sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
72dbd12f520898d722a893f0148da3f3021d99dd0a9c78fc0863d85034d74b01 | MATLAB | 3,408 | 84 | function [Y,Y1] = VSH_spherical_integral(L,M,A)
% L is the L value of the matrix
% M is the M value of the matrix
% A is the side of the cubic ROI - in metres
if isempty(L)
L = [8 8];
end
if isempty(M)
M = [5 5];
end
if isempty(A)
A = 0.1;
end
useV = 0;
useX = 1;
%%%%%%%%%%%%%%% TEST FOR ORTHONO... |
19ee6b5d4a86b6472d0d15c5d9a7b35e62b984e1f645d0cf7743a4111813a523 | MATLAB | 3,409 | 119 | DataPath = '.\Data\Neuro\';
OutPath = '.\Data\Neuro\';
%% Generate and collect KSDs
load([DataPath,'AMDSpks_Sal.mat'],'SpkT_all','cids_all','cpos_all','Mouse_all','SortedStim');
% == KSD parameters ===
minT = -0.2;
maxT = 2.2;
tStep = 0.5e-3;
bws = [1e-3,2e-3,5e-3,10e-3,25e-3,100e-3];
% bws = [10e-3];
tic
fprin... |
9eaaef3f66725dde8d71abd422764fbb9cf44c08135a775c1b47ef618cc35c6a | MATLAB | 3,410 | 121 | clear
[task_info,supPath,MaestroPath] = ...
loadDBAndSpecifyDataPaths('Vermis');
load('sessionMap.mat')
K_FOLD = 10;
DIRECTIONS = 0:45:315;
req_params.grade = 7;
req_params.cell_type = {'PC ss','PC cs', 'CRB','SNR', 'BG msn'};
req_params.task = 'pursuit_8_dir_75and25|saccade_8_dir_75and25';
req_params.num_trials ... |
e0be0c4955e40bfeccff0d271882f74e7c4a0afe5d188b6b7873a9ecb6594413 | MATLAB | 3,413 | 109 | % first stim mark is start of cue
% second stim mark is start of movie
% third stim mark is start of question
% fourth stim mark is end of question
function plotGrpHistISI(sbjList)
ISI = [];
for i = 1:length(sbjList)
sbjNum = sbjList{i};
rawDataDir = ['C:\Users\mn0mn\Documents\ResearchProjects\spatailAt... |
b17f6b40cfd30a3d1e0c00e1bf9126ed42b707641cbf94251cee5497649e0b18 | MATLAB | 3,414 | 112 | function [scratchpad] = train_logreg(trainpats,traintargs,in_args,cv_args)
% Uses logistic regression with regularization to predict your regressors
%
% [SCRATCHPAD] = TRAIN_RIDGE(TRAINPATS,TRAINTARGS,IN_ARGS,CV_ARGS)
%
% Logistic regression, but penalises small weights (like weight
% regularization in backprop), so i... |
1d6cf63192cca8b4abdf486a77178d2337714a18471eee264c6cc3c83d1148f5 | MATLAB | 3,416 | 124 | % Architecture based on de Taillez, for stimulus reconstruction
% Since we're using EEG along with audio waveform, trim EEG segments to
% only active audio part.
% Extract envelope part of audio and downsample to same fs rate as EEG.
whichCond = 1;
sbjNum = 'pk39';
numClasses = 3;
nFolds = 5;
audioDir = 'C:\Users\mn0... |
27ed108086d15e1650a5ef831ef6c6eb359c87ae438c4fc6bbec335d5e25deb7 | MATLAB | 3,417 | 86 | % output = transres_SVM_pattern(decoding_out, chancelevel, cfg, data)
%
% Calculates the pattern according to Haufe et al (2014), Neuroimage. This
% is done by first getting the weights in source space (primal problem), if
% a linear SVM was used (for non-linear methods no weights can be
% calculated for the primal pr... |
e96fa20305f8b03bb16457e26f427139d85a11379dd21eb029a5464269b27843 | MATLAB | 3,419 | 126 | function SetAxesSDGCh(handles, eventdata)
global maingui
if ~exist('eventdata','var')
eventdata = [];
end
hAxesSDG = handles.axesSDG;
iCh = maingui.axesSDG.iCh;
iSrcDet = maingui.axesSDG.iSrcDet;
SD = maingui.dataTree.currElem.GetSDG('2D');
nDataBlks = maingui.dataTree.currElem.GetDataBlocksNum();
ml... |
e98139a4a0b7dc8a44a11f28a8e4b551b90b08523ec305eb2effcf7b4559a1a4 | MATLAB | 3,420 | 103 |
function Process_ConvertBasler2Pos(fbasename,varargin)
syncDatFile = [fbasename '_digitalin.dat'];
syncSampFq = 20000;
syncChan = 1;
syncNbCh = 1;
posSampFq = 60; %in Hz
% Parse options
for i = 1:2:length(varargin),
if ~isa(varargin{i},'char'),
error(['Parameter ' num2str(i+3) ' is not a property (typ... |
2ace6a88bc22c224aff0eeea1f85ded453c8fcdafb50828f47f947caabb23b94 | MATLAB | 3,425 | 115 | function [subj] = statmap_xcorr(subj,data_patname,regsname,selname,new_map_patname,extra_arg)
% Creates statmap using cross correlation
%
% [SUBJ] = STATMAP_XCORR(SUBJ,DATA_PATNAME,REGSNAME,SELNAME,NEW_MAP_PATNAME,EXTRA_ARG)
%
% Creates a pattern containing every voxel for a single timepoint,
% where the value of ... |
711906f3b23d9aa3cd9b6598a88e390fb600e3e4a6ac2610f23e9315f1533174 | MATLAB | 3,426 | 92 | function model = pp_slicetime(sub_id, p)
% function model = pp_slicetime(sub_id, p)
%
% Runs SPM slice time correction on realigned fMRI images.
%
% Input:
% - i_sub: ID of the current subject.
% - p: Structure with analysis parameters.
