sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
11a0d9488595ed1823ded0bf8b843e18e95e5cc81abd83f516a0fd16d9e0ec65 | MATLAB | 5,240 | 165 | function create_quadrature_filters_for_morphon_registration2d(varargin)
% CREATE_QUADRATURE_FILTERS_FOR_MORPHON_REGISTRATION2D Creates 2D quadrature filters for usage in Morphon registration
%
% create_quadrature_filters_for_morphon_registration2d()
%
% INPUT ARGUMENTS
% N/A
%
% OPTIONAL INPUT ARGUMENTS
% See "Advanced... |
3b843db5057b787f9ef4c9d2e1a576ae5b1d4085c2f59b104c24f5806c77da52 | MATLAB | 5,242 | 153 | function [errs warns] = unit_scramble_regressors()
% [ERRS WARNS] = UNIT_SCRAMBLE_REGRESSORS()
%
% This is a script that tests the SCRAMBLE_REGRESSORS function.
% It mainly tests the optional ACTIVE_SELNAME argument to exclude
% time points from being scrambled.
% INPUT ARGUMENTS:
% (none)
%
% OUTPUT ARGUMENTS:
% ERR... |
3faf9279897e527e6daaf0ea76c26819a6a63ab43eae29bdbd8fb66a887684c6 | MATLAB | 5,245 | 228 | function Duszkiewicz2024_Figure_SingleRec_LFP
% This script reporoduces panels from Figure:
% Extended Data Figure 9
% Run in folder A3706-200313 to reproduce manuscript figure
%
% Dependencies:
% TStoolbox
% Buzcode
% TODO: script dependencies, links to external functions
% Copyright (C) 2023 by Adrian Duszkiewi... |
2ec3bcee7e8d9e5147632bb2c0d085e6f5d75d61b472da5e38894169f4576fd2 | MATLAB | 5,255 | 179 | function fo = identifyLinesAndAlignSlide_GoOverAllOptions(f,vLinePositions,hLinePositions,...
pixelSize_um,octVolumeJson,histologyFluorescenceIm)
%This script goes over all options for feducial lines, chooses the best one
%and allows user to manually select if it wants another option
%INPUTS:
% f - fiducialL... |
38e506a7758c8032d73b97742bbb765b758ae69a9cd7a3c72625ebd7e13b89d0 | MATLAB | 5,264 | 171 | % Probability Tuning curves
supPath = 'C:\noga\TD complex spike analysis\Data\albert\pursuit_8_dir_75and25';
load ('C:\noga\TD complex spike analysis\task_info');
bool_task = ~cellfun(@isempty,regexp({task_info.task},'pursuit_8_dir_75and25'));
bool_type = ~cellfun(@isempty,regexp({task_info.cell_type},'PC cs'));
bool... |
d1e8e3c136ffd18bac5b14689a9191646a0207fd4fa78b21fc905b8fd6a1ef5b | MATLAB | 5,266 | 173 | % Probability Tuning curves
supPath = 'C:\noga\TD complex spike analysis\Data\albert\saccade_8_dir_75and25';
load ('C:\noga\TD complex spike analysis\task_info');
bool_task = ~cellfun(@isempty,regexp({task_info.task},'saccade_8_dir_75and25'));
bool_type = ~cellfun(@isempty,regexp({task_info.cell_type},'PC cs'));
bool... |
574f1758786d4ee81654c8dea25a65f8194efd3b1f3b8e03d0e52d6ba61d9cf5 | MATLAB | 5,268 | 198 |
function hopf_DTI_Grange_tbi(s,condition)
%addpath('/Volumes/LASA/TBI_project/TBI_openneuro/Hopf_model/TBI_replication/inputs/');
load(sprintf('results_f_diff_fce_cond%d_ON_tbi.mat',condition));
load(sprintf('empirical_spacorr_rest_cond_%d_ON_tbi.mat',condition));
load SClongrange.mat;
load schaefer_MK.mat;
load('tse... |
a1b5d015a2a0fc19c6fea1ba69ed497843b0478e4f0028f569d8baab92915e89 | MATLAB | 5,285 | 178 | function infocapacity_hopf_errorhete_tbi(s,condition)
rng(s);
measure='err_hete';
if condition==1
G=optG_tbicond1; %OptG TBI ses1
elseif condition==2
G=optG_tbicond2; %OptG TBI ses2
elseif condition==3
G=optG_tbicond3; %OptG TBI ses3
end
NPARCELLS=1000;
NR=400;
NRini=20;
NRfin=380;
NSUBSIM=100; %number... |
06d131b56eb47e5c61625665b9b7c7f3590b301089ffa246768261467a5ef14c | MATLAB | 5,289 | 132 | % Add repts and CI. Used for publication
%
% Each array is channels x time x trial
% This use cross-validation where GLM is computed for each training fold
% and SS beta coefficients from training folds are used for test fold.
% Used for IPS
% STATUS: Inactive(?)
%
% SYNTAX:
% [performanceLDALedoit] = ...
% calcCu... |
17ea7387d622d866fddbbae68ae66d380668f4f0fa00df4738180b20a18e8569 | MATLAB | 5,298 | 121 | function [Ci,Q]=modularity_und(A,gamma)
%MODULARITY_UND Optimal community structure and modularity
%
% Ci = modularity_und(W);
% [Ci Q] = modularity_und(W,gamma);
%
% The optimal community structure is a subdivision of the network into
% nonoverlapping groups of nodes in a way that maximizes the numb... |
7b092db2ae2e77708dc1c9c5bb52d244af27088533f62477e5974ba1c61c6ed3 | MATLAB | 5,300 | 177 | function [subj] = statmap_searchlight(subj,data_patname,regsname,selname,new_map_patname,extra_arg)
% [SUBJ] = STATMAP_SEARCHLIGHT( SUBJ, ...
% DATA_PATNAME,REGSNAME,SELNAME,NEW_MAP_PATNAME,EXTRA_ARG)
%
% OBJ_FUNCT (required, default = ''),
% e.g. 'statmap_classify'
%
% ADJ_LIST (required) - as produced by CREATE_AD... |
2014fe222668a68c5d1890ff8730ba737d881dd2ce77294ca6fbfb01c8f63ee9 | MATLAB | 5,310 | 147 | function [hdr,vol] = mom_read_minc1_octave(file_name,opt)
% Read a MINC1 file in Octave
% To learn more about the MINC format :
% http://en.wikibooks.org/wiki/MINC
%
% SYNTAX:
% [HDR,VOL] = MOM_READ_MINC1_OCTAVE(FILE_NAME)
%
% INPUTS:
% FILE_NAME (string) the name of a minc file
%
% OUTPUTS:
% HDR (structure) the... |
bdb69daf5025a8212fc0c239fe5d76f7c2dd7afd2fdda12a33126bcfdbebdb98 | MATLAB | 5,310 | 142 | function [areas,total_bins,overlap_bins, total_area, overlap_area]=...
