sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
fc4f6f0d1f3f538bbbc2ed55f9faafb595cb95d675f2d92bca111c9e981f0d47 | MATLAB | 1,229 | 38 | function [pat_idx] = convert_mask_coords_to_pat_idx(mask,coords)
% [PAT_IDX] = CONVERT_MASK_COORDS_TO_PAT_IDX(MASK,COORDS)
%
% MASK = X x Y x Z boolean matrix, i.e. a normal mask matrix
%
% COORDS (optional) = nVox x 3 matrix of x,y,z
% coordinates. By default, assumes you want all the active
% voxels in the mask. Oth... |
5cae01dc9bdccf3e6d35338bf852d0eb2af66760701ac978dc304c47085f4631 | MATLAB | 1,231 | 42 | % read in a sample image -- also see letters.png, bagel.png
img = imread('mushroom.png');
figure;imshow(img);
% the standard skeletonization:
figure;imshow(bwmorph(img,'skel',inf));
% the new method:
figure;imshow(bwmorph(skeleton(img)>35,'skel',Inf));
testE= skeleton(skel);
testfigE = bwmorph(testE>35,... |
1cf674526950a504b1fba08263674cb499b99666bcec02373917e920ba5f9e27 | MATLAB | 1,232 | 37 | function [subj] = add_created(subj,objtype,objname,new_creat)
% Makes it easier to add the object-creation data easier
%
% [SUBJ] = ADD_CREATED(SUBJ,OBJTYPE,OBJNAME,NEW_CREAT)
%
% Basically, you can feed it an entire NEW_CREAT structure, and
% it will add any fields that didn't already exist, overwrite any
% that did,... |
8adb677c17f4153e7dab59683b80847a34103285e56d9a35cbefc8fbc22b93af | MATLAB | 1,233 | 37 | % Y = spharm(theta,phi,l,m)
%
% Orthonormal spherical harmonic function (Arfken, p. 681) for quantum
% numbers l and m. Angles theta and phi should be given in radians.
%
function Y = spharm(theta,phi,l,m)
% Copyright (c) 2016, Elekta Oy
% ---------------------------------------
%
% Redistribution and use of the Soft... |
8d94986bb82ee247da481d7bd1aa38cd5deb0aa4a0f53171ff1db0113ebf4adf | MATLAB | 1,233 | 37 | % A simple demo of the NetworkMonitor functionality
% add Offline Analysis Toolbox to path
initOAT;
% init NetworkMonitor
NM = NetworkMonitor('../results/sim_gc can.dat');
% plot network activity
disp('NetworkMonitor.plot')
disp('-------------------')
disp('Press ''p'' to pause.')
disp('Press ''q'' to qu... |
ba9207ff4362e66864065e1542a714eb55d85ae4ccf7d8c78ddece3dbe5a4442 | MATLAB | 1,234 | 46 | %% Plot V4 MI Fano factor d' and bias split at cued and cue-opposite locations (histogram)
%% Code
loc = ["cued","opp"];
loc_flag = input('neural data at cued location or cue-opposite location: 1-cued, 2-opp: '); % loc_flag = 1 for SI Fig 6G
% cue epoch
win_start = 500;
win_end = 2500;
win_str = num2str(win_start)+"... |
82d27bf9b11407d28fdc821c07959cfeebe2c78c20aaa05a241bf30c66330ff9 | MATLAB | 1,235 | 22 | function model = liblinear_train(labels_train,data_train,cfg)
if isstruct(data_train), error('This method requires training vectors in data_train directly. Probably a kernel was passed method is use. This method does not support kernel methods'), end
switch lower(cfg.decoding.method)
case 'classification'
... |
ebb00a20ca4c1977aae74b62bbd92f49596c66bf40f018a7c3b4b07d4424f581 | MATLAB | 1,236 | 60 | function data = Concatenate(transformer, data)
%
% Concatenate columns together.
%
%
% Arguments:
%
% :param Input: **mandatory**. Column(s) to concatenate. Must all be of the same length.
% :type Input: array
%
% :param Output: Optional. Name of the output column.
% :type Output: char
%
%
... |
d0549ce146d5a8c499b812ecf183b6b126bd5e4bc725ab3936f465ba3c7a24f4 | MATLAB | 1,237 | 48 | function [h2,Bt,Bs] = HeadDirectionField_Speed(tsa1,ang,GoodRanges,varargin)
% USAGE
% [h,d,mu,p,kappa] = HeadDirectionField(tsa,ang,GoodRanges)
%
% Inputs:
% tsa: a ts object (typically a cell!)
% ang: a tsd object of head orientation
% GoodRanges: an intervalSet object defining the time of va... |
0df44f4c1d4149e2ccb9cfcd60e9deb2bed4a988c4f6a59fae9753e5969c172c | MATLAB | 1,238 | 51 | function [h X Y] = hist2(Data, XBins, YBins)
% out = hist2(Data, XBins, YBins)
%
% Makess a 2d histogram of the data.
% XBins and YBins are optional arguments
% which give the number of grid segments.
% - default is 50.
if length(XBins) == 1
MinX = min(Data(:,1));
MaxX = max(Data(:,1));
else
MinX = XBins(1... |
bc322814f88080e736375e919ce29c8518e56c345f82c98a6ba01349c91e71c5 | MATLAB | 1,238 | 33 | function a_MLE = powerLawMLEClauset(x_i, x_min)
% returns the MLE of a power law exponent for the observations x_i,
% restricted to observations larger than x_min. If x_min is empty,
% estimates x_min using the Clauset methods
% x_i are assumed to be DISCRETE
% UPDATE 1/18/22: uses a look-up table (as a global variabl... |
c6a740033a11e4dfda2ab900356a49f617b25d56eecf7e68f952d29ac2606c51 | MATLAB | 1,238 | 45 | function bar_GOenrich(data,Y_label,title_label,top_num,output_name)
figure;
% J = customcolormap(linspace(0,1,10), {'#cf1619','#d87323','#e8b52c','#f9fc35','#4fa019','#85fb27','#6a70b1','#422450','#430e32','#000000'});
% colormap(J)
J = colormap;
[ranked_b ind]= sort(data.b,'ascend');
h=barh(ranked_b(1:top_num),'LineW... |
4cc935882208094d1a3221cb0d93887b3704db5679104811678dcacb0134a69b | MATLAB | 1,239 | 57 |
ndim = size(Y, 2);
% ndim = 3;
%%
x = SimpleCSRio;
ev_s = nan(ndim, 1);
for i = 1:ndim
y = Y(:, i);
ev_s(i) = corr(x, y).^2;
end
x = SimpleTemporalRio;
ev_t = nan(ndim, 1);
for i = 1:ndim
y = Y(:, i);
ev_t(i) = corr(x, y).^2;
end
%%
% close all
figure;
subplot(1, 2, 1); hold on
... |
c5691f8704ea346f9620d3691660e8bc5c773c3af30a8d957f8793d4fd295a67 | MATLAB | 1,239 | 42 | function [ output ] = shear3DinDim2(inArray, angle, bReverse, dz, xypixelsize, trans, fillVal)
%shear3DinDim2 -- deskew inArray about the 1st dimension (y in image terms)
%x in sheet-scan terms)
% inArray -- input 3D array
% angle -- skewing angle
% bReverse -- is skewing in reverse direction ? (like in Bi-C... |
ebe63cb14c85777e02430774e82bc91ab419d51657133b373d9ea0cae9a8ad49 | MATLAB | 1,240 | 38 | function trans = headcoordinates_mne(nas, lpa, rpa)
% Returns the homogenous coordinate transformation matrix
% that converts the specified fiducials in any coordinate system (e.g. MRI)
% into the rotated and translated headccordinate system.
