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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>BigNeuro NIPS Workshop</title>
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<h1 class="header-navbar-logo">BigNeuro NIPS Workshop</h1>
<div class="header-navbar-hamburger"></div>
<ul class="header-navbar-items">
<li class="header-navbar-item"><a href="#about" class="header-navbar-link">About</a></li>
<li class="header-navbar-item"><a href="#faq" class="header-navbar-link">FAQ</a></li>
<li class="header-navbar-item"><a href="#schedule" class="header-navbar-link">Schedule</a></li>
<li class="header-navbar-item"><a href="#posters" class="header-navbar-link">Posters</a></li>
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<div class="header-text">
<h2 class="header-text-headline">BigNeuro Workshop @ NIPS
<!-- <span class="beating-heart fa fa-heart"></span> -->
</h2>
<h4 class="header-text-byline">Making sense of big neural data<strong></strong></h4>
<h4 class="header-text-byline">Where: Long Beach Convention Center, Long Beach, CA<strong></strong></h4>
<h4 class="header-text-byline">When: <strong>Saturday Dec. 9th, 2017</strong><br>
<h4 class="header-text-byline">Submission Deadline: <strong>Tuesday Oct. 31st, 2017</strong>
<h4 class="header-text-byline">Notification of Selection: <strong>Friday Nov. 10th, 2017</strong>
</div>
</div>
<div id="about" class="about">
<div class="container">
<div class="row">
<div class="col-lg-12 text-center">
<h1 class="descr-title info-heading">BigNeuro 2017</h1>
<p class="lead info-para">
Datasets in neuroscience are increasing in size at alarming rates relative to our ability to analyze them.
This workshop aims at discussing new frameworks for processing and making sense of large neural datasets. The morning session will focus on approaches for processing large neuroscience datasets. Examples include: distributed + high-performance computing, GPU and other hardware accelerations, spatial databases,
and other compression schemes used for large neuroimaging datasets, online machine learning approaches for handling large data sizes, randomization and stochastic optimization. The afternoon session will focus on abstractions for modelling large neuroscience datasets. Examples include graphs, graphical models, manifolds, mixture models, latent variable models, spatial models, and factor learning.
</p>
</div>
</div>
</div>
</div>
<div id="faq" class="faq">
<h2 class="heading-faq">FAQ</h2>
<p class="faq-additional-questions"><strong>Have a question</strong> that isn't addressed below? Email us at <a href="mailto:[email protected]">[email protected]</a></p>
<div class="faq-column">
<div class="faq-box">
<h3>What is the format?</h3>
<p> The morning will consist of two sessions focused on approaches for processing large neuroscience datasets.
There will be three or four invited talks and one submitted talk per session, as well as time for an open panel discussion.</p>
<p> This format will be repeated in the afternoon, alongside a poster session and spotlight, as well as an open discussion at the end of the day.</p>
</div>
<div class="faq-box">
<h3>Who can participate?</h3>
<p>We welcome participants from the machine learning community, data scientists, and neuroscientists alike!</p>
</div>
<!-- <div class="faq-box">
<h3>What can I win?</h3>
<p>It's not about winning - it's about learning, having fun, and starting the renaissance of medical innovation! In addition, winners will be chosen by a judging panel based on criteria such as technical difficulty, creativity, and impact. Many sponsors will also give out cool prizes for hacks that excel in specific categories.</p>
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<div class="faq-column">
<!-- <div class="faq-box">
<h3>What is a Medical Hackathon?</h3>
<p>Drawing on the popular concept of hackathons being held around the world, we are challenging innovators to design solutions to the most pressing medical issues of our day within 36 hours. MedHackers will work (and play) in interdisciplinary teams over the weekend, utilizing the unique skillset of each team member to build the best solution to a critical medical problem. Along the way, hackers will eat together, code together, learn together, and design together before presenting their finished product (together) to a panel of judges.</p>
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<div class="faq-box">
<h3>But I know nothing about neuroscience?!</h3>
<p>If you don't know about neuroscience, don’t worry - we will introduce the basics behind some promising big neuro datasets and their associated computational challenges! This will set the stage for the afternoon talks and discussion, where we can talk about how machine learning can aid in analyzing these datasets. </p>
</div>
<div class="faq-box">
<h3>When is BigNeuro?</h3>
<p>
The workshop is part of the NIPS workshop program, held at the Long Beach Convention Center, in Long Beach, CA. The event will take place from 8:30-5:30 on Saturday Dec 9th, 2017.