ISI_conduction_time_peak_finding_210909...
(stim,stim_dir,times_struct_stim,stim_duration,parent_dir,if_cw)
% Testing peak finding capabilities across multiple units for elimination
% of zeros
% Fixed saving issue and added bin count for per... |
906345afa615789d6a5da453f01cf9c3fadf7258c9fa1271773b6201c101c564 | MATLAB | 5,312 | 205 | function [errmsgs warnmsgs] = unit_hd_scripts()
% USAGE :[ERRMSGS WARNMSGS] = UNIT_HD_SCRIPTS()
%
% This is a script that tests the move_pattern_to_hd and the
% load_pattern_from_hd functions.
%
% ERRMSGS = cell array holding the error strings
% describing any tests that failed. If this is empty,
% that's a good thi... |
095701be224f37bbc7fdf4ab17b7359a83a10b415194a56fc564ffe31f4e3e91 | MATLAB | 5,313 | 153 | function [pparam, K, clabel] = mv_preprocess_average_kernel(pparam, K, clabel)
% Kernel averaging is the generalization of sample averaging to the
% non-linear case. The samples are not averaged in input space (which is
% non-linear) but rather in Reproducing Kernel Hilbert Space (RKHS), in
% which the data is linearl... |
3e94732d75b162cc991e858ad5bf93753cd93582c1fafb0b9c5d489379968b0f | MATLAB | 5,324 | 132 | %% THREE CLUSTERS ON TWO LEVELS (UFOs, 2 kHz)
% SETTING
clear
rng(29) % seed
Path = [pwd, '\IN_sims\'];
clrs = ["b","r","g","k","c","y"]; % colors
per_clr = [0.9290,0.6940,0.1250]; % periodogram color
% TIME
T = 12000; % time span
dt = 0.01;
% COUPLING
eps1 = 0.06; % within a cluster
eps2 = ... |
74e1d86793796c06ce514dfbe1cbe1a496cc38a5e98f1667ba3eefbe7e49865f | MATLAB | 5,328 | 95 | % Extract single HRF from dcNew variable, the output of GLM fitting function
% save to mat file.
function createSingleTrialHRF_Updated_MultiOnly(sbjNum,movieList,numClasses)
%saveDir = ['C:\Users\mn0mn\Documents\ResearchProjects\spatailAttentionProject\ProcessedDatafNIRS\Experiment' num2str(sbjNum)];
processedDataDir =... |
6bcc3d6f735203eea074aa8821680c2516f42bd2e5bc888a8fa6335e4fedbe5c | MATLAB | 5,333 | 131 | % STATUS: Inactive(?)
%
% SYNTAX:
% [performanceLDALedoitHbO] = ...
% calcCumSum_DiffTLen_AllChn_CompClassifiers_Final_NoSave(sbjNum,...
% trialsTr,trialsTst,movieListTrain,movieListTest,numClasses,...
% mlActAuto,timeLen)
%
% DESCRIPTION:
% Perform cross validation where SS beta coefficients from train... |
2eee27de4ade2b7e4fae215b066f3242122ca9b52de151c729a7474c605f5a82 | MATLAB | 5,340 | 131 | % Fit Helper
%
% Descriptions tba
%
% $Author: Peng Li, Ph.D.
% Division of Sleep Medicine, Brigham & Women's Hospital
% Division of Sleep Medicine, Harvard Medical School
% $Date: Dec 26, 2019
%
% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
% ... |
92d6da14a8e221b7e3d145f7ce58da725a5df2a95dfc123366e64a71f9d76aa8 | MATLAB | 5,342 | 162 | % This script is a demo showing some simple decoding on simulated toy data.
% The toy data are simple matlab matrices and no "real" fMRI or EEG data.
fpath = fileparts(fileparts(mfilename('fullpath')));
addpath(fpath)
clear variables
dbstop if error % if something goes wrong
res_fig = figure;
%% Set the ... |
1b899ec16628318096bfe95c284fcbca040991fdf027d003b8a214b312c3eba2 | MATLAB | 5,345 | 172 | function [Azloo,Azsig,Y,a,v,D,Azloomedian] = single_trial_analysis_EEG(X, truth, duration, skipLOO, perm, eigratio,gamma, method)
% INPUT:
% X (data matrix) =
% [Ch1: Sample1toM (Trial1) Sample1toM (Trial2) .... Sample1toM (TrialN)]
% [Ch2: Sample1toM (Trial1) Sample1toM (Trial2) .... Sample1toM (Tr... |
22ba31dcc8adb834472fb5a7b711ad8fd7cb60ee7d4ad346b36ce16d15b6b81b | MATLAB | 5,345 | 177 | function [theta_est, theta_err, LLF, ctable_mod, ctable_fit, py_corr,theta_init] = mADC_model_fit_sel_angles_resp_error_const(ctable, angles,d_limit,Aeq,beq,lb,ub,r_init)
%% function that fits the m-ADC model to data in a contingency table
%
% Sridhar Devarajan, Sep 2016
% sridhar@cns.iisc.ernet.in
%
% Usage:... |
7cfc0b8ad7f7a331d1b004fd25c23c3c825e6cb9e93b662a7246c47fbbfdb310 | MATLAB | 5,351 | 161 | function simulateBehavior(models,paratype,fbtype,nSubjects)
% input parameter
% - models
% - paratype (
% empprior = parameters drawn from empirical prior distributions
% random = drawn from random (=uniform) distributions
% fitted = drawn from behavioral data
% - fbtype (
% value = magnit... |
20f26384040b720e537c7fc501fe2febddf50859005d0803b6185bb59d0f9ded | MATLAB | 5,362 | 139 | function [synLat,synStrZ,synStrR,sigBounds] = FindSynapse_SameShank(cch,varargin)
% FindSynapse_SameShank - find putatitve monosynaptic connections based on
% cross-correlograms for same-shank cell pairs
%
% USAGE
% [excPairs,inhPairs,excTimes,inhTimes,excStrength,inhStrength,hiBounds,loBounds] = FindSynapse_SameShan... |
6f63a66cf08979ec172069634d77babe8aa2dee95cb51ba78d65ee5fdad513b5 | MATLAB | 5,366 | 173 | % Dependency of LoadPosition.