%
% M1 = headcoordinates(nas, lpa, rpa)
%
% The headcoordinate system in Elek... |
f1aa30f1d2f5da869b9069d6ba37fc341376d711eaae4361421f182a3479ee2e | MATLAB | 1,240 | 27 | classdef dfaPlot < actigraphy
properties (Access = public)
dfaAxis
dfaShallowAxis
end
%% construction and deletion
methods (Access = public)
function app = dfaPlot(varargin)
app@actigraphy(varargin{:});
% change position
... |
f831194a2e3634fa3ce61fe662728a5eeeb2b1edd6d97de7e3feee908fece006 | MATLAB | 1,240 | 52 | function C = corrcoefLocal(q,T,varargin)
% This function computes correlation coefficient getting rid of the global
% fluctuation. Inspired by Renart et al., Science, 2010
% Input:
% q: matrix of binned spike trains
% T: smoothing window. The spk trains will be convolved with a mexican
% hat of T positive wid... |
0e9885f6a602f5a678a036b46bb17e590dc808d9008bb2c411b4e1918e213fcf | MATLAB | 1,241 | 39 | function [SpatialInfo] = spatial_information(binActivity,neuronSL_inlap)
%SPATIAL_INFORMATION ´Ë´¦ÏÔʾÓйش˺¯ÊýµÄÕªÒª
% Default neuronSL_inlap is extracted from binActivity
if nargin ==1 && isstruct(binActivity)
neuronSL_inlap = binActivity.neuronSL_inlap;
end
[n, nbin, lapNum] = size(binActi... |
3523963691ff8d189ae2d8ddb49863b66f236f507712b6eb8bfb66ae71159492 | MATLAB | 1,241 | 76 | clear all
N = 10000; %total
N1 = 4000:1:5000;
N2 = N-N1;
K = 1:1:1000;
snr_ratio = nan(length(N1),length(K));
for i = 1:length(N1)
x(i) = N1(i)/N;
for j = 1:length(K)
% s = ALPHA*((N1(i)-N2(i))/(N1(i)+N2(i)))^2;
% n = (1-ALPHA)*(1/K(j));
snr_ratio(i,j) = K(j)*((N1(i)-N2(i))^2/(N1(i)... |
cdd0d09cb0d17af39226ee3dea5f0d3f90a5b50ddadac19e30782e12d0584383 | MATLAB | 1,241 | 52 | function L = CrossValidationSpatialCheck_Cells(S,lambda,X,Y,ep)
nbEp = 10;
dispBar = 0;
binSize = 0.5; %in cms
epV = regIntervals(ep,nbEp);
L = NaN(nbEp,length(S),length(lambda));
fprintf('Launching Cross-validated spatial info\n')
for ii=1:nbEp
fprintf('.')
ix = (1:nbEp);
ix(ii)=[];
testEp = epV{ii... |
56872eb161dca2f7ec350773bd924e9a26f17354bed742d51642e1e4960d73d5 | MATLAB | 1,242 | 34 | function [Traj_down] = DownSample(Traj,down, ChHome)
% Downsampling to 6msec per sample.
% some trajectories are too short and have only 1 entry after downsampling
% these trajectories will be deleted here to allow the next step
% (interpolation to 200 points)
numTraj = length(Traj);
if numTraj == 0
Traj... |
69ea22c6c1d1b27b8386fdb61fb0a7409039f469b74bd4ef3003f4072a775f46 | MATLAB | 1,242 | 59 | function [cellMod,cellPh] = OscillationPhase_Ripples(lfp,S,ep)
% [thetaMod,thetaPh,thetaTrghs,thetaPks] = ThetaPhase(fbasename,channel,ep)
% optional:
% [thetaMod,thetaPh,thetaTrghs,thetaPks] = ThetaPhase(fbasename,channel,ep,folder)
% to indicate another folder where the lfp file is located
%
% Adrien Peyrache, 2013... |
8d642809bd6e4a04a2c58d1f12d5cc38f93d83bd32fe67181fa0120e1fdc7aec | MATLAB | 1,244 | 43 | function beh_corr_scatter_NKI(x,y,Xlabel1,Xlabel2,Xlabel3,Ylabel,pic_title,output_path)
dotcolor = [231 223 202]./255;
linecolor = [182/255 180/255 194/255];
shadecolor = [154/255 154/255 154/255];
[xData, yData] = prepareCurveData(x, y);
ft = fittype( 'poly1' );
opts = fitoptions( ft );
opts.Lower = [-Inf -Inf];
op... |
8994979379874365993004161571fe931f148ce8534013fddba0d0c9a2685ef7 | MATLAB | 1,245 | 40 | function niiFiles = select_nii_func(cfg, subject, runs, prefix, doMergeRuns)
%SELECT_NII Select functional EPI images of several runs
% Detailed explanation goes here
if doMergeRuns
%% Merge Runs
i_index = 1;
for i_run = 1:length(runs)
c_run = runs(i_run);
... |
655f6bb25abf5f3220dc905173963b3cba771b0a94a2a1c150400bb041e1d0f0 | MATLAB | 1,246 | 50 | function [theta_est, data_fit, eta, b] = choice_theory_dcest_pos(pobs)
% Luce's choice theory analysis to estimate d'(eta) and c(b)
% for comparison with mADC predictions
%
% last row/col taken to be nogo/nostim response, all biases rel to this
% Sridhar Devarajan, Jan 2013
p = pobs'; % such that stim in col a... |
17edbadc193993fb86868dda37a875224f76bbe79ceaddfafcc637ba95e7e675 | MATLAB | 1,247 | 32 | % number of different conditions
numCond = 6;
% number of source-detector channels
numChn = 42;
% HbO, HbR, HbT
numConc = 3;
sizeOneCond = numChn*numConc;
procDataDir = 'C:\Users\mn0mn\Documents\ResearchProjects\spatailAttentionProject\ProcessedDatafNIRS\Experiment08';
homer3GroupDir = 'C:\Users\mn0mn\Documents\Resear... |
a31ebbfe8ee742ac04b65fc480466b7a9f0a5e119424fd33f22880510e0e04d0 | MATLAB | 1,248 | 41 | addpath('src')
T = readtable('raw/E-GEOD-46903.sdrf.txt');
sourceName = strrep(T.SourceName, ' 1', '');
cellType = T.Comment_Sample_description_;
time = T.FactorValue_TIME_;
stimulation = T.FactorValue_ACTIVATIONSTIMULI_;
stimulation = strrep(stimulation, ' 100U/ml', '');
stimulation = strrep(stimulation, ' 800 u/ml... |
