sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
7f88922d22399db41f6515fd048fdf886109c2f47f75575894fa10c32c0cd8fd | MATLAB | 4,164 | 113 | % MY_SPECTRUM Welch spectrum for multiple signals.
%
% call [ PXX, F ] = MY_SPECTRUM( X, NFFT, FS, WINDOW, NOVERLAP, DFLAG )
%
% gets X matrix; trials in columns
% NFFT {128}; length of segments to be averaged
% ... |
128cb9e3814d41d127b551295c5d3423f6699736905c7169be15139e38fc6ea2 | MATLAB | 4,165 | 149 | function [subj] = apply_to_runs(subj, patname, selname, fname, varargin)
% Applies a function to the runs of a given pattern.
%
% [SUBJ] = APPLY_TO_RUNS(SUBJ, PATNAME, SELNAME, FUNCTIONS, ...)
%
% Each function in the cell array FUNCTIONS is applied individually to
% the runs specified by SELNAME of pattern PATNAME. ... |
3d92a990e71d3b9d1c7cea264e230a5d00fafa796e99b814140958b233066396 | MATLAB | 4,167 | 126 | %DEMGPARD Demonstrate ARD using a Gaussian Process.
%
% Description
% The data consists of three input variables X1, X2 and X3, and one
% target variable T. The target data is generated by computing
% SIN(2*PI*X1) and adding Gaussian noise, x2 is a copy of x1 with a
% higher level of added noise, and x3 is sampled r... |
787355d59ae580fe7f5754942e704f4aec51882a0f30d5043d1b5fb5ba62a617 | MATLAB | 4,167 | 129 | function [ptStart_Scan, ptEnd_Scan, ptStart_Extended, ptEnd_Extended] = ScanAndPattern_GeneratePatternToPhotobleach(config)
%This auxilary function will generate line set to photobleach as part of
%the scan and the extended lines
ptStart = [];
ptEnd = [];
ini = yOCTReadProbeIniToStruct(config.octProbePath);
... |
35a1e87ca1bf6a0d9158bedd8807c30acac0057e9df35153430ade30d4023f9e | MATLAB | 4,170 | 142 | function [data, headers, y_labels] = list_events(varargin)
%
% Returns summary of all events for a given task.
%
% USAGE::
%
% [data, headers, y_labels] = bids.internal.list_events(BIDS, ...
% modality, ...
% ... |
0dff8ad8c0c688f62168ceab06bfeba6e9e577a33234ebb08080d6d7b9851139 | MATLAB | 4,171 | 90 | function pd = weibulltails(x,pl,pu,distrib)
if ~exist('distrib','var') | isempty(distrib)
distrib = 'weibull';
end
[yvals xvals ylvals yuvals] = ecdf(x);
yvals = 1-yvals; ylvals = 1-ylvals; yuvals = 1-yuvals; % Use upper tail
xvals = (xvals(1:end-1)+xvals(2:end))/2; % Piecewise linear interpolation, to enble inte... |
f812d466a7de16a87b5470d36a9d4c22b917867e4e90ff78f6523f3996e53167 | MATLAB | 4,173 | 129 | warning('off', 'all');
m = [];
%% Dataset 4
load('.\Dataset4.mat');
fname = 'D4'; fixed_time = 132; voxel_size = [0.267 0.267 2]; current_neuron_arangement = pca_reconstruction(neurons_cleaned,20); % prima sembrava giusto [29.7563 76.6338]; [319.0453 115.6386];
chosen_point = [40 70];
[correspondences,di... |
d34e597880a4b4f25617b1531488f37785a0e7941f454722f7c6c1264a1592d2 | MATLAB | 4,177 | 142 | classdef cosinor < timeseries
% COSINOR define class for cosinor analysis
%
% C = COSINOR(DATA, TIME) creates a COSINOR object to fit the default
% cycle length (24 hour). TIME should be in seconds relative to
% 'StartDateTime'. If 'StartDateTime' is not specificed, the
% ... |
325df97d8b37db1390ca30a74496dba04135bdbff28f346fab98407a67b67ce5 | MATLAB | 4,178 | 122 | function [pparam, X, clabel] = mv_preprocess_pca(pparam, X, clabel)
% Performs Principal Component Analysis (PCA) by projecting the data onto
% the n leading eigenvectors of the covariance matrix.
%
%Usage:
% [pparam, X, clabel] = mv_preprocess_pca(pparam, X, clabel)
%
%Parameters:
% X - [... x ... x ...] ... |
e7ce8f906a663e80ebb1a76e2e96f148d65c1aef22e39ecf78ba6f374c1d3b57 | MATLAB | 4,181 | 102 | function PlotMean_IndDataPts_MultCond( DIST_STRUCT, TITLE, YLABEL, cmap, cmap_num,...
stdOrsem, STATS )
% Michelle Cahill 20211201: modified from violinplot_STATS.m
% PlotMean_IndDataPts_MultCond plots the mean value of each t-series for each
% condition & the std or sem of all t-series. A statistical test ... |
a21e041c4a54d076feface61a0a2a67e2f8cf38abd2d0fe99a8e743627d64de2 | MATLAB | 4,184 | 134 | function response = get_response(Experiment, responseType)
Keys = Experiment.Keys;
Timing = Experiment.Timing;
switch responseType
case 'continue'
% Wait for response
keyIsDown = 0;
while 1 % Infinite loop of keyboard checks
[keyIsDown, secs, keyCode] ... |
aa95c06fac317f322f977db24f69812c12c272be4246f1bfdbf10a4994ef564d | MATLAB | 4,185 | 112 | % Extract the brain activity computed in the GLM2_Test Grid orientation in
% the EC
%% Set up the environment
clc
clear
restoredefaultpath
SPMPath = '/Applications/spm12';
addpath(SPMPath);
%% Add the pm_data path
pmDataPath='/Users/federica/data/GridMagination/3Iso/Script/00_pm_data';
addpath(pmDataPath)
WorkingDi... |
3dffa4fff39598f9b021908c779687bdc05173543266e8a8c2c0f3e9f513e0fc | MATLAB | 4,186 | 121 | function fdln = fdlnIdentifyLinesByRatio(fdln,vLinePositions,hLinePositions)
%This function tries to identify fiducial line structure based on the distance ratios.
