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mainProt_single_channel.m
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%%%TODO'S!!!!
%{
Base: disp number of target hits (without b2base)
BMI: disp number of delivered holo stims
streamline analysis of baseline, pretrain, bmi data, plots in same format
rewards per minute plot
anticipatory licking?
Baseline CA vs stim time
Close-loop E2 not being high-ish
analyze baseline E2
what window around stim triggers reward
%
%For Experiment:
%A protocol for determing reliably stimmed cells, and determing power and
duration of stim
%}
%% Main protocol for the experiment
%--------------------------------------------------------------------------
%BEFORE ANIMAL IN BOX:
%DO:
% Hook up BNCs:
% 1) BMI solenoid, AI5
% 2) Monaco Trig, AI6
% 3) Frame Trig, AI7
% 4) Holo Trig PFI1
%
% Power Arduino:
% (Power supply needed to power solenoid, can't control solenoid on USB power)
% Voltage Recording: All Inputs Active (check 6+7)
%
%Fill syringe with sucrose cuz of gravity
%Run 'main_test_190923.m'
% check nidaq pulses are received in voltage_rec: 1) frame trigger, 2) trig photostim (monaco), 3)
% trig reward (bmi solenoid / arduino)
% calibrate solenoid opening time
%
%load pyctrl expt for the mouse
%In load cell: remove offset. Collect sensor baseline data
% convert to csv
% put the file in pre folder
%
%Put mouse in
%put gel from headbar to ear
%adjust spout so mouse can lick
%put objective
%--------------------------------------------------------------------------
%% CLEAR ALL
clear all
%% DEFINE PATHS
%--------------------------------------------------------------------------
%DO:
%Input 'folder', as directory to write to.
%--------------------------------------------------------------------------
fb_bool = 0;
cd G:\VivekNuria\Code\HoloBMI
[task_settings] = define_BMI_task_settings();
%DEFINE PATH_DATA:
%
%LOAD PATHS:
load_path = define_and_load_bmi_paths()
%SAVE PATHS:
home_dir = 'G:\VivekNuria\Code\HoloBMI'
cd(home_dir)
env_dir = 'G:\VivekNuria\utils'
% define Animal, day and folder where to save
animal = 'NVI22'; day = 'D30';
% folder = 'E:\holobmi_E\191214';
folder = 'H:\holobmi_H\191215';
savePath = fullfile(folder, animal, day);
if ~exist(savePath, 'dir')
mkdir(savePath);
end
path_data.load_path = load_path;
path_data.home_dir = home_dir; %home_dir
path_data.env_dir = env_dir; %contains env files for prairie
path_data.savePath = savePath;
path_data.im = fullfile(savePath, 'im'); %directory for imaging data
if ~exist(path_data.im, 'dir')
mkdir(path_data.im);
end
connectivity_bool = 0;
%%
%DO: enter zoom (either 1.5 or 2)
zoom = 2;
posz = 0;
pl = actxserver('PrairieLink.Application');
pl.Connect(); disp('Connecting to prairie...');
% Prairie variables
px = pl.PixelsPerLine();
py = pl.LinesPerFrame();
micronsPerPixel.x = str2double(pl.GetState('micronsPerPixel', 'XAxis'));
micronsPerPixel.y = str2double(pl.GetState('micronsPerPixel', 'YAxis'));
pl.Disconnect();
disp('Disconnected from prairie');
% px = 512;
% py = 512;
frameRate = 29.989;
chan_data = struct(...
'label', 'g', ...
'chan_idx', 2); %in RGB, G is 2nd
num_chan = length(chan_data);
onacid_bool = false;
%z: 162.90
%%
%{
%--------------------------------------------------------------------------
%DO:
%SUMMARY:
Option 1:
Turn up imaging power, turn on average of 32 frames, save the image
Option2:
%Take 1000 frame video, calc mean, stddev images, use for
%selecting ROI's.
%
%find FOV
%-disable the motor control!!!!
%Option 1 instructiosn: TODO
%Option 2 instructions:
%take 1000 frame video
%convert it using image-block ripping utility
%load the converted tif into imagej
% [mean, std dev image]: Image->Stack->ZProject, choose mean, choose std
% dev.
% Put the path into:
% im_summary_path
%TODO: make an acquisition for this.
%--------------------------------------------------------------------------
%}
%% Select + Scale summary image (to choose ROI)
%--------------------------------------------------------------------------
%DO:
%-Input 'im_summary_path'
%-Run 'scale_im_interactive' : choose percentiles for scaling image
%--------------------------------------------------------------------------
option1_bool = 0;
if option1_bool
pl = actxserver('PrairieLink.Application');
pl.Connect();
disp('Connecting to prairie')
pause(2);
im_summary = pl.GetImage_2(2, px, py);
pl.Disconnect();
else
im_summary_path = ...
fullfile('H:\holobmi_H\191215\NVI22\D30\im\roi_draw', 'AVG_roi_draw-000.tif');
% fullfile('E:\vivek\190822\NY35\D1_test', 'chan_mean.tif');
exist(im_summary_path)
im_summary = imread(im_summary_path);
end
%Scale the image, in order to help see ROIs better.