%
% Output:
% - model: Structure with all SPM settings used for this preproc... |
92b46cf5f5fe1f33ad85982a8612b5c92a585fd6cc1a14dd71dd11566c5ac7ed | MATLAB | 3,426 | 96 | function behav = msExtractBehavoir(behav, trackLength)
%MSEXTRACTBEHAVOIR Summary of this function goes here
% Detailed explanation goes here
ROI = uint16([behav.ROI(1) behav.ROI(1)+behav.ROI(3) behav.ROI(2) behav.ROI(2)+behav.ROI(4)]);
%% Removes background
% takes the median of each pixel from 100 frames spread... |
873c638205d57ba492e6cb3e65072e66140bd4a4e4e2176e0eef972a55f26c50 | MATLAB | 3,427 | 132 | function fig = plot_transients_fragments(step1, step2)
i1 = step2.i1;
i2 = step2.i2;
D = step2.D;
x = step2.x;
zscored = step2.transients_PETA.zscored_matrix;
baseline_period_start_msec = step2.baseline_period_start_msec;
baseline_period_stop_msec = step2.baseline_period_stop_msec;
evt_ts = step2.evt_ts;
%% rename v... |
556fd45f68ad4549a4adf069c6c667f5197c4aebad21881b3f155658ed39b7b7 | MATLAB | 3,429 | 127 | % MY_XCORR3 3 signal cross correlation by cross bispectrum.
%
% compute the cross correlation between 3 stationary signals
%
% (1) Rxyz(m,n) = E[ x(k)y(k+m)z(k+m+n) ]
%
% for the 3 signals case, the extension of WK theorem is
%
% (2) Sxyz( wk, wl ) = sum sum Rxyz( m,n ) * exp( -j*wk*m ) * ex... |
8327f2047448053c93204077ef81ff66b516ab9ded1837a8b74cfd0be6906a4f | MATLAB | 3,430 | 82 | %This analysis code was used in:
%Mares I, Ewing L, Papasavva E, Ducrocq E, Smith FW, Smith MLS (in press).
%Face recognition ability is manifest in early dynamic decoding of face-orientation
%selectivity – evidence from multi-variate pattern analysis of the neural
%response. Cortex.
%The code was originally cre... |
63623b7c72bcc342dedc92dff77a857586c63970a7e4cb5b7b33bfd5763320ba | MATLAB | 3,435 | 83 | function y=fDataROIset(targetpath,savepath,name_sub,name_epi,TR,TE,FA)
%% Set CSF ROI on target slices (4th ventricle)
for cnt=1:length(name_sub)
cd(targetpath);
cd(name_sub(cnt).name);
epitemp = dir(['*' name_epi '*.nii*']);
func = niftiread(epitemp(1).name);
if length(epitemp)>2 && size... |
4a586334ee839bec8081c18c1a86fd8eef0bf2dc04ffac16d7ad84e9a9ebfb23 | MATLAB | 3,436 | 91 | function model = pp_normalize_func(sub_id, p)
% function model = pp_normalize_func(sub_id, p)
%
% Runs SPM normalization on fMRI images that has been realigned and
% slice-time corrected (prefix 'ar'). Uses the deformation fields that were
% calculated with pp_segment.m.
%
% Input:
% - i_sub: ID of the current subje... |
93479ee759c51ffe98cbb86435dc23daa22eb4e02e589317719c391f8a845bab | MATLAB | 3,437 | 84 | function [hdr,vol] = mom_read_minc1_matlab(file_name,opt)
% Read a MINC1 file in Matlab
% To learn more about the MINC format :
% http://en.wikibooks.org/wiki/MINC
%
% SYNTAX:
% [HDR,VOL] = MOM_READ_MINC1_MATLAB(FILE_NAME)
%
% INPUTS:
% FILE_NAME (string) the name of a minc file
%
% OUTPUTS:
% HDR (structure) the... |
ead3b43ab8b231b95ccd36081daee8967ded5e7a2f6df0c7c4efc3f33daac308 | MATLAB | 3,438 | 76 | function [uMF,uMD,uInt,AUCs,dPrime,mice,HR,NTrl] = calcDPrimeFromLatency(RespLatTable,minRespDelay,maxRespDelay,respWin)
% CALCHRDPRIME calculates dPrime, hit rates for AM sounds
% Outputs unique values of the three stimulus parameters
% uMF (modulation frequency)
% uMD (modulation depth)
% uInt (i... |
f52b7f7359725b3aa4ce5d0f566b8696d96e89c3c0b1ce5bc92514fe99be50f8 | MATLAB | 3,439 | 95 | run("/home/nmsutton/Dropbox/CompNeuro/gmu/research/sim_project/code/gc_can/scripts/initOAT.m");
addpath /comp_neuro/Software/Github/CMBHOME_github/
load("example_data/example_spk_x.mat")
load("example_data/example_spk_y.mat")
spikes = [];
display_plot=0;
save_plot=1;
grid_size=30;
shuffles=1000;
binside = 3;
std_smooth... |
296d2f9a8747af78f44d8f885339ea0adabbabb00b4ff9375b38ef55f011bec1 | MATLAB | 3,441 | 141 | function [err,ErrMessage, NameList] = zglobb (Identifiers, Opt)
%
% [err, ErrMessage, List] = zglobb (Identifiers, [Opt])
%
%Purpose:
% returns the list of files specified in Identifiers
%
%
%Input Parameters:
% Identifiers is a cellstr identifying which briks to use
% Identifiers = {'ADzst2r*ir.alnd2AA... |
24f5c68910a4770790f76152e47d68dab30210c74915714daa7d14feaa5349f7 | MATLAB | 3,443 | 116 | function [d, c] = estimate_displacement(im1, im2, kernelsizes1, ...
kernelsizes2, model, method, d0)
% [D, C] = ESTIMATE_DISPLACEMENT(IM1, IM2, KERNELSIZES1, KERNELSIZES2, ...
% MODEL, METHOD, D0)
%
% Estimate displacement according to the algorithms described in chapter 7
% of Gunna... |
3aa8551b091bbe2fd40fe837d8edb47912f90a75ce3a3a55154b2fdbb565cba7 | MATLAB | 3,443 | 125 | function output = model_postCAT(x,data)
% Likelihood function for Q-learning on experiment postCAT v.3 with
% parameters (1)learning rate, (2)inverse temperature, (3)
% perseveration (4)start bias and (5)assignment rate
%
% USAGE: lik = model_postCAT(x,data)
%
% INPUTS:
% x - parameters:
% x(1) - learning rate... |
198793f2906be75c18f303af8756f24fb76782c084d1c409fd49ac2e72d3cc73 | MATLAB | 3,445 | 119 | function [subj] = create_sorted_mask(subj,map_patin,new_maskname,nkeep,varargin)
% Create a boolean mask including only the top OR bottom NKEEP voxels
%
% SUBJ = CREATE_SORTED_MASK(SUBJ,MAP_PATIN,NEW_MASKNAME,NKEEP,...)