%INPUTS:
% fdln - fiducial line structure array
% vLinePositions - crossing points of vertical lines (x=0, y=c) [mm]
% hLinePositions - crossing ... |
9186d480c2e6196eaf69962de50e18909267266f2532725d1febdcd34e8c38c2 | MATLAB | 4,189 | 133 | function vmp = bv_writevmp(fname_out_vmp, data, fname_in_vtc, ...
x_vmr, y_vmr, z_vmr)
% vmp = bv_readvmp(fname)
%
% :description
%
% Writes a VMP file in version 2 (BrainVoyager QX).
%
% :inputs
%
% fname_out_vmp file to be written out, e.g. 'myvolstats.vmp'
% data 3d m... |
0d5faa27b8ee764630496a8f8e9cdc9c6e679dacbf20af560388583f0f506a08 | MATLAB | 4,191 | 125 | function [subj mat] = duplicate_object(subj,objtype,old_objname,new_objname,varargin)
% Duplicate an object
%
% [SUBJ MAT] = DUPLICATE_OBJECT(SUBJ,OBJTYPE,OLD_OBJNAME,NEW_OBJNAME,...)
%
% Calls init_object to create a new obj called NEW_OBJNAME,
% and then copies across the mat from OLD_OBJNAME and sets
% the derived_... |
7efc2fc7a083107ea5c6a073a55b4e475400d93cb58b5c758a79990f435a8d67 | MATLAB | 4,192 | 77 | % Fit a utility logit choice model for intertemporal binary choice data
%
% 'type' : String input indicating the model to fit. One of H, E, GE, GH1, GH2, Q, DD.
% 1 param models (k)
% 'E' : Exponential Model (Samuelson, 1937). U = A * exp(-kD)
% 'H' : Hyperbolic Model (Mazur, 1987). U = A / (1+kD)
%... |
fda37488362f687c6a1e179fa7f33734920226558cfc9a99f92d1b808249b190 | MATLAB | 4,193 | 104 | function mri_reconstruction_permute(p)
% Run the pSVR analysis as for the optimal values for voxel-counts and
% feature-space smoothing FWHM values. First, the results from the
% parameter grid-search analysis (see mri_reconstruction_grid.m) are loaded
% and submitted to a nested cross-validation across subject, in wh... |
19e09c13b6ed040e57b41d38e87eb3b423532d4c8c3218b8a089ca4784835a2d | MATLAB | 4,194 | 119 | %% IN-PHASE AND ANTI-PHASE SYNCHRONY
clear
rng(17) % seed
Path = [pwd, '\ML_sims\'];
clrs = ["b","r","g","k","c","y"]; % colors
per_clr = [0.9290,0.6940,0.1250]; % periodogram color
% TIME
T = 1500; % time span
dt = 0.01;
% COUPLING
epsilon = 0.10;
n = 2;
N = sum(n);
K = getK(n,epsilon); % al... |
5b3553c044876b35cb608aa655330a412b935add42fb0d4e45305f85f8e803f2 | MATLAB | 4,197 | 87 | % This script is a template for people who are interested in carrying out
% between-group decoding of brain imaging data. This is the case where each
% of your subjects carries only one condition. If you have one group and
% have multiple conditions per subject you want to compare between subject,
% see decoding_te... |
3c16cb423078dcb654628e5780902faa76d06ffce49924db9e6618a891571931 | MATLAB | 4,206 | 120 | function [transformationMatrix] = ...
phase_difference_linear_registration2d(moving, movingCertainty, ...
fixed, fixedCertainty, varargin)
% PHASE_DIFFERENCE_LINEAR_REGISTRATION2D Estimates a transformation matrix using phase-difference
%
% [transformationMatrix] = ...
% phase_difference_linear_registration... |
f4d59c61b02a7d27029212f8904afe334b16e70b3a68d61b68ed8c3145c68e2f | MATLAB | 4,211 | 135 | clear all
[task_info, supPath ,~,task_DB_path] = loadDBAndSpecifyDataPaths('Vermis');
req_params.grade = 7;
req_params.cell_type = 'PC cs';
req_params.task = 'speed_2_dir_0,50,100';
req_params.ID = 4000:6000;
req_params.num_trials = 50;
req_params.remove_question_marks = 1;
lines = findLinesInDB (task_info, req_par... |
07b2b3ce8758c19edf88d71908f6ba7b051c838321d550c4b083ce2ee182adfa | MATLAB | 4,214 | 142 | % dehighpass in a data-dependent manner
%
% xd = dehighpass( x, minAmp, minDur, f )
%
% default parameters:
% minAmp 0.01 of the range (max-median)
% minDur 3; [samples]
% f 2; multiples of the instantaneous duration
%
% does
% given a segment of data filtered with a high-p... |
9bae9dfb4de770f220287479fe2979bced645c1127cb6a4b4f92924b2c2ccefb | MATLAB | 4,216 | 147 | clear
[task_info,supPath,MaestroPath] = ...
loadDBAndSpecifyDataPaths('Vermis');
angles = [0,90];
probabilities = [0:25:100];
test_window_form_event = 1:300;
NUM_COND = 10;
req_params.grade = 7;
req_params.cell_type = 'PC ss|CRB';
req_params.task = 'choice';
req_params.ID = 4000:5845;
req_params.num_trials = 100... |
11f3859473a05ee42ddba6d58c96358618c1cec29d5324f9faf30872ded3c853 | MATLAB | 4,220 | 93 | % This script is a template that can be used for a representational
% similarity analysis on brain image data. It is for people who have betas
% available from an SPM.mat and want to automatically extract the relevant
% images used for the similarity analysis, as well as corresponding labels
% and decoding chunk nu... |
56b1bdfc6c1dc1595d64f14724af402f672929598d9fd662cef8b2b606c76f9d | MATLAB | 4,225 | 152 | function [x, options] = linemin(f, pt, dir, fpt, options, ...
varargin)
%LINEMIN One dimensional minimization.
%
% Description
% [X, OPTIONS] = LINEMIN(F, PT, DIR, FPT, OPTIONS) uses Brent's
% algorithm to find the minimum of the function F(X) along the line DIR
% through the point PT. The function value at the start... |
dcf85d886ac40d358396b023cdd6382b50598c50d4b528f58246bc01e5429b51 | MATLAB | 4,229 | 92 | function singlePlaneFit = spfCreateFromUVH (u,v,h,v_,pixelSize_um)
%This function creates a single plane fit structure from u,v,h inputs
%Single plane structure contains some statistics on the plane.