% [CleanWhl GoodRanges] = CleanWhlFR(Whl, StretchLen, JumpSize,Gap);
%
% "cleans up" a wheel file by interpolating missing stretches
% up to StretchLen long (default 20), for which the endpoints
% don't differ by more than JumpSize, (default 30).
%
% also returns the ranges where the whl ... |
8467ac6e4a712335a141b919e68c3bdcd6e4740498791d8abbc33707ebaf9540 | MATLAB | 5,371 | 190 |
clear
[task_info,supPath,MaestroPath] = ...
loadDBAndSpecifyDataPaths('Vermis');
%load('sessionMap.mat')
K_FOLD = 10;
EPOCH = 'cue';
req_params = reqParamsEffectSize("both");
raster_params.align_to = EPOCH;
raster_params.time_before = 0;
raster_params.time_after = 800;
raster_params.smoothing_margins = 100;
ts... |
9d795667714ca66a39a1c094b9eea1afcf49ecda094650211f5b6bc58f526f95 | MATLAB | 5,375 | 206 | classdef Description
%
% Class to deal with dataset_description files.
%
% USAGE::
%
% ds_desc = bids.Description(pipeline, BIDS);
%
% :param pipeline: pipeline name
% :type pipeline: char
%
% :param BIDS: output from BIDS layout to identify the source dataset
% used when creatin... |
b91406f26428f9a6cda2fccef7c945842361c9671e01f6cf45b706de6003cde9 | MATLAB | 5,376 | 122 | function preprocess_param = mv_get_preprocess_param(preprocess, preprocess_param)
% Returns a parameter struct with default parameters for a given
% preprocessing function.
%
%Usage:
% param = mv_get_preprocess_param(preprocess_fun, <param>)
%
%Parameters:
% preprocess_fun - [string] specifying the preprocessing fun... |
d60529d22cbba9a05f38268e2f82c6d2f9f9099dff7f1f0a58a3f419621224bd | MATLAB | 5,391 | 114 | % function output = transres_accuracy_pairwise(decoding_out, chancelevel, varargin)
%
% For more than two classes, rather than getting the mean multiclass
% accuracy across all classes (i.e. chance = 1/n_label), report the mean
% accuracy of all pairwise comparisons (i.e. chance = 1/2).
%
% This code runs faster if all... |
59313252fb60e696ddb23b04a1f243994f6f5bb7118f5b81182e5eb0414c8923 | MATLAB | 5,399 | 208 | clear all
[task_info,supPath,MaestroPath] =...
loadDBAndSpecifyDataPaths('Vermis');
PROBABILITIES = 0:25:100;
NUM_COND = 20;
req_params.remove_repeats = 0;
req_params.grade = 7;
req_params.cell_type = 'CRB|PC|BG|SNR';
req_params.task = 'choice';
req_params.ID = 4000:6000;
req_params.num_trials = 180;
req_params.re... |
ebc064a6cee65273d665ad2c24dbd01297dded9b5fb9a2579610f4af991fa4df | MATLAB | 5,402 | 104 | %Figure 3
%Calculations needed for plotting
%Percent of participants who said object apeared slanted
SL_cont_square_percent = sum(SL_cont_square)./SubjNum*100;
SL_exp_square_percent = sum(SL_exp_square)./SubjNum*100;
SL_cont_phone_percent = sum(SL_cont_phone)./SubjNum*100;
SL_exp_phone_percent = sum(SL_e... |
4500b7b1ccc42dd7fde27b52187568db1053cb0a71438248de7e98be6157ddbe | MATLAB | 5,410 | 194 | function selection = checkboxinputdlg(choices, name)
% Syntax:
%
% selection = checkboxinputdlg(choices)
% selection = checkboxinputdlg(choices, name)
%
% Inputs:
%
% choices: Cell array of strings specifying the radio button choices
% to select from.
% name: (Optional) argument n... |
35b5321c70fa36c01dcae308a4892486eae628df6a80e1eb41ab3e77490ac2b8 | MATLAB | 5,412 | 180 | function [x, options, flog, pointlog] = graddesc(f, x, options, gradf, ...
varargin)
%GRADDESC Gradient descent optimization.
%
% Description
% [X, OPTIONS, FLOG, POINTLOG] = GRADDESC(F, X, OPTIONS, GRADF) uses
% batch gradient descent to find a local minimum of the function F(X)
% whose gradient is given by GRADF(... |
3fc06a347175efc7702f39f8023bee405ec733a1dd89e4b57972bf5d454fbd79 | MATLAB | 5,413 | 183 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Written by James N Ingram, 2023
clear all;
close all;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
L0 = 0.060; % m
L1 = 0.035; % m
L2 = 0.050; % m
% Valid values for the angles between 1st and ... |
15e9e4fb92a02f65f58daf67991a34baa8797afcbd87b7c9f4247a6d9308fb4d | MATLAB | 5,422 | 149 | function output_filenames = create_scans_tsv(varargin)
%
% Create a simple scans.tsv for each participant of a BIDS dataset.
%
% USAGE::
%
% output_filename = bids.util.create_scans_tsv(layout_or_path, ...
% 'use_schema', true, ...