%
% Adds the following objects:
% - mask object OR mask group (if PATGROUP == true)
%
% Takes the M... |
fbb7d47f14383014a75ac78fe009a98b71fa45cb6cae096563d78bc08ff026bb | MATLAB | 3,446 | 150 | % mins = LocalMinima(x, NotCloserThan, LessThan)
%
% finds positions of all local minima in input array
% in the case that it goes down, stays level, then goes up,
% the function finds the earliest point of equality
%
% second optional argument gives minimum distance between
% two minima - if 2 are closer than this, it... |
d09c021d131936b1e56fc208ff14eadecefe002b4fc6ba5e82af32c8c23e80cf | MATLAB | 3,448 | 96 | %% Prepare data tables for R
% Script to process file structure output from pairSyanpses.m to
% make datatables ready for R and further analysis for
% "Differential nanoscale organization of excitatory synapses onto excitatory vs. inhibitory neurons"
% Poorna A Dharmasri, Aaron D Levy, Thomas A Blanpied, PNAS 2024
... |
d16af23f3ff6a5b4c5e70655208f1e184eb25b6d0c3731436bf4194d1384b73c | MATLAB | 3,455 | 125 | function this = qc(this)
%QC qc an ACTI object
%
% $Author: Peng Li
% $Date: Feb 07, 2022
%
len = length(this.Data);
qcMessage = "";
%% 1: consistant off-wrist during nighttime
% time restrition mask between 21:00 and 7:00
[~, ind] = doMask2(len, this.Epoch, this.TimeInfo.StartDate, ...
'21:00', ... |
e1517844cd4872ce1f6c520e0a10a6963e4a8d0dcf31851e6ab5551cfcdd21ad | MATLAB | 3,457 | 113 | %DEMSOM1 Demonstrate SOM for visualisation.
%
% Description
% This script demonstrates the use of a SOM with a two-dimensional
% grid to map onto data in two-dimensional space. Both on-line and
% batch training algorithms are shown.
%
% See also
% SOM, SOMPAK, SOMTRAIN
%
% Copyright (c) Ian T Nabney (1996-2001)
... |
f3d5d21dadb9668ff1edcef64f77650fe2aa48a4292d9d99643250a88c15d3a0 | MATLAB | 3,457 | 94 | load('choice_vs_madc_RFTrials.mat')
%% plot Choice Theory results
cols = [[0 0.7 0.7];[1 0 0];[0.2 0 0.8];[0.2 0 0.8]];
f_cols = [[1 1 1];[1 1 1];[0.2 0 0.8];[1 1 1]];
% individual sessions choice theory
cnt = 0;
for iSess = [4:15 18 20:24]
cnt = cnt +1 ;
sens_choice_indiv.G(cnt,1:2) = theta_est_ch... |
8b9ce6dca99e3554b0e4923c6052178836d670f1fb9f756e48b4aca8e738bb69 | MATLAB | 3,458 | 132 | %% Comparison in time
clear
[task_info,supPath,MaestroPath] = ...
loadDBAndSpecifyDataPaths('Vermis');
req_params.grade = 7;
req_params.cell_type = {'PC ss', 'CRB','SNR', 'BG msn'};
req_params.remove_question_marks = 1;
req_params.remove_repeats = false;
epoch = 'cue';
req_params.num_trials = 120;
req_params.t... |
df154966e41648450b73ccda06554e20174573f763bd4ead1236ce0a417c395e | MATLAB | 3,459 | 106 | DataPathNeuro = '.\Data\Neuro\';
DataPathPCA = '.\Data\PCA_SIM\';
OutPath = '.\Data\PCA_SIM\';
addpath(genpath('.\functions\'))
%% ########### MODEL ###########
KSD_trl = matfile([DataPathNeuro,'AMKSDs_individual_Sal.mat']); % per-trial KSDs
load([DataPathPCA,'PCAResTable_Sal.mat'],'PCARes')
%% Estimate threshold cr... |
fa31845a40968cb5cb1db28d90cc11c19536356060319cc5fe0397a23eb22a8c | MATLAB | 3,463 | 93 | %Code to generate T2 maps for a 140x100x9X15 MRI scan
%This is done using method 3 where apply the ROI mask on the T2 scan and average the T2 values for
%a given ROI and perform only one curve fit for the averaged values for each ROI
%throughout this code, cx1,cy1,cz1,ck1 are the 4 counters for looping through the ... |
2248f174582b73e54156b2264c384859ba861f2eb0c90e113f89a379e2154250 | MATLAB | 3,464 | 94 | % shuffle local features function
% October 26 2022
% Amirhossein Farzmahdi
function [imgs_shuffled,fc6_unit_shuffeled_views_percents] = shuffle_feature(nview,n_repeat,n_shuffle,img_size,bckg,images,pixels,net,layer,unit)
for i_view = 1:nview
% insertion image
img_ = images{i_view};
% shuffle... |
0a8fb5c2438c9a42e8cd20042a046603cea7afd91bb130d61fb2808057363f89 | MATLAB | 3,467 | 132 | % Compute border score as in Solstad 2008 (works with square matrix)
%
% USAGE
%
% Compute_BorderScore(pf,bx,by,<options>)
%
% pf place field (#by x #bx)
% bx x-axis bins
% by y-axis bins
% <options> optional list of property-value pairs (see table below)
%
% ... |
626641bbab13841991db6844faa89f2b641be930a747e1dcb9be1db2f8303e89 | MATLAB | 3,467 | 70 | clear
s=1;
while ~isequal(s,0)
fn = 1;
GC=[];
c=1; %counter
oldFolder=pwd; %records your active folder
prompt = 'How long is your experiment(in frames)?';
exp_length = input(prompt); %input of how many frames of the dff files do you want to grab
disp('select folder containing analysis files,... |
214d0b0f9cf7c50515288adc6c5f4176e1cf654ea574e1beaf72333596eb1551 | MATLAB | 3,469 | 94 | % function sim = pattern_similarity_fast(x,method,param)
%
% This function calculates pattern similarity for the desired similarity
% metric (e.g. 'pearson' for pearson correlation or 'cov' for covariance).