<!-- Hack@NeuroData 2015 will take place from Friday, October 2nd to Sunday, October 4th. -->
</p>
</div>
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<h3>How will I get there?</h3>
<p>Hack@NeuroData is able to offer a limited number of travel grants for participants coming from outside the Baltimore metro area. If you are in need of assistance in attending Hack@NeuroData, and live outside the Baltimore area, please send an email to <a href="mailto:travel@[email protected]">travel@[email protected]</a>. </p>
<p>For participants coming to the Homewood Campus from other Johns Hopkins Institutions, the JHMI shuttle is recommended. The JHMI Shuttle schedule is available <a href="http://ts.jhu.edu/Shuttles/Shuttle_Schedules/Homewood_JHMI_Shuttle_Schedule.pdf">here</a>.</p>
<p>For those traveling to Hack@NeuroData by train (Amtrak and MARC Penn Line) via Baltimore Penn Station, the JHMI shuttle stops at Penn Station and heads north to the Homewood Campus (its northernmost stop). Taxis are also available at Penn Station for the 10 minute drive to the Homewood Campus, and should cost approximately $7.</p>
</div> -->
<div class="faq-box">
<h3>What is the basic schedule?</h3>
<p>Both the morning and afternoon will be broken up into two sessions consisting of talks and a round-table discussion of the speakers. The afternoon will also consist of poster presentations.</p>
</div>
</div>
</div>
<!-- <p class="text-center">*All events take place in the Bloomberg Center for Physics and Astronomy and are subject to change</p> -->
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<div id="submissions" class="about">
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<h1 class="descr-title info-heading">Submission Deadline: Tuesday Oct. 31st, 2017
<p class="lead info-para">Submit your contribution for a talk or poster <a href="https://docs.google.com/forms/d/1YQCYT51jm2t3cZhVJn7qEdU5O2QxiY_8-E4KOujDa4M/viewform?edit_requested=true">here</a>!<br>
The submission deadline is October 31st, 2017, and the decisions will be announced on November 10th, 2017. To be considered for a talk, please include a URL to a pre-print of an unpublished manuscript on arXiv (or related preprint service, such as biorRxiv). To be considered for a poster, please attach a PDF abstract of your proposed topic. Please attach only one of these documents in a single submission.
</p>
</div>
</div>
</div>
<div id="schedule" class="schedule">
<div class="container">
<div class="row">
<h2 class="section-apply-heading text-center">Schedule</h2>
<div class="col-lg-8 col-lg-offset-2">
<p class="lead info-para text-center">Welcome & Introductory Remarks</p>
<table class="table">
<th width="100px">Time</th>
<th width="500px">Presentation</th>
<th>Location</th>
<tr>
<td>8:45-9:00</td>
<td>Eva Dyer, Georgia Institute of Technology</td>
<td>Room 204</td>
</table>
<p class="lead info-para text-center">Deep Learning & Neuro Session</p>
<table class="table">
<th width="100px">Time</th>
<th width="500px">Presentation</th>
<th>Location</th>
<tr>
<td>9:00-9:35</td>
<td>Jim DiCarlo, Massachusetts Institute of Technology,<br /> “Can brain data be used to reverse engineer the algorithms of human perception?”</td>
<td>Room 204</td>
</tr>
<tr>
<td>9:35-9:55</td>
<td>Timothy Lillicrap, Google DeepMind, <br /> “Backpropagation and deep learning in the brain”</td>
<td>Room 204</td>
</tr>
<tr>
<td>9:55-10:15</td>
<td>Viren Jain, Google, <br /> “Algorithms, tools, and progress in connectomic reconstruction of neural circuits”</td>
<td>Room 204</td>
</tr>
<tr><td>10:15-10:35</td>
<td>Bing Brunton, University of Washington, <br /> “Multimodal deep learning for natural human neural recordings and video”</td>
<td>Room 204</td>
</tr>
<tr>
<td>10:35-11:00</td>
<td>Coffee Break</td>
<td>Room 204</td>
</tr>
<tr><td>11:00-11:35</td>
<td>Yoshua Bengio, University of Montreal, <br /> "More Steps towards Biologically Plausible Backprop"</td>
<td>Room 204</td>
</tr>
</table>
<p class="lead info-para text-center">Panel Discussion <br /> <font size="2">Moderator: Will Gray Roncal</font></p>
<table class="table">
<th width="100px">Time</th>
<th width="500px">Presentation</th>
<th>Location</th>
<tr>
<td>11:40-12:20</td>
