sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
f7ed4c774d7a28c9d86c361da7686b828d40576a82bf9d12141439fcac1a9bd5 | MATLAB | 4,887 | 140 | function P1 = fun_my_head_motion_missalignment_estimation(pth, selind,....
DWIname_pre,...
bvals, bvecs,...
mask)
% pth - path where images are s... |
b0425706c6cf068a5046a5522dc493792cf7ac14ab2a77bdc4626b4ce0fa87ca | MATLAB | 4,895 | 125 | function changeTextSettings(textSize, textSpacing)
% Change the text size and the line spacing of the presentation window.
%
% Both settings are global and stay in effect until this function is called
% again or the window is closed. initWindow sets the defaults:
% textSize = 46 (35 with Psychtoolbox older tha... |
31604964c83f03a45fccd50269e1c216039ac74c7f1e2af1f00b338c7c999771 | MATLAB | 4,906 | 98 | function SBJ11c_HFA_conn_peak_stats(proc_id, an_id, model_id, conn_id, swap_Xcorr)
if exist('/home/knight/hoycw/','dir'); root_dir='/home/knight/hoycw/';ft_dir=[root_dir 'Apps/fieldtrip/'];
else root_dir='/Volumes/hoycw_clust/'; ft_dir='/Users/colinhoy/Code/Apps/fieldtrip/';end
addpath([root_dir 'PRJ_Error/scripts/'])... |
82350a582bc78bc192d1acd8d7a9852bb578484deda58825188219358bbae2a0 | MATLAB | 4,913 | 123 | function fr = calculateBaselineFR(spikeData, stimInfo, baselineDur, dt, sortedSpikes)
blankingInterval = .002; % (sec) duration of the stimulation pulse + blanking period
prePTA = -0.005;
if sortedSpikes
unitIds = spikeData.unitIds;
else
unitIds = 0;
end
% Stim Train Information
numTrains = stimInfo.train.nu... |
9e6104dc826565f99596d86f4239105168fb69c7f17896285e79663baa0d8139 | MATLAB | 4,921 | 167 | function [prv ] = surf_registration_adf(subject, th_pval)
%% Tests the overall surface based registration %%
%
%
% surf_registration_afd.m
%
% Original Author: Laurence Wastiaux
% CVS Revision Info:
% $Author: nicks $
% $Date: 2007/01/10 22:55:10 $
% $Revision: 1.2 $
%
% Copyright (C) 2002-2007,
% The Gener... |
df85a09f86993af3288bc098d810197d7f240b0a606ea02d74de9ce57dde0b15 | MATLAB | 4,921 | 147 | function yhat=fun_IterPoissonBM3D(z)
% Iterative Poisson denoising algorithm based on variance stabilization and AWGN denoising (v1.00)
%
% INPUT: z Poisson noisy image z ~ P(y)
% OUTPUT: yhat denoised image, estimate of y
%
%
% L. Azzari and A. Foi, "Variance Stabilization for Noisy+Estimate
% Combination in I... |
a34bb1f9dcd8b1bf277b16d96b475b4e61c58e4ae0cf3a0cf2f6b9ffad6bdcbc | MATLAB | 4,923 | 114 | %{
createPluginFigure - shared parameter pop-up builder for onset/offset detection plugins.
DO NOT EDIT. Every onset/offset detection plugin calls this. It builds the numeric
parameter pop-up, auto-prepends a "Start Time (ms)" field, and loads/saves
the calling plugin's settings file.
Inputs:
app, existFig, appCust... |
fd7b971fb9db8686479f1c3bf134d7327e3e5ae4c0b4c1f5d7f03e90dff979d8 | MATLAB | 4,923 | 111 |
% id=str2num(id);
folder='/projectnb2/npbssmic/ns/Ann_PTSD1/Ann_PTSD1_1/';
datapath=strcat(folder,'dist_corrected/');
% add subfunctions for the script. Change directory if not running on BU SCC
addpath('/projectnb/npbssmic/s/Matlab_code/fitting_code');
addpath('/projectnb/npbssmic/s/Matlab_code/PostProcessing');
add... |
8be4b4e24eb96a06af6c58560c71a6c9a1e705aea7fa9488ad333d2eb0d8f73d | MATLAB | 4,924 | 129 | function [negloglikelihood] = Estimate_beta_k_g3_m3_sigma_nu(beta, k, g3, m3, sigma, nu, Input, config)
% Zilong Ji, UCL, 2022, zilong.ji@ucl.ac.uk
% Find the likelihood with parameters specified in the model function name
% Args (in common between all estimates functions):
% beta: decay factor for the mental distance ... |
4182ef93fdb9e126e906a02e8e1edb6e6004d381c6523e160c2eb4960836e577 | MATLAB | 4,925 | 241 |
%% T2-D example
%simulate a simple image where each voxel has an associated spectrum
dim = [10 10 10];
%load a placenta gradechoinv file
gradechoinv = load('inspect/examples/placenta_gradechoinv.txt');
% %want this in ms
% gradechoinv(:,5) = 10^3 * gradechoinv(:,5);
nmeas = size(gradechoinv,1);
%associated ... |
f6d917d4cd72a9f1fa558e09c7937f91b2eb14ddd5cd86cabd8b3f3cbfcbbe9e | MATLAB | 4,925 | 130 | % The following script can be used to perform the behavioral analyses
% described in the manuscript. More precisely, the script will go through
% the experimental data and extract the trial-by-trial stimulus motion
% direction and the corresponding reported direction of motion for each
% coherence level and each subj... |
846d1cfdc29deae64f1f1a9a9d25460b888ff3506af9cd3920eeb70b60be202d | MATLAB | 4,926 | 132 | clc;clear;close all;
root='..\';
addpath(genpath([root,'NeuralCorrelateEvidenceAcc']))
addpath(genpath([rootpath,'steinmetz-et-al-2019-master\']))
addpath(genpath([rootpath,'npy-matlab-master\']))
%% ------------------------------------------------------------------------
subdir='total_clusterArea_paper';
rootpath='..... |
45d48caff298f81b0163d830422f29ecfd95267e1e992b3d0af382ea8468c6b4 | MATLAB | 4,928 | 165 | function h_group = impolygon(h_parent, x, y)
n = length(x);
if length(y) ~= n,
error('x and y must be the same length');
end;
if ~ishandle(h_parent)
error('Images:impolygon:invalidHandle', ...