% This function is slightly faster than the more general version
% pattern_similarity, because it assumes th... |
8ab4d112bcf5f640204ae43165527ef992a9328d5c24d800209c47726871d8a8 | MATLAB | 3,469 | 67 |
% load all analyzed ccstep files and create analysis overviews for
% inspection
load('D:\Human inhibition\Cell classification\AllenClusterMethod\superclusterdata.mat')
files={SuperCellSummary.File};
files=ChangeFilenameExts(files, 'mat');
users={SuperCellSummary.UserID};
for i=1:numel(files)
fprintf('Open file %1.... |
08091ef9bd3d0a5d0f63521caabd445cfc8e7d6ae286b29365f8ddd5bd5b2820 | MATLAB | 3,474 | 81 | function [Y, X, incrementalCertainty] = ...
estimate_displacement_based_upon_vector_summation2d(solution,...
filterDirection,...
sizeImage,...
numberOfFilterDirections,...
dk,ck,...
t11,t22,t12)
% ESTIMATE_DISPLACEMENT_BASED_UPON_VECTOR_SUMMATION2D Estimates a displacement field based upon vecto... |
ae74baafea7df040c6da7e4ffa28664a29e465054ec63c300ae854ea1903ed34 | MATLAB | 3,474 | 114 | % function [res,run_number] = residuals_from_spm(SPM,masks,Y)
%
% Function to extract residuals from an SPM.mat in case they have not been
% returned by SPM. You don't need SPM for that function to work.
% However, AR(1) is not carried out!
% INPUT:
% SPM: Full path of SPM.mat containing relevant information t... |
0dff37d329845b1da8ecb64e7f990e369275c81bc9ad1492afec6d19d65e71ec | MATLAB | 3,480 | 119 | function [d, c] = one_frame_motion(im, kernelsizes1, kernelsizes2, ...
model, method, d0)
% [D, C] = ESTIMATE_DISPLACEMENT(IM, KERNELSIZES1, KERNELSIZES2, ...
% MODEL, METHOD, D0)
%
% Estimate displacement from a single interlaced frame according to the
% algorithm described in sec... |
22dd6a4a95f9d8e6e0034b0a86d28422647e3297a2636e29ccbd813e2354403a | MATLAB | 3,480 | 119 | function [perfmet] = perfmet_lda(acts,targs,scratchpad,varargin)
%
% function [perfmet] = perfmet_lda(acts,targs,scratchpad,varargin)
%
% The LDA classifier is considered to guess correctly if its
% active row (the unit that it guessed) corresponds to the active
% row of the targs (the unit that was desired) using cl... |
22de6e223376b9cba1ba9b14dc6d9c51064aea2324825c3e470d0e29fa7e96e3 | MATLAB | 3,484 | 91 | function [binCounts, NMI, xbin_ISI,b, keep_time ] = get_spike_delay_hist_210712(down_channel, up_channel, if_flip, if_hist, length_rec_sec)
%creates ISI histogram between ISI of cw_channel and ccw_channel
%Changed from 0.1 to 0.2 1/21/21
min_difference = 0.1; %ms ,ie, velocity = 2 m/s
max_difference = 1; %ms ,ie, velo... |
9bf845c13550ff9937971bd5e4c7314b7719287e059e5ad057bc8d1e833e8469 | MATLAB | 3,486 | 177 | function [c, n2] = nancov(x, varargin)
% [C] = NANCOV(X, NORMALIZEFLAG, DIM) computes the covariance, across all cells in x along
% the dimension dim, accounting for NaNs
% [C] = NANCOV(X, Y, NORMALIZEFLAG, DIM) computes the covariance between all cells in x and y
%
% X (and Y) should be linear cell-array(s) ... |
0ce8a00574b8863103149403adcf7156c6887839d33f9d258d1a5fec2012d609 | MATLAB | 3,494 | 97 | Condition_Name = cond_labels;
Channels = cluster_labels;
%% Load all data
for sub = 1:length(subject_list)
subject = subject_list{sub};
load([TFR_Data subject, '_tf_data_' int2str(1)])
% initialize matrices on 1st subject
if sub == 1
tf_all_PT_Can = zeros([size(tf_data_1) l... |
4988a3055769d25441ac7d16eee30c7605a7bd687d51f602abc0db33fcc86888 | MATLAB | 3,494 | 112 | function new_data = feature_space_smoothing(data, labels, fwhm)
% function new_data = feature_space_smoothing(data, labels, fwhm)
%
% Moves over a continuous feature-space and computes the average of the
% signal of neighboring features, where neighboring features are weighted
% using a Gaussian kernel.
%
% Input:
%... |
411f53de14f3a303c740e3f767c01d1b86505deae8838a8b216368b07a2f1db1 | MATLAB | 3,495 | 75 | % function model = libsvm_train(labels_train,data_train,cfg)
%
% Wrapper function to use libsvm for classification/regression.
% To speed up cross-validation, classification using a precomputed kernel
% is possible. For classification, using a kernel could be implemented, but
% has not been done yet.
%
% General... |
f1c71f57ff8a07eda9df6da6ed9f4b50b8254c260cdb4685c415d5b25b315bc4 | MATLAB | 3,497 | 114 | function [net, gamma, logev] = evidence(net, x, t, num)
%EVIDENCE Re-estimate hyperparameters using evidence approximation.
%
% Description
% [NET] = EVIDENCE(NET, X, T) re-estimates the hyperparameters ALPHA
% and BETA by applying Bayesian re-estimation formulae for NUM
% iterations. The hyperparameter ALPHA can be a ... |
284c3501e6eeca24c3d48f34f4db44eb07cc38836844d37ab84bc20e97d93a19 | MATLAB | 3,500 | 76 | function [ks_D, n_obs_tail, ks_stat] = computeKSStatPowerLaw(x_i, a_val, x_min, x_max_vals)
% Computes the KS statistic (maximum absolute difference between
% cumulative density function) for model and observations
% ks_D is eqn 3.9 from Clauset: this is the KS statistic for x > x_min ONLY
%
max_n_possible = s... |
dc047f7fd116324c1a1ada77ba61f415fc5c12897a4654fde327b5b82de880ba | MATLAB | 3,503 | 135 | function BEH_analysis_regression(exp_name)
%load raw data
load(sprintf('BEHdata-%s.mat',exp_name),'BEHdata')
%load summary data
load(sprintf('BEHsummary-%s.mat',exp_name),'BEHsummary')
%% Regression: calculate regression and plot behavior
wincrit = [40 60];
[fig1,mdl,ds] = calcswitch(exp_name,BEHdata,1,1,1,wincrit);... |
30ec32007963f1d96e60eb11d8e08937ac47efe06a50a0240cf1d051a3a40c6a | MATLAB | 3,505 | 152 | function A = truc(A)
% Find Upstate
% Adapted from Muckovski, Cerebral Cortex, 2007
% Adrien Peyrache 2007
A = getResource(A,'PfcTrace');
A = getResource(A,'Sleep1DeltaEpoch');
A = getResource(A,'Sleep1SpindleEpoch');
A = getResource(A,'Sleep2DeltaEpoch');
A = getResource(A,'Sleep2SpindleEpoch');
A = getResource(A,... |
6f27b81f21e7996f47cdd9671fcc3613bb6a1eb029872367c322ae3ffcec0f82 | MATLAB | 3,507 | 143 | clear all;
clc;
mdir=pwd;
cd('../Data')
load AccuracyDATA.mat
load BaseDATA.mat
cd(mdir);
%% Compare learning index of conjunction learning experiments (1,4) 1)Construct Mat
for ftr_id=1:3
% ftr_id=1; % ori
% ftr_id=2; % color
% ftr_id=3; % joint
Base=BaseDATA(:,ftr_id);
DfCnd{1}=[1:6... |
5254a1dc1cecf317c5144d1f91d285ab029480248b54da3c93f21f2933b1cf9e | MATLAB | 3,513 | 127 | %% Plot PC activity during VORD and cancellation
function [fH_vord, fH_canc, P_gain_vor, P_gain_canc] =...