5f3f0fbb09c040018570e6f3d91aaf6abf1132f56968678e6e6002ea3f1140a3 | MATLAB | 1,249 | 60 | function [l,d,perm] = mchol(A,mu)
% [l,d,perm] = mchol(A,mu)
% Compute the Gill-Murray modified LDL factorization of A,
if nargin < 2
mu = 1e-12;
end
n = size(A,1);
l = eye(n);
d = zeros(n,1);
perm = 1:n;
for i = 1:n
c(i,i) = A(i,i);
end
% Compute modification parameters
gamma = max(abs(diag(A)));
xi = max(... |
aae3ce76613320eb2ce4b11a293977ce4b3aa9fc177d150b24a41f5d80205ce5 | MATLAB | 1,249 | 38 | function EV = expVar(r_w,r_pre,r_post)
% =========================================================================
% expVar
% =========================================================================
%
% USAGE: EV = expVar(r_w,r_pre,r_post)
%
% DESCRIPTION: Computes explained var... |
2853b9db9c3244c1e52cd6e606245c5e20d3e81567a4e9195a591a493e66674d | MATLAB | 1,252 | 38 | function post_scan = post_scan_load(p)
% function post_scan = post_scan_load(p)
%
% Load data of the post scan questionnaires, including the VVIQ, the OSIQ
% and a strategy questionnaire.
%
% Output:
% - post_scan: Table of questionnaire data.
%
% Simon Weber, sweber@bccn-berlin.de, 2021
post_scan = [];
post_scan_... |
4cba5765e419e2e28b79a008940c1e3ffbe30a6fc911af8cac6610e05679ea3c | MATLAB | 1,252 | 53 | function [x] = compress(x)
% Recursively converts datatypes to save memory.
%
% [X] = COMPRESS(X);
%
% Compress converts numeric matrices to single precision and binary
% numeric matrices to logical matrices. Runs recursively on cell
% arrays and all fields of structures.
% License:
%==================================... |
3eb4b15af617b3440b30460be1457c3bcc16a5c32bc5cb903c2443fd2d9c86a5 | MATLAB | 1,255 | 53 | function L = CrossValidationGLM_Cells(Q,Pred,ep)
nbEp = 10;
dispBar = 0;
binSize = 0.5; %in cms
epV = regIntervals(ep,nbEp);
L = NaN(nbEp,length(S),length(lambda));
fprintf('Launching Cross-validated spatial info\n')
for ii=1:nbEp
fprintf('.')
ix = (1:nbEp);
ix(ii)=[];
testEp = epV{ii};
training... |
ae4789bce4dff1b2fb58cef2783d0ad2ea7dea54ea45003fb3e2a4bd50e897c0 | MATLAB | 1,255 | 46 | %% Plot V4 MI Firing Rate d' and bias split at cued and cue-opposite locations (histogram)
%% Code
loc = ["cued","opp"];
loc_flag = 2;%input('neural data at cued location or cue-opposite location: 1-cued, 2-opp: '); % loc_flag = 1 for Fig 2D, loc_flag = 2 for Fig 3B
% cue epoch
win_start = 500;
win_end = 2500;
win_st... |
18e0284116f06ee19740b15aa5a981cf29051e46b1f9897d7839a7b08d415f79 | MATLAB | 1,256 | 35 | function pipeline_svm_state_at_cue_decoding()
cfg = load('/projects/crunchie/moehring/neurovar_v2/BIDS/code/neurovar_cfg.mat').cfg;
for i_sub = 1:44
fprintf(strcat('Decoding Sub_', string(i_sub),'\n'));
pipeline_dir = fullfile(cfg.global.deriv_dir, 'pipeline_svm_state_at_cue_1st',... |
22f6ad685c661f7c2d1f6351dc79dcd7977d30739aa95602794cae7a98724727 | MATLAB | 1,256 | 35 | function [z] = sandwichMx2(x, y)
% z = sandwichMx2(x, y)
% compute x*y*x' provided y = hermitian
% and dimensionality is 2x2xN, x should be Mx2xN
%FIXME build in check for hermitianity
z = complex(zeros(size(x,1),size(x,1),size(x,3)));
xconj = conj(x);
xabs2 = abs(x).^2;
for k = 1:size(x,1)
z(k,k,:,:) = xabs2(... |
9f72ec7ba87fb9bbc21067dd086283d9b602daa41db887a209b1d30cc194f65d | MATLAB | 1,256 | 46 | %% Plot V4 MI Firing Rate d' and bias split at cued and cue-opposite locations (histogram)
%% Code
loc = ["cued","opp"];
loc_flag = 1;% input('neural data at cued location or cue-opposite location: 1-cued, 2-opp: '); % loc_flag = 1 for Fig 2D, loc_flag = 2 for Fig 3B
% cue epoch
win_start = 500;
win_end = 2500;
win_s... |
bd6aa76d6b7f4c822419bf6efe1a4e8d6abcc2a46884525885e4c84b55ae6c21 | MATLAB | 1,256 | 24 | function model = correlation_classifier_train(labels_train,data_train,cfg)
if isstruct(data_train), error('This method requires training vectors in data_train directly. Probably a kernel was passed method is use. This method does not support kernel methods'), end
switch lower(cfg.decoding.method)
case ... |
e200bc374fd16eb9d70d3a62968883d4078be6e42ad35b6e7940d69400aa5bae | MATLAB | 1,256 | 46 | %% Plot V4 MI Firing Rate d' and bias split at cued and cue-opposite locations (histogram)
%% Code
loc = ["cued","opp"];
loc_flag = 1;%input('neural data at cued location or cue-opposite location: 1-cued, 2-opp: '); % loc_flag = 1 for Fig 2D, loc_flag = 2 for Fig 3B
% cue epoch
win_start = 500;
win_end = 2500;
win_st... |
142346df7bda5304b3076c3b74d07b618b57a2d562091a8807a5ea3838e4a98c | MATLAB | 1,257 | 36 | function [goodness ondiags_val offdiags_val] = onoff_metric(mat)
% [GOODNESS ONDIAGS_VAL OFFDIAGS_VAL] = ONOFF_METRIC(MAT)
%
% Calculate the OnOff metric, as in pg 12 of the supporting
% materials of Polyn et al (2005). Subtracts the mean of the
% off-diagonals from the mean of the on-diagonals.