'HPARENT must be a valid graphics handle.');
end
if strcmp(get(h_parent, 'type'), 'axes')
h_axes = h_parent;
else
... |
c16bad7192ad50ae817789392dda97148a3985362f7edeb8caf4500b1d5e6351 | MATLAB | 4,929 | 130 | function [negloglikelihood] = Estimate_alpha_k_g2_g3_m3_sigma_nu(alphap, k, g2, g3, m3, sigma, nu, Input, config)
% Zilong Ji, UCL, 2022, zilong.ji@ucl.ac.uk
% leaky integration with distance (added because one of the reviewers asked)
% Find the likelihood with parameters specified in the model function name
% Args (in... |
91a57d295daa1ea076a0fe14f136e4b4736ee0ca7d96392114a8548918ce77d7 | MATLAB | 4,931 | 180 | % cone_beam_ct_example.m
% Illustrate cone-beam X-ray CT image reconstruction via FDK and iterative.
% Uses a tiny system size because cone-beam projection can be slow.
% Copyright 2005-1-21, Jeff Fessler, University of Michigan
%% First run the FDK example. It generates the true image xtrue
% and noiseless projectio... |
dd3db2bafd6cc7e0b1dc241caf5934b792cbf48618eafa8c701e3c09011ce3c5 | MATLAB | 4,931 | 153 | function A = catstruct(varargin)
% CATSTRUCT Concatenate or merge structures with different fieldnames
% X = CATSTRUCT(S1,S2,S3,...) merges the structures S1, S2, S3 ...
% into one new structure X. X contains all fields present in the various
% structures. An example:
%
% A.name = 'Me' ;
% B.income = 99... |
da5a1e0bbbb4dbf85f45d7ccf9ac110f24c94d41d559c1de1cd917aec9ae0e39 | MATLAB | 4,932 | 121 | function model = step_neurons(model, params, i)
%% inputs
V = model.V_line(:, i);
U = model.U_line(:, 1);
G = model.G(:, i);
G2 = model.G2(:, i);
AG = model.AG(:, i);
eSP_expand_line = model.eSP_expand_line(:, i);
t_Iapp_met = model.T_Iapp_met(i);
array_Iapp = model.Iapp;
Isyn =... |
f22950afc4e3d1cc4d8eb69e4f7554e51fd1e0959d9e393da783f2456f605e5c | MATLAB | 4,933 | 119 |
% Check the Time and Distance cells for a given Phase Precession slope PVal or CorrCoef
% PPSummary provides the mean values of the slope and R2 for a given pValue (<=)
% Then count the cells, for each type and experiment type, complying to the given mean slope criteria
function CheckPhasePrecessionPop
Condit... |
293a577c53486e0fedcc81516521fb9e9059c442297ec48b886795a47fe92589 | MATLAB | 4,936 | 110 | function create_data_directory()
% create_data_directory Set up desired data folder organization
% This function will create the expected file directory organization
% Types of folders to create
folder_names = {'doppler', 'sulcus', 'output', 'simulated', 'aligned'};
%% Iterate over folder types and create folder if n... |
c294995eba3ed2584a1a13518a9923f911dc7655c66f72656d50bac12348d805 | MATLAB | 4,938 | 129 | function [negloglikelihood] = Estimate_beta_k_g2_g3_m3_sigma_nu(beta, k, g2, g3, m3, sigma, nu, Input, config)
% Zilong Ji, UCL, 2022, zilong.ji@ucl.ac.uk
% Find the likelihood with parameters specified in the model function name
% Args (in common between all estimates functions):
% beta: decay factor for the mental di... |
7ac0677ba34ad0a9092883685f8c2bc33844ae47e57feb689bd641b953c8377c | MATLAB | 4,939 | 144 | %% Thomas_Yang_et al, 2023 @Nuo Li lab
%% Fig. 3b # of selective SC neurons contra vs ipsi
clear all
close all
addpath('../func/');
load Figure_3_b_sc_data
sig_selective(isnan(sig_selective))=1;
sig_selective = sig_selective<0.01;%sig_selective<(0.05/size(sig_selective,1)/3); % <----------- This p valu... |
8c18aa8e9ca9a51e92cae3776d15f088ef70a00c35a3aac1106fff0a4c99222c | MATLAB | 4,939 | 129 | function EEG = c_TMSEEG_handleBurstEvents(varargin)
p = inputParser();
p.addRequired('EEG', @isstruct);
p.addParameter('pulseEvent', '', @ischar);
p.addParameter('pulseEvents', {}, @iscellstr); % can specify multiple pulse event types to be treated as the same
p.addParameter('method', 'error', @ischar);
p.addParameter... |
26980f668c8138098838ce0208d6b6be8b80f8d7875f1bc00df5bc22283850dc | MATLAB | 4,941 | 194 | function values = c_convertValuesFromUnitToUnit(values,fromUnit,toUnit)
% c_convertValuesFromUnitToUnit - Convert values between units (e.g. mm to m)
% Can handle {km,m,cm,mm,um,pm,miles,feet}, {kV,V,mV,uV,pV,dBmV,dBuV}, or arbitrary relative scales
%
% Syntax:
% convertedValues = c_convertValuesFromUnitToUnit(values... |
309d7fc4ae6bbdb726c4628f8cfdccb002a58d589c80cb8b3d8cd1249d8abaf4 | MATLAB | 4,941 | 171 | function ll = llbmm(nn,pA,pI,lambda,M)
% ll = llbmm(nn,pA,pI,lambda,<M>)
%
% Computes the log-likelihood of data (nn) given a binomial mixture
% model with parameters pA, pI, and lambda.
%
% nn is the histogram over all voxels of the number of times
% that a given a given number of positives was detected. The
% number ... |
bc09add890e83fdf1abf55d6e7be1488ab7f7c1bc679f6a83b1fb45a5b84aeeb | MATLAB | 4,941 | 184 | function [] = violinPlotsStats(data,opts)
% violinPlotsStats significant box of violins :)
%
% Inputs:
% data: A matrix of data. Each row is an observation. each column is a
% variable.
% opts: a structure of options with potential fields:
% yl: y-limit (e.g. [ymin ymax])
% sigLevel: significan... |
b8016f452f51c8b80763ba81d77eb5df4dbd05283092b8f80c3d7df1d2386819 | MATLAB | 4,944 | 187 | classdef c_GUI_ColorIndicator < c_GUI_handle
% c_GUI_ColorIndicator - simple GUI class to draw a color indicator with an optional label
properties
Units
Position
Callback
end
properties(Dependent)
Label
Color
end
properties(SetAccess=protected)
Parent;
end
properties(Access=protected)
gcont
... |
a0bfd8cc8c88a8d67759ef97db92d22de523f1a86c87743892d473eb214534d7 | MATLAB | 4,946 | 100 | function outResult = IPEMProcessAuditoryModel (varargin)
% Usage:
% IPEMProcessAuditoryModel(inInputFileName,inInputFilePath,...