%INPUTS:
% u,v,h ar 3D vectors defining the plane as u*U+v*V+h. units: mm.
% v_ [optional] - typical value fo... |
0b050bfbcc8531474c7aee572ab673c69fc5bf6efd869ea1e4ab592f3c4dda78 | MATLAB | 4,231 | 125 |
%% Load data
load(fullfile('Data','BehavEpochs'));
load(fullfile('Data','SpikeData'),'S');
load(fullfile('Data','Angle'));
load(fullfile('Data','Velocity'));
% epTemp = csvread(fullfile('Analysis','LED1_TS.csv'));
% wakeEp = intervalSet(epTemp(2,1),epTemp(2,2));... |
4e0d2dfe36eaf8ab7628d670a192d5cb4f21458cf6f5e42366b0a5b4a9bd0ecf | MATLAB | 4,231 | 82 | % Template for creating ML documents. Using the HEP average by trial data.
% X, truth, duration, skipLOO, perm, eigratio,gamma, method)
% Sample length = 1000 Hz Downsampled to 500 Hz
tau = 5; % 10 ms of increment for discrimination
duration = 30; % 60 ms of windo... |
46c50f6842f4c72fbbb3a54941a463d1d80bda8342feee6e35b5414810093653 | MATLAB | 4,235 | 95 | % This demo shows how to apply prevalence inference as statistics to a toy
% dataset. This example shows you what you minimally have to do to PROVIDE
% YOUR OWN DATA (i.e. if you do not want to use TDT or images). Find
% further explanations about the analysis in the paper below.
%
% Alternatively, consider tak... |
2213866e16a8dc52e00579ae4e58d309808773649876160c2b1768a76ae03d3d | MATLAB | 4,236 | 121 | % beta is #sbj x HbX x #Channels x #conditions
% bvar is #sbj x #HxB x #Channels x #conditions
% sigAct is HbX x #Channels x #conditions
% sigDiff is HbX x #Channels x #pairs
% For sbj 12, # of coefficients is 16.
% use welch t-test
% Average beta
% Use Wald's test for single parameter estimation.
% Given each obser... |
533fcbe9608c953e4ab607b90dd10e39154a8b735aee2fd0f56fed60d9ca037b | MATLAB | 4,238 | 175 |
clear
[task_info,supPath] = loadDBAndSpecifyDataPaths('Vermis');
req_params.grade = 7;
req_params.cell_type = {'PC ss', 'PC cs', 'CRB','SNR','BG msn'};
req_params.task = 'choice';
req_params.remove_question_marks = 1;
req_params.remove_repeats = false;
req_params.num_trials = 120;
epoch = 'cue';
lines = findLine... |
71073643d7dd158e4c1c19ca92072db6ab8c7e296cbdb82fb3d14f72780ff87c | MATLAB | 4,240 | 71 | function [h, p, stats, n, stderr_spon, stderr_stim]=no_pad_wilcoxon_210701(table1, table2)
%if conduction time isnt present in both, drop time and spikes etc
for i=1:length(table1)
[rwtb,~]=size(table1{i});
for j=1:rwtb
time_similar_1=zeros(1,length(table1{i}.("Conduction Time"){j}));
... |
cbbea57dc885856996694f4006002c1de8447afa9e7df5cbd24cd845d198ecab | MATLAB | 4,243 | 130 | function [errs warns] = unit_spm_afni_tutcompare(varargin)
% [ERRS WARNS] = UNIT_SPM_ANA_AFNICOMPARE(varargin)
%
% FEXTENSION(*.nii/.img)
% If unset, this will assume you wish to run the tutorial against nifti
% data. If set to .img it will change to using the analyze data set.
%
% Unit test of the spm ana fi... |
c43283f96675410e69656410e636ca6a28dcfcaf111142f6e47296e22fe595bd | MATLAB | 4,250 | 109 | clear,clc
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% This script is used to predict the learning outcomes of week 4
% intervetion
%
% Jin
% 8/17/2022
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% set the file path
current_path = pwd... |
2f28e5c4f31c9113c09b6a7f1e6e24c478a345097bb1d9ae48b19b060f057407 | MATLAB | 4,251 | 135 | clear all;close all;clc;
% import matrix for completion
Y_filepath = 'ccle.xlsx';
[Y, text, alldata] = xlsread(Y_filepath);
Y = Y'; % 491*23 --to--23*491
Y_dim = size(Y);
% Known_index0 = find(isnan(Y)==0);
% Missing_index0 = find(isnan(Y)==1);
% missing_index_1 = randperm(length(Known_index0),500);
% Known_index ... |
c7507668fe2f6530298a6ae52c492525fda8884e79b6a265e739ef4159578e1d | MATLAB | 4,251 | 135 | clear all;close all;clc;
% import matrix for completion
Y_filepath = 'ccle.xlsx';
[Y, text, alldata] = xlsread(Y_filepath);
Y = Y'; % 491*23 --to--23*491
Y_dim = size(Y);
% Known_index0 = find(isnan(Y)==0);
% Missing_index0 = find(isnan(Y)==1);
% missing_index_1 = randperm(length(Known_index0),500);
% Known_index ... |
5b0b211074062757ec58de7f99a7ded1b2c1b5f210fa1b5708dad7205e05daa5 | MATLAB | 4,257 | 107 | % lateralization test using welch's t-test for group.
% beta is (#coefficients x HbX x #Channels x #conditions)
% bvar is #coefficients x # Channels x #HbX
% bvar is square of standard error, not SD
function calcLatTest(sbjList)
% Good news, same for both old and new probe
leftChn = [1:6];
rightChn = [15:... |
b10d276052684d9eb55701d332b373201c449b5653b88a0329a1c5fee5b88d49 | MATLAB | 4,268 | 84 | %% Extended Data Fig. 1a and 3a: Ribosomal-mRNA expression in visual cortex astrocytes from the Farhy-Tselnicker et. al publicly available dataset
% (NCBI Gene Expression Omnibus, GSE161398)
% 1. Load 'GSE161398_FPKM_MasterTable_Development.xlsx' as a table
% 2. Run the following code
% Extended Data Fig. 1a: R... |
b68e40c69be51db4504861f142cd3ce7e1e983f7687a07d95cb7397945c273f0 | MATLAB | 4,271 | 171 | function [success errmsg] = test_tutorial_easy()
% [success errmsg] = test_tutorial_easy()
%
% Runs the new and old version of the tutorial scripts to compare
% them
success = 0;
errmsg = '';
rand_state_int = sum(100*clock);
curdir = pwd;
try
cd ~/fmri/benchmark/
catch
success = -1;
errmsg = 'No benchmark di... |
bbf2a12c41f2867703567af9969d4b833605f5a45e436cc8512ff9748b52b871 | MATLAB | 4,271 | 141 | function [subj] = load_bv_pattern(subj,new_patname,maskname,filenames,varargin)
% Loads an BrainVoyager dataset into a subject structure
%
% [SUBJ] = LOAD_BV_PATTERN-AA(SUBJ,NEW_PATNAME,MASKNAME,FILENAMES,...)