%If no modification to original image needed, just run code, in
% 'scale_im_interactive' set min_perc = 0, max_perc = 100
im_sc_struct = struct(...
'im', [], ...
'minmax_perc', [], ...
'minmax', [], ...
'min', [], ...
'min_perc', [], ...
'max', [], ...
'max_perc', []);
num_im_sc = 0;
[im_sc_struct, num_im_sc] = scale_im_interactive(im_summary, im_sc_struct, num_im_sc);
close all;
%% REMEMBER TO TURN OFF PHASE OFFSET
%% TURN OFF THE MANIPULATOR
%% TURN OFF AUTOSCALE
%%
%--------------------------------------------------------------------------
%DO:
%-Input index to 'im_sc_struct'
%--------------------------------------------------------------------------
%Can input a different index to choose as the Image for choosing ROI
%Defaults to the last image in 'im_sc_struct'
im_bg = im_sc_struct(end).im;
h = figure;
imagesc(im_bg);
axis square
colormap('gray');
title('selected background image for identifying ROI');
%PLOT_IMAGES data:
%'plot_images' contains a set of images so user can tell if ROI selection is
%appropriate.
plot_images = struct('im', [], 'label', '');
plot_images(1).im = im_summary;
plot_images(1).label = 'green mean';
plot_images(2).im = im_bg;
plot_images(2).label = 'scaled';
%% INIT ROI_DATA
%--------------------------------------------------------------------------
%DO:
%-Confirm parameters for automatically identifying ROI
%-Run this cell
%-if template matching sucks, can set 'auto_init' to 0
%--------------------------------------------------------------------------
auto_init = 1; %initializes roi_data using automatic cell detection:
% Parameters for auto cell detection:
% Folylowing were for zoom=2 on bruker soma:
% template_diam = 25; %diamter of difference of Gaussians in pixels
% thres = 0.5; %cell detection threshold as correlation coefficient
% cell_diam = 7; %CELL_DIAM is diameter used for dilation.
% finemode = 1; %imTemplateMatch will be used instead of normxcorr2. It will be slower.
% temmode = 0; % 0 is for full circle (soma) 1 is for donuts (membrane)
template_diam = 15; %diamter of difference of Gaussians in pixels
thres = 0.6; %cell detection threshold as correlation coefficient
cell_diam = 16; %CELL_DIAM is diameter used for dilation.
finemode = 1; %imTemplateMatch will be used instead of normxcorr2. It will be slower.
temmode = 1; % 0 is for full circle (soma) 1 is for donuts (membrane)
if auto_init
%FIND ROI AUTOMATICALLY
[mask_intermediate, ~] = imFindCellsTM (im_bg, template_diam, thres, cell_diam, finemode, temmode);
init_roi_mask = bwlabel(mask_intermediate);
findCenter (init_roi_mask, im_bg);
roi_data = label_mask2roi_data_single_channel(im_bg, init_roi_mask, chan_data);
else
roi_data = init_roi_data(im_bg, num_chan, chan_data);
end
% %%
% %Visualize:
% screen_size = get(0,'ScreenSize');
% h = figure('Position', [screen_size(3)/2 1 screen_size(3)/2 screen_size(4)]);
% hold on;
% imagesc(roi_data.im_roi); %colormap('gray');
% axis square;
% title('ROI footprint overlay in blue');
% % scatter(roi_data.x, roi_data.y, pi*roi_data.r.^2, 'r');
%
% %
% h = figure('Position', [screen_size(3)/2 1 screen_size(3)/2 screen_size(4)]);
% % hold on;
% imagesc(roi_data.roi_mask); %colormap('gray');
% axis square;
% title('ROI Mask');
% % scatter(roi_data.x, roi_data.y, pi*roi_data.r.^2, 'r');
%
% %TESTS
% % roi_data = label_mask2roi_data_single_channel(im_bg, init_roi_mask, chan_data);
% % [roi_ctr] = roi_bin_cell2center_radius(roi_data.roi_bin_cell);
%% Delete ROI if needed
%--------------------------------------------------------------------------
%DO:
%-If auto detected ROI suck, delete ROI
%--------------------------------------------------------------------------
close all;
%Delete ROI if needed:
disp('Deleting ROIs from image!');
