal bias leads to more effective \r\ninhibition under different neuronal operating conditions. Using \r\ncompartmental models based on detailed anatomical measurements of \r\nintracellularly stained Renshaw cells, we compared the inhibition \r\nproduced by GABAA synapses when\r\ndistributed with a proximal bias to the inhibition produced when the \r\nsame synapses were distributed uniformly. See paper for more and details.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 876, + "tag": "SABER" + }, + { + "id": 1145, + "tag": "ModelDB:58957" + } + ], + "timestamp_created": "2024-01-11 15:50:35.180012+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/58957", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "593": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 593, + "name": "IA and IT interact to set first spike latency (Molineux et al 2005)", + "repository_type": "github", + "summary": "Using patch clamp and modeling, we illustrate that spike latency characteristics are the product of an interplay between I(A) and low-threshold calcium current (I(T)) that requires a steady-state difference in the inactivation parameters of the currents. Furthermore, we show that the unique first-spike latency characteristics of stellate cells have important implications for the integration of coincident IPSPs and EPSPs, such that inhibition can shift first-spike latency to differentially modulate the probability of firing.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1146, + "tag": "ModelDB:59479" + } + ], + "timestamp_created": "2024-01-11 15:50:35.645690+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/59479", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "594": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 594, + "name": "Dendritic Na inactivation drives a decrease in ISI (Fernandez et al 2005)", + "repository_type": "github", + "summary": "We use a combination of dynamical analysis and electrophysiological recordings to demonstrate that spike broadening in dendrites is primarily caused by a cumulative inactivation of dendritic Na(+) current. We further show that a reduction in dendritic Na(+) current increases excitability by decreasing the interspike interval (ISI) and promoting burst firing.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1147, + "tag": "ModelDB:59480" + } + ], + "timestamp_created": "2024-01-11 15:50:36.124553+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/59480", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "595": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 595, + "name": "Continuum model of tubulin-driven neurite elongation (Graham et al 2006)", + "repository_type": "github", + "summary": "This model investigates the elongation over time of a single developing neurite (axon or dendrite). Our neurite growth model describes the elongation of a single,unbranched neurite in terms of the rate of extension of the microtubule cytoskeleton. The cytoskeleton is not explicitly modelled, but its construction is assumed to depend on the\r\navailable free tubulin at the growing neurite tip.", + "tags": [ + { + "id": 839, + "tag": "Development" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 1148, + "tag": "ModelDB:59581" + } + ], + "timestamp_created": "2024-01-11 15:50:36.595224+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/59581", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "596": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 596, + "name": "Compartmental models of growing neurites (Graham and van Ooyen 2004)", + "repository_type": "github", + "summary": "Simulator for models of neurite outgrowth. The principle model is a biophysical model of neurite outgrowth described in Graham and van Ooyen (2004). In the model, branching depends on the concentration of a branch-determining substance in each terminal segment. The substance is produced in the cell body and is transported by active transport and diffusion to the terminals. The model reveals that transport-limited effects may give rise to the same modulation of branching as indicated by the stochastic BESTL model. Different limitations arise if transport is dominated by active transport or by diffusion.", + "tags": [ + { + "id": 839, + "tag": "Development" + }, + { + "id": 864, + "tag": "Java" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1149, + "tag": "ModelDB:59582" + } + ], + "timestamp_created": "2024-01-11 15:50:37.053592+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/59582", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "597": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 597, + "name": "Excitability of the soma in central nervous system neurons (Safronov et al 2000)", + "repository_type": "github", + "summary": "The ability of the soma of a spinal dorsal horn neuron, a spinal ventral horn neuron, and a hippocampal pyramidal neuron to generate action potentials was studied using experiments and computer simulations. By comparing recordings ... of a dorsal horn neuron with simulated responses, it was shown that computer models can be adequate for the study of somatic excitability. The modeled somata of both spinal neurons were unable to generate action potentials, showing only passive and local responses to current injections. ... In contrast to spinal neurons, the modeled soma of the hippocampal pyramidal neuron generated spikes with an overshoot of +9 mV. It is concluded that the somata of spinal neurons cannot generate action potentials and seem to resist their propagation from the axon to dendrites. ... See paper for more and details.\r\n", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1150, + "tag": "ModelDB:62266" + } + ], + "timestamp_created": "2024-01-11 15:50:37.517552+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/62266", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "598": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 598, + "name": "Inhibitory control by an integral feedback signal in prefrontal cortex (Miller and Wang 2006)", + "repository_type": "github", + "summary": "The prefrontal cortex (PFC) is known to be critical for inhibitory\r\ncontrol of behavior, but the underlying mechanisms are unclear.\r\nHere, we propose that inhibitory control can be instantiated by an\r\nintegral signal derived from working memory, another key function of the PFC. Specifically, we assume that an integrator converts\r\nexcitatory input into a graded mnemonic activity that provides an\r\ninhibitory signal (integral feedback control) to upstream afferent\r\nneurons. We demonstrate this scenario in a neuronal-network\r\nmodel for a temporal discrimination task... See paper for details\r\nand more.\r\n", + "tags": [ + { + "id": 857, + "tag": "Action Selection/Decision Making" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 877, + "tag": "Working memory" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1151, + "tag": "ModelDB:62268" + } + ], + "timestamp_created": "2024-01-11 15:50:37.988231+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/62268", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "599": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 599, + "name": "A model for pituitary GH(3) lactotroph (Wu and Chang 2005)", + "repository_type": "github", + "summary": "The ATP-sensitive K(+) (K(ATP)) channels are composed of sulfonylurea receptor and inwardly rectifying K(+) channel (Kir6.2) subunit. These channels are regulated by intracellular ADP/ATP ratio and play a role in cellular metabolism. ... The objective of this study was to determine whether Diethyl pyrocarbonate (DEPC) modifies K(ATP)-channel activity in pituitary GH(3) cells. ... Simulation studies also demonstrated that the increased conductance of K(ATP)-channels used to mimic DEPC actions reduced the frequency of spontaneous action potentials and fluctuation of intracellular Ca(2+). The results indicate that chemical modification with DEPC enhances K(ATP)-channel activity and influences functional activities of pituitary GH(3) cells. See paper for more and details.", + "tags": [ + { + "id": 878, + "tag": "ATP-senstive potassium current" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1152, + "tag": "ModelDB:62272" + } + ], + "timestamp_created": "2024-01-11 15:52:51.034955+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/62272", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "600": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 600, + "name": "Spike frequency adaptation in spinal sensory neurones (Melnick et al 2004)", + "repository_type": "github", + "summary": "Using tight-seal recordings from rat spinal cord slices, intracellular\r\nlabelling and computer simulation, we analysed the mechanisms of spike\r\nfrequency adaptation in substantia gelatinosa (SG)\r\nneurones. Adapting-firing neurones (AFNs) generated short bursts of\r\nspikes during sustained depolarization and were mostly found in\r\nlateral SG. ... Ca2 + -dependent conductances do not contribute to\r\nadapting firing. Transient KA current was small and completely\r\ninactivated at resting potential suggesting that adapting firing was\r\nmainly generated by voltage-gated Na+ and delayed-rectifier K+ (KDR )\r\ncurrents. ... Computer simulation has further revealed that\r\ndown-regulation of Na+ conductance represents an effective mechanism\r\nfor the induction of firing adaptation. It is suggested that the\r\ncell-specific regulation of Na+ channel expression can be an important\r\nfactor underlying the diversity of firing patterns in SG neurones.\r\nSee paper for more and details.", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + }, + { + "id": 863, + "tag": "Spike Frequency Adaptation" + }, + { + "id": 1153, + "tag": "ModelDB:62284" + } + ], + "timestamp_created": "2024-01-11 15:52:51.554017+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/62284", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "601": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 601, + "name": "Tonic firing in substantia gelatinosa neurons (Melnick et al 2004)", + "repository_type": "github", + "summary": "Ionic conductances underlying excitability in tonically firing neurons\r\n(TFNs) from substantia gelatinosa (SG) were studied by the patch-clamp\r\nmethod in rat spinal cord slices. ... Suppression of Ca2+ and KCA currents ... did not\r\nabolish the basic pattern of tonic firing, indicating that it was\r\ngenerated by voltage-gated Na+ and K+ currents. ...\r\n on the basis of present data, we created a model of TFN\r\nand showed that Na+ and KDR currents are sufficient to generate a\r\nbasic pattern of tonic firing. It is concluded that the balanced\r\ncontribution of all ionic conductances described here is important for\r\ngeneration and modulation of tonic firing in SG neurons. See paper for more and details.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1154, + "tag": "ModelDB:62285" + } + ], + "timestamp_created": "2024-01-11 15:52:52.058088+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/62285", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "602": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 602, + "name": "Excitation-contraction coupling in an integrative heart cell model (Greenstein et al 2006)", + "repository_type": "github", + "summary": "\"... In this study, we generalize a recently developed analytical\r\napproach for deriving simplified mechanistic models of CICR\r\n(Ca(2+)-induced Ca(2+) release) to formulate an integrative model of\r\nthe canine cardiac myocyte which is computationally efficient. The\r\nresulting model faithfully reproduces experimentally measured\r\nproperties of EC (excitation-contraction) coupling and whole cell\r\nphenomena. The model is used to study the role of local redundancy in\r\nL-type Ca(2+) channel gating and the role of dyad configuration on EC\r\ncoupling. Simulations suggest that the characteristic steep rise in EC\r\ncoupling gain observed at hyperpolarized potentials is a result of\r\nincreased functional coupling between LCCs (L-type Ca(2+) channels)\r\nand RyRs (ryanodine-sensitive Ca(2+) release channels). We also\r\ndemonstrate mechanisms by which alterations in the early\r\nrepolarization phase of the action potential, resulting from reduction\r\nof the transient outward potassium current, alters properties of EC\r\ncoupling.\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1155, + "tag": "ModelDB:62286" + } + ], + "timestamp_created": "2024-01-11 15:52:52.519707+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/62286", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "603": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 603, + "name": "Kv4.3, Kv1.4 encoded K(+) channel in heart cells (Greenstein et al 2000) (XPP)", + "repository_type": "github", + "summary": "A model of canine I:(to1) (the Ca(2+)-independent transient outward current) is formulated as the combination of Kv4.3 and Kv1.4 \r\ncurrents and is incorporated into an existing canine ventricular myocyte model. Simulations demonstrate strong \r\ncoupling between L-type Ca(2+) current and I:(Kv4.3) and predict a bimodal relationship between I:(Kv4.3) \r\ndensity and APD whereby perturbations in I:(Kv4.3) density may produce either prolongation or shortening of APD, \r\ndepending on baseline I:(to1) current level.\r\nThe model files were submitted by:\r\nDr. Sheng-Nan Wu, Dr. Ruey J. Sung, Ya-Jean Wang and Jiun-Shian Wu\r\ne-mail: snwu@mail.ncku.edu.tw\r\n", + "tags": [ + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1156, + "tag": "ModelDB:62287" + } + ], + "timestamp_created": "2024-01-11 15:52:52.961006+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/62287", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "604": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 604, + "name": "Kv4.3, Kv1.4 encoded K channel in heart cells & tachy. (Winslow et al 1999, Greenstein et al 2000)", + "repository_type": "github", + "summary": "(1999) We present a model of the canine midmyocardial ventricular action potential and Ca2+ transient. The model is used to estimate the degree of functional upregulation and downregulation of Na/Ca exchanger protein and sarcoplasmic reticulum Ca ATPase in heart failure using data obtained from 2 different experimental protocols.\r\n(2000): A model of canine I:(to1) (the Ca(2+)-independent transient outward current) is formulated as the combination of Kv4.3 and Kv1.4 \r\ncurrents and is incorporated into an existing canine ventricular myocyte model. Simulations demonstrate strong \r\ncoupling between L-type Ca(2+) current and I:(Kv4.3) and predict a bimodal relationship between I:(Kv4.3) \r\ndensity and APD whereby perturbations in I:(Kv4.3) density may produce either prolongation or shortening of APD, \r\ndepending on baseline I:(to1) current level.\r\nSee each paper for more and details.\r\n", + "tags": [ + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 864, + "tag": "Java" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1157, + "tag": "ModelDB:62654" + } + ], + "timestamp_created": "2024-01-11 15:52:53.415940+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/62654", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "605": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 605, + "name": "Markovian model for cardiac sodium channel (Clancy, Rudy 2002)", + "repository_type": "github", + "summary": "Complex physiological interactions determine the functional consequences of gene abnormalities and make mechanistic interpretation of phenotypes extremely difficult. A recent example is a single mutation in the C terminus of the cardiac Na(+) channel, 1795insD. The mutation causes two distinct clinical syndromes, long QT (LQT) and Brugada, leading to life-threatening cardiac arrhythmias. Coexistence of these syndromes is seemingly paradoxical; LQT is associated with enhanced Na(+) channel function, and Brugada with reduced function. Using a computational approach, we demonstrate that the 1795insD mutation exerts variable effects depending on the myocardial substrate. We develop Markov models of the wild-type and 1795insD cardiac Na(+) channels. See reference for more and details. The model files were submitted by: Dr. Jiun-Shian Wu, Dr. Sheng-Nan Wu, Dr. Ruey J. Sung, Han-Dong Chang.", + "tags": [ + { + "id": 879, + "tag": "Brugada" + }, + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 870, + "tag": "Long-QT" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 872, + "tag": "Pathophysiology" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1158, + "tag": "ModelDB:62661" + } + ], + "timestamp_created": "2024-01-11 15:52:53.873246+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/62661", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "606": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 606, + "name": "Sodium currents activate without a delay (Baranauskas and Martina 2006)", + "repository_type": "github", + "summary": "Hodgkin and Huxley established that sodium currents in the squid giant\r\naxons activate after a delay, which is explained by the model of a\r\nchannel with three identical independent gates that all have to open\r\nbefore the channel can pass current (the HH model). It is assumed that\r\nthis model can adequately describe the sodium current activation time\r\ncourse in all mammalian central neurons, although there is no\r\nexperimental evidence to support such a conjecture. We performed high\r\ntemporal resolution studies of sodium currents gating in three types\r\nof central neurons. ... These results can be explained by a model with\r\ntwo closed states and one open state. ... This\r\nmodel captures all major properties of the sodium current\r\nactivation. In addition, the proposed model reproduces the observed\r\naction potential shape more accurately than the traditional HH model.\r\nSee paper for more and details.\r\n", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1159, + "tag": "ModelDB:62673" + } + ], + "timestamp_created": "2024-01-11 15:52:54.327103+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/62673", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "607": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 607, + "name": "Markovian model for single-channel recordings of Ik_1 in ventricular cells (Matsuoka et al 2003)", + "repository_type": "github", + "summary": "The interaction between many currents in a cardiac ventricular model are examined in this paper. One of the main contributions come from a current called IK_1. An XPP version of this model was supplied by\r\nHsieng-Jung Lai, Jiun-Shian Wu, Sheng-Nan Wu, Ruey J. Sung, Han-Dong\r\nChang. Please see paper and model for more and details.", + "tags": [ + { + "id": 880, + "tag": "Cardiac pacemaking" + }, + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1160, + "tag": "ModelDB:62676" + } + ], + "timestamp_created": "2024-01-11 15:52:54.803239+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/62676", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "608": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 608, + "name": "Dichotomy of action-potential backpropagation in CA1 pyramidal neuron dendrites (Golding et al 2001)", + "repository_type": "github", + "summary": "From reference below and \r\nCorrigendum: J Neurophysiol 87:1a, 2002 (better versions of figures 2, 3, 5 and 7 because of poor print quality in the original article; as of 2/2006, these figures are perfectly fine in the PDF of the original article that is currently available from the publisher's WWW site).\r\nExamines the anatomical and biophysical factors that account for the fact that retrograde invasion of spikes into the apical dendritic tree past 300 um succeeds in some CA1 pyramidal neurons but fails in others.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 819, + "tag": "NEURON (web link to model)" + }, + { + "id": 1161, + "tag": "ModelDB:64167" + } + ], + "timestamp_created": "2024-01-11 15:52:55.256373+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/64167", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "609": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 609, + "name": "Voltage attenuation in CA1 pyramidal neuron dendrites (Golding et al 2005)", + "repository_type": "github", + "summary": "Voltage attenuation in the apical dendritic field of CA1 pyramidal neurons is particularly strong for epsps spreading toward the soma. High cytoplasmic resistivity and high membrane (leak) conductance appear to be the major determinants of voltage attenuation over most of the apical field, but H current may be responsible for as much as half of the attenuation of distal apical epsps.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 871, + "tag": "Electrotonus" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 819, + "tag": "NEURON (web link to model)" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 1162, + "tag": "ModelDB:64170" + } + ], + "timestamp_created": "2024-01-11 15:52:55.713709+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/64170", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "610": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 610, + "name": "Cardiac action potential based on Luo-Rudy phase 1 model (Luo and Rudy 1991), (Wu 2004)", + "repository_type": "github", + "summary": "A mathematical model of the membrane action potential of the mammalian \r\nventricular cell is introduced. The model is based, whenever possible, \r\non recent single-cell and single-channel data and incorporates the \r\npossibility of changing extracellular potassium concentration [K]o. The \r\nfast sodium current, INa, is characterized by fast upstroke velocity (Vmax \r\n= 400 V/sec) and slow recovery from inactivation. The time-independent \r\npotassium current, IK1, includes a negative-slope phase and displays \r\nsignificant crossover phenomenon as [K]o is varied. The time-dependent \r\npotassium current, IK, shows only a minimal degree of crossover. A novel \r\npotassium current that activates at plateau potentials is included in \r\nthe model. The simulated action potential duplicates the experimentally \r\nobserved effects of changes in [K]o on action potential duration and rest \r\npotential. See papers for more and details.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 872, + "tag": "Pathophysiology" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1163, + "tag": "ModelDB:64171" + } + ], + "timestamp_created": "2024-01-11 15:52:56.167022+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/64171", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "611": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 611, + "name": "Stochastic Ih and Na-channels in pyramidal neuron dendrites (Kole et al 2006)", + "repository_type": "github", + "summary": "The hyperpolarization-activated cation current (Ih) plays an important role in regulating neuronal excitability, yet its native single-channel properties in the brain are essentially unknown. Here we use variance-mean analysis to study the properties of single Ih channels in the apical dendrites of cortical layer 5 pyramidal neurons in vitro. ... In contrast to the uniformly distributed single-channel conductance, Ih channel number increases exponentially with distance, reaching densities as high as approximately 550 channels/microm2 at distal dendritic sites. These high channel densities generate significant membrane voltage noise. By incorporating a stochastic model of Ih single-channel gating into a morphologically realistic model of a layer 5 neuron, we show that this channel noise is higher in distal dendritic compartments and increased threefold with a 10-fold increased single-channel conductance (6.8 pS) but constant Ih current density. ... These data suggest that, in the face of high current densities, the small single-channel conductance of Ih is critical for maintaining the fidelity of action potential output. See paper for more and details.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1164, + "tag": "ModelDB:64195" + } + ], + "timestamp_created": "2024-01-11 15:52:56.614109+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/64195", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "612": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 612, + "name": "Vomeronasal sensory neuron (Shimazaki et al 2006)", + "repository_type": "github", + "summary": "NEURON model files from the papers:\r\nShimazaki et al, Chem. Senses, epub ahead of print (2006)\r\nElectrophysiological properties and modeling of murine vomeronasal \r\nsensory neurons in acute slice preparations.\r\n\r\nThe model reproduces quantitatively the experimentally observed \r\nfiring rates of these neurons under a wide range of input currents.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1165, + "tag": "ModelDB:64212" + } + ], + "timestamp_created": "2024-01-11 15:52:57.076879+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/64212", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "613": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 613, + "name": "Reciprocal regulation of rod and cone synapse by NO (Kourennyi et al 2004)", + "repository_type": "github", + "summary": "We constructed models of rod and cone photoreceptors\r\nusing NEURON software to predict how changes in Ca channels\r\nwould affect the light response in these cells and in\r\npostsynaptic horizontal cells.", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 852, + "tag": "I Chloride" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 800, + "tag": "Vision" + }, + { + "id": 1166, + "tag": "ModelDB:64216" + } + ], + "timestamp_created": "2024-01-11 15:52:57.545431+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/64216", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "614": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 614, + "name": "Simulated light response in rod photoreceptors (Liu and Kourennyi 2004)", + "repository_type": "github", + "summary": "We developed a complete computer model of the rod, which accurately reproduced the main features of the light response and allowed us to demonstrate that it was suppression of Kx channels that was essential for slowing SLR and increasing excitability of rods. The results reported in this work further establish the importance of Kx channels in rod photoreceptor function.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 852, + "tag": "I Chloride" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1167, + "tag": "ModelDB:64228" + } + ], + "timestamp_created": "2024-01-11 15:52:58.028140+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/64228", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "615": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 615, + "name": "Parallel network simulations with NEURON (Migliore et al 2006)", + "repository_type": "github", + "summary": "The NEURON simulation environment has been extended to support parallel network simulations.\r\nThe performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters.\r\n", + "tags": [ + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1168, + "tag": "ModelDB:64229" + } + ], + "timestamp_created": "2024-01-11 15:52:58.491150+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/64229", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "616": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 616, + "name": "Biologically-plausible models for spatial navigation (Cannon et al 2003)", + "repository_type": "github", + "summary": "Hypotheses about how parahippocampal and hippocampal structures may be involved in spatial navigation tasks are implemented in a model of a virtual rat navigating through a virtual environment in search of a food reward. The model incorporates theta oscillations to separate encoding from retrieval and yields testable predictions about the phase relations of spiking activity to theta oscillations in different parts of the hippocampal formation at various stages of the behavioral task. See paper for more and details.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 881, + "tag": "Catacomb (web link to model)" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 882, + "tag": "Place cell/field" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 883, + "tag": "Spatial Navigation" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1169, + "tag": "ModelDB:64242" + } + ], + "timestamp_created": "2024-01-11 15:52:58.946211+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/64242", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "617": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 617, + "name": "A dynamical model of the basal ganglia (Leblois et al 2006)", + "repository_type": "github", + "summary": "We propose a new model for the function and dysfunction of the basal ganglia (BG). \r\nThe basal ganglia are a set of cerebral structures involved in motor control which \r\ndysfunction causes high-incidence pathologies such as Parkinson's disease (PD). \r\nTheir precise motor functions remain unknown. \r\nThe classical model of the BG that allowed for the discovery of new treatments \r\nfor PD seems today outdated in several respects. \r\nBased on experimental observations, our model proposes a simple dynamical framework \r\nfor the understanding of how BG may select motor programs to be executed. Moreover, \r\nwe explain how this ability is lost and how tremor-related oscillations in neuronal \r\nactivity may emerge in PD. \r\n", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 872, + "tag": "Pathophysiology" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 1170, + "tag": "ModelDB:64255" + } + ], + "timestamp_created": "2024-01-11 15:52:59.406540+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/64255", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "618": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 618, + "name": "Steady-state Vm distribution of neurons subject to synaptic noise (Rudolph, Destexhe 2005)", + "repository_type": "github", + "summary": "This package simulates synaptic background activity similar to in vivo measurements using a model of fluctuating synaptic conductances, and compares the simulations with analytic estimates. The steady-state membrane potential (Vm) distribution is calculated numerically and compared with the \"extended\" analytic expression provided in the reference (see this paper for details).", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 820, + "tag": "Synaptic noise" + }, + { + "id": 1171, + "tag": "ModelDB:64259" + } + ], + "timestamp_created": "2024-01-11 15:52:59.907991+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/64259", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "619": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 619, + "name": "Learning spatial transformations through STDP (Davison, Fr\u00e9gnac 2006)", + "repository_type": "github", + "summary": "A common problem in tasks involving the integration of spatial information from multiple senses, or in sensorimotor coordination, is that different modalities represent space in different frames of reference. Coordinate transformations between different reference frames are therefore required. One way to achieve this relies on the encoding of spatial information using population codes. The set of network responses to stimuli in different locations (tuning curves) constitute a basis set of functions which can be combined\r\nlinearly through weighted synaptic connections in order to approximate non-linear transformations of the input variables. The question then arises how the appropriate synaptic connectivity is obtained. \r\n\r\nThis model shows that a network of spiking neurons can learn the coordinate transformation from one frame of reference to another, with connectivity that develops continuously in an unsupervised manner, based only on the correlations available in the environment, and with a biologically-realistic plasticity mechanism (spike timing-dependent plasticity).", + "tags": [ + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 886, + "tag": "Unsupervised Learning" + }, + { + "id": 1172, + "tag": "ModelDB:64261" + } + ], + "timestamp_created": "2024-01-11 15:53:00.368108+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/64261", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "620": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 620, + "name": "Simple model of barrel-specific segregation in cortex (Lu et al 2006)", + "repository_type": "github", + "summary": "Mice with a loss-of-function mutation of calcium/calmodulin-activated adenylyl cyclase I (AC1) - barrelless mice - have strikingly abherrent cortical development: the thalamic afferents into the barrel cortex do not segregate into whisker-specific barrels. Our paper investigates the link between this mutation and the \"barrelless\" phenotype, and demonstrates that the loss-of-function mutation leads to deficits in presynaptic mechanisms at the thalamocortical synapse.\r\n\r\nHow might presynaptic deficits disrupt whisker-specific segregation in the barrel cortex? We used a model to demonstrate one possibility: decrease in the release probability at the thalamocortical synapse (which is observed in the barrelless mutant) can influence the balance between LTP and LTD (in favor of LTD), which can disrupt whisker segregaton. Though how this occurs is easily explained with a conceptual model (described succinctly in the associated paper), we also produced a computational simulation of this phenomenon.", + "tags": [ + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1173, + "tag": "ModelDB:64266" + } + ], + "timestamp_created": "2024-01-11 15:53:00.827447+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/64266", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "621": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 621, + "name": "Dopaminergic cell bursting model (Kuznetsov et al 2006)", + "repository_type": "github", + "summary": "Dopaminergic neurons of the midbrain fire spontaneously at rates\r\n<10/s and ordinarily will not exceed this range even when driven with\r\nsomatic current injection. During spontaneous\r\nbursting of dopaminergic neurons in vivo, bursts related to reward\r\nexpectation in behaving animals, and bursts generated by dendritic\r\napplication of N-methyl-D-aspartate (NMDA) agonists, transient firing\r\nattains rates well above this range. We suggest a way such highfrequency\r\nfiring may occur in response to dendritic NMDA receptor\r\nactivation. We have extended the coupled oscillator model of the\r\ndopaminergic neuron, which represents the soma and dendrites as\r\nelectrically coupled compartments with different natural spiking frequencies,\r\nby addition of dendritic AMPA (voltage-independent) or\r\nNMDA (voltage-dependent) synaptic conductance. Both soma and\r\ndendrites contain a simplified version of the calcium-potassium mechanism\r\nknown to be the mechanism for slow spontaneous oscillation\r\nand background firing in dopaminergic cells. We show that because of its voltage dependence,\r\nNMDA receptor activation acts to amplify the effect on the\r\nsoma of the high-frequency oscillation of the dendrites, which is\r\nnormally too weak to exert a large influence on the overall oscillation\r\nfrequency of the neuron.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1174, + "tag": "ModelDB:64285" + } + ], + "timestamp_created": "2024-01-11 15:53:01.279801+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/64285", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "622": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 622, + "name": "Dynamical model of olfactory bulb mitral cell (Rubin, Cleland 2006)", + "repository_type": "github", + "summary": "This four-compartment mitral cell exhibits endogenous subthreshold oscillations, phase resetting, and evoked spike phasing properties as described in electrophysiological studies of mitral cells. It is derived from the prior work of Davison et al (2000) and Bhalla and Bower (1993). See readme.txt for details.", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 830, + "tag": "Delay" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1175, + "tag": "ModelDB:64296" + } + ], + "timestamp_created": "2024-01-11 15:53:01.784476+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/64296", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "623": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 623, + "name": "Computational Model of a Central Pattern Generator (Cataldo et al 2006)", + "repository_type": "github", + "summary": "The buccal ganglia of Aplysia contain a central pattern generator (CPG) that mediates rhythmic movements of the foregut during feeding. This CPG is a multifunctional circuit and generates at least two types of buccal motor patterns (BMPs), one that mediates ingestion (iBMP) and another that mediates rejection (rBMP). The present study used a computational approach to examine the ways in which an ensemble of identified cells and synaptic connections function as a CPG. Hodgkin-Huxley-type models were developed that mimicked the biophysical properties of these cells and synaptic connections. The results suggest that the currently identified ensemble of cells is inadequate to produce rhythmic neural activity and that several key elements of the CPG remain to be identified.", + "tags": [ + { + "id": 852, + "tag": "I Chloride" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1176, + "tag": "ModelDB:65412" + } + ], + "timestamp_created": "2024-01-11 15:53:02.296338+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/65412", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "624": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 624, + "name": "CA1 pyramidal cell: I_NaP and I_M contributions to somatic bursting (Golomb et al 2006)", + "repository_type": "github", + "summary": "To study the mechanisms of bursting, we have constructed a\r\nconductance-based, one-compartment model of CA1 pyramidal neurons. In this neuron model,\r\nreduced [Ca2+]o is simulated by negatively shifting the activation curve of the persistent Na+ current\r\n(INaP), as indicated by recent experimental results. The neuron model accounts, with different\r\nparameter sets, for the diversity of firing patterns observed experimentally in both zero and normal\r\n[Ca2+]o. Increasing INaP in the neuron model induces bursting and increases the number of spikes\r\nwithin a burst, but is neither necessary nor sufficient for bursting. We show, using fast-slow analysis\r\nand bifurcation theory, that the M-type K+ current (IM) allows bursting by shifting neuronal behavior\r\nbetween a silent and a tonically-active state, provided the kinetics of the spike generating currents are\r\nsufficiently, though not extremely, fast. We suggest that bursting in CA1 pyramidal cells can be\r\nexplained by a single compartment *square bursting* mechanism with one slow variable, the\r\nactivation of IM. See paper for more and details.", + "tags": [ + { + "id": 887, + "tag": "Bifurcation" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1177, + "tag": "ModelDB:66268" + } + ], + "timestamp_created": "2024-01-11 15:53:02.783110+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/66268", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "625": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 625, + "name": "CA1 pyramidal neuron synaptic integration (Li and Ascoli 2006, 2008)", + "repository_type": "github", + "summary": "The model shows how different input patterns (irregular & asynchronous,\r\nirregular & synchronous, regular & asynchronous, regular & synchronous)\r\naffect the neuron's output rate when 1000 synapses are distributed in\r\nthe proximal apical dendritic tree of a hippocampus CA1 pyramidal neuron.", + "tags": [ + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 1178, + "tag": "ModelDB:71312" + } + ], + "timestamp_created": "2024-01-11 15:53:03.309694+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/71312", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "626": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 626, + "name": "Activity dependent changes in motoneurones (Dai Y et al 2002, Gardiner et al 2002)", + "repository_type": "github", + "summary": "These two papers review various experimental papers and examine the effects of activity on motoneurons in a similar 5 compartment model with 10 active conductances. Included are slow (S) and fast (F) type and fast fatigue resistant (FR) and fast fatigable (FF) models corresponding to the types of motoneurons. See papers for more and details.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + }, + { + "id": 1179, + "tag": "ModelDB:71317" + } + ], + "timestamp_created": "2024-01-11 15:53:03.813536+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/71317", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "627": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 627, + "name": "Rat subthalamic projection neuron (Gillies and Willshaw 2006)", + "repository_type": "github", + "summary": "A computational model of the rat subthalamic nucleus projection neuron is constructed using electrophysiological and morphological data and a restricted set of channel specifications. The model cell exhibits a wide range of electrophysiological behaviors characteristic of rat subthalamic neurons. It reveals that a key set of three channels play a primary role in distinguishing behaviors: a high-voltage-activated calcium channel (Cav 1.2.-1.3), a low-voltage-activated calcium channel (Cav 3.-), and a small current calcium-activated potassium channel (KCa 2.1-2.3). See paper for more and details.", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 888, + "tag": "I Mixed" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1180, + "tag": "ModelDB:74298" + } + ], + "timestamp_created": "2024-01-11 15:53:04.311792+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/74298", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "628": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 628, + "name": "Excitatory and inhibitory interactions in populations of model neurons (Wilson and Cowan 1972)", + "repository_type": "github", + "summary": "Coupled nonlinear differential equations are derived for the dynamics\r\nof spatially localized populations containing both excitatory and inhibitory model\r\nneurons. Phase plane methods and numerical solutions are then used to investigate\r\npopulation responses to various types of stimuli. The results obtained show simple\r\nand multiple hysteresis phenomena and limit cycle activity. The latter is particularly\r\ninteresting since the frequency of the limit cycle oscillation is found to be a monotonic\r\nfunction of stimulus intensity. Finally, it is proved that the existence of limit cycle\r\ndynamics in response to one class of stimuli implies the existence of multiple stable\r\nstates and hysteresis in response to a different class of stimuli. The relation between\r\nthese findings and a number of experiments is discussed.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1181, + "tag": "ModelDB:76879" + } + ], + "timestamp_created": "2024-01-11 15:53:04.777105+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/76879", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "629": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 629, + "name": "Scaling self-organizing maps to model large cortical networks (Bednar et al 2004)", + "repository_type": "github", + "summary": "Self-organizing computational models\r\nwith specific intracortical connections can\r\nexplain many functional features of visual\r\ncortex, such as topographic orientation and\r\nocular dominance maps. ... This article\r\nintroduces two techniques that make large simulations\r\npractical. \r\n\r\nFirst, we show how parameter\r\nscaling equations can be derived for\r\nlaterally connected self-organizing models.\r\nThese equations result in quantitatively equivalent\r\nmaps over a wide range of simulation\r\nsizes, making it possible to debug small simulations\r\nand then scale them up only when\r\nneeded. ...\r\nSecond, we use parameter\r\nscaling to implement a new growing map\r\nmethod called GLISSOM, which dramatically\r\nreduces the memory and computational\r\nrequirements of large self-organizing networks.\r\n\r\nSee paper for more and details.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 889, + "tag": "Topographica (web link to model)" + }, + { + "id": 886, + "tag": "Unsupervised Learning" + }, + { + "id": 1182, + "tag": "ModelDB:76883" + } + ], + "timestamp_created": "2024-01-11 15:53:05.225528+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/76883", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "630": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 630, + "name": "An oscillatory neural model of multiple object tracking (Kazanovich and Borisyuk 2006)", + "repository_type": "github", + "summary": "An oscillatory neural network model of multiple object tracking is described. The model works with a set of identical visual objects moving around the screen. At the initial stage, the model selects into the focus of attention a subset of objects initially marked as targets. Other objects are used as distractors. The model aims to preserve the initial separation between targets and distractors while objects are moving. This is achieved by a proper interplay of synchronizing and desynchronizing interactions in a multilayer network, where each layer is responsible for tracking a single target. The results of the model simulation are presented and compared with experimental data. In agreement with experimental evidence, simulations with a larger number of targets have shown higher error rates. Also, the functioning of the model in the case of temporarily overlapping objects is presented.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 1183, + "tag": "ModelDB:79145" + } + ], + "timestamp_created": "2024-01-11 15:53:05.677857+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/79145", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "631": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 631, + "name": "Simulation study of Andersen-Tawil syndrome (Sung et al 2006)", + "repository_type": "github", + "summary": "Patients with Andersen-Tawil syndrome (ATS) mostly have mutations on the KCNJ2 gene producing loss of function or \r\ndominant-negative suppression of the inward rectifier K(+) channel Kir2.1. However, clinical manifestations of ATS \r\nincluding dysmorphic features, periodic paralysis (hypo-, hyper-, or normokalemic), long QT, and ventricular arrhythmias \r\n(VA) are considerably variable. Using a modified dynamic Luo-Rudy simulation model of cardiac ventricular myocyte, we \r\nelucidate the mechanisms of VA in ATS. We adopted a kinetic model of KCNJ2 in which channel block by Mg(+2) and \r\nspermine was incorporated. In this study, we attempt to examine the effects of KCNJ2 mutations on the ventricular action \r\npotential (AP), single-channel Markovian models were reformulated and incorporated into the dynamic Luo-Rudy model \r\nfor rapidly and slowly delayed rectifying K(+) currents and KCNJ2 channel. During pacing at 1.0 Hz with [K(+)]o at 5.4 \r\nmM, a stepwise 10% reduction of Kir2.1 channel conductance progressively prolonged the terminal repolarization phase \r\nof AP along with gradual depolarization of the resting membrane potential (RMP). At 90% reduction, early after-\r\ndepolarizations (EADs) became inducible and RMP was depolarized to -55.0 mV (control: -90.1 mV) followed by \r\nemergence of spontaneous action potentials (SAP). Both EADs and SAP were facilitated by a decrease in [K(+)]o and \r\nsuppressed by increase in [K(+)]o. beta-adrenergic stimulation enhanced delayed after-depolarizations (DADs) and could \r\nalso facilitate EADs as well as SAP in the setting of low [K(+)]o and reduced Kir2.1 channel conductance. In conclusion, \r\nthe spectrum of VA in ATS includes (1) triggered activity mediated by EADs and/or DADs, and (2) abnormal automaticity \r\nmanifested as SAP. These VA can be aggravated by a decrease in [K(+)]o and beta-adrenergic stimulation, and may \r\npotentially induce torsades de pointes and cause sudden death. In patients with ATS, the hypokalemic form of periodic \r\nparalysis should have the highest propensity to VA especially during physical activities.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 888, + "tag": "I Mixed" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 870, + "tag": "Long-QT" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 816, + "tag": "Na/K pump" + }, + { + "id": 817, + "tag": "Sodium pump" + }, + { + "id": 1184, + "tag": "ModelDB:79237" + } + ], + "timestamp_created": "2024-01-11 15:53:06.272867+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/79237", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "632": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 632, + "name": "Action potential of striated muscle fiber (Adrian et al 1970)", + "repository_type": "github", + "summary": "1. Membrane currents during step depolarizations were determined by \r\na method in which three electrodes were inserted near the end of a \r\nfibre in the frog's sartorius muscle. The theoretical basis and \r\nlimitations of the method are discussed.\r\n2. Measurements of the membrane capacity (CM) and resting resistance \r\n(RM) derived from the current during a step change in membrane \r\npotential are consistent with values found by other methods.\r\n3. In fibres made mechanically inactive with hypertonic solutions \r\n(Ringer solution plus 350 mM sucrose) step depolarizations produced \r\nionic currents which resembled those of nerve in showing (a) an early \r\ntransient inward current, abolished by tetrodotoxin, which reversed \r\nwhen the depolarization was carried beyond an internal potential of \r\nabout +20 mV, (b) a delayed outward current, with a linear instantaneous \r\ncurrent\u00a1Xvoltage relation, and a mean equilibrium potential with a normal \r\npotassium concentration (2\u00a1P5 mM) of -85 mV.\r\n4. The reversal potential for the early current appears to be consistent\r\nwith the sodium equilibrium potential expected in hypertonic solutions.\r\n5. The variation of the equilibrium potential for the delayed current \r\n(V\u00a1\u00acK) with external potassium concentration suggests that the channel \r\nfor delayed current has a ratio of potassium to sodium permeability of \r\n30:1; this is less than the resting membrane where the ratio appears \r\nto be 100:1. V\u00a1\u00acK corresponds well with the membrane potential at the \r\nbeginning of the negative after-potential observed under similar conditions.\r\n6. The variation of V\u00a1\u00acK with the amount of current which has passed \r\nthrough the delayed channel suggests that potassium ions accumulate in a \r\nspace of between 1/3 and 1/6 of the fibre volume. If potassium accumulates in \r\nthe transverse tubular system (T system) much greater variation in V\u00a1\u00acK \r\nwould be expected.\r\n7. The delayed current is not maintained but is inactivated like the early \r\ncurrent. The inactivation is approximately exponential with a time constant \r\nof 0\u00a1P5 to 1 sec at 20\u00a2X C. The steady-state inactivation of the potassium \r\ncurrent is similar to that for the sodium current, but its voltage \r\ndependence is less steep and the potential for half inactivation is 20 mV \r\nrate more positive.\r\n8. Reconstructions of ionic currents were made in terms of the parameters\r\n(m, n, h) of the Hodgkin\u00a1XHuxley model for the squid axon, using constants \r\nwhich showed a similar dependence on voltage.\r\n9. Propagated action potentials and conduction velocities were computed for \r\nvarious conditions on the assumption that the T system behaves as if it were \r\na series resistance and capacity in parallel with surface capacity and the \r\nchannels for sodium, potassium and leak current. There was reasonable \r\nagreement with observed values, the main difference being that the \r\ncalculated velocities and rates of rise were somewhat less than those \r\nobserved experimentally.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1185, + "tag": "ModelDB:79238" + } + ], + "timestamp_created": "2024-01-11 15:53:06.840507+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/79238", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "633": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 633, + "name": "Cardiac Atrial Cell (Courtemanche et al 1998) (C++)", + "repository_type": "github", + "summary": "The mechanisms underlying many important properties of the human atrial action potential (AP) \r\nare poorly understood. Using specific formulations of the K+, Na+, and Ca2+ currents based on \r\ndata recorded from human atrial myocytes, along with representations of pump, exchange, and \r\nbackground currents, we developed a mathematical model of the AP. The model AP resembles APs \r\nrecorded from human atrial samples and responds to rate changes, L-type Ca2+ current blockade, \r\nNa+/Ca2+ exchanger inhibition, and variations in transient outward current amplitude in a \r\nfashion similar to experimental recordings. Rate-dependent adaptation of AP duration, an \r\nimportant determinant of susceptibility to atrial fibrillation, was attributable to \r\nincomplete L-type Ca2+ current recovery from inactivation and incomplete delayed rectifier \r\ncurrent deactivation at rapid rates. Experimental observations of variable AP morphology \r\ncould be accounted for by changes in transient outward current density, as suggested \r\nexperimentally. We conclude that this mathematical model of the human atrial AP reproduces \r\na variety of observed AP behaviors and provides insights into the mechanisms of clinically \r\nimportant AP properties.\r\n", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 816, + "tag": "Na/K pump" + }, + { + "id": 872, + "tag": "Pathophysiology" + }, + { + "id": 817, + "tag": "Sodium pump" + }, + { + "id": 1186, + "tag": "ModelDB:79461" + } + ], + "timestamp_created": "2024-01-11 15:53:07.360588+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/79461", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "634": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 634, + "name": "FS Striatal interneuron: K currents solve signal-to-noise problems (Kotaleski et al 2006)", + "repository_type": "github", + "summary": "... We show that a transient\r\npotassium (KA) current allows the Fast Spiking (FS) interneuron to strike a balance\r\nbetween sensitivity to correlated input and robustness to noise,\r\nthereby increasing its signal-to-noise ratio (SNR). First, a compartmental\r\nFS neuron model was created to match experimental data from\r\nstriatal FS interneurons in cortex\u2013striatum\u2013substantia nigra organotypic\r\ncultures. Densities of sodium, delayed rectifier, and KA channels\r\nwere optimized to replicate responses to somatic current injection.\r\nSpontaneous AMPA and GABA synaptic currents were\r\nadjusted to the experimentally measured amplitude, rise time, and\r\ninterevent interval histograms. Second, two additional adjustments\r\nwere required to emulate the remaining experimental observations.\r\nGABA channels were localized closer to the soma than AMPA\r\nchannels to match the synaptic population reversal potential. Correlation\r\namong inputs was required to produce the observed firing rate\r\nduring up-states. In this final model, KA channels were essential for\r\nsuppressing down-state spikes while allowing reliable spike generation\r\nduring up-states. ... Our results suggest that KA\r\nchannels allow FS interneurons to operate without a decrease in SNR\r\nduring conditions of increased dopamine, as occurs in response to\r\nreward or anticipated reward. See paper for more and details.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 1187, + "tag": "ModelDB:79465" + } + ], + "timestamp_created": "2024-01-11 15:53:07.858370+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/79465", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "635": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 635, + "name": "Differences between type A and B photoreceptors (Blackwell 2006)", + "repository_type": "github", + "summary": "In Hermissenda crassicornis, the memory of light associated with turbulence\r\nis stored as changes in intrinsic and synaptic currents in both\r\ntype A and type B photoreceptors. These photoreceptor types exhibit\r\nqualitatively different responses to light and current injection, and\r\nthese differences shape the spatiotemporal firing patterns that control\r\nbehavior. Thus the objective of the study was to identify the mechanisms\r\nunderlying these differences. The approach was to develop a\r\ntype B model that reproduced characteristics of type B photoreceptors\r\nrecorded in vitro, and then to create a type A model by modifying a\r\nselect number of ionic currents. Comparison of type A models with\r\ncharacteristics of type A photoreceptors recorded in vitro revealed that\r\ntype A and type B photoreceptors have five main differences, three\r\nthat have been characterized experimentally and two that constitute\r\nhypotheses to be tested with experiments in the future. See paper for more and details.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 860, + "tag": "Chemesis" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1188, + "tag": "ModelDB:79471" + } + ], + "timestamp_created": "2024-01-11 15:53:08.337149+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/79471", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "636": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 636, + "name": "Dynamic dopamine modulation in the basal ganglia: Learning in Parkinson (Frank et al 2004,2005)", + "repository_type": "github", + "summary": "See README file for all info on how to run models under different tasks and simulated Parkinson's and medication conditions.", + "tags": [ + { + "id": 857, + "tag": "Action Selection/Decision Making" + }, + { + "id": 890, + "tag": "Emergent/PDPplusplus" + }, + { + "id": 891, + "tag": "Hebbian plasticity" + }, + { + "id": 865, + "tag": "I Cl,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 872, + "tag": "Pathophysiology" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 892, + "tag": "Reinforcement Learning" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1189, + "tag": "ModelDB:79488" + } + ], + "timestamp_created": "2024-01-11 15:53:08.811081+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/79488", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "637": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 637, + "name": "Cerebellar purkinje cell: interacting Kv3 and Na currents influence firing (Akemann, Knopfel 2006)", + "repository_type": "github", + "summary": "Purkinje neurons spontaneously generate action potentials in the absence of synaptic drive and thereby exert a tonic, yet plastic, input to their target cells in the deep cerebellar nuclei. Purkinje neurons express two ionic currents with biophysical properties that are specialized for high-frequency firing: resurgent sodium currents and potassium currents mediated by Kv3.3. Numerical simulations indicated that Kv3.3 increases the spontaneous firing rate via cooperation with resurgent sodium currents. We conclude that the rate of spontaneous action potential firing of Purkinje neurons is controlled by the interaction of Kv3.3 potassium currents and resurgent sodium currents. See paper for more and details.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1190, + "tag": "ModelDB:80769" + } + ], + "timestamp_created": "2024-01-11 15:53:09.314660+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/80769", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "638": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 638, + "name": "Ih levels roles in bursting and regular-spiking subiculum pyramidal neurons (van Welie et al 2006)", + "repository_type": "github", + "summary": "Pyramidal neurons in the subiculum typically display either bursting\r\nor regular-spiking behavior. ... Here we report that bursting neurons\r\nposses a hyperpolarization-activated cation current (Ih) that is\r\ntwo-fold larger (conductance: 5.3 \u00c2\u00b1 0.5 nS) than in regularspiking\r\nneurons (2.2 \u00c2\u00b1 0.6 nS), while Ih exhibits similar voltage-dependent\r\nand kinetic properties in both classes of neurons. Bursting and\r\nregular-spiking neurons display similar morphology. The difference in\r\nIh between the two classes is not responsible for the distinct firing\r\npatterns, since neither pharmacological blockade of Ih nor enhancement\r\nof Ih using a dynamic clamp affects the qualitative firing\r\npatterns. Instead, the difference in Ih between bursting and\r\nregular-spiking neurons determines the temporal integration of evoked\r\nsynaptic input from the CA1 area. In response to 50 Hz stimulation,\r\nbursting neurons, with a large Ih, show ~50% less temporal summation\r\nthan regular-spiking neurons. ... A computer simulation model of a\r\nsubicular neuron with the properties of either a bursting or a\r\nregular-spiking neuron confirmed the pivotal role of Ih in temporal\r\nintegration of synaptic input. These data suggest that in the\r\nsubicular network, bursting neurons are better suited to discriminate\r\nthe content of high frequency input, such as that occurring during\r\ngamma oscillations, compared to regular-spiking neurons. See paper for more and details.\r\n", + "tags": [ + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 1191, + "tag": "ModelDB:82364" + } + ], + "timestamp_created": "2024-01-11 15:53:09.809712+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/82364", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "639": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 639, + "name": "Information-processing in lamina-specific cortical microcircuits (Haeusler and Maass 2006)", + "repository_type": "github", + "summary": "A major challenge for computational neuroscience is to understand\r\nthe computational function of lamina-specific synaptic connection\r\npatterns in stereotypical cortical microcircuits.We approach this problem by studying ...\r\nthe dynamical system defined by more realistic cortical microcircuit\r\nmodels as a whole and by investigating the influence that its\r\nlaminar structure has on the transmission and fusion of information\r\nwithin this dynamical system. The circuit models that we examine\r\nconsist of Hodgkin--Huxley neurons with dynamic synapses... We investigate to what\r\nextent this cortical microcircuit template supports the accumulation\r\nand fusion of information contained in generic spike inputs into\r\nlayer 4 and layers 2/3 and how well it makes this information\r\naccessible to projection neurons in layers 2/3 and layer 5. ... We conclude that computer simulations\r\nof detailed lamina-specific cortical microcircuit models\r\nprovide new insight into computational consequences of anatomical\r\nand physiological data. See paper for more and details.", + "tags": [ + { + "id": 893, + "tag": "CSIM (web link to model)" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1192, + "tag": "ModelDB:82385" + } + ], + "timestamp_created": "2024-01-11 15:53:10.304622+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/82385", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "640": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 640, + "name": "Computational aspects of feedback in neural circuits (Maass et al 2006)", + "repository_type": "github", + "summary": "It had previously been shown that generic cortical microcircuit models\r\ncan perform complex real-time computations on continuous input\r\nstreams, provided that these computations can be carried out with a\r\nrapidly fading memory. We investigate ... the computational\r\ncapability of such circuits in the more realistic case where not only\r\nreadout neurons, but in addition a few neurons within the circuit have\r\nbeen trained for specific tasks. This is essentially equivalent to\r\nthe case where the output of trained readout neurons is fed back into\r\nthe circuit. We show that this new model overcomes the limitation of\r\na rapidly fading memory. In fact, we prove that in the idealized case\r\nwithout noise it can carry out any conceiv- able digital or analog\r\ncomputation on time-varying inputs. But even with noise the resulting\r\ncomputational model can perform a large class of biologically relevant\r\nreal-time computations that require a non-fading memory. ... In\r\nparticular we show that ... generic cortical microcircuits with\r\nfeedback provide a new model for working memory that is consistent\r\nwith a large set of biological constraints. See paper for more and details.", + "tags": [ + { + "id": 893, + "tag": "CSIM (web link to model)" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1193, + "tag": "ModelDB:82392" + } + ], + "timestamp_created": "2024-01-11 15:53:10.756694+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/82392", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "641": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 641, + "name": "High dimensional dynamics and low dimensional readouts in neural microcircuits (Haeusler et al 2006)", + "repository_type": "github", + "summary": "We investigate generic models for cortical microcircuits,\r\ni.e. recurrent circuits of integrate-and fire neurons with dynamic\r\nsynapses. These complex dynamic systems subserve the amazing\r\ninformation processing capabilities of the cortex, but are at the\r\npresent time very little understood. We analyze the transient dynamics\r\nof models for neural microcircuits from the point of view of one or\r\ntwo readout neurons that collapse the high dimensional transient\r\ndynamics of a neural circuit into a 1- or 2--dimensional output\r\nstream. See paper for more and details.\r\n", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 790, + "tag": "Attractor Neural Network" + }, + { + "id": 893, + "tag": "CSIM (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1194, + "tag": "ModelDB:82394" + } + ], + "timestamp_created": "2024-01-11 15:53:11.312126+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/82394", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "642": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 642, + "name": "Hysteresis in voltage gating of HCN channels (Elinder et al 2006, Mannikko et al 2005)", + "repository_type": "github", + "summary": "We found that HCN2 and HCN4 channels\r\nexpressed in oocytes from the frog Xenopus laevis do not display the\r\nactivation kinetic changes that we (previously) observed in spHCN and\r\nHCN1. However, HCN2 and HCN4 channels display changes in their tail\r\ncurrents, suggesting that these channels also undergo mode shifts and\r\nthat the conformational changes underlying the mode shifts are due to\r\nconserved aspects of HCN channels. With computer modelling, we show\r\nthat in channels with relatively slow opening kinetics and fast\r\nmode-shift transitions, such as HCN2 and HCN4 channels, the mode shift\r\neffects are not readily observable, except in the tail\r\nkinetics. Computer simulations of sino-atrial node action potentials\r\nsuggest that the HCN2 channel, together with the HCN1 channel, are\r\nimportant regulators of the heart firing frequency and that the mode\r\nshift is an important property to prevent arrhythmic firing. We\r\nconclude that although all HCN channels appear to undergo mode shifts\r\n\u2013 and thus may serve to prevent arrhythmic firing\r\n\u2013 it is mainly observable in ionic currents\r\nfrom HCN channels with faster kinetics. See papers for more and details.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 848, + "tag": "QBasic/QuickBasic/Turbo Basic/VBA" + }, + { + "id": 1195, + "tag": "ModelDB:82758" + } + ], + "timestamp_created": "2024-01-11 15:53:11.804568+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/82758", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "643": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 643, + "name": "Irregular spiking in NMDA-driven prefrontal cortex neurons (Durstewitz and Gabriel 2006)", + "repository_type": "github", + "summary": "Slow N-Methyl-D-aspartic acid (NMDA) synaptic currents are assumed to strongly contribute to the persistently elevated firing rates observed in prefrontal cortex (PFC) during working memory. During persistent activity, spiking of many neurons is highly irregular. ... The highest interspike-interval (ISI) variability occurred in a transition regime where the subthreshold membrane potential distribution shifts from mono- to bimodality, ... Predictability within irregular ISI series was significantly higher than expected from a noise-driven linear process, indicating that it might best be described through complex (potentially chaotic) nonlinear deterministic processes. Accordingly, the phenomena observed in vitro could be reproduced in purely deterministic biophysical model neurons. High spiking irregularity in these models emerged within a chaotic, close-to-bifurcation regime characterized by a shift of the membrane potential distribution from mono- to bimodality and by similar ISI return maps as observed in vitro. ... NMDA-induced irregular dynamics may have important implications for computational processes during working memory and neural coding.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 887, + "tag": "Bifurcation" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 877, + "tag": "Working memory" + }, + { + "id": 1196, + "tag": "ModelDB:82784" + } + ], + "timestamp_created": "2024-01-11 15:53:12.331834+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/82784", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "644": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 644, + "name": "Neocortical pyramidal neuron: deep; effects of dopamine (Durstewitz et al 2000)", + "repository_type": "github", + "summary": "\"... Simulated dopamine strongly enhanced high, delay-type activity but not low, spontaneous activity in the model network. Furthermore the strength of an afferent stimulation needed to disrupt delay-type activity increased with the magnitude of the dopamine-induced shifts in network parameters, making the currently active representation much more stable. Stability could be increased by dopamine-induced enhancements of the persistent Na(+) and N-methyl-D-aspartate (NMDA) conductances. Stability also was enhanced by a reduction in AMPA conductances. The increase in GABA(A) conductances that occurs after stimulation of dopaminergic D1 receptors was necessary in this context to prevent uncontrolled, spontaneous switches into high-activity states (i.e., spontaneous activation of task-irrelevant representations). In conclusion, the dopamine-induced changes in the biophysical properties of intrinsic ionic and synaptic conductances conjointly acted to highly increase stability of activated representations in PFC networks and at the same time retain control over network behavior and thus preserve its ability to adequately respond to task-related stimuli. ...\" See paper and references for more and details.", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1197, + "tag": "ModelDB:82849" + } + ], + "timestamp_created": "2024-01-11 15:53:12.816015+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/82849", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "645": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 645, + "name": "Distributed computing tool for NEURON, NEURONPM (screensaver) (Calin-Jageman and Katz 2006)", + "repository_type": "github", + "summary": "\"... To lower the barrier for large-scale model analysis, we have developedNeuronPM,\r\na client/server application that creates a \u201cscreen-saver\u201d cluster\r\nfor running simulations in NEURON (Hines & Carnevale, 1997). ... The NeuronPM client is\r\na Windows-based screen saver, and the NeuronPM server can be hosted\r\non any Apache/PHP/MySQL server. ... Administrative\r\npanels make it simple to upload model files, define the parameters and\r\nconditions to vary, and then monitor client status and work progress.\r\nNeuronPM is open-source freeware and is available for download at\r\nhttp://neuronpm.homeip.net. ...\"", + "tags": [ + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 894, + "tag": "NEURONPM (web link to tool)" + }, + { + "id": 1198, + "tag": "ModelDB:82891" + } + ], + "timestamp_created": "2024-01-11 15:53:13.263322+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/82891", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "646": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 646, + "name": "Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007)", + "repository_type": "github", + "summary": "This package provides a series of codes that simulate networks of spiking neurons (excitatory and inhibitory, integrate-and-fire or Hodgkin-Huxley type, current-based or conductance-based synapses; some of them are event-based). The same networks are implemented in different simulators (NEURON, GENESIS, NEST, NCS, CSIM, XPP, SPLIT, MVAspike; there is also a couple of implementations in SciLab and C++).\r\nThe codes included in this package are benchmark simulations; see\r\nthe associated review paper (Brette et al. 2007). The \r\nmain goal is to provide a series of benchmark simulations of\r\nnetworks of spiking neurons, and demonstrate how these are implemented in the\r\ndifferent simulators overviewed in the paper. See also details in the\r\nenclosed file Appendix2.pdf, which describes these different \r\nbenchmarks. Some of these benchmarks were based on the \r\nVogels-Abbott model (Vogels TP and Abbott LF 2005).\r\n", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 9, + "tag": "Brian" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 895, + "tag": "CSIM" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 896, + "tag": "MVASpike" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 897, + "tag": "NCS" + }, + { + "id": 661, + "tag": "NEST" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 760, + "tag": "PyNN" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 898, + "tag": "SPLIT" + }, + { + "id": 899, + "tag": "SciLab" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1199, + "tag": "ModelDB:83319" + } + ], + "timestamp_created": "2024-01-11 15:53:13.745816+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83319", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "647": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 647, + "name": "A simulation method for the firing sequences of motor units (Jiang et al 2006)", + "repository_type": "github", + "summary": "\" ... a novel model based on the Hodgkin\u2013Huxley (HH) system is proposed, which has the ability to simulate\r\nthe complex neurodynamics of the firing sequences of motor neurons. The model is presented at the cellular level and network level,\r\nand some simulation results from a simple 3-neuron network are presented to demonstrate its applications.\" See paper for more and details.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1200, + "tag": "ModelDB:83320" + } + ], + "timestamp_created": "2024-01-11 15:53:14.224021+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83320", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "648": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 648, + "name": "Pyramidal neuron coincidence detection tuned by dendritic branching pattern (Schaefer et al 2003)", + "repository_type": "github", + "summary": "\"... We examined the relationship between dendritic arborization\r\nand the coupling between somatic and dendritic action potential\r\n(AP) initiation sites in layer 5 (L5) neocortical pyramidal neurons.\r\nCoupling was defined as the relative reduction in threshold for\r\ninitiation of a dendritic calcium AP due to a coincident\r\nback-propagating AP. Simulations based on reconstructions of\r\nbiocytin-filled cells showed that addition of oblique branches of the\r\nmain apical dendrite in close proximity to the soma (d < 140 um)\r\nincreases the coupling between the apical and axosomatic AP initiation\r\nzones, whereas incorporation of distal branches decreases\r\ncoupling. ... We conclude that variation in dendritic arborization may\r\nbe a key determinant of variability in coupling (49+-17%; range\r\n19-83%; n = 37) and is likely to outweigh the contribution made by\r\nvariations in active membrane properties. Thus coincidence detection\r\nof inputs arriving from different cortical layers is strongly\r\nregulated by differences in dendritic arborization.\"", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1201, + "tag": "ModelDB:83344" + } + ], + "timestamp_created": "2024-01-11 15:53:14.722757+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83344", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "649": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 649, + "name": "Homosynaptic plasticity in the tail withdrawal circuit (TWC) of Aplysia (Baxter and Byrne 2006)", + "repository_type": "github", + "summary": "The tail-withdrawal circuit of Aplysia provides a useful model system for investigating synaptic dynamics. Sensory neurons within the circuit manifest several forms of synaptic plasticity. Here, we developed a model of the circuit and investigated the ways in which depression (DEP) and potentiation (POT) contributed to information processing. DEP limited the amount of motor neuron activity that could be elicited by the monosynaptic pathway alone. POT within the monosynaptic pathway did not compensate for DEP. There was, however, a synergistic interaction between POT and the polysynaptic pathway. This synergism extended the dynamic range of the network, and the interplay between DEP and POT made the circuit respond preferentially to long-duration, low-frequency inputs.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 797, + "tag": "Depression" + }, + { + "id": 798, + "tag": "Facilitation" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 847, + "tag": "Post-Tetanic Potentiation" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 845, + "tag": "Sensory processing" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 1202, + "tag": "ModelDB:83472" + } + ], + "timestamp_created": "2024-01-11 15:53:15.423692+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83472", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "650": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 650, + "name": "INa and IKv4.3 heterogeneity in canine LV myocytes (Flaim et al 2006)", + "repository_type": "github", + "summary": "\"The roles of sustained components of INa and IKv43 in shaping the\r\naction potentials (AP) of myocytes isolated from the canine left\r\nventricle (LV) have not been studied in detail. Here we investigate\r\nthe hypothesis that these two currents can contribute substantially to\r\nheterogeneity of early repolarization and arrhythmic\r\nrisk.... The resulting simulations illustrate ways in which KChIP2- and\r\nCa2+- dependent control of IKv43 can result in a sustained outward\r\ncurrent that can neutralize INaL in a rate- and myocyte\r\nsubtype-dependent manner. Both these currents appear to play\r\nsignificant roles in modulating AP duration and rate dependence in\r\nmidmyocardial myocytes. ... By design, these models allow upward\r\nintegration into organ models or may be used as a basis for further\r\ninvestigations into cellular heterogeneities.\" See paper for more\r\nand details.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 900, + "tag": "I_Na,Ca" + }, + { + "id": 901, + "tag": "I_SERCA" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 902, + "tag": "Late Na" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 814, + "tag": "Na/Ca exchanger" + }, + { + "id": 816, + "tag": "Na/K pump" + }, + { + "id": 817, + "tag": "Sodium pump" + }, + { + "id": 1203, + "tag": "ModelDB:83491" + } + ], + "timestamp_created": "2024-01-11 15:53:15.991816+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83491", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "651": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 651, + "name": "Contibutions of input and history to motoneuron output (Powers et al 2005)", + "repository_type": "github", + "summary": "\"The present study presents results based\r\non recordings of noise-driven discharge in rat hypoglossal motoneurones ... First, we show that the hyperpolarizing trough is larger in Average Current Trajectories (ACTs)\r\ncalculated from spikes preceded by long interspike intervals, and minimal or absent in those\r\nbased on short interspike intervals. Second, we show that the trough is present for ACTs\r\ncalculated from the discharge of a threshold-crossing neurone model with a postspike after-\r\nhyperpolarization (AHP), but absent from those calculated from the discharge of a model\r\nwithout an AHP. We show that it is possible to represent noise-driven discharge using a\r\ntwo-component linear model that predicts discharge probability based on the sum of a feedback\r\nkernel and a stimulus kernel. The feedback kernel reflects the influence of prior discharge\r\nmediated by the AHP, and it increases in amplitude when AHP amplitude is increased by\r\npharmacological manipulations. Finally, we show that the predictions of this model are virtually\r\nidentical to those based on the first-order Wiener kernel. This suggests that the Wiener kernel\r\nderived from standard white-noise analysis of noise-driven discharge in neurones actually\r\nreflect the effects of both stimulus and discharge history.\" See paper for more and details.", + "tags": [ + { + "id": 903, + "tag": "IGOR Pro" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1204, + "tag": "ModelDB:83508" + } + ], + "timestamp_created": "2024-01-11 15:53:16.567172+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83508", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "652": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 652, + "name": "Prefrontal cortical mechanisms for goal-directed behavior (Hasselmo 2005)", + "repository_type": "github", + "summary": "\".. a model of prefrontal cortex function emphasizing\r\nthe influence of goal-related activity on the choice\r\nof the next motor output. ...\r\nDifferent neocortical minicolumns represent distinct sensory\r\ninput states and distinct motor output actions. The dynamics\r\nof each minicolumn include separate phases of encoding and\r\nretrieval. During encoding, strengthening of excitatory connections\r\nforms forward and reverse associations between each\r\nstate, the following action, and a subsequent state, which\r\nmay include reward. During retrieval, activity spreads from\r\nreward states throughout the network. The interaction of this\r\nspreading activity with a specific input state directs selection of\r\nthe next appropriate action. Simulations demonstrate how\r\nthese mechanisms can guide performance in a range of goal directed\r\ntasks, and provide a functional framework for some\r\nof the neuronal responses previously observed in the medial\r\nprefrontal cortex during performance of spatial memory tasks\r\nin rats.\"", + "tags": [ + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 892, + "tag": "Reinforcement Learning" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1205, + "tag": "ModelDB:83512" + } + ], + "timestamp_created": "2024-01-11 15:53:17.030918+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83512", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "653": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 653, + "name": "A reinforcement learning example (Sutton and Barto 1998)", + "repository_type": "github", + "summary": "This MATLAB script demonstrates an example of reinforcement learning\r\nfunctions guiding the movements of an agent (a black square) in a\r\ngridworld environment. See at the top of the matlab script and the book for more details.\r\n", + "tags": [ + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 892, + "tag": "Reinforcement Learning" + }, + { + "id": 1206, + "tag": "ModelDB:83514" + } + ], + "timestamp_created": "2024-01-11 15:53:17.481800+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83514", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "654": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 654, + "name": "Hippocampal context-dependent retrieval (Hasselmo and Eichenbaum 2005)", + "repository_type": "github", + "summary": "\"... The model simulates the context-sensitive firing properties of hippocampal neurons including trial-specific firing during spatial\r\nalternation and trial by trial changes in theta phase precession on a linear track. ...\" See paper for more and details.", + "tags": [ + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 892, + "tag": "Reinforcement Learning" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1207, + "tag": "ModelDB:83516" + } + ], + "timestamp_created": "2024-01-11 15:53:17.934196+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83516", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "655": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 655, + "name": "Fixed point attractor (Hasselmo et al 1995)", + "repository_type": "github", + "summary": "\"... In the model, cholinergic suppression of synaptic transmission at excitatory feedback synapses is shown to determine the extent to which activity depends upon new features of the afferent input versus components of previously stored representations. ...\" See paper for more and details. The MATLAB script demonstrates the model of fixed point attractors mediated by excitatory feedback with subtractive inhibition in a continuous firing rate model.\r\n", + "tags": [ + { + "id": 790, + "tag": "Attractor Neural Network" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 892, + "tag": "Reinforcement Learning" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1208, + "tag": "ModelDB:83517" + } + ], + "timestamp_created": "2024-01-11 15:53:18.375463+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83517", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "656": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 656, + "name": "Ambiguous Encoding and Distorted Perception (Carlson and Kawasaki 2006)", + "repository_type": "github", + "summary": "\"... In\r\nthe weakly electric fish Eigenmannia, P- and T-type primary afferent fibers are specialized for encoding the amplitude and phase,\r\nrespectively, of electrosensory stimuli. We used a stimulus estimation technique to quantify the ability of P- and T-units to encode\r\nrandom modulations in amplitude and phase. As expected, P-units exhibited a clear preference for encoding amplitude modulations,\r\nwhereas T-units exhibited a clear preference for encoding phase modulations. Surprisingly, both types of afferents also encoded their\r\nnonpreferred stimulus attribute when it was presented in isolation or when the preferred stimulus attribute was sufficiently weak.\r\nBecause afferent activity can be affected by modulations in either amplitude or phase, it is not possible to unambiguously distinguish\r\nbetween these two stimulus attributes by observing the activity of a single afferent fiber. Simple model neurons with a preference for\r\nencoding either amplitude or phase also encoded their nonpreferred stimulus attribute when it was presented in isolation, suggesting that\r\nsuch ambiguity is unavoidable. ... \" See paper for more and details.", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 904, + "tag": "Sensory coding" + }, + { + "id": 1209, + "tag": "ModelDB:83520" + } + ], + "timestamp_created": "2024-01-11 15:53:18.835808+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83520", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "657": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 657, + "name": "Combination sensitivity and active conductances (Carlson and Kawasaki 2006)", + "repository_type": "github", + "summary": "\"... The weakly electric fish Gymnarchus discriminates the\r\nsign of the frequency difference (Df) between a neighbor\u2019s electric\r\norgan discharge (EOD) and its own EOD by comparing temporal\r\npatterns of amplitude modulation (AM) and phase modulation (PM).\r\nSign-selective neurons in the midbrain respond preferentially to either\r\npositive frequency differences (Df >0 selective) or negative frequency\r\ndifferences (Df <0 selective). To study the mechanisms of\r\ncombination sensitivity, we made whole cell intracellular recordings\r\nfrom sign-selective midbrain neurons in vivo and recorded postsynaptic\r\npotential (PSP) responses to AM, PM, Df >0, and Df <0.\r\n... Responses to the nonpreferred\r\nsign of Df, but not the preferred sign of Df, were substantially weaker\r\nthan linear predictions, causing a significant increase in the actual\r\ndegree of sign selectivity. By using various levels of current clamp\r\nand comparing our results to simple models of synaptic integration,\r\nwe demonstrate that this decreased response to the nonpreferred sign\r\nof Df is caused by a reduction in voltage-dependent excitatory\r\nconductances. This finding reveals that nonlinear decoders, in the\r\nform of voltage-dependent conductances, can enhance the selectivity\r\nof single neurons for particular combinations of stimulus attributes.\" See paper for more and details.", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1210, + "tag": "ModelDB:83521" + } + ], + "timestamp_created": "2024-01-11 15:53:19.285582+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83521", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "658": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 658, + "name": "Subthreshold inact. of K channels modulates APs in bitufted interneurons (Korngreen et al 2005)", + "repository_type": "github", + "summary": "... In this study we show that in bitufted interneurones\r\nfrom layer 2/3 of the somatosensory cortex, the height and width of APs recorded at the\r\nsoma are sensitive to changes in the resting membrane potential, suggesting subthreshold\r\nactivity of voltage-gated conductances. Attributes of K+ currents examined in nucleated\r\npatches revealed a fast subthreshold-inactivating K+ conductance (Kf ) and a slow\r\nsuprathreshold-inactivating K+ conductance (Ks ). Simulations of these K+ conductances,\r\nincorporated into a Hodgkin\u2013Huxley-type model, suggested that during a single AP or during\r\nlow frequency trains of APs, subthreshold inactivation of Kf was the primary modulator of AP\r\nshape, whereas during trains of APs the shape was governed to a larger degree by Ks resulting\r\nin the generation of smaller and broader APs. ... Compartmental simulation\r\nof the back-propagating AP suggested a mechanism for the modulation of the back-propagating\r\nAP height and width by subthreshold activation of Kf . We speculate that this signal may\r\nmodulate retrograde GABA release and consequently depression of synaptic efficacy of excitatory\r\ninput from neighbouring pyramidal neurones.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1211, + "tag": "ModelDB:83523" + } + ], + "timestamp_created": "2024-01-11 15:53:19.845392+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83523", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "659": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 659, + "name": "Cortex learning models (Weber at al. 2006, Weber and Triesch, 2006, Weber and Wermter 2006/7)", + "repository_type": "github", + "summary": "A simulator and the configuration files for three publications are\r\nprovided. First, \"A hybrid generative and predictive model of the motor\r\ncortex\" (Weber at al. 2006) which uses reinforcement learning to set up a\r\ntoy action scheme, then uses unsupervised learning to \"copy\" the learnt\r\naction, and an attractor network to predict the hidden code of the\r\nunsupervised network. Second, \"A Self-Organizing Map of Sigma-Pi Units\"\r\n(Weber and Wermter 2006/7) learns frame of reference transformations on\r\npopulation codes in an unsupervised manner. Third, \"A possible\r\nrepresentation of reward in the learning of saccades\" (Weber and Triesch,\r\n2006) implements saccade learning with two possible learning schemes for\r\nhorizontal and vertical saccades, respectively.", + "tags": [ + { + "id": 790, + "tag": "Attractor Neural Network" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 891, + "tag": "Hebbian plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 892, + "tag": "Reinforcement Learning" + }, + { + "id": 886, + "tag": "Unsupervised Learning" + }, + { + "id": 905, + "tag": "Winner-take-all" + }, + { + "id": 1212, + "tag": "ModelDB:83528" + } + ], + "timestamp_created": "2024-01-11 15:53:20.349284+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83528", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "660": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 660, + "name": "Regulation of the firing pattern in dopamine neurons (Komendantov et al 2004)", + "repository_type": "github", + "summary": "Midbrain dopaminergic (DA) neurons in vivo exhibit two major firing patterns: single-spike firing and burst firing. The firing pattern expressed is dependent on both the intrinsic properties of the neurons and their excitatory and inhibitory synaptic inputs. Experimental data suggest that the activation of NMDA and GABAA receptors is crucial contributor to the initiation and suppression of burst firing, respectively, and that blocking calcium-activated potassium channels can facilitate burst firing. This multi-compartmental model of a DA neuron with a branching structure was developed and calibrated based on in vitro experimental data to explore the effects of different levels of activation of NMDA and GABAA receptors as well as the modulation of the SK current on the firing activity.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 816, + "tag": "Na/K pump" + }, + { + "id": 817, + "tag": "Sodium pump" + }, + { + "id": 1213, + "tag": "ModelDB:83547" + } + ], + "timestamp_created": "2024-01-11 15:53:20.816925+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83547", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "661": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 661, + "name": "Role of active dendrites in rhythmically-firing neurons (Goldberg et al 2006)", + "repository_type": "github", + "summary": "\"The responsiveness of rhythmically-firing neurons to synaptic inputs is characterized by their phase response curve (PRC), which relates how weak somatic perturbations affect the timing of the next action potential. The shape of the somatic PRC is an important determinant of collective network dynamics. Here we study theoretically and experimentally the impact of distally-located synapses and dendritic nonlinearities on the synchronization properties of rhythmically firing neurons. Combining the theories of quasi-active cables and phase-coupled oscillators we derive an approximation for the dendritic responsiveness, captured by the neuron's dendritic PRC (dPRC). This closed-form expression indicates that the dPRCs are linearly-filtered versions of the somatic PRC, and that the filter characteristics are determined by the passive and active properties of the dendrite. ... collective dynamics can be qualitatively different depending on the location of the synapse, the neuronal firing rates and the dendritic nonlinearities.\" See paper for more and details.", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 906, + "tag": "Phase Response Curves" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1214, + "tag": "ModelDB:83558" + } + ], + "timestamp_created": "2024-01-11 15:53:21.277926+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83558", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "662": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 662, + "name": "Spiking neuron model of the basal ganglia (Humphries et al 2006)", + "repository_type": "github", + "summary": "A spiking neuron model of the basal ganglia (BG) circuit (striatum, STN, GP, SNr). Includes: parallel anatomical channels; tonic dopamine; dopamine receptors in striatum, STN, and GP; burst-firing in STN; GABAa, AMPA, and NMDA currents; effects of synaptic location. Model demonstrates selection and switching of input signals. Replicates experimental data on changes in slow-wave (<1 Hz) and gamma-band oscillations within BG nuclei following lesions and pharmacological manipulations.", + "tags": [ + { + "id": 857, + "tag": "Action Selection/Decision Making" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 775, + "tag": "Rebound firing" + }, + { + "id": 802, + "tag": "Sleep" + }, + { + "id": 1215, + "tag": "ModelDB:83559" + } + ], + "timestamp_created": "2024-01-11 15:53:21.733740+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83559", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "663": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 663, + "name": "Population-level model of the basal ganglia and action selection (Gurney et al 2001, 2004)", + "repository_type": "github", + "summary": "We proposed a new functional architecture for the basal ganglia (BG) based on the premise that these brain structures play a central role in behavioural action selection. The papers quantitatively describes the properties of the model using analysis and simulation. In the first paper, we show that the decomposition of the BG into selection and control pathways is supported in several ways. First, several elegant features are exposed--capacity scaling, enhanced selectivity and synergistic dopamine modulation--which might be expected to exist in a well designed action selection mechanism. Second, good matches between model GPe output and GPi and SNr output, and neurophysiological data, have been found. Third, the behaviour of the model as a signal selection mechanism has parallels with some kinds of action selection observed in animals under various levels of dopaminergic modulation.