plotVorCanc(I, K1, K2, K0, flag_gain_up)
% Makes one complete figure for VORD, and one figure for VORC
% One for gain up, and one for gain down
%% Plotting parameters
label_font = 6;
lw = 1.5;
fig_pos = [20... |
84956fad631b548cc5561271f891c6e24c68dbf628c4119bff4ddce758807fab | MATLAB | 3,513 | 108 | clear all
close all
clc
%% Group histogram data
% Define an output folder - the folder which contains all of the
% "exportFolders"
outputFolder = ['D:\Google Drive\Optical Flow Analysis of Actin Waves\Output\HL60\'];
% Include any folder or file names that should NOT be included in the data
% grouping
badNameList = ... |
211e624859cfcbb3abcbb188d2c3306fa6fd727e166a9d1c0e366852f55dce00 | MATLAB | 3,515 | 92 | n = size(Y,2);
p = size(Y,1);
s = nStates;
ldim = size(Xm{1},1)-1;
for subj = 1:nSubjs
Gammas{subj} = QnsCell{subj}';
end
for tt=1:s
parfor ns=1:nSubjs
XmState{ns}(:,:) = preprocess(Xm{tt}(2:end,n*(ns-1)/nSubjs+1:(n*ns/nSubjs)));
end
Ybar = data_arrange(XmState);
if sum(sum(an... |
3c892be22e977d1329457d1f088bed3a1bf2eeb4c63851aecfa949badde4b45f | MATLAB | 3,517 | 134 | % figure S3: mirror symmetric viewpoint tuning index for pool5 and fc6
% layers
% last update: October 27 2022
% Amirhossein Farzmahdi
clear
close all
clc
% add path
addpath(genpath('functions'))
images_dir = 'mat_files/';
% load data
load(images_dir+"imgs")
load(images_dir+"bw_masks")
% settings
nexemplar = 25;
nv... |
9ff16f6a900fb525d5050674a6cf65fab56278050f10c5831116c88a7abaa665 | MATLAB | 3,518 | 124 | function [centres, options, post, errlog] = kmeans(centres, data, options)
%KMEANS Trains a k means cluster model.
%
% Description
% CENTRES = KMEANS(CENTRES, DATA, OPTIONS) uses the batch K-means
% algorithm to set the centres of a cluster model. The matrix DATA
% represents the data which is being clustered, with ea... |
923626473d62b2a4c7a184eb0425b12b3f9f72466d64b3f191469b68b654b384 | MATLAB | 3,520 | 102 | function PlotDistributionRoseplotsOverTime(histExportFolder, filePrefix, distOption, dataBinWidth, showBinWidth, numSmoothedFrames, HistogramTimeData)
%% Assign values
rawCounts = HistogramTimeData.RawCounts;
thrCounts = HistogramTimeData.ThreshCounts;
relCounts = HistogramTimeData.RelCountsWeighted;
magCounts = Histog... |
4a50343db2ae426b6b4322b751b9b78815e6d6e4123a90b1a970936bd1b150c8 | MATLAB | 3,521 | 108 | function [subj results] = tutorial_easy()
% Tutorial script to accompany TUTORIAL_EASY.HTM
%
% [SUBJ RESULTS] = TUTORIAL_EASY()
%
% This is the sample script for the Haxby et al. (Science, 2001) 8-
% categories study. See the accompanying TUTORIAL_EASY.HTM, the
% MVPA manual (MANUAL.HTM) and then TUTORIAL_HARD.HTM.
%... |
113a593ec77553d9218f2ad758e2bb22ecbe78443db34494a6ab816064df78a9 | MATLAB | 3,522 | 109 | % STATUS: Inactive(?)
%
% SYNTAX:
% performanceLDA = calcCumSum_DiffStartT_SingleChn_NoSave(sbjNum,...