% inOutputFileName,inOutputFilePath,...
% inSampleFrequency,inNumOfChannels,...
% inFirstFreq,inFreqDis... |
a2825ef427c6884d678961362829f842b5854b98d0ede71ca9c7b11cade7b829 | MATLAB | 4,946 | 140 | [filename,pathname] = uigetfile({'*.mat;*.tiff;*.tif'},'Please select the Segmentation Data');
h = waitbar(0,'Please wait... loading the data');
[~,~,ext] = fileparts(filename);
if strcmp(ext,'.mat')
temp = load([pathname filename]);
fn = fieldnames(temp);
angio = temp.(fn{1});
elseif strcmp(ext,'... |
06a9bae87315b3f0f1d9ddd329d3db3fc3c2b3b8b1fda0e6372744cc5a1e110f | MATLAB | 4,949 | 89 | %% Run this files to compute the network-human main effect and interactions for all for the VISION experiments
%% Must specify the paths to the required network data mat files.
function [] = run_anova_stats_matlab_network_human_results(EXP_NUM)
rng(517)
addpath('../matlab_statistics_functions')
NUM_BOOTSTRAPS = 1000... |
18032a94e65bc26e7327e84918bc1521a278677887fbce4f2f2a191784a47b13 | MATLAB | 4,950 | 147 | function [GroupIntegrationKernels, SubjectIntegrationKernels, SignificantTimePoints] = calculate_integration_kernels_for_signal_periods(all_responses,SubjectList,nS, mean_stim_streams, stim_streams, trigger_streams,lags)
ExpParameters = 0;
%% loop through subjects and find button presses
for subject = 1 : nS
subj... |
5a2f676f91200fed50beb2680ff7b061b3bdfef133af998b14ef58f8668f2ca9 | MATLAB | 4,950 | 97 | function shuffle_type= shuffle_scoring(shuffled_struct,analysis_type,event_type)
% new function to score shuffles post hoc. allows rerunning of scores
% without having to redo the actual shuffles.
% will overwrite shuffled_tracks if exists
parameters= list_of_parameters;
if isempty(shuffled_struct)
load('Bayesian... |
2cf6ef26d9f0c04051d33b61eea9b9d2fb330cea209fb476ff2e52cac07520aa | MATLAB | 4,951 | 197 | pause(0.1)
[~,ccl] = find(sum(left_fr,1));
[~,ccr] = find(sum(right_fr,1));
len_cc = min([length(ccl) length(ccr)]);
MM = movmean(correct_mat,[100 1]);
% figure;plot(MM)
% ylim([0 1])
pause(0.1)
NNL = movmean(choice_mat(:,ccl),[100 1],2);
NNR = movmean(choice_mat(:,ccr),[100 1],2);
figure;
subplot(2,2,1)
plot(NNL... |
f3ed1e46a465f7712be1b9a33191e82be471e178c57014fdc532d7ad806c894a | MATLAB | 4,951 | 155 | function img = feldkamp_par(interpRect,rectGrid,cg, ig, proj, varargin)
%|function img = feldkamp_par(cg, ig, proj, varargin)
%|
%|
%| Modified by G. Handy; feldkamp can be rewritten to accommodate changes
%|
%| in
%| interpRect set to 1 if you want to use
%| interpolation/extrapolation
%|... |
31700e3afe67a99b19ff50e16635f30faa31756c8ac69c89931be7a83f60ecd3 | MATLAB | 4,952 | 190 | % Gtomo3_test.m
% Test the Gtomo3 object
% f3d_mex('chat', int32(2)) % for debugging
if ~isvar('A1'), printm 'setup Gtomo3_test'
f.chat = 0;
f.option = {};
f.test = '2z'; % todo: na is not last dimension
f.test = '3l'; % todo: diag tests below fail
f.test = '3s';
f3d_mex('free:all') % start with a blank slate ... |
182292ec4c7024e8e067bba0f9f8952d3830a8a0b16124a8db69e595ac6ad037 | MATLAB | 4,955 | 134 | %% Reliability analysis of source reconstructed data
%
% Vinding, M. C., & Oostenveld, R. (2022). Sharing individualised template MRI data for MEG source reconstruction: A solution for open data while keeping subject confidentiality. NeuroImage, 119165. https://doi.org/10.1016/j.neuroimage.2022.119165
%
% Compare the ... |
aa2b9d393134c40bc9be72361e44c4a9f64a7115d62c8bc7f66b8b7b168c584b | MATLAB | 4,955 | 132 | clear
clc
addpath /mridata2/mri_group/xilin_data/misac/mouse_atlas/
addpath /mridata2/mri_group/xilin_data/nifit_code_matlab/
N = 10;
M = 8;
T = 40;
blue_am = 10;
uv_am = 6;
blue = blue_am*randn(N, M, T);
uv = uv_am*randn(N,M,T);
blue_off = repmat( rand(N,M)*10+5, 1,1,T);
uv_off = repmat( rand(N,M)*10+5, 1,1,T);
... |
5d3b6289b19213bd716f586cfdec8b76d64291cdf90f903b86bd6761c6a70e06 | MATLAB | 4,962 | 123 | function [expNewSeq_out, expStates_out, estTR, estE, actualratio_out] = getHMMStates3(track,binSize,onfood,ratio_cutoff,trans,emis,x_offset,train)
%function [estTR estE] = getHMMStates(finalTracks,binSize, trans,emis)
%does R/D classification on the entire track regardless of whether it was
%in or out of the lawn.
fin... |
f009881e0e4e1f21d99197d7a9d504f033c75a67fd2b0b75222085aa242c883a | MATLAB | 4,962 | 108 | function pseudotrials_SVM_artificial_vs_natural_decoding_PDM(subject,task)
%PSEUDOTRIALS_SVM_ARTIFICIAL_VS_NATURAL_DECODING_PDM_FULL_EXPERIMENT Perform category decoding (artificial vs natural) using the SVM classifier.
%
%Input: subject ID, task (1=categorization, 2=fixation)
%
%Output: NxNxP vector of accuracies i... |
cd94f82283a9f786aecf528c6c3215dc20f57a4e753900c8241ecfe0dc5a920e | MATLAB | 4,963 | 143 | function [pE,C,x,pC] = spm_dcm_fmri_priors(A,B,C,D,options)
% Returns the priors for a two-state DCM for fMRI.