\r\n\r\nIn the second paper, we extend the BG model to include new connections, and show that action selection is maintained. In addition, we provide quantitative measures for defining different forms of selection, and methods for assessing performance changes in computational neuroscience models. \r\n\r\n\r\n", + "tags": [ + { + "id": 857, + "tag": "Action Selection/Decision Making" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 907, + "tag": "Simulink" + }, + { + "id": 1216, + "tag": "ModelDB:83560" + } + ], + "timestamp_created": "2024-01-11 15:53:22.186674+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83560", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "664": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 664, + "name": "Basal ganglia-thalamocortical loop model of action selection (Humphries and Gurney 2002)", + "repository_type": "github", + "summary": "We embed our basal ganglia model into a wider circuit containing the motor thalamocortical loop and thalamic reticular nucleus (TRN). Simulation of this extended model showed that the additions gave five main results which are desirable in a selection/switching mechanism. First, low salience actions (i.e. those with low urgency) could be selected. Second, the range of salience values over which actions could be switched between was increased. Third, the contrast between the selected and non-selected actions was enhanced via improved differentiation of outputs from the BG. Fourth, transient increases in the salience of a non-selected action were prevented from interrupting the ongoing action, unless the transient was of sufficient magnitude. Finally, the selection of the ongoing action persisted when a new closely matched salience action became active. The first result was facilitated by the thalamocortical loop; the rest were dependent on the presence of the TRN. Thus, we conclude that the results are consistent with these structures having clearly defined functions in action selection.", + "tags": [ + { + "id": 857, + "tag": "Action Selection/Decision Making" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 907, + "tag": "Simulink" + }, + { + "id": 1217, + "tag": "ModelDB:83562" + } + ], + "timestamp_created": "2024-01-11 15:53:22.638143+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83562", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "665": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 665, + "name": "A theory of ongoing activity in V1 (Goldberg et al 2004)", + "repository_type": "github", + "summary": "Ongoing spontaneous activity in the cerebral cortex exhibits\r\ncomplex spatiotemporal patterns in the absence of sensory stimuli. To elucidate the nature of\r\nthis ongoing activity, we present a theoretical treatment of two contrasting scenarios of cortical dynamics: (1) fluctuations about a single background state\r\nand (2) wandering among multiple \u201cattractor\u201d states, which\r\nencode a single or several stimulus features.\r\nStudying simplified network rate models of the primary\r\nvisual cortex (V1), we show that the single state scenario\r\nis characterized by fast and high-dimensional\r\nGaussian-like fluctuations, whereas in the multiple\r\nstate scenario the fluctuations are slow, low dimensional,\r\nand highly non-Gaussian. Studying a more realistic model that incorporates correlations in the feedforward input, spatially restricted cortical interactions,\r\nand an experimentally derived layout of pinwheels,\r\nwe show that recent optical-imaging data of ongoing\r\nactivity in V1 are consistent with the presence of either\r\na single background state or multiple attractor states\r\nencoding many features.", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1218, + "tag": "ModelDB:83570" + } + ], + "timestamp_created": "2024-01-11 15:53:23.095953+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83570", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "666": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 666, + "name": "Modeling interactions in Aplysia neuron R15 (Yu et al 2004)", + "repository_type": "github", + "summary": "\"The biophysical properties of neuron R15 in Aplysia endow it with the ability to express multiple modes of oscillatory electrical activity, such as beating and bursting. Previous modeling studies examined the ways in which membrane conductances contribute to the electrical activity of R15 and the ways in which extrinsic modulatory inputs alter the membrane conductances by biochemical cascades and influence the electrical activity. The goals of the present study were to examine the ways in which electrical activity influences the biochemical cascades and what dynamical properties emerge from the ongoing interactions between electrical activity and these cascades.\" See paper for more and details.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1219, + "tag": "ModelDB:83575" + } + ], + "timestamp_created": "2024-01-11 15:53:23.584520+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83575", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "667": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 667, + "name": "Input Fluctuations effects on f-I curves (Arsiero et al. 2007)", + "repository_type": "github", + "summary": "\"... We examined in vitro frequency versus current (f-I) relationships of layer 5 (L5) pyramidal cells of the rat medial prefrontal cortex (mPFC) using fluctuating stimuli. ...our results show that mPFC L5 pyramidal neurons retain an increased sensitivity to input fluctuations, whereas their sensitivity to the input mean diminishes to near zero. This implies that the discharge properties of L5 mPFC neurons are well suited to encode input fluctuations rather than input mean in their firing rates, with important consequences for information processing and stability of persistent activity at the network level.\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 863, + "tag": "Spike Frequency Adaptation" + }, + { + "id": 1220, + "tag": "ModelDB:83590" + } + ], + "timestamp_created": "2024-01-11 15:53:24.060994+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/83590", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "668": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 668, + "name": "Linking STDP and Dopamine action to solve the distal reward problem (Izhikevich 2007)", + "repository_type": "github", + "summary": "\"... How does the brain know what firing patterns of what neurons are responsible for the reward if 1) the patterns are no longer there when the reward arrives and 2) all\r\nneurons and synapses are active during the waiting period to the\r\nreward? Here, we show how the conundrum is resolved by a model network\r\nof cortical spiking neurons with spike-timing-dependent plasticity\r\n(STDP) modulated by dopamine (DA). Although STDP is triggered by\r\nnearly coincident firing patterns on a millisecond timescale, slow\r\nkinetics of subsequent synaptic plasticity is sensitive to changes in\r\nthe extracellular DA concentration during the critical period of a few\r\nseconds. ... This study emphasizes the importance of precise firing\r\npatterns in brain dynamics and suggests how a global diffusive\r\nreinforcement signal in the form of extracellular DA can selectively\r\ninfluence the right synapses at the right time.\" See paper for more and details.", + "tags": [ + { + "id": 908, + "tag": "Learning" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1221, + "tag": "ModelDB:84167" + } + ], + "timestamp_created": "2024-01-11 15:53:24.556378+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/84167", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "669": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 669, + "name": "Extracellular Action Potential Simulations (Gold et al 2007)", + "repository_type": "github", + "summary": "This package recreates the the principal experiments described in (Gold, Henze and Koch, 2007) and includes the core code necessary to create your own Extracellular Action Potential Simulations.", + "tags": [ + { + "id": 810, + "tag": "Extracellular Fields" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1222, + "tag": "ModelDB:84589" + } + ], + "timestamp_created": "2024-01-11 15:53:25.009647+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/84589", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "670": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 670, + "name": "Temporal decorrelation by intrinsic cellular dynamics (Wang et al 2003)", + "repository_type": "github", + "summary": "\"... Recent investigations in primary visual (V1) cortical neurons have\r\ndemonstrated that adaptation to prolonged changes in stimulus contrast\r\nis mediated in part through intrinsic ionic currents, a Ca2+\r\nactivated K+ current (IKCa) and especially a Na+ activated K+ current\r\n(IKNa). The present study was designed to test the hypothesis that\r\nthe activation of adaptation ionic currents may provide a cellular\r\nmechanism for temporal decorrelation in V1. A conductance-based\r\nneuron model was simulated, which included an IKCa and an IKNa. We\r\nshow that the model neuron reproduces the adaptive behavior of V1\r\nneurons in response to high contrast inputs. ...\". See paper for details and more.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 800, + "tag": "Vision" + }, + { + "id": 1223, + "tag": "ModelDB:84593" + } + ], + "timestamp_created": "2024-01-11 15:53:25.476465+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/84593", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "671": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 671, + "name": "Synaptic integration of an identified nonspiking interneuron in crayfish (Takashima et al 2006)", + "repository_type": "github", + "summary": "This GENESIS simulation shows how a single or compound excitatory synaptic potential evoked by mechanosensory stimulation spreads over the dendrites of the LDS interneuron that is one of the identified nonspiking interneurons in the central nervous system of crayfish Procambarus clarkii. The model is based on physiological experiments carried out by Akira Takashima using single-electrode voltage clamp techniques and also 3-D morphometry of the interneuron carried out by Ryou Hikosaka using confocal laser scanning microscopic techniques. The physiological and morphological studies were coordinated by Masakazu Takahata.", + "tags": [ + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 1224, + "tag": "ModelDB:84599" + } + ], + "timestamp_created": "2024-01-11 15:53:25.946908+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/84599", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "672": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 672, + "name": "Zonisamide-induced inhibition of the firing of APs in hippocampal neurons (Huang et al. 2007)", + "repository_type": "github", + "summary": "Zonisamide (ZNS), a synthetic benzisoxazole derivative, has been used as an alternative choice in the treatment of epilepsy with a better efficacy and safety profile. However, little is known regarding the mechanism of ZNS actions on ion currents in neurons. We thus investigated its effect on ion currents in differentiated hippocampal 19-7 cells. The ZNS (30 uM) reversibly increased the amplitude of K+ outward currents and paxilline (1 uM) was effective in suppressing ZNS-induced increase of K+ outward currents. In inside-out configuration, ZNS (30 uM) applied to the intracellular face of the membrane did not alter single-channel conductance; however, it did enhance the activity of large-conductance Ca2+-activated K+ (BKCa) channels primarily by decreasing mean closed time. The EC50 value for ZNS-stimulated BKCa channels was 34 uM. This drug caused a left shift in the activation curve of BKCa channels with no change in the gating charge of these channels. ZNS at a concentration greater than 100 uM also reduced the amplitude of A-type K+ current in these cells. A simulation modeling based on hippocampal CA3 pyramidal neurons (Pinsky-Rinzel model) was also analyzed to investigate the inhibitory effect of ZNS on the firing of simulated action potentials. Taken together, this study suggests that in hippocampal neurons, during the exposure to ZNS, the ZNS-mediated effects on BKCa channels and IA could be one of the ionic mechanisms through which it affects neuronal excitability.", + "tags": [ + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1225, + "tag": "ModelDB:84606" + } + ], + "timestamp_created": "2024-01-11 15:53:26.406246+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/84606", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "673": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 673, + "name": "Differential modulation of pattern and rate in a dopamine neuron model (Canavier and Landry 2006)", + "repository_type": "github", + "summary": "\"A stylized, symmetric, compartmental model of a dopamine neuron in vivo shows how rate and pattern can be modulated either concurrently or differentially. If two or more parameters in the model are varied concurrently, the baseline firing rate and the extent of bursting become decorrelated, which provides an explanation for the lack of a tight correlation in vivo and is consistent with some independence of the mechanisms that generate baseline firing rates versus bursting. ...\" See paper for more and details.\r\n", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 850, + "tag": "Intrinsic plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 816, + "tag": "Na/K pump" + }, + { + "id": 817, + "tag": "Sodium pump" + }, + { + "id": 1226, + "tag": "ModelDB:84612" + } + ], + "timestamp_created": "2024-01-11 15:53:26.875176+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/84612", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "674": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 674, + "name": "Method for counting motor units in mice (Major et al 2007)", + "repository_type": "github", + "summary": "\"... Our goal was to develop an efficient method\r\nto determine the number of motor neurons making functional connections\r\nto muscle in a transgenic mouse model of amyotrophic lateral\r\nsclerosis (ALS). We developed a novel protocol for motor unit\r\nnumber estimation (MUNE) using incremental stimulation. The\r\nmethod involves analysis of twitch waveforms using a new software\r\nprogram, ITS-MUNE, designed for interactive calculation of motor\r\nunit number. The method was validated by testing simulated twitch\r\ndata from a mathematical model of the neuromuscular system. Computer\r\nsimulations followed the same stimulus-response protocol and\r\nproduced waveform data that were indistinguishable from experiments.\r\n... The ITS-MUNE analysis method has the potential to quantitatively\r\nmeasure the progression of motor neuron diseases and therefore the\r\nefficacy of treatments designed to alleviate pathologic processes of\r\nmuscle denervation.\" The software is available for download under the \"ITS-MUNE software\" link at \r\n(see below for links).\"", + "tags": [ + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 872, + "tag": "Pathophysiology" + }, + { + "id": 1227, + "tag": "ModelDB:84627" + } + ], + "timestamp_created": "2024-01-11 15:53:27.355942+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/84627", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "675": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 675, + "name": "A cardiac cell simulator (Puglisi and Bers 2001), applied to the QT interval (Busjahn et al 2004)", + "repository_type": "github", + "summary": "\"LabHEART is an easy to use program that simulates the cardiac action potential, calcium transient and ionic currents. Key parameters such as ionic concentration, stimulus waveform and channel conductance can easily be changed by a click on an icon or dragging a slider.\r\n\r\nIt is a powerfull tool for teaching and researching cardiac electrophysiology.\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + }, + { + "id": 1228, + "tag": "ModelDB:84641" + } + ], + "timestamp_created": "2024-01-11 15:53:27.796759+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/84641", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "676": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 676, + "name": "Squid axon (Hodgkin, Huxley 1952) (LabAXON)", + "repository_type": "github", + "summary": "The classic HH model of squid axon membrane implemented in LabAXON. Hodgkin, A.L., Huxley, A.F. (1952)", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1229, + "tag": "ModelDB:84649" + } + ], + "timestamp_created": "2024-01-11 15:53:28.248281+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/84649", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "677": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 677, + "name": "Recording from rod bipolar axon terminals in situ (Oltedal et al 2007)", + "repository_type": "github", + "summary": "\"... Whole cell\r\nrecordings from axon terminals and cell bodies were used to investigate\r\nthe passive membrane properties of rod bipolar cells and analyzed\r\nwith a two-compartment equivalent electrical circuit model\r\ndeveloped by Mennerick et al. For both terminal- and soma-end\r\nrecordings, capacitive current decays were well fitted by biexponential\r\nfunctions. Computer simulations of simplified models of rod bipolar\r\ncells demonstrated that estimates of the capacitance of the axon\r\nterminal compartment can depend critically on the recording location,\r\nwith terminal-end recordings giving the best estimates. Computer\r\nsimulations and whole cell recordings demonstrated that terminal-end\r\nrecordings can yield more accurate estimates of the peak amplitude\r\nand kinetic properties of postsynaptic currents generated at the axon\r\nterminals due to increased electrotonic filtering of these currents when\r\nrecorded at the soma. ...\" See paper for more and details.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1230, + "tag": "ModelDB:84655" + } + ], + "timestamp_created": "2024-01-11 15:53:28.704945+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/84655", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "678": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 678, + "name": "Models of Na channels from a paper on the PKC control of I Na,P (Baker 2005)", + "repository_type": "github", + "summary": "\"The tetrodotoxin-resistant (TTX-r) persistent Na(+) current, attributed to Na(V)1.9, was recorded in small (< 25 mum apparent diameter) dorsal root ganglion (DRG) neurones cultured from P21 rats and from adult wild-type and Na(V)1.8 null mice. ... Numerical simulation of the up-regulation qualitatively reproduced changes in sensory neurone firing properties. ...\" Note: models of NaV1.8 and NaV1.9 and also persistent and transient Na channels that collectively model Nav 1.1, 1.6, and 1.7 are present in this model.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 861, + "tag": "Nociception" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 1231, + "tag": "ModelDB:85112" + } + ], + "timestamp_created": "2024-01-11 15:53:29.160112+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/85112", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "679": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 679, + "name": "Modeling temperature changes in AMPAR kinetics (Postlethwaite et al 2007)", + "repository_type": "github", + "summary": "This model was used to simulate glutamatergic, AMPA receptor mediated mEPSCs (miniature EPSCs, resulting from spontaneous vesicular transmitter release) at the calyx of Held synapse. It was used to assess the influence of temperature (physiological vs. subphysiological) on the amplitude and time course of mEPSCs. In the related paper, simulation results were directly compared to the experimental data, and it was concluded that an increase of temperature accelerates AMPA receptor kinetics.", + "tags": [ + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 909, + "tag": "MCell" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + }, + { + "id": 1232, + "tag": "ModelDB:85981" + } + ], + "timestamp_created": "2024-01-11 15:53:29.609297+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/85981", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "680": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 680, + "name": "TTX-R Na+ current effect on cell response (Herzog et al 2001)", + "repository_type": "github", + "summary": "\"Small dorsal root ganglion (DRG) neurons, which include nociceptors,\r\nexpress multiple voltage-gated sodium currents. In addition to a\r\nclassical fast inactivating tetrodotoxin-sensitive (TTX-S) sodium\r\ncurrent, many of these cells express a TTX-resistant (TTX-R) sodium\r\ncurrent that activates near -70 mV and is persistent at negative\r\npotentials. To investigate the possible contributions of this TTX-R\r\npersistent (TTX-RP) current to neuronal excitability, we carried out\r\ncomputer simulations using the Neuron program with TTX-S and -RP\r\ncurrents, fit by the Hodgkin-Huxley model, that closely matched the\r\ncurrents recorded from small DRG neurons. ...\" See paper for more and details.", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 861, + "tag": "Nociception" + }, + { + "id": 1233, + "tag": "ModelDB:86537" + } + ], + "timestamp_created": "2024-01-11 15:53:30.058015+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/86537", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "681": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 681, + "name": "Model of calcium oscillations in olfactory cilia (Reidl et al. 2006)", + "repository_type": "github", + "summary": "Simulation of experiments on olfactory receptor neurons (ORNs). Focussing on the negative feedback that calcium (through calmodulin) has on its own influx through CNG channels, this model is able to reproduce both calcium oscillations as well as adaptation behaviour as seen in experiments done with ORNs.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 853, + "tag": "I CNG" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1234, + "tag": "ModelDB:86538" + } + ], + "timestamp_created": "2024-01-11 15:53:30.533559+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/86538", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "682": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 682, + "name": "STDP and NMDAR Subunits (Gerkin et al. 2007)", + "repository_type": "github", + "summary": "The paper argues for competing roles of NR2A- and NR2B-containing NMDARs in spike-timing-dependent plasticity. This simple dynamical model recapitulates the results of STDP experiments involving selective blockers of NR2A- and NR2B-containing NMDARs, for which the stimuli are pre- and postsynaptic spikes in varying combinations. Experiments were done using paired recordings from glutamatergic neurons in rat hippocampal cultures. This model focuses on the dynamics of the putative potentiation and depression modules themselves, and their interaction For detailed dynamics involving NMDARs and Ca2+ transients, see Rubin et al., J. Neurophys., 2005.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 903, + "tag": "IGOR Pro" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 905, + "tag": "Winner-take-all" + }, + { + "id": 1235, + "tag": "ModelDB:87216" + } + ], + "timestamp_created": "2024-01-11 15:53:31.011713+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/87216", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "683": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 683, + "name": "Markov models of SCN1A (NaV1.1) applied to abnormal gating and epilepsy (Clancy and Kass 2004)", + "repository_type": "github", + "summary": "\"Recently, some forms of idiopathic epilepsy have been causally related to\r\ngenetic mutations in neuronal ion channels. To understand disease\r\nmechanisms, it is crucial to understand how a gene defect can disrupt\r\nchannel gating, which in turn can affect complex cellular dynamic\r\nprocesses. We develop a theoretical Markovian model of the neuronal\r\nNa+ channel NaV1.1 to explore and explain gating mechanisms underlying\r\ncellular excitability and physiological and pathophysiological\r\nmechanisms of abnormal neuronal excitability in the context of\r\nepilepsy. ...\"", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1236, + "tag": "ModelDB:87278" + } + ], + "timestamp_created": "2024-01-11 15:53:31.481779+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/87278", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "684": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 684, + "name": "Amyloid beta (IA block) effects on a model CA1 pyramidal cell (Morse et al. 2010)", + "repository_type": "github", + "summary": "The model simulations provide evidence oblique dendrites in CA1 pyramidal neurons are susceptible to hyper-excitability by amyloid beta block of the transient K+ channel, IA. See paper for details.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 833, + "tag": "Aging/Alzheimer`s" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 872, + "tag": "Pathophysiology" + }, + { + "id": 1237, + "tag": "ModelDB:87284" + } + ], + "timestamp_created": "2024-01-11 15:53:31.956533+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/87284", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "685": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 685, + "name": "Squid axon (Hodgkin, Huxley 1952) (SBML, XPP, other)", + "repository_type": "github", + "summary": "An SBML (and related XPP and other formats) implementation of the classic HH paper is available in the BIOMODELS database. See far below for links.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 910, + "tag": "SBML (web link to model)" + }, + { + "id": 911, + "tag": "XML (web link to model)" + }, + { + "id": 912, + "tag": "XPP (web link to model)" + }, + { + "id": 1238, + "tag": "ModelDB:87450" + } + ], + "timestamp_created": "2024-01-11 15:53:32.438809+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/87450", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "686": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 686, + "name": "Oscillating neurons in the cochlear nucleus (Bahmer Langner 2006a, b, and 2007)", + "repository_type": "github", + "summary": "\"Based on the physiological and anatomical data, we propose a model consisting of a minimum network of two choppers that are interconnected with a synaptic delay of 0.4 ms (Bahmer and Langner 2006a) . Such minimum delays have been found in different systems and in various animals (e.g. Hackett, Jackson, and Rubel 1982; Borst, Helmchen, and Sakmann 1995). The choppers receive input from both the auditory nerve and an onset neuron. This model can reproduce the mean, standard deviation, and coefficient of variation of the ISI and the dynamic features of AM coding of choppers.\"", + "tags": [ + { + "id": 849, + "tag": "Audition" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1239, + "tag": "ModelDB:87454" + } + ], + "timestamp_created": "2024-01-11 15:53:32.904836+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/87454", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "687": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 687, + "name": "AP shape and parameter constraints in optimization of compartment models (Weaver and Wearne 2006)", + "repository_type": "github", + "summary": "\"... We construct an\r\nobjective function that includes both time-aligned action potential shape error and errors in firing rate and firing regularity. We then\r\nimplement a variant of simulated annealing that introduces a recentering algorithm to handle infeasible points outside the boundary\r\nconstraints. We show how our objective function captures essential features of neuronal firing patterns, and why our boundary\r\nmanagement technique is superior to previous approaches.\"", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 1240, + "tag": "ModelDB:87473" + } + ], + "timestamp_created": "2024-01-11 15:53:33.373696+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/87473", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "688": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 688, + "name": "CA1 pyramidal neurons: binding properties and the magical number 7 (Migliore et al. 2008)", + "repository_type": "github", + "summary": "NEURON files from the paper:\r\n\r\nSingle neuron binding properties and the magical number 7,\r\nby M. Migliore, G. Novara, D. Tegolo, Hippocampus, in press (2008).\r\n\r\nIn an extensive series of simulations with realistic morphologies and active properties, \r\nwe demonstrate how n radial (oblique) dendrites of these neurons may be used to bind n inputs \r\nto generate an output signal. \r\nThe results suggest a possible neural code as the most effective n-ple of dendrites that \r\ncan be used for short-term memory recollection of persons, objects, or places. \r\nOur analysis predicts a straightforward physiological explanation for the observed \r\npuzzling limit of about 7 short-term memory items that can be stored by humans.\r\n", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 857, + "tag": "Action Selection/Decision Making" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 908, + "tag": "Learning" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 877, + "tag": "Working memory" + }, + { + "id": 1241, + "tag": "ModelDB:87535" + } + ], + "timestamp_created": "2024-01-11 15:53:33.864121+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/87535", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "689": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 689, + "name": "CA1 oriens alveus interneurons: signaling properties (Minneci et al. 2007)", + "repository_type": "github", + "summary": "The model supports the experimental findings showing that the dynamic interaction between cells with various firing patterns could differently affect GABAergic signaling, leading to a wide range of interneuronal communication within the hippocampal network.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 1242, + "tag": "ModelDB:87546" + } + ], + "timestamp_created": "2024-01-11 15:53:34.348449+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/87546", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "690": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 690, + "name": "Slow wave propagation in the guinea-pig gastric antrum (Hirst et al. 2006, Edwards and Hirst 2006)", + "repository_type": "github", + "summary": "\"(Edwards and Hirst 2006) provides an electrical description of the propagation of slow waves and pacemaker potentials in the guinea-pig gastric antrum in anal and circumferential directions. As electrical conduction between laterally adjacent circular muscle bundles is regularly interrupted, anal conduction of pacemaker potentials was assumed to occur via an electrically interconnected chain of myenteric interstitial cells of Cajal (ICCMY). ICCMY were also connected resistively to serially connected compartments of longitudinal muscle. Circumferential conduction occurred in a circular smooth muscle bundle that was represented as a chain of electrically connected isopotential compartments: each compartment contained a proportion of intramuscular interstitial cells of Cajal (ICCIM) that are responsible for the regenerative component of the slow wave. The circular muscle layer, which contains ICCIM, and the ICCMY network incorporated a mechanism, modelled as a two-stage chemical reaction, which produces an intracellular messenger. ... The model generates pacemaker potentials and slow waves with propagation velocities similar to those determined in the physiological experiments described in the accompanying paper.\"", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1243, + "tag": "ModelDB:87581" + } + ], + "timestamp_created": "2024-01-11 15:53:34.818516+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/87581", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "691": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 691, + "name": "Self-influencing synaptic plasticity (Tamosiunaite et al. 2007)", + "repository_type": "github", + "summary": "\"... Similar to a previous study (Saudargiene et al., 2004) we employ a differential\r\nHebbian learning rule to emulate spike-timing dependent\r\nplasticity and investigate how the interaction of dendritic\r\nand back-propagating spikes, as the post-synaptic signals,\r\ncould influence plasticity. ...\"", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 905, + "tag": "Winner-take-all" + }, + { + "id": 1244, + "tag": "ModelDB:87582" + } + ], + "timestamp_created": "2024-01-11 15:53:35.266606+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/87582", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "692": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 692, + "name": "Sodium channel mutations causing generalized epilepsy with febrile seizures + (Barela et al. 2006)", + "repository_type": "github", + "summary": "A novel mutation, R859C, in the Nav1.1 sodium channel was identified in a 4-generation, 33-member Caucasian family with a clinical presentation consistent with GEFS+. The mutation neutralizes a positively charged arginine in the domain 2 S4 voltage sensor of the Nav1.1 channel \u0192\u00d1 subunit. When the mutation was placed in the rat Nav1.1 channel and expressed in Xenopus oocytes, the mutant channel displayed a positive shift in the voltage-dependence of sodium channel activation, slower recovery from slow inactivation, and lower levels of current compared to the wild-type channel. Computational analysis suggests that neurons expressing the mutant channel have higher thresholds for firing a single action potential and for firing multiple action potentials, along with decreased repetitive firing. Therefore, this mutation should lead to decreased neuronal excitability, in contrast to most previous GEFS+ sodium channel mutations that have changes predicted to increase neuronal firing.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1245, + "tag": "ModelDB:87585" + } + ], + "timestamp_created": "2024-01-11 15:53:35.720637+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/87585", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "693": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 693, + "name": "Cat auditory nerve model (Zilany and Bruce 2006, 2007)", + "repository_type": "github", + "summary": "\"This paper presents a computational model to simulate normal and impaired auditory-nerve (AN)\r\nfiber responses in cats. The model responses match physiological data over a wider dynamic range\r\nthan previous auditory models. This is achieved by providing two modes of basilar membrane\r\nexcitation to the inner hair cell (IHC) rather than one. ... The model responses are consistent with a wide range of\r\nphysiological data from both normal and impaired ears for stimuli presented at levels spanning the\r\ndynamic range of hearing.\"", + "tags": [ + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1246, + "tag": "ModelDB:87751" + } + ], + "timestamp_created": "2024-01-11 15:53:36.174745+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/87751", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "694": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 694, + "name": "Cochlear implant models (Bruce et al. 1999a, b, c, 2000)", + "repository_type": "github", + "summary": "\"In a recent set of modeling studies we have developed a stochastic threshold model of auditory nerve response to single biphasic electrical pulses (Bruce et al., 1999c) and moderate rate (less than 800 pulses per second) pulse trains (Bruce et al., 1999a). In this article we derive an analytical approximation for the single-pulse model, which is then extended to describe the pulse-train model in the case of evenly timed, uniform pulses. This renewal-process description provides an accurate and computationally efficient model of electrical stimulation of single auditory nerve fibers by a cochlear implant that may be extended to other forms of electrical neural stimulation.\"", + "tags": [ + { + "id": 849, + "tag": "Audition" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1247, + "tag": "ModelDB:87760" + } + ], + "timestamp_created": "2024-01-11 15:53:36.628263+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/87760", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "695": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 695, + "name": "The role of ATP-sensitive potassium channels in a hippocampal neuron (Huang et al. 2007)", + "repository_type": "github", + "summary": "\"Hyperglycemia-related neuronal excitability and epileptic seizures \r\nare not uncommon in clinical practice. However, their underlying \r\nmechanism remains elusive. ATP-sensitive K(+) (K(ATP)) channels are \r\nfound in many excitable cells, including cardiac myocytes, \r\npancreatic beta cells, and neurons. These channels provide a link \r\nbetween the electrical activity of cell membranes and cellular \r\nmetabolism. We investigated the effects of higher extracellular \r\nglucose on hippocampal K(ATP) channel activities and neuronal \r\nexcitability. The cell-attached patch-clamp configuration on \r\ncultured hippocampal cells and a novel multielectrode recording \r\nsystem on hippocampal slices were employed. In addition, a \r\nsimulation modeling hippocampal CA3 pyramidal neurons (Pinsky-Rinzel \r\nmodel) was analyzed to investigate the role of K(ATP) channels in \r\nthe firing of simulated action potentials. ...\"", + "tags": [ + { + "id": 878, + "tag": "ATP-senstive potassium current" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1248, + "tag": "ModelDB:87762" + } + ], + "timestamp_created": "2024-01-11 15:53:37.101322+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/87762", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "696": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 696, + "name": "Olfactory bulb network model of gamma oscillations (Bathellier et al. 2006; Lagier et al. 2007)", + "repository_type": "github", + "summary": "This model implements a network of 100 mitral cells connected with \r\nasynchronous inhibitory \"synapses\" that is meant to reproduce the \r\nGABAergic transmission of ensembles of connected granule cells. \r\nFor appropriate parameters of this special synapse the model generates \r\ngamma oscillations with properties very similar to what is observed \r\nin olfactory bulb slices (See Bathellier et al. 2006, Lagier et al. 2007).\r\nMitral cells are modeled as single compartment neurons with a small \r\nnumber of different voltage gated channels. Parameters were tuned to reproduce the\r\nfast subthreshold oscillation of the membrane potential observed experimentally\r\n(see Desmaisons et al. 1999).\r\n", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 830, + "tag": "Delay" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1249, + "tag": "ModelDB:91387" + } + ], + "timestamp_created": "2024-01-11 15:53:37.709460+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/91387", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "697": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 697, + "name": "Oxytocin and VIP involvement in prolactin secretion (Egli et al. 2004,2006, Bertram et al. 2006)", + "repository_type": "github", + "summary": "\"Prolactin (PRL) is secreted from lactotrophs of the anterior\r\npituitary gland of rats in a unique pattern in response to\r\nuterine cervical stimulation (CS) during mating. Surges of\r\nPRL secretion occur in response to relief from hypothalamic\r\ndopaminergic inhibition and stimulation by hypothalamic releasing\r\nneurohormones. In this study, we characterized the\r\nrole of oxytocin (OT) in this system and the involvement of\r\nvasoactive intestinal polypeptide (VIP) from the suprachiasmatic\r\nnucleus (SCN) in controlling OT and PRL secretion of\r\nCS rats. ... OT\r\nmeasurements of serial blood samples obtained from ovariectomized\r\n(OVX) CS rats displayed a prominent increase at\r\nthe time of the afternoon PRL peak. The injection of VIP antisense\r\noligonucleotides into the SCN abolished the afternoon\r\nincrease of OT and PRL in CS-OVX animals. These findings\r\nsuggest that VIP from the SCN contributes to the regulation\r\nof OT and PRL secretion in CS rats. We propose that in CS rats\r\nthe regulatory mechanism(s) for PRL secretion comprise coordinated\r\naction of neuroendocrine dopaminergic and OT\r\ncells, both governed by the daily rhythm of VIP-ergic output\r\nfrom the SCN. This hypothesis is illustrated with a mathematical model.\"", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 912, + "tag": "XPP (web link to model)" + }, + { + "id": 1250, + "tag": "ModelDB:91893" + } + ], + "timestamp_created": "2024-01-11 15:53:38.261084+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/91893", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "698": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 698, + "name": "Low dose of dopamine may stimulate prolactin secretion by increasing K currents (Tabak et al. 2006)", + "repository_type": "github", + "summary": "\".. We considered\r\nthe fast K+ currents flowing through large-conductance\r\nBK channels and through A-type channels. We developed a\r\nminimal lactotroph model to investigate the effects of these\r\ntwo currents. Both IBK and IA could transform the electrical\r\npattern of activity from spiking to bursting, but through\r\ndistinct mechanisms. IBK always increased the intracellular\r\nCa2+ concentration, while IA could either increase or\r\ndecrease it. Thus, the stimulatory effects of DA could be\r\nmediated by a fast K+ conductance which converts tonically\r\nspiking cells to bursters. In addition, the study illustrates that\r\na heterogeneous distribution of fast K+ conductances could\r\ncause heterogeneous lactotroph firing patterns.\"", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 912, + "tag": "XPP (web link to model)" + }, + { + "id": 1251, + "tag": "ModelDB:91898" + } + ], + "timestamp_created": "2024-01-11 15:53:38.724140+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/91898", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "699": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 699, + "name": "Endothelin action on pituitary latotrophs (Bertram et al. 2006)", + "repository_type": "github", + "summary": "Endothelin (ET-1, -2, and -3 designate three genes which produce different endothelin isopeptides) is a prolactin inhibiting substance of hypothalmic origin. ET-1 binding is part of at least four G protein signaling pathways in lactotrophs. The sequence of events in these pathways following the presentation of nano- and pico-molar concentrations of ET-1 is modeled in the paper.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 901, + "tag": "I_SERCA" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 912, + "tag": "XPP (web link to model)" + }, + { + "id": 1252, + "tag": "ModelDB:91899" + } + ], + "timestamp_created": "2024-01-11 15:53:39.385113+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/91899", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "700": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 700, + "name": "Implementation issues in approximate methods for stochastic Hodgkin-Huxley models (Bruce 2007)", + "repository_type": "github", + "summary": "Four different algorithms for implementing Hodgkin\u2013Huxley models\r\nwith stochastic sodium channels: Strassberg and\r\nDeFelice (1993), Rubinstein (1995), Chow and White\r\n(1996), and Fox (1997) are compared.", + "tags": [ + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1253, + "tag": "ModelDB:93315" + } + ], + "timestamp_created": "2024-01-11 15:53:39.836785+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/93315", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "701": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 701, + "name": "Lamprey spinal CPG neuron (Huss et al. 2007)", + "repository_type": "github", + "summary": "This is a model of a generic locomotor network neuron in the lamprey spinal cord. The given version is assumed to correspond to an interneuron; motoneurons can also be modelled by changing the dendritic tree morphology.", + "tags": [ + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 868, + "tag": "I_Ks" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 775, + "tag": "Rebound firing" + }, + { + "id": 1254, + "tag": "ModelDB:93319" + } + ], + "timestamp_created": "2024-01-11 15:53:40.327300+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/93319", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "702": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 702, + "name": "Activity dependent conductances in a neuron model (Liu et al. 1998)", + "repository_type": "github", + "summary": "\"... We present a model of a\r\nstomatogastric ganglion (STG) neuron in which several Ca2+-dependent\r\npathways are used to regulate the maximal conductances of membrane\r\ncurrents in an activity-dependent manner. Unlike previous models of\r\nthis type, the regulation and modification of maximal conductances by\r\nelectrical activity is unconstrained. The model has seven\r\nvoltage-dependent membrane currents and uses three Ca2+ sensors acting\r\non different time scales. ... The model suggests that neurons may regulate their\r\nconductances to maintain fixed patterns of electrical activity, rather\r\nthan fixed maximal conductances, and that the regulation process\r\nrequires feedback systems capable of reacting to changes of electrical\r\nactivity on a number of different time scales.\"", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 171, + "tag": "Homeostasis" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1255, + "tag": "ModelDB:93321" + } + ], + "timestamp_created": "2024-01-11 15:53:40.787018+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/93321", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "703": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 703, + "name": "Updated Tritonia Swim CPG (Calin-Jagemann et al. 2007)", + "repository_type": "github", + "summary": "Model of the 3-cell core CPG (DSI, C2, and VSI-B) mediating escape swimming in Tritonia diomedea. Cells use a hybrid integrate-and-fire scheme pioneered by Peter Getting. Each model cell is reconstructed from extensive physiological measurements to precisely mimic I-F curves, synaptic waveforms, and functional connectivity.", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1256, + "tag": "ModelDB:93325" + } + ], + "timestamp_created": "2024-01-11 15:53:41.252538+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/93325", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "704": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 704, + "name": "Classic model of the Tritonia Swim CPG (Getting, 1989)", + "repository_type": "github", + "summary": "Classic model developed by Petter Getting of the 3-cell core CPG (DSI, C2, and VSI-B) mediating escape swimming in Tritonia diomedea. Cells use a hybrid integrate-and-fire scheme pioneered by Peter Getting. Each model cell is reconstructed from extensive physiological measurements to precisely mimic I-F curves, synaptic waveforms, and functional connectivity. **However, continued physiological measurements show that Getting may have inadvertently incorporated modulatory and or polysynaptic effects -- the properties of this model do *not* match physiological measurements in rested preparations.** This simulation reconstructs the Getting model as reported in: Getting (1989) 'Reconstruction of small neural networks' In Methods in Neural Modeling, 1st ed, p. 171-196. See also, an earlier version of this model reported in Getting (1983). Every attempt has been made to replicate the 1989 model as precisely as possible.", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1257, + "tag": "ModelDB:93326" + } + ], + "timestamp_created": "2024-01-11 15:53:41.712640+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/93326", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "705": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 705, + "name": "Pyramidal Neuron Deep: Constrained by experiment (Dyhrfjeld-Johnsen et al. 2005)", + "repository_type": "github", + "summary": "\"... As a practical demonstration of the use of CoCoDat we\r\nconstructed a detailed computer model of an intrinsically\r\nbursting (IB) layer V pyramidal neuron from the rat barrel\r\ncortex supplementing experimental data (Schubert et al.,\r\n2001) with information extracted from the database. The\r\npyramidal neuron morphology (Fig. 10B) was reconstructed\r\nfrom histological sections of a biocytin-stained IB neuron\r\nusing the NeuroLucida software package...\"", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1258, + "tag": "ModelDB:93349" + } + ], + "timestamp_created": "2024-01-11 15:53:42.188377+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/93349", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "706": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 706, + "name": "Efficient estimation of detailed single-neuron models (Huys et al. 2006)", + "repository_type": "github", + "summary": "\"Biophysically accurate multicompartmental models of individual neurons ... depend on a large number of parameters that are difficult to estimate. ... We propose a statistical approach to the automatic estimation of various biologically relevant parameters, including 1) the distribution of channel densities, 2) the spatiotemporal pattern of synaptic input, and 3) axial resistances across extended dendrites. ... We demonstrate that the method leads to accurate estimations on a wide variety of challenging model data sets that include up to about 10,000 parameters (roughly two orders of magnitude more than previously feasible) and describe how the method gives insights into the functional interaction of groups of channels.\"", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1259, + "tag": "ModelDB:93390" + } + ], + "timestamp_created": "2024-01-11 15:53:42.640278+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/93390", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "707": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 707, + "name": "Fast population coding (Huys et al. 2007)", + "repository_type": "github", + "summary": "\"Uncertainty coming from the noise in its neurons and the ill-posed nature of many tasks plagues neural computations. Maybe surprisingly, many studies show that the brain manipulates these forms of uncertainty in a probabilistically consistent and normative manner, and there is now a rich theoretical literature on the capabilities of populations of neurons to implement computations in the face of uncertainty. However, one major facet of uncertainty has received comparatively little attention: time. In a dynamic, rapidly changing world, data are only temporarily relevant. Here, we analyze the computational consequences of encoding stimulus trajectories in populations of neurons. ...\"", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1260, + "tag": "ModelDB:93394" + } + ], + "timestamp_created": "2024-01-11 15:53:43.078601+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/93394", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "708": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 708, + "name": "Mapping function onto neuronal morphology (Stiefel and Sejnowski 2007)", + "repository_type": "github", + "summary": "\"... We used an optimization procedure to find neuronal morphological\r\nstructures for two computational tasks: First, neuronal morphologies were selected for\r\nlinearly summing excitatory synaptic potentials (EPSPs); second, structures were\r\nselected that distinguished the temporal order of EPSPs. The solutions resembled the\r\nmorphology of real neurons. In particular the neurons optimized for linear summation\r\nelectrotonically separated their synapses, as found in avian nucleus laminaris neurons,\r\nand neurons optimized for spike-order detection had primary dendrites of significantly\r\ndifferent diameter, as found in the basal and apical dendrites of cortical pyramidal\r\nneurons. ...\"", + "tags": [ + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 886, + "tag": "Unsupervised Learning" + }, + { + "id": 1261, + "tag": "ModelDB:93398" + } + ], + "timestamp_created": "2024-01-11 15:53:43.541394+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/93398", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "709": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 709, + "name": "Periodicity in Na channel properties alters model neuron excitability (Majumdar and Sikdar 2007)", + "repository_type": "github", + "summary": "\"... We have shown earlier that the\r\nduration and amplitude of a prolonged depolarization alter all the steady state and kinetic parameters of rNav1.2a voltage gated Na\r\nchannel in a pseudo-oscillatory fashion. In the present study, we show that the Hodgkin\u2013Huxley voltage and time dependent rate constants\r\nof activation (am and bm) and fast inactivation (ah and bh), obtained from the analyses of Na currents and steady state activation\r\nand inactivation plots, following application of prepulses in both slow (1\u2013100 s) and fast (100\u20131000 ms) ranges, vary with the duration of\r\na prepulse in a pseudo-oscillatory manner. ...\"", + "tags": [ + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1262, + "tag": "ModelDB:93416" + } + ], + "timestamp_created": "2024-01-11 15:53:44.008820+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/93416", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "710": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 710, + "name": "A neural model of Parkinson`s disease (Cutsuridis and Perantonis 2006, Cutsuridis 2006, 2007)", + "repository_type": "github", + "summary": "\"A neural model of neuromodulatory (dopamine) control of arm movements in Parkinson\u2019s disease (PD) bradykinesia was recently introduced [1, 2]. The model is multi-modular consisting of a basal ganglia module capable of selecting the most appropriate motor command in a given context, a cortical module for coordinating and executing the final motor commands, and a spino-musculo-skeletal module for guiding the arm to its final target and providing proprioceptive (feedback) input of the current state of the muscle and arm to higher cortical and lower spinal centers.\r\n... The new (extended) model [3] predicted that the reduced reciprocal disynaptic Ia inhibition in the DA depleted case doesn\u2019t lead to the co-contraction of antagonist motor units.\" See below readme and papers for more and details.", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 872, + "tag": "Pathophysiology" + }, + { + "id": 1263, + "tag": "ModelDB:93422" + } + ], + "timestamp_created": "2024-01-11 15:53:44.450334+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/93422", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "711": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 711, + "name": "Optimal deep brain stimulation of the subthalamic nucleus-a computational study (Feng et al. 2007)", + "repository_type": "github", + "summary": "Here, we use a biophysically-based model of spiking cells in the basal ganglia (Terman et al., Journal of Neuroscience, 22, 2963-2976, 2002; Rubin and Terman, Journal of Computational Neuroscience, 16, 211-235, 2004) to provide computational evidence that alternative temporal patterns of DBS inputs might be equally effective as the standard high-frequency waveforms, but require lower amplitudes. Within this model, DBS performance is assessed in two ways. First, we determine the extent to which DBS causes Gpi (globus pallidus pars interna) synaptic outputs, which are burstlike and synchronized in the unstimulated Parkinsonian state, to cease their pathological modulation of simulated thalamocortical cells. Second, we evaluate how DBS affects the GPi cells' auto- and cross-correlograms.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 834, + "tag": "Deep brain stimulation" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 1264, + "tag": "ModelDB:93449" + } + ], + "timestamp_created": "2024-01-11 15:53:44.920176+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/93449", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "712": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 712, + "name": "Inferring connection proximity in electrically coupled networks (Cali et al. 2007)", + "repository_type": "github", + "summary": "In order to explore electrical coupling in the nervous system and its network-level organization, it is imperative to map the electrical synaptic microcircuits, in analogy with in vitro studies on monosynaptic and disynaptic chemical coupling. However, walking from cell to cell over large distances with a glass pipette is challenging, and microinjection of (fluorescent) dyes diffusing through gap-junctions remains so far the only method available to decipher such microcircuits even though technical limitations exist.\r\n\r\nBased on circuit theory, we derived analytical descriptions of the AC electrical coupling in networks of isopotential cells. We then proposed an operative electrophysiological protocol to distinguish between direct electrical connections and connections involving one or more intermediate cells. \r\n\r\nThis method allows inferring the number of intermediate cells, generalizing the conventional coupling coefficient, which provides limited information. \r\n\r\nWe provide here some analysis and simulation scripts that used to test our method through computer simulations, in vitro recordings, theoretical and numerical methods.\r\n\r\nKey words: Gap-Junctions; Electrical Coupling; Networks; ZAP current; Impedance.\r\n", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 873, + "tag": "PSpice" + }, + { + "id": 913, + "tag": "Sspice Symbolic SPICE" + }, + { + "id": 1265, + "tag": "ModelDB:94321" + } + ], + "timestamp_created": "2024-01-11 15:53:45.428412+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/94321", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "713": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 713, + "name": "Turtle visual cortex model (Nenadic et al. 2003, Wang et al. 2005, Wang et al. 2006)", + "repository_type": "github", + "summary": "This is a model of the visual cortex of freshwater turtles that is based upon the\r\nknown anatomy and physiology of individual neurons. The model was published in three\r\npapers (Nenadic et al., 2003; Wang et al., 2005; Wang et al., 2006), which should be\r\nconsulted for full details on its construction. The model has also been used in several\r\npapers (Robbins and Senseman, 2004; Du et al., 2005; Du et al., 2006). It is\r\nimplemented in GENESIS (Bower and Beeman, 1998).", + "tags": [ + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 800, + "tag": "Vision" + }, + { + "id": 1266, + "tag": "ModelDB:94845" + } + ], + "timestamp_created": "2024-01-11 15:53:45.922540+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/94845", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "714": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 714, + "name": "Rod photoreceptor (Barnes and Hille 1989, Publio et al. 2006, Kourennyi and Liu et al. 2004)", + "repository_type": "github", + "summary": "This a conductance-based model of a rod photoreceptor cell based on other modeling works\r\n(Barnes and Hille 1989 and Publio et al. 2006 and Kourennyi and Liu et al. 2004 ). In this\r\nmodel four types of ionic channels identified in the inner segment of the rod: nonselective cation channel (h), delayed rectifying potassium channel (Kv), noninactivating potassium channel (Kx) and calcium channel (Ca) was used. \r\nThe model accurately reproduces the rod response when stimulated with a simulated photocurrent signal. We can show the effect of nonselective cation channel. The absence of this channel cause increasing the peak amplitude and the time to \r\nreach the peak of voltage response and absence of transient mode in this response.", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 865, + "tag": "I Cl,Ca" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1267, + "tag": "ModelDB:95870" + } + ], + "timestamp_created": "2024-01-11 15:53:46.404229+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/95870", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "715": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 715, + "name": "Dentate gyrus granule cell: subthreshold signal processing (Schmidt-Hieber et al. 2007)", + "repository_type": "github", + "summary": "Detailed compartmental cable models of 8 hippocampal granule cells of adult mice were obtained from dual patch-clamp whole-cell recordings and subsequent 3D reconstructions. This code allows to reproduce figures 6-8 from the paper.", + "tags": [ + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 1268, + "tag": "ModelDB:95960" + } + ], + "timestamp_created": "2024-01-11 15:53:46.858274+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/95960", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "716": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 716, + "name": "Kinetics of the P2X7 receptor as expressed in Xenopus oocytes (Riedel et al. 2007a,b)", + "repository_type": "github", + "summary": "\"Human P2X7 receptors were expressed in Xenopus laevis oocytes and\r\nsingle channels were recorded using the patch-clamp technique in the\r\noutside-out configuration. ATP4- evoked two types of P2X7\r\nreceptor-mediated single channel currents characterized by short-lived\r\nand long-lived openings. ... The kinetics of the short channel\r\nopenings at negative membrane potentials fitted well to a linear\r\nC-C-C-O model with two ATP4- binding steps at equal binding sites\r\n....\" and \"Using the patch-clamp method, we studied the influence of\r\nexternal alkali and organic monovalent cations on the single-channel\r\nproperties of the adenosine triphosphate (ATP)-activated recombinant\r\nhuman P2X(7) receptor.\" See the references for more.", + "tags": [ + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 861, + "tag": "Nociception" + }, + { + "id": 914, + "tag": "QuB" + }, + { + "id": 1269, + "tag": "ModelDB:95990" + } + ], + "timestamp_created": "2024-01-11 15:53:47.333712+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/95990", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "717": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 717, + "name": "Translating network models to parallel hardware in NEURON (Hines and Carnevale 2008)", + "repository_type": "github", + "summary": "Shows how to move a working network model written in NEURON from a serial processor to a parallel machine in such a way that the final result will produce numerically identical results on either serial or parallel hardware.", + "tags": [ + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1270, + "tag": "ModelDB:96444" + } + ], + "timestamp_created": "2024-01-11 15:53:47.808774+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/96444", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "718": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 718, + "name": "Olfactory bulb mitral and granule cell: dendrodendritic microcircuits (Migliore and Shepherd 2008)", + "repository_type": "github", + "summary": "This model shows how backpropagating action potentials in the long lateral dendrites of mitral cells, together with granule cell actions on mitral cells within narrow columns forming glomerular units, can provide a mechanism to activate strong local inhibition between arbitrarily distant mitral cells. The simulations predict a new role for the dendrodendritic synapses in the multicolumnar organization of the granule cells.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 1271, + "tag": "ModelDB:97263" + } + ], + "timestamp_created": "2024-01-11 15:53:48.398399+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/97263", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "719": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 719, + "name": "Vesicular pool simulations of synaptic depression (Aristizabal and Glavinovic 2004)", + "repository_type": "github", + "summary": "\"Synaptic release was simulated using a Simulink sequential storage model with three vesicular pools. Modeling was modular and easily extendable to the systems with greater number of vesicular pools, parallel input, or time-varying parameters. ... Finally, the method was tested experimentally using the rat phrenic-diaphragm neuromuscular junction.\"\r\nSee paper for more and details.", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 907, + "tag": "Simulink" + }, + { + "id": 1272, + "tag": "ModelDB:97274" + } + ], + "timestamp_created": "2024-01-11 15:53:48.873087+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/97274", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "720": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 720, + "name": "Model of DARPP-32 phosphorylation in striatal medium spiny neurons (Lindskog et al. 2006)", + "repository_type": "github", + "summary": "The work describes a model of how transient calcium and dopamine inputs might affect phosphorylation of DARPP-32 in the medium spiny neurons in the striatum. The model is relevant for understanding both the \"three-factor rule\" for synaptic plasticity in corticostriatal synapses, and also for relating reinforcement learning theories to biology.", + "tags": [ + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 892, + "tag": "Reinforcement Learning" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1273, + "tag": "ModelDB:97743" + } + ], + "timestamp_created": "2024-01-11 15:53:49.435447+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/97743", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "721": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 721, + "name": "Fast-spiking cortical interneuron (Golomb et al. 2007)", + "repository_type": "github", + "summary": "Cortical fast-spiking (FS) interneurons display highly variable electrophysiological properties. We hypothesize that this variability emerges naturally if one assumes a continuous distribution of properties in a small set of active channels. We construct a minimal, single-compartment conductance-based model of FS cells that includes transient Na+, delayed-rectifier K+, and slowly inactivating d-type K+ conductances. The model may display delay to firing. Stuttering (elliptic bursting) and subthreshold oscillations may be observed for small Na+ window current.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 830, + "tag": "Delay" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 823, + "tag": "I_KD" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 915, + "tag": "Stuttering" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1274, + "tag": "ModelDB:97747" + } + ], + "timestamp_created": "2024-01-11 15:53:50.390269+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/97747", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "722": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 722, + "name": "Ion channel modeling with whole cell and a genetic algorithm (Gurkiewicz and Korngreen 2007)", + "repository_type": "github", + "summary": "\"... Here we show that a genetic search algorithm in combination with a gradient descent algorithm can be used to\r\nfit whole-cell voltage-clamp data to kinetic models with a high degree of accuracy. Previously, ion channel stimulation\r\ntraces were analyzed one at a time, the results of these analyses being combined to produce a picture of channel\r\nkinetics. Here the entire set of traces from all stimulation protocols are analysed simultaneously. The algorithm was\r\ninitially tested on simulated current traces produced by several Hodgkin-Huxley\u2013like and Markov chain models of\r\nvoltage-gated potassium and sodium channels. ... Finally, the algorithm was used for finding the kinetic parameters of several voltage-gated\r\nsodium and potassium channels models by matching its results to data recorded from layer 5 pyramidal neurons\r\nof the rat cortex in the nucleated outside-out patch configuration. The minimization scheme gives electrophysiologists\r\na tool for reproducing and simulating voltage-gated ion channel kinetics at the cellular level.\"", + "tags": [ + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 819, + "tag": "NEURON (web link to model)" + }, + { + "id": 1275, + "tag": "ModelDB:97756" + } + ], + "timestamp_created": "2024-01-11 15:53:50.883202+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/97756", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "723": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 723, + "name": "Study of augmented Rubin and Terman 2004 deep brain stim. model in Parkinsons (Pascual et al. 2006)", + "repository_type": "github", + "summary": "\" ... The model by Rubin and Terman [31] represents one of the most comprehensive and biologically plausible models of DBS published recently. We examined the validity of the model, replicated its simulations and tested its robustness. While our simulations partially reproduced the results presented by Rubin and Terman [31], several issues were raised including the high complexity of the model in its non simplified form, the lack of robustness of the model with respect to small perturbations, the nonrealistic representation of the thalamus and the absence of time delays. Computational models are indeed necessary, but they may not be sufficient in their current forms to explain the effect of chronic electrical stimulation on the activity of the basal ganglia (BG) network in PD.\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 834, + "tag": "Deep brain stimulation" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1276, + "tag": "ModelDB:97850" + } + ], + "timestamp_created": "2024-01-11 15:53:51.368904+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/97850", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "724": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 724, + "name": "Rejuvenation model of dopamine neuron (Chan et al. 2007)", + "repository_type": "github", + "summary": "Model files for the paper C. Savio Chan, et al. 'Rejuvenation' protects neurons in mouse models of Parkinson's disease, Nature 447, 1081-1086(28 June 2007).", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1277, + "tag": "ModelDB:97860" + } + ], + "timestamp_created": "2024-01-11 15:53:51.898389+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/97860", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "725": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 725, + "name": "Mammalian Ventricular Cell (Beeler and Reuter 1977)", + "repository_type": "github", + "summary": "This classic model of ventricular myocardial fibres was implemented by Francois Gannier. \"... Four individual components of ionic current were formulated mathematically\r\nin terms of Hodgkin-Huxley type equations. The model incorporates\r\ntwo voltage- and time-dependent inward currents, the excitatory\r\ninward sodium current, illa, and a secondary or slow inward current,\r\nis, primarily carried by calcium ions. A time-independent outward\r\npotassium current, iK1, exhibiting inward-going rectification, and a voltage-\r\nand time-dependent outward current, i.1, primarily carried by potassium\r\nions, are further elements of the model....\"\r\n", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1278, + "tag": "ModelDB:97863" + } + ], + "timestamp_created": "2024-01-11 15:53:52.366829+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/97863", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "726": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 726, + "name": "NEURON interfaces to MySQL and the SPUD feature extraction algorithm (Neymotin et al. 2008)", + "repository_type": "github", + "summary": "See the readme.txt for information on setting up this interface to a MySQL server from the NEURON simulator. Note the SPUD feature extraction algorithm includes its own readme in the spud directory.", + "tags": [ + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1279, + "tag": "ModelDB:97868" + } + ], + "timestamp_created": "2024-01-11 15:53:52.856221+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/97868", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "727": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 727, + "name": "Neural Query System NQS Data-Mining From Within the NEURON Simulator (Lytton 2006)", + "repository_type": "github", + "summary": "NQS is a databasing program with a query command modeled loosely on the SQL select command.\r\nPlease see the manual NQS.pdf for details of use.\r\nAn NQS database must be populated with data to be used. This package includes MFP (model fingerprint) which provides an example of NQS use with the model provided in the modeldb folder (see readme for usage).", + "tags": [ + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1280, + "tag": "ModelDB:97874" + } + ], + "timestamp_created": "2024-01-11 15:53:53.301678+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/97874", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "728": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 728, + "name": "Determinants of fast calcium dynamics in dendritic spines and dendrites (Cornelisse et al. 2007)", + "repository_type": "github", + "summary": "\"... Calcium influx time course and calcium extrusion rate were both in the same range for spines and dendrites when fitted with a dynamic multi-compartment model that included calcium binding kinetics and diffusion. In a subsequent analysis we used this model to investigate which parameters are critical determinants in spine calcium dynamics. The model confirmed the experimental findings: a higher SVR (surface-to-volume ratio) is not sufficient by itself to explain the faster rise time kinetics in spines, but only when paired with a lower buffer capacity in spines. Simulations at zero calcium-dye conditions show that calmodulin is more efficiently activated in spines, which indicates that spine morphology and buffering conditions in neocortical spines favor synaptic plasticity. ...\"", + "tags": [ + { + "id": 916, + "tag": "CalC Calcium Calculator" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1281, + "tag": "ModelDB:97903" + } + ], + "timestamp_created": "2024-01-11 15:53:53.794194+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/97903", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "729": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 729, + "name": "Cell splitting in neural networks extends strong scaling (Hines et al. 2008)", + "repository_type": "github", + "summary": "Neuron tree topology equations can be split into two subtrees and solved\r\non different processors with no change in accuracy, stability, or\r\ncomputational effort; communication costs involve only sending and\r\nreceiving two double precision values by each subtree at each time step.\r\nApplication of the cell splitting method to two published\r\nnetwork models exhibits good runtime scaling on twice as many\r\nprocessors as could be effectively used with whole-cell balancing.\r\n", + "tags": [ + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1282, + "tag": "ModelDB:97917" + } + ], + "timestamp_created": "2024-01-11 15:53:54.344931+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/97917", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "730": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 730, + "name": "Roles of subthalamic nucleus and DBS in reinforcement conflict-based decision making (Frank 2006)", + "repository_type": "github", + "summary": "Deep brain stimulation (DBS) of the subthalamic nucleus dramatically improves the motor symptoms of Parkinson's disease, but causes cognitive side effects such as impulsivity. This model from Frank (2006) simulates the role of the subthalamic nucleus (STN) within the basal ganglia circuitry in decision making. The STN dynamically modulates network decision thresholds in proportion to decision conflict. The STN ``hold your horses'' signal adaptively allows the system more time to settle on the best choice when multiple options are valid. The model also replicates effects in Parkinson's patients on and off DBS in experiments designed to test the model (Frank et al, 2007).", + "tags": [ + { + "id": 857, + "tag": "Action Selection/Decision Making" + }, + { + "id": 834, + "tag": "Deep brain stimulation" + }, + { + "id": 890, + "tag": "Emergent/PDPplusplus" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 775, + "tag": "Rebound firing" + }, + { + "id": 892, + "tag": "Reinforcement Learning" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1283, + "tag": "ModelDB:97972" + } + ], + "timestamp_created": "2024-01-11 15:53:54.811419+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/97972", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "731": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 731, + "name": "Neuronal population models of intracerebral EEG (Wendling et al. 2005)", + "repository_type": "github", + "summary": "\"... In this study, the authors relate electrophysiologic patterns typically observed during the transition from interictal to ictal activity in human mesial temporal lobe epilepsy (MTLE) to mechanisms (at a neuronal population level) involved in seizure generation through a computational model of EEG activity. Intracerebral EEG signals recorded from hippocampus in five patients with MTLE during four periods (during interictal activity, just before seizure onset, during seizure onset, and during ictal activity) were used to identify the three main parameters of a model of hippocampus EEG activity (related to excitation, slow dendritic inhibition and fast somatic inhibition). ... . Results demonstrated that the model generates very realistic signals for automatically identified parameters. They also showed that the transition from interictal to ictal activity cannot be simply explained by an increase in excitation and a decrease in inhibition but rather by time-varying ensemble interactions between pyramidal cells and local interneurons projecting to either their dendritic or perisomatic region (with slow and fast GABAA kinetics). Particularly, during preonset activity, an increasing dendritic GABAergic inhibition compensates a gradually increasing excitation up to a brutal drop at seizure onset when faster oscillations (beta and low gamma band, 15 to 40 Hz) are observed. ... These findings obtained from model identification in human temporal lobe epilepsy are in agreement with some results obtained experimentally, either on animal models of epilepsy or on the human epileptic tissue.\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 917, + "tag": "GNUstep NeXTStep/OpenStep" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1284, + "tag": "ModelDB:97983" + } + ], + "timestamp_created": "2024-01-11 15:53:55.274747+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/97983", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "732": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 732, + "name": "Fully Implicit Parallel Simulation of Single Neurons (Hines et al. 2008)", + "repository_type": "github", + "summary": "A 3-d reconstructed\r\nneuron model can be simulated in parallel on a dozen or so processors and experience almost linear\r\nspeedup. Network models can be simulated when\r\nthere are more processors than cells.\r\n", + "tags": [ + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1285, + "tag": "ModelDB:97985" + } + ], + "timestamp_created": "2024-01-11 15:53:55.743955+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/97985", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "733": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 733, + "name": "Theta phase precession in a model CA3 place cell (Baker and Olds 2007)", + "repository_type": "github", + "summary": "\"... The present study concerns a neurobiologically based computational model of the emergence of theta phase precession in which the responses of a single model CA3 pyramidal cell are examined in the context of stimulation by realistic afferent spike trains including those of place cells in entorhinal cortex, dentate gyrus, and other CA3 pyramidal cells. \r\nSpike-timing dependent plasticity in the model CA3 pyramidal cell leads to a spatially correlated associational synaptic drive that subsequently creates a spatially asymmetric expansion of the model cell\u2019s place field. ...\r\nThrough selective manipulations of the model it is possible to decompose theta phase precession in CA3 into the separate contributing factors of inheritance from upstream afferents in the dentate gyrus and entorhinal cortex, the interaction of synaptically controlled increasing afferent drive with phasic inhibition, and the theta phase difference between dentate gyrus granule cell and CA3 pyramidal cell activity.\"", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 888, + "tag": "I Mixed" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 908, + "tag": "Learning" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 882, + "tag": "Place cell/field" + }, + { + "id": 892, + "tag": "Reinforcement Learning" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1286, + "tag": "ModelDB:98003" + } + ], + "timestamp_created": "2024-01-11 15:53:56.263089+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/98003", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "734": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 734, + "name": "D2 dopamine receptor modulation of interneuronal activity (Maurice et al. 2004)", + "repository_type": "github", + "summary": "\"... Using a combination of electrophysiological, molecular, and computational approaches, the studies reported here show that D2 dopamine receptor modulation of Na+ currents underlying autonomous spiking contributes to a slowing of discharge rate, such as that seen in vivo. Four lines of evidence support this conclusion. ... Fourth, simulation of cholinergic interneuron pacemaking revealed that a modest increase in the entry of Na+ channels into the slow-inactivated state was sufficient to account for the slowing of pacemaker discharge. These studies establish a cellular mechanism linking dopamine and the reduction in striatal cholinergic interneuron activity seen in the initial stages of associative learning.\" See paper for more and details.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 1287, + "tag": "ModelDB:98005" + } + ], + "timestamp_created": "2024-01-11 15:53:56.825739+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/98005", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "735": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 735, + "name": "Fronto-parietal visuospatial WM model with HH cells (Edin et al 2007)", + "repository_type": "github", + "summary": "1) J Cogn Neurosci: 3 structural mechanisms that had been hypothesized to underlie vsWM development during childhood were evaluated by simulating the model and comparing results to fMRI. It was concluded that inter-regional synaptic connection strength cause vsWM development.\r\n\r\n2) J Integr Neurosci: Given the importance of fronto-parietal connections, we tested whether connection asymmetry affected resistance to distraction. We drew the conclusion that stronger frontal connections are beneficial. By comparing model results to EEG, we concluded that the brain indeed has stronger frontal-to-parietal connections than vice versa.", + "tags": [ + { + "id": 790, + "tag": "Attractor Neural Network" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 877, + "tag": "Working memory" + }, + { + "id": 1288, + "tag": "ModelDB:98017" + } + ], + "timestamp_created": "2024-01-11 15:53:57.289804+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/98017", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "736": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 736, + "name": "Studies of stimulus parameters for seizure disruption using NN simulations (Anderson et al. 2007)", + "repository_type": "github", + "summary": "Architecturally realistic neocortical model using seven classes of excitatory and inhibitory single compartment Hodgkin-Huxley cells. Wiring is adapted to minicolumn hypothesis and incorporates visual and neocortical data. Simulation demonstrates spontaneous bursting onset and cessation, and activity can be altered with external electric field.", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 800, + "tag": "Vision" + }, + { + "id": 1289, + "tag": "ModelDB:98902" + } + ], + "timestamp_created": "2024-01-11 15:53:57.955740+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/98902", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "737": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 737, + "name": "ERG current in repolarizing plateau potentials in dopamine neurons (Canavier et al 2007)", + "repository_type": "github", + "summary": "\"Blocking the small-conductance (SK) calcium-activated potassium channel promotes\r\nburst firing in dopamine neurons both in vivo and in vitro.\r\n...\r\nWe focus on the underlying plateau potential\r\noscillation generated in the presence of both apamin and TTX, so that\r\naction potentials are not considered. \r\n\r\nWe find that although the plateau\r\npotentials are mediated by a voltage-gated Ca2+ current, they do not\r\ndepend on the accumulation of cytosolic Ca2+, then use a computational\r\nmodel to test the hypothesis that the slowly voltage-activated\r\nether-a-go-go\u2013related gene (ERG) potassium current repolarizes the\r\nplateaus.\r\n\r\nThe model, which includes a material balance on calcium,\r\nis able to reproduce the time course of both membrane potential\r\nand somatic calcium concentration, and can also mimic the induction\r\nof plateau potentials by the calcium chelator BAPTA.\" See paper for more.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 756, + "tag": "FORTRAN" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 874, + "tag": "I_HERG" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1290, + "tag": "ModelDB:100603" + } + ], + "timestamp_created": "2024-01-11 15:53:58.497496+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/100603", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "738": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 738, + "name": "CA3 pyramidal neuron: firing properties (Hemond et al. 2008)", + "repository_type": "github", + "summary": "In the paper, this model was used to identify how relative differences in K+ conductances,\r\nspecifically KC, KM, & KD, between cells contribute to the different characteristics of the\r\nthree types of firing patterns observed experimentally.\r\n", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1291, + "tag": "ModelDB:101629" + } + ], + "timestamp_created": "2024-01-11 15:53:58.995693+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/101629", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "739": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 739, + "name": "Leech S Cell: Modulation of Excitability by Serotonin (Burrell and Crisp 2008)", + "repository_type": "github", + "summary": "Serotonergic modulation of the afterhyperpolarization (AHP) contributes to the regulation of the excitability of the leech S cell, a neuron critical for sensitization of the shortening reflex. Pharmacological and physiological data suggest that three currents contribute to the S cell's afterhyperpolarization: a charybdotoxin-sensitive, fast calcium-dependent potassium current (fAHP); a tubocurare-sensitive, calcium-dependent potassium current (mAHP); and, a saxitoxin-sensitive, afterdepolarization current (ADP). This single-compartment model of the S cell is constructed using fAHP, mAHP and ADP currents, and shows that reduction of the conductances to mimic the effects of serotonin is sufficient to enhance excitability (repetitive firing).", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 1292, + "tag": "ModelDB:102279" + } + ], + "timestamp_created": "2024-01-11 15:53:59.498185+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/102279", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "740": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 740, + "name": "O-LM interneuron model (Lawrence et al. 2006)", + "repository_type": "github", + "summary": "Exploring the kinetics and distribution of the muscarinic potassium channel, IM, in 2 O-LM interneuron morphologies. Modulation of the ion channel by drugs such as XE991 (antagonist) and retigabine (agonist) are simulated in the models to examine the role of IM in spiking properties.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1293, + "tag": "ModelDB:102288" + } + ], + "timestamp_created": "2024-01-11 15:54:00.005100+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/102288", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "741": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 741, + "name": "Elementary mechanisms producing facilitation of Cav2.1 (P/Q-type) channels", + "repository_type": "github", + "summary": "\"The regulation of Ca(V)2.1 (P/Q-type) channels by calmodulin (CaM) showcases the powerful Ca(2+) decoding capabilities of CaM in complex with the family of Ca(V)1-2 Ca(2+) channels. \r\n\r\nThroughout this family, CaM does not simply exert a binary on/off regulatory effect; rather, Ca(2+) binding to either the C- or N-terminal lobe of CaM alone can selectively trigger a distinct form of channel modulation. \r\n...\r\nCa(2+) binding to the C-terminal lobe induces Ca(2+)-dependent facilitation of opening (CDF), whereas the N-terminal lobe yields Ca(2+)-dependent inactivation of opening (CDI). \r\n...\r\nFurthermore, direct single-channel determinations of channel open probability (P(o)) and kinetic simulations demonstrate that CDF represents a genuine enhancement of open probability, without appreciable change of activation kinetics. \r\nThis enhanced-opening mechanism suggests that the CDF evoked during action-potential trains would produce not only larger, but longer-lasting Ca(2+) responses, an outcome with potential ramifications for short-term synaptic plasticity.\"\r\n", + "tags": [ + { + "id": 798, + "tag": "Facilitation" + }, + { + "id": 818, + "tag": "I p,q" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1294, + "tag": "ModelDB:102871" + } + ], + "timestamp_created": "2024-01-11 15:54:00.501267+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/102871", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "742": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 742, + "name": "Ionic basis of alternans and Timothy Syndrome (Fox et al. 2002), (Zhu and Clancy 2007)", + "repository_type": "github", + "summary": "From Zhu and Clancy: \"... Here we\r\nemploy theoretical simulations to examine the effects of a Timothy Syndrome (TS) mutation\r\nin the L-type Ca2+ channel on cardiac dynamics over multiple\r\nscales, from a gene mutation to protein, cell, tissue, and finally the\r\nECG, to connect a defective Ca2+ channel to arrhythmia susceptibility. ...\"", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 868, + "tag": "I_Ks" + }, + { + "id": 870, + "tag": "Long-QT" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 814, + "tag": "Na/Ca exchanger" + }, + { + "id": 816, + "tag": "Na/K pump" + }, + { + "id": 817, + "tag": "Sodium pump" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 918, + "tag": "Timothy Syndrome" + }, + { + "id": 1295, + "tag": "ModelDB:104623" + } + ], + "timestamp_created": "2024-01-11 15:54:01.192215+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/104623", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "743": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 743, + "name": "Excitation-contraction coupling/mitochondrial energetics (ECME) model (Cortassa et al. 2006)", + "repository_type": "github", + "summary": "\"An intricate network of reactions is involved in matching energy supply with demand in the heart. This complexity\r\narises because energy production both modulates and is modulated by the electrophysiological and contractile activity of the\r\ncardiac myocyte. Here, we present an integrated mathematical model of the cardiac cell that links excitation-contraction\r\ncoupling with mitochondrial energy generation. The dynamics of the model are described by a system of 50 ordinary differential\r\nequations. The formulation explicitly incorporates cytoplasmic ATP-consuming processes associated with force generation and\r\nion transport, as well as the creatine kinase reaction. Changes in the electrical and contractile activity of the myocyte are\r\ncoupled to mitochondrial energetics through the ATP, Ca21, and Na1 concentrations in the myoplasmic and mitochondrial\r\nmatrix compartments. ...\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 901, + "tag": "I_SERCA" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 814, + "tag": "Na/Ca exchanger" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1296, + "tag": "ModelDB:105383" + } + ], + "timestamp_created": "2024-01-11 15:54:02.418524+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/105383", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "744": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 744, + "name": "Nav1.6 sodium channel model in globus pallidus neurons (Mercer et al. 2007)", + "repository_type": "github", + "summary": "Model files for the paper Mercer JN, Chan CS, Tkatch T, Held J, Surmeier DJ. Nav1.6 sodium channels are critical to pacemaking and fast spiking in globus pallidus neurons.,J Neurosci. 2007 Dec 5;27(49):13552-66.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 1297, + "tag": "ModelDB:105385" + } + ], + "timestamp_created": "2024-01-11 15:54:02.942224+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/105385", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "745": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 745, + "name": "Temporal integration by stochastic recurrent network (Okamoto et al. 2007)", + "repository_type": "github", + "summary": "\"Temporal integration of externally or internally\r\ndriven information is required for a variety of cognitive\r\nprocesses. This computation is generally linked with graded rate\r\nchanges in cortical neurons, which typically appear during a delay\r\nperiod of cognitive task in the prefrontal and other cortical areas.\r\nHere, we present a neural network model to produce graded (climbing\r\nor descending) neuronal activity. Model neurons are interconnected\r\nrandomly by AMPA-receptor\u2013mediated fast excitatory synapses and\r\nare subject to noisy background excitatory and inhibitory synaptic\r\ninputs. In each neuron, a prolonged afterdepolarizing potential follows\r\nevery spike generation. Then, driven by an external input, the individual\r\nneurons display bimodal rate changes between a baseline state\r\nand an elevated firing state, with the latter being sustained by regenerated\r\nafterdepolarizing potentials. ...\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1298, + "tag": "ModelDB:105501" + } + ], + "timestamp_created": "2024-01-11 15:54:03.413987+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/105501", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "746": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 746, + "name": "A dual-Ca2+-sensor model for neurotransmitter release in a central synapse (Sun et al. 2007)", + "repository_type": "github", + "summary": "\"Ca2+-triggered synchronous neurotransmitter release is well described, but asynchronous release-in fact, its very existence-remains enigmatic. \r\nHere we report a quantitative description of asynchronous neurotransmitter release in calyx-of-Held synapses. \r\n...