%
% Adds the following objects:
% - pattern object called NEW_PATNAME masked by MASKNAME
%
% NEW_PATNAME is t... |
92bfe1dc31ce8f742dbe087ccf341eace20e1de83507921c49a552ebe9e527b9 | MATLAB | 4,272 | 142 | function [yo, fo]=mtcsd1(varargin)
%function [yo, fo]=mtcsd1(x,nFFT,Fs,WinLength,nOverlap,NW,Detrend,nTapers);
% Multitaper Cross-Spectral Density
% function A=mtcsd1(x,nFFT,Fs,WinLength,nOverlap,NW,Detrend,nTapers)
% x : input time series
% nFFT = number of points of FFT to calculate (default 1024)
% Fs = sampling fre... |
9ac01924544c2bd79659218961c5146402f27450c1dae5ebbcf33d5d1bb7fcdd | MATLAB | 4,273 | 108 | function [subj] = scramble_regressors(subj,regsname,selname,new_regsname,varargin)
% Scrambles your regressors for sanity-checking
%
% [SUBJ] = SCRAMBLE_REGRESSORS(SUBJ,REGSNAME,SELNAME,NEW_REGSNAME,...)
%
% SUBJ = the subj structure
%
% REGSNAME = the name of the regressors you want to scramble
%
% SELNAME = the sele... |
c91f26dd78140ff24490425d150521fea3e6d0c9e365b3a78c53f0833c718218 | MATLAB | 4,276 | 132 | % STATUS: Active(?)
%
% SYNTAX:
% [performanceLDALedoit] = ...
% calcCumSum_DiffStartT_AllChn_CompClassifiers_NoSave_LOFO(sbjNum,...
% trialsTr,trialsTst,movieListTrain,movieListTest,numClasses,idxChn,...
% mlActAuto)
%
% DESCRIPTION:
% Format data and perform leave-one-channel-out classification using cross
%... |
0cdfabbfd16bc2f08bea9e5a24121e47aea51d19a1ceb75ae34ac81ee30795f9 | MATLAB | 4,280 | 139 |
clear;
supPath = 'C:\Users\Noga\Documents\Vermis Data';
load ('C:\Users\Noga\Documents\Vermis Data\task_info');
req_params.grade = 7;
req_params.cell_type = 'PC cs';
req_params.task = 'saccade_8_dir_75and25';
req_params.ID = 4000:5000;
% req_params.ID = setdiff(4000:5000,[4243,4269,4575,4692,4718,4722])
req_params.n... |
4dea763568a094a2de5b1146a126d0721d330368305d4d5164018fea8124201f | MATLAB | 4,280 | 86 | % Extract single HRF from dcNew variable, the output of GLM fitting function
% save to mat file.
% Update file name solely for SSBeta Prior
% For sbj 12 and after
function createSingleTrialHRF_SSBeta_MultiOnly(sbjNum,respData,numClasses)
%saveDir = ['C:\Users\mn0mn\Documents\ResearchProjects\spatailAttentionProject\Pr... |
e9edb52d0e891c18f30863aa1037befa38ea509d67bf58da119be6afac529e20 | MATLAB | 4,284 | 104 | % Abffile bulk conversion script
%
% Goal: - convert and analyse abf files
% - skip certain protocols that are not analyzable at the moment
% - only analyse channels with something meaning ful associated to it: so check labbook list to see if channel
% was used... |
0d41fc92d42ba330a37f9828ff8bdba06a01f8aa8d0883ae3eb180fa4e791214 | MATLAB | 4,291 | 113 | function model = pp_segment(sub_id, p)
% function model = pp_segment(sub_id, p)
%
% Runs SPM segmentation on a coregistered high-res anatomical image.
%
% Input:
% - sub_id: ID of the current subject.
% - p: Structure with analysis parameters.
%
% Output:
% - model: Structure with all SPM settings used for this ... |
352168a4a6772873dce0761885e760b3333646af02fd8b623d43e6f52ed076d9 | MATLAB | 4,291 | 106 | %% bulk data extraction from Abffile objects
% When nr. of files is too large to be practical in one Abfbatch object, this function comes in handy. It chucks the list
% of files into smaller batches of files, keeping the total number of MBs per batch roughy constant. Then creates ABFBATCH
% objects for them using par... |
4f7e9b70b379315c39fd65b4ffe138dd4ffcc3cb2f4679ff8f1e066b3c6bf553 | MATLAB | 4,291 | 64 | clear,clc
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% This script is used to perform CCA validation
%
% Jin
% 8/10/2022
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% set the file path
current_path = pwd;
working_path = current_path(1:strfind(cu... |
c9f15e280af1a77ac123479d34cdd01ee7619d5068117946a71a155c3f32daf0 | MATLAB | 4,298 | 191 |
clear; clc;
opts = optimset('Display','off');
[task_info,supPath,~,task_DB_path] = loadDBAndSpecifyDataPaths('Vermis');
EPOCH = 'targetMovementOnset';
DIRECTIONS = 0:45:315;
req_params = reqParamsEffectSize("saccade");
%req_params.ID = [4797];
lines = findLinesInDB (task_info, req_params);
cells = findPathsToCells... |
2e722e0b83f5294fa1198740a341153287e371bcd787e082b2a0afef262b95a4 | MATLAB | 4,303 | 127 | function [subj] = create_blocklabels(subj,regsname,runsname,varargin)
% Creates selector labels for each condition in each run
%
% [SUBJ] = CREATE_BLOCKLABELS(SUBJ,REGSNAME,RUNSNAME,...)