% ... |
8885ccfbe2c8a7b07c8f151e78f4888be2e7252a0be82ea4e53367c6b2ddc758 | MATLAB | 5,430 | 170 | function dimensions = yOCTLoadInterfFromFile_ThorlabsHeader (inputDataFolder, OCTSystem, chirp)
%This function loads dimensions structure header
% INPUTS:
% - inputDataFolder - OCT folder with header.xml file or srr files
% Optional inputs, if they are unknown, we will figure them out
% OCTSystem if we know it... |
6066d52231879ec5527608a026c695ca8e7741a3b9cac7a4a9fad49680e90f58 | MATLAB | 5,432 | 195 |
function hopf_DTI_Grange_tbi_lesions_mask_2SD_bin(s,condition)
%load data
load(sprintf('results_f_diff_fce_cond%d_ON_tbi.mat',condition));
load(sprintf('empirical_spacorr_rest_cond_%d_ON_tbi.mat',condition));
load SClongrange.mat;
load schaefer_MK.mat;
load('tseries_openneuro_tbi_tp123_sch1000.mat');
load('lesion_mas... |
bff8d8dba5ae732ecadf59d9a5e3249f72290679b066a46690d9663cb164f7ca | MATLAB | 5,437 | 163 | function [results_data] = plot_cue_opp_evokResp_bin_rsc(cueMI, oppMI, evokResp_quad, evokResp, iWindow, iMonkey, yl, trial_type);
%% windows
window_names = ["cue onset", "pre blank onset", "post blank onset"];
%% Plots V4 f-MI cued vs opp histograms (locked to onset)
%% Code
cueMI_temp = []; oppMI_temp = [];
evokR... |
ca63088f124ffa1019abcf8006406f31661a018b2309c91a8a193e49d7789a7d | MATLAB | 5,441 | 178 | function [theta_est, theta_err, LLF, ctable_mod, ctable_fit, py_corr,theta_init] = mADC_model_fit_sel_angles_resp_error_const_L2(ctable, angles,d_limit,Aeq,beq,lb,ub,r_init,lambda)
%% function that fits the m-ADC model to data in a contingency table
%
% Sridhar Devarajan, Sep 2016
% sridhar@cns.iisc.ernet.in
%... |
70fc0a285c2bfa6045db0916496e16e4de16317e576ee619c7c27a08dd7c379e | MATLAB | 5,443 | 194 |
function hopf_DTI_Grange_tbi_lesions_mask_2SD_weight(s,condition)
load(sprintf('results_f_diff_fce_cond%d_ON_tbi.mat',condition));
load(sprintf('empirical_spacorr_rest_cond_%d_ON_tbi.mat',condition));
load SClongrange.mat;
load schaefer_MK.mat;
load('tseries_openneuro_tbi_tp123_sch1000.mat');
load('lesion_mask_2SD_we... |
73620f27392f24e79c361d808d049134b2ba01f9a01adab218fee1545b6e38e2 | MATLAB | 5,443 | 155 |
clear
[task_info,supPath] = loadDBAndSpecifyDataPaths('Vermis');
K_FOLD = 10;
req_params.grade = 7;
req_params.cell_type = {'PC ss', 'PC cs', 'CRB','SNR', 'BG msn'};
req_params.task = 'pursuit_8_dir_75and25';
%req_params.ID = [4081,4081,4198,4198,4210,4211,4238,4238,4269,4269,4307,4307,4307,4307,4307,4307,4310,4331,... |
0e6543fb90e7e2e97932a40d3314c69c9c1f3b55cc468697e27029be22ef78b6 | MATLAB | 5,444 | 195 | %% Compare Conjunction test exp1 vs exp3
clear all;
clc;
mdir=pwd;
cd('../Data')
load AccuracyDATA.mat
load BaseDATA.mat
JointTrExp3 = [2; 4; 7; 8; 11; 12]; %%% Subjects who received Conjunction training (in addition to Separate training) in experiment 3
cd(mdir);
%%
PfigDir='../Figures';
Tst1=A... |
f2c23d16851ea5fdab045b8f6a9d7685347f0ed354f1b2b892362eccc91e1b81 | MATLAB | 5,448 | 133 | clear,clc
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% This script is used to predict the learning outcomes of week 8
% intervetion
%
% Jin
% 8/17/2022
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% set the file path
current_path = pwd;
working_p... |
86c35786645c72c1ea5526cfaf2741c93b21ac72fabbb1e613d5ca7eaf3f5a2b | MATLAB | 5,455 | 143 | % READBIN read data from binary file
%
% GENERAL optimized for huge files, multiple (dis)contiguous channels, and multiple (dis)contiguous periods
%
% FILE STRUCTURE sample by sample; in each sample - channel by channel:
%
% c1t1 c2t1 ... cNt1 // sample 1
% ... |
88fa96c2a58a31cf1a70a9524913617ef1b0ee9ccb630132ec50f20b1e2f463f | MATLAB | 5,460 | 155 | % function aligntsp2(file, [front, back])
%
% Making whl file for two colors. Originally from 'aligntsp.m' by Tony Berenyi.
%
% tsp(t,:) = [timestamp, color1-X, color1-Y, color2-X, color2-Y, color3-X, color3-Y]
% 'front' and 'back' assigns which color (1, 2 or 3) should be used. if -1, then all rows are -1.
function a... |
9a1e057e0827c562383a064cda04815f6e4530f693eb4ef86c29bf168eeb536b | MATLAB | 5,464 | 199 | function [scratchpad] = train_svdlr(trainpats,traintargs,in_args,cv_args)
% Experimental classifier: SVD combined with logistic regression.
%
% License:
%=====================================================================
%
% This is part of the Princeton MVPA toolbox, released under
% the GPL. See http://www.csbmb.... |
caf95a2a41b138e97a14cb156cd5d321524e7234c1e0e775095ba73a9b2205a0 | MATLAB | 5,466 | 172 | function create_quadrature_filters_for_morphon_registration3d(varargin)
% CREATE_QUADRATURE_FILTERS_FOR_MORPHON_REGISTRATION3D Creates 3D quadrature filters for usage in Morphon registration
%
% create_quadrature_filters_for_morphon_registration3d()
%
% INPUT ARGUMENTS
% N/A
%
% OPTIONAL INPUT ARGUMENTS
% See "Advanced... |
3945aaea94d4fef50e1682e1e6f8799569e2c2a6034042624ba09e4cf23bc80e | MATLAB | 5,470 | 194 |
function hopf_DTI_Grange_tbi_lesions_mask1halfSD_weight(s,condition)
load(sprintf('results_f_diff_fce_cond%d_ON_tbi.mat',condition));
load(sprintf('empirical_spacorr_rest_cond_%d_ON_tbi.mat',condition));
load SClongrange.mat;
load schaefer_MK.mat;
load('tseries_openneuro_tbi_tp123_sch1000.mat');
load('lesion_mask_1an... |
8255d2900f8dd3f3aee9942c6a21d1c0664d6a41b34aa08bd74ca3277a7f7cc1 | MATLAB | 5,471 | 167 | function [subj] = load_afni_pattern(subj,new_patname,maskname,filenames,varargin)
% Loads an AFNI dataset into a subject structure
%
% [SUBJ] = LOAD_AFNI_PATTERN(SUBJ,NEW_PATNAME,MASKNAME,FILENAMES,...)