% FORMAT:[pE,pC,x,vC] = spm_dcm_fmri_priors(A,B,C,D,options)
%
% options.two_state: (0 or 1) one or two states per region
% options.stochastic: (0 or 1) exogenous or endogenous fluctuations
% options.pr... |
268f61f242bf0a68af197f89e87374a99d4252a3d753ac60b7e52a3fd92ff05e | MATLAB | 4,965 | 160 | %{
Run this file to compile the mex files if you need to
(or if the binaries work for your computer, then you can skip this)
This file also contains two simple test problems
Stephen Becker, Feb 14, 2012 srbecker@alumni.caltech.edu
Updated May 3, 2012 adding the f2c version for Windows userss
Updated ... |
71b821cb2e00fafcc660c1d2e5cc55b86a6b236ee127a379bc6dbe8aed69e507 | MATLAB | 4,965 | 142 | function [M Rex S] = ANALYTIC_SIM(Sim)
% comments here
% last change: 2014/04/03 by PS
Sim.td = calc_td(Sim.tp,Sim.DC); % td is always calculated form the duty-cycle and tp
xZspec = Sim.xZspec;
[S Rex] = rho_s(Sim);
R1rho = S.Rho_full(:);
theta = S.theta(:);
Rex=Rex(:);
... |
f64a7f6502e7c30aecd56498c250400b808fc6c84a8099d160963e2808214338 | MATLAB | 4,967 | 117 | %% Client panel: watching an experiment from the office
% The client panel shows on your office computer how an animal is doing in
% a running session: the camera image of the box, what is on the screen in
% the box, how many trials are done and how many of them were correct.
%
% <<clientPanelExample.jpg>>
%
% The pane... |
3e4acf0196e1faa15e40116eb09c02e0ec58776350c9c108aba33c5c9e2190c1 | MATLAB | 4,968 | 182 | % ir_ct_roi_split1.m
% "frequency split" approach to ROI recon of Lin Fu et al. from Fully 3D 2015.
% Low frequency part of ROI sinogram comes from reprojecting FBP ROI image,
% whereas high frequency part comes from original sinogram.
% ROI reconstructed using PWLS-OS-LALM
% 2015-06-17 Jeff Fessler, University of Mich... |
105eefca7528c1e39a128608316f6bb55d8e5e8609ddce59fbac0ea1e3972896 | MATLAB | 4,970 | 223 | function [xc,good,bad,type] = cornerfinder(xt,I,wintx,winty,wx2,wy2);
%[xc] = cornerfinder(xt,I);
%
%Finds the sub-pixel corners on the image I with initial guess xt
%xt and xc are 2xN matrices. The first component is the x coordinate
%(horizontal) and the second component is the y coordinate (vertical)
%
%Based on H... |
4f1f3b02e3e1eb33fd8afc0ea969046ce5a1924c1cc0aa0c0c9f9082c9af87e6 | MATLAB | 4,970 | 158 | datapath = strcat('C:\Users\jryang\Desktop\Vibratome_data\1002\Axial\30.38opt\');
cd(datapath);
load max.mat;
depth0=mip;
datapath = strcat('C:\Users\jryang\Desktop\Vibratome_data\1002\Axial\30.305\');
cd(datapath);
load max.mat;
depth1=mip;
datapath = strcat('C:\Users\jryang\Desktop\Vibratome_data\1002\Axia... |
247a88dfe2a25cfc0b17582f9eca5d9b3df1e91b7ef874bda60b3d7e73603957 | MATLAB | 4,971 | 246 | function y = ir_conv(x, psf, varargin)
%function y = ir_conv(x, psf, varargin)
%|
%| ND convolution of signal x with psf.
%| This generalizes convn(x, psf, 'same') by supporting and adjoint
%| and by allowing periodic boundary conditions.
%| For the usual case of an odd-sized psf, the 'center' of the psf is [0,0]
%| s... |
57aa8275dd8d974c0d7f21a3aba86ee77e34c4661867996787fc0997beb85aa5 | MATLAB | 4,971 | 157 |
function create_GLM_figures
% PLOT INTER SLEEP GLM
cd 'X:\BendorLab\Drobo\Lab Members\Marta\Analysis\HIPP\Chapter 2\Raw_replay_analysis\figures\GLM'
bar_ind = openfig('GLM_INTER_explained_deviance_individual.fig');
bar_diff = openfig('GLM_INTER_explained_deviance_difference.fig' );
prob_ind = openfig('GLM_INTER_pr... |
231ec5d4e905a1dc8d05b013705613d5e99acfef443ee9445b49b6dce62f1b25 | MATLAB | 4,973 | 142 | function out = getMousePos(waitTime)
% Wait for a mouse click on the stimulus window and return its position.
%
% Calling getMousePos without arguments blocks execution until a click is
% done. A call with the optional argument waitTime exits after a click or
% after the given time, whichever comes first. Pressing ESC ... |
01ee5c377b2d0349afe49602c9da746e4c9b7bf59f32e842bc3917af69b2ddcb | MATLAB | 4,977 | 160 | %% Analyzing Neural Time Series Data
% Matlab code for Chapter 2
% Mike X Cohen
%
% This code accompanies the book, titled "Analyzing Neural Time Series Data"
% (MIT Press). Using the code without following the book may lead to confusion,
% incorrect data analyses, and misinterpretations of results.