%
% See https://compmem.princeton.edu/mvpa_docs/TutorialAvg
%
% This is usually used a precursor to a function like
% AVER... |
b708f2518f8285ec7cfa23cd1bb1c183667cb967ddf89787eeb374b44111c262 | MATLAB | 4,305 | 91 | % function [mask_vol,mask_hdr,sz,mask_vol_each] = load_mask(cfg)
%
% Subfunction for decoding.m
% This function loads one or several masks from cfg.files.mask in which the
% decoding analysis should be performed (e.g. a brain mask for a
% whole-brain or searchlight analysis or a small volume for a region of
% int... |
4e5b5410ed97d8c60c6d126d6d36daff915280a433ed4e30d560c3fb55869581 | MATLAB | 4,307 | 125 | function [output, displacementField] = thin_plate_splines3d(input, outputSize, ...
inputLandmarks, outputLandmarks)
% THIN_PLATE_SPLINES3D Deforms input volume according to given landmarks and thin plate splines
%
% [output] = thin_plate_splines3d(input, outputSize, ...
% inputLandmarks, outputLandmarks)
%
% I... |
c5f1b6a8d848db4dde26a2076b4fa957800b85cd6d1228a83c387bdd15a4b228 | MATLAB | 4,308 | 110 | function pipeline_svm_state_at_cue_2nd()
cfg = load('/projects/crunchie/moehring/neurovar_v2/BIDS/code/neurovar_cfg.mat').cfg;
n_subs = 44;
%% Specify 2nd Level AUC
clear matlabbatch
auc_dir = fullfile(cfg.global.deriv_dir, 'pipeline_svm_state_at_cue_2nd', 'AUC_minus_chance');
... |
1c1d73b8a2c869d032436503c35bf9401ea80e71f2d6f52c6a0cd0f9c42a8df9 | MATLAB | 4,309 | 139 | function dimensions = yOCTLoadInterfFromFile_ThorlabsSRRHeader (inputDataFolder,OCTSystem,chirp)
%In case this is a thorlabs file that is saved in srr format.
%Make sure SRR Name convention is 'Data_Y%04d_Total%d_B%04d_Total%d_%s.srr
%Specifing (by order)
% - Y0001 = Y scan number
% - Total5 = Total number of ... |
890a875f97c9ccec19e16151834b158dab6d248c89c5bf47bd80ad41b5dd2db4 | MATLAB | 4,311 | 133 | % SYNTAX:
% mlActAuto = hmrR_PruneChannels(data, probe, mlActMan, tInc, dRange, SNRthresh, SDrange)
%
% UI NAME:
% Prune_Channels
%
% DESCRIPTION:
% Prune channels from the measurement list if their signal is too weak, too
% strong, or their standard deviation is too great. This function
% updates MeasListAct based on ... |
b5f6bb5cf20ac265588a6d3c7d0632f6b454c72291a273f1b7444d2bd93bd32f | MATLAB | 4,312 | 148 | clear
[task_info,supPath,~,task_DB_path] = loadDBAndSpecifyDataPaths('Vermis');
EPOCH = 'reward';
req_params = reqParamsEffectSize("both");
% req_params.cell_type = {'PC cs'};
%req_params.ID = 4153;
lines = findLinesInDB (task_info, req_params);
cells = findPathsToCells (supPath,task_info,lines);
for ii = 1:lengt... |
d8b223ea8b1890731af577446a3a05ed21905cb10ee2d0a835e64f07bf083548 | MATLAB | 4,312 | 160 | function Duszkiewicz2024_Figure_SingleRec_Decoding
% This script reporoduces panels from:
% Extended Data Figure 1
% Run in folder A3705-200306 to reproduce the manuscript figure
% Dependencies:
% TStoolbox
%TODO: script dependencies, links to external functions
% Copyright (C) 2023 by Adrian Duszkiewic... |
fc0dce4f6c0aaf7b5fd91def8adc4d1be63a288b9ee7df2a7e119456c2ae4653 | MATLAB | 4,313 | 86 | function [all_units] =NWB_SB_extractUnits(nwbAll, load_all_waveforms)
%NWB_SB_extractUnits Takes a cell array of loaded nwb files and returns all
% single unit information across all the files.
% nwbAll: cell array of loaded nwb files
% importRange:
%
% mkyzar 4/27/2023
all_units = {};
for i=1:length(nwbAll)... |
3406258185ec3c0aaedc68403af06059c6e85960b8200bc13552ad59424fecb2 | MATLAB | 4,317 | 72 | function [h, p, ci, stats, n, stderr_spon, stderr_stim]=no_pad_ttest_210319(table1, table2)
%if conduction time isnt present in both, drop time and spikes etc
for i=1:length(table1)
[rwtb,~]=size(table1{i});
for j=1:rwtb
time_similar_1=zeros(1,length(table1{i}.("Conduction Time"){j}));
... |
c997ba5e89592f6e62ef837404952fc7bd7b72b889ae0dfdeed96785e7a21ff4 | MATLAB | 4,321 | 181 | clear
statistic_func = @func;
%statistic_func = @(smpl) var(reshape(squeeze(mean(smpl)),1,[]));
T = 10;
N_TRIAL = 25; %average per condition
base_vals(:,1) = linspace(0,1,T);
base_vals(:,2) = linspace(0,-1,T);
var_vals = [0:20];
permDims = {[2],[3]};
varNames = {'reward'};
for v = 1:length(var_vals)
var_fact ... |
42b5aad19f35ed459ca28ac7edeadf18845c1ff1351dfb90f4fcbaf8bcc611dc | MATLAB | 4,326 | 120 | function [imfStart, imfPeriod, imfAmplitude, imfNadir] = li_upmemd_cycle(comp, fs)
%LI_UPMEMD_CYCLE calculate cycle start point, period, and amplitude of each
% cycle from the component extracted using LI_UPMEMD
%
% $Author: Peng Li, Ph.D.