% trialsTr,trialsTst,movieListTrain,movieListTest,numClasses,mlActAuto)
%
% DESCRIPTION:
% Perform cross Validation where SS beta coefficients from training fold
% is passed to test fold. Perform single-channel... |
5c02e59da548286771694675e9fbdf29fb24cae4585051d50441414dca007ff8 | MATLAB | 3,522 | 138 | function statDist = permVarFrac(response,match,statistic_func, varargin)
permDims = size(match,2);
p = inputParser;
defaultPlot = false;
defaultBootNum = 1000;
defaultVarNames = string(1:permDims);
defaultVarNames = {'total',defaultVarNames{:}};
defaultTimes = 1:size(response,2);
addOptional(p,'plotCell',defaultPl... |
78845c8e728a7f28084ba6afc42fcaf75c2bb423aabf0f857979f1dd988f80b9 | MATLAB | 3,526 | 78 |
if ~exist('index_to_run', 'var'), error('index_to_run needs to be specified'); end
if ~exist('state', 'var'), state = rng(1, 'twister'); end % state of random number generator
if ~exist('ndip', 'var'), ndip = 200; end % number of active dipoles
if ~exist('snr', 'var'), snr = 0.5; en... |
4f1aaad3d7ba794aa6a100a473821b7363c56f298103c6bf24561aa9daa0126b | MATLAB | 3,536 | 104 | function PlotDistributionHistogramsOverTime(histExportFolder, filePrefix, distOption, dataBinWidth, showBinWidth, numSmoothedFrames, HistogramTimeData)
%% Assign values
rawCounts = HistogramTimeData.RawCounts;
thrCounts = HistogramTimeData.ThreshCounts;
relCounts = HistogramTimeData.RelCountsWeighted;
magCounts = Histo... |
a7fa0a52087c43fcceae34c6907ee63203b791eb843e91ba336b98339ea36a27 | MATLAB | 3,537 | 135 |
%% Pursuit task
clear
[task_info,supPath] = loadDBAndSpecifyDataPaths('Vermis');
PROBABILITIES = [25,75];
OUTCOMES =[0,1];
figure
req_params = reqParamsEffectSize("both");
% req_params.cell_type = {'PC cs'};
req_params.ID = 4153;
lines = findLinesInDB (task_info, req_params);
cells = findPathsToCells (supPath,... |
2fcf7affe0335f1a82c0543aa2ebe5c50ad7c0ef806eeb32f8ee2a1bce3eebd7 | MATLAB | 3,539 | 121 | function [subj] = hpfilter_runs(subj,patname,selname,cutoff,tr,varargin)
% Uses spm_filter.m to high-pass filter the time series of each voxel
% in each run
% Adds the following objects: - pattern object
%
% This function creates a new patterns object (PATNAME + '_hp') to store
% the filtered timeseries.
%
% ... |
3d93559869b11942b345edee8522c1dab2af524d6fd9733134431b2691cd734d | MATLAB | 3,546 | 97 | function this = emd(this)
%EMD Perform EMD analysis on an ACTI object
%
% $Author: Peng Li
% $Date: Mar 09, 2022
% $Modif.: Mar 24, 2026
% Revise to enable per cycle output
% Jul 06, 2026
% Add nadir output after LI_UPMEMD_CYCLE. Currently put nadir
% t... |
aedb73659a6aef950225b5deb4e16a77f061e513ac61bc0d893e02e1ae62ec0b | MATLAB | 3,551 | 95 | % to be run after preprocessFNIRS06_FInteraction_MultiOnly.m
function plot_FInteraction(s,numClasses,fefOnlyOp,saveOp)
fs = 50;
timeLen = [0.1 0.2 0.5 1 1.5 2 3 4 5].*fs;
nrept = 10;
kFold = 5;
sbjNum = s.name;
processedDataDir = ['C:\Users\mn0mn\Documents\ResearchProjects\spatailAttentionProject\ProcessedDatafNIR... |
d677ce447fb54a2afaead08b55d463a0290567e754225a7b6177bd7f07d7ad47 | MATLAB | 3,551 | 93 | function pp_partition_rp_files(sub_id, p)
% function pp_partition_rp_files(sub_id, p)
%
% Takes the rp*.txt files containing the realignment-parameters estimated
% with pp_realign.m for each experimental session, and saves them for each
% individual run. This is necessary to include them as regressors of no
% interest... |
348a9c38fc424a872142caee01c5112d935bf604ea1f2efeef9c9e6d26563894 | MATLAB | 3,556 | 122 | function err = hdf5write_safe(fname, name, val, options)
err = -1;
if isempty(val)
return;
end
if ~exist('options','var')
options = '';
end
force_scalar = false;
force_array = false;
if any(strcmp(options, 'array'))
force_array = true;
elseif any(str... |
c82c5e025f142dd9a7990b2194dd2876a281aac76133d4de4ba7b39b654a7c79 | MATLAB | 3,557 | 97 | %This script scans an OCT image of a target or flat surface for analysis
%% Inputs
% IMPORTANT:
% Before running this script make sure target is at focus
% What are we imaging
switch(1)
case 1
%Imaging a flat surface target but placing it on motorized stage so
%we can take multiple imag... |
950833f8e557e10dfb7c61e943d0c59f899b637fd0d370b7e485ac781380fc20 | MATLAB | 3,558 | 101 | close all
clearvars
clc
%get user input
selpath=uigetdir('*.*','select MINFLUX directory for a single construct')
%find result files
files=dir(fullfile(selpath,'**','*reeval.xls'));
%define model for fitting
fitmodel=@(p,x)[p(3).*( exp(-x./p(2))./p(2) .* (x./(p(2)-p(1)) - (1-exp(-x.*(p(2)-p(1))./(p(1)*p(2... |
c0d49faea7ced46504d8e7587af95a90fa79d41d78346d2c9642bf05a311342a | MATLAB | 3,559 | 121 | function h = plot(this, varargin)
%PLOT plot an ACTI object
%
% $Author: Peng Li
% $Date: Dec 02, 2020
% $Modif.: Dec 08, 2025
% Add Diary section.
% Remove Nap as essentially it should be included in Sleep
% Feb 15, 2026
% Enable showing primary sleep ... |
90756fe8daa7a9be189e12b78d4f84e839f444cd3e1a88b698090562f7b300e4 | MATLAB | 3,561 | 133 | function FindHDCells(varargin)
% This function computes some Head Direction cell statistics
% Must be launched from the folder containing data
% Folder must have the same name as position file basename.
% Adrien Peyrache 2012
epIx = [];
if ~isempty(varargin)
epIx = varargin{1};
end
if length(varargin)==2
fn... |
d4459131fdf9a067563ad5558213f7f7e50f034d670722b4e03400b94c72a7f0 | MATLAB | 3,562 | 98 | function spots=bd3d_t_local_filtered(wholestack_local,sizes_local,thresholds_local,sigmas_local,zsizes_local,max_spotnum)
% volume LOG filtering
filtersize=[sizes_local sizes_local zsizes_local];
h=-fspecial3('log',filtersize,sigmas_local); % this can be taken out of the function
If=imfilter(wholestack_local,h,'sym... |
2b890b795606ec775fca1b65afd538cc4d2bd53247cf63cbd838f15a05bdf496 | MATLAB | 3,572 | 121 | function this = fit(this)
% FIT Fit a cosinor object based on LSE
%
% This can be done through minimizing the residual squared error
% to solve normal equations -- RSS's first order derivatives w.r.t. each
% parameter are zero
% notation in Ref. [1] (check COSINOR)
% y = M + Acos(w... |
c8994d5db91a764ea14b2dae5816b81bf027daccf1b245ade6a15842583ab1a4 | MATLAB | 3,572 | 108 | function [hit_rate,false_alarm_rate,p_val] = ...
plot_psychometrics(sessions, beh_dat)
% Initialize variables
% cTab = cell(1,length(sessions));
% p_val = zeros(length(sessions_looped),num_of_iters);
% hit_rate = cell(1,2);
% false_alarm_rate = cell(1,2);
RF=1; % RF = 0... |
369b42a73003db9c6deac12034cfa061c96924691a0b08ffd497682d0f25c8ce | MATLAB | 3,575 | 127 | function [br_min, br_mid, br_max, num_evals] = minbrack(f, a, b, fa, ...
varargin)
%MINBRACK Bracket a minimum of a function of one variable.