%
% Adds the following objects:
% - pattern object called NEW_PATNAME masked by MASKNAME
%
% NEW_PATNAME is the name... |
7658608d2b4941a3fdd1b6861c8f6d7dafca1a5718fb0d7b6d808cbee8f7c3df | MATLAB | 5,476 | 191 | % Implementation of MUSE for comparison (on Matlab R2018b)
% (c) Erpeng Dai, Stanford University
close all
clear
clc
%% Input parameters
MB=3;
Rpe=1;
vcc_flag=0;
par.ky_s = 150;
par.ky_r = 200;
par.kx_r = 200;
par.nSL = 1;
par.nCH = 32;
par.nB = 1;
par.nNEX0 = 1;
par.nNEX = 1;
par.blipsign = 1;
par.nSHOT = 4;
%% SM... |
4b0b9b164c5169fce3c119ac7c34dee3fcad474bcf458d9eefe1b67ddd9f6089 | MATLAB | 5,477 | 185 | %% Plot PC activity during VORD and cancellation
function [fH_vord, fH_canc, fH_vord_split, fH_canc_split] = ...
plotCancellation(I, K1, K2, K0)
ylims_canc = [-.6 2.2];
ylims_vord = .6 * [-1 1];
tt = (1:8000)*K1.dt-K1.dt; % Allow two cycles
% Function to negate gain if abs(phase) > 90 deg
flipGain = @(gain, ph) ... |
e70b9a951c6ba824900f79d5b0522983ef6839b051f8c01be503dcea869d1be7 | MATLAB | 5,481 | 85 | %Evaluates significance and amplitude of SPEP.
%2024/02/25
function evaluate_SPEP_degree(fnames)
%create output folder if it does not exist:
if ~exist([fnames.analysis_folder,'/LFP/spep_erp'],'dir')
mkdir([fnames.analysis_folder,'/LFP/spep_erp']);
end
for ref_method={'Laplacian'}
ERP_r... |
7ef9d92e8f3962b546146dc7e72fa8f76b5a891fa96f679e507878c3de8caed1 | MATLAB | 5,483 | 147 | function varargout = choose_method(varargin)
% CHOOSE_METHOD M-file for choose_method.fig
% CHOOSE_METHOD, by itself, creates a new CHOOSE_METHOD or raises the existing
% singleton*.
%
% H = CHOOSE_METHOD returns the handle to a new CHOOSE_METHOD or the handle to
% the existing singleton*.
%
... |
13c1b08c8fd6c3e2ec7107f88b4ecd8ca646989700115cd57b63f3842ed2c593 | MATLAB | 5,488 | 99 | %Returns a structure with tables, matrices and neuron names for the
%forward & backwards connectome of the C. elegans
function [Celegans]=Organizer(expression_type,diagonal_symmetry,off_diagonal_symmetry)
%Creattion of name ordering for neuron based on Natt. Comm paper
Celegans.NeuronNames.NodesB_gap = {'VA02';'VA03';... |
05be95a4b425bf9ca41a211b7209cdc8584be0425564d85a4cd9c577e3605df6 | MATLAB | 5,492 | 195 |
function hopf_DTI_Grange_tbi_lesions_mask1halfSD_bin(s,condition)
%load data
load(sprintf('results_f_diff_fce_cond%d_ON_tbi.mat',condition));
load(sprintf('empirical_spacorr_rest_cond_%d_ON_tbi.mat',condition));
load SClongrange.mat;
load schaefer_MK.mat;
load('tseries_openneuro_tbi_tp123_sch1000.mat');
load('lesion_... |
c40de687a2729b4961e8d7ce7df5717c1386ef42f6180dfc7449f83d0fa16a70 | MATLAB | 5,501 | 120 | function [CORRECTED_STATS] = DoseResponseCurves(change_struct,tseries_idx_struct,TreatmentLevels, condition,...
TITLE, YLABEL, YLIMS, XLABEL, XLIMS,...
DATACOLORS, stdOrsem, LW, STATS, pairedOrunpaired)
% DoseResponseCurves plots line plots for each condition with mean +- error
% for each treatment level. I... |
d592bd2a73855018667d53cabe5e11576eee6c42f98ae9f2e60226d2173e4976 | MATLAB | 5,511 | 146 | %DEMEV1 Demonstrate Bayesian regression for the MLP.
%
% Description
% The problem consists an input variable X which sampled from a
% Gaussian distribution, and a target variable T generated by computing
% SIN(2*PI*X) and adding Gaussian noise. A 2-layer network with linear
% outputs is trained by minimizing a sum-of-... |
fb9f90ada4de1b58deb21ef19a138ea3e517c680d33a75af0d12ad73e976fa00 | MATLAB | 5,522 | 80 | function [ interpulse_duration_ms, num_pulses, num_bursts, pulseduration_ms, uncaging_start_ms, uncaging_end_ms, uncaging_duration_ms, uncaging_frame_start, uncaging_frame_end, max_voltage ] = square_pulse_detection( r, frameRate )
%square_pulse_detection detects square pulses from a voltage recording
%(column 1: tim... |
cd260b7c065c6a4d20d553247caf978b3f90fa4bc0839ecb44cefa11e1e8029c | MATLAB | 5,523 | 164 | function createInstallFile(options)
global installfilename
global platform
platform = [];
% Start with a clean slate
cleanup('','','start');
installfilename = sprintf('%s_install', lower(getAppname()));
[~, exename] = getAppname();
setNamespace(exename)
% Generate a LastCheckForUpdates.dat in case that we haven't... |
7a247f2e56204853f8630fdd4ff2ed32fed194b2afae7772d8bfc0310534162c | MATLAB | 5,528 | 185 | %This script runs all preprocessing sequentially
%% Inputs
SubjectFolderIn = s3SubjectPath('01');
SubjectFolderOut = SubjectFolderIn; %Where to save folder to
isRunInAutomatedMode = true; %When set to false, will allow human more control to select info
% Specify what kind of processing is needed
isUploadToC... |
79ecd59657849fa4b665a8ff0923b13b4689a7b30ccb4d11b6f66b98086e7c01 | MATLAB | 5,531 | 152 | % function [results, cfg] = decoding_example(decoding_type,labelname1,labelname2,beta_dir,output_dir,radius,cfg)
%
% This is a general function for two class classification with SPM, using a
% linear SVM as implemented in the libsvm software, with accuracy images
% (for searchlight) or variables (for ROI or wholebr... |
d1b0caf252f5fb61a136585b9ee9d795bdc25f286155f61c97f42d93ea077301 | MATLAB | 5,531 | 150 | Condition_Name = cond_labels;
Channels = cluster_labels;
%% Define variables for ROI topoplot
ROI_plot_t = [1200 1700];
ROI_plot_f = [4 7];
%% Load all data
for sub = 1:length(subject_list)
subject = subject_list{sub};
load([TFR_Data subject, '_tf_data_' int2str(1)])
% initialize matri... |
d2c5afb61cc78f9c1ed2023cf966adf3e5f327f40c15f80f236f673ca413762c | MATLAB | 5,535 | 193 | function [mask]=DSC_mri_mask(volumes,options)
% ultima modifica: Marco Castellaro 08/07/2010
%Funzione del pacchetto DSC_mri - DSC_mri_mask
%Autore: Castellaro Marco - Università di Padova - DEI
%
%Calcola le maschere per esami DSC-MRI.