% $Date: Mar 24, 2023
% $Modif.: Jul 06, 2026
% Allow... |
6cc8d2fa7e693cdb8e4d4554cb5a8920517b1f92ff44bea414addf86bad28472 | MATLAB | 4,329 | 130 | function this = cosinorActi(this)
%COSINORACTI Perform cosinor analysis on an ACTI object
% based on cosinor class methods
%
% $Author: Peng Li
% $Date: Nov 11, 2022
% $Modif.: Feb 20, 2026
% Asign fitted data to Circadian property for consistency
% with Sleep and Gap
% ... |
d2ff773f5709cec57f8f5d11c3489746b766606a0af4f9a47ababbd2355288a8 | MATLAB | 4,329 | 146 | function [spkWidth,tr2pk,halfPkW,maxIx,wave] = Make_WaveFormFeatures(waveforms,varargin)
% Extract spike width, trough to peak and peak half-width from waveform(s)
% The program assumes spikes are negative. Exclude positive spikes for
% now.
%
% USAGE
%
% Make_WaveFormFeatures(waveforms,<Fq>)
%
% INPUT
% ... |
f31e08a1cb53307c356136b20c5498efbc4f9a323a6e848eb535c330aab20f44 | MATLAB | 4,342 | 151 | function [v,D] = logistpca(x,y,v,show,lambda,eigvalratio)
% [v,D] = logist(x,y,vinit,show,lambda,eigvalratio)
% Iterative recurcive least squares algorithm for linear logistic model
%
% x - N input samples [N,D]
% y - N binary labels {0,1}
%
% Optional parameters:
% vinit - initialization for faster convergence (optio... |
a6964ef91c1d4faf24db27eb5ddcc6d740904fbc9b776a643e36b7b783bb24fd | MATLAB | 4,344 | 199 | %% for axes alignment
cids = Qual < qualthr;
ndim = size(Y, 2);
%
a = CSctr;
a(cids) = nan;
b = BSctr;
b(cids) = nan;
x = log(median(a(:, ChooseClip)./b(:, ChooseClip), 2, 'omitnan'));
x = x-x';
x(eye(size(x, 1))==1) = nan;
x = x(:);
x(isnan(x)) = [];
ev_s = nan(ndim, 1);
for i = 1:ndim
y = Y(:, i... |
8105fe36dd3ce7a11d62bee5dc694e367d5322d0b559f8d8591637f2a8320893 | MATLAB | 4,348 | 159 | function [errs warns] = unit_wavestrapper_results()
% [ERRS WARNS] = UNIT_WAVESTRAPPER_RESULTS()
errs = {};
warns = {};
errs = very_significant_sine(errs);
errs = not_significant_rand(errs);
errs = varying_significant_sine(errs);
% alert the user if there are any problems
[errs warns] = alert_unit_errors(errs,warn... |
2918ef3d9fafda6c745379acd6915e471c544b16d6833ebfc711823078ea778b | MATLAB | 4,350 | 118 |
steps2run = [false false true];
%folder containing doric data and boris data
%one subfolder per animal with all trials from the same animal
data_folder = fullfile(pwd,'Data');
%folder used to save analyses
%one subfolder per animal with all trials from the same animal
analysis_folder = fullfile(pwd,'Analysis'); %
... |
924de66976c35f0f5ee967a495068ef7e68fbd1aff88b23a575b3014654081f4 | MATLAB | 4,350 | 109 | %% Flow Distribution Data and Plots
function FlowDistributionData(fileParams, flowDistParams)
%% Make Flow Distribution Data
fprintf('\n\n- - - Starting FlowDistributionData - - -\n');
%% Figure Parameters
distOption = flowDistParams.distOption;
dataBinWidth = flowDistParams.dataBinWidth;
showBinWidth = flowDistParams... |
5ed5ef9485a86e1a3397aeef53acbb266d1705457135ea91c3e5cad70bbf116a | MATLAB | 4,358 | 138 | %DEMRBF1 Demonstrate simple regression using a radial basis function network.
%
% Description
% The problem consists of one input variable X and one target variable
% T with data generated by sampling X at equal intervals and then
% generating target data by computing SIN(2*PI*X) and adding Gaussian
% noise. This data ... |
ddec5733b0e576762849a5bbc509f5c41fdc7f547fa17cde88a4603ea5071a33 | MATLAB | 4,358 | 128 | % Option 1: No GLM, just use SS coefficients = 1 to get individual trials
% In this option, no CV is performed here.
% Option 2: Use GLM only for SS coefficients since it's the same for all
% conditions. However, require building design matrix. Also not 100%
% independent of class conditions, still need to know tr... |
c4769b6fcc8be984ed8b93dbdace52d5cba9331fc8e9c9e7ef10f809189d6990 | MATLAB | 4,366 | 125 | function regmod = getRegMod(regtype)
i = 0; %number of models
if strcmp(regtype,'fb-surprise-stepwise')
i = i+1; %standard RL model
regmod(i).name = 'splitRPE-only';
regmod(i).model = 'eeg ~ (valenceP1 * surpriseP1) + (valenceP2 * surpriseP2)';
regmod(i).identVars = {}; regmod(i).identVals = {};
... |
56d84a6b4ab57d65c018115b9680455757efd72b70107f25497eb5532dfeb584 | MATLAB | 4,369 | 124 | function model = train_ridge(param, X, Y)
% Trains a ridge regression or a linear regression model.
%
% Usage:
% model = train_ridge(param, X, Y)
%
%Parameters:
% X - [samples x features] matrix of training samples (should
% not include intercept term/column of 1's)
% Y ... |
1897fbce554dab3d5e06955322df7b5c6bf5e252d6d61bbd404f45f931ab6526 | MATLAB | 4,371 | 95 | % This script is a template that can be used for a decoding analysis on
% brain image data. It is for people who ran one deconvolution per run
% using AFNI and want to automatically extract the relevant images used for
% classification, as well as corresponding labels and decoding chunk numbers
% (e.g. run numbers... |
724eecdcddb153f1723be4410cfc2f0a2afe7acd339c4fc8b2681b25bcaa2977 | MATLAB | 4,371 | 113 | function varargout = write_image_afni(varargin)
% write_image:
% inputs: header, volume (X x Y x Z)
% output: written header (normally not needed)
% History:
% 2019/24/01: Fixed potential issue when blank appears at end of filename
% This code includes parts from the CoSMoMVPA package from the... |
c15e0546ea68d34f834e243e532bb02c0ec05fd72b25fb800d7718d7bd65865d | MATLAB | 4,376 | 119 | % figure S5: measuring symmetry of the weights
% last update: October 28 2022
% Amirhossein Farzmahdi
close all
clear
clc
% add path
addpath(genpath('functions'))
% fixed random seed for regenerating same result
seed = 42; rng(seed)
% load network
trained_net = alexnet;
% create random network
untrained_net = fun_... |
bcb908cc6899bd4fd673ef437985ad7c69d0cfdf8511139a5b494db7a3d83241 | MATLAB | 4,378 | 88 | function [all_units] =NWB_SB_extractUnits(nwbAll, load_all_waveforms)
%NWB_SB_extractUnits Takes a cell array of loaded nwb files and returns all
% single unit information across all the files.