%
% Description
% BRMIN, BRMID, BRMAX, NUMEVALS] = MINBRACK(F, A, B, FA) finds a
% bracket of three points around a local minimum of F. The function F
% must have a one di... |
fdf056612ca3cb659fae7722ce631f7d2ab1e2d77004113b797f79d52638b099 | MATLAB | 3,577 | 102 | function onsets = lvl1_extract_onsets(run_log, experiment, analysis)
% function onsets = lvl1_extract_onsets(run_log, experiment, analysis)
%
% Extract onset times of trial events for SPM first-level analysis.
%
% Input:
% - run_log: Struct with one index per run. log.runNumber is adjusted so
% that it counts ... |
2f8fad94c51547d4e23453ba08ce066a8a88e3d62286ae4673e8c1c07c85a18e | MATLAB | 3,579 | 97 | function scanInfoAdvance(subjectPath)
% This function advances scan info to the latest version written for LD
% Written at March 25, 2020.
if ~exist('subjectPath','var')
subjectPath = s3SubjectPath('02','LD');
end
isLoadConfigFromLog = true;
%% Set paths
volumeJsonPath_Operational = [subjectPath '/OCT... |
e195b4814a9d15c93ae109bf9b380344e18d5b90915f9d6561e92907e7db76c5 | MATLAB | 3,579 | 129 | % TEMPMATCH template matching for time series
%
% [ c, t, p ] = tempmatch( w, x, th )
%
% w template (must be a vector)
% x time series (can be matrix, works on columns)
% th {0.9i} threshold: p-value (real) or corr (imag)
%
% c correlation coefficient
% t decision (1/0: m... |
83b75b0b14f7bd1bc50586165aefd17f56445305be791429855ca5cc0da8b752 | MATLAB | 3,580 | 119 | function [subj] = detrend_runs(subj,patname,selname,varargin)
% [SUBJ] = DETREND_RUNS(SUBJ,PATNAME,SELNAME,...)
%
% For each voxel in each run, detrend_runs removes trends
% according to the order of the specified polynomial
% i.e. 1 = linear, 2 = linear + quadratic, ...
%
% Adds the following objects:
% - ... |
d2942bbb093b47ebacf001036b679db4faa0b49fed949f32617a2a8b8390396c | MATLAB | 3,581 | 90 | function mv_print_classification_info(cfg, X, clabel, X2, clabel2)
% Prints information regarding the classification procedure, including the
% cross-validation procedure and the dimensions of the input data. This
% function is called by mv_crossvalidate, mv_classify_across_time, and
% mv_classify_timextime.
%
% Usage:... |
f20e621a4157eca532758c45590efc818ed8fadae05c74dcde14c200d07fa9f0 | MATLAB | 3,588 | 106 | function [interferogram, apodization,prof] = yOCTLoadInterfFromFile_ThorlabsSRRData(varargin)
%Interface implementation of yOCTLoadInterfFromFile. See help yOCTLoadInterfFromFile
%Reads SRR files
% OUTPUTS:
% - interferogram - interferogram data, apodization corrected.
% Dimensions order (lambda,x,y,AScan... |
31c9431f14a5596e58165ded8c0fb9acd46296b309bdc5ecff8f272277473fdd | MATLAB | 3,589 | 124 | function awsCopyFile_MW2(dest)
%See awsCopyFile_MW1 for documentation
%Will go over all folders in dest to get files out!
%% Input check
if awsIsAWSPath(dest)
awsSetCredentials(1); %We need CLI
end
dest = awsModifyPathForCompetability(dest);
%% Get all the files that need loving
try
ds = fileDatas... |
e3177319058281c3c81420e569120fbdba75741f756509299b522d5dc3b8ff96 | MATLAB | 3,591 | 98 | %% plot Choice Theory results
cols = [[0 0.7 0.7];[1 0 0];[0.2 0 0.8];[0.2 0 0.8]];
f_cols = [[1 1 1];[1 1 1];[0.2 0 0.8];[1 1 1]];
% individual sessions choice theory
cnt = 0;
for iSess = [4:15 18 20:24]
cnt = cnt +1 ;
sens_choice_indiv.G(cnt,1:2) = theta_est_choice_indiv.G(iSess,1:2);
sens_choi... |
ac9747b6f292d590c8cf6f4bc239bd1ee2a3558cad66174b9c1d1f7b1f0415d5 | MATLAB | 3,592 | 107 | function ret = aqua_export( options )
% AQUA_EXPORT Extract and save AQuA events from a dataset
% ret = AQUA_EXPORT(options)
%
% Available keyword options:
% * 'inputPath': Path to the folder with input aqua data
% * 'outputPath': Path to the folder to place results
% * 'experiment': Presets for uncaging sta... |
fdad75d95604d222784ab54f8b4172943b34da372cf58d3edfa1882a4d9cd83a | MATLAB | 3,595 | 71 | function LinePlot_SummaryStat_IndData(mat2plot, TS, TITLE, condition, YLIMS, YLABEL, SUMMARY, stdOrsem, COLOR)
%LinePlot_SummaryStat_IndData:
% 1. Takes a matrix of values
% 2. Plots each row of the matrix as a grey line plot (repeated measures as columns, ex: multiple imaging rounds)
% 3. Plots a summary statist... |
1d6053d3bfe6ceef3ee87c9e930644b4054b2b748728808af1ef30c22272d4be | MATLAB | 3,601 | 94 | function status = unitTest_DefaultProcStream(datafmt, dirname)
global procStreamStyle
global testidx
global logger
if isempty(procStreamStyle)
procStreamStyle = datafmt;
end
if isempty(testidx)
testidx=0;
end
testidx=testidx+1;
status = -1;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
85623123099cf3ff266f4f59b81aa047be811dad9f10e8ba35a941659dfbeb7b | MATLAB | 3,602 | 96 | clear all;
load matching_table_ccw.mat
% loading order of raw data
folders=["ECDGCA3CA1 19914 160127 160217 d21 5minspont000_mat_files",...