<td>"What neural systems can teach us about building better machine learning algorithms?" <br /> Panel: Bing Brunton (U Washington), Jim DiCarlo (MIT), Timothy Lillicrap (DeepMind), Viren Jain (Google), Nathan Kutz (U Washington) </td>
<td>Room 204</td>
</tr>
<tr>
<td>12:20-13:40</td>
<td>Lunch Break</td>
<td>Room 204</td>
</tr>
</table>
<p class="lead info-para text-center">Dimensionality Reduction and Adaptive Experiments Session</p>
<table class="table">
<th width="100px">Time</th>
<th width="500px">Presentation</th>
<th>Location</th>
<tr>
<td>13:40-14:15</td>
<td>Nathan Kutz, University of Washington, <br />“Discovery of governing equations and biological principles from spatio-temporal time-series recordings”</td>
<td>Room 204</td>
</tr>
<tr><td>14:15-14:35</td>
<td>Eftychios Pnevmatikakis, Flatiron Institute, Simons Foundation, <br /> “Large scale calcium imaging data analysis for the 99%”</td>
<td>Room 204</td>
</tr>
<tr><td>14:35-14:55</td>
<td>Gael Varoquaux, INRIA <br /> “Machine learning for cognitive mapping”</td>
<td>Room 204</td>
</tr>
</table>
<p class="lead info-para text-center">Contributed Talks / Poster Spotlights</p>
<table class="table">
<th width="100px">Time</th>
<th width="500px">Presentation</th>
<th>Location</th>
<tr>
<td>14:55-15:15</td>
<td>Pierre Bellec / Christian Dansereau, CRIUGM, <br /> "Dealing with clinical heterogeneity in the discovery of new biomarkers of brain disorders"</td>
<td>Room 204</td>
</tr>
<tr>
<td>15:15-15:25</td>
<td>David Rolnick, MIT, <br /> “Morphological error detection for connectomics”</td>
<td>Room 204</td>
</tr>
<tr>
<td>15:25-15:35</td>
<td>Poster Spotlights: Shariq Iqbal, Milad Makkie, Sang-Yun Oh</td>
<td>Room 204</td>
</tr>
</table>
<p class="lead info-para text-center">Poster Session</p>
<table class="table">
<th width="100px">Time</th>
<th width="500px">Presentation</th>
<th>Location</th>
<tr>
<td>15:45-16:30</td>
<td>Posters</td>
<td>Room 204</td>
</tr>
<tr>
<td>15:15-15:25</td>
<td>Coffee Break</td>
<td>Room 204</td>
</tr>
</table>
<p class="lead info-para text-center">Macroscale Data/Functional Connectivity Session</p>
<table class="table">
<th width="100px">Time</th>
<th width="500px">Presentation</th>
<th>Location</th>
<tr>
<td>16:30-17:05</td>
<td>Bin Yu, UC Berkeley, <br /> “Deep nets meet real neurons: pattern selectivity of V4 through transfer learning and stability analysis”</td>
<td>Room 204</td>
</tr>
<tr>
<td>17:05-17:25</td>
<td>Vince Calhoun, Mind Research Network <br /> “Mapping brain structure and function with deep learning”</td>
<td>Room 204</td>
</tr>
</table>
<p class="lead info-para text-center">Big Data Session <br /> <font size="2">Moderator: Will Gray Roncal</font></p>
<table class="table">
<th width="100px">Time</th>
<th width="500px">Presentation</th>
<th>Location</th>
<tr>
<td>17:25-17:45</td>
<td>Nathan Drenkow, JHU Applied Physics Lab, <br /> “bossDB: A Petascale Database for Large-Scale Neuroscience”</td>
<td>Room 204</td>
</tr>
<tr>
<td>17:45-18:05</td>
<td> Kristin Branson, Janelia Farm, <br /> "Machine vision and learning for extracting a mechanistic understanding of neural computation"</td>
<td>Room 204</td>
</tr>
<tr>
<td>18:05-18:30</td>
<td> Discussion</td>
<td>Room 204</td>
</tr>
</table>
<!-- -->
</div>
</div>
</div>
</div>
<div id="posters" class="posters">
<div class="container">
<div class="row">
<h2 class="section-apply-heading text-center">Posters</h2>
<div class="col-lg-8 col-lg-offset-2">
<li> Ming Bo Cai*, Stephanie S.Y. Chan, Yael Niv: <em>Using a generative adversarial network to explain fMRI data </em></p>
<li> Guillem Cucurull*, Konrad Wagstyl, Arantxa Casanova, Petar Velickovic, Estrid Jakobsen, Adriana Romero, Alan Evans, Yoshua Bengio: <em>Graph Convolutional Neural Networks for Cortical Mesh Segmentation </em></li></p>
<li>Brandon Duderstadt*, Jaewon Chung, Forrest Collman, Joshua Vogelstein: <em>NOMADS: Neurodata’s Opensource Method for Automatic Detection of Synapses</em></li></p>
<li> Alex Fedorov, Eswar Damaraju, Vince Calhoun*, Sergey Plis: <em>An (almost) instant brain atlas segmentation for large-scale studies</em></li></p>
<li>Andrea Giovannucci*, Johannes Friedrich, Matthew Kaufman, Anne Churchland, Dmitri Chklovskii, Liam Paninski, Eftychios A. Pnevmatikakis: <em> OnACID: Online analysis on calcium imaging data in real time</em></li></p>