\r\nOur results reveal that release triggered in wild-type synapses at low Ca2+ concentrations is physiologically asynchronous, and that asynchronous release completely empties the readily releasable pool of vesicles during sustained elevations of Ca2+. \r\nWe propose a dual-Ca2+-sensor model of release that quantitatively describes the contributions of synchronous and asynchronous release under conditions of different presynaptic Ca2+ dynamics.\"", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 903, + "tag": "IGOR Pro" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1299, + "tag": "ModelDB:105506" + } + ], + "timestamp_created": "2024-01-11 15:54:03.871372+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/105506", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "747": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 747, + "name": "Tonic-clonic transitions in a seizure simulation (Lytton and Omurtag 2007)", + "repository_type": "github", + "summary": "\"... The authors have ... computationally manageable networks of moderate size consisting of 1,000 to 3,000 neurons with multiple intrinsic\r\nand synaptic properties. \r\n\r\nExperiments on these simulations demonstrated the presence of epileptiform behavior in the form of\r\nrepetitive high-intensity population events (clonic behavior) or\r\nlatch-up with near maximal activity (tonic behavior). \r\n\r\n...\r\nSeveral simulations revealed the importance of random coincident inputs to shift a network from\r\na low-activation to a high-activation epileptiform state. Finally, a\r\nsimulated anticonvulsant acting on excitability tended to preferentially\r\ndecrease tonic activity.\"\r\n", + "tags": [ + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1300, + "tag": "ModelDB:105507" + } + ], + "timestamp_created": "2024-01-11 15:54:04.330680+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/105507", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "748": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 748, + "name": "Effect of riluzole on action potential in cultured human skeletal muscle cells (Wang YJ et al. 2008)", + "repository_type": "github", + "summary": "Simulation studies also unraveled that both decreased conductance of I(Na) and increased conductance of I(K(Ca)) utilized to mimic riluzole\r\nactions in skeletal muscle cells could combine to decrease the amplitude of action potentials and increase the repolarization of action potentials.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1301, + "tag": "ModelDB:105528" + } + ], + "timestamp_created": "2024-01-11 15:54:04.787489+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/105528", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "749": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 749, + "name": "CA1 pyramidal neuron: calculation of MRI signals (Cassara et al. 2008)", + "repository_type": "github", + "summary": "NEURON mod files from the paper:\r\nCassar\u00e0 AM, Hagberg GE, Bianciardi M, Migliore M, Maraviglia B.\r\nRealistic simulations of neuronal activity: A contribution to the debate on direct detection of neuronal currents by MRI.\r\nNeuroimage. 39:87-106 (2008).\r\n\r\nIn this paper, we use a detailed calculation of the magnetic field produced by the neuronal \r\ncurrents propagating over a hippocampal CA1 pyramidal neuron placed inside a cubic MR voxel of \r\nlength 1.2 mm to estimate the Magnetic Resonance signal.\r\n", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 810, + "tag": "Extracellular Fields" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1302, + "tag": "ModelDB:106551" + } + ], + "timestamp_created": "2024-01-11 15:54:05.459602+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/106551", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "750": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 750, + "name": "JitCon: Just in time connectivity for large spiking networks (Lytton et al. 2008)", + "repository_type": "github", + "summary": "This simulation is primarily an illustration and is not well optimized for actually running large\r\n networks.\r\njitcon.mod contains a large amount of C level code, understanding of which requires some\r\n knowledge of Neuron internals", + "tags": [ + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1303, + "tag": "ModelDB:106891" + } + ], + "timestamp_created": "2024-01-11 15:54:05.962358+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/106891", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "751": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 751, + "name": "Calcium spikes in basal dendrites (Kampa and Stuart 2006)", + "repository_type": "github", + "summary": "This model was published in Kampa & Stuart (2006) J Neurosci 26(28):7424-32. The simulation creates two plots showing voltage and calcium changes in basal dendrites of layer 5 pyramidal neurons during action potential backpropagation. \r\n\r\ncreated by B. Kampa (2006)", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 1304, + "tag": "ModelDB:108458" + } + ], + "timestamp_created": "2024-01-11 15:54:06.434344+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/108458", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "752": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 752, + "name": "STDP depends on dendritic synapse location (Letzkus et al. 2006)", + "repository_type": "github", + "summary": "This model was published in Letzkus, Kampa & Stuart (2006) J Neurosci 26(41):10420-9. The simulation creates several plots showing voltage and NMDA current and conductance changes at different apical dendritic locations in layer 5 pyramidal neurons during STDP induction protocols. \r\n\r\nCreated by B. Kampa (2006).", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1305, + "tag": "ModelDB:108459" + } + ], + "timestamp_created": "2024-01-11 15:54:06.927374+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/108459", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "753": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 753, + "name": "A microcircuit model of the frontal eye fields (Heinzle et al. 2007)", + "repository_type": "github", + "summary": "\" ... we show that the canonical circuit (Douglas et al. 1989, Douglas and Martin 1991) can,\r\nwith a few modifications, model the primate FEF. The spike-based network of integrate-and-fire neurons was tested in tasks that were\r\nused in electrophysiological experiments in behaving macaque monkeys. The dynamics of the model matched those of neurons observed\r\nin the FEF, and the behavioral results matched those observed in psychophysical experiments. The close relationship between the model\r\nand the cortical architecture allows a detailed comparison of the simulation results with physiological data and predicts details of the\r\nanatomical circuit of the FEF.\"", + "tags": [ + { + "id": 857, + "tag": "Action Selection/Decision Making" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 800, + "tag": "Vision" + }, + { + "id": 1306, + "tag": "ModelDB:110022" + } + ], + "timestamp_created": "2024-01-11 15:54:07.411732+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/110022", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "754": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 754, + "name": "Space clamp problems in neurons with voltage-gated conductances (Bar-Yehuda and Korngreen 2008)", + "repository_type": "github", + "summary": "\" ... using numerical simulations, we show that the distortions of voltage-gated K+ and Ca2+ currents are substantial even in neurons with short dendrites. The simulations also demonstrate that passive cable theory cannot be used to justify voltage-clamping of neurons, due to significant shunting to the reversal potential of the voltage-gated conductance during channel activation.\r\n... \"", + "tags": [ + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1307, + "tag": "ModelDB:110560" + } + ], + "timestamp_created": "2024-01-11 15:54:07.962112+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/110560", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "755": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 755, + "name": "Thalamic transformation of pallidal input (Hadipour-Niktarash 2006)", + "repository_type": "github", + "summary": "\"In Parkinson\u2019s disease, neurons of the internal segment of the globus pallidus (GPi) display the low-frequency tremor-related oscillations.\r\n\r\nThese oscillatory activities are transmitted to the thalamic relay nuclei.\r\n\r\nComputer models of the interacting thalamocortical (TC) and thalamic reticular (RE) neurons were used to explore how the TC-RE network processes the low-frequency oscillations of the GPi neurons. ...\"\r\n", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 1308, + "tag": "ModelDB:111870" + } + ], + "timestamp_created": "2024-01-11 15:54:08.454148+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/111870", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "756": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 756, + "name": "Ca-dependent K Channel: kinetics from rat muscle (Moczydlowski, Latorre 1983) XPP", + "repository_type": "github", + "summary": "This is an XPP version of the classic KCa channel from Moczydlowski and Latorre 1983.", + "tags": [ + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1309, + "tag": "ModelDB:111877" + } + ], + "timestamp_created": "2024-01-11 15:54:08.914928+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/111877", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "757": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 757, + "name": "Thalamocortical model of spike and wave seizures (Suffczynski et al. 2004)", + "repository_type": "github", + "summary": "SIMULINK macroscopic model of transitions between normal (spindle) activity and spike and wave (SW) discharges in the thalamocortical network. The model exhibits bistability properties and stochastic fluctuations present in the network may flip the system between the two operational states. The predictions of the model were compared with real EEG data in rats and humans. A possibility to abort an ictal state by a single counter stimulus is suggested by the model. \r\n", + "tags": [ + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 907, + "tag": "Simulink" + }, + { + "id": 803, + "tag": "Spindles" + }, + { + "id": 1310, + "tag": "ModelDB:111880" + } + ], + "timestamp_created": "2024-01-11 15:54:09.367810+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/111880", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "758": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 758, + "name": "Kinetic properties of voltage gated Na channel (Nayak and Sikdar 2007)", + "repository_type": "github", + "summary": "Here we illustrate novel non-linear properties of voltage gated Na+ channel induced by sustained membrane depolarization. In cell-attached patch clamp recordings of rNav1.2 channels expressed in CHO cells, we found complex non-linear changes in the molecular kinetic properties, including channel dwell times and unitary conductance of single Na+ channels that were dependent on the extent of conditioning membrane depolarization.\r\nA \u201cmolecular memory\u201d phenomenon arises at longer depolarization characterized by clusters of dwell time events and strong autocorrelation in dwell times. Hidden Markov Modeling (HMM) ... suggested a possible explanation to the memory phenomenon.\r\nSee paper for more and details.", + "tags": [ + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 919, + "tag": "Yale HMM" + }, + { + "id": 1311, + "tag": "ModelDB:111967" + } + ], + "timestamp_created": "2024-01-11 15:54:09.900312+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/111967", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "759": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 759, + "name": "Properties of aconitine-induced block of KDR current in NG108-15 neurons (Lin et al. 2008)", + "repository_type": "github", + "summary": "\"The effects of aconitine (ACO), a highly toxic alkaloid, on ion currents in differentiated NG108-15 neuronal cells were investigated in this study. ACO (0.3-30 microM) suppressed the amplitude of delayed rectifier K+ current (IK(DR)) in a concentration-dependent manner with an IC50 value of 3.1 microM. The presence of ACO enhanced the rate and extent of IK(DR) inactivation, although it had no effect on the initial activation phase of IK(DR). ... A modeled cell was designed to duplicate its inhibitory effect on spontaneous pacemaking. ... Taken together, the experimental data and simulations show that ACO can block delayed rectifier K+ channels of neurons in a concentration- and state-dependent manner. Changes in action potentials induced by ACO in neurons in vivo can be explained mainly by its blocking actions on IK(DR) and INa.\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 855, + "tag": "I_KHT" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1312, + "tag": "ModelDB:112079" + } + ], + "timestamp_created": "2024-01-11 15:54:10.410412+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/112079", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "760": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 760, + "name": "Carbon nanotubes as electrical interfaces to neurons (Giugliano et al. 2008)", + "repository_type": "github", + "summary": "In the present NEURON model, we explore simple phenomenological models of the extracellular coupling, occurring at the neuron-metal microelectrode junction and (possibly) at the neuron-carbon nanotube junction.", + "tags": [ + { + "id": 810, + "tag": "Extracellular Fields" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1313, + "tag": "ModelDB:112086" + } + ], + "timestamp_created": "2024-01-11 15:54:10.876646+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/112086", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "761": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 761, + "name": "Activity constraints on stable neuronal or network parameters (Olypher and Calabrese 2007)", + "repository_type": "github", + "summary": "\"In this study, we developed a general description of parameter combinations for which specified\r\ncharacteristics of neuronal or network activity are constant.\r\nOur approach is based on the implicit function theorem and is applicable\r\nto activity characteristics that smoothly depend on parameters.\r\nSuch smoothness is often intrinsic to neuronal systems when they are in\r\nstable functional states.\r\nThe conclusions about how parameters compensate each other, developed in this study, can thus be used even\r\nwithout regard to the specific mathematical model describing a particular\r\nneuron or neuronal network. ...\"", + "tags": [ + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1314, + "tag": "ModelDB:112348" + } + ], + "timestamp_created": "2024-01-11 15:54:11.346112+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/112348", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "762": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 762, + "name": "Nigral dopaminergic neurons: effects of ethanol on Ih (Migliore et al. 2008)", + "repository_type": "github", + "summary": "We use a realistic computational model of dopaminergic neurons in vivo to suggest \r\nthat ethanol, through its effects on Ih, modifies the temporal structure of the spiking \r\nactivity. The model predicts that the dopamine level may increase much more during bursting \r\nthan pacemaking activity, especially in those brain regions with a slow dopamine clearance rate. \r\nThe results suggest that a selective pharmacological remedy could thus be devised against the \r\nrewarding effects of ethanol that are postulated to mediate alcohol abuse and addiction, \r\ntargeting the specific HCN genes expressed in dopaminergic neurons.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 920, + "tag": "Alcohol Use Disorder" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 816, + "tag": "Na/K pump" + }, + { + "id": 872, + "tag": "Pathophysiology" + }, + { + "id": 817, + "tag": "Sodium pump" + }, + { + "id": 1315, + "tag": "ModelDB:112359" + } + ], + "timestamp_created": "2024-01-11 15:54:11.958319+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/112359", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "763": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 763, + "name": "CellExcite: an efficient simulation environment for excitable cells (Bartocci et al. 2008)", + "repository_type": "github", + "summary": "\"We have developed CellExcite, a sophisticated simulation environment for excitable-cell networks. CellExcite allows the user to sketch a tissue of excitable cells, plan the stimuli to be applied during simulation, and customize the diffusion model. CellExcite adopts Hybrid Automata (HA) as the computational model in order to efficiently capture both discrete and continuous excitable-cell behavior.\"", + "tags": [ + { + "id": 921, + "tag": "CellExcite" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 1316, + "tag": "ModelDB:112468" + } + ], + "timestamp_created": "2024-01-11 15:54:12.566850+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/112468", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "764": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 764, + "name": "CA1 pyramidal neuron: functional significance of axonal Kv7 channels (Shah et al. 2008)", + "repository_type": "github", + "summary": "The model used in this paper confirmed the experimental findings suggesting that axonal Kv7 channels are critically and uniquely required for determining the inherent spontaneous firing of \r\nhippocampal CA1 pyramids, independently of alterations in synaptic activity. \r\nThe model predicts that the axonal Kv7 density could be 3-5 times that at the soma.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1317, + "tag": "ModelDB:112546" + } + ], + "timestamp_created": "2024-01-11 15:54:13.070419+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/112546", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "765": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 765, + "name": "Spontaneous calcium oscillations in astrocytes (Lavrentovich and Hemkin 2008)", + "repository_type": "github", + "summary": "\" ... We propose here a mathematical model of how spontaneous Ca2+ \r\noscillations arise in astrocytes. This model uses the calcium-induced calcium release and inositol cross-coupling \r\nmechanisms coupled with a receptor-independent method for producing inositol (1,4,5)-trisphosphate as the heart \r\nof the model. By computationally mimicking experimental constraints we have found that this model provides \r\nresults that are qualitatively similar to experiment.\"", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 875, + "tag": "Calcium waves" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1318, + "tag": "ModelDB:112547" + } + ], + "timestamp_created": "2024-01-11 15:54:13.542090+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/112547", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "766": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 766, + "name": "Gap-junction coupled network activity depends on coupled dendrites diameter (Gansert et al. 2007)", + "repository_type": "github", + "summary": "\"... We have previously shown that the amplitude of electrical signals propagating across gap-junctionally coupled passive cables is maximized at a unique diameter. This suggests that threshold-dependent signals may propagate through gap junctions for a finite range of diameters around this optimal value. \r\nHere we examine the diameter dependence of action potential propagation across model networks of dendro-dendritically coupled neurons. The neurons in these models have passive soma and dendrites and an action potential-generating axon. We show that propagation of action potentials across gap junctions occurs only over a finite range of dendritic diameters and that propagation delay depends on this diameter. ...\". See paper for more and details.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 653, + "tag": "Network" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1319, + "tag": "ModelDB:112633" + } + ], + "timestamp_created": "2024-01-11 15:54:14.025430+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/112633", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "767": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 767, + "name": "Cerebellar Golgi cell (Solinas et al. 2007a, 2007b)", + "repository_type": "github", + "summary": "\"... Our results suggest that a complex complement of ionic mechanisms is needed to fine-tune separate aspects of the neuronal response dynamics. Simulations also suggest that the Golgi cell may exploit these mechanisms to obtain a fine regulation of timing of incoming mossy fiber responses and granular layer circuit oscillation and bursting.\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 859, + "tag": "neuroConstruct (web link to model)" + }, + { + "id": 1320, + "tag": "ModelDB:112685" + } + ], + "timestamp_created": "2024-01-11 15:54:14.514003+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/112685", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "768": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 768, + "name": "Afferent Integration in the NAcb MSP Cell (Wolf et al. 2005)", + "repository_type": "github", + "summary": "\"We describe a computational model of the principal cell in the nucleus accumbens (NAcb), the medium spiny projection (MSP) neuron. \r\n\r\nThe model neuron, constructed in NEURON, includes all of the known ionic currents in these cells and receives synaptic input from simulated spike trains via NMDA, AMPA, and GABAA receptors. \r\n\r\n... results suggest that afferent information integration by the NAcb MSP cell may be compromised by pathology in which the NMDA current is altered or modulated, as has been proposed in both schizophrenia and addiction.\"\r\n", + "tags": [ + { + "id": 922, + "tag": "Addiction" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 923, + "tag": "I Krp" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 924, + "tag": "I Q" + }, + { + "id": 925, + "tag": "I R" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 926, + "tag": "Schizophrenia" + }, + { + "id": 1321, + "tag": "ModelDB:112834" + } + ], + "timestamp_created": "2024-01-11 15:54:15.012104+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/112834", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "769": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 769, + "name": "Spike trains in Hodgkin\u2013Huxley model and ISIs of acupuncture manipulations (Wang et al. 2008)", + "repository_type": "github", + "summary": "The Hodgkin-Huxley equations (HH) are parameterized by a number of parameters \r\nand shows a variety of qualitatively different behaviors depending on the \r\nparameter values. Under stimulation of an external periodic voltage, the \r\nISIs (interspike intervals) of a HH model are investigated in this work, \r\nwhile the frequency of the voltage is taken as the controlling parameter. \r\nAs well-known, the science of acupuncture and moxibustion is an important \r\ncomponent of Traditional Chinese Medicine with a long history. Although there \r\nare a number of different acupuncture manipulations, the method for \r\ndistinguishing them is rarely investigated. With the idea of ISI, we study \r\nthe electrical signal time series at the spinal dorsal horn produced by \r\nthree different acupuncture manipulations in Zusanli point and present an \r\neffective way to distinguish them.\r\n", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 927, + "tag": "Complementary and alternative medicine" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1322, + "tag": "ModelDB:112836" + } + ], + "timestamp_created": "2024-01-11 15:54:15.504225+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/112836", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "770": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 770, + "name": "Two-cell inhibitory network bursting dynamics captured in a one-dimensional map (Matveev et al 2007)", + "repository_type": "github", + "summary": "\" ... Here\r\nwe describe a simple method that allows us to investigate\r\nthe existence and stability of anti-phase bursting\r\nsolutions in a network of two spiking neurons, each\r\npossessing a T-type calcium current and coupled by\r\nreciprocal inhibition. \r\nWe derive a one-dimensional map\r\nwhich fully characterizes the genesis and regulation of\r\nanti-phase bursting arising from the interaction of the\r\nT-current properties with the properties of synaptic\r\ninhibition. ...\"", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1323, + "tag": "ModelDB:112914" + } + ], + "timestamp_created": "2024-01-11 15:54:15.969417+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/112914", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "771": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 771, + "name": "Spikes,synchrony,and attentive learning by laminar thalamocort. circuits (Grossberg & Versace 2007)", + "repository_type": "github", + "summary": "\"... The model\r\nhereby clarifies, for the first time, how the following levels of brain organization coexist to realize\r\ncognitive processing properties that regulate fast learning and stable memory of brain representations:\r\nsingle cell properties, such as spiking dynamics, spike-timing-dependent plasticity (STDP), and\r\nacetylcholine modulation; detailed laminar thalamic and cortical circuit designs and their interactions;\r\naggregate cell recordings, such as current-source densities and local field potentials; and single cell and\r\nlarge-scale inter-areal oscillations in the gamma and beta frequency domains. ...\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 928, + "tag": "KInNeSS (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 929, + "tag": "NeuroML (web link to model)" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 883, + "tag": "Spatial Navigation" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 877, + "tag": "Working memory" + }, + { + "id": 911, + "tag": "XML (web link to model)" + }, + { + "id": 1324, + "tag": "ModelDB:112922" + } + ], + "timestamp_created": "2024-01-11 15:54:16.451166+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/112922", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "772": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 772, + "name": "Synchronization by D4 dopamine receptor-mediated phospholipid methylation (Kuznetsova, Deth 2008)", + "repository_type": "github", + "summary": "\"We describe a new molecular mechanism of\r\ndopamine-induced membrane protein modulation that\r\ncan tune neuronal oscillation frequency to attention related\r\ngamma rhythm. This mechanism is based on\r\nthe unique ability of D4 dopamine receptors (D4R)\r\nto carry out phospholipid methylation (PLM) that\r\nmay affect the kinetics of ion channels.\r\nWe show that by deceasing the inertia of the delayed rectifier potassium channel, a transition to 40 Hz oscillations can be achieved. ...\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1325, + "tag": "ModelDB:112968" + } + ], + "timestamp_created": "2024-01-11 15:54:16.947552+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/112968", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "773": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 773, + "name": "Stability of complex spike timing-dependent plasticity in cerebellar learning (Roberts 2007)", + "repository_type": "github", + "summary": "\"Dynamics of spike-timing dependent synaptic\r\nplasticity are analyzed for excitatory and inhibitory synapses\r\nonto cerebellar Purkinje cells. \r\n\r\nThe purpose of this study is\r\nto place theoretical constraints on candidate synaptic learning\r\nrules that determine the changes in synaptic efficacy\r\ndue to pairing complex spikes with presynaptic spikes in\r\nparallel fibers and inhibitory interneurons. \r\n...\"", + "tags": [ + { + "id": 930, + "tag": "Java (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1326, + "tag": "ModelDB:113426" + } + ], + "timestamp_created": "2024-01-11 15:54:17.400455+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/113426", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "774": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 774, + "name": "Computer simulations of neuron-glia interactions mediated by ion flux (Somjen et al. 2008)", + "repository_type": "github", + "summary": "\"...\r\nTo examine the effect of glial K+ uptake, we used a model neuron equipped with Na+, K+, Ca2+ and Cl−\r\nconductances, ion pumps and ion exchangers, surrounded by interstitial space and glia. \r\nThe glial membrane was either \u201cpassive\u201d, incorporating only leak channels and an ion\r\nexchange pump, or it had rectifying K+ channels. We computed ion fluxes, concentration changes and osmotic\r\nvolume changes.\r\n...\r\nWe conclude that voltage gated K+ currents can boost the effectiveness of the glial \u201cpotassium buffer\u201d\r\nand that this buffer function is important even at moderate or low levels of excitation, but especially so in pathological states.\"", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 814, + "tag": "Na/Ca exchanger" + }, + { + "id": 816, + "tag": "Na/K pump" + }, + { + "id": 817, + "tag": "Sodium pump" + }, + { + "id": 1327, + "tag": "ModelDB:113446" + } + ], + "timestamp_created": "2024-01-11 15:54:17.891627+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/113446", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "775": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 775, + "name": "Distributed representation of perceptual categories in the auditory cortex (Kim and Bao 2008)", + "repository_type": "github", + "summary": "Examines the hypothesis that enlargement in cortical stimulus representation is a mechanism of categorical perception. Categorical perception is tested using discrimination and identification ability.", + "tags": [ + { + "id": 849, + "tag": "Audition" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 931, + "tag": "Perceptual Categories" + }, + { + "id": 1328, + "tag": "ModelDB:113459" + } + ], + "timestamp_created": "2024-01-11 15:54:18.356868+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/113459", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "776": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 776, + "name": "Multiple mechanisms of short term plasticity at the calyx of Held (Hennig et al. 2008)", + "repository_type": "github", + "summary": "This is a new model of the short-term dynamics of glutamatergic synaptic transmission, which incorporates multiple mechanisms acting at differing sites and across a range of different time scales (ms to tens of seconds). In the paper, we show that this model can accurately reproduce the experimentally measured time-course of short term depression across different stimulus frequencies at the calyx of Held. The model demonstrates how multiple forms of activity-dependent modulation of release probability and vesicle pool depletion interact, and shows how stimulus-history-dependent recovery from synaptic depression can arise from dynamics on multiple time scales.", + "tags": [ + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + }, + { + "id": 1329, + "tag": "ModelDB:113649" + } + ], + "timestamp_created": "2024-01-11 15:54:18.830151+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/113649", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "777": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 777, + "name": "MEG of Somatosensory Neocortex (Jones et al. 2007)", + "repository_type": "github", + "summary": "\"... To make a direct and principled connection between the SI (somatosensory primary neocortex magnetoencephalography) waveform and underlying neural dynamics, we developed a biophysically realistic\r\ncomputational SI model that contained excitatory and inhibitory neurons in supragranular and infragranular layers. ... our model\r\nprovides a biophysically realistic solution to the MEG signal and can predict the electrophysiological correlates of human perception.\"\r\n", + "tags": [ + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 925, + "tag": "I R" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 932, + "tag": "Magnetoencephalography" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 851, + "tag": "Touch" + }, + { + "id": 1330, + "tag": "ModelDB:113732" + } + ], + "timestamp_created": "2024-01-11 15:54:19.355257+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/113732", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "778": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 778, + "name": "KInNeSS : a modular framework for computational neuroscience (Versace et al. 2008)", + "repository_type": "github", + "summary": "The xml files provided here implement a network of excitatory and inhibitory spiking neurons, governed by either Hodgkin-Huxley or quadratic integrate-and-fire dynamical equations. The code is used to demonstrate the capabilities of the KInNeSS software package for simulation of networks of spiking neurons. The simulation protocol used here is meant to facilitate the comparison of KInNeSS with other simulators reviewed in Brette et al. (2007). See the associated paper \"Versace et al. (2008) KInNeSS : a modular framework for computational neuroscience.\" for an extensive description of KInNeSS .", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 852, + "tag": "I Chloride" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 933, + "tag": "KInNeSS" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 23, + "tag": "NeuroML" + }, + { + "id": 1331, + "tag": "ModelDB:113939" + } + ], + "timestamp_created": "2024-01-11 15:54:19.941483+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/113939", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "779": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 779, + "name": "Dendritic processing of excitatory synaptic input in GnRH neurons (Roberts et al. 2006)", + "repository_type": "github", + "summary": "\"... we used electrophysiological recordings and neuronal reconstructions to generate computer models of (Gonadotopin-Releasing Hormone) GnRH neurons to examine the effects of synaptic inputs at varying distances from the soma along dendrites. ... analysis of reduced morphology models indicated that this population of cells is unlikely to exhibit low-frequency tonic spiking in the absence of synaptic input. ... applying realistic patterns of synaptic input to modeled GnRH neurons indicates that synapses located more than about 30% of the average dendrite length from the soma cannot drive firing at frequencies consistent with neuropeptide release. Thus, processing of synaptic input to dendrites of GnRH neurons is probably more complex than simple summation.\"\r\n", + "tags": [ + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 1332, + "tag": "ModelDB:113949" + } + ], + "timestamp_created": "2024-01-11 15:54:20.441009+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/113949", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "780": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 780, + "name": "Hodgkin-Huxley model of persistent activity in prefrontal cortex neurons (Winograd et al. 2008)", + "repository_type": "github", + "summary": "The paper demonstrate a form of graded persistent activity activated by hyperpolarization. This phenomenon is modeled based on a slow calcium regulation of Ih, similar to that introduced\r\nearlier for thalamic neurons (see Destexhe et al., J Neurophysiol. 1996). The only difference is that the calcium signal is here provided by the high-threshold calcium current (instead of the low-threshold calcium current in thalamic neurons).", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1333, + "tag": "ModelDB:113997" + } + ], + "timestamp_created": "2024-01-11 15:54:20.908337+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/113997", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "781": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 781, + "name": "Hippocampal basket cell gap junction network dynamics (Saraga et al. 2006)", + "repository_type": "github", + "summary": "2 cell network of hippocampal basket cells connected by gap junctions. Paper explores how distal gap junctions and active dendrites can tune network dynamics.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 830, + "tag": "Delay" + }, + { + "id": 871, + "tag": "Electrotonus" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1334, + "tag": "ModelDB:114047" + } + ], + "timestamp_created": "2024-01-11 15:54:21.418472+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114047", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "782": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 782, + "name": "Effect of slowly inactivating IKdr to delayed firing of action potentials (Wu et al. 2008)", + "repository_type": "github", + "summary": "\"The properties of slowly inactivating delayed-rectifier K+ current (IKdr) were investigated in NG108-15 neuronal cells differentiated with long-term exposure to dibutyryl cyclic AMP. \r\n...\r\nThe computer model, in which state-dependent inactivation of IKdr was incorporated, was also implemented to predict the firing behavior present in NG108-15 cells. \r\n...\r\nOur theoretical work and the experimental results led us to propose a pivotal role of slowly inactivating IKdr in delayed firing of APs in NG108-15 cells. The results also suggest that aconitine modulation of IKdr gating is an important molecular mechanism through which it can contribute to neuronal firing.\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 830, + "tag": "Delay" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1335, + "tag": "ModelDB:114108" + } + ], + "timestamp_created": "2024-01-11 15:54:21.907351+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114108", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "783": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 783, + "name": "Systems-level modeling of neuronal circuits for leech swimming (Zheng et al. 2007)", + "repository_type": "github", + "summary": "\"This paper describes a mathematical model of the neuronal central pattern generator (CPG) that controls the rhythmic body motion of the swimming leech. The systems approach is employed to capture the neuronal dynamics essential for generating coordinated oscillations of cell membrane potentials by a simple CPG architecture with a minimal number of parameters. \r\n... parameter estimation leads to predictions regarding the synaptic coupling strength and intrinsic period gradient along the nerve cord, the latter of which agrees qualitatively with experimental observations.\"", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1336, + "tag": "ModelDB:114230" + } + ], + "timestamp_created": "2024-01-11 15:54:22.384904+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114230", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "784": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 784, + "name": "Brette-Gerstner model (Touboul and Brette 2008)", + "repository_type": "github", + "summary": "Brian code to simulate the Brette-Gerstner model and reproduce the figures of Touboul and Brette, Biol Cyber (2008).", + "tags": [ + { + "id": 9, + "tag": "Brian" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 1337, + "tag": "ModelDB:114242" + } + ], + "timestamp_created": "2024-01-11 15:54:22.860460+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114242", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "785": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 785, + "name": "A comparative computer simulation of dendritic morphology (Donohue and Ascoli 2008)", + "repository_type": "github", + "summary": "Morphological aspects of dendritic branching such branch lengths, taper rates,ratios of daughter radii, and bifurcation probabilities are measured from real cells. These morphometrics are then resampled to create virtual trees based on the current branch order, radius, path distance to the soma, or combination of the three.", + "tags": [ + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 864, + "tag": "Java" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1338, + "tag": "ModelDB:114310" + } + ], + "timestamp_created": "2024-01-11 15:54:23.314698+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114310", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "786": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 786, + "name": "Ionic mechanisms of bursting in CA3 pyramidal neurons (Xu and Clancy 2008)", + "repository_type": "github", + "summary": "\"... We present a single-compartment model of a CA3 hippocampal pyramidal neuron based on recent experimental data. We then use the model to determine the roles of primary depolarizing currents in burst generation.\r\nThe single compartment\r\nmodel incorporates accurate representations of sodium (Na+) channels (NaV1.1) and T-type calcium (Ca2+) channel subtypes\r\n(CaV3.1, CaV3.2, and CaV3.3).\r\nOur simulations predict the importance of Na+ and T-type Ca2+ channels in hippocampal\r\npyramidal cell bursting and reveal the distinct contribution of each subtype to burst morphology.\r\nWe also performed fastslow\r\nanalysis in a reduced comparable model, which shows that our model burst is generated as a result of the interaction\r\nof two slow variables, the T-type Ca2+ channel activation gate and the Ca2+-dependent potassium (K+) channel activation\r\ngate.\r\nThe model reproduces a range of experimentally observed phenomena including afterdepolarizing potentials, spike widening at the end of the burst, and rebound.\r\nFinally, we use the model to simulate the effects of two epilepsy-linked\r\nmutations: R1648H in NaV1.1 and C456S in CaV3.2, both of which result in increased cellular excitability.\"\r\n", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1339, + "tag": "ModelDB:114337" + } + ], + "timestamp_created": "2024-01-11 15:54:23.774204+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114337", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "787": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 787, + "name": "Activity dependent changes in dendritic spine density and spine structure (Crook et al. 2007)", + "repository_type": "github", + "summary": "\"... In this work, we extend previous modeling studies [27] by combining a model for activity-dependent spine density with one for\r\ncalcium-mediated spine stem restructuring.\r\n... Additional equations characterize the change in spine density along\r\nthe dendrite, the current balance equation for an individual spine\r\nhead, the change in calcium concentration in the spine head, and the\r\ndynamics of spine stem resistance.\r\n\r\nWe use computational studies to investigate the changes in spine\r\ndensity and structure for differing synaptic inputs and demonstrate\r\nthe effects of these changes on the input-output properties of the\r\ndendritic branch.\r\n... \"", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1340, + "tag": "ModelDB:114342" + } + ], + "timestamp_created": "2024-01-11 15:54:24.243906+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114342", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "788": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 788, + "name": "Homeostatic synaptic plasticity (Rabinowitch and Segev 2006a,b)", + "repository_type": "github", + "summary": "(2006a): \"We investigated analytically and numerically the interplay between two opposing forms of synaptic plasticity: positive-feedback, long-term potentiation/depression (LTP/LTD), and negative-feedback, homeostatic synaptic plasticity (HSP). A detailed model of a CA1 pyramidal neuron, with numerous HSP-modifiable dendritic synapses, demonstrates that HSP may have an important role in selecting which spatial patterns of LTP/LTD are to last.\r\n...\r\nDespite the negative-feedback nature of HSP, under both local and global HSP, numerous synaptic \r\npotentiations/depressions can persist. These experimentally testable results imply that HSP could be significantly involved in shaping the spatial distribution of synaptic weights in the dendrites and not just normalizing it, as is currently believed.\"\r\n(2006b): \"Homeostatic synaptic plasticity (HSP) is an important mechanism attributed with the slow regulation of the neuron's activity. Whenever activity is chronically enhanced, HSP weakens the weights of the synapses in the dendrites and vice versa. Because dendritic morphology and its electrical properties partition the dendritic tree into functional compartments, we set out to explore the interplay between HSP and dendritic compartmentalization.\r\n...\r\nThe spatial distribution of synaptic weights throughout the dendrites will markedly differ under the local versus global HSP mechanisms. We suggest an experimental paradigm to unravel which type of HSP mechanism operates in the dendritic tree. The answer to this question will have important implications to our understanding of the functional organization of the neuron.\"\r\n", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1341, + "tag": "ModelDB:114355" + } + ], + "timestamp_created": "2024-01-11 15:54:24.787852+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114355", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "789": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 789, + "name": "Impact of dendritic size and topology on pyramidal cell burst firing (van Elburg and van Ooyen 2010)", + "repository_type": "github", + "summary": "The code provided here was written to systematically investigate which of the\r\nphysical parameters controlled by dendritic morphology underlies the differences\r\nin spiking behaviour observed in different realizations of the\r\n'ping-pong'-model. Structurally varying dendritic topology and length in a\r\nsimplified model allows us to separate out the physical parameters derived from\r\nmorphology underlying burst firing.\r\nTo perform the parameter scans we created a new NEURON tool the\r\nMultipleRunControl which can be used to easily set up a parameter scan and write\r\nthe simulation results to file.\r\nUsing this code we found that not input conductance but the arrival time of the\r\nreturn current, as measured provisionally by the average electrotonic path\r\nlength, determines whether the pyramidal cell (with ping-pong model dynamics)\r\nwill burst or fire single spikes.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 1342, + "tag": "ModelDB:114359" + } + ], + "timestamp_created": "2024-01-11 15:54:25.329975+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114359", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "790": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 790, + "name": "An allosteric kinetics of NMDARs in STDP (Urakubo et al. 2008)", + "repository_type": "github", + "summary": "\"... We developed a detailed biophysical model of STDP and found\r\nthat the model required spike timing-dependent distinct suppression of NMDARs by Ca2+-calmodulin.\r\n\r\nThis led us to predict an allosteric\r\nkinetics of NMDARs: a slow and rapid suppression of NMDARs by Ca2+-calmodulin with prespiking -> postspiking and postspiking -> prespiking, respectively.\r\n\r\nWe found that the allosteric kinetics, but not the conventional kinetics, is consistent with specific features of\r\namplitudes and peak time of NMDAR-mediated EPSPs in experiments.\r\n...\" See paper for more and details.", + "tags": [ + { + "id": 826, + "tag": "GENESIS (web link to model)" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 1343, + "tag": "ModelDB:114365" + } + ], + "timestamp_created": "2024-01-11 15:54:25.808198+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114365", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "791": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 791, + "name": "Na+ channel dependence of AP initiation in cortical pyramidal neuron (Kole et al. 2008)", + "repository_type": "github", + "summary": "In this simulation action potential initiation, action potential properties and the role of axon initial segment Na+ channels are investigated in a realistic model of a layer 5 pyramidal neuron axon initial segment. The main Na+ channel properties were constrained by experimental data and the axon initial segment was reconstructed. Model parameters were constrained by direct recordings at the axon initial segment.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1344, + "tag": "ModelDB:114394" + } + ], + "timestamp_created": "2024-01-11 15:54:26.280124+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114394", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "792": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 792, + "name": "A simplified model of NMDA oscillations in lamprey locomotor neurons (Huss et al. 2008)", + "repository_type": "github", + "summary": "Using experiments in conjunction with this simplified model, we sought to understand the basic mechanisms behind NMDA-induced oscillations in lamprey locomotor neurons, specifically (a) how the oscillation frequency depends on NMDA concentration and why, and (b) what the minimal number of components for generating NMDA oscillations is (in vitro and in the model).", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1345, + "tag": "ModelDB:114424" + } + ], + "timestamp_created": "2024-01-11 15:54:26.764328+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114424", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "793": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 793, + "name": "Synergistic inhibitory action of oxcarbazepine on INa and IK (Huang et al. 2008)", + "repository_type": "github", + "summary": "\"Oxcarbazepine (OXC), one of the newer anti-epileptic drugs, has been demonstrating its efficacy on wide-spectrum neuropsychiatric disorders. \r\n... With the aid of patch-clamp technology, we first investigated the effects of OXC on ion currents in NG108-15 neuronal cells differentiated with cyclic AMP. \r\n\r\nWe found OXC ... caused a reversible reduction in the amplitude of voltage-gated Na+ current (INa) ...\r\nand produce(d) a significant prolongation in the recovery of INa inactivation. \r\n\r\n...\r\nMoreover, OXC could suppress the amplitude of delayed rectifier K+ current (IK(DR)), with no effect on M-type K+ current (IK(M)). \r\n\r\n...\r\nFurthermore, the simulations, based on hippocampal pyramidal neurons (Pinsky-Rinzel model) and a network of the Hodgkin-Huxley model, were analysed to investigate the effect of OXC on action potentials. \r\n\r\nTaken together, our results suggest that the synergistic blocking effects on INa and IK(DR) may contribute to the underlying mechanisms through which OXC affects neuronal function in vivo.\"\r\n", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1346, + "tag": "ModelDB:114450" + } + ], + "timestamp_created": "2024-01-11 15:54:27.267268+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114450", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "794": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 794, + "name": "Efffect of propofol on potassium current in cardiac H9c2 cells (Liu et al. 2008)", + "repository_type": "github", + "summary": "\"...\r\nThe effects \r\nof propofol, an intravenous anesthetic agent with a distinct chemical structure, \r\non ion currents of differentiated clonal cardiac (H9c2) cells were investigated \r\nin this study. \r\n\r\nPropofol ... suppressed the amplitude of delayed \r\nrectifier K(+) current (I(K(DR))) in a concentration-dependent manner with \r\nan IC(50) value of 36 muM. \r\n...\r\nPropofol (30 muM) had no effect on erg-mediated K(+) \r\ncurrent in these cells; however, it suppressed L-type Ca(2+) current (I(Ca,L)) \r\nof cardiac and skeletal types to a similar extent. \r\n...\r\nNumerical \r\nsimulations of I(K(DR)) based on a Markovian model reproduce the \r\nexperimental results and show that propofol-induced blockade of I(K(DR)) \r\nis associated with an decrease in forward rate of the activation process and \r\nan increase in transitional rate into the inactivated state. \r\n\r\n...\"", + "tags": [ + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 868, + "tag": "I_Ks" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1347, + "tag": "ModelDB:114451" + } + ], + "timestamp_created": "2024-01-11 15:54:27.739364+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114451", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "795": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 795, + "name": "CaMKII system exhibiting bistability with respect to calcium (Graupner and Brunel 2007)", + "repository_type": "github", + "summary": "\"... We present a detailed biochemical model of the CaMKII autophosphorylation and the protein signaling cascade governing the CaMKII dephosphorylation.\r\n... it is shown that the CaMKII system can qualitatively reproduce results of plasticity outcomes in response to spike-timing dependent plasticity (STDP) and presynaptic stimulation protocols.\r\nThis shows that the CaMKII protein network can account for both induction, through LTP/LTD-like transitions, and storage, due to its bistability, of synaptic changes.\"", + "tags": [ + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1348, + "tag": "ModelDB:114452" + } + ], + "timestamp_created": "2024-01-11 15:54:28.248869+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114452", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "796": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 796, + "name": "Contrast invariance by LGN synaptic depression (Banitt et al. 2007)", + "repository_type": "github", + "summary": "\"Simple cells in layer 4 of the primary visual cortex of the cat show contrast-invariant orientation tuning, in which the amplitude of the\r\npeak response is proportional to the stimulus contrast but the width of the tuning curve hardly changes with contrast. \r\n\r\nThis study uses a\r\ndetailed model of spiny stellate cells (SSCs) from cat area 17 to explain this property. \r\n\r\nThe model integrates our experimental data,\r\nincluding morphological and intrinsic membrane properties and the number and spatial distribution of four major synaptic input\r\nsources of the SSC: the dorsal lateral geniculate nucleus (dLGN) and three cortical sources. \r\n\r\n...\r\n\r\nThe model response is in close\r\nagreement with experimental results, in terms of both output spikes and membrane voltage (amplitude and fluctuations), with reasonable\r\nexceptions given that recurrent connections were not incorporated.\"", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 800, + "tag": "Vision" + }, + { + "id": 1349, + "tag": "ModelDB:114637" + } + ], + "timestamp_created": "2024-01-11 15:54:28.740503+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114637", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "797": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 797, + "name": "Globus pallidus multi-compartmental model neuron with realistic morphology (Gunay et al. 2008)", + "repository_type": "github", + "summary": "\"Globus pallidus (GP) neurons recorded in brain slices show significant variability in intrinsic electrophysiological properties. \r\n\r\nTo investigate how this variability arises, we manipulated the biophysical properties of GP neurons using computer simulations. \r\n\r\n\r\n...\r\nOur results indicated that most of the experimental variability could be matched by varying conductance densities, which we confirmed with additional partial block experiments. \r\n\r\nFurther analysis resulted in two key observations: (1) each voltage-gated conductance had effects on multiple measures such as action potential waveform and spontaneous or stimulated spike rates; and (2) the effect of each conductance was highly dependent on the background context of other conductances present. \r\n\r\nIn some cases, such interactions could reverse the effect of the density of one conductance on important excitability measures. \r\n...\"", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 869, + "tag": "KCNQ1" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 1350, + "tag": "ModelDB:114639" + } + ], + "timestamp_created": "2024-01-11 15:54:29.239312+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114639", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "798": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 798, + "name": "High-Res. Recordings Using a Real-Time Computational Model of the Electrode (Brette et al. 2008)", + "repository_type": "github", + "summary": "\"Intracellular recordings of neuronal membrane potential are a central tool in neurophysiology.\r\n...\r\nWe introduce a computer-aided technique, Active Electrode Compensation (AEC), based on a digital model of the electrode interfaced in real time with the electrophysiological setup. \r\n...\r\nAEC should be particularly useful to characterize fast neuronal phenomena intracellularly in vivo.\"", + "tags": [ + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 819, + "tag": "NEURON (web link to model)" + }, + { + "id": 832, + "tag": "Python (web link to model)" + }, + { + "id": 934, + "tag": "Scilab (web link to model)" + }, + { + "id": 1351, + "tag": "ModelDB:114643" + } + ], + "timestamp_created": "2024-01-11 15:54:29.711698+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114643", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "799": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 799, + "name": "Axonal gap junctions produce fast oscillations in cerebellar Purkinje cells (Traub et al. 2008)", + "repository_type": "github", + "summary": "Examines how electrical coupling between proximal axons produces fast oscillations in cerebellar Purkinje cells.\r\nTraub RD, Middleton SJ, Knopfel T, Whittington MA (2008) Model of very fast (>75 Hz) network oscillations generated by electrical coupling between the proximal axons of cerebellar Purkinje cells. European Journal of Neuroscience.", + "tags": [ + { + "id": 756, + "tag": "FORTRAN" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 925, + "tag": "I R" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 818, + "tag": "I p,q" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1352, + "tag": "ModelDB:114654" + } + ], + "timestamp_created": "2024-01-11 15:54:30.406665+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114654", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "800": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 800, + "name": "Laminar connectivity matrix simulation (Weiler et al 2008)", + "repository_type": "github", + "summary": "A routine that simulates the flow of activity within and across laminar levels in the local pyramidal neuron network, based on a connectivity matrix (W) measured by laser scanning photostimulation in mouse somatic motor cortex, and a very simple neural network simulation.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 935, + "tag": "Connectivity matrix" + }, + { + "id": 936, + "tag": "Laminar Connectivity" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1353, + "tag": "ModelDB:114655" + } + ], + "timestamp_created": "2024-01-11 15:54:30.983388+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114655", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "801": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 801, + "name": "Olfactory bulb mitral and granule cell column formation (Migliore et al. 2007)", + "repository_type": "github", + "summary": "In the olfactory bulb, the processing units for odor discrimination are believed \r\nto involve dendrodendritic synaptic interactions between mitral and granule cells. \r\nThere is increasing anatomical evidence that these cells are organized in columns, \r\nand that the columns processing a given odor are arranged in widely distributed arrays. \r\nExperimental evidence is lacking on the underlying learning mechanisms for how these \r\ncolumns and arrays are formed. \r\nWe have used a simplified realistic circuit model to test the hypothesis that \r\ndistributed connectivity can self-organize through an activity-dependent dendrodendritic \r\nsynaptic mechanism. \r\nThe results point to action potentials propagating in the mitral cell lateral dendrites \r\nas playing a critical role in this mechanism, and suggest a novel and robust learning \r\nmechanism for the development of distributed processing units in a cortical structure.\r\n", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 908, + "tag": "Learning" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1354, + "tag": "ModelDB:114665" + } + ], + "timestamp_created": "2024-01-11 15:54:31.453310+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114665", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "802": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 802, + "name": "GPi/GPe neuron models (Johnson and McIntyre 2008)", + "repository_type": "github", + "summary": "Model files for two types of non-human primate neurons used in the paper: simplified versions of 1) a GPi neuron and 2) a GPe axon collateralizing in GPi en route to STN.", + "tags": [ + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 810, + "tag": "Extracellular Fields" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1355, + "tag": "ModelDB:114685" + } + ], + "timestamp_created": "2024-01-11 15:54:31.930249+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114685", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "803": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 803, + "name": "High frequency oscillations induced in three gap-junction coupled neurons (Tseng et al. 2008)", + "repository_type": "github", + "summary": "Here we showed experimentally that high frequency oscillations (up to 600 Hz) were easily induced in a purely gap-junction coupled network by simple two stimuli with very short interval. The root cause is that the second elicited spike suffered from slow propagation speed and failure to transmit through a low-conductance junction. Similiar results were also obtained in these simulation.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 821, + "tag": "Conduction failure" + }, + { + "id": 830, + "tag": "Delay" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1356, + "tag": "ModelDB:114735" + } + ], + "timestamp_created": "2024-01-11 15:54:32.425610+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/114735", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "804": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 804, + "name": "Axonal NaV1.6 Sodium Channels in AP Initiation of CA1 Pyramidal Neurons (Royeck et al. 2008)", + "repository_type": "github", + "summary": "\"...\r\n\r\nWe show that the Na+ channel NaV1.6 displays a striking aggregation at the AIS\r\nof cortical neurons.\r\n\r\n\r\n...\r\nIn combination with simulations using a realistic\r\ncomputer model of a CA1 pyramidal cell, our results imply that a hyperpolarized\r\nvoltage-dependence of activation of AIS NaV1.6 channels is important both in\r\ndetermining spike threshold and localizing spike initiation to the AIS. \r\n\r\n...\r\nThese results suggest that NaV1.6 subunits at the AIS contribute significantly to\r\nits role as spike trigger zone and shape repetitive discharge properties of CA1 neurons.\"\r\n", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 924, + "tag": "I Q" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 818, + "tag": "I p,q" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1357, + "tag": "ModelDB:115356" + } + ], + "timestamp_created": "2024-01-11 15:54:32.946859+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/115356", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "805": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 805, + "name": "Generalized Carnevale-Hines algorithm (van Elburg and van Ooyen 2009)", + "repository_type": "github", + "summary": "Demo illustrating the behaviour of the integrate-and-fire model in the parameter regime relevant for the generalized event-based Carnevale-Hines integration scheme. The demo includes the improved implementation of the IntFire4 mechanism.\r\n", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1358, + "tag": "ModelDB:115357" + } + ], + "timestamp_created": "2024-01-11 15:54:33.419656+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/115357", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "806": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 806, + "name": "Human Attentional Networks: A Connectionist Model (Wang and Fan 2007)", + "repository_type": "github", + "summary": "\"... We describe a connectionist model of human attentional networks to explore the\r\npossible interplays among the networks from a computational\r\nperspective. This model is developed in the framework of\r\nleabra (local, error-driven, and associative, biologically realistic\r\nalgorithm) and simultaneously involves these attentional networks\r\nconnected in a biologically inspired way. ...\r\n\r\n\r\nWe evaluate the model by simulating the empirical data collected on normal human\r\nsubjects using the Attentional Network Test (ANT).\r\n\r\nThe simulation results fit the experimental data well.\r\n\r\nIn addition, we show that the same model, with a single parameter change that\r\naffects executive control, is able to simulate the empirical data collected\r\nfrom patients with schizophrenia.\r\n\r\nThis model represents a plausible connectionist explanation for the functional structure\r\nand interaction of human attentional networks.\"\r\n", + "tags": [ + { + "id": 857, + "tag": "Action Selection/Decision Making" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 890, + "tag": "Emergent/PDPplusplus" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 926, + "tag": "Schizophrenia" + }, + { + "id": 905, + "tag": "Winner-take-all" + }, + { + "id": 1359, + "tag": "ModelDB:115813" + } + ], + "timestamp_created": "2024-01-11 15:54:33.904091+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/115813", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "807": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 807, + "name": "Neural modeling of an internal clock (Yamazaki and Tanaka 2008)", + "repository_type": "github", + "summary": "\"We studied a simple random recurrent inhibitory network. Despite its simplicity, the dynamics was so rich that activity patterns of neurons\r\nevolved with time without recurrence due to random recurrent connections\r\namong neurons. The sequence of activity patterns was generated\r\nby the trigger of an external signal, and the generation was stable against\r\nnoise.... Therefore, a\r\ntime passage from the trigger of an external signal could be represented by\r\nthe sequence of activity patterns, suggesting that this model could work\r\nas an internal clock. ...\"", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1360, + "tag": "ModelDB:115966" + } + ], + "timestamp_created": "2024-01-11 15:54:34.395547+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/115966", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "808": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 808, + "name": "Polychronization: Computation With Spikes (Izhikevich 2005)", + "repository_type": "github", + "summary": "\"We present a minimal spiking network that can polychronize, that is,\r\nexhibit reproducible time-locked but not synchronous firing patterns\r\nwith millisecond precision, as in synfire braids. \r\n\r\nThe network consists\r\nof cortical spiking neurons with axonal conduction delays and spiketiming-\r\ndependent plasticity (STDP); a ready-to-use MATLAB code is\r\nincluded. \r\n\r\nIt exhibits sleeplike oscillations, gamma (40 Hz) rhythms,\r\nconversion of firing rates to spike timings, and other interesting regimes.\r\n\r\n...\r\nTo our surprise, the number of\r\ncoexisting polychronous groups far exceeds the number of neurons in\r\nthe network, resulting in an unprecedented memory capacity of the\r\nsystem. \r\n\r\n...\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1361, + "tag": "ModelDB:115968" + } + ], + "timestamp_created": "2024-01-11 15:54:34.881509+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/115968", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "809": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 809, + "name": "Axonal Projection and Interneuron Types (Helmstaedter et al. 2008)", + "repository_type": "github", + "summary": "\"Interneurons in layer 2/3 (L2/3) of the somatosensory cortex show\r\n4 types of axonal projection patterns with reference to the laminae\r\nand borders of columns in rat barrel cortex (Helmstaedter et al.\r\n2008a). \r\n\r\nHere, we analyzed the dendritic geometry and electrical\r\nexcitability of these interneurons. \r\n\r\n...\r\nWe conclude that\r\n1) dendritic polarity is correlated to intrinsic electrical excitability,\r\nand 2) the axonal projection pattern represents an independent\r\nclassifier of interneurons.\r\n\"", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 819, + "tag": "NEURON (web link to model)" + }, + { + "id": 1362, + "tag": "ModelDB:116053" + } + ], + "timestamp_created": "2024-01-11 15:54:35.369465+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116053", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "810": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 810, + "name": "CA1 pyramidal neuron synaptic integration (Jarsky et al. 2005)", + "repository_type": "github", + "summary": "\"The perforant-path projection to the hippocampus forms synapses in the apical tuft of CA1 pyramidal neurons. \r\n\r\nWe used computer modeling to examine the function of these distal synaptic inputs, which led to three predictions that we confirmed in experiments using rat hippocampal slices. \r\n\r\n...\r\n\r\nThis 'gating' of dendritic spike propagation may be an important activation mode of CA1 pyramidal neurons, and its modulation by neurotransmitters or long-term, activity-dependent plasticity may be an important feature of dendritic integration during mnemonic processing in the hippocampus.\"\r\n", + "tags": [ + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 819, + "tag": "NEURON (web link to model)" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 1363, + "tag": "ModelDB:116084" + } + ], + "timestamp_created": "2024-01-11 15:54:35.839707+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116084", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "811": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 811, + "name": "Code to calc. spike-trig. ave (STA) conduct. from Vm (Pospischil et al. 2007, Rudolph et al. 2007)", + "repository_type": "github", + "summary": "PYTHON code to calculate spike-triggered average (STA) conductances from intracellular recordings, according to the method published by Pospischil et al., J Neurophysiol, 2007. The method consists of a maximum likelihood estimate of the conductance STA, from the voltage STA (which is calculated from the data). The method was tested using models and dynamic-clamp experiments; for details, see the original publication (Pospischil et al., 2007). The first application of this method to experimental data was from intracellular recordings in awake cat cerebral cortex (Rudolph et al., 2007).", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 802, + "tag": "Sleep" + }, + { + "id": 1364, + "tag": "ModelDB:116086" + } + ], + "timestamp_created": "2024-01-11 15:54:36.305812+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116086", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "812": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 812, + "name": "Lateral dendrodenditic inhibition in the Olfactory Bulb (David et al. 2008)", + "repository_type": "github", + "summary": "Mitral cells, the principal output neurons of the olfactory bulb, receive direct synaptic activation from primary sensory neurons. Shunting inhibitory inputs delivered by granule cell interneurons onto mitral cell lateral dendrites are believed to influence spike timing and underlie coordinated field potential oscillations. Lateral dendritic shunt conductances delayed spiking to a degree dependent on both their electrotonic distance and phase of onset. Recurrent inhibition significantly narrowed the distribution of mitral cell spike times, illustrating a tendency towards coordinated synchronous activity. This result suggests an essential role for early mechanisms of temporal coordination in olfaction. The model was adapted from Davison et al, 2003, but include additional noise mechanisms, long lateral dendrite, and specific synaptic point processes.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1365, + "tag": "ModelDB:116094" + } + ], + "timestamp_created": "2024-01-11 15:54:36.781512+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116094", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "813": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 813, + "name": "Biophysical and phenomenological models of spike-timing dependent plasticity (Badoual et al. 2006)", + "repository_type": "github", + "summary": "\"Spike-timing dependent plasticity (STDP) is a form of associative synaptic modification which depends\r\non the respective timing of pre- and post-synaptic spikes. \r\n\r\nThe biophysical mechanisms underlying this\r\nform of plasticity are currently not known. \r\n\r\nWe present here a biophysical model which captures the\r\ncharacteristics of STDP, such as its frequency dependency, and the effects of spike pair or spike triplet\r\ninteractions. \r\n...\r\nA simplified phenomenological\r\nmodel is also derived...\"", + "tags": [ + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1366, + "tag": "ModelDB:116096" + } + ], + "timestamp_created": "2024-01-11 15:54:37.256683+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116096", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "814": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 814, + "name": "Dynamics of Spike Initiation (Prescott et al. 2008)", + "repository_type": "github", + "summary": "\"Transduction of graded synaptic input into trains of all-or-none action potentials (spikes) is a crucial step in neural coding.\r\n\r\nHodgkin identified three classes of neurons with qualitatively different analog-to-digital transduction properties. \r\n\r\nDespite widespread use of this classification scheme, a generalizable explanation of its biophysical basis has not been described. \r\n\r\nWe recorded from spinal sensory neurons representing each class and reproduced their transduction properties in a minimal\r\nmodel. With phase plane and bifurcation analysis, each class of excitability was shown to derive from distinct spike initiating dynamics. \r\n\r\nExcitability could be converted between all three classes by varying single parameters; moreover, several\r\nparameters, when varied one at a time, had functionally equivalent effects on excitability. From this, we conclude that the\r\nspike-initiating dynamics associated with each of Hodgkin\u2019s classes represent different outcomes in a nonlinear competition\r\nbetween oppositely directed, kinetically mismatched currents. ...\"", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 887, + "tag": "Bifurcation" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 904, + "tag": "Sensory coding" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1367, + "tag": "ModelDB:116123" + } + ], + "timestamp_created": "2024-01-11 15:54:37.748957+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116123", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "815": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 815, + "name": "Multimodal stimuli learning in hawkmoths (Balkenius et al. 2008)", + "repository_type": "github", + "summary": "The moth Macroglossum stellatarum can learn the color and sometimes the odor of a rewarding food\r\nsource.\r\n\r\nWe present data from 20 different experiments with different combinations of blue and yellow\r\nartificial flowers and the two odors, honeysuckle and lavender. \r\n\r\n... \r\nThree computational models were tested in the same experimental situations as the real\r\nmoths and their predictions were compared with the experimental data. \r\n\r\n...\r\nNeither the\r\nRescorla\u2013Wagner model nor a learning model with independent learning for each stimulus component\r\nwere able to explain the experimental data. \r\n\r\nWe present the new hawkmoth learning model, which\r\nassumes that the moth learns a template for the sensory attributes of the rewarding stimulus. \r\n\r\nThis\r\nmodel produces behavior that closely matches that of the real moth in all 20 experiments.", + "tags": [ + { + "id": 857, + "tag": "Action Selection/Decision Making" + }, + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 908, + "tag": "Learning" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1368, + "tag": "ModelDB:116312" + } + ], + "timestamp_created": "2024-01-11 15:54:38.277313+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116312", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "816": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 816, + "name": "A Model of Selection between Stimulus and Place Strategy in a Hawkmoth (Balkenius et al. 2004)", + "repository_type": "github", + "summary": "\"In behavioral experiments, the hawkmoth Deilephila elpenor can learn both the \r\ncolor and the position of artificial flowers.\r\n\r\n...\r\n\r\nWe show how a computational model can reproduce the behavior in the experimental situation.\r\n\r\nThe aim of the model is to investigate which learning and behavior selection strategies are \r\nnecessary to reproduce the behavior observed in the experiment.\r\n\r\nThe model is based on behavioral data and the sensitivities of the moth photoreceptors. \r\n\r\nThe model consists of a number of interacting behavior systems that are triggered by \r\nspecific stimuli and control specific behaviors. \r\n\r\nThe ability of the moth to learn the colors of different flowers and the adaptive processes involved \r\nin the choice between stimulus-approach and place-approach strategies are reproduced very accurately by the model. \r\nThe model has implications both for further studies of the ecology of the animal and for robotic systems.\"\r\n", + "tags": [ + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 908, + "tag": "Learning" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1369, + "tag": "ModelDB:116313" + } + ], + "timestamp_created": "2024-01-11 15:54:38.736611+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116313", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "817": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 817, + "name": "Pyramidal neurons switch from integrators to resonators (Prescott et al. 2008)", + "repository_type": "github", + "summary": "During wakefulness, pyramidal neurons in the intact brain are bombarded by synaptic\r\ninput that causes tonic depolarization, increased membrane conductance (i.e. shunting), and\r\nnoisy fluctuations in membrane potential; by comparison, pyramidal neurons in acute slices\r\ntypically experience little background input.\r\n\r\nSuch differences in operating conditions can compromise extrapolation of in vitro data to explain \r\nneuronal operation in vivo.\r\n\r\n...\r\n\r\n in slice experiments, we show that\r\nCA1 hippocampal pyramidal cells switch from integrators to resonators, i.e. from class 1 to class\r\n2 excitability.\r\n\r\nThe switch is explained by increased outward current contributed by the M-type\r\npotassium current IM\r\n...\r\nThus, even so-called \u201cintrinsic\u201d properties may differ qualitatively between in vitro and in vivo conditions.", + "tags": [ + { + "id": 887, + "tag": "Bifurcation" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1370, + "tag": "ModelDB:116386" + } + ], + "timestamp_created": "2024-01-11 15:54:39.203894+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116386", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "818": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 818, + "name": "NEURON + Python (Hines et al. 2009)", + "repository_type": "github", + "summary": "The NEURON simulation program now allows Python to be used alone or in\r\ncombination with NEURON's traditional Hoc interpreter. Adding Python to\r\nNEURON has the immediate benefit of making available a very extensive\r\nsuite of analysis tools written for engineering and science. It also\r\ncatalyzes NEURON software development by offering users a modern programming\r\ntool that is recognized for its flexibility and power to create and \r\nmaintain complex programs. At the same time, nothing is lost because\r\nall existing models written in Hoc, including GUI tools, continue to\r\nwork without change and are also available within the Python context.\r\nAn example of the benefits of Python availability is the use of the xml\r\nmodule in implementing NEURON's Import3D and CellBuild tools to read MorphML and\r\nNeuroML model specifications.", + "tags": [ + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 23, + "tag": "NeuroML" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 1371, + "tag": "ModelDB:116491" + } + ], + "timestamp_created": "2024-01-11 15:54:39.660659+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116491", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "819": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 819, + "name": "Modulation of septo-hippocampal theta activity by GABAA receptors (Hajos et al. 2004)", + "repository_type": "github", + "summary": "Theta frequency oscillation of the septo-hippocampal system has been considered as a prominent activity associated with cognitive function and affective processes.\r\n\r\n...\r\n\r\nIn the present experiments we applied a combination of computational and physiological techniques to explore the functional role of GABAA receptors in theta oscillation.\r\n\r\n...\r\n\r\nIn parallel to these experimental observations, a computational model has been constructed by implementing a septal GABA neuron model with a CA1 hippocampal model containing three types of neurons (including oriens and basket interneurons and pyramidal cells; latter modeled by multicompartmental techniques; for detailed model description with network parameters see online addendum: http://geza.kzoo.edu/theta).\r\n\r\nThis connectivity made the network capable of simulating the responses of the septo-hippocampal circuitry to the modulation of GABAA transmission, and the presently described computational model proved suitable to reveal several aspects of pharmacological modulation of GABAA receptors.\r\n\r\n In addition, computational findings indicated different roles of distinctively located GABAA receptors in theta generation.", + "tags": [ + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1372, + "tag": "ModelDB:116567" + } + ], + "timestamp_created": "2024-01-11 15:54:40.273792+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116567", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "820": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 820, + "name": "A Model of Multiple Spike Initiation Zones in the Leech C-interneuron (Crisp 2009)", + "repository_type": "github", + "summary": "The leech C-interneuron and its electrical synapse with the S-interneuron exhibit unusual properties: an asymmetric delay when impulses travel from one soma to the other, and graded C-interneuron impulse amplitudes under elevated divalent cation concentrations. These properties have been simulated using a SNNAP model in which the C-interneuron has multiple, independent spike initiation zones associated with individual electrical junctions with the C-interneuron.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 1373, + "tag": "ModelDB:116575" + } + ], + "timestamp_created": "2024-01-11 15:54:40.846609+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116575", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "821": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 821, + "name": "Currents contributing to decision making in neurons B31-B32 of Aplysia (Hurwitz et al. 2008)", + "repository_type": "github", + "summary": "\"Biophysical properties of neurons contributing to the ability of an animal to decide whether or not to respond were examined. \r\n\r\nB31/B32, two pairs of bilaterally symmetrical Aplysia neurons, are major participants in deciding to initiate a buccal motor program, the neural correlate of a consummatory feeding response. \r\n\r\nB31/B32 respond to an adequate stimulus after a delay, during which time additional stimuli influence the decision to respond. \r\n\r\nB31/B32 then respond with a ramp depolarization followed by a sustained soma depolarization and axon spiking that is the expression of a commitment to respond to food. \r\n\r\nFour currents contributing to decision making in B31/B32 were characterized, and their functional effects were determined, in current- and voltage-clamp experiments and with simulations. ...\r\n\r\nHodgkin-Huxley kinetic analyses were performed on the outward currents. \r\n\r\nSimulations using equations from these analyses showed that IK-V and IK-A slow the ramp depolarization preceding the sustained depolarization. \r\n\r\nThe three outward currents contribute to braking the B31/B32 depolarization and keeping the sustained depolarization at a constant voltage. \r\n\r\nThe currents identified are sufficient to explain the properties of B31/B32 that play a role in generating the decision to feed.\"", + "tags": [ + { + "id": 857, + "tag": "Action Selection/Decision Making" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 844, + "tag": "SNNAP" + }, + { + "id": 1374, + "tag": "ModelDB:116606" + } + ], + "timestamp_created": "2024-01-11 15:54:41.319320+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116606", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "822": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 822, + "name": "Dentate gyrus granule cell: calcium and calcium-dependent conductances (Aradi and Holmes 1999)", + "repository_type": "github", + "summary": "We have constructed a detailed model of a hippocampal dentate granule (DG) cell that includes nine different channel types. \r\nChannel densities and distributions were chosen to reproduce reported physiological responses observed in normal solution and when blockers were applied. \r\n\r\nThe model was used to explore the contribution of each channel type to spiking behavior with particular emphasis on the mechanisms underlying postspike events. \r\n\r\n...\r\nThe model was used to predict changes in channel densities that could lead to epileptogenic burst discharges and to predict the effect of altered buffering capacity on firing behavior. \r\n\r\nWe conclude that the clustered spatial distributions of calcium related channels, the presence of slow delayed rectifier potassium currents in dendrites, and calcium buffering properties, together, might explain the resistance of DG cells to the development of epileptogenic burst discharges.", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1375, + "tag": "ModelDB:116740" + } + ], + "timestamp_created": "2024-01-11 15:54:41.790325+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116740", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "823": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 823, + "name": "Spine neck plasticity controls postsynaptic calcium signals (Grunditz et al. 2008)", + "repository_type": "github", + "summary": "This model was set up to dissect the relative contribution of different channels to \r\nthe spine calcium transients measured at single spines.\r\n", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 925, + "tag": "I R" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1376, + "tag": "ModelDB:116769" + } + ], + "timestamp_created": "2024-01-11 15:54:42.253924+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116769", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "824": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 824, + "name": "Robust Reservoir Generation by Correlation-Based Learning (Yamazaki & Tanaka 2008)", + "repository_type": "github", + "summary": "\"Reservoir computing (RC) is a new framework for neural computation. \r\nA reservoir is usually a recurrent neural network with fixed random connections. \r\n\r\nIn this article, we propose an RC model in which the connections in the reservoir are modifiable. \r\n\r\n...\r\nWe apply our RC model to trace eyeblink conditioning. \r\n\r\nThe reservoir bridged the gap of an interstimulus interval between the conditioned and unconditioned stimuli, and a readout neuron was able to learn and express the timed conditioned response.\"\r\n", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 908, + "tag": "Learning" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1377, + "tag": "ModelDB:116806" + } + ], + "timestamp_created": "2024-01-11 15:54:42.711463+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116806", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "825": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 825, + "name": "Broadening of activity with flow across neural structures (Lytton et al. 2008)", + "repository_type": "github", + "summary": "\"Synfire chains have long been suggested as a substrate for perception and information processing in the nervous system. \r\n\r\nHowever, embedding activation chains in a densely connected nervous matrix risks spread of signal that will obscure or obliterate the message. \r\n\r\nWe used computer modeling and physiological measurements in rat hippocampus to assess this problem of activity broadening.\r\n\r\nWe simulated a series of neural modules with feedforward propagation and random connectivity within each module and from one module to the next. ...\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1378, + "tag": "ModelDB:116830" + } + ], + "timestamp_created": "2024-01-11 15:54:43.160562+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116830", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "826": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 826, + "name": "Multicompartmental cerebellar granule cell model (Diwakar et al. 2009)", + "repository_type": "github", + "summary": "A detailed multicompartmental model was used to study neuronal electroresponsiveness of cerebellar granule cells in rats. Here we show that, in cerebellar granule cells, Na+ channels are enriched in the axon, especially in the hillock, but almost absent from soma and dendrites. Numerical simulations indicated that granule cells have a compact electrotonic structure allowing EPSPs to diffuse with little attenuation from dendrites to axon. The spike arose almost simultaneously along the whole axonal ascending branch and invaded the hillock, whose activation promoted spike back-propagation with marginal delay (<200 micros) and attenuation (<20 mV) into the somato-dendritic compartment. For details check the cited article.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 850, + "tag": "Intrinsic plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1379, + "tag": "ModelDB:116835" + } + ], + "timestamp_created": "2024-01-11 15:54:43.662245+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116835", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "827": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 827, + "name": "Reward modulated STDP (Legenstein et al. 2008)", + "repository_type": "github", + "summary": "\"...\r\nThis article provides tools for an analytic treatment of reward-modulated\r\nSTDP, which allows us to predict under which conditions reward-modulated STDP will achieve a desired learning\r\neffect.\r\n\r\nThese analytical results imply that neurons can learn through reward-modulated STDP to classify not only spatial but\r\nalso temporal firing patterns of presynaptic neurons. \r\n\r\nThey also can learn to respond to specific presynaptic firing patterns\r\nwith particular spike patterns. \r\n\r\nFinally, the resulting learning theory predicts that even difficult credit-assignment problems,\r\nwhere it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system,\r\ncan be solved in a self-organizing manner through reward-modulated STDP. \r\n\r\nThis yields an explanation for a fundamental\r\nexperimental result on biofeedback in monkeys by Fetz and Baker. \r\n\r\nIn this experiment monkeys were rewarded for\r\nincreasing the firing rate of a particular neuron in the cortex and were able to solve this extremely difficult credit assignment\r\nproblem. \r\n...\r\nIn addition our model\r\ndemonstrates that reward-modulated STDP can be applied to all synapses in a large recurrent neural network without\r\nendangering the stability of the network dynamics.\"", + "tags": [ + { + "id": 937, + "tag": "Biofeedback" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 938, + "tag": "PCSIM" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 892, + "tag": "Reinforcement Learning" + }, + { + "id": 939, + "tag": "Reward-modulated STDP" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 1380, + "tag": "ModelDB:116837" + } + ], + "timestamp_created": "2024-01-11 15:54:44.150171+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116837", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "828": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 828, + "name": "The virtual slice setup (Lytton et al. 2008)", + "repository_type": "github", + "summary": "\"In an effort to design a simulation environment that is more similar to that of neurophysiology, we introduce a virtual slice setup in the NEURON simulator. \r\n\r\nThe virtual slice setup runs continuously and permits parameter changes, including changes to synaptic weights and time course and to intrinsic cell properties. \r\n\r\nThe virtual slice setup permits shocks to be applied at chosen locations and activity to be sampled intra- or extracellularly from chosen locations. ...\"", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1381, + "tag": "ModelDB:116838" + } + ], + "timestamp_created": "2024-01-11 15:54:44.612169+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116838", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "829": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 829, + "name": "Thalamic interneuron multicompartment model (Zhu et al. 1999)", + "repository_type": "github", + "summary": "This is an attempt to recreate a set of simulations originally performed in 1994 under NEURON version 3 and last tested in 1999. When I ran it now it did not behave exactly the same as previously which I suspect is due to some minor mod file changes on my side rather than due to any differences among versions. \r\n\r\nAfter playing around with the parameters a little bit I was able to get something that looks generally like a physiological trace in \r\nJ Neurophysiol, 81:702--711, 1999, fig. 8b top trace. \r\n\r\nThis sad preface is simply offered in order to encourage anyone who is interested in this model to make and post fixes. I'm happy to help out.\r\n\r\nSimulation by JJ Zhu\r\n\r\nTo run\r\nnrnivmodl\r\nnrngui.hoc\r\n\r\n", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1382, + "tag": "ModelDB:116862" + } + ], + "timestamp_created": "2024-01-11 15:54:45.075278+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116862", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "830": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 830, + "name": "High frequency stimulation of the Subthalamic Nucleus (Rubin and Terman 2004)", + "repository_type": "github", + "summary": "\" ... Using a computational model, this paper considers the hypothesis that DBS works by replacing pathologically rhythmic\r\nbasal ganglia output with tonic, high frequency firing.\r\n\r\nIn our simulations of parkinsonian conditions, rhythmic inhibition from GPi to the thalamus compromises the ability of thalamocortical relay (TC) cells to respond to depolarizing inputs, such as sensorimotor signals.\r\n\r\nHigh frequency stimulation of STN regularizes GPi firing, and this restores TC responsiveness, despite the increased frequency and amplitude of GPi inhibition to thalamus that result.\r\n\r\nWe provide a mathematical phase plane analysis of the mechanisms that determine TC relay capabilities in\r\nnormal, parkinsonian, and DBS states in a reduced model.\r\n\r\nThis analysis highlights the differences in deinactivation of the low-threshold calcium T -current that we observe in TC cells in these different conditions. ...