"ECDGCA3CA1 19914 160127 160303 d37 5minspont000_mat_files",...
"ECDGCA3CA1 19908 150729 150823 d25 5minspont000_mat_files",...
"ECDGCA3CA1 24088 160127 160302 d36 5mi... |
778348cfa74113fe22c9ccb1cf3dc27890f232d1a9f403a8aac72798ce202933 | MATLAB | 3,608 | 113 | function [P, F, stats] = anova1_mvpa(mat,conds,sels,chunksize)
% Performs a 1 way between-subjects anova
%
% [P F STATS] = ANOVA1_MVPA(MAT,CONDS,SELS,CHUNKSIZE)
%% Performs a 1 way between-subjects ANOVA on a matrix MAT. Condition
% membership is contained within CONDS, which must be a boolean matrix of
% size [M, N]... |
8c525fe84ec466a9adf81fe7d2419d9c3b4777056fac51b9369ef9c181c65408 | MATLAB | 3,609 | 113 | function [X,clabel,Y] = simulate_spiral_data(nsamples, nrevolutions, nclasses, prop, scale, do_plot)
% Creates spiral-shaped data in two dimensions.
% Useful for testing non-linear classifiers eg SVM with RBF kernel.
%
% Usage: [X,clabel,Y,M] = simulate_spiral_data(nsamples, nrevolutions, nclasses, prop, scale, do_plo... |
fcde01296b57330561366225b986760a00085bb6286991378a52b12ad2b95161 | MATLAB | 3,610 | 176 | function O = intervalSet(varargin)
% Constructor for intervalSet objects
%
% USAGE:
% O = intervalSet(s, e, fixit, PropertyName, PropertyValue, ....)
%
% INPUTS:
% if no inputs, return a null intervalSet object
% s, e - vectors containing start and stop times for intervals
%
% 'PropertyName', Pr... |
95a441e51306afd65a79944b3258627a7f73d284b4778bd6713a902825452e23 | MATLAB | 3,611 | 119 | % function table = display_design(cfg)
% or
% function table = display_design(cfg.design)
%
% Displays the design matrices (train, test & label) in a nice(r) form.
%
% For a graphical representation, see plot_design.m
%
% See also PLOT_DESIGN
% Potential improvements
%
% - Multiple blocks for long designs (e.g. afte... |
5ea4640d1616e5374c11fee9360d5d82a6a7f2fa5043a576dbeaf939d484cd40 | MATLAB | 3,612 | 128 | clear;
% INT549:thr=7
prompt={'tau','threshold_scaling'};
answer=inputdlg(prompt);
% set to folder with tiffs
root = 'D:\McCormick_Data_May27_2021-\2021\Jun\4004_210610_E266_meso_am_1\2P';
file='4004_210610_E266_0_00012_00001.tif';
fs = dir(fullfile(root,file ));
fname = fs(1).name;
fname = fullfile(root, fn... |
a6b43dbc898e143c86b7e4d5827d62f126cc5e0291240528e47cc23707d1dd86 | MATLAB | 3,618 | 175 | function [c] = cellnancov(x, varargin)
% [C] = CELLNANCOV(X, NORMALIZEFLAG, DIM) computes the covariance, across all cells in x along
% the dimension dim, accounting for NaNs
% [C] = CELLNANCOV(X, Y, NORMALIZEFLAG, DIM) computes the covariance between all cells in x and y
%
% X (and Y) should be linear cell-a... |
d599c63eaa1424aebed2838eb69120485a5d294f03770092d8343c61513d1a28 | MATLAB | 3,621 | 98 | function test_suite = test_file_utils %#ok<*STOUT>
try % assignment of 'localfunctions' is necessary in Matlab >= 2016
test_functions = localfunctions(); %#ok<*NASGU>
catch % no problem; early Matlab versions can use initTestSuite fine
end
initTestSuite;
end
function test_file_utils_basic()
%% test to g... |
f344b073d727b1c76bdda7468257b2f895949e067cebab005653d4e469b6cd93 | MATLAB | 3,621 | 107 | function sortImagesTrainTest(baseFolder,trainSetSubjects)
% This function sorts images to train & test folders
% train_A, test_A contains OCT, _B contains histology.
% baseFolder - main folder to sort
% filesInTestingSet - cell array containing template of files to be
% included in test set, all the rest wil... |
dd177ebca753f3d1a97e9338e206a3b85cd5b3eaa417fb817d6b3d239a39221d | MATLAB | 3,623 | 100 | clear all;
load matching_table_ccw.mat
% loading order of raw data
folders=["ECDGCA3CA1 19914 160127 160217 d21 5minspont000_mat_files",...
"ECDGCA3CA1 19914 160127 160303 d37 5minspont000_mat_files",...
"ECDGCA3CA1 19908 150729 150823 d25 5minspont000_mat_files",...
"ECDGCA3CA1 24088 160127 160302 d36 5minspont0... |
6d935f330d61694ffcc6bb051ffb213d3789ac59f16e22b9bd6fe202f2db838b | MATLAB | 3,626 | 92 | % This function tests knobes to and from transform
%% Basic tests
% 2 microns per pixel, no rotation, no shrinkage, x translation of 10 microns
T = knobesToTransform(0,0,10,0,2,1);
assert(all([0 0 1]*T==[10 0 1]),'Baseline translation');
assert(all([1 0 1]*T==[12 0 1]),'1 pixel translation x');
assert(all([0 ... |
81e1acbd1b7529f1cba8873fa040bfbf02af212e218d372447c43eb5b7e6df97 | MATLAB | 3,628 | 114 | function json = s3LoadAllSubjectSectionJSONs(varargin)
% This function loads all JSONs to one structure for us to reference
% json = s3LoadAllSubjectSectionJSONs(subjectPath [, sectionName, parameters ...)
% INPUTS:
% - subjectPath - path to subject.
% - sectionName - example: 'Slide01_Section02'. If empty, ... |
4ab1059098fbf30a3558def31232b2cab14484ce556b768c90d529497abf0ecb | MATLAB | 3,637 | 99 | function compute_NMI_210726(folder_name, parent_dir, if_cw)
%Computes Normalized Matching Index matrix(NMI) for all output of wave_clus
%tunnel pairs in the folder indicated by folder_name
%
%Normalized Matching Index(NMI) is used to indentify which using in
%tunnel-1 corresponds to unit in tunnel-2 to form an axon, w... |
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