%
%Parametri in ingresso: volumes (Matrice 4D) che contiene gli andamenti del
%seg... |
3bdc47662cc309aa8616e273d8cb97439374db4064251355741046a951cc9beb | MATLAB | 5,536 | 131 | function [ ResBVC ] = getCellTypeProportions( Res, area, varargin )
%UNTITLED3 Summary of this function goes here
% Detailed explanation goes here
%
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
prms.ageBins = [16 18; 19 21; 22 25; 40 40]; % de... |
5e4f86b863b9ab99dc619e300dd7d029587f27c80023242705c7eeaa3bd8d7e6 | MATLAB | 5,540 | 163 | % Parallel medial axis thinning of a 3D binary volume
%
% MATLAB vectorized implementation of the algorithm by
% Ta-Chih Lee, Rangasami L. Kashyap and Chong-Nam Chu
% "Building skeleton models via 3-D medial surface/axis thinning algorithms."
% Computer Vision, Graphics, and Image Processing, 56(6):462–478, 1994.
... |
baa1da953226f9ed77ff2633d1c2d6f2d10802b5549a202f4a450c7546b61a7d | MATLAB | 5,542 | 114 | % different from redefineStimulusClassesUpdated02.
% removed prune channels step to observe auditory cortical regions.
function plotSingleTrialHRF_UpdatedDCNew(sbjNum)
saveDir = ['C:\Users\mn0mn\Documents\ResearchProjects\spatailAttentionProject\ProcessedDatafNIRS\Experiment' num2str(sbjNum)];
saveDir2 = ['C:\Users\mn0... |
c2c5d538f3644257b6a5c8dbe359ad7bf91d356a5fb86b6b19d2728b91977072 | MATLAB | 5,542 | 150 | function [gamma_fit_mean, gamma_fit_se, gamma_range, full_res] = extractGammaFitRegion(pfit_G)
% starting at each point, how many decades of scaling can you have before
% the standard deviation of the mean gamma fits (scaled by sqrt(4), because
% we have overlapping quarters) is > 3 SE of an typical individual gamma
% ... |
225eddb1a862018d606177e9eb4de0a052150c11835202c44538224bea7c6476 | MATLAB | 5,544 | 113 | %Plots flicker modulation significance and amplitude on 3D model of subject's brain.
%2024/02/25
function plot_ind_entrainment_3d(fnames,ref_method)
%initialize subject structure:
subject=fetch_subject_data(fnames.root_dir,{fnames.subjectID},'anat');
subject_nber=1;
%get mni template data:
mni... |
49b4e1e2c8917d9503ddac99058a3299cc65414869be430da1097854e8d11ff0 | MATLAB | 5,545 | 213 |
clear; clc
[task_info, supPath] = loadDBAndSpecifyDataPaths('Golda');
req_params.grade = 7;
req_params.cell_type = 'PC|CRB';
req_params.task = 'pursuit_8_dir_75and25';
req_params.num_trials = 50;
req_params.remove_question_marks = 1;
raster_params.align_to = 'targetMovementOnset';
raster_params.cue_time = 500;
raste... |
4cdf6b89cfb22b515cc1b4d3386574ebe533feafa5d714b970ef13e2f5ade6e0 | MATLAB | 5,546 | 95 | function [G,h] = build_G_h_linear3d(b,delta_c,transformationModel)
% BUILD_G_H_LINEAR3D Builds the equation system G*p = h
%
% INPUT ARGUMENTS
% b - b = [b2 b1 b3]'
% delta_c - c_fixed - c_moving
% transformationModel - Transformation model (translation or affine)
%
% OPTIONAL INPUT... |
8a9c7aca71fe5ad598ebf32b81f20b913bc4abafc882dfb167813c45c49337d4 | MATLAB | 5,547 | 159 | % test if distribution of two sets are different from randomly sample from
% the sets
nIter = 2000;
nSample = 10;
ndim = 2;
kldmethod = 4; % kldmethod 2: only avaiable for ndim = 2
Zt = nan(nIter, 1);
Zr = nan(nIter, nSample);
p = nan(nIter, nSample);
fullset = unique(tabd(:,1));
fullset(isnan(fullset)) = [];... |
d0a27f9132b1e5559f20f9182b0cab13053bfd855b0f91101db485d8880a4888 | MATLAB | 5,568 | 139 | function [rho, pval, regionList, xRegional, yRegional] = regionalCorr(atlasFile, xFiles, yFile, corrMethod)
% regionalCorr computes correlations between regional cell proportion values
% (extracted from a list of NIfTI files, one per cell type) and a neuroimaging phenotype.
%
% INPUTS:
% atlasFile - string; filename ... |
18fadabf635362065075dcc6dc3f3fda86de721a4e31df100391a1c7623aaf43 | MATLAB | 5,579 | 194 | %DEMGTM2 Demonstrate GTM for visualisation.
%
% Description
% This script demonstrates the use of a GTM with a two-dimensional
% latent space to visualise data in a higher dimensional space. This is
% done through the use of the mean responsibility and magnification
% factors.
