sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
311ecf7fe6b7d72050521b1d5ee9a9e4d0edd1a3be280af22fe0e19f07ee3550 | MATLAB | 4,845 | 135 | function varargout = load_xls(varargin)
% LOAD_XLS M-file for load_xls.fig
% LOAD_XLS, by itself, creates a new LOAD_XLS or raises the existing
% singleton*.
%
% H = LOAD_XLS returns the handle to a new LOAD_XLS or the handle to
% the existing singleton*.
%
% LOAD_XLS('CALLBACK',hObject,eventDa... |
8c197966c1a0f80fb9efbc74a5a5d09768d3fc4bb05e52e457d343596e0b8461 | MATLAB | 4,846 | 151 | function [ec2Instance] = awsEC2StartInstance(ec2RunStructure,instanceType,numberOfInstances,v)
%This function starts an EC2 instance, returns instance hendle, it will
%wait until instance is up and ready to recive commands
%INPUT:
% ec2RunStructure - see awsEC2BuildRunStructure for details
% instanceType - see... |
72dca11a07189e08681936ff28ab59534fec5c401f06b02560d13df715cfa0c1 | MATLAB | 4,858 | 137 | %This script generates histology instructions
%% Inputs
subjectFolder = s3SubjectPath('01');
yourName = 'MyUser';
iteration = 1;
isCutOnDotSide = 1; %1-true, -1 - oposite side
%Distance to OCT origin:
%In iteration #1 - distance from full face to origin
%In following iterations - distance from current pos... |
870a10f0d564aa3c3901ce73b00ce7cade8aba4649001b433e77cafc8d8b3f85 | MATLAB | 4,862 | 175 | function [cmds, errs, msgs] = downloadSharedLibs(options, appname)
cmds = {};
errs = 0;
msgs = {};
if ~exist('options','var') || (isnumeric(options) && options==0)
options = 'init';
end
s = parseGitSubmodulesFile();
if ~optionExists_startup(options,'init') && ~optionExists_startup(options,'update')
addSearch... |
e4f616cc5e2ae75d35855531646b4cf09a63a12ca65bd5764079a81460a06889 | MATLAB | 4,880 | 142 | function this = ci(this)
%CI statistical inference
%
% Note:
% Only works when one component is fitted.
% For multiple components fit, math hasn't been worked out
%
% see also: FIT, PLOT, COSINOR
%
% $Author: Peng Li
% Brigham and Women's Hospital
% Harvard Medical School
% ... |
341b930046c22a4d540fd3a2027ca0c43f507ebf1ff366234dbef8b0643e9d04 | MATLAB | 4,881 | 160 | classdef FileLoadSaveClass < matlab.mixin.Copyable
properties (Access = private)
filename;
fileformat;
supportedFomats;
err;
dataStorageScheme;
end
methods
% ----------------------------------------------------------------------... |
58dfa1f9c88b5922ecc18f34e89a2117b31336eb1b6696bd7e523f33d2cb8bbe | MATLAB | 4,884 | 139 | function [h, p, ci, stats, n, stderr1, stderr2]=total_ttest_downreg_210326(table1, table2)
% Assumes tables 1 and 2 are already organized
% input the metadata from the spontaneous and stimulated datasets into this function
% if all(~ismember({'Electrode Pairs','Spike Area','Conduction Time','Unit Pairs','Direction'},... |
8ca41b2071b6bd63cfa07db399f714c0e51a32435a4e9f078ce89547998a9385 | MATLAB | 4,886 | 147 | %DEMGMM5 Demonstrate density modelling with a PPCA mixture model.
%
% Description
% The problem consists of modelling data generated by a mixture of
% three Gaussians in 2 dimensions with a mixture model using full
% covariance matrices. The priors are 0.3, 0.5 and 0.2; the centres
% are (2, 3.5), (0, 0) and (0,2); t... |
f5814cc9889758c53d3168a350cbc8f9a7b5b652dd49367f8120a9ee2824f831 | MATLAB | 4,886 | 99 |
function [event_no_error,deleted_events] = Event_clearComments(event_orig,verbose)
deleted_events.newSegment = [];
deleted_events.overflow = [];
deleted_events.Comment = [];
deleted_events.deleteOrder = {};
... |
5326e671cb22c76b3c2bd04701e599b7db66f02c15386ac4d476c88541c52547 | MATLAB | 4,889 | 147 | %DEMGTM1 Demonstrate EM for GTM.
%
% Description
% This script demonstrates the use of the EM algorithm to fit a one-
% dimensional GTM to a two-dimensional set of data using maximum
% likelihood. The location and spread of the Gaussian kernels in the
% data space is shown during training.