% nwbAll: cell array of loaded nwb files
% importRange:
%
% mkyzar 4/27/2023
% updated JD 01/2025 (correction of ... |
d2f5dc12ca977f0e6beb6c7ae7eb027e91221c9e53b1ac62b04752d987ad3fc2 | MATLAB | 4,385 | 121 |
%% CLEAR AND INITIALIZE
clear;close all;clc;tic;
%% DEFINE THE STRUCTURE THAT WILL HOLD ALL THE PARAMETERS
params = [];
%% DEFINE INPUTS AND OUTPUTS
params.data_folder = 'E:\NAS_SD\SuiviClient\Triffilieff\2023\Data\RWD_TestForLola';
params.analysis_folder = 'E:\NAS_SD\SuiviClient\Triffilieff\2023\Analyses\RWD_TestF... |
ee06aaaf38aec0a11300e6c3f2be5f7d9037db25cd51ac281da8b1158f62af09 | MATLAB | 4,386 | 135 | function [subj fn] = save_subj(subj,varargin)
% This just saves the subj structure to a file.
%
% [SUBJ FN] = SAVE_SUBJ(SUBJ,...)
%
% It's useful because it does lots of book-keeping, and won't
% overwrite existing files unless you tell it to
%
% FN = the filename it saved itself under. This is useful in cases
% where... |
7ba2c08b76c0c49f7f1dafd31c25e8da3c9a17efee9b65ca63cf9a54cd8cc571 | MATLAB | 4,391 | 98 | % This script is a template that can be used for a decoding analysis on
% brain imaging data. It is for people who neither have betas available
% nor brain image files, but who want to pass their brain imaging data
% directly. This also supports the use of data of other modalities, e.g.
% EEG data. Labels, decodi... |
eb4c29acc736af2c1a04a2286fa1ec0dd799c9d72360825d5ea9efb6763afadd | MATLAB | 4,393 | 102 | function [v, regions, params, startpoints] = velocity_segment(T, options)
% [V, REGIONS, PARAMS, STARTPOINTS] = VELOCITY_SEGMENT(T, OPTIONS)
%
% Compute simultaneous segmentation and velocity estimation from the
% spatiotemporal orientation tensor field T. The algorithm is described
% in section 6.4 of Gunnar Far... |
ad62d896867b249dbfc5a1f43064ae7de4388ef5d4eec6ceb27730742c620e2a | MATLAB | 4,394 | 139 | function [t,x_new,f_new,g_new,funEvals,H] = ArmijoBacktrack(...
x,t,d,f,fr,g,gtd,c1,LS_interp,LS_multi,progTol,debug,doPlot,saveHessianComp,funObj,varargin)
% [t,x_new,f_new,g_new,funEvals,H] = ArmijoBacktrack(...
% x,t,d,f,fr,g,gtd,c1,LS_interp,LS_multi,progTol,debug,doPlot,saveHessianComp,funObj,varargin)
%
% ... |
c5d2f1408c08eab9bedc8e9c97c9228f385f922e7a916be2c1b3e572fd444654 | MATLAB | 4,394 | 107 | clear
clc
close all
%% Load raw data and set parameters
pathname = uigetdir('pick a folder that store tracking data');
pattern = fullfile(pathname,'*TNT*');
filelist = dir(pattern);
framerate = 400; % in Hz
ratio = 3; % the interpolation ratio
inter = framerate; % how many frames to reconstruct a ... |
d8a0ee0371dc198caae352661c6b58a09664bf9f300d3ff481e9da7b353d994c | MATLAB | 4,405 | 102 | % function indexindex = get_ind(cfg,mask_index,i_decodingstep,sz,sl_template,passed_data)
%
% Subfunction for decoding.m
% This function gets the indices for the current step of the decoding
% analysis (i.e. the current searchlight for searchlight decoding or the
% current ROI for ROI decoding).
%
% Masks (e.g. ... |
e0391697178318a03e3a1a667c0d7a7ce0e1ba31967d5e67be49079e68282cc7 | MATLAB | 4,405 | 131 | function [errs warns] = unit_template()
% [ERRS WARNS] = UNIT_TEMPLATE()
%
% This is the template for a unit test. Unit tests should
% take in no arguments (or only varargin optional
% arguments), and return ERRS and WARNS cell arrays of
% error messages. If both are empty, the test has passed.
%
% The unit test shou... |
7f0a2a208b6bc15315020328983a65c75dd160c1884d4d119212b1a7baa05812 | MATLAB | 4,408 | 101 | % @title Create synaptic connection matrix for a group
% @file gen_syncon.m
% @author Romain Beaubois
% @date 04 Aug 2021
% @copyright
% SPDX-FileCopyrightText: © 2021 Romain Beaubois <refbeaubois@yahoo.com>
% SPDX-License-Identifier: GPL-3.0-or-later
%
% @details
% > **04 Aug 2021** : file creati... |
89086be845a86a62c04cb48efc7234f2754bce47ff0da6d41323d1b198758920 | MATLAB | 4,408 | 137 | clear all;close all;clc;
% import matrix for completion
Y_filepath = 'gdsc.csv';
[Y, text, alldata] = xlsread(Y_filepath);
%Y = Y'; % 969*295 --to--295*969
Y_dim = size(Y);
% Known_index0 = find(isnan(Y)==0);
% Missing_index0 = find(isnan(Y)==1);
% missing_index_1 = randperm(length(Known_index0),500);
% Known_inde... |
f2089f98d60180f54bcdcfd4b3ec7d46c729f28eacbfcac9a1bf9b4231cad3e4 | MATLAB | 4,409 | 114 | % Script for a power simulation for the detection of correlation effects,
% depending on whether variables are pre-selected or not.