\"\r\n", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 834, + "tag": "Deep brain stimulation" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1383, + "tag": "ModelDB:116867" + } + ], + "timestamp_created": "2024-01-11 15:54:45.537308+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116867", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "831": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 831, + "name": "Python demo of the VmT method to extract conductances from single Vm traces (Pospischil et al. 2009)", + "repository_type": "github", + "summary": "This python code implements a method to estimate synaptic conductances from single membrane potential traces (the \"VmT method\"), as described in Pospischil et al. (2009). The method uses a maximum likelihood procedure and was successfully tested using models and dynamic-clamp experiments in vitro (see paper for details).", + "tags": [ + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 1384, + "tag": "ModelDB:116870" + } + ], + "timestamp_created": "2024-01-11 15:54:45.985361+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116870", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "832": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 832, + "name": "A spiking model of cortical broadcast and competition (Shanahan 2008)", + "repository_type": "github", + "summary": "\"This paper presents a computer model of cortical broadcast and competition based on spiking neurons and inspired by\r\nthe hypothesis of a global neuronal workspace underlying conscious information processing in the human brain. In the\r\nmodel, the hypothesised workspace is realised by a collection of recurrently interconnected regions capable of sustaining\r\nand disseminating a reverberating spatial pattern of activation. ...\"", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 877, + "tag": "Working memory" + }, + { + "id": 1385, + "tag": "ModelDB:116871" + } + ], + "timestamp_created": "2024-01-11 15:54:46.445856+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116871", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "833": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 833, + "name": "Synaptic plasticity can produce and enhance direction selectivity (Carver et al, 2008)", + "repository_type": "github", + "summary": "\" ... We propose a parsimonious model of motion processing that generates direction selective responses using short-term synaptic depression and can reproduce salient features of direction selectivity found in a population of neurons in the midbrain of the weakly electric fish Eigenmannia virescens. \r\n\r\nThe model achieves direction selectivity with an elementary Reichardt motion detector: information from spatially separated receptive fields converges onto a neuron via dynamically different pathways. \r\n\r\nIn the model, these differences arise from convergence of information through distinct synapses that either exhibit or do not exhibit short-term synaptic depression\u2014short-term depression produces phase-advances relative to nondepressing synapses. ...\"\r\n", + "tags": [ + { + "id": 797, + "tag": "Depression" + }, + { + "id": 837, + "tag": "Direction Selectivity" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 854, + "tag": "Synaptic Convergence" + }, + { + "id": 1386, + "tag": "ModelDB:116901" + } + ], + "timestamp_created": "2024-01-11 15:54:46.896494+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116901", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "834": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 834, + "name": "Spike frequency adaptation in the LGMD (Peron and Gabbiani 2009)", + "repository_type": "github", + "summary": "This model is used in the referenced paper to demonstrate that a model of an SK-like calcium-sensitive potassium (KCa) conductance can replicate the spike frequency adaptation (SFA) of the locust lobula giant movement detector (LGMD) neuron. The model simulates current injection experiments with and without KCa block in the LGMD, as well as visual stimulation experiments with and without KCa block.", + "tags": [ + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 923, + "tag": "I Krp" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 863, + "tag": "Spike Frequency Adaptation" + }, + { + "id": 800, + "tag": "Vision" + }, + { + "id": 1387, + "tag": "ModelDB:116945" + } + ], + "timestamp_created": "2024-01-11 15:54:47.349885+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116945", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "835": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 835, + "name": "Fly lobular plate VS cell (Borst and Haag 1996, et al. 1997, et al. 1999)", + "repository_type": "github", + "summary": "In a series of papers the authors conducted experiments to develop understanding and models of fly visual system HS, CS, and VS neurons. This model recreates the VS neurons from those papers with enough success to merit approval by Borst although some discrepancies remain (see readme).", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 940, + "tag": "I_K,Na" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 838, + "tag": "Reliability" + }, + { + "id": 800, + "tag": "Vision" + }, + { + "id": 1388, + "tag": "ModelDB:116956" + } + ], + "timestamp_created": "2024-01-11 15:54:47.807932+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116956", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "836": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 836, + "name": "Data-driven, HH-type model of the lateral pyloric (LP) cell in the STG (Nowotny et al. 2008)", + "repository_type": "github", + "summary": "This model was developed using voltage clamp data and existing LP models to assemble an initial set of currents which were then adjusted by extensive fitting to a long data set of an isolated LP neuron. The main points of the work are\r\na) automatic fitting is difficult but works when the method is carefully adjusted to the problem (and the initial guess is good enough).\r\nb) The resulting model (in this case) made reasonable predictions for manipulations not included in the original data set, e.g., blocking some of the ionic currents.\r\nc) The model is reasonably robust against changes in parameters but the different parameters vary a lot in this respect.\r\nd) The model is suitable for use in a network and has been used for this purpose (Ivanchenko et al. 2008)", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 941, + "tag": "Parameter sensitivity" + }, + { + "id": 1389, + "tag": "ModelDB:116957" + } + ], + "timestamp_created": "2024-01-11 15:54:48.328387+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116957", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "837": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 837, + "name": "Effects of synaptic location and timing on synaptic integration (Rall 1964)", + "repository_type": "github", + "summary": "Reproduces figures 5 - 8 from \r\nRall, W.\r\nTheoretical significance of dendritic trees for neuronal input-output relations.\r\nIn: Neural Theory and Modeling, ed. Reiss, R.F., Palo Alto: Stanford University Press (1964).", + "tags": [ + { + "id": 871, + "tag": "Electrotonus" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 1390, + "tag": "ModelDB:116981" + } + ], + "timestamp_created": "2024-01-11 15:54:48.820088+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116981", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "838": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 838, + "name": "CA1 pyramidal neuron: h channel-dependent deficit of theta oscill. resonance (Marcelin et al. 2008)", + "repository_type": "github", + "summary": "This model was used to confirm and support experimental data\r\nsuggesting that the neuronal/circuitry changes associated with temporal lobe epilepsy,\r\nincluding Ih-dependent inductive mechanisms, can disrupt hippocampal theta function.\r\n", + "tags": [ + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1391, + "tag": "ModelDB:116983" + } + ], + "timestamp_created": "2024-01-11 15:54:49.325478+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/116983", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "839": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 839, + "name": "Working memory circuit with branched dendrites (Morita 2008)", + "repository_type": "github", + "summary": "This is a rate-coding model of the neocortical spatial working memory circuit incorporating multiple dendritic branches of the individual pyramidal cell in order to examine how nonlinear dendritic integration, combined with the nonuniform distribution of the external input, affects the behavior of the whole circuit.", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 877, + "tag": "Working memory" + }, + { + "id": 1392, + "tag": "ModelDB:117204" + } + ], + "timestamp_created": "2024-01-11 15:54:49.858740+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/117204", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "840": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 840, + "name": "Excitability of PFC Basal Dendrites (Acker and Antic 2009)", + "repository_type": "github", + "summary": "\".. We\r\ncarried out multi-site voltage-sensitive dye imaging of membrane potential transients from thin basal\r\nbranches of prefrontal cortical pyramidal neurons before and after application of channel blockers. We\r\nfound that backpropagating action potentials (bAPs) are predominantly controlled by voltage-gated\r\nsodium and A-type potassium channels. In contrast, pharmacologically blocking the delayed rectifier\r\npotassium, voltage-gated calcium or Ih, conductance had little effect on dendritic action potential\r\npropagation. Optically recorded bAP waveforms were quantified and multicompartmental modeling\r\n(NEURON) was used to link the observed behavior with the underlying biophysical properties. The\r\nbest-fit model included a non-uniform sodium channel distribution with decreasing conductance with\r\ndistance from the soma, together with a non-uniform (increasing) A-type potassium conductance. AP\r\namplitudes decline with distance in this model, but to a lesser extent than previously thought. We used\r\nthis model to explore the mechanisms underlying two sets of published data involving high frequency\r\ntrains of action potentials, and the local generation of sodium spikelets. ...\"", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 1393, + "tag": "ModelDB:117207" + } + ], + "timestamp_created": "2024-01-11 15:54:50.393715+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/117207", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "841": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 841, + "name": "Hodgkin-Huxley simplifed 2D and 3D models (Lundstrom et al. 2009)", + "repository_type": "github", + "summary": "\"Neuronal responses are often characterized by the\r\nfiring rate as a function of the stimulus mean, or the f\u2013I curve.\r\n\r\nWe introduce a novel classification of neurons into Types A,\r\nB−, and B+ according to how f\u2013I curves are modulated by\r\ninput fluctuations. ...\"\r\n", + "tags": [ + { + "id": 887, + "tag": "Bifurcation" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1394, + "tag": "ModelDB:117330" + } + ], + "timestamp_created": "2024-01-11 15:54:50.928349+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/117330", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "842": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 842, + "name": "Quantal neurotransmitter release kinetics with fixed and mobile Ca2+ buffers (Gilmanov et al. 2008)", + "repository_type": "github", + "summary": "\"... In this work, we used computer modeling of\r\nquanta release kinetics with various levels of calcium influx\r\nand in the presence of endogenous calcium buffers with\r\nvarying mobilities. \r\n\r\nThe results of this modeling revealed\r\nthe desynchronization of quanta release under low calcium\r\ninflux in the presence of an endogenous fixed calcium\r\nbuffer, with a diffusion coefficient much smaller than that\r\nof free Ca2+, and synchronization occurred upon adding a\r\nmobile buffer. This corresponds to changes in secretion\r\ntime course parameters found experimentally ...\"\r\n", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 942, + "tag": "Pascal (web link to model)" + }, + { + "id": 1395, + "tag": "ModelDB:117351" + } + ], + "timestamp_created": "2024-01-11 15:54:51.374019+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/117351", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "843": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 843, + "name": "Numerical Integration of Izhikevich and HH model neurons (Stewart and Bair 2009)", + "repository_type": "github", + "summary": "The Parker-Sochacki method is a new technique for the numerical integration of differential equations applicable to many neuronal models.\r\nUsing this method, the solution order can be adapted according to the local conditions at each time step, enabling adaptive error control without changing the integration timestep. \r\nWe apply the Parker-Sochacki method to the Izhikevich \u2018simple\u2019 model and a Hodgkin-Huxley\r\ntype neuron, comparing the results with those obtained using the Runge-Kutta and Bulirsch-Stoer methods.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1396, + "tag": "ModelDB:117361" + } + ], + "timestamp_created": "2024-01-11 15:54:51.832932+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/117361", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "844": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 844, + "name": "Competition for AP initiation sites in a circuit controlling simple learning (Cruz et al. 2007)", + "repository_type": "github", + "summary": "\"The spatial and temporal patterns of action potential initiations were studied in a behaving leech preparation to determine the basis of increased firing that accompanies sensitization, a form of non-associative learning requiring the\r\nS-interneurons.\r\n\r\n...\r\nThe S-interneurons, one in each ganglion and linked by electrical synapses with both neighbors to form a chain, are interposed between sensory\r\nand motor neurons.\r\n\r\n...\r\n the single site with the largest initiation rate, the S-cell in the\r\nstimulated segment, suppressed initiations in adjacent ganglia.\r\n\r\nExperiments showed this was both because (1) it received the earliest, greatest input and (2) the delayed synaptic\r\ninput to the adjacent S-cells coincided with the action potential refractory period.\r\n\r\nA compartmental model of the S-cell and its inputs showed that a simple, intrinsic mechanism of inexcitability after each action potential may account for suppression of impulse initiations.\r\n\r\nThus, a non-synaptic competition between neurons alters synaptic integration in the chain.\r\n\r\nIn one mode, inputs to different sites sum independently, whereas in another, synaptic input to a single site precisely specifies the overall pattern of activity.\"\r\n", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 1397, + "tag": "ModelDB:117459" + } + ], + "timestamp_created": "2024-01-11 15:54:52.303829+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/117459", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "845": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 845, + "name": "Rat alpha7 nAChR desensitization is modulated by W55 (Gay et al. 2008)", + "repository_type": "github", + "summary": "\"The rat alpha7 nicotinic acetylcholine receptor (nAChR) can undergo rapid onset of desensitization; however, the mechanisms of desensitization are largely unknown. \r\n\r\nThe contribution of a tryptophan (W) residue at position 55 of the rat alpha7 nAChR subunit, which lies within the beta2 strand, was studied by mutating it to other hydrophobic and/or aromatic amino acids, followed by voltage-clamp experiments in Xenopus oocytes. \r\n\r\nWhen mutated to alanine, the alpha7-W55A nAChR desensitized more slowly, and recovered from desensitization more rapidly, than wildtype alpha7 nAChRs. \r\n\r\nThe contribution of desensitization was validated by kinetic modelling. ...\"", + "tags": [ + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 943, + "tag": "Pascal/Delphi" + }, + { + "id": 1398, + "tag": "ModelDB:117508" + } + ], + "timestamp_created": "2024-01-11 15:54:52.896627+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/117508", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "846": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 846, + "name": "Reverse-time correlation analysis for idealized orientation tuning dynamics (Kovacic et al. 2008)", + "repository_type": "github", + "summary": "\"A theoretical analysis is presented of a\r\nreverse-time correlation method used in experimentally\r\ninvestigating orientation tuning dynamics of\r\nneurons in the primary visual cortex.\r\n\r\nAn exact mathematical\r\ncharacterization of the method is developed,\r\nand its connection with the Volterra\u2013Wiener nonlinear\r\nsystems theory is described.\r\n\r\nVarious mathematical\r\nconsequences and possible physiological implications\r\nof this analysis are illustrated using exactly solvable\r\nidealized models of orientation tuning.\"", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 800, + "tag": "Vision" + }, + { + "id": 1399, + "tag": "ModelDB:117514" + } + ], + "timestamp_created": "2024-01-11 15:54:53.344407+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/117514", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "847": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 847, + "name": "Experimental and modeling studies of desensitization of P2X3 receptors (Sokolova et al. 2006)", + "repository_type": "github", + "summary": "\"The function of ATP-activated P2X3 receptors involved in pain sensation is modulated by desensitization, a phenomenon poorly understood.\r\n\r\nThe present study used patch-clamp recording from cultured rat or mouse sensory neurons and kinetic\r\nmodeling to clarify the properties of P2X3 receptor desensitization.\r\n\r\n...\r\nDesensitization properties were well accounted for by a cyclic model in which\r\nreceptors could be desensitized from either open or closed\r\nstates.\r\n\r\nRecovery was assumed to be a multistate process with distinct kinetics dependent on the agonist-dependent dissociation rate from desensitized receptors.\r\n\r\n...\r\nBy using subthreshold concentrations of an HAD (high-affinity desensitization)-potent agonist, it might be possible\r\nto generate sustained inhibition of P2X3 receptors for controlling\r\nchronic pain.\"", + "tags": [ + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 861, + "tag": "Nociception" + }, + { + "id": 943, + "tag": "Pascal/Delphi" + }, + { + "id": 1400, + "tag": "ModelDB:117691" + } + ], + "timestamp_created": "2024-01-11 15:54:53.867596+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/117691", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "848": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 848, + "name": "Simulation system of spinal cord motor nuclei and assoc. nerves and muscles (Cisi and Kohn 2008)", + "repository_type": "github", + "summary": "A Web-based simulation system of the spinal\r\ncord circuitry responsible for muscle control is described.\r\nThe simulator employs two-compartment motoneuron\r\nmodels for S, FR and FF types, with synaptic inputs acting\r\nthrough conductance variations. \r\n\r\nFour motoneuron pools\r\nwith their associated interneurons are represented in the\r\nsimulator, with the possibility of inclusion of more than\r\n2,000 neurons and 2,000,000 synapses. \r\n\r\n... \r\nInputs to the motoneuron pool come from populations of\r\ninterneurons (Ia reciprocal inhibitory interneurons, Ib\r\ninterneurons, and Renshaw cells) and from stochastic point\r\nprocesses associated with descending tracts. \r\n\r\n...\r\nThe generation of the H-reflex\r\nby the Ia-motoneuron pool system and its modulation\r\nby spinal cord interneurons is included in the simulation\r\nsystem.\r\n", + "tags": [ + { + "id": 930, + "tag": "Java (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1401, + "tag": "ModelDB:117810" + } + ], + "timestamp_created": "2024-01-11 15:54:54.308995+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/117810", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "849": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 849, + "name": "Spike Response Model simulator (Jolivet et al. 2004, 2006, 2008)", + "repository_type": "github", + "summary": "The Spike Response Model (SRM) optimized on the experimental data in the Single-Neuron modelling Competition ( www.incf.org/community/competitions ) for edition 2007 and edition 2008. The Spike Response Model is a simplified model of neuronal excitability where current linearly integrates to an artificial threshold. After the spike, the threshold is augmented and the voltage follows a voltage kernel that is the average voltage trace during and after a spike. The parameters were chosen to best fit the observed spike times with a method outlined in Jolivet et al. (2006).", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1402, + "tag": "ModelDB:117966" + } + ], + "timestamp_created": "2024-01-11 15:54:54.983908+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/117966", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "850": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 850, + "name": "Roles of I(A) and morphology in AP prop. in CA1 pyramidal cell dendrites (Acker and White 2007)", + "repository_type": "github", + "summary": "\" ...Using conductance-based models of CA1 pyramidal cells, we show that underlying \u201ctraveling wave attractors\u201d control action potential propagation in the apical dendrites. \r\n\r\nBy computing these attractors, we dissect and quantify the effects of IA channels and dendritic morphology on bAP amplitudes. \r\n\r\nWe find that non-uniform activation properties of IA can lead to backpropagation failure similar to that observed experimentally in these cells. \r\n... \"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1403, + "tag": "ModelDB:118014" + } + ], + "timestamp_created": "2024-01-11 15:54:55.479531+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118014", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "851": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 851, + "name": "Reduction of nonlinear ODE systems possessing multiple scales (Clewley et al. 2005)", + "repository_type": "github", + "summary": "\" ... We introduce a combined numerical and analytical technique that aids the identification of structure in a class of systems of nonlinear ordinary differential\r\nequations (ODEs) that are commonly applied in dynamical models of physical processes.\r\n... \r\nThese methods have been incorporated into a new software tool named Dssrt, which we demonstrate\r\non a limit cycle of a synaptically driven Hodgkin\u2013Huxley neuron model.\"", + "tags": [ + { + "id": 887, + "tag": "Bifurcation" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 944, + "tag": "Multiscale" + }, + { + "id": 912, + "tag": "XPP (web link to model)" + }, + { + "id": 1404, + "tag": "ModelDB:118020" + } + ], + "timestamp_created": "2024-01-11 15:54:55.955685+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118020", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "852": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 852, + "name": "A model of the femur-tibia control system in stick insects (Stein et al. 2008)", + "repository_type": "github", + "summary": "We studied the femur-tibia joint control system of the insect leg, and its switch between resistance reflex in posture control and \"active reaction\" in walking. The \"active reaction\" is basically a reversal of the resistance reflex. Both responses are elicited by the same sensory input and the same neuronal network (the femur-tibia network). \r\nThe femur-tibia network was modeled by fitting the responses of model neurons to those obtained in animals. Each implemented neuron has a physiological counterpart. The strengths of 16 interneuronal pathways that integrate sensory input were then assigned three different values and varied independently, generating a database of more than 43 million network variants. The uploaded version contains the model that best represented the resistance reflex. Please see the README for more help.\r\nWe demonstrate that the combinatorial code of interneuronal pathways determines motor output. A switch between different behaviors such as standing to walking can thus be achieved by altering the strengths of selected sensory integration pathways.\r\n", + "tags": [ + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 945, + "tag": "MadSim" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 1405, + "tag": "ModelDB:118092" + } + ], + "timestamp_created": "2024-01-11 15:54:56.402799+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118092", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "853": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 853, + "name": "Ca3 pyramidal neuron: membrane response near rest (Hemond et al. 2009)", + "repository_type": "github", + "summary": "In this paper, the model was used to show how the temporal summation of excitatory inputs in CA3 pyramidal neurons was affected by the presence of Ih in the dendrites in a frequency- and distance-dependent fashion.", + "tags": [ + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 1406, + "tag": "ModelDB:118098" + } + ], + "timestamp_created": "2024-01-11 15:54:56.843963+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118098", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "854": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 854, + "name": "Evaluation of stochastic diff. eq. approximation of ion channel gating models (Bruce 2009)", + "repository_type": "github", + "summary": "Fox and Lu derived an algorithm based on stochastic differential equations for approximating the kinetics of ion channel gating that is simpler and faster than \"exact\" algorithms for simulating Markov process models of channel gating.\r\n\r\nHowever, the approximation may not be sufficiently accurate to predict statistics of action potential generation in some cases.\r\n\r\nThe objective of this study was to develop a framework for analyzing the inaccuracies and determining their origin.\r\n\r\nSimulations of a patch of membrane with voltage-gated sodium and potassium channels were performed using an exact algorithm for the kinetics of channel gating and the approximate algorithm of Fox & Lu.\r\n...\r\nThe results indicate that: (i) the source of the inaccuracy is that the Fox & Lu algorithm does not adequately describe the combined behavior of the multiple activation particles in each sodium and potassium channel, and (ii) the accuracy does not improve with increasing numbers of channels.", + "tags": [ + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1407, + "tag": "ModelDB:118195" + } + ], + "timestamp_created": "2024-01-11 15:54:57.294984+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118195", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "855": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 855, + "name": "Tag Trigger Consolidation (Clopath and Ziegler et al. 2008)", + "repository_type": "github", + "summary": "This model simulates different phases of LTP/D, i.e. the induction or early phase, the setting of synaptic tags, a trigger process for protein synthesis, and a slow transition leading to synaptic consolidation namely the late phase of synaptic plasticity. The model explains a large body of experimental data on synaptic tagging and capture, cross-tagging, and the late phases of LTP and LTD. Moreover, the model accounts for the dependence of LTP and LTD induction on voltage and presynaptic stimulation frequency.", + "tags": [ + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 858, + "tag": "Maintenance" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1408, + "tag": "ModelDB:118199" + } + ], + "timestamp_created": "2024-01-11 15:54:57.750723+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118199", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "856": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 856, + "name": "Multisensory integration in the superior colliculus: a neural network model (Ursino et al. 2009)", + "repository_type": "github", + "summary": "\" ... The model includes three distinct neural areas:\r\ntwo unimodal areas (auditory and visual) are devoted to a\r\ntopological representation of external stimuli, and communicate\r\nvia synaptic connections with a third downstream\r\narea (in the SC) responsible for multisensory integration.\r\n\r\nThe present simulations show that the model, with a single\r\nset of parameters, can mimic various responses to different\r\ncombinations of external stimuli including the inverse\r\neffectiveness, both in terms of multisensory enhancement\r\nand contrast, the existence of within- and cross-modality\r\nsuppression between spatially disparate stimuli, a reduction\r\nof network settling time in response to cross-modal stimuli\r\ncompared with individual stimuli.\r\n...\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 849, + "tag": "Audition" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 800, + "tag": "Vision" + }, + { + "id": 1409, + "tag": "ModelDB:118261" + } + ], + "timestamp_created": "2024-01-11 15:54:58.352204+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118261", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "857": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 857, + "name": "Moose/PyMOOSE: interoperable scripting in Python for MOOSE (Ray and Bhalla 2008)", + "repository_type": "github", + "summary": "\" ... We report the integration of Python scripting with the Multi-scale Object Oriented Simulation Environment (MOOSE). MOOSE is a general-purpose simulation system for compartmental neuronal models and for models of signaling pathways based on chemical kinetics. We show how the Python-scripting version of MOOSE, PyMOOSE, combines the power of a compiled simulator with the versatility and ease of use of Python. ... \"", + "tags": [ + { + "id": 946, + "tag": "MOOSE/PyMOOSE (web link to method)" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 1410, + "tag": "ModelDB:118326" + } + ], + "timestamp_created": "2024-01-11 15:54:58.913727+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118326", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "858": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 858, + "name": "Gap junction coupled network of striatal fast spiking interneurons (Hjorth et al. 2009)", + "repository_type": "github", + "summary": "Gap junctions between striatal FS neurons has very weak ability to synchronise spiking. Input uncorrelated between neighbouring neurons is shunted, while correlated input is not.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 940, + "tag": "I_K,Na" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 1411, + "tag": "ModelDB:118389" + } + ], + "timestamp_created": "2024-01-11 15:54:59.379307+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118389", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "859": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 859, + "name": "Phase oscillator models for lamprey central pattern generators (Varkonyi et al. 2008)", + "repository_type": "github", + "summary": "In our paper, Varkonyi et al. 2008, we derive phase oscillator models for the lamprey central pattern generator from two biophysically based segmental models. We\r\nstudy intersegmental coordination and show how these models can provide stable intersegmental phase lags observed in real animals.\r\n", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 941, + "tag": "Parameter sensitivity" + }, + { + "id": 906, + "tag": "Phase Response Curves" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1412, + "tag": "ModelDB:118392" + } + ], + "timestamp_created": "2024-01-11 15:54:59.847179+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118392", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "860": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 860, + "name": "Odor supported place cell model and goal navigation in rodents (Kulvicius et al. 2008)", + "repository_type": "github", + "summary": "\" ...\r\nHere we model odor supported place cells by using a simple feed-forward network and analyze the impact of olfactory cues on place cell formation and spatial navigation. \r\n\r\nThe obtained place cells are used to solve a goal navigation task by a novel mechanism based on self-marking by odor patches combined with a Q-learning algorithm. \r\n\r\nWe also analyze the impact of place cell remapping on goal directed behavior when switching between two environments. \r\n...\"", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 882, + "tag": "Place cell/field" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 892, + "tag": "Reinforcement Learning" + }, + { + "id": 883, + "tag": "Spatial Navigation" + }, + { + "id": 1413, + "tag": "ModelDB:118434" + } + ], + "timestamp_created": "2024-01-11 15:55:00.399007+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118434", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "861": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 861, + "name": "Virtual Retina: biological retina simulator, with contrast gain control (Wohrer and Kornprobst 2009)", + "repository_type": "github", + "summary": "\"We propose a new retina simulation software, called Virtual Retina, which transforms a video into spike trains.\r\n\r\nOur goal is twofold: Allow large scale simulations (up to 100,000 neurons) in reasonable processing times and keep a strong biological plausibility, taking into account implementation constraints. \r\n\r\n...\r\nThis software will be an evolutionary tool for neuroscientists that need realistic large-scale input spike trains in subsequent treatments, and for educational purposes.\"", + "tags": [ + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1414, + "tag": "ModelDB:118524" + } + ], + "timestamp_created": "2024-01-11 15:55:00.858577+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118524", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "862": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 862, + "name": "Calyx of Held, short term plasticity (Yang Z et al. 2009)", + "repository_type": "github", + "summary": "This model investigates mechanisms contributing to short term plasticity at the calyx of Held, a giant glutamatergic synapse in the mammalian brainstem auditory system. It is a stochastic version of the model described in:\r\n \r\nHennig, M., Postlethwaite, M., Forsythe, I.D. and Graham, B.P. (2007). A biophysical model of short-term plasticity at the calyx of Held. \r\nNeurocomputing, 70:1626-1629.\r\n\r\nThis version introduces stochastic vesicle recycling and release. It has been used to investigate the information transmission\r\nproperties of this synapse, as detailed in:\r\n\r\nYang, Z., Hennig, M., Postlethwaite, M., Forsythe, I.D. and Graham, B.P. (2008). \r\nWide-band information transmission at the calyx of Held. Neural Computation, 21(4):991-1018.\r\n", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + }, + { + "id": 829, + "tag": "Vestibular" + }, + { + "id": 1415, + "tag": "ModelDB:118554" + } + ], + "timestamp_created": "2024-01-11 15:55:01.307465+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118554", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "863": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 863, + "name": "Response properties of neocort. neurons to temporally modulated noisy inputs (Koendgen et al. 2008)", + "repository_type": "github", + "summary": "Neocortical neurons are classified by current\u2013frequency relationship. This is a static description and it may be inadequate to interpret neuronal responses to time-varying stimuli. \r\n\r\nTheoretical studies (Brunel et al., 2001; Fourcaud-Trocm\u00e9 et al. 2003; Fourcaud-Trocm\u00e9 and Brunel 2005; Naundorf et al. 2005) suggested that single-cell dynamical response properties are necessary to interpret ensemble responses to fast input transients. Further, it was shown that input-noise linearizes and boosts the response bandwidth, and that the interplay between the barrage of noisy synaptic currents and the spike-initiation mechanisms determine the dynamical properties of the firing rate. \r\n\r\nIn order to allow a reader to explore such simulations, we prepared a simple NEURON implementation of the experiments performed in K\u00f6ndgen et al., 2008 (see also Fourcaud-Trocm\u00e9 al. 2003; Fourcaud-Trocm\u00e9 and Brunel 2005).\r\n\r\nIn addition, we provide sample MATLAB routines for exploring the sandwich model proposed in K\u00f6ndgen et al., 2008, employing a simple frequdency-domain filtering.\r\n\r\nThe simulations and the MATLAB routines are based on the linear response properties of layer 5 pyramidal cells estimated by injecting a superposition of a small-amplitude sinusoidal wave and a background noise, as in K\u00f6ndgen et al., 2008.", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 1416, + "tag": "ModelDB:118631" + } + ], + "timestamp_created": "2024-01-11 15:55:01.803237+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118631", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "864": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 864, + "name": "Drosophila projection neuron electrotonic structure (Gouwens and Wilson 2009)", + "repository_type": "github", + "summary": "We address the issue of how electrical signals propagate in Drosophila neurons by modeling the electrotonic structure of the antennal lobe projection neurons innervating glomerulus DM1. The readme file contains instructions for running the model.", + "tags": [ + { + "id": 871, + "tag": "Electrotonus" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1417, + "tag": "ModelDB:118662" + } + ], + "timestamp_created": "2024-01-11 15:55:02.265462+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118662", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "865": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 865, + "name": "Permeation and inactivation of CaV1.2 Ca2+ channels (Babich et al. 2007)", + "repository_type": "github", + "summary": "The authors present data and a kinetics model of the CaV1.2 channel supporting the idea that Ca2+ block of the pore generates the U-shaped inactivation curve.", + "tags": [ + { + "id": 947, + "tag": "CalC Calcium Calculator (web link to model)" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1418, + "tag": "ModelDB:118759" + } + ], + "timestamp_created": "2024-01-11 15:55:02.723558+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118759", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "866": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 866, + "name": "Facilitation model based on bound Ca2+ (Matveev et al. 2006)", + "repository_type": "github", + "summary": "\"Facilitation is a transient stimulation-induced increase in synaptic response, a\r\nubiquitous form of short-term synaptic plasticity that can regulate\r\nsynaptic transmission on fast time scales. \r\n\r\nIn their pioneering work, Katz and Miledi and Rahamimoff demonstrated the dependence of\r\nfacilitation on presynaptic Ca2+ influx and proposed that facilitation\r\nresults from the accumulation of residual Ca2+ bound to vesicle\r\nrelease triggers. \r\n\r\nHowever, this bound Ca2+ hypothesis appears to contradict the evidence that \r\nfacilitation is reduced by exogenous Ca2+ buffers. \r\n\r\nThis conclusion led to a widely held view that facilitation\r\nmust depend solely on the accumulation of Ca2+ in free form. \r\n\r\nHere we consider a more realistic implementation of the bound Ca2+\r\nmechanism, taking into account spatial diffusion of Ca2+, and show\r\nthat a model with slow Ca2+ unbinding steps can retain sensitivity to\r\nfree residual Ca2+. \r\n...\"", + "tags": [ + { + "id": 947, + "tag": "CalC Calcium Calculator (web link to model)" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 798, + "tag": "Facilitation" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1419, + "tag": "ModelDB:118797" + } + ], + "timestamp_created": "2024-01-11 15:55:03.174512+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118797", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "867": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 867, + "name": "Loss of phase-locking in non-weakly coupled inhib. networks of type-I neurons (Oh and Matveev 2009)", + "repository_type": "github", + "summary": "...\r\nHere we examine the loss of\r\nsynchrony caused by an increase in inhibitory coupling\r\nin networks of type-I Morris\u2013Lecar model oscillators,\r\nwhich is characterized by a period-doubling cascade\r\nand leads to mode-locked states with alternation in the\r\nfiring order of the two cells, as reported recently by\r\nMaran and Canavier (J Comput Nerosci, 2008) for a\r\nnetwork of Wang-Buzs\u00e1ki model neurons. \r\n\r\nAlthough\r\nalternating-order firing has been previously reported as\r\na near-synchronous state, we show that the stable phase\r\ndifference between the spikes of the two Morris\u2013Lecar\r\ncells can constitute as much as 70% of the unperturbed\r\noscillation period. \r\n\r\nFurther, we examine the generality\r\nof this phenomenon for a class of type-I oscillators that\r\nare close to their excitation thresholds, and provide\r\nan intuitive geometric description of such \u201cleap-frog\u201d\r\ndynamics. \r\n...\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1420, + "tag": "ModelDB:118799" + } + ], + "timestamp_created": "2024-01-11 15:55:03.657446+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118799", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "868": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 868, + "name": "Stochastic automata network Markov model descriptors of coupled Ca2+ channels (Nguyen et al. 2005)", + "repository_type": "github", + "summary": "\"... Here we present a formalism by which mathematical models for Ca2+-regulated Ca2+ release sites\r\nare derived from stochastic models of single-channel gating that include Ca2+ activation, Ca2+\r\ninactivation, or both. \r\n\r\nSuch models are stochastic automata networks (SANs) that involve a large\r\nnumber of functional transitions, that is, the transition probabilities of the infinitesimal generator\r\nmatrix of one of the automata (i.e., an individual channel) may depend on the local [Ca2+] and\r\nthus the state of the other channels. Simulation and analysis of the SAN descriptors representing\r\nhomogeneous clusters of intracellular Ca2+ channels show that (1) release site density can modify\r\nboth the steady-state open probability and stochastic excitability of Ca2+ release sites, (2) Ca2+\r\ninactivation is not a requirement for Ca2+ puffs or sparks, and (3) a single-channel model with a\r\nbell-shaped open probability curve does not lead to release site activity that is a biphasic function of\r\nrelease site density.\r\n...\"", + "tags": [ + { + "id": 947, + "tag": "CalC Calcium Calculator (web link to model)" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1421, + "tag": "ModelDB:118894" + } + ], + "timestamp_created": "2024-01-11 15:55:04.125582+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118894", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "869": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 869, + "name": "CA1 pyramidal neurons: effects of a Kv7.2 mutation (Miceli et al. 2009)", + "repository_type": "github", + "summary": "NEURON mod files from the paper:\r\nMiceli et al, Neutralization of a unique, negatively-charged residue in the voltage sensor \r\nof K(V)7.2 subunits in a sporadic case of benign familial neonatal seizures, Neurobiol Dis., in press (2009).\r\nIn this paper, the model revealed that the gating changes introduced by a mutation in K(v)7.2 \r\ngenes encoding for the neuronal KM current in a case of benign familial neonatal seizures,\r\nincreased cell firing frequency, thereby triggering the neuronal hyperexcitability which underlies the observed neonatal epileptic condition.\r\n", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1422, + "tag": "ModelDB:118986" + } + ], + "timestamp_created": "2024-01-11 15:55:04.641924+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/118986", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "870": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 870, + "name": "Facilitation through buffer saturation (Matveev et al. 2004)", + "repository_type": "github", + "summary": "\"... Using computer simulations, we\r\nstudy the magnitude of synaptic facilitation (SF) that can be achieved by a buffer saturation mechanism (BSM), and explore its dependence on the\r\nendogenous buffering properties.\r\n...\"", + "tags": [ + { + "id": 947, + "tag": "CalC Calcium Calculator (web link to model)" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 798, + "tag": "Facilitation" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1423, + "tag": "ModelDB:119153" + } + ], + "timestamp_created": "2024-01-11 15:55:05.120315+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/119153", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "871": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 871, + "name": "STDP promotes synchrony of inhibitory networks in the presence of heterogeneity (Talathi et al 2008)", + "repository_type": "github", + "summary": "\"Recently Haas et al. (J Neurophysiol 96:\r\n3305\u20133313, 2006), observed a novel form of spike timing\r\ndependent plasticity (iSTDP) in GABAergic synaptic\r\ncouplings in layer II of the entorhinal cortex. Depending\r\non the relative timings of the presynaptic input at\r\ntime tpre and the postsynaptic excitation at time tpost,\r\nthe synapse is strengthened (delta_t = t(post) - t(pre) > 0) or\r\nweakened (delta_t < 0). The temporal dynamic range of\r\nthe observed STDP rule was found to lie in the higher\r\ngamma frequency band (> or = 40 Hz), a frequency range\r\nimportant for several vital neuronal tasks. In this paper\r\nwe study the function of this novel form of iSTDP in\r\nthe synchronization of the inhibitory neuronal network.\r\nIn particular we consider a network of two unidirectionally\r\ncoupled interneurons (UCI) and two mutually\r\ncoupled interneurons (MCI), in the presence of\r\nheterogeneity in the intrinsic firing rates of each coupled\r\nneuron. ...\"", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 1424, + "tag": "ModelDB:119159" + } + ], + "timestamp_created": "2024-01-11 15:55:05.572013+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/119159", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "872": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 872, + "name": "New and corrected simulations of synaptic facilitation (Matveev et al. 2002)", + "repository_type": "github", + "summary": "\"A three-dimensional presynaptic calcium diffusion model developed to account for characteristics of transmitter release\r\nwas modified to provide for binding of calcium to a receptor and subsequent triggering of exocytosis. \r\n\r\nWhen low affinity (20 FLM) and\r\nrapid kinetics were assumed for the calcium receptor triggering exocytosis, and stimulus parameters were selected to match those\r\nof experiments, the simulations predicted a virtual invariance of the time course of transmitter release to paired stimulation,\r\nstimulation with pulses of different amplitude, and stimulation in different calcium solutions. \r\n...\"", + "tags": [ + { + "id": 947, + "tag": "CalC Calcium Calculator (web link to model)" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 798, + "tag": "Facilitation" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1425, + "tag": "ModelDB:119214" + } + ], + "timestamp_created": "2024-01-11 15:55:06.023705+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/119214", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "873": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 873, + "name": "Modelling reduced excitability in aged CA1 neurons as a Ca-dependent process (Markaki et al. 2005)", + "repository_type": "github", + "summary": "\"We use a multi-compartmental model of a CA1 pyramidal cell to study changes in hippocampal excitability that result from aging-induced alterations in calcium-dependent membrane mechanisms.\r\n\r\nThe model incorporates N- and L-type calcium channels which are respectively coupled to fast and slow afterhyperpolarization potassium channels.\r\n\r\nModel parameters are calibrated using physiological data.\r\n\r\nComputer simulations reproduce the decreased excitability of aged CA1 cells, which results from increased internal calcium accumulation, subsequently larger postburst slow afterhyperpolarization, and enhanced spike frequency adaptation.\r\n\r\nWe find that aging-induced alterations in CA1 excitability can be modelled with simple coupling mechanisms that selectively link specific types of calcium channels to specific calcium-dependent potassium channels.\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 833, + "tag": "Aging/Alzheimer`s" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 925, + "tag": "I R" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1426, + "tag": "ModelDB:119266" + } + ], + "timestamp_created": "2024-01-11 15:55:06.522618+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/119266", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "874": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 874, + "name": "Computational neuropharmacology of CA1 pyramidal neuron (Ferrante et al. 2008)", + "repository_type": "github", + "summary": "In this paper, the model was used to show how neuroactive drugs targeting different neuronal mechanisms affect the signal integration in CA1 pyramidal neuron. Ferrante M, Blackwell KT, Migliore M, Ascoli GA (2008)", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 833, + "tag": "Aging/Alzheimer`s" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 872, + "tag": "Pathophysiology" + }, + { + "id": 926, + "tag": "Schizophrenia" + }, + { + "id": 863, + "tag": "Spike Frequency Adaptation" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 1427, + "tag": "ModelDB:119283" + } + ], + "timestamp_created": "2024-01-11 15:55:07.006831+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/119283", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "875": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 875, + "name": "Transmitter release and Ca diffusion models (Yamada and Zucker 1992)", + "repository_type": "github", + "summary": "\"A three-dimensional presynaptic calcium diffusion model developed to account for characteristics of transmitter release was modified to provide for binding of calcium to a receptor and subsequent triggering of exocytosis. ...\"", + "tags": [ + { + "id": 947, + "tag": "CalC Calcium Calculator (web link to model)" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 798, + "tag": "Facilitation" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1428, + "tag": "ModelDB:120115" + } + ], + "timestamp_created": "2024-01-11 15:55:07.474607+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/120115", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "876": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 876, + "name": "TTX-R Na+ current effect on cell response (Herzog et al 2001) (MATLAB)", + "repository_type": "github", + "summary": "\"Small dorsal root ganglion (DRG) neurons, which include nociceptors, express multiple voltage-gated sodium currents. In addition to a classical fast inactivating tetrodotoxin-sensitive (TTX-S) sodium current, many of these cells express a TTX-resistant (TTX-R) sodium current that activates near -70 mV and is persistent at negative potentials. To investigate the possible contributions of this TTX-R persistent (TTX-RP) current to neuronal excitability, we carried out computer simulations using the Neuron program with TTX-S and -RP currents, fit by the Hodgkin-Huxley model, that closely matched the currents recorded from small DRG neurons. ...\" See paper for more and details.", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 861, + "tag": "Nociception" + }, + { + "id": 1429, + "tag": "ModelDB:120117" + } + ], + "timestamp_created": "2024-01-11 15:55:08.039361+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/120117", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "877": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 877, + "name": "A finite volume method for stochastic integrate-and-fire models (Marpeau et al. 2009)", + "repository_type": "github", + "summary": "\"The stochastic integrate and fire neuron is\r\none of the most commonly used stochastic models\r\nin neuroscience.\r\n\r\nAlthough some cases are analytically\r\ntractable, a full analysis typically calls for numerical\r\nsimulations.\r\n\r\nWe present a fast and accurate finite volume\r\nmethod to approximate the solution of the associated\r\nFokker-Planck equation. ...\"", + "tags": [ + { + "id": 948, + "tag": "FORTRAN (web link to a model)" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 1430, + "tag": "ModelDB:120137" + } + ], + "timestamp_created": "2024-01-11 15:55:08.492990+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/120137", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "878": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 878, + "name": "A kinetic model unifying presynaptic short-term facilitation and depression (Lee et al. 2009)", + "repository_type": "github", + "summary": "\"...\r\n\r\nHere, we propose a unified theory of synaptic short-term plasticity based on realistic yet tractable and testable model descriptions of the underlying intracellular biochemical processes. \r\n\r\nAnalysis of the model equations leads to a closed-form solution of the resonance frequency, a function of several critical biophysical parameters, as the single key indicator of the propensity for synaptic facilitation or depression under repetitive stimuli. \r\n\r\nThis integrative model is supported by a broad range of transient and frequency response experimental data including those from facilitating, depressing or mixed-mode synapses. \r\n... the model provides the reasons behind the switching behavior between facilitation and depression observed in experiments. ...\"\r\n", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 797, + "tag": "Depression" + }, + { + "id": 798, + "tag": "Facilitation" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 907, + "tag": "Simulink" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 1431, + "tag": "ModelDB:120184" + } + ], + "timestamp_created": "2024-01-11 15:55:08.961391+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/120184", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "879": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 879, + "name": "Multistability of clustered states in a globally inhibitory network (Chandrasekaran et al. 2009)", + "repository_type": "github", + "summary": "\"We study a network of m identical excitatory cells projecting excitatory synaptic connections onto a single inhibitory interneuron, which is reciprocally coupled to all excitatory cells through inhibitory synapses possessing short-term synaptic depression.\r\n\r\nWe find that such a network with global inhibition possesses multiple stable activity patterns with distinct periods, characterized by the clustering of the excitatory cells into synchronized sub-populations.\r\n\r\nWe prove the existence and stability of n-cluster solutions in a m-cell network.\r\n\r\n... Implications for temporal coding and memory storage are discussed.\"", + "tags": [ + { + "id": 790, + "tag": "Attractor Neural Network" + }, + { + "id": 797, + "tag": "Depression" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 1432, + "tag": "ModelDB:120227" + } + ], + "timestamp_created": "2024-01-11 15:55:09.443319+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/120227", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "880": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 880, + "name": "Contribution of ATP-sensitive potassium channels in the neuronal network (Huang et al. 2009)", + "repository_type": "github", + "summary": "Epileptic seizures in diabetic hyperglycemia (DH) are not uncommon. \r\n\r\nThis study aimed to determine the acute behavioral, pathological, and electrophysiological effects of status epilepticus (SE) on diabetic animals. \r\n\r\n...\r\n\r\nWe also used a simulation model to evaluate intracellular adenosine triphosphate (ATP) and neuroexcitability.\r\n\r\n...\r\nIn the simulation, increased intracellular ATP concentration promoted action potential firing.\r\n\r\nThis finding that rats with DH had more brain damage after SE than rats without diabetes suggests the importance of intensively treating hyperglycemia and seizures in diabetic patients with epilepsy.", + "tags": [ + { + "id": 878, + "tag": "ATP-senstive potassium current" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1433, + "tag": "ModelDB:120243" + } + ], + "timestamp_created": "2024-01-11 15:55:09.927637+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/120243", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "881": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 881, + "name": "Action potential of adult rat ventricle (Wang et al. 2008)", + "repository_type": "github", + "summary": "\"Aconitine (ACO), a highly toxic diterpenoid alkaloid, is recognized to have effects \r\non cardiac voltage-gated Na(+) channels. However, it remains unknown whether it has \r\nany effects on K(+) currents. The effects of ACO on ion currents in differentiated \r\nclonal cardiac (H9c2) cells and in cultured neonatal rat ventricular myocytes were \r\ninvestigated in this study. ...\" The rat action potential in this simulation was played back into the cell for experiments reported in this paper.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 846, + "tag": "XPPAUT" + }, + { + "id": 1434, + "tag": "ModelDB:120246" + } + ], + "timestamp_created": "2024-01-11 15:55:10.434368+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/120246", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "882": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 882, + "name": "A nicotinic acetylcholine receptor kinetic model (Edelstein et al. 1996)", + "repository_type": "github", + "summary": "Nicotinic acetylcholine receptors are transmembrane\r\noligomeric proteins that mediate interconversions\r\nbetween open and closed channel states under the\r\ncontrol of neurotransmitters. \r\n\r\n..\r\n\r\nIn order to represent the functional properties of such\r\nreceptors, we have developed a kinetic model that links\r\nconformational interconversion rates to agonist binding\r\nand extends the general principles of the Monod-\r\nWyman-Changeux model of allosteric transitions. \r\n\r\n...\r\nApplication of the model to the peripheral nicotinic acetylcholine receptor\r\n(nAChR) accounts for the main properties of ligand-gating,\r\nincluding single-channel events, and several new\r\nrelationships are predicted.\r\n...\r\nIn terms of future developments, the analysis presented here provides\r\na physical basis for constructing more biologically realistic\r\nmodels of synaptic modulation that may be applied to\r\nartificial neural networks.\r\n", + "tags": [ + { + "id": 949, + "tag": "BioPAX (web link to model)" + }, + { + "id": 950, + "tag": "CellML (web link to model)" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 910, + "tag": "SBML (web link to model)" + }, + { + "id": 934, + "tag": "Scilab (web link to model)" + }, + { + "id": 867, + "tag": "Virtual Cell (web link to model)" + }, + { + "id": 911, + "tag": "XML (web link to model)" + }, + { + "id": 912, + "tag": "XPP (web link to model)" + }, + { + "id": 1435, + "tag": "ModelDB:120320" + } + ], + "timestamp_created": "2024-01-11 15:55:10.928308+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/120320", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "883": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 883, + "name": "Nodose sensory neuron (Schild et al. 1994, Schild and Kunze 1997)", + "repository_type": "github", + "summary": "This is a simulink implementation of the model described in Schild et al. 1994, and Schild and Kunze 1997 papers on Nodose sensory neurons. These papers describe the sensitivity these models have to their parameters and the match of the models to experimental data.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 814, + "tag": "Na/Ca exchanger" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 941, + "tag": "Parameter sensitivity" + }, + { + "id": 1436, + "tag": "ModelDB:120521" + } + ], + "timestamp_created": "2024-01-11 15:55:11.419426+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/120521", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "884": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 884, + "name": "Sodium potassium ATPase pump (Chapman et al. 1983)", + "repository_type": "github", + "summary": "The electrochemical properties of a widely accepted six-step reaction scheme for the Na,K-ATPase have been studied by computer simulation.", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 816, + "tag": "Na/K pump" + }, + { + "id": 817, + "tag": "Sodium pump" + }, + { + "id": 1437, + "tag": "ModelDB:120692" + } + ], + "timestamp_created": "2024-01-11 15:55:11.907479+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/120692", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "885": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 885, + "name": "Generating oscillatory bursts from a network of regular spiking neurons (Shao et al. 2009)", + "repository_type": "github", + "summary": "Avian nucleus isthmi pars parvocellularis (Ipc) neurons are reciprocally connected with the tectal layer 10 (L10) neurons and respond with oscillatory bursts to visual stimulation. To elucidate mechanisms of oscillatory bursting in this network of regularly spiking neurons, we investigated an experimentally constrained model of coupled leaky integrate-and-fire neurons with spike-rate adaptation. The model reproduces the observed Ipc oscillatory bursting in response to simulated visual stimulation.", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 800, + "tag": "Vision" + }, + { + "id": 1438, + "tag": "ModelDB:120783" + } + ], + "timestamp_created": "2024-01-11 15:55:12.390998+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/120783", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "886": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 886, + "name": "Membrane potential changes in dendritic spines during APs and synaptic input (Palmer & Stuart 2009)", + "repository_type": "github", + "summary": "\" ...\r\nFinally, we used simulations of our experimental observations in\r\nmorphologically realistic models to estimate spine neck resistance.\r\n\r\nThese simulations indicated that spine neck resistance ranges up\r\nto ~500 M Ohm.\r\n\r\nSpine neck resistances of this magnitude reduce somatic EPSPs by ~15%,\r\nindicating that the spine neck is unlikely to act as a physical device\r\nto significantly modify synaptic strength.\"", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 1439, + "tag": "ModelDB:120798" + } + ], + "timestamp_created": "2024-01-11 15:55:12.836195+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/120798", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "887": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 887, + "name": "A model of beta-adrenergic modulation of IKs in the guinea-pig ventricle (Severi et al. 2009)", + "repository_type": "github", + "summary": "Detailed understanding of IKs gating complexity may provide clues on the mechanisms of cardiac repolarization instability and the resulting arrhythmias. We developed and tested a kinetic Markov model to interpret physiologically relevant IKs properties, including pause-dependency and modulation by beta-adrenergic receptors (beta-AR). The model was developed from the Silva & Rudy formulation. Parameters were optimized on control and ISO experimental data, respectively.", + "tags": [ + { + "id": 868, + "tag": "I_Ks" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 907, + "tag": "Simulink" + }, + { + "id": 1440, + "tag": "ModelDB:120835" + } + ], + "timestamp_created": "2024-01-11 15:55:13.280930+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/120835", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "888": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 888, + "name": "Mechanisms of very fast oscillations in axon networks coupled by gap junctions (Munro, Borgers 2010)", + "repository_type": "github", + "summary": "Axons connected by gap junctions can produce very fast oscillations (VFOs, > 80 Hz) when stimulated randomly at a low rate. The models here explore the mechanisms of VFOs that can be seen in an axonal plexus, (Munro & Borgers, 2009): a large network model of an axonal plexus, small network models of axons connected by gap junctions, and an implementation of the model underlying figure 12 in Traub et al. (1999) .\r\n\r\nThe large network model consists of 3,072 5-compartment axons connected in a random network. The 5-compartment axons are the 5 axonal compartments from the CA3 pyramidal cell model in Traub et al. (1994) with a fixed somatic voltage. The random network has the same parameters as the random network in Traub et al. (1999), and axons are stimulated randomly via a Poisson process with a rate of 2/s/axon. \r\n\r\nThe small network models simulate waves propagating through small networks of axons connected by gap junctions to study how local connectivity affects the refractory period.\r\n", + "tags": [ + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 821, + "tag": "Conduction failure" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1441, + "tag": "ModelDB:120907" + } + ], + "timestamp_created": "2024-01-11 15:55:13.777532+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/120907", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "889": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 889, + "name": "Electrically-coupled Retzius neurons (Vazquez et al. 2009)", + "repository_type": "github", + "summary": "\"Dendritic electrical coupling increases the number of effective synaptic inputs\r\nonto neurons by allowing the direct spread of synaptic potentials from one\r\nneuron to another. \r\n\r\nHere we studied the summation of excitatory postsynaptic potentials (EPSPs) produced \r\nlocally and arriving from the coupled neuron (transjunctional) in pairs of \r\nelectrically-coupled Retzius neurons of the leech.\r\n\r\nWe combined paired recordings of EPSPs, the production of artificial EPSPs\r\n(APSPs) in neuron pairs with different coupling coefficients and simulations of\r\nEPSPs produced in the coupled dendrites. \r\n...\"", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 1442, + "tag": "ModelDB:120910" + } + ], + "timestamp_created": "2024-01-11 15:55:14.243256+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/120910", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "890": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 890, + "name": "Effects of KIR current inactivation in NAc Medium Spiny Neurons (Steephen and Manchanda 2009)", + "repository_type": "github", + "summary": "\"Inward rectifying potassium (KIR) currents in medium spiny (MS) neurons of nucleus accumbens inactivate significantly in ~40% of the neurons but not in the rest, which may lead to differences in input processing by these two groups. \r\n\r\nUsing a 189-compartment computational model of the MS neuron, we investigate the influence of this property using injected current as well as spatiotemporally distributed synaptic inputs. \r\n\r\nOur study demonstrates that KIR current inactivation facilitates depolarization, firing frequency and firing onset in these neurons. ...\"", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 830, + "tag": "Delay" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 923, + "tag": "I Krp" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 925, + "tag": "I R" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 818, + "tag": "I p,q" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 1443, + "tag": "ModelDB:121060" + } + ], + "timestamp_created": "2024-01-11 15:55:14.730641+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/121060", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "891": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 891, + "name": "Submyelin Potassium accumulation in Subthalamic neuron (STN) axons (Bellinger et al. 2008)", + "repository_type": "github", + "summary": "\"To better understand the direct effects of DBS (Deep brain stimulation) on central neurons, a computational model of a myelinated axon has been constructed which includes the effects of K+ accumulation within the peri-axonal space.\r\nUsing best estimates of anatomic and electrogenic model parameters for in vivo STN axons, the model predicts a functional block along the axon due to K+ accumulation in the submyelin space.\r\n...\r\nThese results suggest that therapeutic DBS of the STN likely results in a functional block for many STN axons, although a subset of STN axons may also be activated at the stimulating frequency.\r\n\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 834, + "tag": "Deep brain stimulation" + }, + { + "id": 951, + "tag": "Depolarization block" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 868, + "tag": "I_Ks" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 816, + "tag": "Na/K pump" + }, + { + "id": 817, + "tag": "Sodium pump" + }, + { + "id": 1444, + "tag": "ModelDB:121253" + } + ], + "timestamp_created": "2024-01-11 15:55:15.213528+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/121253", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "892": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 892, + "name": "Small world networks of Type I and Type II Excitable Neurons (Bogaard et al. 2009)", + "repository_type": "github", + "summary": "Implemented with NEURON 5.9, four model neurons with varying excitability properties affect the spatiotemporal patterning of small world networks of homogeneous and heterogeneous cell population.", + "tags": [ + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 1445, + "tag": "ModelDB:121259" + } + ], + "timestamp_created": "2024-01-11 15:55:15.698075+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/121259", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "893": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 893, + "name": "Thalamic neuron: Modeling rhythmic neuronal activity (Meuth et al. 2005)", + "repository_type": "github", + "summary": "The authors use an in vitro cell model of a single acutely isolated thalamic neuron in the NEURON simulation environment to address and discuss questions in an undergraduate course. Topics covered include passive electrical properties, composition of action potentials, trains of action potentials, multicompartment modeling, and research topics. The paper includes detailed instructions on how to run the simulations in the appendix.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + }, + { + "id": 1446, + "tag": "ModelDB:121600" + } + ], + "timestamp_created": "2024-01-11 15:55:16.171551+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/121600", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "894": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 894, + "name": "Software for teaching neurophysiology of neuronal circuits (Grisham et al. 2008)", + "repository_type": "github", + "summary": "\"To circumvent the many problems in teaching neurophysiology as a \u201cwet lab,\u201d we developed SWIMMY, a virtual fish that swims by moving its virtual tail by means of a virtual neural circuit.\r\n...\r\nUsing SWIMMY, students (1) review the basics of neurophysiology, (2) identify the neurons in the circuit, (3) ascertain the neurons\u2019 synaptic interconnections, (4) discover which cells generate the motor pattern of swimming, (5) discover how the rhythm is generated, and finally (6) use an animation that corresponds to the activity of the motoneurons to discover the behavioral effects produced by various lesions and explain them in terms of their neural underpinnings.\r\n...\"", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 819, + "tag": "NEURON (web link to model)" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + }, + { + "id": 1447, + "tag": "ModelDB:121628" + } + ], + "timestamp_created": "2024-01-11 15:55:16.636540+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/121628", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "895": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 895, + "name": "Model of SK current`s influence on precision in Globus Pallidus Neurons (Deister et al. 2009)", + "repository_type": "github", + "summary": "\" ... In numerical simulations, the availability of both Na+ and A-type K+ channels during autonomous firing were reduced when SK channels were removed, and a nearly equal reduction in Na+ and K+ subthreshold-activated ion channel availability produced a large decrease in the neuron's slope conductance near threshold. \r\n\r\nThis change made the neuron more sensitive to intrinsically generated noise.\r\n\r\nIn vivo, this change would also enhance the sensitivity of GP (Globus Pallidus) neurons to small synaptic inputs.\"\r\n", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 952, + "tag": "Noise Sensitivity" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1448, + "tag": "ModelDB:122329" + } + ], + "timestamp_created": "2024-01-11 15:55:17.104658+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/122329", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "896": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 896, + "name": "Investigation of different targets in deep brain stimulation for Parkinson`s (Pirini et al. 2009)", + "repository_type": "github", + "summary": "\"We investigated by a computational model of the basal ganglia the different network effects of deep brain stimulation (DBS) for Parkinson\u2019s disease (PD) in different target sites in the subthalamic nucleus (STN), the globus pallidus pars interna (GPi), and the globus pallidus pars externa (GPe). \r\n\r\nA cellular-based model of the basal ganglia system (BGS), based on the model proposed by Rubin and Terman (J Comput Neurosci 16:211\u2013235, 2004), was developed.\r\n...\r\nOur results suggest that DBS in the STN could functionally restore the TC relay activity, while DBS in the GPe and in the GPi could functionally over-activate and inhibit it, respectively.\r\n\r\nOur results are consistent with the experimental and the clinical evidences on the network effects of DBS.\"\r\n", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 834, + "tag": "Deep brain stimulation" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 1449, + "tag": "ModelDB:122369" + } + ], + "timestamp_created": "2024-01-11 15:55:17.564382+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/122369", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "897": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 897, + "name": "Tight junction model of CNS myelinated axons (Devaux and Gow 2008)", + "repository_type": "github", + "summary": "Two models are included:\r\n1) a myelinated axon is represented by an equivalent circuit with a double cable design but includes a tight junction in parallel with the myelin membrane RC circuit (called double cable model, DCM).\r\n\r\n2) a myelinated axon is represented by an equivalent circuit with a double cable design but includes a tight junction in series with the myelin RC circuit (called tight junction model, TJM).\r\n\r\nThese models have been used to simulate data from compound action potentials measured in mouse optic nerve from Claudin 11-null mice in Fig. 6 of:\r\nDevaux, J.J. & Gow, A. (2008) Tight Junctions Potentiate The Insulative Properties Of Small CNS Myelinated Axons. J Cell Biol 183, 909-921.\r\n", + "tags": [ + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1450, + "tag": "ModelDB:122442" + } + ], + "timestamp_created": "2024-01-11 15:55:18.028455+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/122442", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "898": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 898, + "name": "CONFIGR: a vision-based model for long-range figure completion (Carpenter et al. 2007)", + "repository_type": "github", + "summary": "\"CONFIGR (CONtour FIgure GRound) is a computational model based on\r\nprinciples of\r\nbiological vision that completes sparse and noisy image figures.\r\n\r\nWithin an integrated\r\nvision/recognition system, CONFIGR posits an initial recognition\r\nstage which identifies figure\r\npixels from spatially local input information.\r\n\r\nThe resulting, and typically incomplete, figure is\r\nfed back to the \u201cearly vision\u201d stage for long-range completion via\r\nfilling-in.\r\n\r\nThe reconstructed\r\nimage is then re-presented to the recognition system for global\r\nfunctions such as object\r\nrecognition.\r\n\r\n...\r\nMulti-scale simulations illustrate the\r\nvision/recognition system. \r\n\r\n...\"\r\n", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 1451, + "tag": "ModelDB:123086" + } + ], + "timestamp_created": "2024-01-11 15:55:18.509570+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/123086", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "899": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 899, + "name": "Effect of voltage sensitive fluorescent proteins on neuronal excitability (Akemann et al. 2009)", + "repository_type": "github", + "summary": "\"Fluorescent protein voltage sensors are recombinant proteins that are designed as genetically encoded cellular\r\nprobes of membrane potential using mechanisms of voltage-dependent modulation of fluorescence. \r\n\r\nSeveral such proteins,\r\nincluding VSFP2.3 and VSFP3.1, were recently reported with reliable function in mammalian cells. \r\n\r\n...\r\n\r\nExpression of these proteins in cell membranes is accompanied by additional dynamic membrane capacitance, ...\r\n\r\nWe used recordings of\r\nsensing currents and fluorescence responses of VSFP2.3 and of VSFP3.1 to derive kinetic models of the voltage-dependent\r\nsignaling of these proteins. \r\n\r\nUsing computational neuron simulations, we quantitatively investigated the perturbing effects of\r\nsensing capacitance on the input/output relationship in two central neuron models, a cerebellar Purkinje and a layer 5 pyramidal\r\nneuron. \r\n... \". The Purkinje cell model is included in ModelDB.", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1452, + "tag": "ModelDB:123453" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:53:04.023316+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/123453", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "900": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 900, + "name": "Hodgkin-Huxley models of different classes of cortical neurons (Pospischil et al. 2008)", + "repository_type": "github", + "summary": "\"We review here the development of Hodgkin-\r\nHuxley (HH) type models of cerebral cortex and thalamic\r\nneurons for network simulations.\r\n\r\nThe intrinsic electrophysiological\r\nproperties of cortical neurons were analyzed from\r\nseveral preparations, and we selected the four most prominent\r\nelectrophysiological classes of neurons.\r\n\r\nThese four classes\r\nare 'fast spiking', 'regular spiking', 'intrinsically bursting'\r\nand 'low-threshold spike' cells. For each class, we fit 'minimal'\r\nHH type models to experimental data.\r\n...\"", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1453, + "tag": "ModelDB:123623" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:53:04.876123+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/123623", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "901": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 901, + "name": "Encoding and retrieval in a model of the hippocampal CA1 microcircuit (Cutsuridis et al. 2009)", + "repository_type": "github", + "summary": "This NEURON code implements a small network model (100 pyramidal cells\r\nand 4 types of inhibitory interneuron) of storage and recall of patterns\r\nin the CA1 region of the mammalian hippocampus. Patterns of PC activity\r\nare stored either by a predefined weight matrix generated by Hebbian learning,\r\nor by STDP at CA3 Schaffer collateral AMPA synapses.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 935, + "tag": "Connectivity matrix" + }, + { + "id": 908, + "tag": "Learning" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1454, + "tag": "ModelDB:123815" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 1455, + "tag": "Storage/recall" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + } + ], + "timestamp_created": "2024-01-12 09:53:05.671368+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/123815", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "902": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 902, + "name": "Na channel mutations in the dentate gyrus (Thomas et al. 2009)", + "repository_type": "github", + "summary": "These are source files to generate the data in Figure 6 from\r\n\"Mossy fiber sprouting interacts with sodium channel\r\nmutations to increase dentate gyrus excitability\" Thomas EA, Reid CA, Petrou S,\r\nEpilepsia (2009)", + "tags": [ + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1456, + "tag": "ModelDB:123848" + }, + { + "id": 1457, + "tag": "parplex" + } + ], + "timestamp_created": "2024-01-12 09:53:06.240696+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/123848", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "903": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 903, + "name": "Action Potential initiation and backpropagation in Neocortical L5 Pyramidal Neuron (Hu et al. 2009)", + "repository_type": "github", + "summary": "\"...Previous computational studies have yielded conflicting conclusions\r\nabout the role of Na+ channel density and biophysical properties in\r\naction potential initiation as a result of inconsistent estimates of\r\nchannel density. Our modeling studies integrated the immunostaining\r\nand electrophysiological results and showed that the lowest\r\nthreshold for action potential initiation at the distal AIS was largely\r\ndetermined by the density of low-threshold Nav1.6 channels ... Distinct from the function of Nav1.6 channel, the Nav1.2 channel\r\nmay control action potential backpropagation because of its high\r\ndensity at the proximal AIS and high threshold. ... In conclusion, distal AIS accumulation of Nav1.6 channels determines\r\nthe low threshold for action potential initiation; whereas\r\nproximal AIS accumulation of Nav1.2 channels sets the threshold for\r\nthe generation of somatodendritic potentials and ensures action\r\npotential backpropagation to the soma and dendrites. Thus, Nav1.6\r\nand Nav1.2 channels serve distinct functions in action potential\r\ninitiation and backpropagation.\"", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1458, + "tag": "ModelDB:123897" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:53:06.812633+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/123897", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "904": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 904, + "name": "Complex CA1-neuron to study AP initiation (Wimmer et al. 2010)", + "repository_type": "github", + "summary": "Complex model of a pyramidal CA1-neuron, adapted from Royeck, M., et al. Role of axonal NaV1.6 sodium channels in action potential\r\ninitiation of CA1 pyramidal neurons. Journal of neurophysiology 100, 2361-2380\r\n(2008).\r\nIt contains a biophysically realistic morphology comprising 265 compartments (829 segments) and 15 different distributed Ca2+- and/or voltage-dependent conductances.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 818, + "tag": "I p,q" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1459, + "tag": "ModelDB:123927" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:53:07.469953+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/123927", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "905": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 905, + "name": "Oscillations, phase-of-firing coding and STDP: an efficient learning scheme (Masquelier et al. 2009)", + "repository_type": "github", + "summary": "The model demonstrates how a common oscillatory drive for a group of neurons formats and reliabilizes their spike times - through an activation-to-phase conversion - so that repeating activation patterns can be easily detected and learned by a downstream neuron equipped with STDP, and then recognized in just one oscillation cycle.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 9, + "tag": "Brian" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1460, + "tag": "ModelDB:123928" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 886, + "tag": "Unsupervised Learning" + } + ], + "timestamp_created": "2024-01-12 09:53:08.179581+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/123928", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "906": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 906, + "name": "Oscillation and coding in a proposed NN model of insect olfaction (Horcholle-Bossavit et al. 2007)", + "repository_type": "github", + "summary": "\"For the analysis of coding mechanisms in the insect olfactory system, a fully connected network of synchronously updated\r\nMcCulloch and Pitts neurons (MC-P type) was (previously) developed. ... Considering the update time as an intrinsic clock, this \u201cDynamic\r\nNeural Filter\u201d (DNF), which maps regions of input space into spatio-temporal sequences of neuronal activity, is able to produce\r\nexact binary codes extracted from the synchronized activities recorded at the level of projection neurons (PN) in the locust antennal\r\nlobe (AL) in response to different odors\r\n...\r\nWe find synaptic matrices which lead to both the emergence of robust oscillations and spatio-temporal patterns, using a\r\nformal criterion, based on a Normalized Euclidian Distance (NED), in order to measure the use of the temporal dimension as a\r\ncoding dimension by the DNF. Similarly to biological PN, the activity of excitatory neurons in the model can be both phase-locked\r\nto different cycles of oscillations which (is reminiscent of the) local field potential (LFP), and nevertheless exhibit dynamic behavior complex\r\nenough to be the basis of spatio-temporal codes.\"", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1461, + "tag": "ModelDB:123986" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + } + ], + "timestamp_created": "2024-01-12 09:53:08.840070+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/123986", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "907": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 907, + "name": "Neural model of frog ventilatory rhythmogenesis (Horcholle-Bossavit and Quenet 2009)", + "repository_type": "github", + "summary": "\"In the adult frog respiratory system, periods of rhythmic movements of the buccal floor are interspersed\r\nby lung ventilation episodes. \r\n\r\nThe ventilatory activity results from the interaction of two hypothesized\r\noscillators in the brainstem. \r\n\r\nHere, we model these oscillators with two coupled neural networks, whose\r\nco-activation results in the emergence of new dynamics. \r\n\r\n..\r\nThe biological interest of this formal model is illustrated by\r\nthe persistence of the relevant dynamical features when perturbations are introduced in the model, i.e.\r\ndynamic noises and architecture modifications. \r\n\r\nThe implementation of the networks with clock-driven\r\ncontinuous time neurones provides simulations with physiological time scales.\"", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1462, + "tag": "ModelDB:123987" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + } + ], + "timestamp_created": "2024-01-12 09:53:09.490198+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/123987", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "908": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 908, + "name": "Synaptic integration in tuft dendrites of layer 5 pyramidal neurons (Larkum et al. 2009)", + "repository_type": "github", + "summary": "Simulations used in the paper. Voltage responses to current injections in different tuft locations; NMDA and calcium spike generation. Summation of multiple input distribution.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 818, + "tag": "I p,q" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1463, + "tag": "ModelDB:124043" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:53:10.121932+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/124043", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "909": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 909, + "name": "A network model of the vertebrate retina (Publio et al. 2009)", + "repository_type": "github", + "summary": "In this work, we use a minimal conductance-based model of the ON rod pathways in the vertebrate retina to study the effects of electrical synaptic coupling via gap junctions among rods and among AII amacrine cells on the dynamic range of the retina. The model is also used to study the effects of the maximum conductance of rod hyperpolarization activated current Ih on the dynamic range of the retina, allowing a study of the interrelations between this intrinsic membrane parameter with those two retina connectivity characteristics.", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1464, + "tag": "ModelDB:124063" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 845, + "tag": "Sensory processing" + } + ], + "timestamp_created": "2024-01-12 09:53:10.640502+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/124063", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "910": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 910, + "name": "A contracting model of the basal ganglia (Girard et al. 2008)", + "repository_type": "github", + "summary": "Basal ganglia model : selection processes between channels, dynamics controlled by contraction analysis, rate-coding model of neurons based on locally projected dynamical systems (lPDS).", + "tags": [ + { + "id": 857, + "tag": "Action Selection/Decision Making" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1465, + "tag": "ModelDB:124111" + }, + { + "id": 89, + "tag": "Python" + } + ], + "timestamp_created": "2024-01-12 09:53:11.242074+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/124111", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "911": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 911, + "name": "Dentate Gyrus Feed-forward inhibition (Ferrante et al. 2009)", + "repository_type": "github", + "summary": "In this paper, the model was used to show how that FFI can change a steeply sigmoidal input-output (I/O) curve into a double-sigmoid typical of buffer systems.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1466, + "tag": "ModelDB:124291" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 952, + "tag": "Noise Sensitivity" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2024-01-12 09:53:11.824848+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/124291", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "912": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 912, + "name": "Alternative time representation in dopamine models (Rivest et al. 2009)", + "repository_type": "github", + "summary": "Combines a long short-term memory (LSTM) model of the cortex to a temporal difference learning (TD) model of the basal ganglia. Code to run simulations similar to the published data: Rivest, F, Kalaska, J.F., Bengio, Y. (2009) Alternative time representation in dopamine models. Journal of Computational Neuroscience. \r\nSee http://dx.doi.org/10.1007/s10827-009-0191-1 for details.", + "tags": [ + { + "id": 891, + "tag": "Hebbian plasticity" + }, + { + "id": 864, + "tag": "Java" + }, + { + "id": 908, + "tag": "Learning" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1467, + "tag": "ModelDB:124329" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 892, + "tag": "Reinforcement Learning" + }, + { + "id": 886, + "tag": "Unsupervised Learning" + }, + { + "id": 877, + "tag": "Working memory" + } + ], + "timestamp_created": "2024-01-12 09:53:12.444280+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/124329", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "913": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 913, + "name": "Epilepsy may be caused by very small functional changes in ion channels (Thomas et al. 2009)", + "repository_type": "github", + "summary": "We used a previously published model of the dentate gyrus with varying degrees of mossy fibre sprouting.We preformed a sensitivity analysis where we systematically varied individual properties of ion channels. The results predict that genetic variations in the properties of sodium channels are likely to have the biggest impact on network excitability. Furthermore, these changes may be as small as 1mV, which is currently undetectable using standard experimental practices.", + "tags": [ + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1468, + "tag": "ModelDB:124392" + }, + { + "id": 1457, + "tag": "parplex" + } + ], + "timestamp_created": "2024-01-12 09:53:12.972493+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/124392", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "914": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 914, + "name": "Dendritic Na+ spike initiation and backpropagation of APs in active dendrites (Nevian et al. 2007)", + "repository_type": "github", + "summary": "NEURON model used to create simulations shown in figure 6 of the paper. The model includes two point processes; one for dendritic spike initiation and the other for somatic action potential generation. The effect of filtering by imperfect recording electrode can be examined in somatic and dendritic locations.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1469, + "tag": "ModelDB:124394" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:53:13.572270+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/124394", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "915": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 915, + "name": "Dentate gyrus (Morgan et al. 2007, 2008, Santhakumar et al. 2005, Dyhrfjeld-Johnsen et al. 2007)", + "repository_type": "github", + "summary": "This model was implemented by Rob Morgan in the Soltesz lab at UC Irvine. It is a scaleable model of the rat dentate gyrus including four cell types. This model runs in serial (on a single processor) and has been published at the size of 50,000 granule cells (with proportional numbers of the other cells).", + "tags": [ + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1470, + "tag": "ModelDB:124513" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:53:14.162515+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/124513", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "916": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 916, + "name": "Reduced leech heart interneuron (Channell et al. 2009)", + "repository_type": "github", + "summary": "\"Spiking and bursting patterns of neurons are characterized by a high degree of variability.\r\n\r\nA single neuron can demonstrate endogenously various bursting patterns, changing in response to external disturbances\r\ndue to synapses, or to intrinsic factors such as channel noise.\r\n\r\nWe argue that in a model of the leech heart interneuron existing variations of bursting patterns are\r\nsignificantly enhanced by a small noise.\r\n\r\nIn the absence of noise this model shows periodic bursting with fixed\r\nnumbers of interspikes for most parameter values. ...