% License:
%=========... |
42159c6d01491e71452546419ff9179678489a839619592b55d96c81ce227c19 | MATLAB | 1,257 | 42 | % plot average of ss coefficients across all subjects for each channel
sbjList = {'12','13','14','15','16','17'};
figSaveDir = ['C:\Users\mn0mn\Documents\ResearchProjects\spatailAttentionProject\ProcessedDatafNIRS\GroupResultAll\Figures'];
% hardcoded
ssBetaTotal = zeros(49,36,2);
ssBetaList = zeros(length(sbjList)... |
6da12797cc544742d4e60a88ff6498e823106889f33597305e96d3efc4676c39 | MATLAB | 1,257 | 60 | function transients = get_transients(t, dff, debug)
lowPassTh_Hz= 0.1;
highPassTh_Hz= 2;
filterType = 'bandpass';
fc = [lowPassTh_Hz highPassTh_Hz];
fs = 1/median(diff(t));
[b,c] = butter(4,fc/(fs/2));
filtered_sig = filtfilt(b, c, dff);
two_MAD_th = median(filtered_sig) + (2*mad(filtered_sig));
one_MAD_th = median(... |
117a3f389e2f10cf087b89e9ea36127d950133df24a6e4cd25e8a3e96dda3248 | MATLAB | 1,258 | 33 | function h = plotEnv(ax,env,tt,yOffset,yScale,Color)
% h returns handle of the plotting
% ax: handle of axis in which to plot
% env: 1D vector the (positive) magnitude of the envelope
% tt: 1D vector; time axis for env; must be same dimension
% yOffset: how much to offset the envelope in y-axis
... |
3a6a65455f27a82e944a53e45f5df66cf82c5f7dd09e1a2b873adebdde29b95f | MATLAB | 1,258 | 75 | function t = threshold(tsa, thr, varargin)
% Threshold crossings
%
% USAGE:
% t = threshold(tsa, OptionName, OptionValue)
%
% INPUTS:
% tsa - a tsd object
% thr - a threshold value
%
% OPTIONS:
% 'Crossing' - type of crossing, possible values are:
% 'Rising' (default)
% 'Falling'
% c... |
7335d8621ab37b429b0c05b0ad034e6d240b86018faf7526639ae171500f376e | MATLAB | 1,259 | 39 | function analyse_mri(p)
% Run the preprocessing, modeling and reconstruction of the fMRI data, and
% plot the results.
% Run preprocessing of fMRI data
mri_preprocessing(p);
% Create binary Region of Interest (ROI) masks from probability maps in MNI
% space
create_rois_from_prob_maps(p, 'V1-3');
% Warp ROIs into sub... |
c0b5bcfc4c07068fd7f146037f26fea4a945dfb44c72331572147c59588148e4 | MATLAB | 1,259 | 40 | clear all
load('../data/opt.mat')
load([opt.intermediate_data_path 'clab_arti.mat'])
for pair = 1:length(opt.participant_pair)
clear data_pair EEG
fprintf('Computing ICA for pair session %d: %s - %s \n',...
pair,opt.participant_pair{pair,1},opt.participant_pair{pair,2})
pair_sessio... |
bf028af81c0eacb3ba72f29825c4e36659b26d3c7c173b71a8080c7b923d7fbe | MATLAB | 1,260 | 41 | %% Plot V4 MI Firing Rate d' and bias split at cued and cue-opposite locations (moving window plots locked to cue onset/offset)
%% Code
loc = ["cued","opp"];
loc_flag = 1; %('neural data at cued location or cue-opposite location: 1-cued, 2-opp: '); % loc_flag = 1 for Fig 2C, loc_flag = 2 for Fig 3A
load('V4_FR.mat')... |
9439617efa14dc7085bf75ec97f97eb2c25bd94918274e6291990f3e100f02b6 | MATLAB | 1,261 | 48 | function awsWriteJSON(json,fp)
%This function write a JSON file from AWS or locally
%json - configuration file
%fp - file path, can be local or in AWS
%% Checks
if (awsIsAWSPath(fp))
%Load Data from AWS
isAWS = true;
fpToSave = 'tmp.json';
else
isAWS = false;
fpToSave = awsModifyPathFor... |
24109c1792eaed936b4e130c9b9631fcc61a45062baab84d89c22019fef13d24 | MATLAB | 1,262 | 58 | function V = saveMRImage(data, filename, mat, descrip)
% saves a 3d-matrix to an MR image file, using SPM routines
%
% V = saveMRImage(data, filename, mat = eye(4), descrip = 'saveMRImage')
%
% data: image data to be written, 3d matrix
% filename: name of the file to write to
% mat: transformation... |
7398247ca7a48c50153f61a90fe319b9d5c11bb4f1179439a4a0b1e21ea70f79 | MATLAB | 1,262 | 47 | function [v] = nanvar(x, normalizeflag, dim, flag)
% [V] = NANVAR(X, NORMALIZEFLAG, DIM, FLAG) computes the variance,
% across all cells in x along the dimension dim, accounting for NaNs.
% Normalizeflag = 1 normalizes by N, normalizeflag = [] or 0 normalizes by N-1.
%
% X should be an linear cell-array of matrices... |
d26b044ac4ca0f3278894a22496561a7a2bf668fccc8bda4f43fb0396b708ddb | MATLAB | 1,262 | 32 | %% This script specifies the path to the data
% Please make sure the folder "Data" from the source data is extracted
% directly at the root directory of the github code, so that it contains:
% github_dir\Data\
% github_dir\Data\Neuro\
% github_dir\Data\Behavior\
% etc.