% Size of original sample (i.e., respondents to the VVIQ)
n = 200;
% Size of quartiles
quart = n/4;
% Number of selected samples per group (total is n_sel*2)
n_sel = 20;
sigma ... |
9633a6659fffb26ce423e065019cb673b8c234d16d2c739b8981c0ab63510a05 | MATLAB | 4,414 | 138 | clear all;close all;clc;
% import matrix for completion
Y_filepath = 'gdsc.csv';
[Y, text, alldata] = xlsread(Y_filepath);
%Y = Y'; % 969*295 --to--295*969
Y_dim = size(Y);
% Known_index0 = find(isnan(Y)==0);
% Missing_index0 = find(isnan(Y)==1);
% missing_index_1 = randperm(length(Known_index0),500);
% Known_inde... |
d4997c248ec003d247814eb0f153cb2e3042eaf44895d9105f5ecbb22217c803 | MATLAB | 4,420 | 104 | function [acts scratchpad] = test_adaboost(testpats,testtargs,scratchpad)
% [ACTS SCRATCHPAD] = TEST_ADABOOST(TESTPATS,TESTTARGS,SCRATCHPAD)
%
% Implements the testing function for AdaBoost.MH/AdaBoost.MO. See Allwein
% et al. (2001) for details on the implementation of the multi-class to
% binary reduction evaluatio... |
dffec02f9eb28a4f58be9e55c8508414e1c71cdc2f23510ba10cd12307dedf1a | MATLAB | 4,420 | 124 | function [Y, mask] = spmReadVolsMasked(V, mask, output)
% read in-mask MR image data from an array of SPM volume structs
%
% Y = spmReadVolsMasked(V, mask)
% [Y, mask] = spmReadVolsMasked(V)
%
% V: array of volume structs (or filenames)
% mask: logical array with dimensions matching data volumes
% if not spec... |
0cdab55d99f10105141eab5b201d54b84a3e10b1416dcc82e8f5736eb08dfe8f | MATLAB | 4,422 | 120 | function this = sleepDet(this)
%SLEEPDET Perform sleep detection on an ACTI object
%
% $Author: Peng Li
% $Date: Dec 02, 2020
% $Modif.: May 17, 2021
% generate window from doMask2 instead of recalculate based
% on mask series
% Jun 24, 2021
% see comme... |
5d1005a80b62144cec2a2510e27841ccb293971bc5e52e4ebfe31964cc8688d9 | MATLAB | 4,422 | 132 | %DEMHMC2 Demonstrate Bayesian regression with Hybrid Monte Carlo sampling.
%
% Description
% The problem consists of one input variable X and one target variable
% T with data generated by sampling X at equal intervals and then
% generating target data by computing SIN(2*PI*X) and adding Gaussian
% noise. The model is ... |
b00ecd180b0bb94530b1ed782de7cb097dc1c06b5e0c8c49a7747c96fe5aa18a | MATLAB | 4,431 | 162 | function [L, model] = pure_mf_learner(param, behav_data)
%% Read behav_data
n_trials = behav_data.n_trials;
state_switch_on_trial = behav_data.state_switch_on_trial;
reward_switch_on_trial = behav_data.reward_switch_on_trial;
max_reward = behav_data.max_reward;
%% Parse parameter values
%(transformations n... |
fcb377ba032e26b60cba19351561c79ada3d5f5b44f281801cea7a6da14e3c76 | MATLAB | 4,431 | 134 | % references: https://www.mathworks.com/help/matlab/ref/area.html
% https://www.mathworks.com/matlabcentral/fileexchange/5105-making-surface-plots-from-scatter-data
% possible alt. plots: https://www.mathworks.com/help/matlab/ref/gradient.html#bvhqkfr
downsample_colormap = 1; % activate colormap downsampling
downsampl... |
9fd83e7907e480bb3b37976d6f8f8078b115cf276c7c93d4a90220f254c6252c | MATLAB | 4,434 | 127 | function [err,Indx] = AFNI_XYZcontinuous2Index (XYZmm, Info, CoordCode, IndexDim)
%
% [err,Indx] = AFNI_XYZcontinuous2Index (XYZmm, Info, [CoordCode], [IndexDim])
%
%Purpose:
% Change from voxel XYZ in mm to XYZindex (called Voxel Coords in AFNI)
% The mm and voxel coordinates refer to the values displayed
% ... |
7017d451e26ea62366a0da4c3d5c63818e5a46274c097e6361b216ad2165ac52 | MATLAB | 4,437 | 101 |
%Group level analysis in which we contrast the brain activity of Nav>Math
%of boht group withouth permutation and withouth masking the brain.
%% Set up the environment
clc
clear
restoredefaultpath
SPMPath = '/Applications/spm12';
addpath(SPMPath);
spm('defaults', 'fMRI');
spm_jobman('initcfg');
pmdataPath='/Users/fe... |
037711a9fcce0e3fa84b432de01dd2f1b4f2fa03c9c76b35425e9281b124a0f9 | MATLAB | 4,439 | 156 | %% This code is to calculate the curve using max projection image of the side view
clc;
clear;
close all
acq_number=3;
imagePath = 'F:\Bingying\Curvedprops_mode\embyro1'; % load the side-view image
imageName = 'MAX_Reslice of stack1.tif';
curvename=['scanning_curve_0901-' num2str(acq_number) '.mat']; % savin... |
89d73b84d4cf71a8c10dd8d49759f350d8cac2067e63e6086666d3fce06b295b | MATLAB | 4,445 | 123 | function net = rbf(nin, nhidden, nout, rbfunc, outfunc, prior, beta)
%RBF Creates an RBF network with specified architecture
%
% Description
% NET = RBF(NIN, NHIDDEN, NOUT, RBFUNC) constructs and initialises a
% radial basis function network returning a data structure NET. The
% weights are all initialised with a zero ... |
5952f8e26f49f2570089f64aff1a5ad21b10ea11a6c518a9f756e7f7fe49125b | MATLAB | 4,452 | 166 | set(0,'defaultAxesFontSize',16)
set(0,'defaultAxesTickLength',[0.04,0.08])
set(0,'defaultaxeslinewidth',2)
region=["EC-DG","DG-CA3","CA3-CA1","CA1-EC"];
cd 'graph'
if ~exist('tunnel_cor', 'dir')
mkdir('tunnel_cor');
end
cd 'tunnel_cor'
Anova_name=[];
Anova_cor=[];
for fi=1:9
corr=[];
title=region;
for... |
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