\"\r\n", + "tags": [ + { + "id": 1471, + "tag": "CONTENT" + }, + { + "id": 1472, + "tag": "Dynamics Solver" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1473, + "tag": "ModelDB:125125" + } + ], + "timestamp_created": "2024-01-12 09:53:14.649087+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/125125", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "917": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 917, + "name": "CA1 pyramidal neuron to study INaP properties and repetitive firing (Uebachs et al. 2010)", + "repository_type": "github", + "summary": "A model of a CA1 pyramidal neuron containing a biophysically realistic morphology and 15 distributed voltage and Ca2+-dependent conductances. Repetitive firing is modulated by maximal conductance and the\r\nvoltage dependence of the persistent Na+ current (INaP).", + "tags": [ + { + "id": 878, + "tag": "ATP-senstive potassium current" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 818, + "tag": "I p,q" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1474, + "tag": "ModelDB:125152" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:53:15.186697+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/125152", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "918": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 918, + "name": "Simulation studies on mechanisms of levetiracetam-mediated inhibition of IK(DR) (Huang et al. 2009)", + "repository_type": "github", + "summary": "Levetiracetam (LEV) is an S-enantiomer pyrrolidone derivative with established antiepileptic \r\nefficacy in generalized epilepsy and partial epilepsy. However, its effects on ion currents \r\nand membrane potential remain largely unclear. In this study, we investigated the effect of \r\nLEV on differentiated NG108-15 neurons. \r\n...\r\nSimulation studies in a modified \r\nHodgkin-Huxley neuron and network unraveled that the reduction of slowly inactivating IK(DR) resulted \r\nin membrane depolarization accompanied by termination of the firing of action potentials in a \r\nstochastic manner. Therefore, the inhibitory effects on slowly inactivating IK(DR) (Kv3.1-encoded \r\ncurrent) may constitute one of the underlying mechanisms through which LEV affects neuronal activity \r\nin vivo.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 855, + "tag": "I_KHT" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1475, + "tag": "ModelDB:125154" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:53:15.749090+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/125154", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "919": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 919, + "name": "Maximum entropy model to predict spatiotemporal spike patterns (Marre et al. 2009)", + "repository_type": "github", + "summary": "This MATLAB code implements a model-based analysis of spike trains.\r\nThe analysis predicts the occurrence of spatio-temporal patterns of\r\nspikes in the data, and is based on a maximum entropy principle by\r\nincluding both spatial and temporal correlations. The approach is\r\napplicable to unit recordings from any region of the brain.\r\n\r\nThe code is based on Marre, et al., 2009.\r\n\r\nThe MATLAB code was written by Sami El Boustani and Olivier Marre.\r\n", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 1476, + "tag": "Maximum entropy models" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1477, + "tag": "ModelDB:125290" + } + ], + "timestamp_created": "2024-01-12 09:53:16.310786+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/125290", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "920": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 920, + "name": "Availability of low-threshold Ca2+ current in retinal ganglion cells (Lee SC et al. 2003)", + "repository_type": "github", + "summary": "\"... we measured T-type current of isolated\r\ngoldfish retinal ganglion cells with perforated-patch voltageclamp\r\nmethods in solutions containing a normal extracellular Ca2+\r\nconcentration.\r\n\r\nThe voltage sensitivities and rates of current activation,\r\ninactivation, deactivation, and recovery from inactivation were similar\r\nto those of expressed +1G (CaV3.1) Ca2+ channel clones, except that\r\nthe rate of deactivation was significantly faster.\r\n\r\nWe reproduced the\r\namplitude and kinetics of measured T currents with a numerical\r\nsimulation based on a kinetic model developed for an +1G Ca2+\r\nchannel.\r\n\r\nFinally, we show that this model predicts the increase of\r\nT-type current made available between resting potential and spike\r\nthreshold by repetitive hyperpolarizations presented at rates that are\r\nwithin the bandwidth of signals processed in situ by these neurons.\"", + "tags": [ + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1478, + "tag": "ModelDB:125378" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:53:16.825063+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/125378", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "921": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 921, + "name": "Spike propagation in dendrites with stochastic ion channels (Diba et al. 2006)", + "repository_type": "github", + "summary": "\"We investigate the effects of the stochastic nature\r\nof ion channels on the faithfulness, precision and reproducibility\r\nof electrical signal transmission in weakly active,\r\ndendritic membrane under in vitro conditions. \r\n\r\n...\r\n\r\nWe numerically simulate the effects of stochastic ion\r\nchannels on the forward and backward propagation of dendritic\r\nspikes in Monte-Carlo simulations on a reconstructed\r\nlayer 5 pyramidal neuron. \r\n\r\nWe report that in most instances\r\nthere is little variation in timing or amplitude for a single\r\nBPAP, while variable backpropagation can occur for trains\r\nof action potentials. \r\n\r\nAdditionally, we find that the generation\r\nand forward propagation of dendritic Ca2+ spikes are\r\nsusceptible to channel variability. This indicates limitations\r\non computations that depend on the precise timing of Ca2+\r\nspikes.\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1479, + "tag": "ModelDB:125385" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:53:17.342966+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/125385", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "922": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 922, + "name": "Respiratory central pattern generator network in mammalian brainstem (Rubin et al. 2009)", + "repository_type": "github", + "summary": "This model is a reduced version of a spatially organized respiratory central pattern generation network consisting of four neuronal populations (pre-I, early-I, post-I, and aug-E). In this reduction, each population is represented by a single neuron, in an activity-based framework (which includes the persistent sodium current for the pre-I population). The model includes three sources of external drive and can produce several experimentally observed rhythms.", + "tags": [ + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1480, + "tag": "ModelDB:125529" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:53:17.948018+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/125529", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "923": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 923, + "name": "Ca2+-activated I_CAN and synaptic depression promotes network-dependent oscil. (Rubin et al. 2009)", + "repository_type": "github", + "summary": "\"... the preBotzinger complex...\r\nwe present and analyze a\r\nmathematical model demonstrating an unconventional mechanism\r\nof rhythm generation in which glutamatergic synapses and the\r\nshort-term depression of excitatory transmission play key rhythmogenic\r\nroles. \r\n\r\nRecurrent synaptic excitation triggers postsynaptic Ca2+-\r\nactivated nonspecific cation current (ICAN) to initiate a network-wide\r\nburst. \r\n\r\nRobust depolarization due to ICAN also causes voltage-dependent\r\nspike inactivation, which diminishes recurrent excitation and\r\nthus attenuates postsynaptic Ca2+ accumulation. \r\n...\"", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1481, + "tag": "ModelDB:125649" + }, + { + "id": 1482, + "tag": "NeuronetExperimenter (web link to model)" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 912, + "tag": "XPP (web link to model)" + } + ], + "timestamp_created": "2024-01-12 09:53:18.442077+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/125649", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "924": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 924, + "name": "Ca2+ current versus Ca2+ channel cooperativity of exocytosis (Matveev et al. 2009)", + "repository_type": "github", + "summary": "\"... While varying extracellular or intracellular Ca2+ concentration assesses the intrinsic biochemical Ca2+ cooperativity of \r\nneurotransmitter release, varying the number of open Ca2+ channels using pharmacological channel block or the tail current \r\ntitration probes the cooperativity between individual Ca2+ channels in triggering exocytosis. \r\n...\r\n\r\nHere we provide a detailed analysis of the Ca2+ sensitivity measures probed by these experimental\r\nprotocols, present simple expressions for special cases, and demonstrate the distinction between the Ca2+ current cooperativity, defined\r\nby the relationship between exocytosis rate and the whole-terminal Ca2+ current magnitude, and the underlying Ca2+ channel cooperativity,\r\ndefined as the average number of channels involved in the release of a single vesicle. \r\n...\r\nFurther, we use three-dimensional computational modeling of buffered Ca2+\r\ndiffusion to analyze these distinct Ca2+ cooperativity measures, and demonstrate the role of endogenous Ca2+ buffers on such measures.\r\n\r\nWe show that buffers can either increase or decrease the Ca2+ current cooperativity of exocytosis, depending on their concentration and\r\nthe single-channel Ca2+ current.\"", + "tags": [ + { + "id": 947, + "tag": "CalC Calcium Calculator (web link to model)" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1483, + "tag": "ModelDB:125676" + } + ], + "timestamp_created": "2024-01-12 09:53:18.951254+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/125676", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "925": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 925, + "name": "Deterministic chaos in a mathematical model of a snail neuron (Komendantov and Kononenko 1996)", + "repository_type": "github", + "summary": "\"Chaotic regimes in a mathematical model of pacemaker activity in the bursting neurons of a snail Helix pomatia, have been investigated.\r\n\r\nThe model includes a slow-wave generating mechanism, a spike-generating mechanism, an inward Ca current, intracellular Ca ions, [Ca2+]in, their fast buffering and uptake by intracellular Ca stores, and a [Ca2+]in-inhibited Ca current.\r\n\r\nChemosensitive voltage-activated conductance, gB*, responsible for termination of the spike burst, and chemosensitive sodium conductance, gNa*, responsible for the depolarization phase of the slow-wave, were used as control parameters.\r\n...\r\nTime courses of the membrane potential and [Ca2+]in were employed to analyse different regimes in the model. \r\n...\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1484, + "tag": "ModelDB:125683" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:53:19.496156+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/125683", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "926": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 926, + "name": "Balance of excitation and inhibition (Carvalho and Buonomano 2009)", + "repository_type": "github", + "summary": "\" ... \r\nHere, theoretical analyses reveal that excitatory synaptic\r\nstrength controls the threshold of the neuronal\r\ninput-output function, while inhibitory plasticity\r\nalters the threshold and gain.\r\n\r\nExperimentally, changes in the balance of excitation and inhibition\r\nin CA1 pyramidal neurons also altered their input-output\r\nfunction as predicted by the model.\r\n\r\nThese results support the existence of two functional\r\nmodes of plasticity that can be used to optimize\r\ninformation processing: threshold and gain plasticity.\"", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1485, + "tag": "ModelDB:125689" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:53:20.124531+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/125689", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "927": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 927, + "name": "Hippocampus CA1: Temporal sensitivity of signaling pathways underlying LTP (Kim et al. 2010)", + "repository_type": "github", + "summary": "Temporal sensitivity of signaling pathways underlying L-LTP. Single compartment, deterministic model of calcium and dopamine activated pathways, leading to CaMKII and PKA activation. Experimental verification of model prediction.", + "tags": [ + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1486, + "tag": "ModelDB:125733" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:53:20.654218+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/125733", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "928": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 928, + "name": "Calcium waves in neuroblastoma cells (Fink et al. 2000)", + "repository_type": "github", + "summary": "Uses a model of IP3-mediated release of Ca from endoplasmic reticulum (ER) to study how initiation and propagation of Ca waves are affected by cell geometry, spatial distributions of ER and IP3 generation, and diffusion of Ca and mobile buffer.\r\n", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 875, + "tag": "Calcium waves" + }, + { + "id": 901, + "tag": "I_SERCA" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1487, + "tag": "ModelDB:125745" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 867, + "tag": "Virtual Cell (web link to model)" + } + ], + "timestamp_created": "2024-01-12 09:53:21.184721+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/125745", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "929": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 929, + "name": "Huntington`s disease model (Gambazzi et al. 2010)", + "repository_type": "github", + "summary": "\"Although previous studies of Huntington\u2019s disease (HD) have addressed many potential mechanisms of striatal neuron dysfunction and death, it is also known based on clinical findings that cortical function is dramatically disrupted in HD. With respect to disease etiology, however, the specific molecular and neuronal circuit bases for the cortical effects of mutant huntingtin (htt) have remained largely unknown. \r\n\r\nIn the present work we studied the relation between the molecular effects of mutant htt fragments in cortical cells and the corresponding behavior of cortical neuron microcircuits using a novel cellular model of HD. We observed that a transcript-selective diminution in activity-dependent BDNF expression preceded the onset of a synaptic connectivity deficit in ex vivo cortical networks, which manifested as decreased spontaneous collective burst-firing behavior measured by multi-electrode array substrates. Decreased BDNF expression was determined to be a significant contributor to network-level dysfunction, as shown by the ability of exogenous BDNF to ameliorate cortical microcircuit burst firing. \r\n\r\nThe molecular determinants of the dysregulation of activity-dependent BDNF expression by mutant htt appear to be distinct from previously elucidated mechanisms, as they do not involve known NRSF/REST-regulated promoter sequences, but instead result from dysregulation of BDNF exon IV and VI transcription. These data elucidate a novel HD-related deficit in BDNF gene regulation as a plausible mechanism of cortical neuron hypoconnectivity and cortical function deficits in HD. Moreover, the novel model paradigm established here is well-suited to further mechanistic and drug screening research applications.\r\n\r\nA simple mathematical model is proposed to interpret the observations and to explore the impact of specific synaptic dysfunctions on network activity. Interestingly, the model predicts a decrease in synaptic connectivity to be an early effect of mutant huntingtin in cortical neurons, supporting the hypothesis of decreased, rather than increased, synchronized cortical firing in HD.\"", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 1488, + "tag": "Huntington's" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1489, + "tag": "ModelDB:125748" + }, + { + "id": 872, + "tag": "Pathophysiology" + } + ], + "timestamp_created": "2024-01-12 09:53:21.740554+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/125748", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "930": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 930, + "name": "Long-term adaptation with power-law dynamics (Zilany et al. 2009)", + "repository_type": "github", + "summary": "...\r\nA model of rate adaptation at the synapse between inner hair cells and auditory-nerve\r\n(AN) fibers that includes both exponential and power-law dynamics is presented here.\r\n\r\nExponentially\r\nadapting components with rapid and short-term time constants, which are mainly responsible for\r\nshaping onset responses, are followed by two parallel paths with power-law adaptation that provide\r\nslowly and rapidly adapting responses.\r\n\r\n...\r\nThe proposed model is capable of accurately predicting several sets of AN data,\r\nincluding amplitude-modulation transfer functions, long-term adaptation, forward masking, and\r\nadaptation to increments and decrements in the amplitude of an ongoing stimulus.", + "tags": [ + { + "id": 849, + "tag": "Audition" + }, + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1490, + "tag": "ModelDB:125855" + } + ], + "timestamp_created": "2024-01-12 09:53:22.262581+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/125855", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "931": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 931, + "name": "A Fast Rhythmic Bursting Cell: in vivo cell modeling (Lee 2007)", + "repository_type": "github", + "summary": "One of the cellular mechanisms underlying the generation of gamma oscillations is a type of cortical pyramidal neuron named fast rhythmic bursting (FRB) cells. After cells from cats' primary visual cortices were filled with Neurobiotin, the brains were cut, and the cells were photographed. One FRB cell was chosen to be confocaled, reconstructed with Neurolucida software, and generated a detailed multi-compartmental model in the NEURON program. We explore firing properties of FRB cells and the role of enhanced Na+ conductance.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 940, + "tag": "I_K,Na" + }, + { + "id": 900, + "tag": "I_Na,Ca" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1491, + "tag": "ModelDB:125857" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 800, + "tag": "Vision" + } + ], + "timestamp_created": "2024-01-12 09:53:22.794960+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/125857", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "932": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 932, + "name": "Integrate and fire model code for spike-based coincidence-detection (Heinz et al. 2001, others)", + "repository_type": "github", + "summary": "Model code relevant to three papers; two on level discrimination and one on masked detection at low frequencies.", + "tags": [ + { + "id": 849, + "tag": "Audition" + }, + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1492, + "tag": "ModelDB:126052" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:53:23.383411+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/126052", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "933": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 933, + "name": "Model of neural responses to amplitude-modulated tones (Nelson and Carney 2004)", + "repository_type": "github", + "summary": "\"A phenomenological model with time-varying excitation and inhibition was developed to study\r\npossible neural mechanisms underlying changes in the representation of temporal envelopes along\r\nthe auditory pathway. A modified version of an existing auditory-nerve model (Zhang et al., J.\r\nAcoust. Soc. Am. 109, 648\u2013670 (2001) was used to provide inputs to higher hypothetical\r\nprocessing centers. \r\n\r\nModel responses were compared directly to published physiological data at three\r\nlevels: the auditory nerve, ventral cochlear nucleus, and inferior colliculus.\r\n...\"", + "tags": [ + { + "id": 849, + "tag": "Audition" + }, + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1493, + "tag": "ModelDB:126096" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2024-01-12 09:53:23.889097+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/126096", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "934": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 934, + "name": "A kinetic model of dopamine- and calcium-dependent striatal synaptic plasticity (Nakano et al. 2010)", + "repository_type": "github", + "summary": "A signaling pathway model of spines that express D1-type dopamine receptors was constructed to analyze the dynamic mechanisms of dopamine- and calcium-dependent plasticity. \r\n\r\nThe model incorporated all major signaling molecules, including dopamine- and cyclic AMP-regulated phosphoprotein with a molecular weight of 32 kDa (DARPP32), as well as AMPA receptor trafficking in the post-synaptic membrane. Simulations with dopamine and calcium inputs reproduced dopamine- and calcium-dependent plasticity. \r\n\r\nFurther in silico experiments revealed that the positive feedback loop consisted of protein kinase A (PKA), protein phosphatase 2A (PP2A), and the phosphorylation site at threonine 75 of DARPP-32 (Thr75) served as the major switch for inducing LTD and LTP. \r\n\r\nThe present model elucidated the mechanisms involved in bidirectional regulation of corticostriatal synapses and will allow for further exploration into causes and therapies for dysfunctions such as drug addiction.\"", + "tags": [ + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1494, + "tag": "ModelDB:126098" + }, + { + "id": 827, + "tag": "Signaling pathways" + } + ], + "timestamp_created": "2024-01-12 09:53:24.395091+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/126098", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "935": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 935, + "name": "Encoding and discrimination of vowel-like sounds (Tan and Carney 2005)", + "repository_type": "github", + "summary": "\"The sensitivity of listeners to changes in the center frequency of vowel-like harmonic complexes as\r\na function of the center frequency of the complex cannot be explained by changes in the level of the\r\nstimulus [Lyzenga and Horst, J. Acoust. Soc. Am. 98, 1943\u20131955 (1995)]. \r\n\r\nRather, a complex pattern\r\nof sensitivity is seen; for a spectrum with a triangular envelope, the greatest sensitivity occurs when\r\nthe center frequency falls between harmonics, whereas for a spectrum with a trapezoidal envelope,\r\ngreatest sensitivity occurs when the center frequency is aligned with a harmonic.\r\n\r\nIn this study, the\r\nthresholds of a population model of auditory-nerve (AN) fibers were quantitatively compared to\r\nthese trends in psychophysical thresholds.\r\n\r\nSingle-fiber and population model responses were\r\nevaluated in terms of both average discharge rate and the combination of rate and timing\r\ninformation.\r\n...\"", + "tags": [ + { + "id": 849, + "tag": "Audition" + }, + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1495, + "tag": "ModelDB:126371" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + } + ], + "timestamp_created": "2024-01-12 09:53:24.890963+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/126371", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "936": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 936, + "name": "Auditory nerve spontaneous rate histograms (Jackson and Carney 2005)", + "repository_type": "github", + "summary": "Histograms of spontaneous rate estimates of auditory nerve are well reproduced by models with two or three spontaneous rates and long range dependence.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 849, + "tag": "Audition" + }, + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1496, + "tag": "ModelDB:126389" + } + ], + "timestamp_created": "2024-01-12 09:53:25.393909+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/126389", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "937": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 937, + "name": "Response properties of an integrate and fire model (Zhang and Carney 2005)", + "repository_type": "github", + "summary": "\"A computational technique is described for calculation of the interspike interval and poststimulus time histograms for the responses of an\r\nintegrate-and-fire model to arbitrary inputs.\r\n...\r\nFor stationary inputs, the regularity of the output was studied in detail for various model parameters.\r\n\r\nFor nonstationary inputs, the effects of the model parameters on the output synchronization index were explored.\r\n\r\n... these response properties have been reported for some cells in the ventral cochlear nucleus in the auditory brainstem.\r\n\"", + "tags": [ + { + "id": 849, + "tag": "Audition" + }, + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1497, + "tag": "ModelDB:126392" + } + ], + "timestamp_created": "2024-01-12 09:53:26.039288+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/126392", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "938": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 938, + "name": "Spike-Timing-Based Computation in Sound Localization (Goodman and Brette 2010)", + "repository_type": "github", + "summary": "\" ...\r\nIn neuron models consisting of spectro-temporal filtering and spiking\r\nnonlinearity, we found that the binaural structure induced by spatialized sounds is mapped to synchrony patterns that\r\ndepend on source location rather than on source signal. \r\n\r\nLocation-specific synchrony patterns would then result in the\r\nactivation of location-specific assemblies of postsynaptic neurons. \r\n\r\nWe designed a spiking neuron model which exploited\r\nthis principle to locate a variety of sound sources in a virtual acoustic environment using measured human head-related\r\ntransfer functions.\r\n...\"", + "tags": [ + { + "id": 9, + "tag": "Brian" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1498, + "tag": "ModelDB:126465" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2024-01-12 09:53:26.652745+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/126465", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "939": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 939, + "name": "Asynchronous irregular and up/down states in excitatory and inhibitory NNs (Destexhe 2009)", + "repository_type": "github", + "summary": "\"Randomly-connected networks of integrate-and-fire (IF) neurons are known to display asynchronous irregular (AI) activity states, which resemble the discharge activity recorded in the cerebral cortex of awake animals. \r\n\r\n...\r\nHere, we investigate the occurrence of AI states in networks of nonlinear IF neurons, such as the adaptive exponential IF (Brette-Gerstner-Izhikevich) model. This model can display intrinsic properties such as low-threshold spike (LTS), regular spiking (RS) or fast-spiking (FS). We successively investigate the oscillatory and AI dynamics of thalamic, cortical and thalamocortical networks using such models.\r\n...\"", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1499, + "tag": "ModelDB:126466" + }, + { + "id": 760, + "tag": "PyNN" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + } + ], + "timestamp_created": "2024-01-12 09:53:27.335451+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/126466", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "940": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 940, + "name": "Cardiac sarcomere dynamics (Negroni and Lascano 1996)", + "repository_type": "github", + "summary": "\"A muscle model establishing the link between cross-bridge dynamics and intracellular Ca2+ kinetics was assessed by simulation of experiments performed in isolated cardiac muscle.\r\n\r\nThe model is composed by the series arrangement of muscle units formed by inextensible thick and thin filaments in parallel with an elastic element.\r\n\r\nAttached cross-bridges act as independent force generators whose force is linearly related to the elongation of their elastic structure.\r\n\r\nCa2+ kinetics is described by a four-state system of sites on the thin filament associated with troponin C: sites with free troponin C (T), sites with Ca2+ bound to troponin C (TCa); sites with Ca2+ bound to troponin C and attached cross-bridges (TCa*); and sites with troponin C not associated with Ca2+ and attached cross-bridges (T*).\r\n\r\nThe intracellular Ca2+ concentration ([Ca2+]) is controlled solely by the sarcoplasmic reticulum through an inflow function and a saturated outflow pump function.\r\n...\"", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1500, + "tag": "ModelDB:126467" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:53:27.961423+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/126467", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "941": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 941, + "name": "Formation of synfire chains (Jun and Jin 2007)", + "repository_type": "github", + "summary": "\"Temporally precise sequences of neuronal spikes that span hundreds of milliseconds are observed in many brain areas,\r\nincluding songbird premotor nucleus, cat visual cortex, and primary motor cortex.\r\n\r\nSynfire chains\u2014networks in which groups of\r\nneurons are connected via excitatory synapses into a unidirectional chain\u2014are thought to underlie the generation of such\r\nsequences. \r\n\r\nIt is unknown, however, how synfire chains can form in local neural circuits, especially for long chains. \r\n\r\nHere, we\r\nshow through computer simulation that long synfire chains can develop through spike-time dependent synaptic plasticity and\r\naxon remodeling\u2014the pruning of prolific weak connections that follows the emergence of a finite number of strong\r\nconnections. \r\n...\"", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1501, + "tag": "ModelDB:126471" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 800, + "tag": "Vision" + } + ], + "timestamp_created": "2024-01-12 09:53:28.616724+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/126471", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "942": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 942, + "name": "Models analysis for auditory-nerve synapse (Zhang and Carney 2005)", + "repository_type": "github", + "summary": "\"A general mathematical approach was proposed to study phenomenological models of the\r\ninner-hair-cell and auditory-nerve (AN) synapse complex. Two models (Meddis, 1986; Westerman\r\nand Smith, 1988) were studied using this unified approach. The responses of both models to a\r\nconstant-intensity stimulus were described mathematically, and the relationship between model\r\nparameters and response characteristics was investigated. ...\". The paper then modifies these\r\nto make a more physiologically realistic model.\r\n", + "tags": [ + { + "id": 849, + "tag": "Audition" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1502, + "tag": "ModelDB:126489" + } + ], + "timestamp_created": "2024-01-12 09:53:29.116859+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/126489", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "943": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 943, + "name": "Predicting formant-frequency discrimination in noise (Tan and Carney 2006)", + "repository_type": "github", + "summary": "\"To better understand how the auditory system extracts speech signals in the presence of noise,\r\ndiscrimination thresholds for the second formant frequency were predicted with simulations of\r\nauditory-nerve responses.\r\n\r\nThese predictions employed either average-rate information or combined\r\nrate and timing information, and either populations of model fibers tuned across a wide range of\r\nfrequencies or a subset of fibers tuned to a restricted frequency range.\r\n\r\nIn general, combined temporal\r\nand rate information for a small population of model fibers tuned near the formant frequency was\r\nmost successful in replicating the trends reported in behavioral data for formant-frequency\r\ndiscrimination. ...\"", + "tags": [ + { + "id": 849, + "tag": "Audition" + }, + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1503, + "tag": "ModelDB:126598" + } + ], + "timestamp_created": "2024-01-12 09:53:29.709692+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/126598", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "944": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 944, + "name": "Models for diotic and dichotic detection (Davidson et al. 2009)", + "repository_type": "github", + "summary": "Several psychophysical models for masked detection were evaluated using reproducible noises.\r\n\r\nThe data were hit and false-alarm rates from three psychophysical studies of detection of 500-Hz tones\r\nin reproducible noise under diotic (N0S0) and dichotic (N0Spi) conditions with four stimulus\r\nbandwidths (50, 100, 115, and 2900 Hz).\r\n\r\nDiotic data were best predicted by an energy-based multiple-detector model that linearly combined stimulus energies at the outputs of several\r\ncritical-band filters.\r\n\r\nThe tone-plus-noise trials in the dichotic data were best predicted by models that\r\nlinearly combined either the average values or the standard deviations of interaural time and level\r\ndifferences; however, these models offered no predictions for noise-alone responses.\r\n\r\n...\". The Breebart et al. 2001 and the Dau et al. 1996 models are supplied at the Carney lab web site.", + "tags": [ + { + "id": 849, + "tag": "Audition" + }, + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1504, + "tag": "ModelDB:126636" + } + ], + "timestamp_created": "2024-01-12 09:53:30.447579+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/126636", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "945": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 945, + "name": "A simplified cerebellar Purkinje neuron (the PPR model) (Brown et al. 2011)", + "repository_type": "github", + "summary": "These models were implemented in NEURON by Sherry-Ann Brown in the laboratory of Leslie M. Loew.\r\n\r\nThe files reproduce Figures 2c-f from Brown et al, 2011 \"Virtual NEURON: a Strategy For Merged Biochemical and Electrophysiological Modeling\".\r\n", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 818, + "tag": "I p,q" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 850, + "tag": "Intrinsic plasticity" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1505, + "tag": "ModelDB:126637" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:53:30.998699+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/126637", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "946": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 946, + "name": "NAcc medium spiny neuron: effects of cannabinoid withdrawal (Spiga et al. 2010)", + "repository_type": "github", + "summary": "Cannabinoid withdrawal produces a hypofunction of dopaminergic neurons targeting medium spiny neurons (MSN) of the forebrain. Administration of a CB1 receptor antagonist to control rats provoked structural abnormalities, reminiscent of those observed in withdrawal conditions and support the regulatory role of cannabinoids in neurogenesis, axonal growth and synaptogenesis. Experimental observations were incorporated into a realistic computational model which predicts a strong reduction in the excitability of morphologically-altered MSN, yielding a significant reduction in action potential output. These paper provided direct morphological evidence for functional abnormalities associated with cannabinoid dependence at the level of dopaminergic neurons and their post synaptic counterpart, supporting a hypodopaminergic state as a distinctive feature of the \u201caddicted brain\u201d.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 922, + "tag": "Addiction" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 923, + "tag": "I Krp" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1506, + "tag": "ModelDB:126640" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:53:31.558518+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/126640", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "947": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 947, + "name": "CA1 pyramidal neuron: rebound spiking (Ascoli et al.2010)", + "repository_type": "github", + "summary": "The model demonstrates that CA1 pyramidal neurons support rebound spikes mediated by hyperpolarization-activated inward current (Ih), and normally masked by A-type potassium channels (KA). Partial KA reduction confined to one or few branches of the apical tuft may be sufficient to elicit a local spike following a train of synaptic inhibition. These data suggest that the plastic regulation of KA can provide a dynamic switch to unmask post-inhibitory spiking in CA1 pyramidal neurons, further increasing the signal processing power of the CA1 synaptic microcircuitry.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 850, + "tag": "Intrinsic plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1507, + "tag": "ModelDB:126776" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:53:33.286060+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/126776", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "948": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 948, + "name": "CA3 pyramidal neuron (Safiulina et al. 2010)", + "repository_type": "github", + "summary": "In this review some of the recent work carried out in our laboratory concerning the functional\r\nrole of GABAergic signalling at immature mossy fibres (MF)-CA3 principal cell synapses has\r\nbeen highlighted. To compare the relative strength of CA3 pyramidal cell\r\noutput in relation to their MF glutamatergic or GABAergic inputs in postnatal\r\ndevelopment, a realistic model was constructed taking into account the different\r\nbiophysical properties of these synapses.\r\n", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1508, + "tag": "ModelDB:126814" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 863, + "tag": "Spike Frequency Adaptation" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + } + ], + "timestamp_created": "2024-01-12 09:53:33.837383+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/126814", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "949": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 949, + "name": "CN bushy, stellate neurons (Rothman, Manis 2003) (Brian)", + "repository_type": "github", + "summary": "Cochlear neuron model of Rothman & Manis (2003). Adapted from the Neuron implementation.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 9, + "tag": "Brian" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1509, + "tag": "ModelDB:126899" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + } + ], + "timestamp_created": "2024-01-12 09:53:34.698246+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/126899", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "950": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 950, + "name": "Alcohol excites Cerebellar Golgi Cells by inhibiting the Na+/K+ ATPase (Botta et al.2010)", + "repository_type": "github", + "summary": "Patch-clamp in cerebellar slices and computer modeling show that ethanol excites Golgi cells by inhibiting the Na+/K+ ATPase. In particular, voltage-clamp recordings of Na+/K+ ATPase currents indicated that ethanol partially inhibits this pump and this effect could be mimicked by low concentrations of the Na+/K+ ATPase blocker ouabain. The partial inhibition of Na+/K+ ATPase in a computer model of the Golgi cell reproduced these experimental findings that established a novel mechanism of action of ethanol on neural excitability.", + "tags": [ + { + "id": 920, + "tag": "Alcohol Use Disorder" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1510, + "tag": "ModelDB:127021" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 816, + "tag": "Na/K pump" + } + ], + "timestamp_created": "2024-01-12 09:53:35.256288+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/127021", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "951": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 951, + "name": "Control of vibrissa motoneuron firing (Harish and Golomb 2010)", + "repository_type": "github", + "summary": "We construct and analyze a single-compartment, conductance-based model of vibrissa motoneurons. Low firing rates are supported in extended regimes by adaptation currents and the minimal firing rate decreases with the persistent sodium conductance gNaP and increases with M-potassium \r\nand h-cation conductances. Suprathreshold resonance results from the locking properties of vMN firing to stimuli and from reduction of firing rates at low frequencies by slow M and afterhyperpolarization potassium conductances. h conductance only slightly affects the suprathreshold resonance. When a vMN is subjected to a small periodic CPG input, serotonergically induced gNaP elevation may transfer the system from quiescence to a firing state that is highly\r\nlocked to the CPG input.\r\n", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 1512, + "tag": "Locking, mixed mode" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1513, + "tag": "ModelDB:127022" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:53:35.893007+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/127022", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "952": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 952, + "name": "Pipette and membrane patch geometry effects on GABAa currents patch-clamp exps (Moroni et al. 2011)", + "repository_type": "github", + "summary": "Ion currents, mediated by GABAa-receptors in outside-out membrane patches, may alter the concentration of Chloride ions inside the pipette and the membrane patch.\r\n\r\nGABAa-receptors are in fact ionotropic synaptic receptors, selective to Chloride ions. Therefore, chloride fluxes across the membrane patch correlate to GABAa-receptor opening.\r\n\r\nChloride ions accumulation, depletion and diffusion, inside the pipette and the membrane patch, affect by definition the Chloride equilibrium (i.e. Nernst) electrical potential. This in turn changes the ionic driving force underlying GABAa-mediated currents.\r\n\r\nIt follows that, in case of very small volumes and confined geometries, voltage-clamp recordings of GABAa-receptor currents carry information on both i) Chloride diffusion and ii) receptor kinetics.\r\n\r\nThe relevance of (i) and (ii) have been studied numerically by defining a 1-dimensional biophysical model, released here to the interested user.", + "tags": [ + { + "id": 852, + "tag": "I Chloride" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1514, + "tag": "ModelDB:127190" + } + ], + "timestamp_created": "2024-01-12 09:53:36.491152+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/127190", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "953": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 953, + "name": "Network topologies for producing limited sustained activation (Kaiser and Hilgetag 2010)", + "repository_type": "github", + "summary": "Uses networks of cellular automata to test hypotheses about network topologies that can produce limited, sustained activity. Inspired by empirically-based ideas about neocortical architecture, but conceived and implemented at a level of abstraction that is not closely linked to empirical observations.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 935, + "tag": "Connectivity matrix" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1515, + "tag": "ModelDB:127192" + } + ], + "timestamp_created": "2024-01-12 09:53:37.004445+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/127192", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "954": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 954, + "name": "Spatial summation of excitatory and inhibitory inputs in pyramidal neurons (Hao et al. 2010)", + "repository_type": "github", + "summary": "\"... Based on realistic modeling and experiments in rat hippocampal\r\nslices, we derived a simple arithmetic rule for spatial summation\r\nof concurrent excitatory glutamatergic inputs (E) and inhibitory\r\nGABAergic inputs (I). \r\n\r\nThe somatic response can be well approximated\r\nas the sum of the excitatory postsynaptic potential (EPSP), the inhibitory\r\npostsynaptic potential (IPSP), and a nonlinear term proportional\r\nto their product (k*EPSP*IPSP), where the coefficient k reflects the\r\nstrength of shunting effect.\r\n...\"", + "tags": [ + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1516, + "tag": "ModelDB:127305" + }, + { + "id": 819, + "tag": "NEURON (web link to model)" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:53:37.673786+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/127305", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "955": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 955, + "name": "A model of the temporal pattern generator of C. elegans egg-laying behavior (Zhang et. al 2010)", + "repository_type": "github", + "summary": "\"... We suggest that the HSN neuron is the executive neuron driving egg-laying events. We propose that the VC neurons act as \"single egg counters\" that inhibit HSN activity for short periods in response to individual egg-laying events. We further propose that the uv1 neuroendocrine cells are \"cluster counters\", which inhibit HSN activity for longer periods and are responsible for the time constant of the inactive phase. Together they form an integrated circuit that drives the clustered egg-laying pattern. ...\"", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1517, + "tag": "ModelDB:127321" + }, + { + "id": 1518, + "tag": "R (web link to model)" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + } + ], + "timestamp_created": "2024-01-12 09:53:38.204812+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/127321", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "956": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 956, + "name": "A two-stage model of dendritic integration in CA1 pyramidal neurons (Katz et al. 2009)", + "repository_type": "github", + "summary": "\"... In a two-stage integration model, inputs contribute directly to dendritic spikes, and outputs from multiple branches sum in the axon. ... We used serial-section electron microscopy to reconstruct individual apical oblique dendritic branches of CA1 pyramidal neurons and observe a synapse distribution consistent with the two-stage integration model. Computational modeling suggests that the observed synapse distribution enhances the contribution of each dendritic branch to neuronal output.\"", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1519, + "tag": "ModelDB:127351" + }, + { + "id": 819, + "tag": "NEURON (web link to model)" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:53:38.745388+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/127351", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "957": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 957, + "name": "Prediction for the presence of voltage-gated Ca2+ channels in myelinated central axons (Brown 2003)", + "repository_type": "github", + "summary": "\"The objective of this current study was to investigate whether voltage gated Ca(2+) channels are present \r\non axons of the adult rat optic nerve (RON).\r\n\r\nSimulations of axonal excitability using a Hodgkin-Huxley based \r\none-compartment model incorporating I(Na), I(K) and leak currents were used to predict conditions under \r\nwhich the potential contribution of a Ca(2+) current to an evoked action potential could be measured. \r\n\r\n... , as predicted by the simulation, reducing the repolarizing effect of I(K) by adding the \r\nK(+) channel blocker 4-AP revealed a Ca(2+) component on the repolarizing phase of the action potential that \r\nwas blocked by the Ca(2+) channel inhibitor nifedipine.\"\r\n", + "tags": [ + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1520, + "tag": "ModelDB:127355" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:53:39.264416+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/127355", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "958": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 958, + "name": "Basal ganglia network model of subthalamic deep brain stimulation (Hahn and McIntyre 2010)", + "repository_type": "github", + "summary": "Basal ganglia network model of parkinsonian activity and subthalamic deep brain stimulation in non-human primates from the article \r\n\r\nInstructions are provided in the README.txt file. Contact hahnp@ccf.org if you have any questions about the implementation of the model. Please include \"ModelDB - BGnet\" in the subject heading.", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 834, + "tag": "Deep brain stimulation" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1521, + "tag": "ModelDB:127388" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 884, + "tag": "Parkinson's" + } + ], + "timestamp_created": "2024-01-12 09:53:40.089273+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/127388", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "959": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 959, + "name": "Regulation of firing frequency in a midbrain dopaminergic neuron model (Kuznetsova et al. 2010)", + "repository_type": "github", + "summary": "A dopaminergic (DA) neuron model with a morphologicaly realistic dendritic architecture. The model captures several salient features of DA neurons under different pharmacological manipulations and exhibits depolarization block for sufficiently high current pulses applied to the soma.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1522, + "tag": "ModelDB:127507" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 816, + "tag": "Na/K pump" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + } + ], + "timestamp_created": "2024-01-12 09:53:40.649677+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/127507", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "960": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 960, + "name": "Temporal and spatial characteristics of vibrissa responses to motor commands (Simony et al. 2010)", + "repository_type": "github", + "summary": "\"A mechanistic description of the generation of whisker movements is essential for understanding the control of whisking and vibrissal\r\nactive touch. We explore how facial-motoneuron spikes are translated, via an intrinsic muscle, to whisker movements. This is achieved by\r\nconstructing, simulating, and analyzing a computational, biomechanical model of the motor plant, and by measuring spiking to movement\r\ntransformations at small and large angles using high-precision whisker tracking in vivo. \r\n...\r\nThe model provides a direct translation from motoneuron spikes to whisker movements\r\nand can serve as a building block in closed-loop motor\u2013sensory models of active touch.\"", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1523, + "tag": "ModelDB:127512" + } + ], + "timestamp_created": "2024-01-12 09:53:41.221818+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/127512", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "961": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 961, + "name": "Comparison of full and reduced globus pallidus models (Hendrickson 2010)", + "repository_type": "github", + "summary": "In this paper, we studied what features of realistic full model activity patterns can and cannot be preserved by morphologically reduced models. To this end, we reduced the morphological complexity of a full globus pallidus neuron model possessing active dendrites and compared its spontaneous and driven responses to those of the reduced models.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1524, + "tag": "ModelDB:127728" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:53:41.748125+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/127728", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "962": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 962, + "name": "Global structure, robustness, and modulation of neuronal models (Goldman et al. 2001)", + "repository_type": "github", + "summary": "\"The electrical characteristics of many neurons are remarkably\r\nrobust in the face of changing internal and external conditions.\r\n\r\nAt the same time, neurons can be highly sensitive to neuromodulators.\r\n\r\nWe find correlates of this dual robustness and\r\nsensitivity in a global analysis of the structure of a\r\nconductance-based model neuron.\r\n...\"", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1525, + "tag": "ModelDB:127878" + }, + { + "id": 906, + "tag": "Phase Response Curves" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + } + ], + "timestamp_created": "2024-01-12 09:53:42.374083+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/127878", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "963": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 963, + "name": "Spectral method and high-order finite differences for nonlinear cable (Omurtag and Lytton 2010)", + "repository_type": "github", + "summary": "We use high-order approximation schemes for the space derivatives in the nonlinear cable equation and investigate the behavior of numerical solution errors by using exact solutions, where available, and grid convergence. The space derivatives are numerically approximated by means of differentiation matrices. A flexible form for the injected current is used that can be adjusted smoothly from a very broad to a narrow peak, which leads, for the passive cable, to a simple, exact solution. We provide comparisons with exact solutions in an unbranched passive cable, the convergence of solutions with progressive refinement of the grid in an active cable, and the simulation of spike initiation in a biophysically realistic single-neuron model.\r\n", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 940, + "tag": "I_K,Na" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1526, + "tag": "ModelDB:127887" + } + ], + "timestamp_created": "2024-01-12 09:53:43.039401+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/127887", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "964": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 964, + "name": "Generating coherent patterns of activity from chaotic neural networks (Sussillo and Abbott 2009)", + "repository_type": "github", + "summary": "\"Neural circuits display complex activity patterns both\r\nspontaneously and when responding to a stimulus or\r\ngenerating a motor output.\r\n\r\nHow are these two forms\r\nof activity related? \r\n\r\nWe develop a procedure called\r\nFORCE learning for modifying synaptic strengths\r\neither external to or within a model neural network\r\nto change chaotic spontaneous activity into a wide\r\nvariety of desired activity patterns.\r\n...\r\n Our results\r\nreproduce data on premovement activity in motor\r\nand premotor cortex, and suggest that synaptic plasticity\r\nmay be a more rapid and powerful modulator of\r\nnetwork activity than generally appreciated.\"", + "tags": [ + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1527, + "tag": "ModelDB:127967" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + } + ], + "timestamp_created": "2024-01-12 09:53:43.869408+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/127967", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "965": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 965, + "name": "Accurate and fast simulation of channel noise in conductance-based model neurons (Linaro et al 2011)", + "repository_type": "github", + "summary": "We introduce and operatively present a general method to simulate channel noise in conductance-based model neurons, with modest computational overheads.\r\n\r\nOur approach may be considered as an accurate generalization of previous proposal methods,\r\nto the case of voltage-, ion-, and ligand-gated channels with arbitrary complexity.\r\n\r\nWe focus on the discrete Markov process descriptions, routinely employed in experimental\r\nidentification of voltage-gated channels and synaptic receptors.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1528, + "tag": "ModelDB:127992" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:53:44.477421+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/127992", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "966": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 966, + "name": "Olfactory bulb cluster formation (Migliore et al. 2010)", + "repository_type": "github", + "summary": "Functional roles of distributed synaptic clusters in the mitral-granule cell network of the olfactory bulb.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1529, + "tag": "ModelDB:127995" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:53:45.023734+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/127995", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "967": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 967, + "name": "Rapid desynchronization of an electrically coupled Golgi cell network (Vervaeke et al. 2010)", + "repository_type": "github", + "summary": "Electrical synapses between interneurons contribute to synchronized firing and network oscillations in the brain. However, little is known about how such networks respond to excitatory synaptic input. In addition to detailed electrophysiological recordings and histological investigations of electrically coupled Golgi cells in the cerebellum, a detailed network model of these cells was created. The cell models are based on reconstructed Golgi cell morphologies and the active conductances are taken from an earlier abstract Golgi cell model (Solinas et al 2007, accession no. 112685). Our results show that gap junction coupling can sometimes be inhibitory and either promote network synchronization or trigger rapid network desynchronization depending on the synaptic input. The model is available as a neuroConstruct project and can executable scripts can be generated for the NEURON simulator.", + "tags": [ + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1530, + "tag": "ModelDB:127996" + }, + { + "id": 929, + "tag": "NeuroML (web link to model)" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 859, + "tag": "neuroConstruct (web link to model)" + } + ], + "timestamp_created": "2024-01-12 09:53:45.762904+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/127996", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "968": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 968, + "name": "Stochastic ion channels and neuronal morphology (Cannon et al. 2010)", + "repository_type": "github", + "summary": "\"... We introduce and\r\nvalidate new computational tools that enable efficient generation and simulation of models containing stochastic ion\r\nchannels distributed across dendritic and axonal membranes.\r\n\r\nComparison of five morphologically distinct neuronal cell\r\ntypes reveals that when all simulated neurons contain identical densities of stochastic ion channels, the amplitude of\r\nstochastic membrane potential fluctuations differs between cell types and depends on sub-cellular location.\r\n\r\n...\" The code is downloadable and more information is available at http://www.psics.org/", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1531, + "tag": "ModelDB:128043" + }, + { + "id": 1532, + "tag": "PSICS" + } + ], + "timestamp_created": "2024-01-12 09:53:46.676727+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/128043", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "969": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 969, + "name": "Generic Bi-directional Real-time Neural Interface (Zrenner et al. 2010)", + "repository_type": "github", + "summary": "Matlab/Simulink toolkit for generic multi-channel short-latency bi-directional neural-computer interactions. High-bandwidth (> 10 megabit per second) neural recording data can be analyzed in real-time while simultaneously generating specific complex electrical stimulation feedback with deterministically timed responses at sub-millisecond resolution. The commercially available 60-channel extracellular multi-electrode recording and stimulation set-up (Multichannelsystems GmbH MEA60) is used as an example hardware implementation.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1533, + "tag": "ModelDB:128068" + }, + { + "id": 907, + "tag": "Simulink" + } + ], + "timestamp_created": "2024-01-12 09:53:47.226193+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/128068", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "970": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 970, + "name": "Fast sodium channel gating in mossy fiber axons (Schmidt-Hieber et al. 2010)", + "repository_type": "github", + "summary": "\"... To study the mechanisms underlying AP initiation in unmyelinated hippocampal mossy fibers of adult mice, we recorded sodium currents in axonal and somatic membrane patches. \r\n\r\nWe demonstrate that sodium channel density in the proximal axon is ~5 times higher than in the soma. \r\n\r\nFurthermore, sodium channel activation and inactivation are ~2 times faster. \r\n\r\nModeling revealed that the fast activation localized the initiation site to the proximal axon even upon strong synaptic stimulation, while fast inactivation contributed to energy-efficient membrane charging during APs. ...\"", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1534, + "tag": "ModelDB:128079" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:53:47.759245+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/128079", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "971": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 971, + "name": "Short term plasticity at the cerebellar granule cell (Nieus et al. 2006)", + "repository_type": "github", + "summary": "The model reproduces short term plasticity of the mossy fibre to granule cell synapse. To reproduce synaptic currents recorded in experiments, a model of presynaptic release was used to determine the concentration of glutamate in the synaptic cleft that ultimately determined a synaptic response. The parameters of facilitation and depression were determined deconvolving AMPA EPSCs.", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1535, + "tag": "ModelDB:128446" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:53:48.308400+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/128446", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "972": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 972, + "name": "Leaky integrate-and-fire model of spike frequency adaptation in the LGMD (Gabbiani and Krapp 2006)", + "repository_type": "github", + "summary": "This will reproduce Figure 9 of Gabbiani and Krapp (2006) J Neurophysiol 96:2951-2962.\r\n\r\nThe figure simply shows that a leaky-integrate-and-fire model cannot reproduce spike frequency adaptation as it is seen experimentally in \r\nthe LGMD neuron.\r\n", + "tags": [ + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1536, + "tag": "ModelDB:128449" + }, + { + "id": 863, + "tag": "Spike Frequency Adaptation" + } + ], + "timestamp_created": "2024-01-12 09:53:48.876718+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/128449", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "973": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 973, + "name": "On stochastic diff. eq. models for ion channel noise in Hodgkin-Huxley neurons (Goldwyn et al. 2010)", + "repository_type": "github", + "summary": "\" ... We analyze three SDE models that have been proposed as approximations to the Markov chain model: one that describes the states of the ion channels and two that describe the states of the ion channel subunits. We show that the former channel-based approach can capture the distribution of channel noise and its effect on spiking in a Hodgkin-Huxley neuron model to a degree not previously demonstrated, but the latter two subunit-based approaches cannot. ...\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 756, + "tag": "FORTRAN" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1537, + "tag": "ModelDB:128502" + }, + { + "id": 952, + "tag": "Noise Sensitivity" + } + ], + "timestamp_created": "2024-01-12 09:53:49.544070+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/128502", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "974": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 974, + "name": "Mathematical model for windup (Aguiar et al. 2010)", + "repository_type": "github", + "summary": "\"Windup is characterized as a frequency-dependent\r\nincrease in the number of evoked action potentials in dorsal\r\nhorn neurons in response to electrical stimulation of afferent C-fibers.\r\n... \r\nThe approach presented here relies on mathematical and\r\ncomputational analysis to study the mechanism(s) underlying windup.\r\n\r\nFrom experimentally obtained windup profiles, we extract the time\r\nscale of the facilitation mechanisms that may support the characteristics\r\nof windup.\r\n\r\nGuided by these values and using simulations of a\r\nbiologically realistic compartmental model of a wide dynamic range\r\n(WDR) neuron, we are able to assess the contribution of each\r\nmechanism for the generation of action potentials windup.\r\n...\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1538, + "tag": "ModelDB:128559" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:53:50.232469+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/128559", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "975": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 975, + "name": "Grid cell oscillatory interference with noisy network oscillators (Zilli and Hasselmo 2010)", + "repository_type": "github", + "summary": "To examine whether an oscillatory interference model of grid cell activity could work if the oscillators were noisy neurons, we implemented these simulations. Here the oscillators are networks (either synaptically- or gap-junction--coupled) of one or more noisy neurons (either Izhikevich's simple model or a Hodgkin-Huxley--type biophysical model) which drive a postsynaptic cell (which may be integrate-and-fire, resonate-and-fire, or the simple model) which should fire spatially as a grid cell if the simulation is successful.", + "tags": [ + { + "id": 1539, + "tag": "Grid cell" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1540, + "tag": "ModelDB:128812" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 863, + "tag": "Spike Frequency Adaptation" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2024-01-12 09:53:50.808386+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/128812", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "976": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 976, + "name": "Medial reticular formation of the brainstem: anatomy and dynamics (Humphries et al. 2006, 2007)", + "repository_type": "github", + "summary": "A set of models to study the medial reticular formation (mRF) of the brainstem. We developed a collection of algorithms to derive the adult-state wiring of the model: one set a stochastic model; the other set mimicking the developmental process. We found that the anatomical models had small-world properties, irrespective of the choice of algorithm; and that the cluster-like organisation of the mRF may have arisen to minimise wiring costs. (The model code includes options to be run as dynamic models; papers examining these dynamics are included in the .zip file).", + "tags": [ + { + "id": 857, + "tag": "Action Selection/Decision Making" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 935, + "tag": "Connectivity matrix" + }, + { + "id": 839, + "tag": "Development" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1541, + "tag": "ModelDB:128816" + }, + { + "id": 845, + "tag": "Sensory processing" + } + ], + "timestamp_created": "2024-01-12 09:53:51.537689+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/128816", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "977": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 977, + "name": "Dopamine-modulated medium spiny neuron, reduced model (Humphries et al. 2009)", + "repository_type": "github", + "summary": "We extended Izhikevich's reduced model of the striatal medium spiny neuron (MSN) to account for dopaminergic modulation of its intrinsic ion channels and synaptic inputs. We tuned our D1 and D2 receptor MSN models using data from a recent (Moyer et al, 2007) large-scale compartmental model. Our new models capture the input-output relationships for both current injection and spiking input with remarkable accuracy, despite the order of magnitude decrease in system size. They also capture the paired pulse facilitation shown by MSNs. Our dopamine models predict that synaptic effects dominate intrinsic effects for all levels of D1 and D2 receptor activation. Our analytical work on these models predicts that the MSN is never bistable. Nonetheless, these MSN models can produce a spontaneously bimodal membrane potential similar to that recently observed in vitro following application of NMDA agonists. We demonstrate that this bimodality is created by modelling the agonist effects as slow, irregular and massive jumps in NMDA conductance and, rather than a form of bistability, is due to the voltage-dependent blockade of NMDA receptors", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 887, + "tag": "Bifurcation" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1542, + "tag": "ModelDB:128818" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:53:52.204870+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/128818", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "978": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 978, + "name": "Striatal GABAergic microcircuit, dopamine-modulated cell assemblies (Humphries et al. 2009)", + "repository_type": "github", + "summary": "To begin identifying potential dynamically-defined computational elements within the striatum, we constructed a new three-dimensional model of the striatal microcircuit's connectivity, and instantiated this with our dopamine-modulated neuron models of the MSNs and FSIs. A new model of gap junctions between the FSIs was introduced and tuned to experimental data. We introduced a novel multiple spike-train analysis method, and apply this to the outputs of the model to find groups of synchronised neurons at multiple time-scales. We found that, with realistic in vivo background input, small assemblies of synchronised MSNs spontaneously appeared, consistent with experimental observations, and that the number of assemblies and the time-scale of synchronisation was strongly dependent on the simulated concentration of dopamine. We also showed that feed-forward inhibition from the FSIs counter-intuitively increases the firing rate of the MSNs.", + "tags": [ + { + "id": 857, + "tag": "Action Selection/Decision Making" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 935, + "tag": "Connectivity matrix" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1543, + "tag": "ModelDB:128874" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + } + ], + "timestamp_created": "2024-01-12 09:53:52.895832+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/128874", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "979": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 979, + "name": "Synthesis of spatial tuning functions from theta cell spike trains (Welday et al., 2011)", + "repository_type": "github", + "summary": "A single compartment model reproduces the firing rate maps of place, grid, and boundary cells by receiving inhibitory inputs from theta cells. The theta cell spike trains are modulated by the rat's movement velocity in such a way that phase interference among their burst pattern creates spatial envelope function which simulate the firing rate maps.", + "tags": [ + { + "id": 1544, + "tag": "Envelope synthesis" + }, + { + "id": 1539, + "tag": "Grid cell" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1545, + "tag": "ModelDB:129067" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 882, + "tag": "Place cell/field" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2024-01-12 09:53:53.465566+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/129067", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "980": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 980, + "name": "Continuous time stochastic model for neurite branching (van Elburg 2011)", + "repository_type": "github", + "summary": "\"In this paper we introduce a continuous time stochastic neurite branching model closely related to the discrete time stochastic BES-model. The discrete time BES-model is underlying current attempts to simulate cortical development, but is difficult to analyze. The new continuous time formulation facilitates analytical treatment thus allowing us to examine the structure of the model more closely. ...\"", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 839, + "tag": "Development" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1546, + "tag": "ModelDB:129071" + } + ], + "timestamp_created": "2024-01-12 09:53:53.983614+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/129071", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "981": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 981, + "name": "Increased computational accuracy in multi-compartmental cable models (Lindsay et al. 2005)", + "repository_type": "github", + "summary": "Compartmental models of dendrites are the most widely used tool for investigating their electrical\r\nbehaviour. \r\n\r\nTraditional models assign a single potential to a compartment. \r\n\r\nThis potential is associated with the membrane potential at the centre of the segment represented by the compartment.\r\n\r\nAll input to that segment, independent of its location on the segment, is assumed to act at the centre of the segment with the potential of the\r\ncompartment. \r\n\r\nBy contrast, the compartmental model introduced in this article assigns a potential to each end of a\r\nsegment, and takes into account the location of input to a segment on the model solution by partitioning the effect of\r\nthis input between the axial currents at the proximal and distal boundaries of segments.\r\n\r\nFor a given neuron, the new and traditional approaches to compartmental modelling use the same number of locations at which the membrane\r\npotential is to be determined, and lead to ordinary differential equations that are structurally identical. However, the\r\nsolution achieved by the new approach gives an order of magnitude better accuracy and precision than that achieved\r\nby the latter in the presence of point process input.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1547, + "tag": "ModelDB:129149" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:53:54.940603+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/129149", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "982": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 982, + "name": "Intracortical synaptic potential modulation by presynaptic somatic potential (Shu et al. 2006, 2007)", + "repository_type": "github", + "summary": "\" ... Here we show that the voltage fluctuations associated with\r\ndendrosomatic synaptic activity propagate significant distances\r\nalong the axon, and that modest changes in the somatic membrane\r\npotential of the presynaptic neuron modulate the amplitude\r\nand duration of axonal action potentials and, through a Ca21-\r\ndependent mechanism, the average amplitude of the postsynaptic\r\npotential evoked by these spikes. \r\n\r\nThese results indicate that\r\nsynaptic activity in the dendrite and soma controls not only the\r\npattern of action potentials generated, but also the amplitude of\r\nthe synaptic potentials that these action potentials initiate in local\r\ncortical circuits, resulting in synaptic transmission that is a\r\nmixture of triggered and graded (analogue) signals.\"", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 823, + "tag": "I_KD" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1548, + "tag": "ModelDB:135787" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:53:56.231844+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/135787", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "983": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 983, + "name": "Action potential reconstitution from measured current waveforms (Alle et al. 2009)", + "repository_type": "github", + "summary": "This NEURON code reconstitutes action potentials in a model of a hippocampal mossy fiber from experimentally measured sodium, potassium and calcium current waveforms as described in Alle et al. (2009).\r\n", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1549, + "tag": "ModelDB:135838" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:53:56.800557+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/135838", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "984": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 984, + "name": "AP back-prop. explains threshold variability and rapid rise (McCormick et al. 2007, Yu et al. 2008)", + "repository_type": "github", + "summary": "This simple axon-soma model explained how the rapid rising phase in the somatic spike is derived from the propagated axon initiated spike,\r\nand how the somatic spike threshold variance is affected by spike propagation.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1550, + "tag": "ModelDB:135839" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:53:57.374089+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/135839", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "985": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 985, + "name": "Selective control of cortical axonal spikes by a slowly inactivating K+ current (Shu et al. 2007)", + "repository_type": "github", + "summary": "We discovered a low-threshold, slowly inactivating K+ current, containing Kv1.2 alpha subunits, in axon initial segment, playing a key role in the modulation of spike threshold and spike duration as well as\r\nthe spike timing in prefrontal cortex layer V pyramidal cell of ferrets and rats.\r\nA kd.mod file implements this D current and put it in the axonal model: Neuron_Dcurrent.hoc. Run the model to see the gradual modulation effect over seconds on spike shape.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1551, + "tag": "ModelDB:135898" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:53:58.029963+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/135898", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "986": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 986, + "name": "High frequency oscillations in a hippocampal computational model (Stacey et al. 2009)", + "repository_type": "github", + "summary": "\"... Using a physiological computer model of hippocampus, we investigate random synaptic activity\r\n(noise) as a potential initiator of HFOs (high-frequency oscillations).\r\n\r\nWe explore parameters necessary to produce these oscillations and quantify the response\r\nusing the tools of stochastic resonance (SR) and coherence resonance\r\n(CR).\r\n... \r\n\r\nOur results show that, under normal coupling conditions, synaptic noise was able to produce\r\ngamma (30\u2013100 Hz) frequency oscillations.\r\n\r\n\r\nSynaptic noise generated HFOs in the ripple range (100\u2013200 Hz) when the network had\r\nparameters similar to pathological findings in epilepsy: increased gap\r\njunctions or recurrent synaptic connections, loss of inhibitory interneurons\r\nsuch as basket cells, and increased synaptic noise.\r\n\r\n...\r\nWe propose that increased synaptic noise and physiological coupling mechanisms are sufficient to generate gamma\r\noscillations and that pathologic changes in noise and coupling similar\r\nto those in epilepsy can produce abnormal ripples.\"\r\n", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1552, + "tag": "ModelDB:135902" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + } + ], + "timestamp_created": "2024-01-12 09:53:58.619056+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/135902", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "987": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 987, + "name": "Network recruitment to coherent oscillations in a hippocampal model (Stacey et al. 2011)", + "repository_type": "github", + "summary": "\"... Here we demonstrate, via a detailed computational model, a mechanism whereby physiological noise and coupling initiate oscillations and then recruit neighboring tissue, in a manner well described by a combination of Stochastic Resonance and Coherence Resonance.\r\n\r\n We develop a novel statistical method to quantify recruitment using several measures of network synchrony.\r\n\r\nThis measurement demonstrates that oscillations spread via preexisting network connections such as interneuronal connections, recurrent synapses, and gap junctions, provided that neighboring cells also receive sufficient inputs in the form of random synaptic noise.\r\n\r\n...\"\r\n", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1553, + "tag": "ModelDB:135903" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + } + ], + "timestamp_created": "2024-01-12 09:53:59.189198+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/135903", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "988": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 988, + "name": "Modelling enteric neuron populations and muscle fed-state motor patterns (Chambers et al. 2011)", + "repository_type": "github", + "summary": "\"After a meal, the gastrointestinal tract exhibits a set of behaviours known as the fed state.\r\n... Segmentation manifests as rhythmic local constrictions that do not propagate along the intestine.\r\n...\r\nWe investigated the enteric circuits that regulate segmentation focusing on a central feature of the ENS: a recurrent excitatory network of intrinsic sensory neurons (ISNs) which are characterized by prolonged after-hyperpolarizing potentials (AHPs) following their action potentials.\r\n...\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1554, + "tag": "ModelDB:136024" + }, + { + "id": 907, + "tag": "Simulink" + } + ], + "timestamp_created": "2024-01-12 09:53:59.736086+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/136024", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "989": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 989, + "name": "Functional structure of mitral cell dendritic tuft (Djurisic et al. 2008)", + "repository_type": "github", + "summary": "The computational modeling component of Djurisic et al. 2008 addressed two primary questions: whether amplification by active currents is necessary to explain the relatively mild attenuation suffered by tuft EPSPs spreading along the primary dendrite to the soma; what accounts for the relatively uniform peak EPSP amplitude throughout the tuft. These simulations show that passive spread from tuft to soma is sufficient to yield the low attenuation of tuft EPSPs, and that random distribution of a biologically plausible number of excitatory synapses throughout the tuft can produce the experimentally observed uniformity of depolarization.\r\n", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1555, + "tag": "ModelDB:136026" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:54:00.337636+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/136026", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "990": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 990, + "name": "Synaptic information transfer in computer models of neocortical columns (Neymotin et al. 2010)", + "repository_type": "github", + "summary": "\"...\r\nWe sought to measure how the activity of the network alters information flow from inputs to output patterns. \r\nInformation handling by the network reflected the degree of internal connectivity. ...\r\nWith greater connectivity strength, the recurrent network translated activity and information due to contribution of activity from intrinsic network dynamics. \r\n...\r\nAt still higher internal synaptic strength, the network corrupted the external information, producing a state where little external information came through.\r\nThe association of increased information retrieved from the network with increased gamma power supports the notion of gamma oscillations playing a role in information processing.\"\r\n", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 1556, + "tag": "Information transfer" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1557, + "tag": "ModelDB:136095" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:54:01.123875+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/136095", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "991": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 991, + "name": "Multiscale model of olfactory receptor neuron in mouse (Dougherty 2009)", + "repository_type": "github", + "summary": "Collection of XPP (.ode) files simulating the signal transduction (slow) and action potential (fast) currents in the olfactory receptor neuron of mouse. Collection contains model configured for dual odorant pulse delivery and model configured for prolonged odorant delivery. For those interested more in transduction processes, each whole cell recording model comes with a counter part file configured to show just the slow transduction current for ease of use and convenience. These transduction-only models typically run faster than the full multi-scale models but do not demonstrate action potentials.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 866, + "tag": "G-protein coupled" + }, + { + "id": 1558, + "tag": "I ANO2" + }, + { + "id": 853, + "tag": "I CNG" + }, + { + "id": 865, + "tag": "I Cl,Ca" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 940, + "tag": "I_K,Na" + }, + { + "id": 900, + "tag": "I_Na,Ca" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1559, + "tag": "ModelDB:136097" + }, + { + "id": 944, + "tag": "Multiscale" + }, + { + "id": 814, + "tag": "Na/Ca exchanger" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:54:01.720380+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/136097", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "992": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 992, + "name": "Cerebellar Nucleus Neuron (Steuber, Schultheiss, Silver, De Schutter & Jaeger, 2010)", + "repository_type": "github", + "summary": "This is the GENESIS 2.3 implementation of a multi-compartmental deep cerebellar nucleus (DCN) neuron model with a full dendritic morphology and appropriate active conductances. We generated a good match of our simulations with DCN current clamp data we recorded in acute slices, including the heterogeneity in the rebound responses. We then examined how inhibitory and excitatory synaptic input interacted with these intrinsic conductances to control DCN firing. We found that the output spiking of the model reflected the ongoing balance of excitatory and inhibitory input rates and that changing the level of inhibition performed an additive operation. Rebound firing following strong Purkinje cell input bursts was also possible, but only if the chloride reversal potential was more negative than -70 mV to allow de-inactivation of rebound currents. Fast rebound bursts due to T-type calcium current and slow rebounds due to persistent sodium current could be differentially regulated by synaptic input, and the pattern of these rebounds was further influenced by HCN current. Our findings suggest that active properties of DCN neurons could play a crucial role for signal processing in the cerebellum.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 850, + "tag": "Intrinsic plasticity" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1560, + "tag": "ModelDB:136175" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 775, + "tag": "Rebound firing" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:54:02.398987+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/136175", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "993": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 993, + "name": "CA1 stratum radiatum interneuron multicompartmental model (Katona et al. 2011)", + "repository_type": "github", + "summary": "The model examines dendritic NMDA-spike generation and propagation\r\nin the dendrites of CA1 stratum radiatum interneurons. It contains\r\nNMDA-channels in a clustered pattern on a dendrite and K-channels. The\r\nsimulation shows the whole NMDA spike and the rising phase of the\r\ntraces in separate windows.\r\n", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1561, + "tag": "ModelDB:136176" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:54:02.992635+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/136176", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "994": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 994, + "name": "Myelinated nerve fibre myelin resistance dependent on extracellular K+ level (Brazhe et al. 2010)", + "repository_type": "github", + "summary": "Excitation leads to rise in paranodal [K]e under the myelin. This causes structural changes in myelin structure and resistance. Current model aims to simulate this aspect. This is a space-clamped model of a double-cable nerve fibre.", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 868, + "tag": "I_Ks" + }, + { + "id": 1562, + "tag": "Intermittent block" + }, + { + "id": 1563, + "tag": "Lua" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1564, + "tag": "ModelDB:136296" + }, + { + "id": 816, + "tag": "Na/K pump" + } + ], + "timestamp_created": "2024-01-12 09:54:03.528844+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/136296", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "995": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 995, + "name": "Wang-Buzsaki Interneuron (Talathi et al., 2010)", + "repository_type": "github", + "summary": "The submitted code provides the relevant C++ files, matlabfiles and the data files essential to reproduce the figures in the JCNS paper titled Control of neural synchrony using channelrhodopsin-2: A computational study.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1565, + "tag": "ModelDB:136308" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2024-01-12 09:54:04.146909+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/136308", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "996": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 996, + "name": "Neocort. pyramidal cells subthreshold somatic voltage controls spike propagation (Munro Kopell 2012)", + "repository_type": "github", + "summary": "There is suggestive evidence that pyramidal cell axons in neocortex may be coupled by gap junctions into an ``axonal plexus\" capable of generating Very Fast Oscillations (VFOs) with frequencies exceeding 80 Hz. It is not obvious, however, how a pyramidal cell in such a network could control its output when action potentials are free to propagate from the axons of other pyramidal cells into its own axon. We address this problem by means of simulations based on 3D reconstructions of pyramidal cells from rat somatosensory cortex. We show that somatic depolarization enables propagation via gap junctions into the initial segment and main axon, while somatic hyperpolarization disables it. We show further that somatic voltage cannot effectively control action potential propagation through gap junctions on minor collaterals; action potentials may therefore propagate freely from such collaterals regardless of somatic voltage. In previous work, VFOs are all but abolished during the hyperpolarization phase of slow-oscillations induced by anesthesia in vivo. This finding constrains the density of gap junctions on collaterals in our model and suggests that axonal sprouting due to cortical lesions may result in abnormally high gap junction density on collaterals, leading in turn to excessive VFO activity and hence to epilepsy via kindling.", + "tags": [ + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1566, + "tag": "ModelDB:136309" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + } + ], + "timestamp_created": "2024-01-12 09:54:04.700911+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/136309", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "997": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 997, + "name": "Modeling local field potentials (Bedard et al. 2004)", + "repository_type": "github", + "summary": "This demo simulates a model of local field potentials (LFP) with\r\nvariable resistivity. This model reproduces the low-pass\r\nfrequency filtering properties of extracellular potentials. The\r\nmodel considers inhomogeneous spatial profiles of conductivity\r\nand permittivity, which result from the multiple media (fluids,\r\nmembranes, vessels, ...) composing the extracellular space around\r\nneurons. Including non-constant profiles of conductivity enables\r\nthe model to display frequency filtering properties, ie slow\r\nevents such as EPSPs/IPSPs are less attenuated than fast events\r\nsuch as action potentials. The demo simulates Fig 6 of the\r\npaper.", + "tags": [ + { + "id": 810, + "tag": "Extracellular Fields" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1567, + "tag": "ModelDB:136310" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:54:05.224906+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/136310", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "998": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 998, + "name": "Globus pallidus neuron models with differing dendritic Na channel expression (Edgerton et al., 2010)", + "repository_type": "github", + "summary": "A set of 9 multi-compartmental rat GP neuron models (585 compartments) differing only in their expression of dendritic fast sodium channels were compared in their synaptic integration properties. Dendritic fast sodium channels were found to increase the importance of distal synapses (both excitatory AND inhibitory), increase spike timing variability with in vivo-like synaptic input, and make the model neurons highly sensitive to clustered synchronous excitation.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1568, + "tag": "ModelDB:136315" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:54:05.891961+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/136315", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "999": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 999, + "name": "Neural transformations on spike timing information (Tripp and Eliasmith 2007)", + "repository_type": "github", + "summary": "\" ... Here we employ computational methods to show that an ensemble\r\nof neurons firing at a constant mean rate can induce arbitrarily\r\nchosen temporal current patterns in postsynaptic cells. ...\"\r\n", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1569, + "tag": "ModelDB:136380" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + } + ], + "timestamp_created": "2024-01-12 09:54:34.375194+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/136380", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1000": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1000, + "name": "Action potential-evoked Na+ influx are similar in axon and soma (Fleidervish et al. 2010)", + "repository_type": "github", + "summary": "\"In cortical pyramidal neurons, the axon initial segment (AIS) is pivotal in synaptic integration.\r\n\r\nIt has been asserted that this is because there is a high density of Na+ channels in the AIS. \r\n\r\nHowever, we found that action potential-associated Na+ flux, as measured by high-speed fluorescence Na+ imaging, was about threefold larger in the rat AIS than in the soma. \r\n\r\nSpike-evoked Na+ flux in the AIS and the first node of Ranvier was similar and was eightfold lower in basal dendrites.\r\n\r\n...\r\nIn computer simulations, these data were consistent with the known features of action potential generation in these neurons.