% and from the code repository:
% github_dir\ru... |
5b269fd20f17110843a92f8c1c30cd3a994959c8201db9af12044973aec536aa | MATLAB | 1,263 | 61 | function [ang,GoodRanges,ep] = HeadDirection_Wrapper(fbasename,varargin)
% loads head-direction from a position file file (ending in .whl)
%
% USAGE
% [ang,GoodRanges] = HeadDirectionWhl(fbasename)
%
% INPUT:
% fbasename: session file basename
%
% OUTPUT
% ang: a tsd object of angular values
% G... |
d8ab9fa07835f389d336cfd8ac977e29d27e2b51947db2e870418d89a0de1188 | MATLAB | 1,264 | 45 | function data_averaged = moving_average(data, winsize)
% function data_av = moving_average(data, winsize)
%
% Computes moving average across TRs, which serves as temporal smoothing.
%
% Input:
% - data: [n_tr, n_voxel, n_run] array with data.
% - winsize: Size of moving average window, i.e. number of subsequent T... |
d71fb3e0676c97881902c4dbc647743affd999a979776e09c7fde0d9d8454fa6 | MATLAB | 1,265 | 41 | %% Plot V4 MI Firing Rate d' and bias split at cued and cue-opposite locations (moving window plots locked to cue onset/offset)
%% Code
loc = ["cued","opp"];
loc_flag = 2; %input('neural data at cued location or cue-opposite location: 1-cued, 2-opp: '); % loc_flag = 1 for Fig 2C, loc_flag = 2 for Fig 3A
load('V4_FR.... |
99b8932ee4ec11380eda4249024c00bc9338ab8d54d951c10612fa6d43f277fc | MATLAB | 1,266 | 44 | function [type_matches_str type_matches_vec] = find_obj(subj,objname)
% Auxiliary function
%
% [TYPE_MATCHES_STR TYPE_MATCHES_VEC] = FIND_OBJ(SUBJ,OBJNAME)
%
% Zips through the SUBJ structure looking for objects of any type that
% match OBJNAME.
%
% It returns a string with the name of the types that contain an
% OBJN... |
cc75de2301784538c159f517bd11a87ab7936b0e4dd874f95f056d06e2f86284 | MATLAB | 1,266 | 58 | % Binary search.
% Search 'sval' in sorted vector 'x', returns index of 'sval' in 'x'
%
% INPUT:
% x: vector of numeric values, x should already be sorted in ascending order
% (e.g. 2,7,20,...120)
% sval: numeric value to be search in x
%
% OUTPUT:
% index: index of sval with respect to x. If sval is not found ... |
28d0001ea63ea519506b0ea926111e5946ce77a3f5fd49832bdb7848556fe9f2 | MATLAB | 1,267 | 64 | clear
[task_info,supPath] = loadDBAndSpecifyDataPaths('Vermis');
PLOT_INDIVIDUAL = 0;
runningWindow = -300:300; %ms
req_params = reqParamsEffectSize("both");
lines = findCspkSspkPairs(task_info,req_params);
for ii = 1:length(lines)
cells = findPathsToCells (supPath,task_info,[lines(1,ii),lines(2,ii)]);
... |
52c7934486a99cbc48585d370ad7a43ec2bfd6a2ece61967bc959efd5a86fe5b | MATLAB | 1,267 | 45 | function url = awsGenerateTemporarySharableLink(s3Path ,isUseCash)
% This function generates a temprary sharable link to an object in s3
% INPUTS:
% - s3Path - s3 object path: 's3://bucketname/...'
% - isUseCash, if set to true, and in case this object was already
% generated recently, pull it from cash ... |
65403b0e88130ac6bdba950b3c0a8de0511715c2a1dcab187f2a1ebea6686d0a | MATLAB | 1,267 | 38 | function draw_text(Experiment, whichText, response)
% Draw Text
% Input: Struct 'Experiment' with fields 'Display', string 'drawText' with
% text.
% Gets flipped on screen directly.
mainPath = Experiment.mainPath;
Display = Experiment.Display;
Subject = Experiment.Subject;
if ~exist('response','var') || isempty(resp... |
94654bf1cddd14d77087872bf925ad5e5f512f42c0b6c9e4e8d5ce478991c747 | MATLAB | 1,268 | 33 | function [xdx, xdy, xdz, ydx, ydy, ydz, zdx, zdy, zdz] = jacobian_matrix3d(vectorField)
% JACOBIAND_MATRIX3D Estimates the elements of the jacobian matrix
%
% function [xdx xdy xdz ydx ydy ydz zdx zdy zdz] = jacobian_matrix3d(vectorField)
%
% INPUT ARGUMENTS
% vectorField - Vectorfield to estimate the Jacobian fo... |
17576533ee826eebe60d1ac8e4ab1da40c5c1e77bba44cdb7ec4d603ad4487a7 | MATLAB | 1,270 | 29 | %% Plots a raster plot of spikes for correlations
%% Load prerequisite data
clear
clc
load("D:\Brewer lab data\HFS\Temporal Analysis\40 HFS\dataInfo.mat")
load("D:\Brewer lab data\HFS\Temporal Analysis\40 HFS\full_idx_allregion_unit_matched_stim.mat")
data_folder_addr="D:\Brewer lab data\HFS\HFS stim\full_index_pseudo... |
008441923bec31cfbe06f3c1db8502af5681825059d15b8ca82b195e3e720c0e | MATLAB | 1,271 | 52 | function O = realign(tsa, varargin)
% Realign timestamps
%
% USAGE
% tso = realign(tsa, OptionName, OptionValue)
%
% INPUTS:
% O: a tsd object
%
% OUTPUTS:
% O: the realigned tsd
%
% OPTIONS:
% 'ZeroFirst' - if set to non-zero, the timestamp will be realigned so
% that the first t... |
5bf07618f3287fedb4f54398097f126fe1dd2dbf1ceb57bb80fc93c436b58c0b | MATLAB | 1,271 | 40 | function layer = softmaxLayer( nameValueArgs )
% softmaxLayer Softmax layer
%
% layer = softmaxLayer() creates a softmax layer. This layer is
% useful for classification problems.