[roi_data] = delete_roi_2chan(plot_images, roi_data);
close all;
%% Add ROI if needed
%--------------------------------------------------------------------------
%DO:
%-Run cell to manually draw additional ROI
%--------------------------------------------------------------------------
disp('Adding ROIs to image!');
[roi_data] = draw_roi_g_chan(plot_images, roi_data);
close all
%% SEE ROI if needed
see_roi_data = 1;
if see_roi_data
%HoloMask
holoMask = roi_data.roi_mask;
screen_size = get(0,'ScreenSize');
h = figure('Position', [screen_size(3)/2 1 screen_size(3)/2 screen_size(4)]);
imagesc(holoMask)
axis square;
title(['holoMask num roi: ' num2str(roi_data.num_rois)]);
%im_roi:
screen_size = get(0,'ScreenSize');
h = figure('Position', [screen_size(3)/2 1 screen_size(3)/2 screen_size(4)]);
% hold on;
imagesc(roi_data.im_roi); %colormap('gray');
axis square;
title(['ROI footprint overlay in blue. Num ROI: ' num2str(roi_data.num_rois)]);
end
%% Save roi_data
roi_data_file = fullfile(savePath, 'roi_data.mat');
roi_mask = roi_data.roi_mask;
save(roi_data_file, 'roi_mask', 'plot_images', 'im_sc_struct', 'roi_data');
% filetosave = fullfile(savePath, 'red.mat');
% load(filetosave)
% h = figure;
% imshow(holoMask)
%%
% roi_data_file = fullfile(savePath, 'roi_data.mat');
% load(roi_data_file)
%% prepare SEQUENTIAL HOLO STIM of individual neurons
%Load environment to prairie
% load environment
env_path = fullfile(env_dir, "Tseries_VivekNuria_holo_all.env")
exist(env_path)
pl = actxserver('PrairieLink.Application');
pl.Connect();
loadCommand = "-tsl " + env_path;
pl.SendScriptCommands(loadCommand);
pl.Disconnect()
%% %Create gpl, xml files for individual points
%DEFINE GPL FILE
spiral_size_conversion = 1/49;
%a coefficient needed to accurately load desired ROI size
%Empirically measured
init_markpoints = struct(...
'UncagingLaserPower', 0.4, ...
'Duration', 100, ...
'SpiralSize', 0.7, ...
'SpiralRevolutions', 10);
%Darcy recommends 5-10 spirals
markpoints_data = repmat(init_markpoints, [roi_data.num_rois 1]);
for roi_i = 1:roi_data.num_rois
spiral_size_um = 2*roi_data.r(roi_i)*micronsPerPixel.x;
spiral_size = spiral_size_conversion*spiral_size_um;
markpoints_data(roi_i).SpiralSize = spiral_size; %double(ceil(10*spiral_size)/10);
end
% creates holos
createGplFile_v2(savePath, markpoints_data, roi_data.x, roi_data.y, posz, roi_data.r, px, zoom)
%% XML: Sequential Single Cell Stim
xml_seq_path = fullfile(savePath, 'seq_single_stim.xml');
%{
Params Summary:
-num_sequences
-Initial Delay: time (ms) between each stimulation
-power
-numSpirals
-Repetitions
-Iter - number of iterations
-IterDelay - time beteen iterations
-InterPointDelay
-
%}
numberNeurons= roi_data.num_rois; %max(max(holoMask));
% numberNeurons=max(max(holoMask));
num_sequences = 1;
stim_sequence = repmat(1:numberNeurons, [1 num_sequences]);
power_conversion = 0.004; %0.2 -> 50, 0.4->100
seq_stim_params.UncagingLaser = "Monaco";
seq_stim_params.AllPointsAtOnce = "False"
seq_stim_params.Iter = 1; %how many times to go through and stim each cell.
seq_stim_params.IterDelay = 1000; %Time (ms) between iterations
%
InitialDelay = 2000; %(ms) time bw stim delivery
seq_stim_params.InitialDelayVector = InitialDelay*ones(1,numberNeurons);
%
power = 40;
power_converted = power*power_conversion;
seq_stim_params.PowerVector = power_converted*ones(1,numberNeurons);
%2
Duration = 30;
seq_stim_params.DurationVector = Duration*ones(1,numberNeurons);
%
numSpirals = 10;
seq_stim_params.SpiralVector = numSpirals*ones(1,numberNeurons);
%
Repetitions = 1;
seq_stim_params.RepetitionsVector = Repetitions*ones(1,numberNeurons);
%Darcy sometimes recommends increasing 'Repetitions' and decreasing
%Duration. This changes the distribution of the spirals in time over the
%cell.