\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1570, + "tag": "ModelDB:136715" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:54:34.931557+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/136715", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1001": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1001, + "name": "STDP allows fast rate-modulated coding with Poisson-like spike trains (Gilson et al. 2011)", + "repository_type": "github", + "summary": "The model demonstrates that a neuron equipped with STDP robustly detects repeating rate patterns among its afferents, from which the spikes are generated on the fly using inhomogenous Poisson sampling, provided those rates have narrow temporal peaks (10-20ms) - a condition met by many experimental Post-Stimulus Time Histograms (PSTH).", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 9, + "tag": "Brian" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 1556, + "tag": "Information transfer" + }, + { + "id": 908, + "tag": "Learning" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1571, + "tag": "ModelDB:136717" + }, + { + "id": 952, + "tag": "Noise Sensitivity" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 886, + "tag": "Unsupervised Learning" + } + ], + "timestamp_created": "2024-01-12 09:54:35.477558+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/136717", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1002": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1002, + "name": "Single neuron with dynamic ion concentrations (Cressman et al. 2009)", + "repository_type": "github", + "summary": "These are the full and reduced models of a generic single neuron with dynamic ion concentrations as described in Cressman et al., Journal of Computational Neuroscience (2009) 26:159\u2013170.", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1572, + "tag": "ModelDB:136773" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:54:36.081706+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/136773", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1003": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1003, + "name": "Population models of temporal differentiation (Tripp and Eliasmith 2010)", + "repository_type": "github", + "summary": "\"Temporal derivatives are computed by a wide variety of neural circuits, but the problem of performing this computation accurately has received little theoretical study. \r\n\r\nHere we systematically compare the performance of diverse networks that calculate derivatives using cell-intrinsic adaptation and synaptic depression dynamics, feedforward network dynamics, and recurrent network dynamics. \r\n\r\nExamples of each type of network are compared by quantifying the errors they introduce into the calculation and their rejection of high-frequency input noise. \r\n...\"", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1573, + "tag": "ModelDB:136799" + }, + { + "id": 726, + "tag": "Nengo" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + } + ], + "timestamp_created": "2024-01-12 09:54:36.592463+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/136799", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1004": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1004, + "name": "Biophysically realistic neural modeling of the MEG mu rhythm (Jones et al. 2009)", + "repository_type": "github", + "summary": "\"Variations in cortical oscillations in the alpha (7\u201314 Hz) and beta (15\u201329 Hz) range have been correlated with attention, working memory, and stimulus detection. The mu rhythm recorded with magnetoencephalography (MEG) is a prominent oscillation generated by Rolandic cortex containing alpha and beta bands. Despite its prominence, the neural mechanisms regulating mu are unknown. We characterized the ongoing MEG mu rhythm from a localized source in the finger representation of primary somatosensory (SI) cortex. Subjects showed variation in the relative expression of mu-alpha or mu-beta, which were nonoverlapping for roughly 50% of their respective durations on single trials. To delineate the origins of this rhythm, a biophysically principled computational neural model of SI was developed, with distinct laminae, inhibitory and excitatory neurons, and feedforward (FF, representative of lemniscal thalamic drive) and feedback (FB, representative of higher-order cortical drive or input from nonlemniscal thalamic nuclei) inputs defined by the laminar location of their postsynaptic effects. ...\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1574, + "tag": "ModelDB:136803" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 851, + "tag": "Touch" + } + ], + "timestamp_created": "2024-01-12 09:54:37.100604+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/136803", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1005": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1005, + "name": "Democratic population decisions result in robust policy-gradient learning (Richmond et al. 2011)", + "repository_type": "github", + "summary": "This model demonstrates the use of GPU programming (with CUDA) to simulate a two-layer network of Integrate-and-Fire neurons with varying degrees of recurrent connectivity and to investigate its ability to learn a simplified navigation task using a learning rule stemming from Reinforcement Learning, a policy-gradient rule.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 908, + "tag": "Learning" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1575, + "tag": "ModelDB:136807" + }, + { + "id": 905, + "tag": "Winner-take-all" + } + ], + "timestamp_created": "2024-01-12 09:54:37.983223+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/136807", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1006": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1006, + "name": "Parameter estimation for Hodgkin-Huxley based models of cortical neurons (Lepora et al. 2011)", + "repository_type": "github", + "summary": "Simulation and fitting of two-compartment (active soma, passive dendrite) \r\nfor different classes of cortical neurons. The fitting technique \r\nindirectly matches neuronal currents derived from somatic membrane \r\npotential data rather than fitting the voltage traces directly. \r\nThe method uses an analytic solution for the somatic ion channel \r\nmaximal conductances given approximate models of the channel kinetics, \r\nmembrane dynamics and dendrite. This approach is tested on model-derived \r\ndata for various cortical neurons. \r\n", + "tags": [ + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1576, + "tag": "ModelDB:136808" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 941, + "tag": "Parameter sensitivity" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:54:38.487221+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/136808", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1007": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1007, + "name": "A model of unitary responses from A/C and PP synapses in CA3 pyramidal cells (Baker et al. 2010)", + "repository_type": "github", + "summary": "The model was used to reproduce experimentally determined mean synaptic response characteristics of unitary AMPA and NMDA synaptic stimulations in CA3 pyramidal cells with the objective of inferring the most likely response properties of the corresponding types of synapses. The model is primarily concerned with passive cells, but models of active dendrites are included.", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1577, + "tag": "ModelDB:137259" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:54:38.990312+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/137259", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1008": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1008, + "name": "Effect of trp-like current on APs during exposure to sinusoidal voltage (Chen et al. 2010)", + "repository_type": "github", + "summary": "\"...\r\nPrevious work showed that magnetic electrical field-induced antinoceptive action is mediated by activation of capsaicin-sensitive sensory afferents. In this study, a modified Hodgkin-Huxley model, in which TRP-like current (I-TRP) was incorporated, was implemented to predict the firing behavior of action potentials (APs), as the model neuron was exposed to sinusoidal changes in externally-applied voltage. \r\n\r\n...\r\nOur simulation results suggest that modulation of TRP-like channels functionally expressed in small-diameter peripheral sensory neurons should be an important mechanism through which it can contribute to the firing pattern of APs.\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 1578, + "tag": "I trp" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1579, + "tag": "ModelDB:137263" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:54:39.573585+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/137263", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1009": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1009, + "name": "Striatal GABAergic microcircuit, spatial scales of dynamics (Humphries et al, 2010)", + "repository_type": "github", + "summary": "The main thrust of this paper was the development of the 3D anatomical network of the striatum's GABAergic microcircuit. We grew dendrite and axon models for the MSNs and FSIs and extracted probabilities for the presence of these neurites as a function of distance from the soma. From these, we found the probabilities of intersection between the neurites of two neurons given their inter-somatic distance, and used these to construct three-dimensional striatal networks. These networks were examined for their predictions for the distributions of the numbers and distances of connections for all the connections in the microcircuit. We then combined the neuron models from a previous model (Humphries et al, 2009; ModelDB ID: 128874) with the new anatomical model. We used this new complete striatal model to examine the impact of the anatomical network on the firing properties of the MSN and FSI populations, and to study the influence of all the inputs to one MSN within the network.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 935, + "tag": "Connectivity matrix" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1580, + "tag": "ModelDB:137502" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 905, + "tag": "Winner-take-all" + } + ], + "timestamp_created": "2024-01-12 09:54:40.107970+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/137502", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1010": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1010, + "name": "Model of long range transmission of gamma oscillation (Murray 2007)", + "repository_type": "github", + "summary": "\"...\r\nA minimal mathematical model was developed for a\r\npreliminary study of long-range neural transmission of gamma\r\noscillation from the CA3 to the entorhinal cortex via the CAI\r\nregion of the hippocampus, a subset within a larger complex set of\r\npathways. A module was created for each local population of\r\nneurons with common intrinsic properties and connectivity to\r\nsimplify the connection process and make the model more flexible.\r\nThree modules were created using MATLAB Simulink\u00ae and tested\r\nto confirm that they transmit gamma through the system. The\r\nmodel also revealed that a portion of the signal from CAI to the\r\nentorhinal cortex may be lost in transmission under certain\r\nconditions.\"", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1581, + "tag": "ModelDB:137505" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:54:40.592240+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/137505", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1011": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1011, + "name": "Sensory feedback in an oscillatory interference model of place cell activity (Monaco et al. 2011)", + "repository_type": "github", + "summary": "Many animals use a form of dead reckoning known as 'path integration' to maintain a sense of their location as they explore the world. However, internal motion signals and the neural activity that integrates them can be noisy, leading inevitably to inaccurate position estimates. The rat hippocampus and entorhinal cortex support a flexible system of spatial representation that is critical to spatial learning and memory. The position signal encoded by this system is thought to rely on path integration, but it must be recalibrated by familiar landmarks to maintain accuracy. To explore the interaction between path integration and external landmarks, we present a model of hippocampal activity based on the interference of theta-frequency oscillations that are modulated by realistic animal movements around a track. We show that spatial activity degrades with noise, but introducing external cues based on direct sensory feedback can prevent this degradation. When these cues are put into conflict with each other, their interaction produces a diverse array of response changes that resembles experimental observations. Feedback driven by attending to landmarks may be critical to navigation and spatial memory in mammals.", + "tags": [ + { + "id": 1544, + "tag": "Envelope synthesis" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1582, + "tag": "ModelDB:137676" + }, + { + "id": 952, + "tag": "Noise Sensitivity" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1583, + "tag": "Phase interference" + }, + { + "id": 882, + "tag": "Place cell/field" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2024-01-12 09:54:41.093318+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/137676", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1012": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1012, + "name": "Laminar analysis of excitatory circuits in vibrissal motor and sensory cortex (Hooks et al. 2011)", + "repository_type": "github", + "summary": "\"...\r\nWe mapped local excitatory pathways in each area (primary motor cortex (vM1), primary somatosensory cortex (vS1;\r\nbarrel cortex), and secondary somatosensory cortex (S2)) across all cortical layers using glutamate uncaging and laser scanning photostimulation. \r\n\r\nWe analyzed these maps to derive laminar connectivity matrices describing the average strengths of pathways between individual neurons in\r\ndifferent layers and between entire cortical layers. ...\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 935, + "tag": "Connectivity matrix" + }, + { + "id": 936, + "tag": "Laminar Connectivity" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1584, + "tag": "ModelDB:137743" + }, + { + "id": 851, + "tag": "Touch" + }, + { + "id": 840, + "tag": "Whisking" + } + ], + "timestamp_created": "2024-01-12 09:54:41.623436+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/137743", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1013": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1013, + "name": "CA1 pyramidal: Stochastic amplification of KCa in Ca2+ microdomains (Stanley et al. 2011)", + "repository_type": "github", + "summary": "This minimal model investigates stochastic amplification of calcium-activated potassium (KCa) currents. Amplification results from calcium being released in short high amplitude pulses associated with the stochastic gating of calcium channels in microdomains. This model predicts that such pulsed release of calcium significantly increases subthreshold SK2 currents above what would be produced by standard deterministic models. However, there is little effect on a simple sAHP current kinetic scheme. This suggests that calcium stochasticity and microdomains should be considered when modeling certain KCa currents near subthreshold conditions.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1585, + "tag": "ModelDB:137745" + }, + { + "id": 952, + "tag": "Noise Sensitivity" + } + ], + "timestamp_created": "2024-01-12 09:54:42.120328+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/137745", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1014": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1014, + "name": "Spike exchange methods for a Blue Gene/P supercomputer (Hines et al., 2011)", + "repository_type": "github", + "summary": "Tests several spike exchange methods on a Blue Gene/P supercomputer on up to 64K cores.", + "tags": [ + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1586, + "tag": "ModelDB:137845" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:54:42.621165+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/137845", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1015": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1015, + "name": "GP Neuron, somatic and dendritic phase response curves (Schultheiss et al. 2011)", + "repository_type": "github", + "summary": "Phase response analysis of a GP neuron model showing type I PRCs for somatic inputs and type II PRCs for dendritic excitation. Analysis of intrinsic currents underlying type II dendritic PRCs.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1587, + "tag": "ModelDB:137846" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 906, + "tag": "Phase Response Curves" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2024-01-12 09:54:43.123166+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/137846", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1016": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1016, + "name": "Vectorized algorithms for spiking neural network simulation (Brette and Goodman 2011)", + "repository_type": "github", + "summary": "\"... We describe a set of algorithms to simulate large spiking neural networks\r\nefficiently with high-level languages using vector-based operations.\r\nThese algorithms constitute the core of Brian, a spiking neural network\r\nsimulator written in the Python language. Vectorized simulation makes\r\nit possible to combine the flexibility of high-level languages with the\r\ncomputational efficiency usually associated with compiled languages.\"", + "tags": [ + { + "id": 831, + "tag": "Brian (web link to method)" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1588, + "tag": "ModelDB:137989" + }, + { + "id": 832, + "tag": "Python (web link to model)" + } + ], + "timestamp_created": "2024-01-12 09:54:43.621270+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/137989", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1017": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1017, + "name": "HMM of Nav1.7 WT and F1449V (Gurkiewicz et al. 2011)", + "repository_type": "github", + "summary": "Neuron mod files for the WT and F1449V Na+ currents from the paper:\r\nKinetic Modeling of Nav1.7 Provides Insight Into Erythromelalgia-associated F1449V Mutation\r\nM. Gurkiewicz, A. Korngreen, S. Waxman, and A. Lampert. J.Neurophysiol. (2011).\r\n\r\nThe parameters for the K65, K53 and K63 transitions were derived from microscopic reversibility relationships in the model.", + "tags": [ + { + "id": 1589, + "tag": "Erythromelalgia" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1590, + "tag": "ModelDB:138082" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 861, + "tag": "Nociception" + } + ], + "timestamp_created": "2024-01-12 09:54:44.149807+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/138082", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1018": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1018, + "name": "CA1 pyramidal neuron: schizophrenic behavior (Migliore et al. 2011)", + "repository_type": "github", + "summary": "NEURON files from the paper: A modeling study suggesting how a reduction in the context-dependent input on CA1 pyramidal neurons could generate schizophrenic behavior. by M. Migliore, I. De Blasi, D. Tegolo, R. Migliore, Neural Networks,(2011), doi:10.1016/j.neunet.2011.01.001. Starting from the experimentally supported assumption on hippocampal neurons we explore an experimentally testable prediction at the single neuron level. The model shows how and to what extent a pathological hypofunction of a contextdependent distal input on a CA1 neuron can generate hallucinations by altering the normal recall of objects on which the neuron has been previously tuned. The results suggest that a change in the context during the recall phase may cause an occasional but very significant change in the set of active dendrites used for features recognition, leading to a distorted perception of objects.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 1591, + "tag": "Hallucinations" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1592, + "tag": "ModelDB:138205" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 926, + "tag": "Schizophrenia" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:54:44.646071+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/138205", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1019": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1019, + "name": "Stochastic 3D model of neonatal rat spinal motoneuron (Ostroumov 2007)", + "repository_type": "github", + "summary": "\" ... Although existing models of motoneurons have indicated the distributed role of certain conductances in\r\nregulating firing, it is unclear how the spatial distribution of certain currents is ultimately shaping motoneuron output. \r\n\r\nThus, it would be helpful to\r\nbuild a bridge between histological and electrophysiological data. \r\n\r\nThe present report is based on the construction of a 3D motoneuron model based\r\non available parameters applicable to the neonatal spinal cord. ...\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1593, + "tag": "ModelDB:138273" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:54:45.162457+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/138273", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1020": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1020, + "name": "Mechanisms of magnetic stimulation of central nervous system neurons (Pashut et al. 2011)", + "repository_type": "github", + "summary": "Transcranial magnetic stimulation (TMS) is a widely applied tool for probing cognitive function in humans and is one of the best tools for clinical treatments and interfering with cognitive tasks. Surprisingly, while TMS has been commercially available for decades, the cellular mechanisms underlying magnetic stimulation remain unclear. Here we investigate these mechanisms using compartmental modeling. We generated a numerical scheme allowing simulation of the physiological response to magnetic stimulation of neurons with arbitrary morphologies and active properties. Computational experiments using this scheme suggested that TMS affects neurons in the central nervous system (CNS) primarily by somatic stimulation.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 1594, + "tag": "Magnetic stimulation" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1595, + "tag": "ModelDB:138321" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:54:45.681357+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/138321", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1021": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1021, + "name": "Emergence of physiological oscillation frequencies in neocortex simulations (Neymotin et al. 2011)", + "repository_type": "github", + "summary": "\"Coordination of neocortical oscillations has been hypothesized to underlie the \"binding\" essential to cognitive function.\r\n\r\nHowever, the mechanisms that generate neocortical oscillations in physiological frequency bands remain unknown.\r\n\r\nWe hypothesized that interlaminar relations in neocortex would provide multiple intermediate loops that would play particular roles in generating oscillations, adding different dynamics to the network.\r\n\r\nWe simulated networks from sensory neocortex using 9 columns of event-driven rule-based neurons wired according to anatomical data and driven with random white-noise synaptic inputs.\r\n...\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 936, + "tag": "Laminar Connectivity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1596, + "tag": "ModelDB:138379" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2024-01-12 09:54:46.158816+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/138379", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1022": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1022, + "name": "Controlling KCa channels with different Ca2+ buffering models in Purkinje cell (Anwar et al. 2012)", + "repository_type": "github", + "summary": "In this work, we compare the dynamics of different buffering models during generation of a dendritic Ca2+ spike in a single compartment model of a Purkinje cell dendrite. The Ca2+ buffering models used are 1) a single Ca2+ pool, 2) two Ca2+ pools respectively for the fast and slow transients, 3) a detailed calcium model with buffers, pump (Schmidt et al., 2003), and diffusion and 4) a calcium model with buffers, pump and diffusion compensation. The parameters of single pool and double pool are tuned, using Neurofitter (Van Geit et al., 2007), to approximate the behavior of detailed calcium dynamics over range of 0.5 \u00c2\u00b5M to 8 \u00c2\u00b5M of intracellular calcium. The diffusion compensation is modeled using a buffer-like mechanism called DCM. To use DCM robustly for different diameter compartments, its parameters are estimated, using Neurofitter (Van Geit et al., 2007), as a function of compartment diameter (0.8 \u00c2\u00b5m-20 \u00c2\u00b5m).", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1597, + "tag": "ModelDB:138382" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:54:46.641165+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/138382", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1023": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1023, + "name": "Gamma and theta rythms in biophysical models of hippocampus circuits (Kopell et al. 2011)", + "repository_type": "github", + "summary": "\" ... the main rhythms displayed by the hippocampus, the gamma (30\u201390 Hz) and theta (4\u201312 Hz) rhythms. We concentrate on modeling\r\nin vitro experiments, but with an eye toward possible in vivo implications. ...\r\nWe use simpler biophysical models; all cells have a single compartment only, and the\r\ninterneurons are restricted to two types: fast-spiking (FS) basket cells and oriens\r\nlacunosum-moleculare (O-LM) cells. \r\n... , we aim not so much at reproducing dynamics in great detail, but at clarifying the essential mechanisms underlying the production of the rhythms and their interactions (Kopell, 2005). ...\"\r\n", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1598, + "tag": "ModelDB:138421" + }, + { + "id": 787, + "tag": "Oscillations" + } + ], + "timestamp_created": "2024-01-12 09:54:47.120367+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/138421", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1024": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1024, + "name": "Prefrontal\u2013striatal Parkinsons comp. model of multicue category learning (Moustafa and Gluck 2011)", + "repository_type": "github", + "summary": "\"...\r\nIn this\r\nmodel, PFC dopamine is key for attentional learning, whereas\r\nbasal ganglia dopamine, consistent with other models, is key for\r\nreinforcement and motor learning.\r\n\r\nThe model assumes that competitive\r\ndynamics among PFC neurons is the neural mechanism\r\nunderlying stimulus selection with limited attentional resources,\r\nwhereas competitive dynamics among striatal neurons is the\r\nneural mechanism underlying action selection. \r\n\r\nAccording to our\r\nmodel, PD is associated with decreased phasic and tonic dopamine\r\nlevels in both PFC and basal ganglia. \r\n...\"", + "tags": [ + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1599, + "tag": "ModelDB:138631" + }, + { + "id": 884, + "tag": "Parkinson's" + } + ], + "timestamp_created": "2024-01-12 09:54:47.611080+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/138631", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1025": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1025, + "name": "A neurocomputational model of classical conditioning phenomena (Moustafa et al. 2009)", + "repository_type": "github", + "summary": "\"... Here, we show that the same information-processing function proposed\r\nfor the hippocampal region in the Gluck and Myers (1993) model can also be implemented in\r\na network without using the backpropagation algorithm. Instead, our newer instantiation of\r\nthe theory uses only (a) Hebbian learning methods which match more closely with synaptic\r\nand associative learning mechanisms ascribed to the hippocampal region and (b) a more\r\nplausible representation of input stimuli. \r\nWe demonstrate here that this new more\r\nbiologically plausible model is able to simulate various behavioral effects, including latent\r\ninhibition, acquired equivalence, sensory preconditioning, negative patterning, and context\r\nshift effects. \r\n...\"", + "tags": [ + { + "id": 908, + "tag": "Learning" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1600, + "tag": "ModelDB:138634" + }, + { + "id": 845, + "tag": "Sensory processing" + } + ], + "timestamp_created": "2024-01-12 09:54:48.142643+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/138634", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1026": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1026, + "name": "Stochastic versions of the Hodgkin-Huxley equations (Goldwyn, Shea-Brown 2011)", + "repository_type": "github", + "summary": "A Matlab gui for simulating different channel noise models using the Hodgkin-Huxley equations. Methods provided and reviewed in Goldwyn and Shea-Brown (2011) are: current noise, subunit noise, conductance noise, and Markov chain, as well as the standard deterministic Hodgkin-Huxley model.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1601, + "tag": "ModelDB:138950" + } + ], + "timestamp_created": "2024-01-12 09:54:48.677757+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/138950", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1027": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1027, + "name": "Modular grid cell responses as a basis for hippocampal remapping (Monaco and Abbott 2011)", + "repository_type": "github", + "summary": "\"Hippocampal place fields, the local regions of activity recorded from place cells in exploring rodents, can undergo large changes in relative\r\nlocation during remapping.\r\n\r\nThis process would appear to require some form of modulated global input.\r\n\r\nGrid-cell responses recorded from layer II of medial entorhinal cortex in rats have been observed to realign concurrently with hippocampal remapping, making them a candidate input source.\r\n\r\nHowever, this realignment occurs coherently across colocalized ensembles of grid cells (Fyhn et al., 2007).\r\n\r\nThe hypothesized entorhinal contribution to remapping depends on whether this coherence extends to all grid cells, which is currently\r\nunknown.\r\n\r\nWe study whether dividing grid cells into small numbers of independently realigning modules can both account for this\r\nlocalized coherence and allow for hippocampal remapping.\r\n...\"", + "tags": [ + { + "id": 935, + "tag": "Connectivity matrix" + }, + { + "id": 1539, + "tag": "Grid cell" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1602, + "tag": "ModelDB:138951" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 882, + "tag": "Place cell/field" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + } + ], + "timestamp_created": "2024-01-12 09:54:49.181011+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/138951", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1028": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1028, + "name": "A Neural mass computational model of the Thalamocorticothalamic circuitry (Bhattacharya et al. 2011)", + "repository_type": "github", + "summary": "The model presented here is a bio-physically plausible version of a simple thalamo-cortical neural mass computational model proposed by Lopes da Silva in 1974 to simulate brain EEG activity within the alpha band (8-13 Hz). The thalamic and cortical circuitry are presented as separate modules in this model with cell populations as in biology. The connectivity between cell populations are as reported by Sherman, S. in Scholarpedia, 2006. The values of the synaptic connectivity parameters are as reported by Van Horn et al, 2000. In our paper (doi:10.1016/j.neunet.2011.02.009), we study the model behaviour while varying the values of the synaptic connectivity parameters (Cyyy) in the model about their respective 'basal' (intial) values.", + "tags": [ + { + "id": 833, + "tag": "Aging/Alzheimer`s" + }, + { + "id": 1603, + "tag": "Brain Rhythms" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1604, + "tag": "ModelDB:138970" + }, + { + "id": 907, + "tag": "Simulink" + } + ], + "timestamp_created": "2024-01-12 09:54:49.713689+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/138970", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1029": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1029, + "name": "Interacting synaptic conductances during, distorting, voltage clamp (Poleg-Polsky and Diamond 2011)", + "repository_type": "github", + "summary": "This simulation examines the accuracy of the voltage clamp technique\r\nin detecting the excitatory and the inhibitory components of the\r\nsynaptic drive.", + "tags": [ + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1605, + "tag": "ModelDB:139150" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:54:50.466667+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/139150", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1030": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1030, + "name": "Single neuron with ion concentrations to model anoxic depolarization (Zandt et al. 2011)", + "repository_type": "github", + "summary": "A minimal single neuron model, including changing ion concentrations and homeostasis mechanisms. It shows the sudden depolarization that occurs after prolonged anoxia/ischemia.", + "tags": [ + { + "id": 1606, + "tag": "Anoxic depolarization" + }, + { + "id": 1607, + "tag": "I Cl, leak" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 1608, + "tag": "I Na, leak" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1609, + "tag": "ModelDB:139266" + }, + { + "id": 816, + "tag": "Na/K pump" + } + ], + "timestamp_created": "2024-01-12 09:54:50.946576+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/139266", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1031": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1031, + "name": "A spatially extended model for macroscopic spike-wave discharges (Taylor and Baier 2011)", + "repository_type": "github", + "summary": "A spatially extended neural field model for generating spike-wave based on the Amari (1977) model implemented in MATLAB.", + "tags": [ + { + "id": 1603, + "tag": "Brain Rhythms" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1610, + "tag": "ModelDB:139296" + } + ], + "timestamp_created": "2024-01-12 09:54:51.534834+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/139296", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1032": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1032, + "name": "Bursting respiratory net: clustered architecture gives large phase diff`s (Fietkiewicz et al 2011)", + "repository_type": "github", + "summary": "Using a previous model of respiratory rhythm generation, we modified the network architecture such that cells can be segregated into two clusters. Cells within a given cluster burst with smaller phase differences than do cells from different clusters. This may explain the large phase differences seen experimentally, as reported in the paper.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1611, + "tag": "ModelDB:139418" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1612, + "tag": "Respiratory control" + } + ], + "timestamp_created": "2024-01-12 09:54:52.054495+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/139418", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1033": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1033, + "name": "Ketamine disrupts theta modulation of gamma in a computer model of hippocampus (Neymotin et al 2011)", + "repository_type": "github", + "summary": "\"Abnormalities in oscillations have been suggested to play a role in schizophrenia. \r\n\r\nWe studied theta-modulated gamma oscillations in a computer model of hippocampal CA3 in vivo with and\r\nwithout simulated application of ketamine, an NMDA receptor antagonist and psychotomimetic.\r\n\r\nNetworks of 1200 multi-compartment neurons (pyramidal, basket and oriens-lacunosum moleculare,\r\nOLM, cells) generated theta and gamma oscillations from intrinsic network dynamics: basket cells\r\nprimarily generated gamma and amplified theta, while OLM cells strongly contributed to theta.\r\n...\"", + "tags": [ + { + "id": 1603, + "tag": "Brain Rhythms" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 1556, + "tag": "Information transfer" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1613, + "tag": "ModelDB:139421" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 872, + "tag": "Pathophysiology" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 926, + "tag": "Schizophrenia" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 822, + "tag": "Therapeutics" + } + ], + "timestamp_created": "2024-01-12 09:54:52.587161+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/139421", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1034": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1034, + "name": "Mathematics for Neuroscientists (Gabbiani and Cox 2010)", + "repository_type": "github", + "summary": "This textbook provides a good source for learning the mathematics relevant to computational neuroscience and also the neuroscience itself. There are 232 computer code examples from the book available through the http://www.elsevierdirect.com/companions/9780123748829/pictures/code/index.html code link here and in the below page copied from the books companion web site.", + "tags": [ + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1614, + "tag": "ModelDB:139457" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + } + ], + "timestamp_created": "2024-01-12 09:54:53.181373+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/139457", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1035": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1035, + "name": "L5b PC model constrained for BAC firing and perisomatic current step firing (Hay et al., 2011)", + "repository_type": "github", + "summary": "\"...\r\nL5b pyramidal cells have been the subject of extensive experimental and modeling studies, yet conductance-based models of these cells that faithfully reproduce both their perisomatic Na+-spiking behavior as well as key dendritic active properties, including Ca2+ spikes and back-propagating action potentials, are still lacking.\r\n\r\nBased on a large body of experimental recordings from both the soma and dendrites of L5b pyramidal cells in adult rats, we characterized key features of the somatic and dendritic firing and quantified their statistics.\r\n\r\nWe used these features to constrain the density of a set of ion channels over the soma and dendritic surface via multi-objective optimization with an evolutionary algorithm, thus generating a set of detailed conductance-based models that faithfully replicate the back-propagating action potential activated Ca2+ spike firing and the perisomatic firing response to current steps, as well as the experimental variability of the properties.\r\n...\r\nThe models we present provide several experimentally-testable predictions and can serve as a powerful tool for theoretical investigations of the contribution of single-cell dynamics to network activity and its computational capabilities.\r\n\"", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1615, + "tag": "ModelDB:139653" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 774, + "tag": "Parameter Fitting" + } + ], + "timestamp_created": "2024-01-12 09:54:53.680731+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/139653", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1036": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1036, + "name": "Frog second-order vestibular neuron models (Rossert et al. 2011)", + "repository_type": "github", + "summary": "This implements spiking Hodgkin-Huxley type models of tonic and phasic second-order vestibular neurons. Models fitted to intracellular spike and membrane potential recordings from frog (Rana temporaria). The models can be stimulated by intracellular step current, frequency current (ZAP) or synaptic stimulation.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1616, + "tag": "ModelDB:139654" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 845, + "tag": "Sensory processing" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 829, + "tag": "Vestibular" + } + ], + "timestamp_created": "2024-01-12 09:54:54.225619+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/139654", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1037": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1037, + "name": "Hippocampus CA1: Simulations of LTP signaling pathways (Kim M et al. 2011)", + "repository_type": "github", + "summary": "This is a multi-compartmental, stochastic version of the Kim et al. 2010 paper. There are a few additional reactions, and some of the rate constants have been updated. It addresses the role of molecule anchoring in PKA dependent hippocampal LTP.\r\n", + "tags": [ + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1617, + "tag": "ModelDB:139655" + }, + { + "id": 1618, + "tag": "NeuroRD" + }, + { + "id": 827, + "tag": "Signaling pathways" + } + ], + "timestamp_created": "2024-01-12 09:54:54.718156+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/139655", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1038": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1038, + "name": "Cerebellar cortex oscil. robustness from Golgi cell gap jncs (Simoes de Souza and De Schutter 2011)", + "repository_type": "github", + "summary": "\" ... Previous one-dimensional network modeling of the cerebellar granular layer has been successfully\r\nlinked with a range of cerebellar cortex oscillations observed in vivo. However, the recent discovery of gap\r\njunctions between Golgi cells (GoCs), which may cause oscillations by themselves, has raised the question of how\r\ngap-junction coupling affects GoC and granular-layer oscillations. To investigate this question, we developed a\r\nnovel two-dimensional computational model of the GoC-granule cell (GC) circuit with and without gap junctions\r\nbetween GoCs. ...\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1619, + "tag": "ModelDB:139656" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2024-01-12 09:54:55.191021+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/139656", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1039": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1039, + "name": "Superior paraolivary nucleus neuron (Kopp-Scheinpflug et al. 2011)", + "repository_type": "github", + "summary": "This is a model of neurons in the brainstem superior paraolivary nucleus (SPN), which produce very salient offset firing during sound stimulation. Rebound offset firing is triggered by IPSPs coming from the medial nucleus of the trapezoid body (MNTB). This model shows that AP firing can emerge from inhibition through integration of large IPSPs, driven by an\r\nextremely negative chloride reversal potential, combined with a large hyperpolarization-\r\nactivated non-specific cationic current (IH), with a secondary contribution from a T-type calcium conductance (ITCa). As a result, tiny gaps in sound stimuli of just 3-4ms can elicit reliable APs that signal such brief offsets.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1620, + "tag": "ModelDB:139657" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 775, + "tag": "Rebound firing" + } + ], + "timestamp_created": "2024-01-12 09:54:55.678985+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/139657", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1040": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1040, + "name": "Model of peristalsis in the dorsal blood vessel of Lumbriculus variegatus (Halfmann and Crisp 2011)", + "repository_type": "github", + "summary": "The blackworm, Lumbriculus variegatus, has a segmented dorsal blood vessel that acts as a peristaltic pump to move blood through its closed circulatory system. Here, we conducted a kinematic study using videography and computational modeling as a first step toward understanding the control of DBV pulsation. A simple feed-forward system of distributed, coupled neuronal oscillators is a sufficient model was a sufficient model to explain the control of pulsation in the blackworm.", + "tags": [ + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1621, + "tag": "ModelDB:139760" + }, + { + "id": 89, + "tag": "Python" + } + ], + "timestamp_created": "2024-01-12 09:54:56.162980+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/139760", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1041": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1041, + "name": "Reconstructing cerebellar granule layer evoked LFP using convolution (ReConv) (Diwakar et al. 2011)", + "repository_type": "github", + "summary": "The model allows reconstruction of evoked local field potentials as seen in the cerebellar granular layer. The approach uses a detailed model of cerebellar granule neuron to generate data traces and then uses a \"ReConv\" or jittered repetitive convolution technique to reproduce post-synaptic local field potentials in the granular layer. The algorithm was used to generate both in vitro and in vivo evoked LFP and reflected the changes seen during LTP and LTD, when such changes were induced in the underlying neurons by modulating release probability of synapses and sodium channel regulated intrinsic excitability of the cells.", + "tags": [ + { + "id": 1622, + "tag": "Evoked LFP" + }, + { + "id": 810, + "tag": "Extracellular Fields" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 1607, + "tag": "I Cl, leak" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1623, + "tag": "ModelDB:139883" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 274, + "tag": "Octave" + } + ], + "timestamp_created": "2024-01-12 09:54:56.668548+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/139883", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1042": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1042, + "name": "Hyperconnectivity, slow synapses in PFC mental retardation and autism model (Testa-Silva et al 2011)", + "repository_type": "github", + "summary": "The subdirectory 'matlab' contains MATLAB scripts (The Mathworks, USA) that can be used to reproduce the panels of Figures 4-5.\r\n\r\nThis directory contains files to reproduce sample computer simulations presented in the 2011 paper authored by\r\n\r\nMeredith, R., Testa-Silva, G., Loebel, A., Giugliano, M., de Kock, C.; Mansvelder, H. \r\n\"Hyperconnectivity and slow synapses in prefrontal cortex of a model for mental retardation and autism\".\r\n\r\n\r\nABSTRACT\r\n\"... We propose that these findings are tightly linked: using a network model, we show that slower synapses are essential to counterbalance hyperconnectivity in order to maintain a dynamic range of excitatory activity. However, the slow synaptic time constants induce decreased responsiveness to low frequency stimulation, which may explain deficits in integration and information processing in attentional neuronal networks in neurodevelopmental disorders.\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 790, + "tag": "Attractor Neural Network" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1624, + "tag": "ModelDB:140033" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:54:57.181175+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/140033", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1043": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1043, + "name": "Dorsal root ganglion (DRG) neuronal model (Kovalsky et al. 2009)", + "repository_type": "github", + "summary": "This model, diverged from oscillatory parameters seen in live cells and failed to produce characteristic ectopic discharge patterns. Here we show that use of a more complete set of Na+ conductances--which includes several delayed components--enables simulation of the entire repertoire of oscillation-triggered electrogenic phenomena seen in live dorsal root ganglion (DRG) neurons. This includes a physiological window of induction and natural patterns of spike discharge. An INa+ component at 2-20 ms was particularly important, even though it represented only a tiny fraction of overall INa+ amplitude. With the addition of a delayed rectifier IK+ the singlet firing seen in some DRG neurons can also be simulated. The model reveals the key conductances that underlie afferent ectopia, conductances that are potentially attractive targets in the search for more effective treatments of neuropathic pain.", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 902, + "tag": "Late Na" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1625, + "tag": "ModelDB:140038" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 872, + "tag": "Pathophysiology" + } + ], + "timestamp_created": "2024-01-12 09:54:57.685464+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/140038", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1044": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1044, + "name": "A simple integrative electrophysiological model of bursting GnRH neurons (Csercsik et al. 2011)", + "repository_type": "github", + "summary": "In this paper a modular model of the GnRH\r\nneuron is presented. For the aim of simplicity, the\r\ncurrents corresponding to fast time scales and action\r\npotential generation are described by an impulsive system,\r\nwhile the slower currents and calcium dynamics\r\nare described by usual ordinary differential equations\r\n(ODEs). The model is able to reproduce the depolarizing\r\nafterpotentials, afterhyperpolarization, periodic\r\nbursting behavior and the corresponding calcium transients\r\nobserved in the case of GnRH neurons.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1626, + "tag": "ModelDB:140246" + } + ], + "timestamp_created": "2024-01-12 09:54:58.205642+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/140246", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1045": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1045, + "name": "A multi-compartment model for interneurons in the dLGN (Halnes et al. 2011)", + "repository_type": "github", + "summary": "This model for dLGN interneurons is presented in two parameterizations (P1 & P2), which were fitted to current-clamp data from two different interneurons (IN1 & IN2). The model qualitatively reproduces the responses in IN1 & IN2 under 8 different experimental condition, and quantitatively reproduces the I/O-relations (#spikes elicited as a function of injected current).", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 888, + "tag": "I Mixed" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1627, + "tag": "ModelDB:140249" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 775, + "tag": "Rebound firing" + } + ], + "timestamp_created": "2024-01-12 09:54:58.720082+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/140249", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1046": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1046, + "name": "Firing patterns in stuttering fast-spiking interneurons (Klaus et al. 2011)", + "repository_type": "github", + "summary": "This is a morphologically extended version of the fast-spiking interneuron by Golomb et al. (2007).\r\n\r\nThe model captures the stuttering firing pattern and subthreshold oscillations in response to step current input as observed in many cortical and striatal fast-spiking cells.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1628, + "tag": "ModelDB:140254" + }, + { + "id": 1629, + "tag": "PGENESIS" + }, + { + "id": 915, + "tag": "Stuttering" + } + ], + "timestamp_created": "2024-01-12 09:54:59.276333+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/140254", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1047": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1047, + "name": "Four cortical interneuron subtypes (Kubota et al. 2011)", + "repository_type": "github", + "summary": "\" ... Using electron microscopy and serial reconstructions, we analyzed the dendritic trees of\r\nfour morphologically distinct neocortical interneuron subtypes to reveal two underlying organizational\r\nprinciples common to all.\r\n\r\nFirst, cross-sectional areas at any given point within a dendrite were proportional\r\nto the summed length of all dendritic segments distal to that point.\r\n\r\n...\r\nSecond, dendritic cross-sections\r\nbecame progressively more elliptical at more proximal, larger diameter, dendritic locations.\r\n\r\nFinally,\r\ncomputer simulations revealed that these conserved morphological features limit distance dependent\r\nfiltering of somatic EPSPs and facilitate distribution of somatic depolarization into all dendritic\r\ncompartments.\r\n...\"", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1630, + "tag": "ModelDB:140299" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:54:59.974182+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/140299", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1048": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1048, + "name": "Calcium and potassium currents of olfactory bulb juxtaglomerular cells (Masurkar and Chen 2011)", + "repository_type": "github", + "summary": "Inward and outward currents of the olfactory bulb juxtaglomerular cells are characterized in the experiments and modeling in these two Masurkar and Chen 2011 papers.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 855, + "tag": "I_KHT" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1631, + "tag": "ModelDB:140462" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + } + ], + "timestamp_created": "2024-01-12 09:55:00.475583+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/140462", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1049": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1049, + "name": "Cancelling redundant input in ELL pyramidal cells (Bol et al. 2011)", + "repository_type": "github", + "summary": "The paper investigates the property of the electrosensory lateral line lobe (ELL) of the brain of weakly electric fish to cancel predictable stimuli. Electroreceptors on the skin encode all signals in their firing activity, but superficial pyramidal (SP) cells in the ELL that receive this feedforward input do not respond to constant sinusoidal signals. This cancellation putatively occurs using a network of feedback delay lines and burst-induced synaptic plasticity between the delay lines and the SP cell that learns to cancel the redundant input. Biologically, the delay lines are parallel fibres from cerebellar-like granule cells in the eminentia granularis posterior.\r\n\r\nA model of this network (e.g. electroreceptors, SP cells, delay lines and burst-induced plasticity) was constructed to test whether the current knowledge of how the network operates is sufficient to cancel redundant stimuli. \r\n", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 937, + "tag": "Biofeedback" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 908, + "tag": "Learning" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1632, + "tag": "ModelDB:140465" + }, + { + "id": 952, + "tag": "Noise Sensitivity" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 886, + "tag": "Unsupervised Learning" + } + ], + "timestamp_created": "2024-01-12 09:55:01.305077+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/140465", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1050": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1050, + "name": "Shaping of action potentials by different types of BK channels (Jaffe et al., 2011)", + "repository_type": "github", + "summary": "Dentate gyrus granule cells highly express the beta4 accessory subunit which confer BK channels with type II properties. The properties of heterologously-expressed BK channels (with and without the beta4 subunit) were used to construct channel models. These were then used to study how they affect single action potentials and trains of spikes in a model dentate gyrus granule cells (based on Aradi and Holmes, 1999).", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1633, + "tag": "ModelDB:140471" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:55:01.820904+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/140471", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1051": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1051, + "name": "Dipole Localization Kit (Mechler & Victor, 2012)", + "repository_type": "github", + "summary": "We localize a single neuron from the spatial sample of its EAP\r\namplitudes recorded with a multisite probe (with 6 or more independent\r\nmeasurement sites or channels, e.g., a silicon polytrode, a stepped\r\ntetrode, etc.) This is an inverse problem and we solve it by fitting a\r\nmodel to the EAPs that consists of a volume conductor model of the\r\nneural tissue (known), a realistic model of the probe (known), and a\r\nsingle dipole current source of the model neuron (unknown). The dipole\r\nis free to change position, size, and orientation (a total of 6\r\nparameters) at each moment during the action potential.", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1634, + "tag": "ModelDB:140599" + } + ], + "timestamp_created": "2024-01-12 09:55:02.307235+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/140599", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1052": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1052, + "name": "CA3 Radiatum/Lacunosum-Moleculare interneuron, Ih (Anderson et al. 2011)", + "repository_type": "github", + "summary": "\"The present study examines the biophysical\r\nproperties and functional implications of Ih in hippocampal\r\narea CA3 interneurons with somata in strata radiatum and\r\nlacunosum-moleculare.... The functional\r\nconsequences of Ih were examined with regard to temporal\r\nsummation and impedance measurements. ...\r\nFrom impedance measurements, we\r\nfound that Ih did not confer theta-band resonance, but\r\nflattened the impedance\u2013frequency relations instead. ... Finally, a model of Ih was employed in\r\ncomputational analyses to confirm and elaborate upon the\r\ncontributions of Ih to impedance and temporal summation.\"", + "tags": [ + { + "id": 794, + "tag": "I h" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1635, + "tag": "ModelDB:140732" + } + ], + "timestamp_created": "2024-01-12 09:55:02.809376+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/140732", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1053": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1053, + "name": "Motoneuron model of self-sustained firing after spinal cord injury (Kurian et al. 2011)", + "repository_type": "github", + "summary": "\" ...\r\nDuring the acute-stage of spinal cord injury (SCI), the endogenous ability to generate plateaus is lost; however, during the chronic-stage of SCI, plateau potentials reappear with prolonged self-sustained firing that has been implicated in the development of spasticity. In this work, we extend previous modeling studies to systematically investigate the mechanisms underlying the generation of plateau potentials in motoneurons, including the influences of specific ionic currents, the morphological characteristics of the soma and dendrite, and the interactions between persistent inward currents and synaptic input.\r\n...\"", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 1636, + "tag": "I Ca,p" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 850, + "tag": "Intrinsic plasticity" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1637, + "tag": "ModelDB:140788" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:55:03.335108+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/140788", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1054": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1054, + "name": "Detailed passive cable model of Dentate Gyrus Basket Cells (Norenberg et al. 2010)", + "repository_type": "github", + "summary": "Fast-spiking, parvalbumin-expressing basket cells (BCs) play a key role in feedforward and feedback inhibition in the hippocampus. \r\n...\r\nTo quantitatively address this question, we developed detailed passive cable models of BCs in the dentate gyrus based on dual somatic or somatodendritic recordings and complete morphologic reconstructions. \r\n\r\nBoth specific membrane capacitance and axial resistivity were comparable to those of pyramidal neurons, but the average somatodendritic specific membrane resistance (R(m)) was substantially lower in BCs. \r\n\r\nFurthermore, R(m) was markedly nonuniform, being lowest in soma and proximal dendrites, intermediate in distal dendrites, and highest in the axon. \r\n...\r\nFurther computational analysis revealed that these unique cable properties accelerate the time course of synaptic potentials at the soma in response to fast inputs, while boosting the efficacy of slow distal inputs. \r\n\r\nThese properties will facilitate both rapid phasic and efficient tonic activation of BCs in hippocampal microcircuits.\r\n", + "tags": [ + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1638, + "tag": "ModelDB:140789" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 774, + "tag": "Parameter Fitting" + } + ], + "timestamp_created": "2024-01-12 09:55:03.827318+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/140789", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1055": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1055, + "name": "Dendritic Discrimination of Temporal Input Sequences (Branco et al. 2010)", + "repository_type": "github", + "summary": "Compartmental model of a layer 2/3 pyramidal cell in the rat somatosensory cortex, exploring NMDA-dependent sensitivity to the temporal sequence of synaptic activation.", + "tags": [ + { + "id": 837, + "tag": "Direction Selectivity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1639, + "tag": "ModelDB:140828" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:55:04.343718+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/140828", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1056": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1056, + "name": "Computational Surgery (Lytton et al. 2011)", + "repository_type": "github", + "summary": "Figure 2 in\r\n Neocortical simulation for epilepsy surgery guidance: Localization and intervention,\r\n by William W. Lytton, Samuel A. Neymotin, Jason C. Wester, and Diego Contreras\r\n in Computational Surgery and Dual Training, Springer, 2011\r\n", + "tags": [ + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1640, + "tag": "ModelDB:140881" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:55:04.829196+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/140881", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1057": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1057, + "name": "Event-related simulation of neural processing in complex visual scenes (Mihalas et al. 2011)", + "repository_type": "github", + "summary": "\"... We here present an environment for the\r\nimplementation of large networks of generalized integrate-and-fire\r\nneurons which uses an asynchronous event-based algorithm.\r\n...\r\nThe neuronal network to be simulated\r\nand all parameters are defined in extendible markup language.\r\nA model of the primate early visual system is implemented. The\r\nuse of the tool is illustrated by simulating the processing of both\r\nsimple and complex visual scenes through retina, thalamus and\r\nprimary visual cortex.\"", + "tags": [ + { + "id": 1641, + "tag": "ERNST (Event Related Neuronal Simulation Tool) (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1642, + "tag": "ModelDB:140964" + }, + { + "id": 845, + "tag": "Sensory processing" + }, + { + "id": 800, + "tag": "Vision" + }, + { + "id": 911, + "tag": "XML (web link to model)" + } + ], + "timestamp_created": "2024-01-12 09:55:05.376284+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/140964", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1058": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1058, + "name": "Measuring neuronal identification quality in ensemble recordings (isoitools) (Neymotin et al. 2011)", + "repository_type": "github", + "summary": "\"... Here we describe information theoretic\r\nmeasures of action potential waveform isolation applicable to any dataset, that have an intuitive,\r\nuniversal interpretation, and that are not dependent on the methods or choice of parameters for\r\nsingle unit isolation, and that have been validated using a dataset.\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 810, + "tag": "Extracellular Fields" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1643, + "tag": "ModelDB:141061" + } + ], + "timestamp_created": "2024-01-12 09:55:05.875235+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/141061", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1059": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1059, + "name": "Relative spike time coding and STDP-based orientation selectivity in V1 (Masquelier 2012)", + "repository_type": "github", + "summary": "Phenomenological spiking model of the cat early visual system. We show how natural vision can drive spike time correlations on sufficiently fast time scales to lead to the acquisition of orientation-selective V1 neurons through STDP. This is possible without reference times such as stimulus onsets, or saccade landing times. But even when such reference times are available, we demonstrate that the relative spike times encode the images more robustly than the absolute ones.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 839, + "tag": "Development" + }, + { + "id": 1556, + "tag": "Information transfer" + }, + { + "id": 908, + "tag": "Learning" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1644, + "tag": "ModelDB:141062" + }, + { + "id": 1645, + "tag": "Orientation selectivity" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 886, + "tag": "Unsupervised Learning" + }, + { + "id": 800, + "tag": "Vision" + }, + { + "id": 905, + "tag": "Winner-take-all" + } + ], + "timestamp_created": "2024-01-12 09:55:06.421376+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/141062", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1060": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1060, + "name": "Role of Ih in firing patterns of cold thermoreceptors (Orio et al., 2012)", + "repository_type": "github", + "summary": "\" ... Here we investigated the role of Ih in cold-sensitive (CS) nerve endings, where cold sensory transduction actually takes place. Corneal CS nerve endings in mice show a rhythmic spiking activity at neutral skin temperature that switches to bursting mode when the temperature is lowered. \r\n...\r\nMathematical modeling shows that the firing phenotype of CS nerve endings from HCN1-/- mice can be reproduced by replacing HCN1 channels with the slower HCN2 channels rather than by abolishing Ih. We propose that Ih carried by HCN1 channels helps tune the frequency of the oscillation and the length of bursts underlying regular spiking in cold thermoreceptors, having important implications for neural coding of cold sensation. \r\n\"", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1646, + "tag": "ModelDB:141063" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 904, + "tag": "Sensory coding" + }, + { + "id": 845, + "tag": "Sensory processing" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:55:06.980789+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/141063", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1061": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1061, + "name": "A four compartmental model for ABPD complex in crustacean pyloric network (Maran et al. 2011)", + "repository_type": "github", + "summary": "\"Central pattern generators (CPGs) frequently include bursting neurons that serve as pacemakers for rhythm generation.\r\n\r\nPhase resetting curves (PRCs) can provide insight into mechanisms underlying phase locking in such circuits.\r\n\r\nPRCs were constructed for a pacemaker bursting complex in the pyloric circuit in the stomatogastric ganglion of the lobster and crab.\r\n...\"", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1647, + "tag": "ModelDB:141192" + } + ], + "timestamp_created": "2024-01-12 09:55:07.479242+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/141192", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1062": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1062, + "name": "Multiscale interactions between chemical and electric signaling in LTP (Bhalla 2011)", + "repository_type": "github", + "summary": "\"Synaptic plasticity leads to long-term changes in excitability, whereas cellular homeostasis maintains\r\nexcitability. Both these processes involve interactions between molecular events, electrical events, and\r\nnetwork activity. Here I explore these intersections with a multilevel model that embeds molecular events\r\nfollowing synaptic calcium influx into a multicompartmental electrical model of a CA1 hippocampal\r\nneuron. ...\"", + "tags": [ + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 171, + "tag": "Homeostasis" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1648, + "tag": "ModelDB:141226" + }, + { + "id": 944, + "tag": "Multiscale" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:55:08.046852+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/141226", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1063": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1063, + "name": "Cerebellar long-term depression (LTD) (Antunes and De Schutter 2012)", + "repository_type": "github", + "summary": "Many cellular processes involve small number of molecules and undergo stochastic fluctuations in their levels of activity. Among these processes is cerebellar long-term depression (LTD), a form of synaptic plasticity expressed as a reduction in the number of synaptic AMPA receptors (AMPARs) in Purkinje cells. Using a stochastic model of the signaling network and mechanisms of AMPAR trafficking involved in LTD, we show that the network activity in single synapses switches between two discrete stable states (LTD and non-LTD). Stochastic fluctuations affecting more intensely the level of activity of a few components of the network lead to the probabilistic induction of LTD and threshold dithering. The non-uniformly distributed stochasticity of the network allows the stable occurrence of several different macroscopic levels of depression, determining the experimentally observed sigmoidal relationship between the magnitude of depression and the concentration of the triggering signal.", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1649, + "tag": "ModelDB:141270" + }, + { + "id": 1650, + "tag": "STEPS" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:55:08.638947+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/141270", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1064": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1064, + "name": "Simple and accurate Diffusion Approximation algor. for stochastic ion channels (Orio & Soudry 2012)", + "repository_type": "github", + "summary": "\" ... We derived the (Stochastic Differential Equations) SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable \u2013 allowing an easy, transparent and efficient (Diffusion Approximation) DA implementation, avoiding unnecessary approximations. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as (Markov Chains) MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods, except when short time steps or low channel numbers were used.\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1651, + "tag": "ModelDB:141272" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 899, + "tag": "SciLab" + }, + { + "id": 1652, + "tag": "Stochastic simulation" + } + ], + "timestamp_created": "2024-01-12 09:55:09.169919+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/141272", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1065": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1065, + "name": "Engaging distinct oscillatory neocortical circuits (Vierling-Claassen et al. 2010)", + "repository_type": "github", + "summary": "\"Selective optogenetic drive of fast-spiking (FS) interneurons (INs) leads to enhanced local field potential (LFP) power across the traditional \u201cgamma\u201d frequency band (20\u201380 Hz; Cardin et al., 2009).\r\n\r\nIn contrast, drive to regular-spiking (RS) pyramidal cells enhances power at lower frequencies, with a peak at 8 Hz.\r\n\r\nThe first result is consistent with previous computational studies emphasizing the role of FS and the time constant of GABAA synaptic inhibition in gamma rhythmicity.\r\n\r\nHowever, the same theoretical models do not typically predict low-frequency LFP enhancement with RS drive.\r\n\r\nTo develop hypotheses as to how the same network can support these contrasting behaviors, we constructed a biophysically principled network model of primary somatosensory neocortex containing FS, RS, and low-threshold spiking (LTS) INs. ...\"", + "tags": [ + { + "id": 1603, + "tag": "Brain Rhythms" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 1622, + "tag": "Evoked LFP" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1653, + "tag": "ModelDB:141273" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 851, + "tag": "Touch" + } + ], + "timestamp_created": "2024-01-12 09:55:09.679788+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/141273", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1066": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1066, + "name": "Biophysically detailed model of the mouse sino-atrial node cell (Kharche et al. 2011)", + "repository_type": "github", + "summary": "This model is developed to study the role of various electrophysiological mechanisms in generating cardiac pacemaking action potentials (APs).The model incorporates membrane ionic currents and intracellular mechanisms contributing to spontaneous mouse SAN APs. The model was validated by testing the functional roles of individual membrane currents in one and multiple parameter analyses.The roles of intracellular Ca2+-handling mechanisms on cardiac pacemaking were also investigated in the model.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 880, + "tag": "Cardiac pacemaking" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 874, + "tag": "I_HERG" + }, + { + "id": 869, + "tag": "KCNQ1" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1654, + "tag": "ModelDB:141274" + }, + { + "id": 814, + "tag": "Na/Ca exchanger" + }, + { + "id": 816, + "tag": "Na/K pump" + }, + { + "id": 787, + "tag": "Oscillations" + } + ], + "timestamp_created": "2024-01-12 09:55:10.242361+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/141274", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1067": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1067, + "name": "Prosthetic electrostimulation for information flow repair in a neocortical simulation (Kerr 2012)", + "repository_type": "github", + "summary": "This model is an extension of a model ( http://modeldb.yale.edu/138379 ) recently published in Frontiers in Computational Neuroscience. This model consists of 4700 event-driven, rule-based neurons, wired according to anatomical data, and driven by both white-noise synaptic inputs and a sensory signal recorded from a rat thalamus. Its purpose is to explore the effects of cortical damage, along with the repair of this damage via a neuroprosthesis.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 1603, + "tag": "Brain Rhythms" + }, + { + "id": 834, + "tag": "Deep brain stimulation" + }, + { + "id": 852, + "tag": "I Chloride" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 1556, + "tag": "Information transfer" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1655, + "tag": "ModelDB:141505" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:55:10.759457+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/141505", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1068": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1068, + "name": "Network model with neocortical architecture (Anderson et al 2007,2012; Azhar et al 2012)", + "repository_type": "github", + "summary": "Architecturally realistic neocortical model using seven classes of excitatory and inhibitory single compartment Hodgkin-Huxley cells. This is an addendum to ModelDB Accession # 98902, Studies of stimulus parameters for seizure disruption (Anderson et al. 2007). Wiring is adapted from the minicolumn hypothesis and incorporates visual and neocortical wiring data. Simulation demonstrates spontaneous bursting onset and cessation. This activity can be induced by random fluctuations in the surrounding background input.", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1656, + "tag": "ModelDB:141507" + }, + { + "id": 800, + "tag": "Vision" + } + ], + "timestamp_created": "2024-01-12 09:55:11.281812+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/141507", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1069": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1069, + "name": "Basal ganglia-thalamic network model for deep brain stimulation (So et al. 2012)", + "repository_type": "github", + "summary": "This is a model of the basal ganglia-thalamic network, modified from the Rubin and Terman model (High frequency stimulation of the Subthalamic Nucleus, Rubin and Terman 2004). We subsequently used this model to investigate the effectiveness of STN and GPi DBS as well as lesion when various proportions of local cells and fibers of passage were activated or silenced. The BG network exhibited characteristics consistent with published experimental data, both on the level of single cells and on the network level. Perhaps most notably, and in contrast to the original RT model, the changes in the thalamic error index with changes in the DBS frequency matched well the changes in clinical symptoms with changes in DBS frequency.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 834, + "tag": "Deep brain stimulation" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 1607, + "tag": "I Cl, leak" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1657, + "tag": "ModelDB:141699" + }, + { + "id": 884, + "tag": "Parkinson's" + } + ], + "timestamp_created": "2024-01-12 09:55:11.807355+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/141699", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1070": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1070, + "name": "Mathematical Foundations of Neuroscience (Ermentrout and Terman 2010)", + "repository_type": "github", + "summary": "Ermentrout and Terman's book on dynamical systems and computational methods in neuroscience has associated computer code available at http://www.math.pitt.edu/~bard/bardware/neurobook/allodes.html. The main emphasis in the book is on single-neuron biophysics and there is also systems neuroscience theory and applications to networks. The electronic text of the book is freely available!", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1658, + "tag": "ModelDB:141835" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + }, + { + "id": 912, + "tag": "XPP (web link to model)" + } + ], + "timestamp_created": "2024-01-12 09:55:12.298285+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/141835", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1071": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1071, + "name": "Two Models for synaptic input statistics for the MSO neuron model (Jercog et al. 2010)", + "repository_type": "github", + "summary": "The model is a point neuron model with ionic currents from Rothman & Mannis (2003) and with an update of the low threshold potassium current (IKLT) measured in-vitro by Mathews & Jercog et al (2010).\r\nThis model in conjunction with the synaptic input models presented here has been used to gain insight into mechanisms that account for experimentally observed asymmetries in ITD tuning (Brand et al, 2002).\r\nAsymmetry and displacement of the ITD response function is achieved in the model by the interplay between asymmetry of the excitatory inputs arriving from the two sides and the precise voltage dependent activation of IKLT.\r\nIn Jercog et al (2010) we propose two different mathematical ways, physiologically plausible scenarios, of generating the asymmetry in the bilateral synaptic input events. Here, we present two models for simulating the stochastic synaptic input trains.", + "tags": [ + { + "id": 849, + "tag": "Audition" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1659, + "tag": "ModelDB:141991" + }, + { + "id": 1660, + "tag": "Synaptic-input statistic" + } + ], + "timestamp_created": "2024-01-12 09:55:12.833961+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/141991", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1072": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1072, + "name": "Efficient simulation environment for modeling large-scale cortical processing (Richert et al. 2011)", + "repository_type": "github", + "summary": "\"We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich neuron networks, having spike-timing dependent plasticity and short-term plasticity. ...\"", + "tags": [ + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1661, + "tag": "ModelDB:142062" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:55:13.317658+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/142062", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1073": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1073, + "name": "Hippocampal CA3 network and circadian regulation (Stanley et al. 2013)", + "repository_type": "github", + "summary": "This model produces the hippocampal CA3 neural network model used \r\nin the paper below. It has two modes of operation, a default mode and a circadian mode. In the circadian mode, parameters are swept through a range of values. \r\nThis model can be quite easily adapted to produce theta and gamma oscillations, as certain parameter sweeps will reveal (see Figures). BASH scripts interact with GENESIS\r\n2.3 to implement parameter sweeps.\r\nThe model contains four cell types derived from prior papers.\r\nCA3 pyramidal are derived from Traub et al (1991); Basket, \r\nstratum oriens (O-LM), and Medial Septal GABAergic\r\n(MSG) interneurons are taken from Hajos et al (2004).", + "tags": [ + { + "id": 1603, + "tag": "Brain Rhythms" + }, + { + "id": 1662, + "tag": "Circadian Rhythms" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1663, + "tag": "ModelDB:142104" + } + ], + "timestamp_created": "2024-01-12 09:55:13.854362+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/142104", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1074": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1074, + "name": "Rate model of a cortical RS-FS-LTS network (Hayut et al. 2011)", + "repository_type": "github", + "summary": "A rate model of cortical networks composed of RS, FS and LTS neurons. Synaptic depression is modelled according to the Tsodyks-Markram scheme.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1664, + "tag": "ModelDB:142199" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:55:14.359915+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/142199", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1075": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1075, + "name": "FRAT: An amygdala-centered model of fear conditioning (Krasne et al. 2011)", + "repository_type": "github", + "summary": "Model of Pavlovian fear conditioning and extinction (due to neuromodulator-controlled LTP on principal cells and inhibory interneurons)occur in amygdala and contextual representations are learned in hippocampus. Many properties of fear conditioning are accounted for.", + "tags": [ + { + "id": 908, + "tag": "Learning" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1665, + "tag": "ModelDB:142273" + } + ], + "timestamp_created": "2024-01-12 09:55:14.847418+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/142273", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1076": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1076, + "name": "Ion concentration dynamics as a mechanism for neuronal bursting (Barreto & Cressman 2011)", + "repository_type": "github", + "summary": "\"We describe a simple conductance-based model neuron that includes intra and\r\nextracellular ion concentration dynamics and show that this model exhibits periodic\r\nbursting. The bursting arises as the fast-spiking behavior of the neuron is modulated\r\nby the slow oscillatory behavior in the ion concentration variables and vice versa. By\r\nseparating these time scales and studying the bifurcation structure of the neuron, we catalog\r\nseveral qualitatively different bursting profiles that are strikingly similar to those seen in\r\nexperimental preparations. Our work suggests that ion concentration dynamics may play an\r\nimportant role in modulating neuronal excitability in real biological systems.\"", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 951, + "tag": "Depolarization block" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1666, + "tag": "ModelDB:142630" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:55:15.370254+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/142630", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1077": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1077, + "name": "Fisher and Shannon information in finite neural populations (Yarrow et al. 2012)", + "repository_type": "github", + "summary": "Here we model populations of rate-coding neurons with bell-shaped tuning curves and multiplicative Gaussian noise. This Matlab code supports the calculation of information theoretic (mutual information, stimulus-specific information, stimulus-specific surprise) and Fisher-based measures (Fisher information, I_Fisher, SSI_Fisher) in these population models. The information theoretic measures are computed by Monte Carlo integration, which allows computationally-intensive decompositions of the mutual information to be computed for relatively large populations (hundreds of neurons).", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1667, + "tag": "ModelDB:142990" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + } + ], + "timestamp_created": "2024-01-12 09:55:15.918197+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/142990", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1078": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1078, + "name": "ClC-2 channels regulate neuronal excitability, not intracellular Cl- levels (Ratte & Prescott 2011)", + "repository_type": "github", + "summary": "\"The model is for a generic, single compartment neuron with multiple ion currents. The most notable mechanisms include ClC-2 (a rectifying chloride-leak channel) and KCC2 (potassium chloride co-transporter 2). A significant feature of the model is that it tracks intracellular chloride concentration. Moreover, the GABA-A receptor is modeled as passing both chloride and bicarbonate ions, which is important for proper calculation of the GABA reversal potential. Ornstein-Unlenbeck processes to simulate synaptic inhibition and excitation are also included.\"\r\n", + "tags": [ + { + "id": 1668, + "tag": "Chloride regulation" + }, + { + "id": 1607, + "tag": "I Cl, leak" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1669, + "tag": "ModelDB:142993" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:55:16.406647+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/142993", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1079": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1079, + "name": "Explaining pathological changes in axonal excitability by dynamical analysis (Coggan et al. 2011)", + "repository_type": "github", + "summary": "\"... To help decipher the biophysical basis for \u2018paroxysmal\u2019\r\nspiking, we replicated afterdischarge (i.e. continued spiking after a brief stimulus) in a\r\nminimal conductance-based axon model.\r\n\r\n...\r\nA perturbation\r\ncould abruptly switch the system between two (quasi-)stable attractor states: rest and\r\nrepetitive spiking.\r\n...\r\n Initiation of afterdischarge was explained by activation of the\r\npersistent inward current forcing the system to cross a saddle point that separates the basins of\r\nattraction associated with each attractor.\r\n\r\nTermination of afterdischarge was explained by the\r\nattractor associated with repetitive spiking being destroyed.\r\n...\r\nThe model also explains other features of\r\nparoxysmal symptoms, including temporal summation and refractoriness.\"", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1670, + "tag": "ModelDB:143072" + }, + { + "id": 861, + "tag": "Nociception" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:55:16.899075+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/143072", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1080": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1080, + "name": "STDP and oscillations produce phase-locking (Muller et al. 2011)", + "repository_type": "github", + "summary": "\"... In this note, we investigate a simple mechanism for learning precise LFP-to-spike coupling in feed-forward networks \u2013 the reliable, periodic modulation of presynaptic firing rates during oscillations, coupled with spike-timing dependent plasticity. When oscillations are within the biological range (2\u2013150 Hz), firing rates of the inputs change on a timescale highly relevant to spike-timing dependent plasticity (STDP). Through analytic and computational methods, we find points of stable phase-locking for a neuron with plastic input synapses.\r\n\r\nThese points correspond to precise phase-locking behavior in the feed-forward network. The location of these points depends on the oscillation frequency of the inputs, the STDP time constants, and the balance of potentiation and de-potentiation in the STDP rule.\r\n...\"\r\n", + "tags": [ + { + "id": 9, + "tag": "Brian" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1671, + "tag": "ModelDB:143083" + }, + { + "id": 89, + "tag": "Python" + } + ], + "timestamp_created": "2024-01-12 09:55:17.433579+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/143083", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1081": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1081, + "name": "Phase response curve of a globus pallidal neuron (Fujita et al. 2011)", + "repository_type": "github", + "summary": "We investigated how changes in ionic conductances alter the phase response curve (PRC) of a globus pallidal (GP) neuron and stability of a synchronous activity of a GP network, using a single-compartmental conductance-based neuron model. The results showed the PRC and the stability were influenced by changes in the persistent sodium current, the Kv3 potassium, the M-type potassium and the calcium-dependent potassium current.", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 869, + "tag": "KCNQ1" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1672, + "tag": "ModelDB:143100" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 906, + "tag": "Phase Response Curves" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2024-01-12 09:55:17.935825+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/143100", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1082": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1082, + "name": "Effects of the membrane AHP on the Lateral Superior Olive (LSO) (Zhou & Colburn 2010)", + "repository_type": "github", + "summary": "This simulation study investigated how membrane afterhyperpolarization (AHP) influences spiking activity of neurons in the Lateral Superior Olive (LSO). The model incorporates a general integrate-and-fire spiking mechanism with a first-order adaptation channel. Simulations focus on differentiating the effects of GAHP, tauAHP, and input strength on (1) spike interval statistics, such as negative serial correlation and chopper onset, and (2) neural sensitivity to interaural level difference (ILD) of LSO neurons. The model simulated electrophysiological data collected in cat LSO (Tsuchitani and Johnson, 1985).", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 849, + "tag": "Audition" + }, + { + "id": 951, + "tag": "Depolarization block" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1673, + "tag": "ModelDB:143114" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 863, + "tag": "Spike Frequency Adaptation" + } + ], + "timestamp_created": "2024-01-12 09:55:18.474262+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/143114", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1083": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1083, + "name": "Extraction and classification of three cortical neuron types (Mensi et al. 2012)", + "repository_type": "github", + "summary": "This script proposes a new convex fitting procedure that allows the parameters estimation of a large class of stochastic Integrate-and-Fire model upgraded with spike-triggered current and moving threshold from patch-clamp experiments (i.e. given the injected current and the recorded membrane potential).\r\nThis script applies the method described in the paper to estimate the parameters of a reference model from a single voltage trace and the corresponding input current and evaluate the performance of the fitted model on a separated test set.", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1674, + "tag": "ModelDB:143148" + } + ], + "timestamp_created": "2024-01-12 09:55:18.999723+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/143148", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1084": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1084, + "name": "Phase precession through acceleration of local theta rhythm (Castro & Aguiar 2011)", + "repository_type": "github", + "summary": "\"... Here we\r\npresent a biophysical spiking model for phase precession in\r\nhippocampal CA1 which focuses on the interaction between\r\nplace cells and local inhibitory interneurons.\r\n\r\nThe model\u2019s\r\nfunctional block is composed of a place cell (PC) connected\r\nwith a local inhibitory cell (IC) which is modulated by the\r\npopulation theta rhythm.\r\n\r\nBoth cells receive excitatory inputs\r\nfrom the entorhinal cortex (EC).\r\n...\"\r\n", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1675, + "tag": "ModelDB:143248" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 882, + "tag": "Place cell/field" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2024-01-12 09:55:19.584003+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/143248", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1085": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1085, + "name": "Effects of eugenol on the firing of action potentials in NG108-15 neurons (Huang et al. 2011)", + "repository_type": "github", + "summary": "\"Rationale: Eugenol (EUG, 4-allyl-2-methoxyphenol), the main component of essential oil extracted from cloves, has various uses in medicine because of its potential to modulate neuronal excitability. \r\n\r\nHowever, its effects on the ionic mechanisms remains incompletely understood. \r\n\r\nObjectives: We aimed to investigate EUG`s effects on neuronal ionic currents and excitability, especially on voltage-gated ion currents, and to verify the effects on a hyperexcitability-temporal lobe seizure model. \r\n\r\nMethods: With the aid of patch-clamp technology, we first investigated the effects of EUG on ionic currents in NG108-15 neuronal cells differentiated with cyclic AMP. We then used modified Pinsky-Rinzel simulation modeling to evaluate its effects on spontaneous action potentials (APs). \r\n\r\nFinally, we investigated its effects on pilocarpine-induced seizures in rats. \r\n\r\nResults: EUG depressed the transient and late components of INa in the neurons. \r\n\r\nIt not only increased the degree of INa inactivation, but specifically suppressed the non-inactivating INa (INa(NI)). \r\n\r\n... In addition, EUG diminished L-type Ca2+ current and delayed rectifier K+ current only at higher concentrations. EUG`s effects on APs frequency reduction was verified by the simulation modeling. \r\n\r\nIn pilocarpine-induced seizures, the EUG-treated rats showed no shorter seizure latency but a lower seizure severity and mortality than the control rats. \r\n\r\n...\r\nConclusion: The synergistic blocking effects of INa and INa(NI) contributes to the main mechanism through which EUG affects the firing of neuronal APs and modulate neuronal hyperexcitability such as pilocarpine-induced temporal lobe seizures.