%
% layer = softmaxLayer('PARAM1', VAL1) specifies optional parameter
% name/value pairs for creating the layer:
%
% ... |
e80bc089beb1da7be53e524619cedaeb639f91d0a05d3480d26c7986e053d3a3 | MATLAB | 1,273 | 76 | function O = diff(varargin)
% Computes the set difference of intervalSet's
%
% USAGE:
% O = diff(O1, O2)
%
% INPUTS:
% O1, O2 - intervalSet objects
%
% OUTPUTS:
% O - intervalSet object corresponding to O1 - O2
% copyright (c) 2004 Francesco P. Battaglia
% This software is released under the GNU... |
ed9bd73e64b89b55b98299281058b2a8ac68806de91af88c9e1d30432e89d20a | MATLAB | 1,273 | 37 | function varargout = arrayloop(varargin)
% ARRAYLOOP Implicit loop over some of the array dimensions.
%
% Example:
% [x, dx] = arrayloop(2, a, b, 'lscov', v);
%
% is equivalent to, but differently implemented than,
%
% for i = 1:size(a, 1)
% for j = 1:size(a, 2)
% [x(i, j, :) dx(i, j, :)] = ...
% lscov(squeez... |
14efa8f443974fb17098f9a33d357f12a0b5d0cf49db5aa907fbcf09b6d0587e | MATLAB | 1,274 | 33 | function [sfe] = spatialfilter(V,x,y,dscale)
% - V: voltage in indexes for spatial filtering (electrodes)
% for simplicity, just have one timepoint rn
% - x, y: x and y coordinates of these data
% - dscale: scale of the distance value
% https://www.frontiersin.org/articles/10.3389/fneur.2019.00325/full
% • Fo... |
3df56165e7469e31024baa6d5ec9d8704cf4b1e60584b4d4cc4554a4d5786f17 | MATLAB | 1,274 | 33 | function black=BlackIndex(windowPtrOrScreenNumber)
% color=BlackIndex(windowPtrOrScreenNumber)
% Returns the CLUT index to produce black at the current screen depth,
% assuming a standard color lookup table for that depth. E.g.
% black=BlackIndex(w);
% Screen(w,'FillRect',black);
%
% See WhiteIndex.
%
% When the scre... |
70c458c2eaf5540c796b4609666b1ce7cb0485a600a8eaae6638c404241e124b | MATLAB | 1,275 | 49 | function [e, edata, eprior] = errbayes(net, edata)
%ERRBAYES Evaluate Bayesian error function for network.
%
% Description
% E = ERRBAYES(NET, EDATA) takes a network data structure NET together
% the data contribution to the error for a set of inputs and targets.
% It returns the regularised error using any zero mean ... |
07aa6628e0e229ee7a5f252aaaba70faa0150534c928489f2f29fa772f1ae1e1 | MATLAB | 1,276 | 68 | function Errors = TestSelf;
% Errors = tsd/TestSelf;
adrlib;
Errors = {};
t = 1:10;
xd = reshape(1:60, 10, 2, 3);
x = tsd(t, xd);
% Data
if Data(x) ~= xd
Errors = [Errors, {'Data(x) failed.'}];
end
if reshape(Data(x, [5 6]), 1,12) ~= [5 6 15 16 25 26 35 36 45 46 55 56]
Errors = [Errors, {'Data(x, []) failed.... |
2714573df4d02acd80c0e07bacc145bd265c1d10b77a592078e535036ece857a | MATLAB | 1,277 | 49 | function [subj] = set_type(subj,objtype,objcell)
% Replace the entire cell array of this objtype with a new one
%
% [SUBJ] = SET_TYPE(SUBJ,OBJTYPE,OBJCELL)
%
% Recommended for internal use only
%
% See the discussion in the manual about memory efficiency for why
% this isn't an incredibly costly way to do things
% Li... |
86b41af28960e0c93c125502fa9a768025085154c2f1a224d5aec4a6d179a8ff | MATLAB | 1,277 | 59 | function t = thresholdHysteresis(tsa, thr1, thr2, varargin)
% Hysteresis thresholding
%
% USAGE:
% t = thresholdHysteresis(tsd, thr1, thr2, varargin)
%
% returns the crossings of thr2 that are associated to crossing of thr1
%
% INPUTS:
% tsa - a tsd object
% thr1 - the "high threshold"
% th... |
02fb8ecc3baa1d022c73a3720e2a061b82707190805cc9738346110fad12d0e0 | MATLAB | 1,279 | 41 | %% Plot V4 MI Firing Rate d' and bias split at cued and cue-opposite locations (moving window plots locked to cue onset/offset)
%% Code
loc = ["cued","opp"];
loc_flag = 1; %('neural data at cued location or cue-opposite location: 1-cued, 2-opp: '); % loc_flag = 1 for Fig 2C, loc_flag = 2 for Fig 3A
load('V4_FR_input... |
77e750698c9f469cfc2315d5ee61a453759c8715368ea48da4d092cf0952753a | MATLAB | 1,279 | 48 | clear
clc
%%
mat_idx = 7;
t1_idx = 2;
t2_idx = 3;
t3_idx = 4;
t4_idx = 5;
%
toplot_co = [];
for fn = 1:size(fn_idx1,1)
%load
load(fn_idx1{fn,mat_idx});
tmp = sum(recordingFile.emg,1);
figure
win = [fn_idx1{fn,t1_idx}:fn_idx1{fn,t4_idx}];
% tmp = smooth(eegRatio,50);
tmp = t... |
2f0bfa15c82e6c74bae15fbe3fb30df2454d2aa4a250ffddaf62608cfa6bd853 | MATLAB | 1,280 | 37 | function [payoff,dichot] = doWalk(options)
start = (options.upperBound-options.lowerBound).*rand(options.nB,options.nS) + options.lowerBound;
dichot = []; %draw from P(rew|correct)
reward_pre = start;
payoff = nan(options.nB,options.nS,options.nT);
payoff(:,:,1) = start;
for iB = 1:options.nB
for iS = 1:options... |
891d51fed6c41754983b0e95169c312079382442da53e182cf3153c8388f3542 | MATLAB | 1,280 | 48 | function [Ehat, Phat, varargout] = getFreq(KF, I, freqs)
% function [Ehat, Phat, E_gain, E_phase, P_gain, P_phase] = getFreq(K/F, I, RL_data)
% Get impulse response if needed
if I.impulse_or_closedloop
if isfield(KF,'Kb_eye')
F = getImpulse(KF, I, 1);
else
F = KF;
end
else
K = KF;
end
... |
b3214c6db851fa4f3a5e48eb766405ef5f1bf7811743a8d996138ace1fe92b7e | MATLAB | 1,281 | 37 | function links=LAP_linker_local_s(framenum_id,non_linking_cost,spots_s,xy_pixel_size,z_pixel_size)
try
movement_correction_factor=0.45;
z_pixel_size=z_pixel_size*movement_correction_factor;
% Subpixel centroid extraction in previous frame (t-1), (xc_t0,yc_t0,zc_t0)
xc_t0 = xy_pixel_size .* spots_... |
e4b49df17c3be060a0ef5c7cdec2c11aba76efe6a1262ed1c4b3d1bd69eb8351 | MATLAB | 1,281 | 48 | function [sigma] = wincov(x, y, percentx, percenty, dim)
% Compute the %percent Winsorized covariance across dimension dim, between
% the rows or columns of matrices x and y.