%
InterPointDelay = 0.12;
seq_stim_params.InterPointDelayVector = InterPointDelay*ones(1,numberNeurons);
% seq_stim_params
createXmlFile_sequential_single_cell(xml_seq_path, seq_stim_params, stim_sequence);
% Update prairie view repetitions based on num neurons to stim
stim_time_per_neuron = InitialDelay/1000+InterPointDelay;
num_reps_seq_stim = ceil(numberNeurons*stim_time_per_neuron*frameRate);
len_seq_stim = numberNeurons*stim_time_per_neuron/60;
disp(['Number of Repetitions in PrairieView: ' num2str(num_reps_seq_stim)])
disp(['Stim time per neuron (s): ' num2str(stim_time_per_neuron)]);
disp(['Num neurons: ' num2str(numberNeurons)]);
disp(['Length (min): ' num2str(len_seq_stim)])
%--------------------------------------------------------------------------
%DO:
% upload .gpl in MarkPoints (Top half)
% upload .xml in MarkPoints (Bot half)
% update T-series repetitions in Prairie View with above number
% Make sure Voltage Recording has all channels enabled
% Make sure you turn on the laser power and pmt's
% TODO: automate uploading
%--------------------------------------------------------------------------
%% Run HOLO STIM to check stim-able neurons
%This stims one neuron at a time.
%--------------------------------------------------------------------------
%DO:
%1) Do live scan check
%--------------------------------------------------------------------------
clear s
expt_str = 'holostim_seq'; %previously 'holostim'
mask = roi_data.roi_mask;
expectedLengthExperiment = ceil(num_reps_seq_stim*1.5);
HoloAcqnvsPrairie_v2(path_data, expt_str, mask, expectedLengthExperiment)
% HoloAcqnvsPrairie(folder, animal, day, holoMask)
%TODO: make this closed loop, and wait for the neurons to be inactive
%before stimming them.
%{
%--------------------------------------------------------------------------
%DO:
%Image-block ripping utility: Convert the holostim acqn (2 files)
%Load holostim_seqX.mat
%Load voltage recording for plotting.
% Use 'Import Data' in matlab
% Import csv to matlab: output type is Numeric Matrix.
% Name it "voltageRec")
%--------------------------------------------------------------------------
%}
%%
%(Image-Block Ripping Utility) Convert holostim file with bruker converter
% load the VoltageRec to check the results of holoStim
min_duration = 40; %stims can't occur within this number of samples of voltageRec
plot_win = 1000;
plotHoloStimTimeLock(holoActivity, voltageRec, min_duration, plot_win)
%
%ToDo: allow us to select the idxs of neurons to plot
%ToDo: for plotting, do sliding window deltaf/
%ToDo: a version that just plots each individual neuron, we type 'Y' or 'N'
%to make it a candidate
%%
%--------------------------------------------------------------------------
%DO:
%Summary: choose good candidate stim neurons based on single stim.
%Enter E2_base
% (I often choose more than 4 neurons, manually stim the neurons.
% then re-run once you've chosen your 4.)
%--------------------------------------------------------------------------
% E2_candidate =unique([23 42 35 36 14 4 28 26 38]); %unique also sorts 47 38 19 34 9 45
% E2_candidate = 1:roi_data.num_rois%[8 28 15 22] ; %[]
E2_candidate = [66 64 34 30]
E1_base = [59 42 89 90];
% E2_base = E2_candidate
% E2_base = sort([21 36 127 196], 'ascend')
%% Holo stim of Ensemble neurons
% Make GPL (points), BOT (measure activity)
% -select markpoints_data for
close all
sel_idxs = E2_candidate; %unique(E2_candidate);
[sel_roi_data, sel_idxs] = select_roi_data(roi_data, sel_idxs);
sel_markpoints_data = markpoints_data(sel_idxs);
%--
%GPL for Stim Ensemble
% savePrairieFiles(savePath, pl, 'GPL_candidates_')
% creates holos
gpl_candidates_path = createGplFile_v2(savePath, sel_markpoints_data, sel_roi_data.x, sel_roi_data.y, posz, sel_roi_data.r, px, zoom, 'GPL_candidates_');
% createGplFile(savePath, StimMask, posz, px, 'ensemble_')
%BOT
bot_candidates_path = fullfile(savePath, 'BOT_candidates.cfg');
createBot_v2(bot_candidates_path, sel_roi_data.x, sel_roi_data.y, sel_roi_data.r)
% createBot(savePath, x(E2_base),y(E2_base))
%NOTE:
%If it can't be loaded in prairie because prairie says the file is in use
%by another program, it can be because matlab didn't release the file.