\"", + "tags": [ + { + "id": 878, + "tag": "ATP-senstive potassium current" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1676, + "tag": "ModelDB:143253" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:55:20.165550+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/143253", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1086": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1086, + "name": "Spike repolarization in axon collaterals (Foust et al. 2011)", + "repository_type": "github", + "summary": "Voltage sensing dye experiments and simulations characterize the location and re-polarizing function of Kv1 channels in cortical neurons.\r\n\"... (the papers) results indicate that action potential-induced synaptic transmission may operate through a mix of analog\u2013digital transmission owing to the properties of Kv1 channels in axon collaterals and presynaptic boutons.\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 823, + "tag": "I_KD" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1677, + "tag": "ModelDB:143442" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:55:20.749288+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/143442", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1087": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1087, + "name": "Simulations of oscillations in piriform cortex (Wilson & Bower 1992)", + "repository_type": "github", + "summary": "\"1. A large-scale computer model of the piriform cortex was\r\nconstructed on the basis of the known anatomic and physiological\r\norganization of this region.\r\n2. The oscillatory field potential and electroencephalographic\r\n(EEG) activity generated by the model was compared with actual\r\nphysiological results. The model was able to produce patterns of\r\nactivity similar to those recorded physiologically in response to\r\nboth weak and strong electrical shocks to the afferent input. The\r\nmodel also generated activity patterns similar to EEGs recorded in\r\nbehaving animals.\r\n3. ...\"", + "tags": [ + { + "id": 1622, + "tag": "Evoked LFP" + }, + { + "id": 810, + "tag": "Extracellular Fields" + }, + { + "id": 826, + "tag": "GENESIS (web link to model)" + }, + { + "id": 852, + "tag": "I Chloride" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1678, + "tag": "ModelDB:143446" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 787, + "tag": "Oscillations" + } + ], + "timestamp_created": "2024-01-12 09:55:21.292864+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/143446", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1088": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1088, + "name": "Principles of Computational Modelling in Neuroscience (Book) (Sterratt et al. 2011)", + "repository_type": "github", + "summary": "\"... This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signaling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modeling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience.\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 834, + "tag": "Deep brain stimulation" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1679, + "tag": "ModelDB:143602" + }, + { + "id": 819, + "tag": "NEURON (web link to model)" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + } + ], + "timestamp_created": "2024-01-12 09:55:22.084158+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/143602", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1089": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1089, + "name": "Compartmentalization of GABAergic inhibition by dendritic spines (Chiu et al. 2013)", + "repository_type": "github", + "summary": "A spiny dendrite model supports the hypothesis that only inhibitory inputs on spine heads, not shafts, compartmentalizes inhibition of calcium signals to spine heads as seen in paired inhibition with back-propagating action potential experiments on prefrontal cortex layer 2/3 pyramidal neurons in mouse (Chiu et al. 2013).", + "tags": [ + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1680, + "tag": "ModelDB:143604" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:55:22.641311+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/143604", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1090": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1090, + "name": "Thalamic network model of deep brain stimulation in essential tremor (Birdno et al. 2012)", + "repository_type": "github", + "summary": "\"... Thus the decreased effectiveness\r\nof temporally irregular DBS trains is due to long pauses in the\r\nstimulus trains, not the degree of temporal irregularity alone.\r\n\r\nWe also\r\nconducted computer simulations of neuronal responses to the experimental\r\nstimulus trains using a biophysical model of the thalamic\r\nnetwork.\r\n\r\nTrains that suppressed tremor in volunteers also suppressed\r\nfluctuations in thalamic transmembrane potential at the frequency\r\nassociated with cerebellar burst-driver inputs.\r\n\r\nClinical and computational\r\nfindings indicate that DBS suppresses tremor by masking burst-driver\r\ninputs to the thalamus and that pauses in stimulation prevent\r\nsuch masking. Although stimulation of other anatomic targets may\r\nprovide tremor suppression, we propose that the most relevant neuronal\r\ntargets for effective tremor suppression are the afferent cerebellar\r\nfibers that terminate in the thalamus.\"\r\n", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 834, + "tag": "Deep brain stimulation" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1681, + "tag": "ModelDB:143633" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 822, + "tag": "Therapeutics" + } + ], + "timestamp_created": "2024-01-12 09:55:23.194750+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/143633", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1091": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1091, + "name": "Active dendritic action potential propagation (Casale & McCormick 2011)", + "repository_type": "github", + "summary": "This model explores the dendritic sodium and potassium conductances needed to recapitulate voltage-sensitive dye optical recordings of thalamic interneuron dendrites in the dorsal lateral geniculate nucleus. Model ion channels were selected based on pharmacological data.", + "tags": [ + { + "id": 1682, + "tag": "Conductance distributions" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 855, + "tag": "I_KHT" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1683, + "tag": "ModelDB:143635" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:55:23.739082+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/143635", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1092": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1092, + "name": "Simulations of motor unit discharge patterns (Powers et al. 2011)", + "repository_type": "github", + "summary": "\" ...\r\nTo estimate the potential contributions of PIC (Persistent Inward Current) activation and synaptic input patterns to\r\nmotor unit discharge patterns, we examined the responses of a set of cable\r\nmotoneuron models to different patterns of excitatory and inhibitory\r\ninputs.\r\n\r\nThe models were first tuned to approximate the current- and\r\nvoltage-clamp responses of low- and medium-threshold spinal motoneurons\r\nstudied in decerebrate cats and then driven with different patterns of\r\nexcitatory and inhibitory inputs.\r\n\r\nThe responses of the models to excitatory\r\ninputs reproduced a number of features of human motor unit\r\ndischarge.\r\n\r\nHowever, the pattern of rate modulation was strongly influenced\r\nby the temporal and spatial pattern of concurrent inhibitory inputs.\r\n\r\nThus, even though PIC activation is likely to exert a strong influence on\r\nfiring rate modulation, PIC activation in combination with different\r\npatterns of excitatory and inhibitory synaptic inputs can produce a wide\r\nvariety of motor unit discharge patterns.\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1684, + "tag": "ModelDB:143671" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:55:24.351096+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/143671", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1093": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1093, + "name": "CA1 pyramidal neuron: depolarization block (Bianchi et al. 2012)", + "repository_type": "github", + "summary": "NEURON files from the paper: On the mechanisms underlying the depolarization block in the spiking dynamics of CA1 pyramidal neurons\r\nby D.Bianchi, A. Marasco, A.Limongiello, C.Marchetti, H.Marie,B.Tirozzi, M.Migliore (2012). J Comput. Neurosci. In press. DOI: 10.1007/s10827-012-0383-y.\r\nExperimental findings shown that under sustained input current of increasing strength neurons eventually stop firing, entering a depolarization block.\r\nWe analyze the spiking dynamics of CA1 pyramidal neuron models using the same set of ionic currents on both an accurate morphological reconstruction and on its reduction to a single-compartment.\r\n The results show the specic ion channel properties and kinetics that are needed to\r\nreproduce the experimental findings, and how their interplay can drastically modulate the neuronal dynamics and the input current range leading to depolarization block.", + "tags": [ + { + "id": 887, + "tag": "Bifurcation" + }, + { + "id": 951, + "tag": "Depolarization block" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 1685, + "tag": "Mathematica" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1686, + "tag": "ModelDB:143719" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:55:24.928802+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/143719", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1094": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1094, + "name": "Inhibitory plasticity balances excitation and inhibition (Vogels et al. 2011)", + "repository_type": "github", + "summary": "\"Cortical neurons receive balanced excitatory and inhibitory synaptic currents.\r\n\r\nSuch a balance could be\r\nestablished and maintained in an experience-dependent manner by synaptic plasticity at inhibitory\r\nsynapses.\r\n\r\nWe show that this mechanism provides an explanation for the sparse firing patterns observed\r\nin response to natural stimuli and fits well with a recently observed interaction of excitatory and\r\ninhibitory receptive field plasticity.\r\n\r\n...\r\nOur results suggest an essential\r\nrole of inhibitory plasticity in the formation and maintenance of functional cortical circuitry.\"", + "tags": [ + { + "id": 9, + "tag": "Brian" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1687, + "tag": "ModelDB:143751" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:55:25.522720+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/143751", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1095": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1095, + "name": "Competition model of pheromone ratio detection (Zavada et al. 2011)", + "repository_type": "github", + "summary": "For some closely related sympatric moth species, recognizing a specific pheromone component concentration ratio is essential for mating success. We propose and test a minimalist competition-based feed-forward neuronal model capable of detecting a certain ratio of pheromone components independently of overall concentration. This model represents an elementary recognition unit for binary mixtures which we propose is entirely contained in the macroglomerular complex (MGC) of the male moth. A set of such units, along with projection neurons (PNs), can provide the input to higher brain centres. We found that (1) accuracy is mainly achieved by maintaining a certain ratio of connection strengths between olfactory receptor neurons (ORN) and local neurons (LN), much less by properties of the interconnections between the competing LNs proper. (2) successful ratio recognition is achieved using latency-to-first-spike in the LN populations which. (3) longer durations of the competition process between LNs did not result in higher recognition accuracy.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 1688, + "tag": "CNrun" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1689, + "tag": "ModelDB:143753" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 845, + "tag": "Sensory processing" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 319, + "tag": "neuroConstruct" + } + ], + "timestamp_created": "2024-01-12 09:55:26.061199+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/143753", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1096": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1096, + "name": "Point process framework for modeling electrical stimulation of auditory nerve (Goldwyn et al. 2012)", + "repository_type": "github", + "summary": "A point process model of the auditory nerve that provides a compact and accurate description of neural responses to electric stimulation. Inspired by the framework of generalized linear models, the model consists of a cascade of linear and nonlinear stages. A semi-analytical procedure uniquely determines each parameter in the model on the basis of fundamental statistics from recordings of single fiber responses to electric stimulation, including threshold, relative spread, jitter, and chronaxie. The model also accounts for refractory and summation effects that influence the responses of auditory nerve fibers to high pulse rate stimulation.", + "tags": [ + { + "id": 849, + "tag": "Audition" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1690, + "tag": "ModelDB:143760" + } + ], + "timestamp_created": "2024-01-12 09:55:26.611800+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/143760", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1097": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1097, + "name": "Grid cell spatial firing models (Zilli 2012)", + "repository_type": "github", + "summary": "This package contains MATLAB implementations of most models (published from 2005 to 2011) of the hexagonal firing field arrangement of grid cells.", + "tags": [ + { + "id": 790, + "tag": "Attractor Neural Network" + }, + { + "id": 1539, + "tag": "Grid cell" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1691, + "tag": "ModelDB:144006" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 883, + "tag": "Spatial Navigation" + } + ], + "timestamp_created": "2024-01-12 09:55:27.162312+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144006", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1098": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1098, + "name": "LGMD Variability and logarithmic compression in dendrites (Jones and Gabbiani, 2012, 2012B)", + "repository_type": "github", + "summary": "A compartmental model of the LGMD with a simplified, rake shaped, excitatory dendrite. It receives spontaneous input and excitatory and inhibitory synaptic inputs triggered by visual stimuli. It generates realistic responses to looming through the velocity dependent scaling and delay of individual excitatory synaptic inputs, with variability. We use the model to show that the key determinants of output variability are spontaneous input and temporal jitter of the excitatory inputs, rather than variability in magnitude of individual inputs (2012B, J Neurophysiol). We also use the model to analyze the transformation of the excitatory signals through the visual pathway; concluding that the representation of stimulus velocity is transformed from an expansive relationship at the level of the LGMD inputs to a logarithmic one at the level of its membrane potential (2012, J Neurosci).\r\n", + "tags": [ + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1692, + "tag": "ModelDB:144007" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 845, + "tag": "Sensory processing" + }, + { + "id": 800, + "tag": "Vision" + } + ], + "timestamp_created": "2024-01-12 09:55:27.714932+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144007", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1099": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1099, + "name": "Epileptic seizure model with Morris-Lecar neurons (Beverlin and Netoff 2011)", + "repository_type": "github", + "summary": "Here we use phase-response curves (PRC) from Morris-Lecar (M-L) model neurons with synaptic depression and gradually decrease input current to cells within a network simulation. This method effectively decreases firing rates resulting in a shift to greater network synchrony illustrating a possible mechanism of the transition phenomenon. PRCs are measured from the M-L conductance based model cell with a range of input currents within the limit cycle. A large network of 3000 excitatory neurons is simulated with a network topology generated from second-order statistics which allows a range of population synchrony. The population synchrony of the oscillating cells is measured with the Kuramoto order parameter, which reveals a transition from tonic to clonic phase exhibited by our model network.", + "tags": [ + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1693, + "tag": "ModelDB:144010" + } + ], + "timestamp_created": "2024-01-12 09:55:28.506683+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144010", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1100": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1100, + "name": "Perturbation sensitivity implies high noise and suggests rate coding in cortex (London et al. 2010)", + "repository_type": "github", + "summary": "\"... The network simulations\r\nwere also based on a previously published model(Latham et al. 2000), but with modifications to\r\nallow the addition and detection of extra spikes (see Supplementary Information,\r\nsection 7).\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1694, + "tag": "ModelDB:144027" + } + ], + "timestamp_created": "2024-01-12 09:55:29.139664+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144027", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1101": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1101, + "name": "Synchrony by synapse location (McTavish et al. 2012)", + "repository_type": "github", + "summary": "This model considers synchrony between mitral cells induced via shared\r\ngranule cell interneurons while taking into account the spatial\r\nconstraints of the system. In particular, since inhibitory inputs\r\ndecay passively along the lateral dendrites, this model demonstrates\r\nthat an optimal arrangement of the inhibitory synapses will be near\r\nthe cell bodies of the relevant mitral cells.", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1695, + "tag": "ModelDB:144054" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2024-01-12 09:55:29.794155+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144054", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1102": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1102, + "name": "Layer V PFC pyramidal neuron used to study persistent activity (Sidiropoulou & Poirazi 2012)", + "repository_type": "github", + "summary": "\"... Here, we use a compartmental modeling approach to search for discriminatory features in the properties of incoming stimuli to a PFC pyramidal neuron and/or its response that signal which of these stimuli will result in persistent activity emergence. \r\n\r\nFurthermore, we use our modeling approach to study cell-type specific differences in persistent activity properties, via implementing a regular spiking (RS) and an intrinsic bursting (IB) model neuron. \r\n...\r\nCollectively, our results pinpoint to specific features of the neuronal response to a given stimulus that code for its ability to induce persistent activity and predict differential roles of RS and IB neurons in persistent activity expression.\r\n\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1696, + "tag": "ModelDB:144089" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:55:30.367648+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144089", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1103": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1103, + "name": "Surround Suppression in V1 via Withdraw of Balanced Local Excitation in V1 (Shushruth 2012)", + "repository_type": "github", + "summary": "The model is mean-field network models, which is set up as a so-called ring-model, i. e. it is a highly idealized model of an orientation hypercolumn in primary visual cortex. Long-range intra-areal and inter-areal feedback connections are modeled phenomenologically as an external input. In this model, there are recurrent interactions via short-range local connections between orientation columns, but not between hypercolumns.", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1697, + "tag": "ModelDB:144096" + }, + { + "id": 800, + "tag": "Vision" + } + ], + "timestamp_created": "2024-01-12 09:55:30.906760+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144096", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1104": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1104, + "name": "Cortical pyramidal neuron, phase response curve (Stiefel et al 2009)", + "repository_type": "github", + "summary": "Three models of increasing complexity all showing a switch from type II (biphasic) to type I (monophasic) phase response curves with a cholinergic down-modulation of K+ conductances.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1698, + "tag": "ModelDB:144372" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:55:31.439276+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144372", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1105": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1105, + "name": "Hodgkin-Huxley model of persistent activity in PFC neurons (Winograd et al. 2008) (NEURON python)", + "repository_type": "github", + "summary": "The paper demonstrate a form of graded persistent activity activated by hyperpolarization. This phenomenon is modeled based on a slow calcium regulation of Ih, similar to that introduced\r\nearlier for thalamic neurons (see Destexhe et al., J Neurophysiol. 1996). The only difference is that the calcium signal is here provided by the high-threshold calcium current (instead of the low-threshold calcium current in thalamic neurons).", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1699, + "tag": "ModelDB:144376" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:55:31.974243+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144376", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1106": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1106, + "name": "A multiphysics neuron model for cellular volume dynamics (Lee et al. 2011)", + "repository_type": "github", + "summary": "This paper introduces a novel neuron model, where the cell volume is a time-varying variable and multiple physical principles are combined to build governing equations. Using this model, we analyzed neuronal volume responses during excitation, which elucidated the waveforms of fast intrinsic optical signals observed experimentally across the literature. In addition, we analyzed volume responses on a longer time scale with repetitive stimulation to study the characteristics of slow cell swelling.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 1700, + "tag": "Cellular volume dynamics" + }, + { + "id": 1607, + "tag": "I Cl, leak" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 940, + "tag": "I_K,Na" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1701, + "tag": "ModelDB:144380" + }, + { + "id": 816, + "tag": "Na/K pump" + }, + { + "id": 1702, + "tag": "Osmosis-driven water flux" + } + ], + "timestamp_created": "2024-01-12 09:55:32.674645+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144380", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1107": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1107, + "name": "Dendro-dendritic synaptic circuit (Shepherd Brayton 1979)", + "repository_type": "github", + "summary": "A NEURON simulation has been created to model the passive spread of an EPSP from a mitral cell synapse on a granule cell spine. The EPSP was shown to propagate subthreshold through the dendritic shaft into an adjacent spine with significant amplitude (figure 2B).", + "tags": [ + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1703, + "tag": "ModelDB:144385" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + } + ], + "timestamp_created": "2024-01-12 09:55:33.248170+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144385", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1108": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1108, + "name": "Lobster STG pyloric network model with calcium sensor (Gunay & Prinz 2010) (Prinz et al. 2004)", + "repository_type": "github", + "summary": "This pyloric network model simulator is a C/C++ program that saves 384 different calcium sensor values that are candidates for activity sensors (Gunay and Prinz, 2010). The simulator was used to scan all of the 20 million pyloric network models that were previously collected in a database (Prinz et al, 2004).", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 171, + "tag": "Homeostasis" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1704, + "tag": "ModelDB:144387" + } + ], + "timestamp_created": "2024-01-12 09:55:33.836435+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144387", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1109": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1109, + "name": "CA1 pyramidal neurons: effects of Kv7 (M-) channels on synaptic integration (Shah et al. 2011)", + "repository_type": "github", + "summary": "NEURON mod files from the paper:\r\nShah et al., 2011.\r\n\r\nIn this study, using a combination of electrophysiology\r\nand computational modelling, we show that these channels selectively influence peri-somatic but not dendritic post-synaptic excitatory synaptic potential (EPSP) integration in CA1 pyramidal cells. This may be important for their relative contributions to physiological processes such as synaptic plasticity as well as patho-physiological conditions such as epilepsy.", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1705, + "tag": "ModelDB:144392" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:55:34.677679+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144392", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1110": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1110, + "name": "CA1 pyramidal cell: reconstructed axonal arbor and failures at weak gap junctions (Vladimirov 2011)", + "repository_type": "github", + "summary": "Model of pyramidal CA1 cells connected by gap junctions in their axons.\r\nCell geometry is based on anatomical reconstruction of rat CA1 cell (NeuroMorpho.Org ID: NMO_00927) with long axonal arbor.\r\n\r\nModel init_2cells.hoc shows failures of second spike propagation in a spike doublet, depending on conductance of an axonal gap junction.\r\n\r\nModel init_ring.hoc shows that spike failure result in reentrant oscillations of a spike in a loop of axons connected by gap junctions, where one gap junction is weak.\r\n\r\nThe paper shows that in random networks of axons connected by gap junctions, oscillations are driven by single pacemaker loop of axons. The shortest loop, around which a spike can travel, is the most likely pacemaker.\r\nThis principle allows us to predict the frequency of oscillations from network connectivity and visa versa. We propose that this type of oscillations corresponds to so-called fast ripples in epileptic hippocampus.", + "tags": [ + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 821, + "tag": "Conduction failure" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1706, + "tag": "ModelDB:144401" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + } + ], + "timestamp_created": "2024-01-12 09:55:35.396963+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144401", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1111": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1111, + "name": "A Moth MGC Model-A HH network with quantitative rate reduction (Buckley & Nowotny 2011)", + "repository_type": "github", + "summary": "We provide the model used in Buckley & Nowotny (2011). It consists of a network of Hodgkin Huxley neurons coupled by slow GABA_B synapses which is run alongside a quantitative reduction described in the associated paper.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 887, + "tag": "Bifurcation" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 1608, + "tag": "I Na, leak" + }, + { + "id": 924, + "tag": "I Q" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1707, + "tag": "ModelDB:144403" + }, + { + "id": 944, + "tag": "Multiscale" + } + ], + "timestamp_created": "2024-01-12 09:55:35.973108+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144403", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1112": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1112, + "name": "Cerebellar gain and timing control model (Yamazaki & Tanaka 2007)(Yamazaki & Nagao 2012)", + "repository_type": "github", + "summary": "This paper proposes a hypothetical computational mechanism for unified gain and timing control in the cerebellum. The hypothesis is justified by computer simulations of a large-scale spiking network model of the cerebellum.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 908, + "tag": "Learning" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1708, + "tag": "ModelDB:144416" + }, + { + "id": 845, + "tag": "Sensory processing" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + } + ], + "timestamp_created": "2024-01-12 09:55:36.510256+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144416", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1113": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1113, + "name": "Linear vs non-linear integration in CA1 oblique dendrites (G\u00f3mez Gonz\u00e1lez et al. 2011)", + "repository_type": "github", + "summary": "The hippocampus in well known for its role in learning and memory processes. The CA1 region is the output of the hippocampal formation and pyramidal neurons in this region are the elementary units responsible for the processing and transfer of information to the cortex. Using this detailed single neuron model, it is investigated the conditions under which individual CA1 pyramidal neurons process incoming information in a complex (non-linear) as opposed to a passive (linear) manner.\r\nThis detailed compartmental model of a CA1 pyramidal neuron is based on one described previously (Poirazi, 2003). The model was adapted to five different reconstructed morphologies for this study, and slightly modified to fit the experimental data of (Losonczy, 2006), and to incorporate evidence in pyramidal neurons for the non-saturation of NMDA receptor-mediated conductances by single glutamate pulses. We first replicate the main findings of (Losonczy, 2006), including the very brief window for nonlinear integration using single-pulse stimuli. We then show that double-pulse stimuli increase a CA1 pyramidal neuron\u2019s tolerance for input asynchrony by at last an order of magnitude. Therefore, it is shown using this model, that the time window for nonlinear integration is extended by more than an order of magnitude when inputs are short bursts as opposed to single spikes.\r\n", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1709, + "tag": "ModelDB:144450" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:55:37.058781+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144450", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1114": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1114, + "name": "Connection-set Algebra (CSA) for the representation of connectivity in NN models (Djurfeldt 2012)", + "repository_type": "github", + "summary": "\"The connection-set algebra (CSA) is a novel\r\nand general formalism for the description of connectivity\r\nin neuronal network models, from small-scale to\r\nlarge-scale structure. ... The expressiveness of CSA\r\nmakes prototyping of network structure easy. A C++\r\nversion of the algebra has been implemented and used\r\nin a large-scale neuronal network simulation (Djurfeldt\r\net al., IBM J Res Dev 52(1/2):31\u201342, 2008b) and an\r\nimplementation in Python has been publicly released.\"", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1710, + "tag": "ModelDB:144455" + }, + { + "id": 89, + "tag": "Python" + } + ], + "timestamp_created": "2024-01-12 09:55:37.617797+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144455", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1115": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1115, + "name": "CA1 pyramidal neuron dendritic spine with plasticity (O`Donnell et al. 2011)", + "repository_type": "github", + "summary": "Biophysical model of a dendritic spine and adjacent dendrite with synapse. Model parameters adjusted to fit CA3-CA1 Shaffer collateral synapse data from literature. Model includes both electrical and Ca2+ dynamics, including AMPARs, NMDARs, 4 types of CaV channel, and leak conductance. Spine and synapse are plastic according to Ca2+ dependent rule.\r\n\r\nThe aim of the model is to explore the effects of dendritic spine structural plasticity on the rules of synaptic plasticity.", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1711, + "tag": "ModelDB:144463" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:55:38.174161+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144463", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1116": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1116, + "name": "Markov Chain-based Stochastic Shielding Hodgkin Huxley Model (Schmandt, Galan 2012)", + "repository_type": "github", + "summary": "Markov Chain-based Stochastic Shielding Hodgkin Huxley Model (Schmandt, Galan 2012)", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1712, + "tag": "ModelDB:144468" + } + ], + "timestamp_created": "2024-01-12 09:55:38.742839+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144468", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1117": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1117, + "name": "Functional consequences of cortical circuit abnormalities on gamma in schizophrenia (Spencer 2009)", + "repository_type": "github", + "summary": "\"Schizophrenia is characterized by cortical circuit abnormalities, which might be reflected in\r\ngamma-frequency (30\u2013100 Hz) oscillations in the electroencephalogram. Here we used a computational\r\nmodel of cortical circuitry to examine the effects that neural circuit abnormalities might have\r\non gamma generation and network excitability. The model network consisted of 1000 leaky integrateand-\r\nfi re neurons with realistic connectivity patterns and proportions of neuron types [pyramidal\r\ncells (PCs), regular-spiking inhibitory interneurons, and fast-spiking interneurons (FSIs)].\r\n...\r\nThe results of this study suggest that a multimodal\r\napproach, combining non-invasive neurophysiological and structural measures, might be\r\nable to distinguish between different neural circuit abnormalities in schizophrenia patients.\r\n...\"\r\n", + "tags": [ + { + "id": 1603, + "tag": "Brain Rhythms" + }, + { + "id": 1713, + "tag": "IDL" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1714, + "tag": "ModelDB:144477" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 926, + "tag": "Schizophrenia" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:55:39.313604+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144477", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1118": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1118, + "name": "Nodes of Ranvier with left-shifted Nav channels (Boucher et al. 2012)", + "repository_type": "github", + "summary": "The two programs CLSRanvier.f and propagation.f simulate the excitability of a myelinated axon with injured nodes of Ranvier.\r\nThe injury is simulated as the Coupled Left Shift (CLS) of the activation(V) and inactivation(V) (availability) of a fraction of Nav channels.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 821, + "tag": "Conduction failure" + }, + { + "id": 951, + "tag": "Depolarization block" + }, + { + "id": 756, + "tag": "FORTRAN" + }, + { + "id": 171, + "tag": "Homeostasis" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 1608, + "tag": "I Na, leak" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1715, + "tag": "ModelDB:144481" + }, + { + "id": 816, + "tag": "Na/K pump" + }, + { + "id": 872, + "tag": "Pathophysiology" + }, + { + "id": 817, + "tag": "Sodium pump" + } + ], + "timestamp_created": "2024-01-12 09:55:40.064608+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144481", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1119": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1119, + "name": "Pyramidal neuron conductances state and STDP (Delgado et al. 2010)", + "repository_type": "github", + "summary": "Neocortical neurons in vivo process each of their individual inputs in the context of ongoing synaptic background activity, produced by the thousands of presynaptic partners a typical neuron has. That background activity affects multiple aspects of neuronal and network function. However, its effect on the induction of spike-timing dependent plasticity (STDP) is not clear.\r\nUsing the present biophysically-detailed computational model, it is not only able to replicate the conductance-dependent shunting of dendritic potentials (Delgado et al,2010), but show that synaptic background can truncate calcium dynamics within dendritic spines, in a way that affects potentiation more strongly than depression.\r\nThis program uses a simplified layer 2/3 pyramidal neuron constructed in NEURON.\r\nIt was similar to the model of Traub et al., J Neurophysiol. (2003), and consisted of a soma, an apical shaft, distal dendrites, five basal dendrites, an axon, and a single spine. The spine\u2019s location was variable along the apical shaft (initial 50 μm) and apical. The axon contained an axon hillock region, an initial segment, segments with myelin, and nodes of Ranvier, in order to have realistic action potential generation. For more information about the model see supplemental material, Delgado et al 2010.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 849, + "tag": "Audition" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 1682, + "tag": "Conductance distributions" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1716, + "tag": "ModelDB:144482" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 885, + "tag": "STDP" + } + ], + "timestamp_created": "2024-01-12 09:55:40.815188+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144482", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1120": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1120, + "name": "Reflected SDE Hodgkin-Huxley Model (Dangerfield et al. 2012)", + "repository_type": "github", + "summary": "Matlab code for simulating channel noise using the original Hodgkin-Huxley equations and a variant of the Hodkgin-Huxley model from (Bruce, Annals Bio Eng, Vol 36, pp 824-838, 2009). Methods used in simulation are SSA, SDE method and RSDE method.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1717, + "tag": "ModelDB:144489" + } + ], + "timestamp_created": "2024-01-12 09:55:41.367042+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144489", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1121": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1121, + "name": "CA1 pyramidal neuron: synaptically-induced bAP predicts synapse location (Sterratt et al. 2012)", + "repository_type": "github", + "summary": "This is an adaptation of Poirazi et al.'s (2003) CA1 model that is used to measure BAP-induced voltage and calcium signals in spines after simulated Schaffer\r\ncollateral synapse stimulation. In the model, the peak calcium concentration is highly\r\ncorrelated with soma-synapse distance under a number of physiologically-realistic\r\nsuprathreshold stimulation regimes and for a range of dendritic morphologies. There are also simulations demonstrating that peak calcium can be used to set up a synaptic democracy\r\nin a homeostatic manner, whereby synapses regulate their synaptic strength on the\r\nbasis of the difference between peak calcium and a uniform target value.", + "tags": [ + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 888, + "tag": "I Mixed" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 925, + "tag": "I R" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1718, + "tag": "ModelDB:144490" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:55:41.927003+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144490", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1122": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1122, + "name": "Stochastic versions of the Hodgkin-Huxley equations (Goldwyn, Shea-Brown 2011) (pylab)", + "repository_type": "github", + "summary": "A pylab version from Alan Leggitt for simulating different channel noise models using the Hodgkin-Huxley equations. Methods provided and reviewed in Goldwyn and Shea-Brown (2011) are: current noise, subunit noise, conductance noise, and Markov chain, as well as the standard deterministic Hodgkin-Huxley model.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1719, + "tag": "ModelDB:144499" + }, + { + "id": 89, + "tag": "Python" + } + ], + "timestamp_created": "2024-01-12 09:55:42.595819+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144499", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1123": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1123, + "name": "Inferior Olive, subthreshold oscillations (Torben-Nielsen, Segev, Yarom 2012)", + "repository_type": "github", + "summary": "The Inferior Olive is a brain structure in which neurons are solely connected to each other through gap-junctions. Its behavior is characterized by spontaneous subthreshold oscillation, frequency changes in the subthreshold oscillation, stable phase differences between neurons, and propagating waves of activity.\r\n\r\nOur model based on actual IO topology can reproduce these behaviors and provides a mechanistic explanation thereof.", + "tags": [ + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1720, + "tag": "ModelDB:144502" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + } + ], + "timestamp_created": "2024-01-12 09:55:43.129267+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144502", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1124": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1124, + "name": "Duration-tuned neurons from the inferior colliculus of the big brown bat (Aubie et al. 2009)", + "repository_type": "github", + "summary": "dtnet is a generalized neural network simulator written in C++ with an easy to use XML description language to generate arbitrary neural networks and then run simulations covering many different parameter values. For example, you can specify ranges of parameter values for several different connection weights and then automatically run simulations over all possible parameters. Graphing ability is built in as long as the free, open-source, graphing application GLE (http://glx.sourceforge.net/) is installed.\r\n\r\nIncluded in the examples folder are simulation descriptions that were used to generate the results in Aubie et al. (2009). Refer to the README file for instructions on compiling and running these examples.\r\n\r\nThe most recent source code can be obtained from GitHub: https://github.com/baubie/dtnet\r\n", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 830, + "tag": "Delay" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1721, + "tag": "ModelDB:144509" + }, + { + "id": 775, + "tag": "Rebound firing" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:55:43.649118+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144509", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1125": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1125, + "name": "Duration-tuned neurons from the inferior colliculus of vertebrates (Aubie et al. 2012)", + "repository_type": "github", + "summary": "These models reproduce the responses of duration-tuned neurons in the auditory midbrain of the big brown bat, the rat, the mouse and the frog (Aubie et al. 2012). They are written in the Python interface to NEURON and a subset of the figures from Aubie et al. (2012) are pre-set in run.py (raw data is generated and a separate graphing program must be used to visualize the results).", + "tags": [ + { + "id": 849, + "tag": "Audition" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 1722, + "tag": "Duration Selectivity" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1723, + "tag": "ModelDB:144511" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 845, + "tag": "Sensory processing" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:55:44.322394+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144511", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1126": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1126, + "name": "Half-center oscillator database of leech heart interneuron model (Doloc-Mihu & Calabrese 2011)", + "repository_type": "github", + "summary": "We have created a database (HCO-db) of instances of a half-center oscillator computational model [Hill et al., 2001] for analyzing how neuronal parameters influence network activity. We systematically explored the parameter space of about 10.4 million simulated HCO instances and corresponding isolated neuron model simulations obtained by varying a set of selected parameters (maximal conductance of intrinsic and synaptic currents) in all combinations using a brute-force approach. We classified these HCO instances by their activity characteristics into identifiable groups. We built an efficient relational database table (HCO-db) with the resulting instances characteristics.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1724, + "tag": "ModelDB:144518" + }, + { + "id": 1725, + "tag": "MySQL (web link to model)" + }, + { + "id": 787, + "tag": "Oscillations" + } + ], + "timestamp_created": "2024-01-12 09:55:44.900289+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144518", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1127": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1127, + "name": "Cardiac action potentials and pacemaker activity of sinoatrial node (DiFrancesco & Noble 1985)", + "repository_type": "github", + "summary": "\"Equations have been developed to describe cardiac action potentials and pacemaker activity. The model takes account of extensive developments in experimental work ...\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1726, + "tag": "ModelDB:144520" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 814, + "tag": "Na/Ca exchanger" + }, + { + "id": 816, + "tag": "Na/K pump" + } + ], + "timestamp_created": "2024-01-12 09:55:45.541668+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144520", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1128": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1128, + "name": "STD-dependent and independent encoding of Input irregularity as spike rate (Luthman et al. 2011)", + "repository_type": "github", + "summary": "\"... We use a\r\nconductance-based model of a CN neuron to study the\r\neffect of the regularity of Purkinje cell spiking on CN\r\nneuron activity.\r\n\r\nWe find that increasing the irregularity of\r\nPurkinje cell activity accelerates the CN neuron spike rate\r\nand that the mechanism of this recoding of input irregularity\r\nas output spike rate depends on the number of Purkinje\r\ncells converging onto a CN neuron.\r\n...\"\r\n", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1727, + "tag": "ModelDB:144523" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + } + ], + "timestamp_created": "2024-01-12 09:55:46.161543+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144523", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1129": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1129, + "name": "State and location dependence of action potential metabolic cost (Hallermann et al., 2012)", + "repository_type": "github", + "summary": "With this model of a layer 5 pyramidal neuron the state and location dependence of the ATP usage and the metabolic efficiency of action potentials can be analyzed. Model parameters were constrained by direct subcellular recordings at dendritic, somatic and axonal compartments.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1728, + "tag": "ModelDB:144526" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:55:46.775546+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144526", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1130": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1130, + "name": "Reinforcement learning of targeted movement (Chadderdon et al. 2012)", + "repository_type": "github", + "summary": "\"Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint \u201cforearm\u201d to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. ...\"", + "tags": [ + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1729, + "tag": "ModelDB:144538" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 892, + "tag": "Reinforcement Learning" + }, + { + "id": 939, + "tag": "Reward-modulated STDP" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:55:47.307875+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144538", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1131": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1131, + "name": "CA1 pyramidal neuron: Ih current (Migliore et al. 2012)", + "repository_type": "github", + "summary": "NEURON files from the paper:\r\nMigliore M, Migliore R (2012) Know Your Current Ih: Interaction with a Shunting Current Explains the Puzzling Effects of Its Pharmacological or\r\nPathological Modulations. PLoS ONE 7(5): e36867.\r\ndoi:10.1371/journal.pone.0036867.\r\n\r\n\r\nExperimental findings on the effects of Ih current modulation, which is particularly involved in epilepsy, appear to be inconsistent. In the paper, using a realistic model we show how and why a shunting current, such as that carried by TASK-like channels, dependent on the Ih peak conductance is able to explain virtually all experimental findings on Ih up- or down-regulation by modulators or pathological conditions.\r\n", + "tags": [ + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1730, + "tag": "ModelDB:144541" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:55:47.933867+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144541", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1132": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1132, + "name": "Hopfield and Brody model (Hopfield, Brody 2000) (NEURON+python)", + "repository_type": "github", + "summary": "Demonstration of Hopfield-Brody snychronization using artificial cells in NEURON+python.", + "tags": [ + { + "id": 790, + "tag": "Attractor Neural Network" + }, + { + "id": 791, + "tag": "Coincidence Detection" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1731, + "tag": "ModelDB:144549" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + } + ], + "timestamp_created": "2024-01-12 09:55:48.477509+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144549", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1133": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1133, + "name": "Rhesus Monkey Layer 3 Pyramidal Neurons: V1 vs PFC (Amatrudo, Weaver et al. 2012)", + "repository_type": "github", + "summary": "Whole-cell patch-clamp recordings and high-resolution 3D morphometric analyses of layer 3 pyramidal neurons in in vitro slices of monkey primary visual cortex (V1) and dorsolateral granular prefrontal cortex (dlPFC) revealed that neurons in these two brain areas possess highly distinctive structural and functional properties. ... Three-dimensional reconstructions of V1 and dlPFC neurons were incorporated into computational models containing Hodgkin-Huxley and AMPA- and GABAA-receptor gated channels. Morphology alone largely accounted for observed passive physiological properties, but led to AP firing rates that differed more than observed empirically, and to synaptic responses that opposed empirical results. Accordingly, modeling predicts that active channel conductances differ between V1 and dlPFC neurons. The unique features of V1 and dlPFC neurons are likely fundamental\r\ndeterminants of area-specific network behavior. The compact electrotonic arbor and increased excitability of V1 neurons support the rapid signal integration required for early processing of visual information. The greater connectivity and dendritic complexity of dlPFC neurons likely\r\nsupport higher level cognitive functions including working memory and planning.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 1682, + "tag": "Conductance distributions" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 871, + "tag": "Electrotonus" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1732, + "tag": "ModelDB:144553" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 800, + "tag": "Vision" + } + ], + "timestamp_created": "2024-01-12 09:55:49.058810+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144553", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1134": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1134, + "name": "Computing with neural synchrony (Brette 2012)", + "repository_type": "github", + "summary": "\"... In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. ...\"", + "tags": [ + { + "id": 9, + "tag": "Brian" + }, + { + "id": 171, + "tag": "Homeostasis" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1733, + "tag": "ModelDB:144560" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 775, + "tag": "Rebound firing" + }, + { + "id": 838, + "tag": "Reliability" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2024-01-12 09:55:49.686810+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144560", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1135": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1135, + "name": "Voltage-based STDP synapse (Clopath et al. 2010)", + "repository_type": "github", + "summary": "Implementation of the STDP rule by Clopath et al., Nat. Neurosci. 13(3):344-352,2010\r\nSTDP mechanism added to the AlphaSynapse in NEURON.", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1734, + "tag": "ModelDB:144566" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 885, + "tag": "STDP" + } + ], + "timestamp_created": "2024-01-12 09:55:50.284923+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144566", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1136": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1136, + "name": "Large scale model of the olfactory bulb (Yu et al., 2013)", + "repository_type": "github", + "summary": "The readme file currently contains links to the results for all the 72 odors investigated in the paper, and the movie showing the network activity during learning of odor k3-3 (an aliphatic ketone).\r\n", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1735, + "tag": "ModelDB:144570" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 886, + "tag": "Unsupervised Learning" + } + ], + "timestamp_created": "2024-01-12 09:55:50.839599+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144570", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1137": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1137, + "name": "Thalamic neuron, zebra finch DLM: Integration of pallidal and cortical inputs (Goldberg et al. 2012)", + "repository_type": "github", + "summary": "This is a single-compartment model of a zebra finch thalamic relay neuron from nucleus DLM. It is used to explore the interaction between cortex-like glutamatergic input and pallidum-like GABAergic input as they control the spiking output of these neurons.", + "tags": [ + { + "id": 1685, + "tag": "Mathematica" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1736, + "tag": "ModelDB:144572" + }, + { + "id": 775, + "tag": "Rebound firing" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:55:51.397988+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144572", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1138": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1138, + "name": "Parametric computation and persistent gamma in a cortical model (Chambers et al. 2012)", + "repository_type": "github", + "summary": "Using the Traub et al (2005) model of the cortex we determined how 33 synaptic strength parameters control gamma oscillations. We used fractional factorial design to reduce the number of runs required to 4096. We found an expected multiplicative interaction between parameters.", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 940, + "tag": "I_K,Na" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1737, + "tag": "ModelDB:144579" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 941, + "tag": "Parameter sensitivity" + } + ], + "timestamp_created": "2024-01-12 09:55:51.940681+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144579", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1139": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1139, + "name": "Boolean network-based analysis of the apoptosis network (Mai and Liu 2009)", + "repository_type": "github", + "summary": "\"To understand the design principles of the molecular interaction network associated with the\r\nirreversibility of cell apoptosis and the stability of cell surviving, we constructed a Boolean network integrating both the intrinsic and extrinsic pro-apoptotic pathways with pro-survival signal transduction pathways.\r\nWe performed statistical analyses of the dependences of cell fate on initial\r\nstates and on input signals.\r\nThe analyses reproduced the well-known pro- and anti-apoptotic effects of\r\nkey external signals and network components. We found that the external GF signal by itself did not change the apoptotic ratio from randomly chosen initial states when there is no external TNF signal, but can significantly offset apoptosis induced by the TNF signal. ...\"", + "tags": [ + { + "id": 1738, + "tag": "Apoptosis" + }, + { + "id": 1739, + "tag": "Boolean network" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1740, + "tag": "ModelDB:144586" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 827, + "tag": "Signaling pathways" + } + ], + "timestamp_created": "2024-01-12 09:55:52.482057+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144586", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1140": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1140, + "name": "Modulation of hippocampal rhythms by electric fields and network topology (Berzhanskaya et al. 2013)", + "repository_type": "github", + "summary": "\u201c\u2026 Here we present experimental and computational evidence of the interplay among hippocampal synaptic circuitry, neuronal morphology, external electric fields, and network activity.\r\n\r\nElectrophysiological data are used to constrain and validate an anatomically and biophysically realistic model of area CA1 containing pyramidal cells and two interneuron types: dendritic- and perisomatic-targeting.\r\n\r\nWe report two lines of results: addressing the network structure capable of generating theta-modulated gamma rhythms, and demonstrating electric field effects on those rhythms. First, theta-modulated gamma rhythms require specific inhibitory connectivity.\r\n\r\n\u2026\r\n\r\nThe second major finding is that subthreshold electric fields robustly alter the balance between different rhythms.\r\n\r\n\u2026\u201d", + "tags": [ + { + "id": 1603, + "tag": "Brain Rhythms" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1741, + "tag": "ModelDB:144589" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:55:52.998410+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144589", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1141": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1141, + "name": "Network model with dynamic ion concentrations (Ullah et al. 2009)", + "repository_type": "github", + "summary": "This is a network model composed of 100 excitatory and 100 inhibitory neurons with dynamic ion concentrations as described in \"The Influence of Sodium and Potassium Dynamics on Excitability, Seizures, and the Stability of Persistent States: II. Network and Glia Dynamics (2009) Journal of Computational Neuroscience, 26:171-183\".", + "tags": [ + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 756, + "tag": "FORTRAN" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1742, + "tag": "ModelDB:144590" + } + ], + "timestamp_created": "2024-01-12 09:55:53.530155+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144590", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1142": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1142, + "name": "CA1 pyramidal neurons: effects of Alzheimer (Culmone and Migliore 2012)", + "repository_type": "github", + "summary": "The model predicts possible therapeutic treatments of Alzheimers's Disease in terms of pharmacological manipulations of channels' kinetic and activation properties. The results suggest how and which mechanism can be targeted by a drug to restore the original firing conditions. The simulations reproduce somatic membrane potential in control conditions, when 90% of membrane is affected by AD (Fig.4A of the paper), and after treatment (Fig.4B of the paper).\r\n", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 833, + "tag": "Aging/Alzheimer`s" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1743, + "tag": "ModelDB:144976" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:55:54.058120+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144976", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1143": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1143, + "name": "Method for deriving general HH neuron model`s spiking input-output relation (Soudry & Meir 2014)", + "repository_type": "github", + "summary": "We derived in paper a method to find semi-analytic input-output relations for general HH-like neuron models (firing rates, spectra, linear filters)under sparse spike stimulation. Here we demonstrate the applicability of this method to various HH-type models (HH with slow sodium inactivation, with slow pottasium inactivation, with synaptic STD and other various extensions).", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1744, + "tag": "ModelDB:144993" + } + ], + "timestamp_created": "2024-01-12 09:55:54.591095+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/144993", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1144": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1144, + "name": "Hypocretin and Locus Coeruleus model neurons (Carter et al 2012)", + "repository_type": "github", + "summary": "Conductance based model of the hypocretin neurons (HCRT) and another one of the Locus Coeruleus one (LC). The HCRT drive the LCs via the HCRT receptor on the LCs. The LCs lead to the awakening of the mice if the number of spikes raises over 10 spikes in 10 seconds window.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1745, + "tag": "ModelDB:145162" + } + ], + "timestamp_created": "2024-01-12 09:55:55.115617+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/145162", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1145": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1145, + "name": "Neurophysiological impact of inactivation pathways in A-type K+ channels (Fineberg et al 2012)", + "repository_type": "github", + "summary": "These models predict the differential effects of varying pathways of inactivation (closed state inactivation, CSI, or open state inactivation, OSI). Specifically, Markov models of Kv4 potassium channels with CSI or CSI+OSI were inserted into the CA1 pyramidal neuron model from Migliore et al (1999; ModelDB accession #2796) to determine the neurophysiological impact of inactivation pathways. Furthermore, Markov models of Kv4.2 and Kv3.4 channels are used to illustrate a method by which to test what pathway of inactivation a channel uses.", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 1746, + "tag": "IonChannelLab" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1747, + "tag": "ModelDB:145672" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:55:55.654092+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/145672", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1146": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1146, + "name": "Circadian clock model based on protein sequestration (simple version) (Kim & Forger 2012)", + "repository_type": "github", + "summary": "\"\u2026 To understand the biochemical mechanisms of this timekeeping, we have developed a detailed mathematical model of the mammalian circadian clock. \r\n\r\nOur model can accurately predict diverse experimental data including the phenotypes of mutations or knockdown of clock genes as well as the time courses and relative expression of clock transcripts and proteins. \r\n\r\nUsing this model, we show how a universal motif of circadian timekeeping, where repressors tightly bind activators rather than directly binding to DNA, can generate oscillations when activators and repressors are in stoichiometric balance. \r\n\u2026\"", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 1662, + "tag": "Circadian Rhythms" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 1685, + "tag": "Mathematica" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1748, + "tag": "ModelDB:145800" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:55:56.334557+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/145800", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1147": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1147, + "name": "Circadian clock model in mammals (detailed version) (Kim & Forger 2012)", + "repository_type": "github", + "summary": "\"\u2026 To understand the biochemical mechanisms of this timekeeping, we have developed a detailed mathematical model of the mammalian circadian clock. \r\n\r\nOur model can accurately predict diverse experimental data including the phenotypes of mutations or knockdown of clock genes as well as the time courses and relative expression of clock transcripts and proteins. \r\n\r\nUsing this model, we show how a universal motif of circadian timekeeping, where repressors tightly bind activators rather than directly binding to DNA, can generate oscillations when activators and repressors are in stoichiometric balance. \r\n\u2026\"", + "tags": [ + { + "id": 1603, + "tag": "Brain Rhythms" + }, + { + "id": 1662, + "tag": "Circadian Rhythms" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 1685, + "tag": "Mathematica" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1749, + "tag": "ModelDB:145801" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:55:56.875569+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/145801", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1148": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1148, + "name": "Oversampling method to extract excitatory and inhibitory conductances (Bedard et al. 2012)", + "repository_type": "github", + "summary": "\" ...\r\nWe present here a new method that allows\r\nextracting estimates of the full time course of excitatory and inhibitory conductances from single-trial\r\nVm recordings.\r\n\r\nThis method is based on oversampling of the Vm . We test the method numerically using\r\nmodels of increasing complexity.\r\n\r\nFinally, the method is evaluated using controlled conductance injection\r\nin cortical neurons in vitro using the dynamic-clamp technique.\r\n...\"", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1750, + "tag": "ModelDB:145803" + }, + { + "id": 774, + "tag": "Parameter Fitting" + } + ], + "timestamp_created": "2024-01-12 09:55:57.470358+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/145803", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1149": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1149, + "name": "A fast model of voltage-dependent NMDA Receptors (Moradi et al. 2013)", + "repository_type": "github", + "summary": "These are two or triple-exponential models of the voltage-dependent NMDA receptors. Conductance of these receptors increase voltage-dependently with a \"Hodgkin and Huxley-type\" gating style that is also depending on glutamate-binding. Time course of the gating of these receptors in response to glutamate are also changing voltage-dependently. Temperature sensitivity and desensitization of these receptor are also taken into account.\r\nThree previous kinetic models that are able to simulate the voltage-dependence of the NMDARs are also imported to the NMODL. These models are not temperature sensitive. \r\n\r\nThese models are compatible with the \"event delivery system\" of NEURON. Parameters that are reported in our paper are applicable to CA1 pyramidal cell dendrites.", + "tags": [ + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1751, + "tag": "ModelDB:145836" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:55:58.362242+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/145836", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1150": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1150, + "name": "A CORF computational model of a simple cell that relies on LGN input (Azzopardi & Petkov 2012)", + "repository_type": "github", + "summary": "\"...\r\n\r\nWe propose a computational model that uses as afferent inputs the responses of model LGN cells with center-surround receptive fields (RFs) and we refer to it as a Combination of Receptive Fields (CORF) model. \r\n\r\nWe use shifted gratings as test stimuli and simulated reverse correlation to explore the nature of the proposed model. \r\n\r\nWe study its behavior regarding the effect of contrast on its response and orientation bandwidth as well as the effect of an orthogonal mask on the response to an optimally oriented stimulus.\r\n\r\nWe also evaluate and compare the performances of the CORF and GF (Gabor Filter) models regarding contour detection, using two public data sets of images of natural scenes with associated contour ground truths.\r\n\r\n...\r\n\r\nThe proposed CORF model is more realistic than the GF model and is more effective in contour detection, which is assumed to be the primary biological role of simple cells.\"", + "tags": [ + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1752, + "tag": "ModelDB:145882" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:55:58.924821+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/145882", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1151": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1151, + "name": "NMDA subunit effects on Calcium and STDP (Evans et al. 2012)", + "repository_type": "github", + "summary": "Effect of NMDA subunit on spike timing dependent plasticity.", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1753, + "tag": "ModelDB:145917" + }, + { + "id": 885, + "tag": "STDP" + } + ], + "timestamp_created": "2024-01-12 09:55:59.543065+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/145917", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1152": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1152, + "name": "A generic MAPK cascade model for random parameter sampling analysis (Mai and Liu 2013)", + "repository_type": "github", + "summary": "A generic three-tier MAPK cascade model constructed by comparing previous MAPK models covering a range of biosystems. Pseudo parameters and random sampling were employed for qualitative analysis. A range of kinetic behaviors of MAPK activation, including ultrasensitivity, bistability, transient activation and oscillation, were successfully reproduced in this generic model. The mechanisms were revealed by statistic analysis of the parameter sets.", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1754, + "tag": "ModelDB:146024" + } + ], + "timestamp_created": "2024-01-12 09:56:00.728963+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/146024", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1153": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1153, + "name": "A set of reduced models of layer 5 pyramidal neurons (Bahl et al. 2012)", + "repository_type": "github", + "summary": "These are the NEURON files for 10 different models of a reduced L5 pyramidal neuron. The parameters were obtained by automatically fitting the models to experimental data using a multi objective evolutionary search strategy. Details on the algorithm can be found at \r\nhttp://www.g-node.org/emoo and in Bahl et al. (2012).\r\n", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1755, + "tag": "ModelDB:146026" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:56:01.381318+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/146026", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1154": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1154, + "name": "Olfactory bulb mitral cell gap junction NN model: burst firing and synchrony (O`Connor et al. 2012)", + "repository_type": "github", + "summary": "In a network of 6 mitral cells connected by gap junction in the apical dendrite tuft, continuous current injections of 0.06 nA are injected into 20 locations in the apical tufts of two of the mitral cells. The current injections into one of the cells starts 10 ms after the other to generate asynchronous firing in the cells (Migliore et al. 2005 protocol). Firing of the cells is asynchronous for the first 120 ms. However after the burst firing phase is completed the firing in all cells becomes synchronous.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1756, + "tag": "ModelDB:146030" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2024-01-12 09:56:02.023185+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/146030", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1155": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1155, + "name": "Opponent-channel model of the cortical representation of auditory space (Briley et al., 2012)", + "repository_type": "github", + "summary": "This is the computational opponent-channel model used by Briley et al. (2012) to model electroencephalographic (EEG) responses from human auditory cortex to abrupt shifts in sound-source location and to predict psychophysical measures of spatial acuity. The zip file contains both a Matlab and an Excel implementation of the model. Details of use are contained within each file.", + "tags": [ + { + "id": 849, + "tag": "Audition" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1757, + "tag": "ModelDB:146050" + }, + { + "id": 845, + "tag": "Sensory processing" + } + ], + "timestamp_created": "2024-01-12 09:56:03.114178+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/146050", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1156": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1156, + "name": "Ca1 pyramidal neuron: reduction model (Marasco et al. 2012)", + "repository_type": "github", + "summary": "\"... Here we introduce a new, automatic and fast method to map realistic neurons into equivalent reduced models running up to >40 times faster while maintaining a very high accuracy of the membrane potential dynamics during synaptic inputs, and a direct link with experimental observables. The mapping of arbitrary sets of synaptic inputs, without additional fine tuning, would also allow the convenient and efficient implementation of a new generation of large-scale simulations of brain regions reproducing the biological variability observed in real neurons, with unprecedented advances to understand higher brain functions.\"", + "tags": [ + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1758, + "tag": "ModelDB:146376" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:56:03.646188+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/146376", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1157": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1157, + "name": "Mean Field Equations for Two-Dimensional Integrate and Fire Models (Nicola and Campbell, 2013)", + "repository_type": "github", + "summary": "The zip file contains the files used to perform numerical simulation and bifurcation studies of large networks of two-dimensional integrate and fire neurons and of the corresponding mean field models derived in our paper. The neural models used are the Izhikevich model and the Adaptive Exponential model.", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1759, + "tag": "ModelDB:146499" + } + ], + "timestamp_created": "2024-01-12 09:56:04.154788+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/146499", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1158": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1158, + "name": "Paradoxical GABA-mediated excitation (Lewin et al. 2012)", + "repository_type": "github", + "summary": "\"GABA is the key inhibitory neurotransmitter in the adult central nervous system, but in some circumstances can lead to a paradoxical excitation that has been causally implicated in diverse pathologies from endocrine stress responses to diseases of excitability including neuropathic pain and temporal lobe epilepsy. \r\n\r\nWe undertook a computational modeling approach to determine plausible ionic mechanisms of GABAA-dependent excitation in isolated post-synaptic CA1 hippocampal neurons because it may constitute a trigger for pathological synchronous epileptiform discharge. \r\n\r\nIn particular, the interplay intracellular chloride accumulation via the GABAA receptor and extracellular potassium accumulation via the K/Cl co-transporter KCC2 in promoting GABAA-mediated excitation is complex. \r\n...\"", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 1760, + "tag": "Ca pump" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 1761, + "tag": "KCC2" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1762, + "tag": "ModelDB:146504" + }, + { + "id": 1763, + "tag": "NKCC1" + }, + { + "id": 814, + "tag": "Na/Ca exchanger" + }, + { + "id": 816, + "tag": "Na/K pump" + } + ], + "timestamp_created": "2024-01-12 09:56:04.859004+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/146504", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1159": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1159, + "name": "Functional impact of dendritic branch point morphology (Ferrante et al., 2013)", + "repository_type": "github", + "summary": "\" ... Here, we first quantified the morphological variability of branch points from two-photon images of rat CA1 pyramidal neurons. We then investigated the geometrical features affecting spike initiation, propagation, and timing with a computational model validated by glutamate uncaging experiments. The results suggest that even subtle membrane readjustments at branch point could drastically alter the ability of synaptic input to generate, propagate, and time action potentials.\"", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 887, + "tag": "Bifurcation" + }, + { + "id": 821, + "tag": "Conduction failure" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 1556, + "tag": "Information transfer" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1764, + "tag": "ModelDB:146509" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:56:05.604880+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/146509", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1160": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1160, + "name": "Reconstrucing sleep dynamics with data assimilation (Sedigh-Sarvestani et al., 2012)", + "repository_type": "github", + "summary": "We have developed a framework, based on the unscented Kalman filter, for estimating hidden states and parameters of a network model of sleep. The network model includes firing rates and neurotransmitter output of 5 cell-groups in the rat brain.", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1765, + "tag": "ModelDB:146554" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 802, + "tag": "Sleep" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + }, + { + "id": 1766, + "tag": "unscented Kalman filter" + } + ], + "timestamp_created": "2024-01-12 09:56:06.152708+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/146554", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1161": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1161, + "name": "Nonlinear dendritic processing in barrel cortex spiny stellate neurons (Lavzin et al. 2012)", + "repository_type": "github", + "summary": "This is a multi-compartmental simulation of a spiny stellate neuron which is stimulated by a thalamocortical (TC) and cortico-cortical (CC) inputs. No other cells are explicitly modeled; the presynaptic network activation is represented by the number of active synapses. Preferred and non \u2013preferred thalamic directions thus correspond to larder/smaller number of TC synapses. This simulation revealed that randomly activated synapses can cooperatively trigger global NMDA spikes, which involve participation of most of the dendritic tree. Surprisingly, we found that although the voltage profile of the cell was uniform, the calcium influx was restricted to \u2018hot spots\u2019 which correspond to synaptic clusters or large conductance synapses", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 1760, + "tag": "Ca pump" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 837, + "tag": "Direction Selectivity" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1767, + "tag": "ModelDB:146565" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 840, + "tag": "Whisking" + } + ], + "timestamp_created": "2024-01-12 09:56:06.715733+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/146565", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1162": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1162, + "name": "Olfactory bulb network: neurogenetic restructuring and odor decorrelation (Chow et al. 2012)", + "repository_type": "github", + "summary": "Adult neurogenesis in the olfactory bulb has been shown experimentally\r\nto contribute to perceptual learning. Using a computational network\r\nmodel we show that fundamental aspects of the adult neurogenesis\r\nobserved in the olfactory bulb -- the persistent addition of new\r\ninhibitory granule cells to the network, their activity-dependent\r\nsurvival, and the reciprocal character of their synapses with the\r\nprincipal mitral cells -- are sufficient to restructure the network\r\nand to alter its encoding of odor stimuli adaptively so as to reduce\r\nthe correlations between the bulbar representations of similar\r\nstimuli. The model captures the experimentally observed\r\nrole of neurogenesis in perceptual learning and the enhanced response\r\nof young granule cells to novel stimuli. Moreover, it makes specific\r\npredictions for the type of odor enrichment that should be effective\r\nin enhancing the ability of animals to discriminate similar odor\r\nmixtures. NSF grant DMS-0719944.\r\n", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 1738, + "tag": "Apoptosis" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1768, + "tag": "ModelDB:146583" + }, + { + "id": 1769, + "tag": "Neurogenesis" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 845, + "tag": "Sensory processing" + } + ], + "timestamp_created": "2024-01-12 09:56:07.241903+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/146583", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1163": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1163, + "name": "Modeling the effects of dopamine on network synchronization (Komek et al. 2012)", + "repository_type": "github", + "summary": "Dopamine modulates cortical circuit activity in part through its actions on GABAergic interneurons, including increasing the excitability of fast-spiking interneurons. Though such effects have been demonstrated in single cells, there are no studies that examine how such mechanisms may lead to the effects of dopamine at a neural network level. In this study, we investigated the effects of dopamine on synchronization in two simulated neural networks; one biophysical model composed of Wang-Buzsaki neurons and a reduced model with theta neurons. In both models, we show that parametrically varying the levels of dopamine, modeled through the changes in the excitability of interneurons, reveals an inverted-U shaped relationship, with low gamma band power at both low and high dopamine levels and optimal synchronization at intermediate levels. Moreover, such a relationship holds when the external input is both tonic and periodic at gamma band range. Together, our results indicate that dopamine can modulate cortical gamma band synchrony in an inverted-U fashion and that the physiologic effects of dopamine on single fast-spiking interneurons can give rise to such non-monotonic effects at the network level.", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1770, + "tag": "ModelDB:146734" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:56:07.794719+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/146734", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1164": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1164, + "name": "ACh modulation in olfactory bulb and piriform cortex (de Almeida et al. 2013;Devore S, et al. 2014)", + "repository_type": "github", + "summary": "This matlab code was used in the papers de Almeida, Idiart and Linster, (2013), Devore S, de Almeida L, Linster C (2014) .\r\nThis work uses a computational model of the OB and PC and their common cholinergic inputs to investigate how bulbar cholinergic modulation affects cortical odor processing.", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1771, + "tag": "ModelDB:146813" + }, + { + "id": 952, + "tag": "Noise Sensitivity" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2024-01-12 09:56:08.495398+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/146813", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1165": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1165, + "name": "Motor cortex microcircuit simulation based on brain activity mapping (Chadderdon et al. 2014)", + "repository_type": "github", + "summary": "\"...\r\nWe developed a computational\r\nmodel based primarily on a unified set of brain activity mapping\r\nstudies of mouse M1.\r\nThe simulation consisted of 775 spiking neurons of\r\n10 cell types with detailed population-to-population connectivity.\r\n\r\nStatic\r\nanalysis of connectivity with graph-theoretic tools revealed that the corticostriatal\r\npopulation showed strong centrality, suggesting that would\r\nprovide a network hub.\r\n...\r\nBy demonstrating the effectiveness of combined static\r\nand dynamic analysis, our results show how static brain maps can be\r\nrelated to the results of brain activity mapping.\"", + "tags": [ + { + "id": 936, + "tag": "Laminar Connectivity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1772, + "tag": "ModelDB:146949" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + } + ], + "timestamp_created": "2024-01-12 09:56:09.063638+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/146949", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1166": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1166, + "name": "Constructed Tessellated Neuronal Geometries (CTNG) (McDougal et al. 2013)", + "repository_type": "github", + "summary": "We present an algorithm to form watertight 3D surfaces consistent with\r\nthe point-and-diameter based neuronal morphology descriptions widely\r\nused with spatial electrophysiology simulators.\r\n\r\n...\r\n\r\nThis (point-and-diameter)\r\nrepresentation is well-suited for electrophysiology simulations, where\r\nthe space constants are larger than geometric ambiguities. However,\r\nthe simple interpretations used for pure electrophysiological\r\nsimulation produce geometries unsuitable for multi-scale models that\r\nalso involve three-dimensional reaction\u2013diffusion, as such models have\r\nsmaller space constants.\r\n\r\n...\r\n\r\nAlthough one cannot exactly reproduce an\r\noriginal neuron's full shape from point-and-diameter data, our new\r\nconstructive tessellated neuronal geometry (CTNG) algorithm uses\r\nconstructive solid geometry to define a plausible reconstruction\r\nwithout gaps or cul-de-sacs.\r\n\r\nCTNG then uses \u201cconstructive cubes\u201d to\r\nproduce a watertight triangular mesh of the neuron surface, suitable\r\nfor use in reaction\u2013diffusion simulations.\r\n...\"", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 1773, + "tag": "Cython" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1774, + "tag": "ModelDB:146950" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 89, + "tag": "Python" + } + ], + "timestamp_created": "2024-01-12 09:56:09.630681+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/146950", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1167": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1167, + "name": "Motion Clouds: Synthesis of random textures for motion perception (Leon et al. 2012)", + "repository_type": "github", + "summary": "We describe a framework to generate random texture movies with controlled information content. In particular, these stimuli can be made closer to naturalistic textures compared to usual stimuli such as gratings and random-dot kinetograms. We simplified the definition to parametrically define these \"Motion Clouds\" around the most prevalent feature axis (mean and bandwith): direction, spatial frequency, orientation.", + "tags": [ + { + "id": 1544, + "tag": "Envelope synthesis" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1775, + "tag": "ModelDB:146953" + }, + { + "id": 1776, + "tag": "Motion Detection" + }, + { + "id": 952, + "tag": "Noise Sensitivity" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 931, + "tag": "Perceptual Categories" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 845, + "tag": "Sensory processing" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + } + ], + "timestamp_created": "2024-01-12 09:56:10.210062+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/146953", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1168": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1168, + "name": "A large-scale model of the functioning brain (spaun) (Eliasmith et al. 2012)", + "repository_type": "github", + "summary": "\" ... In this work, we present a\r\n2.5-million-neuron model of the brain (called \u201cSpaun\u201d) that bridges this gap (between neural activity and biological function) by exhibiting many different\r\nbehaviors. The model is presented only with visual image sequences, and it draws all of its responses with\r\na physically modeled arm. Although simplified, the model captures many aspects of neuroanatomy,\r\nneurophysiology, and psychological behavior, which we demonstrate via eight diverse tasks.\"", + "tags": [ + { + "id": 908, + "tag": "Learning" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1777, + "tag": "ModelDB:147103" + }, + { + "id": 726, + "tag": "Nengo" + }, + { + "id": 892, + "tag": "Reinforcement Learning" + }, + { + "id": 800, + "tag": "Vision" + } + ], + "timestamp_created": "2024-01-12 09:56:10.741337+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147103", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1169": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1169, + "name": "Structure-dynamics relationships in bursting neuronal networks revealed (M\u00e4ki-Marttunen et al. 2013)", + "repository_type": "github", + "summary": "This entry includes tools for generating and analyzing network structure, and for running the neuronal network simulations on them.", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 935, + "tag": "Connectivity matrix" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1778, + "tag": "ModelDB:147117" + }, + { + "id": 661, + "tag": "NEST" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2024-01-12 09:56:11.277365+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147117", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1170": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1170, + "name": "Synaptic scaling balances learning in a spiking model of neocortex (Rowan & Neymotin 2013)", + "repository_type": "github", + "summary": "Learning in the brain requires complementary mechanisms: potentiation and activity-dependent homeostatic scaling. We introduce synaptic scaling to a biologically-realistic spiking model of neocortex which can learn changes in oscillatory rhythms using STDP, and show that scaling is necessary to balance both positive and negative changes in input from potentiation and atrophy. We discuss some of the issues that arise when considering synaptic scaling in such a model, and show that scaling regulates activity whilst allowing learning to remain unaltered.", + "tags": [ + { + "id": 171, + "tag": "Homeostasis" + }, + { + "id": 908, + "tag": "Learning" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1779, + "tag": "ModelDB:147141" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:56:11.821098+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147141", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1171": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1171, + "name": "Biophysical model for field potentials of networks of I&F neurons (beim Graben & Serafim 2013)", + "repository_type": "github", + "summary": "\"...\r\nStarting from a reduced three-compartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential (DFP) that contributes to the local field potential (LFP) of a neural population. \r\n...\r\nOur reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire (LIF) models, which facilitates comparison with existing neural network and observation models. \r\n...\"", + "tags": [ + { + "id": 9, + "tag": "Brian" + }, + { + "id": 810, + "tag": "Extracellular Fields" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1780, + "tag": "ModelDB:147172" + }, + { + "id": 89, + "tag": "Python" + } + ], + "timestamp_created": "2024-01-12 09:56:12.453583+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147172", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1172": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1172, + "name": "Neuronvisio: a gui with 3D capabilities for NEURON (Mattioni et al. 2012)", + "repository_type": "github", + "summary": "\"The NEURON simulation environment is a commonly used tool to perform electrical simulation of neurons and neuronal networks. \r\n\r\nThe NEURON User Interface, based on the now discontinued InterViews library, provides some limited facilities to explore models and to plot their simulation results. \r\n\r\nOther limitations include the inability to generate a three-dimensional visualization, no standard mean to save the results of simulations, or to store the model geometry within the results. \r\n\r\nNeuronvisio (http://neuronvisio.org) aims to address these deficiencies through a set of well designed python APIs and provides an improved UI, allowing users to explore and interact with the model. \r\n...\"", + "tags": [ + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1781, + "tag": "ModelDB:147185" + }, + { + "id": 929, + "tag": "NeuroML (web link to model)" + }, + { + "id": 1782, + "tag": "Neuronvisio (web link to model)" + } + ], + "timestamp_created": "2024-01-12 09:56:12.948699+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147185", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1173": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1173, + "name": "Dendritic signals command firing dynamics in a Cerebellar Purkinje Cell model (Genet et al. 2010)", + "repository_type": "github", + "summary": "This model endows the dendrites of a reconstructed Purkinje cells (PC) with the mechanism of Ca-dependent plateau potentials and spikes described in Genet, S., and B. Delord. 2002. A biophysical model of nonlinear dynamics underlying plateau potentials and calcium spikes in Purkinje cell dendrites. J. Neurophysiol. 88:2430\u20132444). It is a part of a comprehensive mathematical study suggesting that active electric signals in the dendrites of PC command epochs of firing and silencing of the PC soma.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 1760, + "tag": "Ca pump" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 1682, + "tag": "Conductance distributions" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 1636, + "tag": "I Ca,p" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1783, + "tag": "ModelDB:147218" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:56:13.453344+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147218", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1174": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1174, + "name": "Composite spiking network/neural field model of Parkinsons (Kerr et al 2013)", + "repository_type": "github", + "summary": "This code implements a composite model of Parkinson's disease (PD). The\r\ncomposite model consists of a leaky integrate-and-fire spiking neuronal\r\nnetwork model being driven by output from a neural field model (instead\r\nof the more usual white noise drive). Three different sets of parameters\r\nwere used for the field model: one with basal ganglia parameters based\r\non data from healthy individuals, one based on data from individuals\r\nwith PD, and one purely thalamocortical model. The aim of this model is\r\nto explore how the different dynamical patterns in each each of these\r\nfield models affects the activity in the network model.", + "tags": [ + { + "id": 852, + "tag": "I Chloride" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 1556, + "tag": "Information transfer" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1784, + "tag": "ModelDB:147366" + }, + { + "id": 819, + "tag": "NEURON (web link to model)" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 89, + "tag": "Python" + } + ], + "timestamp_created": "2024-01-12 09:56:13.950679+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147366", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1175": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1175, + "name": "IP3R model comparison (Hituri and Linne 2013)", + "repository_type": "github", + "summary": "In this study, four models of IP3R (Othmer and Tang, 1993; Dawson et al., 2003; Fraiman and Dawson, 2004; Doi et al., 2005) were selected among many to examine their behavior and compare them with experimental data available in literature.\r\n\r\nThe provided MATLAB script (run_IP3R_P0.m) will run the simulations and plot Figure 2A in \r\nthe paper.\r\n", + "tags": [ + { + "id": 875, + "tag": "Calcium waves" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1785, + "tag": "ModelDB:147367" + }, + { + "id": 1650, + "tag": "STEPS" + } + ], + "timestamp_created": "2024-01-12 09:56:14.480096+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147367", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1176": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1176, + "name": "Direct recruitment of S1 pyramidal cells and interneurons via ICMS (Overstreet et al., 2013)", + "repository_type": "github", + "summary": "Study of the pyramidal cells and interneurons recruited by intracortical microstimulation in primary somatosensory cortex. Code includes morphological models for seven types of pyramidal cells and eight types of interneurons, NEURON code to simulate ICMS, and an artificial reconstruction of a 3D slab of cortex implemented in MATLAB.", + "tags": [ + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 868, + "tag": "I_Ks" + }, + { + "id": 1786, + "tag": "Intracortical Microstimulation" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1787, + "tag": "ModelDB:147460" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:56:14.975434+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147460", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1177": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1177, + "name": "Storing serial order in intrinsic excitability: a working memory model (Conde-Sousa & Aguiar 2013)", + "repository_type": "github", + "summary": "\" \u2026 Here we present a model for working\r\nmemory which relies on the modulation of the intrinsic excitability properties of neurons, instead of synaptic plasticity, to retain novel information for periods of seconds to minutes. \r\n\r\nWe show that it is possible to effectively use this mechanism to store the serial order in a sequence of patterns of activity.\r\n\u2026\r\nThe presented model exhibits properties which\r\nare in close agreement with experimental results in working\r\nmemory. ...\r\n\"\r\n", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1788, + "tag": "ModelDB:147461" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 877, + "tag": "Working memory" + } + ], + "timestamp_created": "2024-01-12 09:56:15.472772+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147461", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1178": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1178, + "name": "A detailed and fast model of extracellular recordings (Camunas-Mesa & Qurioga 2013)", + "repository_type": "github", + "summary": "\"We present a novel method to generate realistic simulations of extracellular recordings. The simulations were obtained by superimposing the activity of neurons placed randomly in a cube of brain tissue. Detailed models of individual neurons were used to reproduce the extracellular action potentials of close-by neurons. ...\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 810, + "tag": "Extracellular Fields" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1789, + "tag": "ModelDB:147487" + } + ], + "timestamp_created": "2024-01-12 09:56:15.968248+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147487", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1179": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1179, + "name": "Altered complexity in layer 2/3 pyramidal neurons (Luuk van der Velden et al. 2012)", + "repository_type": "github", + "summary": "\" ... Our experimental results show that hypercomplexity of the apical dendritic tuft of layer 2/3 pyramidal neurons affects neuronal excitability by reducing the amount of spike frequency adaptation. \r\n\r\nThis difference in firing pattern, related to a higher dendritic complexity, was accompanied by an altered development of the afterhyperpolarization slope with successive action potentials. \r\n\r\nOur abstract and realistic neuronal models, which allowed manipulation of the dendritic complexity, showed similar effects on neuronal excitability and confirmed the impact of apical dendritic complexity.\r\n\r\nAlterations of dendritic complexity, as observed in several pathological conditions such as neurodegenerative diseases or neurodevelopmental disorders, may thus not only affect the input to layer 2/3 pyramidal neurons but also shape their firing pattern and consequently alter the information processing in the cortex.\"", + "tags": [ + { + "id": 1760, + "tag": "Ca pump" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1790, + "tag": "ModelDB:147514" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:56:16.477352+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147514", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1180": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1180, + "name": "BCM-like synaptic plasticity with conductance-based models (Narayanan Johnston, 2010)", + "repository_type": "github", + "summary": "\" ...\r\nAlthough the BCM-like plasticity framework\r\nhas been a useful formulation to understand synaptic plasticity\r\nand metaplasticity, a mechanism for the activity-dependent regulation\r\nof this modification threshold has remained an open question. In this\r\nsimulation study based on CA1 pyramidal cells, we use a modification\r\nof the calcium-dependent hypothesis proposed elsewhere and show\r\nthat a change in the hyperpolarization-activated, nonspecific-cation h\r\ncurrent is capable of shifting the modification threshold. \r\n...\"", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1791, + "tag": "ModelDB:147538" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:56:16.967420+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147538", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1181": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1181, + "name": "Resonance properties through Chirp stimulus responses (Narayanan Johnston 2007, 2008)", + "repository_type": "github", + "summary": "...we constructed a simple, single-compartment\r\nmodel with Ih as the only active current... we found that both resonance frequency and resonance strength increased monotonically with the increase in the h conductance, supporting the notion of a direct, graded relationship between h conductance and resonance properties... (Narayanan and Johnston, 2007). ...we show that the h channels introduce an apparent negative delay in the local voltage response of these neurons with respect to the injected current within the theta frequency range... we found that the total inductive phase increased monotonically with the h conductance, whereas it had a bell-shaped dependence on both the membrane voltage and the half-maximal activation voltage for the h conductance. (Narayanan and Johnston, 2008).", + "tags": [ + { + "id": 1682, + "tag": "Conductance distributions" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1792, + "tag": "ModelDB:147539" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:56:17.463467+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147539", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1182": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1182, + "name": "Chirp stimulus responses in a morphologically realistic model (Narayanan and Johnston, 2007)", + "repository_type": "github", + "summary": "...we built a multicompartmental model with a morphologically realistic three-dimensional reconstruction of a CA1 pyramidal neuron. The only active conductance we added to the model was the h conductance. ... We conclude that experimentally observed gradient in density of h channels could theoretically account for experimentally observed gradient in resonance properties (Narayanan and Johnston, 2007).", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1793, + "tag": "ModelDB:147578" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:56:17.957873+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147578", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1183": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1183, + "name": "Cytoplasmic electric fields and electroosmosis (Andreev 2013)", + "repository_type": "github", + "summary": "The paper presents two mathematical models describing the role of electroosmosis in the transport of the negatively charged messenger proteins to the negatively charged nucleus and in the recovery of the fluorescence after photobleaching. The parameters of the models were derived from the extensive review of the literature data. Computer simulations were performed within the COMSOL 4.2a software environment. The first model demonstrated that the presence of electroosmosis might intensify the flux of messenger proteins to the nucleus and allow the efficient transport of the negatively charged phosphorylated messenger proteins against the electrostatic repulsion of the negatively charged nucleus. The second model revealed that the presence of the electroosmotic flow made the time of fluorescence recovery dependent on the position of the bleaching spot relative to cellular membrane.", + "tags": [ + { + "id": 1794, + "tag": "COMSOL (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1795, + "tag": "ModelDB:147740" + } + ], + "timestamp_created": "2024-01-12 09:56:18.460988+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147740", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1184": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1184, + "name": "Human sleep/wake cycle (Rempe et al. 2010)", + "repository_type": "github", + "summary": "This model simulates sleep in the human brain and is consistent with both the flip/flop concept and the two-process model of sleep regulation. The model also gives a possible mechanism for the changes in sleep timing seen in narcolepsy.", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1796, + "tag": "ModelDB:147748" + }, + { + "id": 802, + "tag": "Sleep" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:56:19.157771+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147748", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1185": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1185, + "name": "Region-specific atrophy in dendrites (Narayanan, Narayan, Chattarji, 2005)", + "repository_type": "github", + "summary": "...in this study, we develop an algorithm that uses statistics from precise morphometric analyses to systematically remodel neuronal reconstructions. We use the distribution function of the ratio of two normal distributed random variables to specify the probabilities of remodeling along various regions of the dendritic arborization. We then use these probabilities to drive an iterative algorithm for manipulating the dendritic tree in a region-specific manner. As a test, we apply this framework to a well characterized example of dendritic remodeling: stress-induced dendritic atrophy in hippocampal CA3 pyramidal cells. We show that our pruning algorithm is capable of eliciting atrophy that matches biological data from rodent models of chronic stress.\r\n
", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1797, + "tag": "ModelDB:147756" + } + ], + "timestamp_created": "2024-01-12 09:56:19.741616+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147756", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1186": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1186, + "name": "Amyloid-beta effects on release probability and integration at CA3-CA1 synapses (Romani et al. 2013)", + "repository_type": "github", + "summary": "The role of amyloid beta (A\u00df) in brain function and in the pathogenesis of Alzheimer\u2019s disease remains elusive.\r\nRecent publications reported that an increase in A\u00df concentration perturbs presynaptic release in hippocampal neurons, in particular by increasing release probability of CA3-CA1 synapses. The model predics how this alteration can affect synaptic plasticity and signal integration. The results suggest that the perturbation of release probability induced by increased A\u00df can significantly alter the spike probability of CA1 pyramidal neurons and thus contribute to abnormal hippocampal function during Alzheimer\u2019s disease.", + "tags": [ + { + "id": 833, + "tag": "Aging/Alzheimer`s" + }, + { + "id": 797, + "tag": "Depression" + }, + { + "id": 798, + "tag": "Facilitation" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1798, + "tag": "ModelDB:147757" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:56:20.293875+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147757", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1187": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1187, + "name": "Impact of dendritic atrophy on intrinsic and synaptic excitability (Narayanan & Chattarji, 2010)", + "repository_type": "github", + "summary": "These simulations examined the atrophy induced changes in electrophysiological properties of CA3 pyramidal neurons. We found these neurons change from bursting to regular spiking as atrophy increases. Region-specific atrophy induced region-specific increases in synaptic excitability in a passive dendritic tree. All dendritic compartments of an atrophied \r\nneuron had greater synaptic excitability and a larger voltage transfer to the soma than the control neuron.\r\n", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 1682, + "tag": "Conductance distributions" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1799, + "tag": "ModelDB:147867" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:56:20.976170+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147867", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1188": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1188, + "name": "Development of orientation-selective simple cell receptive fields (Rishikesh and Venkatesh, 2003)", + "repository_type": "github", + "summary": "Implementation of a computational model for the development of\r\nsimple-cell receptive fields spanning the regimes before and after eye-opening. The before eye-opening period is governed by a correlation-based rule from Miller (Miller, J. Neurosci., 1994), and the post eye-opening period is governed by a self-organizing, experience-dependent dynamics derived in the reference below.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 839, + "tag": "Development" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1800, + "tag": "ModelDB:147929" + }, + { + "id": 1645, + "tag": "Orientation selectivity" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 886, + "tag": "Unsupervised Learning" + }, + { + "id": 800, + "tag": "Vision" + }, + { + "id": 905, + "tag": "Winner-take-all" + } + ], + "timestamp_created": "2024-01-12 09:56:21.496288+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147929", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1189": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1189, + "name": "Towards a virtual C. elegans (Palyanov et al. 2012)", + "repository_type": "github", + "summary": "\"... Here we present a detailed demonstration of a virtual C. elegans\r\naimed at integrating these data in the form of a 3D dynamic model operating in a simulated physical environment. Our current\r\ndemonstration includes a realistic flexible worm body model, muscular system and a partially implemented ventral neural cord.\r\nOur virtual C. elegans demonstrates successful forward and backward locomotion when sending sinusoidal patterns of neuronal\r\nactivity to groups of motor neurons. \r\n...\"", + "tags": [ + { + "id": 862, + "tag": "C or Cplusplus program (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1801, + "tag": "ModelDB:147938" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + } + ], + "timestamp_created": "2024-01-12 09:56:22.006077+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/147938", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1190": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1190, + "name": "Spiking GridPlaceMap model (Pilly & Grossberg, PLoS One, 2013)", + "repository_type": "github", + "summary": "Development of spiking grid cells and place cells in the entorhinal-hippocampal system to represent positions in large spaces", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 1603, + "tag": "Brain Rhythms" + }, + { + "id": 935, + "tag": "Connectivity matrix" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 839, + "tag": "Development" + }, + { + "id": 1539, + "tag": "Grid cell" + }, + { + "id": 1607, + "tag": "I Cl, leak" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 908, + "tag": "Learning" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1802, + "tag": "ModelDB:148035" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 882, + "tag": "Place cell/field" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 886, + "tag": "Unsupervised Learning" + } + ], + "timestamp_created": "2024-01-12 09:56:22.537734+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/148035", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1191": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1191, + "name": "CA1 pyramidal neuron: effects of R213Q and R312W Kv7.2 mutations (Miceli et al. 2013)", + "repository_type": "github", + "summary": "NEURON mod files from the paper:\r\n\r\nMiceli et al, Genotype\u2013phenotype correlations in neonatal epilepsies caused by mutations in the voltage sensor of Kv7.2 potassium channel subunits, PNAS 2013 Feb 25. [Epub ahead of print]\r\n\r\nIn this paper, functional studies revealed that in homomeric or heteromeric configuration with KV7.2 and/or KV7.3 subunits, R213W and \r\nR213Q mutations markedly destabilized the open state, causing a dramatic decrease in channel voltage sensitivity.\r\nModeling these channels in CA1 hippocampal pyramidal cells revealed that both mutations increased cell firing frequency,\r\nwith the R213Q mutation prompting more dramatic functional changes compared with the R213W mutation.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1803, + "tag": "ModelDB:148094" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:56:23.109537+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/148094", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1192": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1192, + "name": "Effects of Chloride accumulation and diffusion on GABAergic transmission (Jedlicka et al 2011)", + "repository_type": "github", + "summary": "\"In the CNS, prolonged activation of GABA(A) receptors (GABA(A)Rs) has been shown to evoke biphasic postsynaptic responses, consisting of an initial hyperpolarization followed by a depolarization. \r\n\r\nA potential mechanism underlying the depolarization is an acute chloride (Cl(-)) accumulation resulting in a shift of the GABA(A) reversal potential (E(GABA)).\r\n\r\nThe amount of GABA-evoked Cl(-) accumulation and accompanying depolarization depends on presynaptic and postsynaptic properties of GABAergic transmission, as well as on cellular morphology and regulation of Cl(-) intracellular concentration ([Cl(-)](i)).\r\n\r\nTo analyze the influence of these factors on the Cl(-) and voltage behavior, we studied spatiotemporal dynamics of activity-dependent [Cl(-)](i) changes in multicompartmental models of hippocampal cells based on realistic morphological data.\r\n...\"", + "tags": [ + { + "id": 1668, + "tag": "Chloride regulation" + }, + { + "id": 852, + "tag": "I Chloride" + }, + { + "id": 1804, + "tag": "I_HCO3" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1805, + "tag": "ModelDB:148253" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:56:23.689784+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/148253", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1193": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1193, + "name": "Circadian clock model in mammals (PK/PD model) (Kim & Forger 2013)", + "repository_type": "github", + "summary": "A systems pharmacology model of the mammalian circadian clock including PF-670462 (CK1d/e inhibitor).", + "tags": [ + { + "id": 1662, + "tag": "Circadian Rhythms" + }, + { + "id": 1685, + "tag": "Mathematica" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1806, + "tag": "ModelDB:148320" + }, + { + "id": 906, + "tag": "Phase Response Curves" + }, + { + "id": 822, + "tag": "Therapeutics" + } + ], + "timestamp_created": "2024-01-12 09:56:24.172790+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/148320", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1194": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1194, + "name": "Failure of Deep Brain Stimulation in a basal ganglia neuronal network model (Dovzhenok et al. 2013)", + "repository_type": "github", + "summary": "\"\u2026 Recently, a lot of interest has been devoted to desynchronizing delayed feedback deep brain stimulation (DBS).\r\n...\r\nThis study explores the action of delayed feedback stimulation on partially synchronized oscillatory dynamics, similar to what one observes experimentally in parkinsonian patients.\r\n\u2026\"\r\n\r\nImplemented by Andrey Dovzhenok, to whom questions should be addressed.", + "tags": [ + { + "id": 834, + "tag": "Deep brain stimulation" + }, + { + "id": 1636, + "tag": "I Ca,p" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1807, + "tag": "ModelDB:148637" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:56:24.686698+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/148637", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1195": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1195, + "name": "Neuronal morphology goes digital ... (Parekh & Ascoli 2013)", + "repository_type": "github", + "summary": "An illustration of a NEURON model and why reconstructing morphologies\r\nis useful in this regard (i.e. investigating spatial/temporal aspect\r\nof how different currents and voltage propagate in dendrites).", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1808, + "tag": "ModelDB:148644" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + } + ], + "timestamp_created": "2024-01-12 09:56:25.209423+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/148644", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1196": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1196, + "name": "CA1 pyramidal neuron: action potential backpropagation (Gasparini & Migliore 2015)", + "repository_type": "github", + "summary": "\" ... the investigation of AP backpropagation and its functional roles has greatly benefitted from computational models that use biophysically and morphologically accurate implementations. ...\" This model entry recreates figures 2 and 4 from the paper illustrating how conductance densities of voltage gated channels (fig 2) and the timing of synaptic input with backpropagating action potentials (fig 4) affects membrane voltage trajectories.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1809, + "tag": "ModelDB:148646" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:56:25.765563+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/148646", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1197": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1197, + "name": "Using Strahler's analysis to reduce realistic models (Marasco et al, 2013)", + "repository_type": "github", + "summary": "Building on our previous work (Marasco et al., (2012)), we present a general reduction method based on Strahler's analysis of neuron\r\nmorphologies. We show that, without any fitting or tuning procedures, it is\r\npossible to map any morphologically and biophysically accurate neuron model\r\ninto an equivalent reduced version. Using this method for Purkinje cells, we\r\ndemonstrate how run times can be reduced up to 200-fold, while accurately taking into account the effects of arbitrarily located and activated\r\nsynaptic inputs.\r\n", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 1760, + "tag": "Ca pump" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1810, + "tag": "ModelDB:149000" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:56:26.273677+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/149000", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1198": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1198, + "name": "Preserving axosomatic spiking features despite diverse dendritic morphology (Hay et al., 2013)", + "repository_type": "github", + "summary": "The authors found that linearly scaling the ion channel conductance densities of a reference model with the conductance load in 28 3D reconstructed layer 5 thick-tufted pyramidal cells was necessary to match the experimental statistics of these cells electrical firing properties.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1811, + "tag": "ModelDB:149100" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 941, + "tag": "Parameter sensitivity" + } + ], + "timestamp_created": "2024-01-12 09:56:27.269560+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/149100", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1199": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1199, + "name": "Rescue of plasticity by a computationally predicted protocol (Liu et al. 2013)", + "repository_type": "github", + "summary": "\" ... A computational model,\r\nwhich simulated molecular processes underlying long-term\r\nsynaptic facilitation (LTF) induction, predicted a rescue protocol of five pulses of 5-HT at non-uniform interstimulus\r\nintervals that overcame the consequences of reduced CREB-binding protein (CBP) and restored LTF. ...\"", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1812, + "tag": "ModelDB:149162" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:56:28.050407+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/149162", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1200": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1200, + "name": "State dependent drug binding to sodium channels in the dentate gyrus (Thomas & Petrou 2013)", + "repository_type": "github", + "summary": "A Markov model of sodium channels was developed that includes drug binding to fast inactivated states. This was incorporated into a model of the dentate gyrus to investigate the effects of anti-epileptic drugs on neuron and network properties.", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 1813, + "tag": "Drug binding" + }, + { + "id": 572, + "tag": "Epilepsy" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 824, + "tag": "Markov-type model" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1814, + "tag": "ModelDB:149174" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:56:28.627097+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/149174", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1201": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1201, + "name": "Roles of essential kinases in induction of late hippocampal LTP (Smolen et al., 2006)", + "repository_type": "github", + "summary": "\"\u2026 Convergence of multiple kinase activities to induce L-LTP helps to generate a threshold whereby the amount of L-LTP varies steeply with the number of brief (tetanic) electrical stimuli. The model simulates tetanic, -burst, pairing-induced, and chemical L-LTP, as well as L-LTP due to synaptic tagging. The model also simulates inhibition of L-LTP by inhibition of MAPK, CAMKII, PKA, or CAMKIV. The model predicts results of experiments to delineate mechanisms underlying L-LTP induction and expression.\r\n\u2026\"\r\n", + "tags": [ + { + "id": 1815, + "tag": "CREB" + }, + { + "id": 864, + "tag": "Java" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1816, + "tag": "ModelDB:149175" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:56:29.137959+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/149175", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1202": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1202, + "name": "Olfactory Computations in Mitral-Granule cell circuits (Migliore & McTavish 2013)", + "repository_type": "github", + "summary": "Model files for the entry \"Olfactory Computations in Mitral-Granule Cell Circuits\" of the Springer Encyclopedia of Computational Neuroscience by Michele Migliore and Tom Mctavish.\r\n\r\nThe simulations illustrate two typical Mitral-Granule cell circuits in the olfactory bulb of vertebrates: distance-independent lateral inhibition and gating effects.\r\n", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 850, + "tag": "Intrinsic plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1817, + "tag": "ModelDB:149415" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + } + ], + "timestamp_created": "2024-01-12 09:56:29.631813+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/149415", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1203": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1203, + "name": "Modeling conductivity profiles in the deep neocortical pyramidal neuron (Wang K et al. 2013)", + "repository_type": "github", + "summary": "\"With the rapid increase in the number of technologies aimed at observing\r\nelectric activity inside the brain, scientists have felt the urge to create\r\nproper links between intracellular- and extracellular-based experimental\r\napproaches. Biophysical models at both physical scales have been formalized\r\nunder assumptions that impede the creation of such links. In this\r\nwork, we address this issue by proposing amulticompartment model that\r\nallows the introduction of complex extracellular and intracellular resistivity\r\nprofiles. This model accounts for the geometrical and electrotonic\r\nproperties of any type of neuron through the combination of four devices:\r\nthe integrator, the propagator, the 3D connector, and the collector. ...\"", + "tags": [ + { + "id": 1682, + "tag": "Conductance distributions" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 1622, + "tag": "Evoked LFP" + }, + { + "id": 810, + "tag": "Extracellular Fields" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 868, + "tag": "I_Ks" + }, + { + "id": 771, + "tag": "Influence of Dendritic Geometry" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1818, + "tag": "ModelDB:149737" + }, + { + "id": 819, + "tag": "NEURON (web link to model)" + } + ], + "timestamp_created": "2024-01-12 09:56:30.271338+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/149737", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1204": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1204, + "name": "A two-layer biophysical olfactory bulb model of cholinergic neuromodulation (Li and Cleland 2013)", + "repository_type": "github", + "summary": "This is a two-layer biophysical olfactory bulb (OB) network model to study cholinergic neuromodulation. Simulations show that nicotinic receptor activation sharpens mitral cell receptive field, while muscarinic receptor activation enhances network synchrony and gamma oscillations. This general model suggests that the roles of nicotinic and muscarinic receptors in OB are both distinct and complementary to one another, together regulating the effects of ascending cholinergic inputs on olfactory bulb transformations.", + "tags": [ + { + "id": 777, + "tag": "I A" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 1636, + "tag": "I Ca,p" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 1607, + "tag": "I Cl, leak" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 868, + "tag": "I_Ks" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1819, + "tag": "ModelDB:149739" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1820, + "tag": "Neuromodulation" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 904, + "tag": "Sensory coding" + }, + { + "id": 845, + "tag": "Sensory processing" + } + ], + "timestamp_created": "2024-01-12 09:56:30.911250+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/149739", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1205": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1205, + "name": "Competing oscillator 5-cell circuit and Parameterscape plotting (Gutierrez et al. 2013)", + "repository_type": "github", + "summary": "Our 5-cell model consists of competing fast and slow oscillators connected to a hub neuron with electrical and inhibitory synapses. Motivated by the Stomatogastric Ganglion (STG) circuit in the crab, we explored the patterns of coordination in the network as a function of the electrical coupling and inhibitory synapse strengths with the help of a novel visualization method that we call the \"Parameterscape.\" The code submitted here will allow you to run circuit simulations and to produce a Parameterscape with the results.", + "tags": [ + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1821, + "tag": "ModelDB:149910" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:56:31.433463+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/149910", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1206": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1206, + "name": "Distributed synaptic plasticity and spike timing (Garrido et al. 2013)", + "repository_type": "github", + "summary": "Here we have used a computational model to simulate the impact of multiple distributed synaptic weights in the cerebellar granular layer network. In response to mossy fiber bursts, synaptic weights at multiple connections played a crucial role to regulate spike number and positioning in granule cells. Interestingly, different combinations of synaptic weights optimized either first-spike timing precision or spike number, efficiently controlling transmission and filtering properties. These results predict that distributed synaptic plasticity regulates the emission of quasi-digital spike patterns on the millisecond time scale and allows the cerebellar granular layer to flexibly control burst transmission along the mossy fiber pathway.", + "tags": [ + { + "id": 1822, + "tag": "EDLUT" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1823, + "tag": "ModelDB:149913" + } + ], + "timestamp_created": "2024-01-12 09:56:31.931382+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/149913", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1207": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1207, + "name": "Prob. Inference of Short-Term Synaptic Plasticity in Neocort. Microcircuits (Costa et al. 2013)", + "repository_type": "github", + "summary": "\" ... As a solution (for Short Term Plasticity (STP) inference), we introduce a Bayesian formulation, which yields the posterior distribution over the model parameters given the data. First, we show that common STP protocols yield broad distributions over some model parameters. Using our result we propose a experimental protocol to more accurately determine synaptic dynamics parameters. Next, we infer the model parameters using experimental data from three different neocortical excitatory connection types. This reveals connection-specific distributions, which we use to classify synaptic dynamics. Our approach to demarcate connection-specific synaptic dynamics is an important improvement on the state of the art and reveals novel features from existing data.\"", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1824, + "tag": "ModelDB:149914" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:56:32.403636+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/149914", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1208": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1208, + "name": "KV1 channel governs cerebellar output to thalamus (Ovsepian et al. 2013)", + "repository_type": "github", + "summary": "The output of the cerebellum to the motor axis of the central nervous system is\r\norchestrated mainly by synaptic inputs and intrinsic pacemaker activity of deep cerebellar nuclear\r\n(DCN) projection neurons. Herein, we demonstrate that the soma of these cells is enriched with\r\nKV1 channels produced by mandatory multi-merization of KV1.1, 1.2 alpha andKV beta2 subunits. Being\r\nconstitutively active, the K+ current (IKV1) mediated by these channels stabilizes the rate and\r\nregulates the temporal precision of self-sustained firing of these neurons. \r\n...\r\nThrough the use of multi-compartmental modelling and ... the physiological significance of the described functions for processing\r\nand communication of information from the lateral DCN to thalamic relay nuclei is established.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 868, + "tag": "I_Ks" + }, + { + "id": 850, + "tag": "Intrinsic plasticity" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1825, + "tag": "ModelDB:150024" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 775, + "tag": "Rebound firing" + } + ], + "timestamp_created": "2024-01-12 09:56:32.918918+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150024", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1209": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1209, + "name": "An attractor network model of grid cells and theta-nested gamma oscillations (Pastoll et al 2013)", + "repository_type": "github", + "summary": "A two population spiking continuous attractor model of grid cells. This model combines the attractor dynamics with theta-nested gamma oscillatory activity. It reproduces the behavioural response of grid cells (grid fields) in medial entorhinal cortex, while at the same time allowing for nested gamma oscillations of post-synaptic currents.", + "tags": [ + { + "id": 790, + "tag": "Attractor Neural Network" + }, + { + "id": 9, + "tag": "Brian" + }, + { + "id": 1539, + "tag": "Grid cell" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1826, + "tag": "ModelDB:150031" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 89, + "tag": "Python" + } + ], + "timestamp_created": "2024-01-12 09:56:33.435168+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150031", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1210": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1210, + "name": "Distributed cerebellar plasticity implements adaptable gain control (Garrido et al., 2013)", + "repository_type": "github", + "summary": "We tested the role of plasticity distributed over multiple synaptic sites (Hansel et al., 2001; Gao et al., 2012) by generating an analog cerebellar model embedded into a control loop connected to a robotic simulator. The robot used a three-joint arm and performed repetitive fast manipulations with different masses along an 8-shape trajectory. In accordance with biological evidence, the cerebellum model was endowed with both LTD and LTP at the PF-PC, MF-DCN and PC-DCN synapses. This resulted in a network scheme whose effectiveness was extended considerably compared to one including just PF-PC synaptic plasticity. Indeed, the system including distributed plasticity reliably self-adapted to manipulate different masses and to learn the arm-object dynamics over a time course that included fast learning and consolidation, along the lines of what has been observed in behavioral tests. In particular, PF-PC plasticity operated as a time correlator between the actual input state and the system error, while MF-DCN and PC-DCN plasticity played a key role in generating the gain controller. This model suggests that distributed synaptic plasticity allows generation of the complex learning properties of the cerebellum.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1827, + "tag": "ModelDB:150067" + }, + { + "id": 907, + "tag": "Simulink" + } + ], + "timestamp_created": "2024-01-12 09:56:33.913750+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150067", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1211": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1211, + "name": "Biologically Constrained Basal Ganglia model (BCBG model) (Lienard, Girard 2014)", + "repository_type": "github", + "summary": "We studied the physiology and function of the basal ganglia through the design of mean-field models of the whole basal ganglia. The parameterizations are optimized with multi-objective evolutionary algorithm to respect best a collection of numerous anatomical data and electrophysiological data. The main outcomes of our study are: \u2022 The strength of the GPe to GPi/SNr connection does not support opposed activities in the GPe and GPi/SNr. \u2022 STN and MSN target more the GPe than the GPi/SNr. \u2022 Selection arises from the structure of the basal ganglia, without properly segregated direct and indirect pathways and without specific inputs from pyramidal tract neurons of the cortex. Selection is enhanced when the projection from GPe to GPi/SNr has a diffuse pattern.", + "tags": [ + { + "id": 857, + "tag": "Action Selection/Decision Making" + }, + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1828, + "tag": "ModelDB:150206" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 872, + "tag": "Pathophysiology" + }, + { + "id": 905, + "tag": "Winner-take-all" + } + ], + "timestamp_created": "2024-01-12 09:56:34.394063+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150206", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1212": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1212, + "name": "A Method for Prediction of Receptor Activation in the Simulation of Synapses (Montes et al. 2013)", + "repository_type": "github", + "summary": "A machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the activation of synaptic receptors, at very low computational cost. The method is designed to learn patterns and general principles from previous Monte Carlo simulations and to predict synapse behavior from them. The resulting procedure is accurate, automatic and can predict synapse behavior under experimental conditions that are different to the ones used during the learning phase. Since our method efficiently reduces the computational costs, it is suitable for the simulation of the vast number of synapses that occur in the mammalian brain.", + "tags": [ + { + "id": 864, + "tag": "Java" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1829, + "tag": "ModelDB:150207" + }, + { + "id": 1830, + "tag": "R" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:56:34.934203+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150207", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1213": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1213, + "name": "Emergence of Connectivity Motifs in Networks of Model Neurons (Vasilaki, Giugliano 2014)", + "repository_type": "github", + "summary": "Recent evidence suggests that short-term dynamics of excitatory synaptic transmission is correlated to stereotypical connectivity motifs. \r\nWe show that these connectivity motifs emerge in networks of model neurons, from the interactions between short-term synaptic dynamics (SD) and long-term spike-timing dependent plasticity (STDP).", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 935, + "tag": "Connectivity matrix" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1831, + "tag": "ModelDB:150211" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 776, + "tag": "Simplified Models" + } + ], + "timestamp_created": "2024-01-12 09:56:35.520888+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150211", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1214": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1214, + "name": "Reliability of Morris-Lecar neurons with added T, h, and AHP currents (Zeldenrust et al. 2013)", + "repository_type": "github", + "summary": "We investigated the reliability of the timing of spikes in a spike train in a Morris-Lecar model with several extensions. A frozen Gaussian noise current, superimposed on a DC current, was injected. The neuron responded with spike trains that showed trial-to-trial variability. The reliability depends on the shape (steepness) of the current input versus spike frequency output curve. The model also allowed to study the contribution of three relevant ionic membrane currents to reliability: a T-type calcium current, a cation selective h-current and a calcium dependent potassium current in order to allow bursting, investigate the consequences of a more complex current-frequency relation and produce realistic firing rates.", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1832, + "tag": "ModelDB:150217" + }, + { + "id": 838, + "tag": "Reliability" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:56:36.035753+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150217", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1215": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1215, + "name": "Fast convergence of cerebellar learning (Luque et al. 2015)", + "repository_type": "github", + "summary": "The cerebellum is known to play a critical role in learning relevant patterns of activity for adaptive motor control, but the underlying network mechanisms are only partly understood. The classical long-term synaptic plasticity between parallel fibers (PFs) and Purkinje cells (PCs), which is driven by the inferior olive (IO), can only account for limited aspects of learning. Recently, the role of additional forms of plasticity in the granular layer, molecular layer and deep cerebellar nuclei (DCN) has been considered. In particular, learning at DCN synapses allows for generalization, but convergence to a stable state requires hundreds of repetitions. In this paper we have explored the putative role of the IO-DCN connection by endowing it with adaptable weights and exploring its implications in a closed-loop robotic manipulation task. Our results show that IO-DCN plasticity accelerates convergence of learning by up to two orders of magnitude without conflicting with the generalization properties conferred by DCN plasticity. Thus, this model suggests that multiple distributed learning mechanisms provide a key for explaining the complex properties of procedural learning and open up new experimental questions for synaptic plasticity in the cerebellar network.", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1833, + "tag": "ModelDB:150225" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 907, + "tag": "Simulink" + } + ], + "timestamp_created": "2024-01-12 09:56:36.531524+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150225", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1216": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1216, + "name": "MEC layer II stellate cell: Synaptic mechanisms of grid cells (Schmidt-Hieber & Hausser 2013)", + "repository_type": "github", + "summary": "This study investigates the cellular mechanisms of grid field generation in Medial Entorhinal Cortex (MEC) layer II stellate cells.", + "tags": [ + { + "id": 790, + "tag": "Attractor Neural Network" + }, + { + "id": 1539, + "tag": "Grid cell" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 853, + "tag": "I CNG" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1834, + "tag": "ModelDB:150239" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 882, + "tag": "Place cell/field" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 883, + "tag": "Spatial Navigation" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:56:37.047505+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150239", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1217": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1217, + "name": "A Model Circuit of Thalamocortical Convergence (Behuret et al. 2013)", + "repository_type": "github", + "summary": "\u201c\u2026\r\nUsing dynamic-clamp techniques in thalamic slices in vitro, we combined theoretical and experimental\r\napproaches to implement a realistic hybrid retino-thalamo-cortical pathway mixing biological cells and simulated circuits.\r\n\u2026\r\nThe study of\r\nthe impact of the simulated cortical input on the global retinocortical signal transfer efficiency revealed a novel control\r\nmechanism resulting from the collective resonance of all thalamic relay neurons. \r\nWe show here that the transfer efficiency\r\nof sensory input transmission depends on three key features: i) the number of thalamocortical cells involved in the many-to-one\r\nconvergence from thalamus to cortex, ii) the statistics of the corticothalamic synaptic bombardment and iii) the level of\r\ncorrelation imposed between converging thalamic relay cells. \r\nIn particular, our results demonstrate counterintuitively that\r\nthe retinocortical signal transfer efficiency increases when the level of correlation across thalamic cells decreases. \r\n\u2026\u201d\r\n", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1835, + "tag": "ModelDB:150240" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 854, + "tag": "Synaptic Convergence" + } + ], + "timestamp_created": "2024-01-12 09:56:37.618862+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150240", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1218": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1218, + "name": "Sensorimotor cortex reinforcement learning of 2-joint virtual arm reaching (Neymotin et al. 2013)", + "repository_type": "github", + "summary": "\"...\r\nWe developed a model of sensory and motor neocortex consisting\r\nof 704 spiking model-neurons. Sensory and motor populations included excitatory cells\r\nand two types of interneurons. Neurons were interconnected with AMPA/NMDA, and\r\nGABAA synapses. We trained our model using spike-timing-dependent reinforcement\r\nlearning to control a 2-joint virtual arm to reach to a fixed target. \r\n...\r\n\"", + "tags": [ + { + "id": 908, + "tag": "Learning" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1836, + "tag": "ModelDB:150245" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 892, + "tag": "Reinforcement Learning" + }, + { + "id": 939, + "tag": "Reward-modulated STDP" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 845, + "tag": "Sensory processing" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:56:38.240537+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150245", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1219": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1219, + "name": "Multiscale simulation of the striatal medium spiny neuron (Mattioni & Le Novere 2013)", + "repository_type": "github", + "summary": "\"\u2026 We present a new event-driven algorithm to synchronize different neuronal\r\nmodels, which decreases computational time and avoids superfluous synchronizations.\r\n\r\nThe algorithm is implemented in the TimeScales framework. \r\n\r\nWe demonstrate its use by simulating a new multiscale model of the Medium Spiny Neuron of the Neostriatum.\r\n\r\nThe model comprises over a thousand dendritic spines, where the electrical model interacts with the\r\nrespective instances of a biochemical model.\r\n\r\nOur results show that a multiscale model is able to exhibit changes of synaptic\r\nplasticity as a result of the interaction between electrical and biochemical signaling.\r\n\u2026\"", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 923, + "tag": "I Krp" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 924, + "tag": "I Q" + }, + { + "id": 925, + "tag": "I R" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1837, + "tag": "ModelDB:150284" + }, + { + "id": 944, + "tag": "Multiscale" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:56:38.926678+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150284", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1220": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1220, + "name": "A 1000 cell network model for Lateral Amygdala (Kim et al. 2013)", + "repository_type": "github", + "summary": "1000 Cell Lateral Amygdala model for investigation of plasticity and memory storage during Pavlovian Conditioning.", + "tags": [ + { + "id": 1760, + "tag": "Ca pump" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 908, + "tag": "Learning" + }, + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1838, + "tag": "ModelDB:150288" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1820, + "tag": "Neuromodulation" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:56:39.539251+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150288", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1221": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1221, + "name": "Network bursts in cultured NN result from different adaptive mechanisms (Masquelier & Deco 2013)", + "repository_type": "github", + "summary": "It is now well established that cultured neuron networks are spontaneously active, and tend to synchronize. Synchronous events typically involve the whole network, and have thus been termed \u201cnetwork spikes\u201d (NS). Using experimental recordings and numerical simulations, we show here that the inter-NS interval statistics are complex, and allow inferring the neural mechanisms at work, in particular the adaptive ones, and estimating a number of parameters to which we cannot access experimentally.", + "tags": [ + { + "id": 9, + "tag": "Brian" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 797, + "tag": "Depression" + }, + { + "id": 798, + "tag": "Facilitation" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1839, + "tag": "ModelDB:150437" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + }, + { + "id": 863, + "tag": "Spike Frequency Adaptation" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + } + ], + "timestamp_created": "2024-01-12 09:56:40.119999+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150437", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1222": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1222, + "name": "Spontaneous weakly correlated excitation and inhibition (Tan et al. 2013)", + "repository_type": "github", + "summary": "Brian code for Tan et al. 2013.", + "tags": [ + { + "id": 9, + "tag": "Brian" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1840, + "tag": "ModelDB:150440" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 800, + "tag": "Vision" + } + ], + "timestamp_created": "2024-01-12 09:56:40.695351+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150440", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1223": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1223, + "name": "Interaural time difference detection by slowly integrating neurons (Vasilkov Tikidji-Hamburyan 2012)", + "repository_type": "github", + "summary": "For localization of a sound source, animals and humans process the microsecond interaural time differences of arriving sound waves. How nervous systems, consisting of elements with time constants of about and more than 1 ms, can reach such high precision is still an open question. This model shows that population of 10000 slowly integrating Hodgkin-Huxley neurons with inhibitory and excitatory inputs (EI neurons) can detect minute temporal disparities in input signals which are significantly less than any time constant in the system.", + "tags": [ + { + "id": 849, + "tag": "Audition" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1841, + "tag": "ModelDB:150445" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:56:41.174051+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150445", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1224": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1224, + "name": "Synaptic gating at axonal branches, and sharp-wave ripples with replay (Vladimirov et al. 2013)", + "repository_type": "github", + "summary": "The computational model of in vivo sharp-wave ripples with place cell replay. Excitatory post-synaptic potentials at dendrites gate antidromic spikes arriving from the axonal collateral, and thus determine when the soma and the main axon fire. The model allows synchronous replay of pyramidal cells during sharp-wave ripple event, and the replay is possible in both forward and reverse directions.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 1603, + "tag": "Brain Rhythms" + }, + { + "id": 821, + "tag": "Conduction failure" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1842, + "tag": "ModelDB:150446" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 882, + "tag": "Place cell/field" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2024-01-12 09:56:41.732021+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150446", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1225": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1225, + "name": "L5 pyr. cell spiking control by oscillatory inhibition in distal apical dendrites (Li et al 2013)", + "repository_type": "github", + "summary": "This model examined how distal oscillatory inhibition influences the firing of a biophysically-detailed layer 5 pyramidal neuron model.", + "tags": [ + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 1608, + "tag": "I Na, leak" + }, + { + "id": 850, + "tag": "Intrinsic plasticity" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1843, + "tag": "ModelDB:150538" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:56:42.290083+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150538", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1226": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1226, + "name": "Calcium waves and mGluR-dependent synaptic plasticity in CA1 pyr. neurons (Ashhad & Narayanan 2013)", + "repository_type": "github", + "summary": "A morphologically realistic, conductance-based model equipped with kinetic schemes that govern several calcium signalling modules and pathways in CA1 pyramidal neurons", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 1760, + "tag": "Ca pump" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 875, + "tag": "Calcium waves" + }, + { + "id": 866, + "tag": "G-protein coupled" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1844, + "tag": "ModelDB:150551" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 827, + "tag": "Signaling pathways" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:56:42.861582+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150551", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1227": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1227, + "name": "Single compartment Dorsal Lateral Medium Spiny Neuron w/ NMDA and AMPA (Biddell and Johnson 2013)", + "repository_type": "github", + "summary": "A biophysical single compartment model of the dorsal lateral striatum medium spiny neuron is presented here. The model is an implementation then adaptation of a previously described model (Mahon et al. 2002). The model has been adapted to include NMDA and AMPA receptor models that have been fit to dorsal lateral striatal neurons. The receptor models allow for excitation by other neuron models.", + "tags": [ + { + "id": 834, + "tag": "Deep brain stimulation" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 923, + "tag": "I Krp" + }, + { + "id": 1608, + "tag": "I Na, leak" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 868, + "tag": "I_Ks" + }, + { + "id": 908, + "tag": "Learning" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1845, + "tag": "ModelDB:150556" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:56:43.601002+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150556", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1228": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1228, + "name": "Striatal Output Neuron (Mahon, Deniau, Charpier, Delord 2000)", + "repository_type": "github", + "summary": "Striatal output neurons (SONs) integrate glutamatergic synaptic inputs originating from the cerebral cortex. In vivo electrophysiological data have shown that a prior depolarization of SONs induced a short-term (1 sec)increase in their membrane excitability, which facilitated the ability of corticostriatal synaptic potentials to induce firing. Here we propose, using a computational model of SONs, that the use-dependent, short-term increase in the responsiveness of SONs mainly results from the slow kinetics of a voltage-dependent, slowly inactivating potassium A-current. This mechanism confers on SONs a form of intrinsic short-term memory that optimizes the synaptic input\u00e2\u20ac\u201coutput relationship as a function of their past activation.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 923, + "tag": "I Krp" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 868, + "tag": "I_Ks" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1846, + "tag": "ModelDB:150621" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 799, + "tag": "Short-term Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:56:44.178259+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150621", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1229": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1229, + "name": "Revised opponent-channel model of auditory space cortical representation (Briley & Summerfield 2013)", + "repository_type": "github", + "summary": "This is the computational opponent-channel model used by Briley et al. (2013) to model electroencephalographic (EEG) responses from the auditory cortices of young, younger-old and older-old adults to abrupt shifts in sound-source location, and to predict each groups' psychophysical measures of spatial acuity.", + "tags": [ + { + "id": 849, + "tag": "Audition" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1847, + "tag": "ModelDB:150622" + }, + { + "id": 845, + "tag": "Sensory processing" + } + ], + "timestamp_created": "2024-01-12 09:56:44.730278+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150622", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1230": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1230, + "name": "Stochastic calcium mechanisms cause dendritic calcium spike variability (Anwar et al. 2013)", + "repository_type": "github", + "summary": "\" ...\r\nIn single Purkinje cells, spontaneous and synaptically evoked\r\ndendritic calcium bursts come in a variety of shapes with a variable number of\r\nspikes. \r\n\r\nThe mechanisms causing this variability have never been investigated thoroughly.\r\n\r\nIn this study, a detailed computational model employing novel simulation routines is\r\napplied to identify the roles that stochastic ion channels, spatial arrangements of ion\r\nchannels and stochastic intracellular calcium have towards producing calcium burst\r\nvariability.\r\n\u2026\r\nOur findings suggest that stochastic intracellular calcium\r\nmechanisms play a crucial role in dendritic calcium spike generation and are, therefore, an\r\nessential consideration in studies of neuronal excitability and plasticity.\"", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 1760, + "tag": "Ca pump" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 818, + "tag": "I p,q" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1848, + "tag": "ModelDB:150635" + }, + { + "id": 1849, + "tag": "STEPS (web link to model)" + } + ], + "timestamp_created": "2024-01-12 09:56:45.273983+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150635", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1231": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1231, + "name": "Auditory cortex layer IV network model (Beeman 2013)", + "repository_type": "github", + "summary": "\"... The primary objective of this modeling study was to determine the effects of axonal conduction velocity (often neglected, but significant), as well as synaptic time constants, on the ability of such a network to create and propagate cortical waves. ... The model is also being used to study the interaction between single and two-tone input and normal background activity, and the effects of synaptic depression from thalamic inputs. The simulation scripts have the additional purpose of serving as tutorial examples for the construction of cortical networks with GENESIS. The present model has fostered the development of the G-3 Python network analysis and visualization tools used in this study... It is my hope that this short tutorial and the example\r\nsimulation scripts can provide a head start for a graduate student or\r\npostdoc who is beginning a cortical modeling project.\r\n\"", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1850, + "tag": "ModelDB:150678" + }, + { + "id": 808, + "tag": "Tutorial/Teaching" + } + ], + "timestamp_created": "2024-01-12 09:56:45.876065+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150678", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1232": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1232, + "name": "Model of arrhythmias in a cardiac cells network (Casaleggio et al. 2014)", + "repository_type": "github", + "summary": "\" ... Here we explore the possible processes leading to the occasional onset and termination of the (usually) non-fatal arrhythmias widely observed in the heart.\r\n\r\nUsing a computational model of a two-dimensional network of cardiac cells, we tested the hypothesis that an ischemia alters the properties of the gap junctions inside the ischemic area.\r\n...\r\n In conclusion, our model strongly supports the hypothesis that non-fatal arrhythmias can develop from post-ischemic alteration of the electrical connectivity in a relatively small area of the cardiac cell network, and suggests experimentally testable predictions on their possible treatments.\"", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 1682, + "tag": "Conductance distributions" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1851, + "tag": "ModelDB:150691" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + } + ], + "timestamp_created": "2024-01-12 09:56:46.404434+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150691", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1233": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1233, + "name": "Crayfish hybrid experimental model (Chung et al. 2015)", + "repository_type": "github", + "summary": "The Crayfish hybrid experimental model is an AnimatLab v1 neuromechanical model of the crayfish thorax and 5th walking leg that provides a virtual periphery to the live crayfish central nervous system. Run in real time, levator and depressor muscles are excited by motor nerve discharges of the CNS. Up and down movement of the leg shortens and stretches a model stretch receptor that controls movement of a real stretch receptor that provides sensory feedback to the CNS. Real-time sensory feedback provided by the model increases the locomotor cycle frequency by three-fold.", + "tags": [ + { + "id": 1852, + "tag": "AnimatLab v1" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1853, + "tag": "ModelDB:150697" + } + ], + "timestamp_created": "2024-01-12 09:56:46.929899+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150697", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1234": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1234, + "name": "Crayfish hybrid simulation model (Bacque-Cazenave et al. 2014)", + "repository_type": "github", + "summary": "A neuromechanical model of the crayfish leg and thorax and the postural and locomotor circuitry built and run in AnimatLab v1. The model simulates experiments run with the BCI preparation model in which the model was linked in real time to the in vivo crayfish thoracic nerve cord. The model shows that current understanding of the neural circuitry can account for the increase in locomotor frequency when the sensori-motor feedback loop is intact.", + "tags": [ + { + "id": 1852, + "tag": "AnimatLab v1" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1854, + "tag": "ModelDB:150698" + }, + { + "id": 1855, + "tag": "Posture and locomotion" + } + ], + "timestamp_created": "2024-01-12 09:56:47.469520+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150698", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1235": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1235, + "name": "Computational modelling of channelrhodopsin-2 photocurrent characteristics (Stefanescu et al. 2013)", + "repository_type": "github", + "summary": "The codes are directly related with the results presented in the manuscript; in brief, it is a computational investigation on the effects of optogenetic actuation on excitatory and inhibitory neurons when 3- and 4- state model is used to implement the ChR2 kinetics. Different parameters of optostimulation are investigated and the results compared with experimental data previously published by other research groups.", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1856, + "tag": "ModelDB:150804" + } + ], + "timestamp_created": "2024-01-12 09:56:48.010517+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150804", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1236": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1236, + "name": "Dynamic cortical interlaminar interactions (Carracedo et al. 2013)", + "repository_type": "github", + "summary": "\"... Here we demonstrate the mechanism underlying a purely neocortical delta rhythm generator and show a remarkable laminar, cell subtype and local subcircuit delineation between delta\r\nand nested theta rhythms. We show that spike timing during delta-nested theta rhythms controls an iterative, reciprocal interaction between deep and superficial cortical layers resembling the unsupervised learning processes proposed for laminar neural networks by Hinton and colleagues ... and mimicking the alternating cortical dynamics of sensory and memory processing during wakefulness.\"\r\n", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 756, + "tag": "FORTRAN" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1857, + "tag": "ModelDB:150806" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 802, + "tag": "Sleep" + } + ], + "timestamp_created": "2024-01-12 09:56:48.573868+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150806", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1237": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1237, + "name": "Grid cells from place cells (Castro & Aguiar, 2014)", + "repository_type": "github", + "summary": "\" ...Here we present a novel model for the emergence of gridlike firing patterns that stands on two key hypotheses: (1) spatial information in GCs is provided from PC activity and (2) grid fields result from a combined synaptic plasticity mechanism involving inhibitory and excitatory neurons mediating the connections between PCs and GCs. ...\"", + "tags": [ + { + "id": 1539, + "tag": "Grid cell" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1858, + "tag": "ModelDB:150846" + }, + { + "id": 882, + "tag": "Place cell/field" + }, + { + "id": 836, + "tag": "Rate-coding model neurons" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2024-01-12 09:56:49.431782+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150846", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1238": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1238, + "name": "Calcium influx during striatal upstates (Evans et al. 2013)", + "repository_type": "github", + "summary": "\"...\r\nTo\r\ninvestigate the mechanisms that underlie the relationship between\r\ncalcium and AP timing, we have developed a realistic biophysical\r\nmodel of a medium spiny neuron (MSN).\r\n...\r\nUsing this model, we found that either the slow inactivation of\r\ndendritic sodium channels (NaSI) or the calcium inactivation of\r\nvoltage-gated calcium channels (CDI) can cause high calcium corresponding\r\nto early APs and lower calcium corresponding to later APs.\r\n\r\nWe found that only CDI can account for the experimental observation\r\nthat sensitivity to AP timing is dependent on NMDA receptors.\r\n\r\nAdditional simulations demonstrated a mechanism by which MSNs\r\ncan dynamically modulate their sensitivity to AP timing and show that\r\nsensitivity to specifically timed pre- and postsynaptic pairings (as in\r\nspike timing-dependent plasticity protocols) is altered by the timing of\r\nthe pairing within the upstate.\r\n\u2026\"", + "tags": [ + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 923, + "tag": "I Krp" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 925, + "tag": "I R" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1859, + "tag": "ModelDB:150912" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 885, + "tag": "STDP" + } + ], + "timestamp_created": "2024-01-12 09:56:50.073108+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/150912", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1239": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1239, + "name": "Effects of increasing CREB on storage and recall processes in a CA1 network (Bianchi et al. 2014)", + "repository_type": "github", + "summary": "Several recent results suggest that boosting the CREB pathway improves hippocampal-dependent memory in healthy rodents and restores this type of\r\nmemory in an AD mouse model. However, not much is known about how CREB-dependent neuronal alterations in synaptic strength, excitability and\r\nLTP can boost memory formation in the complex architecture of a neuronal network. Using a model of a CA1 microcircuit, we investigate whether\r\nhippocampal CA1 pyramidal neuron properties altered by increasing CREB activity may contribute to improve memory storage and recall. With a set of patterns presented to a network, we find that the pattern recall quality under AD-like conditions is significantly better when boosting CREB function with respect to control. The results are robust and consistent upon increasing the synaptic damage expected by AD progression, supporting the idea that the use of CREB-based therapies could provide a new approach\r\nto treat AD.", + "tags": [ + { + "id": 833, + "tag": "Aging/Alzheimer`s" + }, + { + "id": 1815, + "tag": "CREB" + }, + { + "id": 1760, + "tag": "Ca pump" + }, + { + "id": 951, + "tag": "Depolarization block" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 1607, + "tag": "I Cl, leak" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1860, + "tag": "ModelDB:151126" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 1455, + "tag": "Storage/recall" + } + ], + "timestamp_created": "2024-01-12 09:56:50.818713+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/151126", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1240": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1240, + "name": "Ih tunes oscillations in an In Silico CA3 model (Neymotin et al. 2013)", + "repository_type": "github", + "summary": "\" ... We investigated oscillatory control using a multiscale computer model of hippocampal CA3, where each cell class\r\n(pyramidal, basket, and oriens-lacunosum moleculare cells), contained type-appropriate isoforms of Ih.\r\n\r\nOur model\r\ndemonstrated that modulation of pyramidal and basket Ih allows tuning theta and gamma oscillation frequency and\r\namplitude. Pyramidal Ih also controlled cross-frequency coupling (CFC) and allowed shifting gamma generation towards\r\nparticular phases of the theta cycle, effected via Ih\u2019s ability to set pyramidal excitability. ...\"", + "tags": [ + { + "id": 1603, + "tag": "Brain Rhythms" + }, + { + "id": 1682, + "tag": "Conductance distributions" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1861, + "tag": "ModelDB:151282" + }, + { + "id": 944, + "tag": "Multiscale" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 89, + "tag": "Python" + } + ], + "timestamp_created": "2024-01-12 09:56:51.514926+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/151282", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1241": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1241, + "name": "Gating of steering signals through phasic modulation of reticulospinal neurons (Kozlov et al. 2014)", + "repository_type": "github", + "summary": "\" ... We use the lamprey as a model for investigating the role of this\r\nphasic modulation of the reticulospinal activity, because the\r\nbrainstem\u2013spinal cord networks are known down to the cellular level in\r\nthis phylogenetically oldest extant vertebrate.\r\n\r\nWe describe how the phasic modulation of reticulospinal activity from\r\nthe spinal CPG ensures reliable steering/turning commands without the\r\nneed for a very precise timing of on- or offset, by using a\r\nbiophysically detailed large-scale (19,600 model neurons and 646,800\r\nsynapses) computational model of the lamprey brainstem\u2013spinal cord\r\nnetwork.\r\n\r\nTo verify that the simulated neural network can control body\r\nmovements, including turning, the spinal activity is fed to a\r\nmechanical model of lamprey swimming.\r\n...\"", + "tags": [ + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 649, + "tag": "GENESIS" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 868, + "tag": "I_Ks" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1862, + "tag": "ModelDB:151338" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 863, + "tag": "Spike Frequency Adaptation" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + } + ], + "timestamp_created": "2024-01-12 09:56:52.079022+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/151338", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1242": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1242, + "name": "Excitatory synaptic interactions in pyramidal neuron dendrites (Behabadi et al. 2012)", + "repository_type": "github", + "summary": "\" ...\r\nWe hypothesized that if two excitatory pathways bias their synaptic projections towards proximal vs. distal ends of the basal branches, the very different local spike thresholds and attenuation factors for inputs near and far from the soma might provide the basis for a classical-contextual functional asymmetry. Supporting this possibility, we found both in compartmental models and electrophysiological recordings in brain slices that the responses of basal dendrites to spatially separated inputs are indeed strongly asymmetric.\r\n...\"", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1863, + "tag": "ModelDB:151404" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:56:52.650672+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/151404", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1243": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1243, + "name": "Recurrent discharge in a reduced model of cat spinal motoneuron (Balbi et al, 2013)", + "repository_type": "github", + "summary": "Following a distal stimulation of a motor fibre, only a fraction of spinal motoneurons are able to produce a re-excitation of the initial segment leading to an orthodromically conducted action potential, known as recurrent discharge. In order to show the reciprocal interplay of the axonal initial segment and the soma leading to recurrent discharge in detail, a reduced model of a cat spinal motoneuron was developed.", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1864, + "tag": "ModelDB:151443" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 1865, + "tag": "Recurrent Discharge" + } + ], + "timestamp_created": "2024-01-12 09:56:53.200754+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/151443", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1244": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1244, + "name": "Calcium response prediction in the striatal spines depending on input timing (Nakano et al. 2013)", + "repository_type": "github", + "summary": "We construct an electric compartment model of the striatal medium spiny neuron with a realistic morphology and predict the calcium responses in the synaptic spines with variable timings of the glutamatergic and dopaminergic inputs and the postsynaptic action potentials.\r\nThe model was validated by reproducing the responses to current inputs and could predict the electric and calcium responses to glutamatergic inputs and back-propagating action potential in the proximal and distal synaptic spines during up and down states.", + "tags": [ + { + "id": 1760, + "tag": "Ca pump" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 806, + "tag": "I A, slow" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 1636, + "tag": "I Ca,p" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 923, + "tag": "I Krp" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 1608, + "tag": "I Na, leak" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 924, + "tag": "I Q" + }, + { + "id": 925, + "tag": "I R" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1866, + "tag": "ModelDB:151458" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 892, + "tag": "Reinforcement Learning" + }, + { + "id": 939, + "tag": "Reward-modulated STDP" + }, + { + "id": 885, + "tag": "STDP" + } + ], + "timestamp_created": "2024-01-12 09:56:53.784845+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/151458", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1245": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1245, + "name": "DBS of a multi-compartment model of subthalamic nucleus projection neurons (Miocinovic et al. 2006)", + "repository_type": "github", + "summary": "We built a comprehensive computational model of subthalamic nucleus (STN) deep brain stimulation (DBS) in parkinsonian macaques to study the effects of stimulation in a controlled environment. The model consisted of three fundamental components: 1) a three-dimensional (3D) anatomical model of the macaque basal ganglia, 2) a finite element model of the DBS electrode and electric field transmitted to the tissue medium, and 3) multicompartment biophysical models of STN projection neurons, GPi fibers of passage, and internal capsule fibers of passage. Populations of neurons were positioned within the 3D anatomical model. Neurons were stimulated with electrode positions and stimulation parameters defined as clinically effective in two parkinsonian monkeys. The model predicted axonal activation of STN neurons and GPi fibers during STN DBS. Model predictions regarding the degree of GPi fiber activation matched well with experimental recordings in both monkeys.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 834, + "tag": "Deep brain stimulation" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 1608, + "tag": "I Na, leak" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1867, + "tag": "ModelDB:151460" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 884, + "tag": "Parkinson's" + } + ], + "timestamp_created": "2024-01-12 09:56:54.360456+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/151460", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1246": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1246, + "name": "Sympathetic Preganglionic Neurone (Briant et al. 2014)", + "repository_type": "github", + "summary": "A model of a sympathetic preganglionic neurone of muscle vasoconstrictor-type.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1868, + "tag": "ModelDB:151482" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 774, + "tag": "Parameter Fitting" + }, + { + "id": 941, + "tag": "Parameter sensitivity" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + } + ], + "timestamp_created": "2024-01-12 09:56:54.905297+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/151482", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1247": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1247, + "name": "Simulating ion channel noise in an auditory brainstem neuron model (Schmerl & McDonnell 2013)", + "repository_type": "github", + "summary": "\" ... Here we demonstrate that biophysical models of channel noise can give rise to two\r\nkinds of recently discovered stochastic facilitation effects in a Hodgkin-Huxley-like model of auditory brainstem\r\nneurons. The first, known as slope-based stochastic resonance (SBSR), enables phasic neurons to emit action\r\npotentials that can encode the slope of inputs that vary slowly relative to key time constants in the model. \r\n\r\nThe\r\nsecond, known as inverse stochastic resonance (ISR), occurs in tonically firing neurons when small levels of\r\nnoise inhibit tonic firing and replace it with burstlike dynamics. ...\"\r\nPreprint available at http://arxiv.org/abs/1311.2643", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 849, + "tag": "Audition" + }, + { + "id": 887, + "tag": "Bifurcation" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1869, + "tag": "ModelDB:151483" + }, + { + "id": 952, + "tag": "Noise Sensitivity" + } + ], + "timestamp_created": "2024-01-12 09:56:55.458084+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/151483", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1248": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 1248, + "name": "Voltage and light-sensitive Channelrhodopsin-2 model (ChR2) (Williams et al. 2013)", + "repository_type": "github", + "summary": "\" ... \r\nFocusing on one of the most widely used ChR2 mutants\r\n(H134R) with enhanced current, we collected a comprehensive\r\nexperimental data set of the response of this ion channel to different\r\nirradiances and voltages, and used these data to develop a model of\r\nChR2 with empirically-derived voltage- and irradiance- dependence,\r\nwhere parameters were fine-tuned via simulated annealing\r\noptimization.\r\n\r\nThis ChR2 model offers: 1) accurate inward rectification\r\nin the current-voltage response across irradiances; 2)\r\nempirically-derived voltage- and light-dependent kinetics (activation,\r\ndeactivation and recovery from inactivation); and 3) accurate\r\namplitude and morphology of the response across voltage and irradiance\r\nsettings. \r\n\r\nTemperature-scaling factors (Q10) were derived and model kinetics was\r\nadjusted to physiological temperatures.\r\n... \"", + "tags": [ + { + "id": 1870, + "tag": "Channelrhodopsin (ChR)" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 835, + "tag": "MATLAB (web link to model)" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1871, + "tag": "ModelDB:151549" + } + ], + "timestamp_created": "2024-01-12 09:56:55.969373+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/151549", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1249": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1249, + "name": "Tapered whiskers are required for active tactile sensation (Hires et al. 2013)", + "repository_type": "github", + "summary": "\" ... \r\nThe diverse shapes of facial whiskers reflect distinct ecological niches. Rodent whiskers\r\nare conical, often with a remarkably linear taper. \r\n\r\nHere we use theoretical and experimental methods\r\nto analyze interactions of mouse whiskers with objects. \r\n... \"\r\n\r\nThis is a quasi-static solution of the bending of an isolated whisker.\r\n\r\nFor Fig. 2, stable solution, use: theta=-0.174533 rad.\r\n\r\nUse \"Bndryval -> Show\" in XPPAUT.\r\n", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1872, + "tag": "ModelDB:151677" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2024-01-12 09:56:56.755679+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/151677", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1250": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1250, + "name": "3D model of the olfactory bulb (Migliore et al. 2014)", + "repository_type": "github", + "summary": "This entry contains a link to a full HD version of movie 1 and the NEURON code of the paper:\r\n\"Distributed organization of a brain microcircuit analysed by three-dimensional modeling: the olfactory bulb\" by M Migliore, F Cavarretta, ML Hines, and GM Shepherd.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 764, + "tag": "Bursting" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1873, + "tag": "ModelDB:151681" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 792, + "tag": "Pattern Recognition" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + }, + { + "id": 789, + "tag": "Synchronization" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 886, + "tag": "Unsupervised Learning" + } + ], + "timestamp_created": "2024-01-12 09:56:57.293651+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/151681", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1251": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1251, + "name": "Current Dipole in Laminar Neocortex (Lee et al. 2013)", + "repository_type": "github", + "summary": "Laminar neocortical model in NEURON/Python, adapted from Jones et al 2009.\r\n\r\nhttps://bitbucket.org/jonescompneurolab/corticaldipole", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 1874, + "tag": "Current Dipole" + }, + { + "id": 1875, + "tag": "Gamma oscillations" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 932, + "tag": "Magnetoencephalography" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1876, + "tag": "ModelDB:151685" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 819, + "tag": "NEURON (web link to model)" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 832, + "tag": "Python (web link to model)" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + }, + { + "id": 851, + "tag": "Touch" + } + ], + "timestamp_created": "2024-01-12 09:56:57.868273+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/151685", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1252": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1252, + "name": "Stochastic model of the olfactory cilium transduction and adaptation (Antunes et al 2014)", + "repository_type": "github", + "summary": "\" ... In this work, we have combined stochastic computational modeling and\r\n a systematic pharmacological study of different signaling pathways to\r\n investigate their impact during short-term adaptation (STA).\r\n\r\n...\r\nThese results suggest that G-coupled\r\n receptors (GPCRs) cycling is involved with the occurrence of STA. To\r\n gain insights on the dynamical aspects of this process, we developed\r\n a stochastic computational model. The model consists of the olfactory\r\n transduction currents mediated by the cyclic nucleotide gated (CNG)\r\n channels and calcium ion (Ca2+)-activated chloride (CAC) channels,\r\n and the dynamics of their respective ligands, cAMP and Ca2+, and it\r\n simulates the EOG (electroolfactogram)\r\nresults obtained under different experimental\r\n conditions through changes in the amplitude and duration of cAMP and\r\n Ca2+ response, two second messengers implicated with STA\r\n occurrence. The model reproduced the experimental data for each\r\n pharmacological treatment and provided a mechanistic explanation for\r\n the action of GPCR cycling in the levels of second messengers\r\n modulating the levels of STA. All together, these experimental and\r\n theoretical results indicate the existence of a mechanism of\r\n regulation of STA by signaling pathways that control GPCR cycling and\r\n tune the levels of second messengers in OSNs, and not only by CNG\r\n channel desensitization as previously thought. \"", + "tags": [ + { + "id": 1877, + "tag": "COPASI" + }, + { + "id": 765, + "tag": "Calcium dynamics" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1878, + "tag": "ModelDB:151686" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 827, + "tag": "Signaling pathways" + } + ], + "timestamp_created": "2024-01-12 09:56:58.434102+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/151686", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1253": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1253, + "name": "Statistics of symmetry measure for networks of neurons (Esposito et al. 2014)", + "repository_type": "github", + "summary": "The code reproduces Figures 1, 2, 3A and 3C from Esposito et al \"Measuring symmetry, asymmetry and randomness in neural networks\". It provides the statistics of the symmetry measure defined in the paper for networks of neurons with random connections drawn from uniform and gaussian distributions.", + "tags": [ + { + "id": 935, + "tag": "Connectivity matrix" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1879, + "tag": "ModelDB:151692" + } + ], + "timestamp_created": "2024-01-12 09:56:59.442233+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/151692", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1254": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1254, + "name": "CA1 pyramidal neurons: effect of external electric field from power lines (Cavarretta et al. 2014)", + "repository_type": "github", + "summary": "The paper discusses the effects induced by an electric field at power lines frequency.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 810, + "tag": "Extracellular Fields" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1880, + "tag": "ModelDB:151731" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2024-01-12 09:57:00.015664+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/151731", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1255": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1255, + "name": "Model of repetitive firing in Grueneberg ganglion olfactory neurons (Liu et al., 2012)", + "repository_type": "github", + "summary": "This model is constructed based on properties of Na+ and K+ currents observed in whole-cell patch clamp recordings of mouse Grueneberg ganglion neurons in acute slices. Two distinct Na+ conductances representing the TTX-sensitive and TTX-resistant currents and one delayed rectifier K+ currrent are included. By modulating the maximal conductances of Na+ currents, one can reproduce the regular, phasic, and sporadic patterns of repetitive firing found in the patch clamp experiments.", + "tags": [ + { + "id": 801, + "tag": "Action Potential Initiation" + }, + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1881, + "tag": "ModelDB:151817" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 74, + "tag": "Olfaction" + }, + { + "id": 775, + "tag": "Rebound firing" + }, + { + "id": 1865, + "tag": "Recurrent Discharge" + }, + { + "id": 793, + "tag": "Temporal Pattern Generation" + } + ], + "timestamp_created": "2024-01-12 09:57:00.620889+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/151817", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1256": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1256, + "name": "Ionic mechanisms of dendritic spikes (Almog and Korngreen 2014)", + "repository_type": "github", + "summary": "We used a combined experimental and numerical parameter peeling procedure was implemented to optimize a detailed ionic mechanism for the generation and propagation of dendritic spikes in neocortical L5 pyramidal neurons. \r\n\r\nRun the cc_run.hoc to get a demo for dendritic calcium spike generated by coincidence of a back-propagating AP and distal synaptic input.", + "tags": [ + { + "id": 1760, + "tag": "Ca pump" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 1882, + "tag": "ModelDB:151825" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2024-01-12 09:57:01.196251+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/151825", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "1257": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 1257, + "name": "Electrodiffusive astrocytic and extracellular ion concentration dynamics model (Halnes et al. 2013)", + "repository_type": "github", + "summary": "An electrodiffusive formalism was developed for computing the dynamics of the membrane potential\r\nand ion concentrations in the intra- and extracellular space in a one-dimensional geometry (cable). This (general) formalism was implemented in a model of astrocytes exchanging K+, Na+ and Cl- ions with the extracellular space (ECS). \r\nA limited region (0< x