% Implementation according to Wilcox; Robust Estimation
% and Hypothesis Testing. As opposed to matlab's implementation
% the percent is taken ... |
69d145d485f833c6adbb170228e92dd650cc91a1183941eca83d433ddb7ae0b4 | MATLAB | 1,282 | 43 | function theImage = MaximizeContrast(matrix)
%MaximizeContrast Normalize an image to 0..1 where 0 is the minimum, 1 the maximum
% theImage = MaximizeContrast(matrix)
%
% Used by Matrix2Grayscale
%
%See also Matrix2Grayscale
%
%History
%
% 8/20/2002 bds part of matwave package, D.R.Williams' lab
% 8/2... |
23c9fb26b8e8661d4b4718875c630e737e940f94ef9916327355d6e132ce589c | MATLAB | 1,284 | 38 | %% EXAMPLE OF DATA FORMATTING
% This script shows how to create files should be MAT-files with a
% defined structure called RecordingMatfiles (see files in 'Test data'
% folder).
%% Add path to class and functions
addpath(genpath('../Toolbox/'));
%% Output file path
%% Prepare fake recording data
... |
2f4089f52df6190b82a82d28373cb7784a294e8c387d883bc3cc051f06be17ea | MATLAB | 1,285 | 41 | function p = ME_vMMM_pdf(y, r, mu, ka, Ik)
% _
% Probability Density Function for a von Mises Mixture Model
% FORMAT p = ME_vMMM_pdf(y, r, mu, ka, Ik)
%
% y - an N x 1 vector of response deviations
% r - a 1 x K vector of mixture frequencies
% mu - a 1 x K vector of means
% ka - a 1 x K vector of ... |
2086f6483d4004fd9fac2e9ce8979f1722efc136c82a4467d94be62f3db071ad | MATLAB | 1,287 | 49 | %% Plot V4 MI Firing rates cued vs cue-opp for superficial and deep layers (histogram)
%% Code
% cue epoch
win_start = 500;
win_end = 2500;
win_str = num2str(win_start)+"_to_"+num2str(win_end);
layer_id = 1:2; fig_id = ["a","b"];
layer_labels = {'superficial','deep'};
for layer_flag = layer_id
... |
8ef8cbae2e7eb779224f07a2fbcedc398694f5ed25d7bea310d7e7ce52cbbd15 | MATLAB | 1,287 | 36 | function cca_mode_beh_weight(x,label,output_path)
figure('position',[100,100,350,450],'PaperPositionMode','auto')
% J = customcolormap(linspace(0,1,11), {'#ff8200','#fdad31','#e7b62b','#fcda70','#fefec2','#ffffff','#d0fefe','#b3f7fe','#7ce2fe','#33b0fc','#007bf3'});
% J = customcolormap(linspace(0,1,11), {'#7abace','#... |
5b3d216ca1319f8d8d2e6b4d7dbc7877359619112010f2993b8d4e1fb6a5ee27 | MATLAB | 1,288 | 42 | %% Plot V4 MI Firing Rate d' and bias split at cued and cue-opposite locations (moving window plots locked to cue onset/offset)
%% Code
loc = ["cued","opp"];
loc_flag = 1;%input('neural data at cued location or cue-opposite location: 1-cued, 2-opp: '); % loc_flag = 1 for Fig 2C, loc_flag = 2 for Fig 3A
load('V4_FR.m... |
b45e2cae6251be22384dc29e157642d75461fe228abd67fd64190cd6f094e629 | MATLAB | 1,289 | 28 | function [z] = mtimes4xN(x, y)
% MTIMES4XN computes x*y where x = 4x4xM (or Mx4x4) and y = 4xNxM (or
% Mx4xN), and output dimensionatity is 4xNxM (or Mx4xN).
% if M==4 it is treated as Mx4x4
siz = size(x);
sizy = [size(y) 1];
z = complex(zeros(sizy));
%xconj = conj(x);
if siz(2)==siz(3)
for k = 1:sizy(3)
... |
9b379b26a442127d045366d984d2a9a65fe7ab296bf92ae05b4dc80c9d63f1ff | MATLAB | 1,290 | 29 | function T = remove_isotropic(T)
% T = remove_isotropic(T)
%
% T is assumed to be an MxNx3x3 tensor field, where each 3x3 matrix is
% assumed to be symmetric. The function computes the smallest eigenvalue of
% each 3x3 matrix and subtracts this eigenvalue times the identity matrix.
% The algorithm follows appendix G of... |
a116e68b9774b45be8aae0c6d6572b750c8197376fc00cea72fb13196446d08d | MATLAB | 1,290 | 49 | function jac = rbfjacob(net, x)
%RBFJACOB Evaluate derivatives of RBF network outputs with respect to inputs.
%
% Description
% G = RBFJACOB(NET, X) takes a network data structure NET and a matrix
% of input vectors X and returns a three-index matrix G whose I, J, K
% element contains the derivative of network output K... |
c187492b30eec75abc0b358c7f9da0ac4f082de611bac1ff4db4566651d85238 | MATLAB | 1,290 | 72 | function O = tsdArray(varargin)
% Constructor for the tsdArray class
%
% USAGE:
% O = tsdArray(varargin) constructor for the tsdArray class
%
% CALLING CONVENTIONS:
% O = tsdArray(m, n), with m and n two integers, makes an empty 2-dimensional tsdArray of size m x n
% O = tsdArray(S), where S is one ts(d) ... |
daca98dedf23034072a6748c99c6a5006ad8c1d4ef5ad853ee8eb1833756aef7 | MATLAB | 1,292 | 33 | function [processedEMG] = processEMG(EMG, SR, epochLen)
% processEMG Compute the RMS in each epoch of the EMG signal
% Zeke Barger 100119
%
% Arguments:
% EMG - a 1-D matrix of EMG data
% SR - sampling rate, in Hz
% epochLen - length of each epoch, in seconds
%
% Output:
% processedEMG - the proc... |
e1cff25f3bda4369c19ee8210bda4650c2b412f542e8b8707a1ce8c85e5109fd | MATLAB | 1,292 | 52 | function [val x] = getpropval(x, property, default)
% GETPROPVAL - In-line propval.