%Re run, saving to a different filenaem
%XML:
numberNeurons = length(E2_candidate);
ens_default_params.numberNeurons = numberNeurons;
%SPECIFY:
ens_default_params.PowerVector = 30*power_conversion*ones(1,numberNeurons);
ens_default_params.DurationVector = 20*ones(1,numberNeurons);
ens_default_params.RepetitionsVector = 1*ones(1,numberNeurons);
ens_default_params.SpiralVector = 10*ones(1,numberNeurons);
%Don't Change:
ens_default_params.InitialDelayVector = 0*ones(1,numberNeurons);
ens_default_params.UncagingLaser = "Monaco";
ens_default_params.AllPointsAtOnce = "False"
ens_default_params.Iter = 1; %how many times to go through and stim each cell.
ens_default_params.IterDelay = 0; %Time (ms) between iterations
InterPointDelay = 0.12;
ens_default_params.InterPointDelayVector = InterPointDelay*ones(1,numberNeurons);
xml_candidates_path = fullfile(savePath, 'XML_candidates.xml');
createXmlFile_basic(xml_candidates_path, ens_default_params);
pl = actxserver('PrairieLink.Application');
pl.Connect();
loadCommand = "-tsl " + fullfile(path_data.env_dir, "Tseries_VivekNuria_holo_4.env");
pl.SendScriptCommands(loadCommand);
pl.Disconnect();
%--------------------------------------------------------------------------
%DO:
%1) upload the GPL file
%2) upload XML file
%3) Click BOT in Image Window, load BOT.cfg, only display ROI of interest
%4) Run BOT, and adjust (duration, power, repetitions) of each neuron's stim
%5) Note down below the params for each neuron
%--------------------------------------------------------------------------
%%
%{
%DO:
%In the GUI Mark Points Series, remove bad neurons
% 1) from Points/Groups,
2) from Mark Point Series
3) from BOT
%For chosen neurons, Enter Stim Parameters you like in GUI.
EXPORT each of the above, with the following names:
%}
%%
%--------------------------------------------------------------------------
%DO:
%1) Choose E2_base, if different than E2_candidate
%2) Specify: PowerVector, DurationVector, RepetitionsVector, SpiralVector
% else:
gpl_path = fullfile(savePath, 'GPL_ens.gpl');
xml_path = fullfile(savePath, 'XML_ens.xml');
bot_path = fullfile(savePath, 'BOT_ens.cfg');
copyfile(gpl_candidates_path, gpl_path)
copyfile(bot_candidates_path, bot_path)
copyfile(xml_candidates_path, xml_path)
%--------------------------------------------------------------------------
% % E2_base = E2_base([2 11 9 15])
E2_base = E2_candidate;
% %GPL (define the points):
% sel_idxs = unique(E2_base);
% [stim_roi_data, stim_idxs] = select_roi_data(roi_data, sel_idxs);
% stim_markpoints_data = markpoints_data(stim_idxs);
% StimMask = stim_roi_data.roi_mask;
% numberNeurons = length(E2_base);
% createGplFile_v2(savePath, stim_markpoints_data, ...
% stim_roi_data.x, stim_roi_data.y, posz, stim_roi_data.r, px, 'GPL_ens_')
%
% %XML:
% ens_stim_params.numberNeurons = length(E2_base);
% %SPECIFY:
% ens_stim_params.PowerVector = [15 35]*power_conversion;
% ens_stim_params.DurationVector = [20 20];
% ens_stim_params.RepetitionsVector = 1*[1 1];
% ens_stim_params.SpiralVector = [10 10];
% %Don't Change:
% ens_stim_params.InitialDelayVector = 0*ones(1,numberNeurons);
% ens_stim_params.UncagingLaser = "Monaco";
% ens_stim_params.AllPointsAtOnce = "False"
% ens_stim_params.Iter = 1; %how many times to go through and stim each cell.
% ens_stim_params.IterDelay = 0; %Time (ms) between iterations
% InterPointDelay = 0.12;
% ens_stim_params.InterPointDelayVector = InterPointDelay*ones(1,numberNeurons);
% xml_ens_path = fullfile(savePath, 'XML_ens.xml');
% createXmlFile_basic(xml_ens_path, ens_stim_params);
%
% %BOT
% botPath = fullfile(savePath, 'BOT_ens.cfg');
% createBot_v2(botPath, stim_roi_data.x, stim_roi_data.y, stim_roi_data.r)
%% Baseline acquisition
%Note: loads the result of OnAcid / holoMask
%Do this after we confirm we can stim some cells
%--------------------------------------------------------------------------
%DO:
%Remove Red Channel from Image Window 1 (prairie view).
%0) (zero pmt+power) put water
% check FOV didn't move
close all
imshow(im_bg)
%1) start video
%2) start load cells
%3) run and start pyctrl
%4) Run following cell
%% --------------------------------------------------------------------------
if ~onacid_bool
AComp = 0;
else
load(roi_data_file);
end
% Baseline environment already removes MARKPOINTS and set the reps to 27000
holoMask = roi_data.roi_mask;
[base_mat_path, base_dat_path] = ...