<li> Shariq Iqbal*, John Pearson: <em>A Goal-Based Movement Model for Continuous Multi-Agent Tasks</em> </li></p>
<li> Christopher Kim*, Carson Chow: <em>Learning arbitrary patterns in recurrent spiking networks </em></li></p>
<li>Milad Makkie*, Heng Huang, Yu Zhao, Athanasios V. Vasilakos, Tianming Liu: <em>Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI Big Data Analytics</em></li></p>
<li>Matthias Minderer*, Kristen Brown, Christopher D. Harvey: <em>Unsupervised mapping of visual-motor representations in mouse cortex during navigation</em></li></p>
<li> Daniel Moyer*, Greg ver Steeg, Joshua Faskowitz, Paul M. Thompson: <em>How many tracts should we sample?</em></li></p>
<li>Rahul Nadkarni*, Nicholas J. Foti, Emily B. Fox: <em> Learning Dynamic Functional Connectivity Networks from Infant Magnetoencephalography Data</em></li></p>
<li> Sang-Yun Oh*, Alnur Ali*, Penporn Koanantakool, Ariful Azad, Aydin Buluc, Dmitriy Morozov, Leonid Oliker, Katherine Yelick: <em> Whole-brain Functional Connectivity Mapping and Region Segmentation from Distributed Estimation of Voxel-level Sparse Precision Matrix</em></li></p>
<li>Ravi Tejwani*, Adam Liska, Hongyuan You, Jenna Reinen, Payel Das: <em> Autism Classification Using Brain Functional Connectivity Dynamics and Machine Learning</em></li></p>
<li>Andrew Warrington*, Frank Wood: <em> Updating the VESICLE-CNN Synapse Detector</em></li></p>
<li>Hongyuan You*, Adam Liska, Vinay Shahidhar, Payel Das: <em> The Underlying Brain Functional Landscape of an Individual as revealed by Graph Embedding</em></li></p>
<li>Michael Shvartsman*, Narayanan Sundaram, Mikio Aoi, Adam Charles, Theodore Wilke, Jonathan D. Cohen: <em> Matrix-normal models for fMRI analysis</em></li></p>
</div>
</div>
</div>
<div class="page-footer">
<div class="container">
<div class="row" id="footer-text">
<div class="col-lg-10 col-lg-offset-1 text-center">
<h4><strong>BigNeuro Workshop</strong></h4>
<p>Want to get in touch? Contact <a href="mailto:[email protected]">[email protected]</a>!</p>
<p>Organized by <a href="http://dyerlab.gatech.edu">Eva Dyer</a>,
<a href="http://gkiar.me/" target="_blank">Gregory Kiar</a>,
<a href="http://willgray.weebly.com/" target="_blank">Will Gray Roncal</a>,
<a href="https://www.mcgill.ca/neuro/research/researchers/evans">Alan C. Evans</a>,
<a href="http://bigneuro.com/" target="_blank">Konrad P. Kording</a>,
<a href="http://jovo.me/" target="_blank">Joshua T. Vogelstein</a>.
</p>
</p>
<p>Copyright © Eva Dyer 2017</p>
</div>
</div>
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$('html,body').animate({scrollTop: 0}, 500);
$('html').removeClass('nav-open');
});
$('.header-navbar-hamburger').click(function() {
$('html').toggleClass('nav-open');
});
$('body').click(function(e) {
if($('html').hasClass('nav-open') && $(e.target).parents('.header-navbar').length === 0) {
$('html').removeClass('nav-open');
}
});
if(isMobile()) {
$('.header-navbar').addClass('header-navbar-scrolled');
}
});
</script>
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$(function() {
var ekg = document.getElementById('ekg'),
path = ekg.getElementsByTagName('path')[0],
totalLength = path.getTotalLength()
thumper = document.getElementById('thumper'),
style = document.createElement('style'),
resizeTimeout = null;
document.getElementsByTagName('head')[0].appendChild(style);
var stylesheet = style.sheet;
function resizeEkg() {
resizeTimeout = null;
var width = document.body.clientWidth + 200;
ekg.setAttribute('width', width);
var viewBox = ekg.getAttribute('viewBox').split(' ');
viewBox[2] = width;
ekg.setAttribute('viewBox', viewBox.join(' '));
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path.setAttribute('d', 'M0 39h' + side + 'l2.798 -5.740l5.525 10.763l12.629 -48.956l13.705 71.031l12.315 -35.070l4.691 14.717l5.727 -13.101l3.655 5.925h' + side);
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resizeEkg();
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$(window).on('resize', function() {
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clearTimeout(resizeTimeout);
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