% Mike X Cohen ... |
f9d1237b08cddd93c49db3c7573dd9001cb023398b25b637f0582ce988d37418 | MATLAB | 4,981 | 190 | function [grad, out, com] = ir_hct_rgrad(x, varargin)
%|function [grad, out, com] = ir_hct_rgrad(x, varargin)
%|
%| compute 3D regularizer gradient using hct binary
%| for UM testing only
%|
%| in
%| x in xyz order
%| option
%| (many - see below)
%|
%| Notes: because hct2 uses 'zxy' order, it is essential to use Reg1... |
5134bf9a25cb0b93437220ee2dca2e339123e1e2157c226ca9f3a6d44e0131b2 | MATLAB | 4,986 | 160 | function [P] = spm_dcm_fit(P,use_parfor)
% Bayesian inversion of DCMs using Variational Laplace
% FORMAT [DCM] = spm_dcm_fit(P)
%
% P - {N x M} DCM structure array (or filenames) from N subjects
% use_parfor - if true, will attempt to run in parallel (default: false)
% NB: all DCMs are loaded in... |
a7d95850f0f03a771d16698668b8f372fe324f56a0c3f26e11ba5b6753d238ac | MATLAB | 4,986 | 206 | % LICENSE
%
% _This file is Copyright 2018 by the Image Processing and Analysis Group (BioImage Suite Team). Dept. of Radiology & Biomedical Imaging, Yale School of Medicine._
%
% BioImage Suite Web is licensed under the Apache License, Version 2.0 (the "License");
%
% - you may not use this software except in compl... |
38e799164a172a85abdcb80ef1fdec6d0a0c3fdc8e673ce17379580e36b5d5a0 | MATLAB | 4,987 | 142 | % read angiogram and segmentation
[filename,pathname] = uigetfile({'*.mat;*.tiff;*.tif'},'Please select the Angiogram Data');
h = waitbar(0,'Please wait... loading the data');
[~,~,ext] = fileparts(filename);
if strcmp(ext,'.mat')
temp = load([pathname filename]);
fn = fieldnames(temp);
angio = temp.(fn{1... |
411f5aade25412ed2a27df325c589135c48a7ea4a8411649dd8b397a38453534 | MATLAB | 4,990 | 161 | function DCM = spm_dcm_fmri_csd_data(DCM)
% Get cross-spectral density data-features using a VAR model
% FORMAT DCM = spm_dcm_fmri_csd_data(DCM)
% DCM - DCM structure or fMRI
%
% sets
%
% DCM.Y.pst - Peristimulus Time [ms] sampled
% DCM.Y.dt - sampling in seconds [s] (down-sampled)
% DCM.Y.csd ... |
d837fe6a75c78356a59e4569a993b6bcb305efd9db13be3a32829ede182033b1 | MATLAB | 4,993 | 199 | function ft = bspline_1d_interp(fn, ti, varargin)
%function ft = bspline_1d_interp(fn, ti, varargin)
%|
%| given f[n], the "unit spaced" samples of a continuous signal f(t),
%| perform b-spline interpolation at sample locations t_i using the model:
%| f(t) = \sum_{k=0}^{N-1} coef_k b(t - k)
%| where the coefficients a... |
9fcbc43d40eea37216642a3bc075319c8838938bafaed5e0feebc3867a2edb15 | MATLAB | 4,996 | 170 | function [wt scale180] = fbp_fan_short_wt(sg, varargin)
%function [wt scale180] = fbp_fan_short_wt(sg, [options])
%|
%| Sinogram weighting for fan-beam short scan, aka 'Parker weighting'
%|
%| in
%| sg strum sino_geom (sg.orbit_start is ignored)
%| or ct_geom
%|
%| option
%| 'type' 'parker' from parker:82:oss (Med... |
260adb572efac92959d98b77a6ca5530b6c6f47e48ff281843624d47da391b5d | MATLAB | 4,998 | 187 | function s = c_toString(c,varargin)
% c_toString Convert various data types to a string
% Handles reasonable printing of scalars, vectors, matrices, cells, etc.
%
% Examples:
% c_toString(rand(2,3))
% c_toString(rand(2,3),'doPreferMultiline',true)
% c_saySingleMultiline('%s',c_toString(rand(2,3),'doPreferMultiline',tru... |
4741e09f63045a217a9f2f5576187daa8a94a2817311ad123841d62a01d3208c | MATLAB | 5,000 | 146 | %Extract LabChart Block Data
%Inputs: LabChart data
%Outputs: Number of channels, number of blocks, Block data, sample rate for all blocks (assumed to be the same between all blocks)
function [numChannels,numBlocks,Block,AllSampleRate]=ExtractLB_BlockData(labChart_Data)
%Extract base data
%Channel Titles
ChannelTitle... |
90fc3bceb9966c3e97b589c80bf78c67c0f55435d212aa7433e11bc630ce80d0 | MATLAB | 5,002 | 77 | fig7.fig = figure('Name', 'Figure 7: Stimulation Effects on Decoding');
set(fig7.fig, 'Units', 'centimeters', 'Position', [40 10 8.8 3], 'NumberTitle', 'off');
fig7.failureRate = axes('Position', [.1 .15 .375 .8]);
annotation('textbox', [0, 0.9, .1, .1], 'String', 'a', 'FitBoxToText', 'on', 'LineStyle', 'none', 'Rotati... |
943be6fcf5951a0aea3d6c730798c22a1d51a09c8196e022a5849941fe9f51de | MATLAB | 5,007 | 123 | function [OFC_chan_idx,fro_chan_idx,temp_chan_idx,insula_chan_idx,cingulate_chan_idx,ACC_chan_idx,EC_chan_idx,...
CA3_chan_idx,CA1_chan_idx,HC_chan_idx,MTL_chan_idx] = get_elecs_clean(subj)
if strcmp('39', subj)
OFC_chan_idx = [1:2, 58:59];
fro_chan_idx = [16:19, 25:26, 28, 64, 66, 70:74];
... |
3fdd64997307d93c7ff3a1e87dd70225b63379612ea1c9979109a12151911873 | MATLAB | 5,010 | 125 | function findAttractorDynamicsbySession(cells,save_figs,folderName)
% folderName = "wt_cell_gainchange_only";
close all;
seshes = unique(cellfun(@num2str,cells.metadata(:,1),'uni',0));
% slopeMatrix = nan(numel(seshes),4);
pearson_rhoMatrix = nan(numel(seshes),12);
for i = 1:numel(seshes)
seshIndx = ismember... |
0990621c36fa0761a246f17df256acfdf11802b067a73707abdab22507f63fb9 | MATLAB | 5,011 | 153 | % Simulate a feedforward network (without recurrent connections) of Poisson spiking neurons
% Wen-Hao Zhang, July 1, 2021
% University of Chicago
%% Parameters of the model
parsHawkesNet;
parsMdl.tauIsynDecay = 2;
parsMdl.dt = 0.1; % Unit: ms
parsMdl.bSample_ufwd = 0;
parsMdl.jxe = 1e-2;
parsMdl.ratiojie = 5;
par... |
f1f540377412d8c4937883d131c4e1041320718c3ef72c8a0986a6f8577b9f59 | MATLAB | 5,013 | 174 | % Example script to reproduce the spectral plots shown in the paper
%
% Combining Magnetoencephalography with Telemetric Streaming of Intracranial
% Recordings and Deep Brain Stimulation – a Feasibility Study