%
% See also
% DEMGTM1, GTM, GTMEM, GTMP... |
6d1d67a056df6ceff1d1018e2f100341389a2da1d56c9a4da960523ced8b7b0c | MATLAB | 5,585 | 200 | %% polar histogram for phase angle
cutoff=[25,15,15,15];
set(0,'defaultAxesFontSize',20)
set(0,'defaultAxesTickLength',[0.04,0.08])
set(0,'defaultaxeslinewidth',2)
if ~exist('graph', 'dir')
mkdir('graph');
end
cw=load('matching_table_cw.mat');
ccw=load('matching_table_ccw.mat');
region=["EC-DG","DG-CA3","CA3-CA1"... |
76777a1b6b61b48d55a0100761e0a9acbb5bb8e9e6fd25483c4d32e5dd43c1a4 | MATLAB | 5,585 | 115 | % load your file
% variable name in the .mat file for WT = ["tf_wt_global", "tf_wt_delta",
% "tf_wt_theta", "tf_wt_alpha", "tf_wt_beta", "tf_wt_gamma"," tf_wt_HFO"]
% variable name in the .mat file for KO = ["tf_ko_global", "tf_ko_delta",
% "tf_ko_theta", "tf_ko_alpha", "tf_ko_beta", "tf_ko_gamma"," tf_ko_HFO"]... |
d44b4cdb81c0ccd49dcdaabf530cefc9827eabc8e92f97057fd691384303d815 | MATLAB | 5,587 | 192 | function [A,node,link] = Skel2Graph3D(skel,THR)
% SKEL2GRAPH3D Calculate the network graph of a 3D voxel skeleton
%
% [A,node,link] = SKEL2GRAPH3D(skel,THR)
%
% where "skel" is the input 3D binary image, and "THR" is a threshold for
% the minimum length of branches. A is the adjacency matrix, and node/link
% ar... |
fcd3c81998705e95e82eeba1432c4ec343f974f84418049bf406e6276cd6f495 | MATLAB | 5,587 | 190 | function infocapacity_hopf_errorhete_tbi(s,condition)
rng(s);
measure='err_hete';
load('optG_tbi_mask1halfSD_bin_allsub_cond_1.mat')
%load('optG_tbi_mask1halfSD_bin_allsub_cond_2.mat')
%load('optG_tbi_mask1halfSD_bin_allsub_cond_3.mat')
for sbj=1:12
if condition==1
G=optG_tbicond1_sub(1,sbj); %OptG TBI ... |
e578c2c489e3e8d76ce8a72cf0567b56a43ec62c69a6737b39cac05907754bdb | MATLAB | 5,588 | 184 | % tCFS_prepE2_data
%this script concats group effects for b-CFS and re-CFS, and exports in
%JASP friendly format.
% reorient load.
job=[];
job.concatGFX= 1;
% job.plotPFX=1;
% job.plotGFX=0;
%% set up some directories.
%PC
% homedir = 'C:\Users\mdav0285\OneDrive - The University of Sydney (Staff)\De... |
5f05c2d466a67b083cb84c654d6bb45e0c596bf321a11a54eb9193bfb7058d97 | MATLAB | 5,599 | 195 | % SINDETECT automatic detection of sine waves in an otherwise random signal
%
% CALL [ pidx, aidx, trains, meandurs, si, zi, clu ] = sindetect( x, minAmp, minCC, minDC, minDur, rmvMed )
%
% GETS x signal
% minAmp threshold for noise {1}
% minCC thr... |
9130138160b5993d03e1eda79f8edc58baf4940cd5c7f352e0fb319f007ab12a | MATLAB | 5,600 | 172 | clear all
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 ss|CRB';
req_params.task = 'saccade_8_dir_75and25';
req_params.ID = 4000:5000;
req_params.num_trials = 50;
req_params.remove_question_marks = 1;
raster_pa... |
641e3b18934bf0e3612dbeaffbdb1ee4eda988beffc7e64211cef8a64213d792 | MATLAB | 5,601 | 176 | function infocapacity_hopf_errorhete(s,condition)
%%This script introduces perturbation in the Hopf model by varying the
%%value of the bifurcation parameter a
rng(s);
measure='err_hete';
if condition==1
G=optG_hc_tp1;
end
NPARCELLS=1000;
NR=400;
NRini=20;
NRfin=380;
NSUBSIM=100; %number of iterations, 100... |
606bfa10c9430fd874ae1f868a1205602b017db0403226627aa02af39cc96246 | MATLAB | 5,606 | 204 | function p = parse_filename(filename, fields, tolerant, verbose)
%
% Split a filename into its building constituents
%
% USAGE::
%
% p = bids.internal.parse_filename(filename, fields, tolerant, verbose)
%
% :param filename: filename to parse that follows the pattern
% ``sub-label[_e... |
0ff1ad402b2dfe3b371c8f12d33e1e29e2c76576ff2c8fe6b5e20532409888dc | MATLAB | 5,618 | 145 | %Code to generate T2 maps for a 140x100x9X15 MRI scan
%This is done using method 2 where we first apply the ROI mask on the T2 scan
%and then generate T2 maps only for the ROI's
%each pixel in the ROI is fit with a monoexponential decay for 15 time points.
%Here, we DO NOT average T2 values for a ROI thereby pro... |
2e0a056626d96f79f68e240c1c0b7db22a093736fe79230c9bbfe4945c5a8677 | MATLAB | 5,627 | 155 | function tform = align_recording_to_allen_Vickers_A1V1M2_affine(image,alignareas,affine)
%% Align recording to Allen CCF given an image
% INPUTSa
% image - this could be Kmeans map, or a mean of the gcamp activity
%
% alignareas - additional areas in the form 'L area', with a space between
% L/R and area name. the use... |
c130ec0746f0c315c518e8171feaca4559e24a0d0750336e6d028c7eef2f3c3d | MATLAB | 5,632 | 145 | function [slideJson,isIdentifySuccssful] = identifyLinesAndAlignSlide(slideJson,octVolumeJson,identifyMethod,SlidesJsonsStack,histologyFluorescenceIm)
%This function preforms the basic preprocessing of a slide, identify the
%lines and do a single plane alignment
%INPUTS:
% - slideJson - Single slide JSON file
% ... |
ed0aa6338f87d2304cf4f1f8d951d0110d8dd6f5d89bcb609ead55344f0a3c9c | MATLAB | 5,638 | 178 | function [adj_list_pat_idx scratch adj_list_mask_idx] = adj_cuboid(mask,varargin)
% Creates adjacency list using cuboid neighborhoods
%
% [adj_list_pat_idx scratch adj_list_mask_idx] = adj_cuboid(mask,varargin)
%
% Create an adjacency list using a rectangular neighborhood
% function. See GET_ADJACENCY and ADJ_SPHERE f... |
158c5e094e32a313e6c51befb2276730c2ac1a86e9a086baad469169f34333c8 | MATLAB | 5,648 | 129 | function [cfg,sl_template] = decoding_prepare_searchlight(cfg)
% function [cfg,sl_template] = decoding_prepare_searchlight(cfg)
%
% This function computes a reference sphere of coordinates around zero to
% use in the definition of searchlights. When shifted to the current
% searchlight center, it can be used to g... |
9b3bb889fcbcf90ee5ab40cf1800cfff3cad19299273f18d9568b8d85064eaae | MATLAB | 5,663 | 141 | % Plot voltage for article figure
% Note: SpikeReader returns entries with 1 less index then NeuronReader.
% E.g., length(stel_nV.v) vs. length(stel_nS).