BaselineAcqnvsPrairie(folder, animal, day, AComp, holoMask, task_settings);
% BaselineAcqnvsPrairie(folder, animal, day, AComp, holoMaskRedGreen, onacid_bool, frameRate);
% saves in [savePath, 'baselineActivity.dat'] the activity of all the
% neurons of the mask (Acomp+red)
% saves in baseOnline.mat the baseline activityim_bg
%--------------------------------------------------------------------------
%D0:
%0) Abort T-series (cuz of voltage recording)
%1) pyctrl stop
%2) load cells stop
%3) video stop
%4) Drag load cell data to folder
%5) Drag video to folder
%--------------------------------------------------------------------------
%% Selection of neurons
% plots neurons so we can select which ones we like the most
%Copy paste base_file path:
base_file = base_mat_path;
%Can manually enter a previous path:
% base_file = fullfile(savePath, 'BaselineOnline190526T113422.mat')
if onacid_bool
totalneurons = min(size(AComp,2), 20);
else
totalneurons = max(max(roi_data.num_rois));
CComp = [];
YrA = [];
end
load(base_file);
% totalneurons = 40;
plotNeuronsBaseline(baseActivity, CComp, YrA, totalneurons)
% plotNeuronsBaseline(baseActivity, CComp, YrA, 20)
%TODO:
%ToDo: for plotting, do sliding window deltaf/f
%%
% base_file = fullfile('H:\holobmi_H\191214\NVI20\D29',
%%
%--------------------------------------------------------------------------
%D0:
%1) Choose E1_base
%--------------------------------------------------------------------------
%
visual_rois = [2 12 4 8 ]
%%
%Manually enter and confirm the BMI neurons:
% E2_candidate = unique([9 15 23 29]); %unique also sorts
% 27 24 28 36]); %unique also sorts 9 6 4 37
% E1_base = sort([10 21 56 6], 'ascend') % 16 9 17
ensembleNeurons = [E1_base, E2_base];
plotNeuronsEnsemble(baseActivity, ensembleNeurons, [ones(1,length(E1_base)) 2*ones(1,length(E2_base))])
select_roi_data(roi_data, [E2_base, unique(E1_base)]);
% E2_candidates = [39 45 59 37 88 6 26 46 78 48 22 20 33]
%% for E3 experiments:
% E2_candidate = unique([36 42 22 26]); %unique also sorts 47 38 19 34 9 45
E3_base = unique([36 42 22 26]); %unique also sorts 50 12 15 8 67 53
% E2_base = sort([47 38 19 34], 'ascend'); %
%%
%OPTION: Use previously collected BMI data as the baseline data:
%
% bmi_file = fullfile(savePath, 'BMI_online190515T010526.mat');
% bmi_data = load(bmi_file);
% bmi_base = fullfile(savePath, ['base_' 'BMI_online190515T010526.mat']);
% baseActivity = bmi_data.data.bmiAct(:, ~isnan(bmi_data.data.bmiAct(1,:)));
% save(bmi_base, 'baseActivity');
%
% E1_base = [1 2 3 4];
% E2_base = [5 6 7 8];
%%
% base_file = fullfile('H:\holobmi_H\191214\NVI20\D29', 'BaselineOnline191214T213601.mat')
%% Calibrate Target with Baseline simulation
%--------------------------------------------------------------------------
%D0: (nothing)
%1) Parameters:
% - sec_per_reward_range
% - f0_win (F0: how many frames to average over)
% - dff_win (F for Dff: how many frames to average over)
%--------------------------------------------------------------------------
% base_file = fullfile(savePath, 'BaselineOnline190514T221822.mat')
% base_file = bmi_base;
exist(base_file)
n_f_file = base_file;
ndata = load(n_f_file);
num_base_samples = sum(~isnan(ndata.baseActivity(1,:)));
baseline_frameRate = num_base_samples/(15*60);
A_file = roi_data_file; %fullfile(savePath, 'red.mat');
exist(A_file)
onacid_bool = 0
sec_per_reward_range = [120 90];
frames_per_reward_range = sec_per_reward_range*baseline_frameRate;
disp('Time (s) per reward range: ');
disp(sec_per_reward_range);
disp('Frames per reward range: ');
disp(frames_per_reward_range)
% sec_per_reward_range must be higher than 80seconds (to keep the
% occurence of artificial vs natural higher than 80%
E2mE1_prctile = 98;
target_on_cov_bool = 0
prefix_win = 40
f0_win_bool = 1
f0_win = 2*60*ceil(frameRate)
dff_win_bool = 1
dff_win = 4
reward_per_frame_range = 1./frames_per_reward_range
cursor_zscore_bool = 0;
f0_init_slide = 0;
close all
[target_info_path, target_cal_ALL_path] = baseline2target_vE1strict(n_f_file, A_file, onacid_bool, ...