% By M. Fahimi Hnazaee, M. Sure, G. O'Neill, G. Leogrande, A. Schnitzler, E. Florin, V. Litvak
% Vladim... |
8d9d88d2ac917013a04be9b23d81f8230edf6c9667279fb5da989ee9f7ecc79a | MATLAB | 5,014 | 123 | function create_place_field_stability_figure
% PLACE FIELD STABILITY - CENTRE OF MASS & DECODING ERROR
cd 'X:\BendorLab\Drobo\Lab Members\Marta\Analysis\HIPP\Chapter 1\Decoding_error\figures\Decoding error within exposure'
DecErr_lastlaps = openfig('Median decoding error per lap using last laps from same exposure_all... |
f2f7cc2e8f9ad7955e809e86611e5a5bdd9aacb49c4b6bd786404488f0e377b8 | MATLAB | 5,014 | 157 | function[prm] = plot_conditioned_psychometrics(data, ax_postcorr, ax_posterr, varargin)
% data.pokedR, data.gamma, data.hits
% axes for post corr, post err
% trans is unfortunately, whether or not to plot in transparency true/false
p = inputParser;
addParameter(p,'xreg', 'gamma');
addParameter(p,'plot_data', []);
add... |
f5f3254dea20c0333bc087a928d291644e3bae8d82c9a4e40b706f9d9a3ec02b | MATLAB | 5,015 | 174 | % Example script to reproduce the spectral plots shown in the paper
%
% Combining Magnetoencephalography with Telemetric Streaming of Intracranial
% Recordings and Deep Brain Stimulation – a Feasibility Study
% By M. Fahimi Hnazaee, M. Sure, G. O'Neill, G. Leogrande, A. Schnitzler, E. Florin, V. Litvak
% Vladim... |
0df9b45b9619aab6054c91f33a38f4724ad5b9ee506d94cf1b445c25044a6404 | MATLAB | 5,017 | 168 | % output:
%1. data :notch filtered LFP data for elecs of interest
%2. trial_data :epoched data based on photodiode
clear all; close all; clc
subj = '84'
addpath(genpath('/media/SSanDra/Pattern_Separation/analysis_pipeline_final/')) % FT folder
ft_defaults
script_path = '/media/SSanDra/Pattern_Separation/analysis_pipe... |
6bd47a805d050b04f762cb3b327cda8a713e960f5b435cbc2cf47dc261d07e13 | MATLAB | 5,020 | 162 | function [components,cliques,CC] = k_clique(k,M)
% k-clique algorithm for detecting overlapping communities in a network
% as defined in the paper "Uncovering the overlapping
% community structure of complex networks in nature and society" -
% G. Palla, I. Derényi, I. Farkas, and T. Vicsek - Nature 435, 814–818 (200... |
aa8c0923910048bd5bb24f14986dff5808e6b04c2ec35b6bd3b3f862d31b091f | MATLAB | 5,026 | 137 | function result = punishment(wTime, varargin)
% Use this function for punishment if a task wasn't successfully finished.
% During punishment the answers of the animal are recorded and available
% in the output variable.
% This function uses three options for punishment implicitly (all are
% defined in myHardwareSetup/ ... |
e3eaf10d1ce3dcd1df532a0be4eb108257e1a24b7975be215a778611e44bc0fa | MATLAB | 5,026 | 128 | function Prepare_TestData_HR_LR_Noise()
clear all; close all; clc
path_original = './OriginalTestData';
%dataset = {'Set5', 'Set14', 'B100', 'Urban100', 'Manga109'};
%dataset = {'720P', '1080P', '4K'};
% dataset = {'720P'};
ext = {'*.jpg', '*.png', '*.bmp', '*.tif'};
degradation = 'Gray_Noise'; % Noise, Blur, JPEG,... |
f65b2f407c5f5a7789bb3cf4165f340390438b4357f8fbdf0cb621aff13ef0df | MATLAB | 5,029 | 149 | function [fNum,stateFigure,kinFigure] = plotKNNKinematicsSTD(f_orcoAll,state2Cons,tim2Cons,plotType,colortype,fNum)
% loop through each genotype
k=ones(1,numel(state2Cons));
nGen_per_page = 3;
nPage_all_gen = ceil(numel(f_orcoAll)./nGen_per_page);
fNum_byState = fNum:nPage_all_gen:numel(state2Cons).*nPage_all_gen;
nGe... |
d7c63283479d0974945bcba7c41ea5b98419f27d59e3765ad52fe13a4b976d9c | MATLAB | 5,030 | 179 |
% 1062 rows, 1599 columns in 'Orange_on_the_tree.jpg' photo
Nrow2 = 900;
Ncol2 = 1500;
dh = 77;
OR = imread('../../results/Figure_8/Orange_on_the_tree.jpg');
OR = double(OR(1+dh:Nrow2+dh,1:Ncol2,:))/255;
% Make hexagons template
N_hs = 30;
Bn = make_numbered_hexagons(N_hs,Nrow2,Ncol2);
N_hex = max(Bn(:));
% Calcula... |
a07fb782efe5a224849a34f21933afa7e75bc721ccfd83a735e3e07b75630280 | MATLAB | 5,032 | 142 | function [big_table, waves] = get_big_table()
dbeh_path='C:\Users\xscogno\MATLAB\Flavio2\Waves\Tracking data\';
rec_data_path = 'C:\Users\xscogno\MATLAB\Flavio2\Preprocessed_data\';
dpath_spikes='C:\Users\xscogno\MATLAB\Flavio2\Preprocessed_data\';
dpath_sorting='C:\Users\xscogno\MATLAB\Flavio2\Waves\Sorting\';
dpath=... |
0e4603c8887139a10da3bb4ac7f4a21fa3ee7388834bda3098961f1a990225de | MATLAB | 5,033 | 156 | classdef Clusters < handle
% AUTHORSHIP
% Primary Developer: Stephen Meehan <swmeehan@stanford.edu>
% Math Lead & Secondary Developer: Connor Meehan <connor.gw.meehan@gmail.com>
% Bioinformatics Lead: Wayne Moore <wmoore@stanford.edu>
% Copyright (c) 2022 The Board of Trustees of the Leland Stanford Junior... |
2289605b0cdd674e65d596cbfdc0aa65de721141a37240268e52eb00867f9bd6 | MATLAB | 5,037 | 103 | %% IR68 Processing Variables
[root_dir, app_dir] = fn_get_root_dir(); ft_dir = [app_dir 'fieldtrip/'];
if isempty(strfind(path,'fieldtrip')); addpath(ft_dir); ft_defaults; end
%--------------------------------------
% Basics
%--------------------------------------
SBJ_vars.SBJ = 'IR68';
SBJ_vars.raw_file = {'... |
ca91476058986da6f887efdc425fe4f12c568aaad050c100afde915d55379ea8 | MATLAB | 5,043 | 254 | function f = mri_brainweb_params(label, varargin)
%function f = mri_brainweb_params(label, varargin)
%|
%| Tissue parameters for BrainWeb synthetic brain images:
%| 1.5T values from BrainWeb database:
%| http://mouldy.bic.mni.mcgill.ca/brainweb/tissue_mr_parameters.txt
%| 3.0T T1, T2 values taken from literature when ... |
efc1b848c5d2ea26aa3ac1ff409b7c0b17a0a395810c4da9bcb6e81f5d834345 | MATLAB | 5,045 | 145 | function [fNum,stateFigure,kinFigure] = plotKNNKinematics2...