%
% Usage:
%
% [VALUE VARARGIN] = GETPROPVAL(VARARGIN, PROPERTY, DEFAULT)
%
% GETPROPVAL is for obtaining property-value pairs from a VARARGIN
% cell array without requiring the strict PROPVAL format. This
% looks for the first instance... |
0600db930473c74972f4095668485546b52fb97a85187343e5211f0bfa705817 | MATLAB | 1,293 | 46 | function [merged] = mergestructs(s1, s2, prefix)
% Merges two structures together.
%
% [MERGED] = MERGESTRUCTS(S1, S2)
%
% Merges the fields of two structures, S1 and S2. Conflicts are
% resolved by overwriting any S2 field with the S1 field.
%
% [MERGED] = MERGESTRUCTS(S1, S2, PREFIX)
%
% This usage prepends the strin... |
70b056e698d6a231ee1124560b54fbded3db1b362b57e424df4ebb4421d8c249 | MATLAB | 1,294 | 25 | filepath = "F:\Data Lola PAVLOV Doric\M_58665\58665_20221117_sess03_rec1_def_nac.csv"
% This function take a path to a doric csv file and return the headers
% of the columns in header_raw as well as all the data
n_line_header = 2; % In 2021 Doric csv file has two lines of header
fp = fopen(filepath,'r'); % Open ... |
78ff6936782a0b0bdeefd53a94e7bcd8dca0b184e68acd278e5b7065fdd68327 | MATLAB | 1,295 | 48 | function [x] = winsorize(x, percent, dim)
% WINSORIZE winsorizes sample x in dimension dim setting the
% percent percent observations in each of the tails
% to the value of the observation at threshold.
% Implemented according to Wilcox Robust estimation
% and hypothesis testing. If percent is a 2 element vector, it ... |
a4eea930176406a01a84d31dd688a90f9994768b2c721037e184c9f9e7a83d88 | MATLAB | 1,295 | 36 | function y=fProcess3(targetdata,targetpath,savepath,calendar)
%%
isDir=0;
realtargetdata0 = targetdata;
realtargetpath = targetpath;
realcalendar0 = calendar;
for sbjl0=1:length(realtargetdata0)
dataset0 = realtargetdata0(sbjl0).name;
cd([savepath '\' dataset0]);
analysis_list0 = dir(['*' num... |
1c3c2732bb86860e8853201779a0604f0e9bd7e2d111d7228723676741c0c732 | MATLAB | 1,296 | 36 | fn='D:\Allen Data\Ephys\Human\H19.29.144\H19.29.144.11.41\H19.29.144.11.41.01\H19_29_144_11_41_01.nwb';
info=h5info(fn);
swps={info.Groups(1).Groups(2).Groups.Name};
stimswps={info.Groups(6).Groups(1).Groups.Name};
swpstimnames={};
for i=1:numel(swps)
swpstimnames(i)=h5read(fn, [swps{i} '/stimulus_description']);
... |
b07e9537b34333425710443c9fbbdd5925274f3a6d21f39b96538c442aba0b50 | MATLAB | 1,299 | 44 | function y=cut(X,pre,at,post,varargin);
% function y=cut(X,pre,at,post,varargin);
% at: at which frame will be cut
% pre: how many frames before
% post: How many frames after
% varargin:
% 'padding': padding when time that is not available
% 'nan': pad with nans
% 'zero': pad with z... |
5746c3d760838dfd255eb31ed4275584e15d5b11d1d4bd513dfb2eb8ae28da2f | MATLAB | 1,301 | 53 | % DEMO_RPROP_2 Compare the four Rprop methods
%
%
% Copyright (c) 2011 Roberto Calandra
% $Revision: 0.55 $
%% Init
funcgrad = @costfunction; % Function to optimize
a.max = 3;
a.min = 0;
x0 = Utils.rrand([5000,1],a);
p.verbosity = 1; % Increase verbosity to print something
p.MaxIter ... |
4e568c1626861bafa0200027556ea2b634058b7150f79e56047b5e9a4414d11e | MATLAB | 1,302 | 46 | load ('C:\noga\TD complex spike analysis\task_info');
req_params.grade = 7;
req_params.cell_type = 'CRB';
req_params.task = 'pursuit_8_dir_75and25';
req_params.ID = 4000:5000;
req_params.num_trials = 50;
req_params.remove_question_marks = 1;
lines = findLinesInDB (task_info, req_params);
pursuitIDs = [task_info(lines... |
79d16026915bf6ff45b4970ded2c9a7081850968172245f4db58d96d5aba1285 | MATLAB | 1,304 | 35 | function [stats_group, stats_subj] = vector_autoregressive_model(data, group_model, subject_model, n_iter, p_ref_value)
%Inputs:
% data : {data_subj1, data_subj2,...,data_subjS}, data_subj1 is a D-by-N matrix where D is dimension
% group_model : BSDS group_model
% subject_model : mod... |
fc74dc0fdbaf008325bafe1b2bd9ffeb839c8090999c3176d3ca74cf1024e0dc | MATLAB | 1,304 | 37 | function [h,theImage] = MakeGrayImageFigureForMatrix(matrix,figname,figScale)
%MakeGrayImageFigureForMatrix Make a grayscale image figure out of a matrix
% [h,theImage] = MakeGrayImageFigureForMatrix(matrix,figname,figScale)
% matrix = 2D matrix to make a gray image of
% figname = name of figure
% figScal... |
a377782fc4ee5379670cb36894ee4ab90828c72e658dfe0a4ca59fe167eb7278 | MATLAB | 1,305 | 40 | function [pat_idx] = convert_mask_to_pat_idx(mask,mask_idx)
% [PAT_IDX] = CONVERT_MASK_TO_PAT_IDX(MASK,MASK_IDX)
%
% MASK_IDX = a vector of indices into 3D boolean matrix
% mask. [I don't remember how it works if MASK_IDX only
% indexes into a subset of the active voxels in mask.]
%
% Returns PAT_IDX, a vector of indi... |
7abed34a212f9565377b9f5d851de0111fdd76a2ba72da76ca166963f241328f | MATLAB | 1,306 | 45 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function [Cm, I_stim, Vinit, GVal, EVal, TauMax, Sm] = NeuronTypeVtest()
%% RS Hodgkin-Huxley parameter
%Size of neuron
... |
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