E1_base, E2_base, frames_per_reward_range, target_on_cov_bool, ...
prefix_win, f0_win_bool, f0_win, dff_win_bool, dff_win, savePath, ...
cursor_zscore_bool, f0_init_slide, E2mE1_prctile);
%NURIA NOTE: accept calibration only if T value is between 0.2 and 0.5.
%--------------------------------------------------------------------------
%D0:
%Note down:
% - T value
% T: 0.4335
% num_valid_hits: 7//7
% num_hits:141//211
%--------------------------------------------------------------------------
%% Holo stim checking connectivity
% create randomize run for each individual neuron of the ensemple
%--------------------------------------------------------------------------
%D0:
%-Manually enter: powerVector, durationVector
%--------------------------------------------------------------------------
%% Runs connectivity
%--------------------------------------------------------------------------
%D0:
% choose 'num_conn' = number of times to stim each neuron
%--------------------------------------------------------------------------
if connectivity_bool
if pl.Connected()
pl.Disconnect();
end
savePrairieFiles(savePath, pl, 'connectivity_pre')
numberNeurons = stim_roi_data.num_rois;
num_conn = 10;
conn_sequence = repmat(1:numberNeurons, [1 num_conn]);
%Randomize order:
conn_sequence = conn_sequence(randperm(length(conn_sequence)));
ens_conn_params = ens_stim_params;
ens_conn_params.IterDelay = 3000; %Time (ms) between iterations
InitialDelay = 3000; %(ms) time bw stim delivery
ens_conn_params.InitialDelayVector = InitialDelay*ones(1,numberNeurons);
% seq_stim_params
xml_conn_pre_path = fullfile(savePath, 'conn_pre.xml');
createXmlFile_sequential_single_cell(xml_conn_pre_path, ens_conn_params, conn_sequence);
pl = actxserver('PrairieLink.Application');
pl.Connect();
loadCommand = "-tsl " + fullfile(path_data.env_dir, "Tseries_VivekNuria_holo_4.env");
pl.SendScriptCommands(loadCommand);
pl.Disconnect();
end
%% Run
%--------------------------------------------------------------------------
%D0:
%1) (zero PMT, power) Add water to imaging window if needed
%2) Load xml
%--------------------------------------------------------------------------
if connectivity_bool
clear s
StimMask = stim_roi_data.roi_mask; %reload just in case
expectedLengthExperiment = 3*7000;
ConnectivityAcqnvsPrairie_v2(savePath, expectedLengthExperiment, StimMask, 'PRE')
end
%% create stims for pretrain
%DO:
%Change iterations to 121 in the mark point series
% iterations = 121;
% pretrain_xml_path = fullfile(path_data.savePath, 'XML_pretrain.xml');
% pretrain_params = ens_stim_params;
% pretrain_params.Iter = iterations;
% createXmlFile_basic(pretrain_xml_path, pretrain_params);
% createXmlFile(savePath, numberNeurons, reps, initDelay, durationVector, powerVector, spiralVector, iterations, 'preTrain', false)
%%
%Compute vectorHolo
%--------------------------------------------------------------------------
%D0:
%1) Confirm IHSI mean, range
%2) seedBase - if we will seed the baseline, then set to 1.
% - if seedBase 0, we wait for baseline before starting stims
%--------------------------------------------------------------------------
frameRate = 30 %baseline_frameRate
baseFrames = 2*60*30;
expectedLengthExperiment = 75*60*frameRate
% IHSImean, IHSIrange
IHSImean = 20;
IHSIrange = 10;
[vectorHolo, ISI] = createVectorHolo(frameRate, expectedLengthExperiment, IHSImean, IHSIrange, false);
seedBase = 0; %Set this to 1 if you will seed the baseline
if ~seedBase
vectorHolo = vectorHolo + baseFrames;
end
% num imaging reps should be 75600 = 72000+3600
%% Load Ensemble BOT: create masks bot and image to check during experiment
%In BOT, load 'BOT_ens.cfg'
%If it was some how modified:
make_ens_bot = 0;
if make_ens_bot
BOT_ens_path = fullfile(path_data.savePath, 'BOT_ens.cfg');
createBot_v2(BOT_ens_path, stim_roi_data.x,stim_roi_data.y,stim_roi_data.r);
end
%% run Pre-training
%%
%Seed BMI baseVal using Pretrain
%--------------------------------------------------------------------------
%D0: (only seeded)
%1) seedBase - if we will seed the baseline, then set to 1.