(f_orcoAll,state2Cons,tim2Cons,plotType,colortype,fNum)
% loop through each genotype
k=ones(1,numel(state2Cons));
nGen_per_page = 3;
nPage_all_gen = ceil(numel(f_orcoAll)./nGen_per_page);
fNum_byState = fNum:nPage_all_gen:numel(state2Cons).*nPage... |
147b2c586c4aecac5d51e301b77c49618a52199bb400543b909b0aca93b5edbe | MATLAB | 5,047 | 270 | % Gblock_test.m
% Test the Gblock object in all of its versions.
% This also tests several system objects:
% Gsparse, Gtomo2_dscmex, and Gtomo2_wtmex
%
% Copyright 2002-2-19, Jeff Fessler, University of Michigan
% image and sparse matrix
if ~isvar('A0'), printm 'setup Gblock_test'
ig = image_geom('nx', 20, 'ny', 22, ... |
1df4f0a9b6c1fdaccaf8b787d9b008785df854931e0ecbe98d39898a99ad9410 | MATLAB | 5,053 | 134 | function [f_orco_all] = GenerateEmpiricalFlies(genAll,meta)
% GenerateEmpiricalFlies Generates a fly object, then calculate the KNN
% space, baseline and boundary conditions, and crossing turn rate/
% kinematic filters
%
% Inputs: genAll = cell array of genotype labels to create fly objects for
% ... |
f6e557b3041429c7e4e5e6340f8f8e9c98fee2bc899b2a989373d440d5fd396a | MATLAB | 5,057 | 127 | function compare_program_metrics_bias_variance(tbl)
metrics_cell = {'Vessel_Length_Density','Vessel_Area_Fraction','Mean_Diameter','Num_BranchPoints'};
metrics_abbrev_cell = {'VLD','VAF','Diam','BP'};
error_from_gt_tbl = normalize_matched_table(tbl,'Program','Manual','Image_Name',...
metrics_cell,@(x,gt) x-gt);
... |
ef59d92dfdc409594b7f36c24bb0997d504e33805d6f546ed5874acf31635ec0 | MATLAB | 5,065 | 192 | % Simulation for time segmentation code.
% for mri object, must use mexfile mvmult.c
% to mex, simply type: mex mvmult.c at command prompt
%
% This file will form the mri structures and compare iterative
% reconstruction times for field-corrected imaging
%
% Brad Sutton, University of Michigan
% June 2002
%
% Code cont... |
1d059cdc2a5054e06f3d357b5486fbd05f7262cfacb7d08013fce1c590176ef6 | MATLAB | 5,067 | 155 |
function run_format_settings(figure_handle,varargin)%axis_handle
%varargin - settings that are not for the main figure
% match_ax, match_Xax, match_Yax: match limits of multiple axes. Can choose both, or only X or Y
% ...any other type of figure that doesn't run with the main settings
%%%% Load settings
form = forma... |
55fb4c249982c79a215b6d13399eac37f369bd56a696e4a9d4a1a166ececa30d | MATLAB | 5,067 | 144 | function out = spm_shoot_scalmom(job)
% Generate ``scalar momenta'' for use as features in pattern recognition
% FORMAT out = spm_shoot_scalmom(job)
%
% See:
% Singh, Nikhil, P. Fletcher, J. Preston, Linh Ha, Richard King,
% J. Marron, Michael Wiener, and Sarang Joshi. "Multivariate
% statistical analysis of deformatio... |
4647fc2b9cb943321b4f06825fdedca9074c379cb4fb77dd674147746c6dd9fe | MATLAB | 5,069 | 215 | % Code by David Sterratt
%
% Normally SCcol and RGCcol are not specified by the user
function fig = plotGradients2D(obj, invertSCephrinA, forwardOnly, markIsl2)
silentFlag = 1;
if (exist('forwardOnly') && ~forwardOnly)
forwardOnly = false;
else
forwardOnly = true;
end
if (exist('markIsl2') && mar... |
8ac48d82616996a640b41d27fd7a8245653781978a599fed948a85f6bba6c57b | MATLAB | 5,071 | 119 | function ieegHGAll = extractHGDataWithROI(Subject,options)
% extractHGDataWithROI - Extracts high-gamma (HG) band data with region of interest (ROI) selection
%
% Usage:
% ieegHGAll = extractHGDataWithROI(Subject, options)
%
% Inputs:
% - Subject: struct
% subject output of populated task
% - options: struct,... |
912ae09c9ba65aa53eecf55dcbe5bf1f68a3c7d9fe906b1b77836e715e2421de | MATLAB | 5,071 | 138 | function [paramsBest, params, sqerr] = piecewiseFit(X,y,c, slope2)
% Fits data with a series of lines joined at hinges
%
% [paramsBest, params, sqerr] = piecewiseFit(X,y,c)
%
% Inputs:
%
% X - Vector of x measurements
% y - Vector of y measurements
% c - Seedpoints for cutpoint or hinge parameters. There... |
0b86a4667c1fff8c9aeb63610aa30dcd911109829f5fbf72c471e34ce6b4b7d0 | MATLAB | 5,075 | 125 | function [expNewSeq_out, expStates_out, estTR, estE, actualratio_out] = getHMMStatesConcatenatedTracks(speed,angspeed,binSize,onfood,ratio_cutoff,trans,emis,x_offset,train)
%function [estTR estE] = getHMMStates(finalTracks,binSize, trans,emis)
%does R/D classification on the entire track regardless of whether it was
%i... |
226e742d5d18c387f1fe1fa8725cc1c995bd94ee8f24bc0b8954378611616b7e | MATLAB | 5,075 | 111 | folder0={'/projectnb2/npbssmic/ns/Ann_PTSD1/Ann_PTSD1_1/','/projectnb2/npbssmic/ns/Ann_PTSD1/Ann_PTSD1_2/','/projectnb2/npbssmic/ns/Ann_PTSD1/Ann_PTSD1_3/'};
id=str2num(id);
folder='/projectnb2/npbssmic/ns/Ann_PTSD1/Ann_PTSD1_2/';
datapath=strcat(folder,'dist_corrected/');
% add subfunctions for the script. Change dir... |
48517da99d699e7d43c60cbd5e5fdce4ccbabf2035ddd8c6b372b07c669fdf73 | MATLAB | 5,075 | 111 | folder0={'/projectnb2/npbssmic/ns/Ann_PTSD1/Ann_PTSD1_1/','/projectnb2/npbssmic/ns/Ann_PTSD1/Ann_PTSD1_2/','/projectnb2/npbssmic/ns/Ann_PTSD1/Ann_PTSD1_3/'};
id=str2num(id);
folder='/projectnb2/npbssmic/ns/Ann_PTSD1/Ann_PTSD1_3/';
datapath=strcat(folder,'dist_corrected/');
% add subfunctions for the script. Change dir... |
933599061a12826148be7795d71c45e62a1f11ddf9f10bfddc7e6d65d84899a8 | MATLAB | 5,075 | 105 | function input_graph = fun_graph_split_node_DT_guided(input_graph, vessel_mask_dt, vessel_skl, node_label_list)
% fun_graph_split_node_DT_guided deletes the node of degree 4, pairs the
% remaining 4 and connect pairs by linear regression and shortest path
% searching.