% Todo: This could be looked into to see if the indices are offset by 1.
initOAT;
plot_voltage=1; % choose to plot current or voltage
% read in voltages
stel_nR = Ne... |
2667940be31b0a9e4eb9147a1576009130a9e0ff91033fa05f1628f5871c62c6 | MATLAB | 5,668 | 183 | function [subj] = load_bv_pattern(subj,new_patname,maskname,filenames,varargin)
% Loads a BrainVoyager .vtc pattern using the MVPA toolbox
%
% [SUBJ] = LOAD_BV_PATTERN(SUBJ,NEW_PATNAME,MASKNAME,FILENAMES,VARARGIN)
%
% Adds the following objects:
% - pattern object called NEW_PATNAME masked by MASKNAME
%
% In order to ... |
f38f906015eee947353bb72f33cca8aaadafd304ca1bd2563258d1edfb9cfc5f | MATLAB | 5,668 | 193 |
function hopf_DTI_Grange_hc(s,condition)
%load data
load(sprintf('results_f_diff_fce_cond%d_ON_hc.mat',condition)); % load frequency with maximal power (of narrowly filtered data) by area computed in script Compute_Hopf_Freq_hc.m
load(sprintf('empirical_spacorr_rest_cond_%d_ON_hc.mat',condition)); %load empirical FC ... |
ec8b6e9c0a660162ce03ceea7556b8b3d0b37589349bfc85a614a4e289e0a74f | MATLAB | 5,671 | 196 |
function hopf_DTI_Grange_hc(s,condition)
%load data
load(sprintf('results_f_diff_fce_cond%d_ON_hc.mat',condition)); % load frequency with maximal power (of narrowly filtered data) by area computed in script Compute_Hopf_Freq_hc.m
load(sprintf('empirical_spacorr_rest_cond_%d_ON_hc.mat',condition)); %load empirical ... |
67edc69c3b032f2bd2b35045093bb0b47b4e76ea8ab7c3832a7a83da38d5bfd1 | MATLAB | 5,675 | 196 | clc; close all; warning off all; clearvars;
% preparation
fileScript = matlab.desktop.editor.getActiveFilename;
[pathProject,name,ext] = fileparts(fileScript) ; % path to working directory/current folder % path to working directory/main folder
addpath (pathProject)
addp... |
af1d3e4234933d2076e49ddd4f09a1e0cb723a486071de15d930142140b0e15b | MATLAB | 5,683 | 203 | % tCFS_E2_figure3
% job=[];
% job.concatGFX= 1;
% job.plotPFX=0;
% job.plotGFX=0;
% plotType==1; % as per other MS figure
%% set up some directories.
% homedir = 'C:\Users\mdav0285\OneDrive - The University of Sydney (Staff)\Desktop\tracking-CFS';
homedir = 'C:\Users\mdav0285\Documents\GitHub\tCFS-ver1';... |
7d15afebbe191338344739ce2a6a33a288baf383e47ece3b6bd5bbb62bd75f74 | MATLAB | 5,692 | 163 |
%% Parameters and data
% parameters
nbins = 360;
smoothTh = 0:12;
velTh = 2; % velocity threshold (2 = default)
mergeTh = 0; % threshold for merging run epochs (0 = no merging)
%load data
load(fullfile('Data','BehavEpochs'));
load(fullfile('Data','SpikeData'),'S');
load(fullfile('Dat... |
7d5543b703184d0d4c4af39de4272fb36de5b812b3e64e1efb314941c4608ea2 | MATLAB | 5,692 | 186 | function [cbv,cbf,mtt,cbv_lc,ttp,mask,aif,conc,s0,fwhm,K2_map]=DSC_mri_core(volumes,te,tr,optionsIN,aif,mask)
% last modification: Marco Castellaro 26/05/2018
addpath('utils')
%----- CHECK OF INPUT PARAMETERS - PART 1 ----------------------
% Check that input and output has the minimum number of mandatory parameters ... |
84379d6dd4f635c5dc46f77cdb5cf56abcb6b85f208c357156094b56b379dd07 | MATLAB | 5,693 | 142 | function [params, fitcurve, costv, errs] = estimateCenterSurround(inputs, options)
nROI = size(inputs, 1);
IsPCA = 0;
RmvPeakRange = [1:5 14:30];
IsDebug = 0;
DataOrgnization = [7 30];%[7 30][7 33]
nTime = DataOrgnization(2);
EstimationType = 'DoG'; % 'DoG', 'Amp'
Dmt = [10 25 50 100 200 300 400];
AmpDiame... |
8a0fab0e5cf04c2fbec21ce723c1c013bfa91c0797ebbffc457781a628ef4162 | MATLAB | 5,693 | 177 | function infocapacity_hopf_errorhete_hc_tp2(s,condition)
%%This script introduces perturbation in the Hopf model by varying the
%%value of the bifurcation parameter a
rng(s);
measure='err_hete';
if condition==1
G=optG_hc_tp2; %Optimal G weight for healthy controls, manual input
end
NPARCELLS=1000;
NR=400;
NR... |
feb98da8c67487c88919ba0cf0b44fdc0e3a0f092c333e33597c924cf975dda5 | MATLAB | 5,695 | 209 | function [s,mxy] = isochromat_GRE_BM(theta,phi,TR,T1x,T2x,f,ka,Niso,varargin)
% [F0,Fn,Zn,F] = isochromat_GRE_BM(theta,phi,TR,T1x,T2x,f,ka,Niso,varargin)
%
% Isochromat based simualtion of Bloch McConnell coupled systems w/ gradient echo sequences
%
% arguments:
% theta: vector of flip an... |
7c859b34f82b142006bae1a5c1a15fce4c59e103aa1638c5f7f05bb28e9aac72 | MATLAB | 5,697 | 250 | function [errmsgs warnmsgs] = unit_class_gnb(varargin)
% USAGE :[ERRMSGS WARNMSGS] = TEST_(...)
%
% This is a script that tests
%
% INPUT ARGUMENTS:
%
% STDOUT (optional, default = true) If true, prints errors and
% warnings to screen as they occur.
%
% OUTPUT ARGUMENTS:
%
% ERRMSGS = cell array holding the error s... |
0ec4d66ced8aca1a7ed9f13abddb6fd4d15047338a85600985eda77936589dc8 | MATLAB | 5,699 | 194 | function [errs warns] = unit_check_1ofn_regressors() %#ok<STOUT>%ignore that errs and warns arn't used, since no code has been written yet...
% [ERRS WARNS] = UNIT_CHECK_1OFN_REGRESSORS()
%
%Description:
%{
This function will test the behavior of the check 1ofn_regressors
function to make sure it meets the re... |
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