% - if seedBase 0, we wait for baseline before starting stims
%2) Copy-paste BMI_target_info filename (into 'pretrain_file')
%--------------------------------------------------------------------------
seedBase = 0;
baseValSeed = ones(length(E1_base)+length(E2_base), 1)+nan
if seedBase
%TODO:
pretrain_file = 'BMI_online191025T195532'
load(fullfile(savePath, pretrain_file));
pretrain_base = data.baseVector;
pretrain_base(:, isnan(pretrain_base(1,:))) = [];
baseValSeed = pretrain_base(:,end)
end
%%
% Pre-training
%--------------------------------------------------------------------------
%D0:
%
%Confirm 'target_info_path'
% %Change the Mark Points:
% %Clear Point Series, Load pretrain.xml
%Make 121 iterations,
% IMPORTANT put "Wait for Trigger" = First Reptition, Trigger
%Trigger Selection: Start with External, PFI1
%
% Then, before running cell:
%0) zero pmt, laser. put water under objective.
%1) start video
%2) start load cells
%3) start pyctrl
%--------------------------------------------------------------------------
close all
imshow(im_bg)
clear s
baselineCalibrationFile = target_info_path;
vectorVTA = []
%expt_str:
% expt_cell = {...
% 'BMI', ...
% 'HoloVTA_pretrain', ...
% 'Holo_pretrain', ...
% 'VTA_pretrain'};
expt_str = 'HoloVTA_pretrain';
debug_bool = 0;
debug_input = [];
BMIAcqnvsPrairienoTrialsHoloCL_debug_enable_v4(folder, animal, day, ...
expt_str, baselineCalibrationFile, frameRate, vectorHolo, vectorVTA, ...
cursor_zscore_bool, debug_bool, debug_input, baseValSeed)
% BMIAcqnvsPrairienoTrialsHoloCL_debug_enable_v3(folder, animal, day, ...
% expt_str, baselineCalibrationFile, frameRate, vectorHolo, vectorVTA, ...
% cursor_zscore_bool, debug_bool, debug_input);
%TODO: add seed functionality
%
% BMIAcqnvsPrairienoTrialsHoloCL_debug_enable_v2(path_data.savePath, path_data.env_dir, ...
% expt_str, baselineCalibrationFile, frameRate, vectorHolo, vectorVTA, ...
% cursor_zscore_bool, debug_bool, debug_input, baseValSeed);
%saves filename with expt_str
% BMIAcqnvsPrairienoTrialsHoloCL(folder, animal, day, expt_str, baselineCalibrationFile, baseline_frameRate, vectorHolo, vectorVTA, cursor_zscore_bool)
%--------------------------------------------------------------------------
%D0:
%Stop:
%1) pyctrl
%2) load cells
%3) video
%--------------------------------------------------------------------------
%% run BMI
%--------------------------------------------------------------------------
%D0:
% Remove mark points!!!!
% Then, before running cell:
%0) put water under objective
%1) start video
%2) start load cells
%3) start pyctrl
%--------------------------------------------------------------------------
%Get baseValSeed from HoloVTA_pretrain! load file, take the last valid
%baseVal
% load_baseVal = 0;
% if load_baseVal
% baselineCalibrationFile = 'BMI_target_info_20190523T220638.mat';
pretrain_file = 'BMI_online191018T160622'
load(fullfile(savePath, pretrain_file));
pretrain_base = data.baseVector;
pretrain_base(:, isnan(pretrain_base(1,:))) = [];
baseValSeed = pretrain_base(:,end)
%%
baseValSeed = ones(length(E1_base)+length(E2_base), 1)+nan
baselineCalibrationFile = target_info_path;
%%
close all
% imshow(im_bg)
% baseValSeed = ones(length(E1_base)+length(E2_base), 1)+nan
vectorHolo = [];
vectorVTA= [];
debug_bool = 0;
debug_input = [];
cursor_zscore_bool = 0;
%T1 = 0.27
expt_str = 'BMI';
BMIAcqnvsPrairienoTrialsHoloCL_debug_enable_v4(folder, animal, day, ...
expt_str, baselineCalibrationFile, frameRate, vectorHolo, vectorVTA, ...
cursor_zscore_bool, debug_bool, debug_input, baseValSeed)
% BMIAcqnvsPrairienoTrialsHoloCL_debug_enable_v3(folder, animal, day, ...
% 'BMI', baselineCalibrationFile, frameRate, vectorHolo, vectorVTA, ...
% cursor_zscore_bool, debug_bool, debug_input);
% BMIAcqnvsPrairienoTrialsHoloCL_debug_enable_v2(path_data.savePath, path_data.env_dir, ...
% 'BMI', baselineCalibrationFile, frameRate, vectorHolo, vectorVTA, ...
% cursor_zscore_bool, debug_bool, debug_input, baseValSeed)
%Stop:
%1) pyctrl
%2) load cells
%3) video
%%
%DO: bmi no reward
%Clear vectorVTA
%Clear vectorHolo
%CLEAR MARK POINTS!!!
bmi_no_reward_bool = 1;
baselineCalibrationFile = target_info_path;
baseValSeed = ones(length(E1_base)+length(E2_base), 1)+nan;