% Input:
% input_graph: structure generated by ... |
9dd03886e1130b593d97e36aaebbd02d3b228cdcd080208e2743586a0180c61a | MATLAB | 5,075 | 146 | function P1 = fun_my_head_motion_correction_v1(pth, selind, DWIname_pre, ...
bvals, bvecs, mask, ...
DWIname_orig)
% pth - path where images are saved
% selind - index of volumes where motion did not occour (FSL index + 1)
% DWIn... |
35b93431463cb69952f3cd52a5ecd7fa71e5a1021d7da8fd39bf6d9e0b05febf | MATLAB | 5,081 | 200 | %
%
% Alessandra Griffa
% University of Geneva
% Ecole polytechnique federale de Lausanne EPFL | MIPLab
% Jun 2021
%
function [k1,k2,k3,k4,k5] = colorsurf_2hemi_5perspectives_macaqueRM82(qinfo, CM, llist, my_lighting, nview)
% Initialize figures handles
k1 = NaN;
k2 = NaN;
k3 = NaN;
k4 = NaN;
k5 = NaN;
% FACEs and... |
b0bf27e4e506835e30a5a390efce60e3d1cb864755a70dbed6c403a32448ff5a | MATLAB | 5,081 | 88 | % BATCH ANALYSIS FOR REPLAY CONTROL FOR 1 LAP TRACK
% MH_2020
% For 1 lap session, this code takes each lap from T1, calculate it's place fields and compares it to T2 1 LAP (e.g. ratemaps Lap 5 T1 vs Lap1 T2).
% Then it used these new place fields to decode replay events, and runs the same shuffle analysis as for norma... |
51e15bf43043a2c089fd889efee70bb375618f0563f5bb871f0e1946e678150a | MATLAB | 5,082 | 108 | % DECODING ERROR COMPARISON BETWEEN PROTOCOLS
function plot_compare_thetaseq_decodingError(bayesian_control)
PP = plotting_parameters;
sessions = data_folders;
session_names = fieldnames(sessions);
if ~isempty(bayesian_control)
path1 = '\Bayesian controls\Only first exposure';
path2 = '\Bayesian controls\Onl... |
293c64b5db5507a7056b82f41ac5dd1dc3896f66b72f70053fab20c87d9d9517 | MATLAB | 5,086 | 227 | function ob = Gtomo2_wtmex(arg1, varargin)
%function ob = Gtomo2_wtmex(sg, ig, options)
%function ob = Gtomo2_wtmex(file, options)
%function ob = Gtomo2_wtmex(arg_pairs, options)
%
% Generate a tomographic system model based on aspire's wtfmex() routine.
%
% in
% sg strum sino_geom()
% ig strum image_geom()
%
% opti... |
1dd7b00a1266ad9a3efd8be482946bbc9e2beba9392e70ede0cac95a5765d874 | MATLAB | 5,087 | 191 | function ndf2swc(input_file, output_file, scale_factor)
[input_filepath, input_name, input_ext] = fileparts(input_file);
% Check if output file is specified
if (nargin<2)
if(isempty(input_filepath))
output_file = strcat(input_name,'.swc');
else
output_file = strcat(input_filepath,'/',input_nam... |
04c1e8901b7b4145e0ca57525d6d4498687e7c2e477122a80d04e958b1c4b177 | MATLAB | 5,089 | 134 | function bootstrap_partialcorr_eeg_rnn_rt(conditions)
%BOOTSTRAP_PARTIALCORR_EEG_RNN_RT Calculate the peak latencies and confidence intervals
%for the partial correlations of EEG-RT and RNN-RT.
%
%Input: conditions ('artificial','natural' or 'both')
%
%Output: peak latency and confidence interval
%
%
%% Add paths
addp... |
87bfa5d6e7cdd07d429b70a6c36965f32346a766cdaa183bc4a6f2feaa4ca2d9 | MATLAB | 5,091 | 110 | %% NuCLear
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% NuCLear neural network cell type classification algorithm
% Version: 4.1
% ... |
9a65e55d7c68bff1bba9f299d7ab7142890690bfd18239dec0e1e27a09d24f37 | MATLAB | 5,094 | 140 | function Statistical_IndividualAnalysis_PLV_BH(PLV_MI, PLV_Baseline, subject_ID, fig_path, db_path, numperm, nb_ROIs_DK, p_thresh, freqband)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Perform individual-level statistical analysis - PLV
% Authors: MCC
% Date: 26/10/2022
%%%... |
cdea2e8617e9b482334c13b98e145e38fdb9722db716fc220c610f093b3aed39 | MATLAB | 5,106 | 100 | %Z-score delay all cells
close all;
clear;
load('U:\rg-yoshida\Yoshida_lab_shared_folder\Shared Data\Antonio\In vivo data\Babak\preliminary analysis\Space_bin\Space_bin_filt30Kalman.mat')
load('U:\rg-yoshida\Yoshida_lab_shared_folder\Shared Data\Antonio\In vivo data\Babak\preliminary analysis\Space_bin\Spatial_... |
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