%
% See also
% DEMGTM2, GTM,... |
0b6fef1dcb45affa68627da9e22d8eab8cec968f526365d4c553d92d4095d0b0 | MATLAB | 4,894 | 185 | % Test 3: transient SPGR, WM simulation
addpath(genpath('lib'));
addpath(genpath('EPGX-src'));
%%% White matter model (see Gloor 2008)
f = 0.1166; %% F=0.132 = f/(1-f) => f=0.1166
kf = 4.3e-3;
kb = kf * (1-f)/f;
R1f = 1/779; % ms^-1
R1b = 1/779; %<- Gloor fix as T1f
R2f = 1/45;
R1obs = 0.5*(R1f + kf + R1b + kb)-0.5*... |
2375e8461f6741ffeca730d215169a05e4ac934a9c283bf3f24fdad5bf0321e4 | MATLAB | 4,894 | 133 | function [areas,total_bins,overlap_bins, total_area, overlap_area]=...
conduction_time_peak_finding_210816(stim, stim_dir, parent_dir,if_cw)
% Testing peak finding capabilities across multiple units for elimination
% of zeros
% Fixed saving issue and added bin count for percent overlap bins 7/7/21
total_bins=0;
ov... |
ed02af8cbf7e4d09bb7990be4e4c03f118ad5acf46c1b7644836e27fcdad0b54 | MATLAB | 4,896 | 102 | % This script is the default template, but with a modification that allows
% balancing confounds. The two main approaches for controlling for
% confounds are balancing confounds (by subsampling, supersampling,
% or weighted classification) or analyzing data separately for each
% confound. The approach in this file ... |
9e356fd79f822fbf9637db5887387fd1df16b8f9b554c1c089ad8682122a9721 | MATLAB | 4,897 | 188 | clear all
[task_info,supPath,MaestroPath] =...
loadDBAndSpecifyDataPaths('Golda');
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 = 4000:5845;
req_params.num_trials = 180;
req_params.remove_question_marks =0;
re... |
11c24c5ad4e6b5626949c706e1cc69a31a99f52055b5c20d61c80aeaa976c675 | MATLAB | 4,901 | 181 | function [net, options, errlog, pointlog] = olgd(net, options, x, t)
%OLGD On-line gradient descent optimization.
%
% Description
% [NET, OPTIONS, ERRLOG, POINTLOG] = OLGD(NET, OPTIONS, X, T) uses on-
% line gradient descent to find a local minimum of the error function
% for the network NET computed on the input data... |
3fda4ce750c95fe0e6b0a510818cf4fb6380a14d1a6e013a8d1da4f1fa9c207b | MATLAB | 4,902 | 126 | function varargout = load_data_1_var(varargin)
% LOAD_DATA_1_VAR M-file for load_data_1_var.fig
% LOAD_DATA_1_VAR, by itself, creates a new LOAD_DATA_1_VAR or raises the existing
% singleton*.
%
% H = LOAD_DATA_1_VAR returns the handle to a new LOAD_DATA_1_VAR or the handle to
% the existing s... |
498d425f2d1d38d6544554063292b0b66a969f819e7e53c251f15d883ff53e58 | MATLAB | 4,902 | 129 | %% COLLECTIVE ANTI-PHASE BEHAVIOR
% SETTING
clear
rng(170) % 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 = 1200; % time span
dt = 0.01;
% COUPLING
eps1 = 0.10;
eps2 = 0.10;
epsilon = [ eps1, eps2; ...
... |
04e45c2418e0d64c301cd817ac69b60fd0b20f61f1482103895efe696ac7ba1e | MATLAB | 4,903 | 181 | clear
[task_info,supPath,MaestroPath] = loadDBAndSpecifyDataPaths('Vermis');
TASK= "both";
MONKEY = "albert";
req_params = reqParamsEffectSize(TASK,MONKEY);
behavior_params.time_after = 1500;
behavior_params.time_before = 1000;
behavior_params.smoothing_margins = 100; % ms
behavior_params.SD = 20; % ms
ts = -behavi... |
c6629a9b942f1f5bec845cd691ab020f26c4077fd7d1fe943641af82dc53f21e | MATLAB | 4,911 | 143 | function this = ci(this)
%CI statistical inference
%
% Note:
% Only works when one component is fitted.
% For multiple components fit, math hasn't been worked out
%
% see also: FIT, PLOT, COSINOR
%
% $Author: Peng Li
% Brigham and Women's Hospital
% Harvard Medical School
% ... |
f8b1236f4c3c2013caf6decaced5967dbf3a734cdbfc42461215c0a0b356edd4 | MATLAB | 4,913 | 135 | % modeNoiseAxis: 1 use PCA, 2 use covariance matrix of entire data, 3 use average noise covariance matrix
% Node for modeNoiseAxis=2: this will also include signal correlations. This is useful when comparing
% against a within-group scramble, in which signal corrs will be preserved.
%
%urut/aug23
function [errRate... |
10b3017a20631c8a396a36669f72f5b44038468e08b4a729f418050c24faf9b1 | MATLAB | 4,914 | 140 | function [h, p, ci, stats, n, stderr1, stderr2]=total_ttest_upreg_210326(table1, table2)
% Assumes tables 1 and 2 are already organized
% input the metadata from the spontaneous and stimulated datasets into this function
% if all(~ismember({'Electrode Pairs','Spike Area','Conduction Time','Unit Pairs','Direction'}, t... |
474094a0baa027331e628aeb9efb39d8558efd3b243b8e51fc72a01dc9789fec | MATLAB | 4,915 | 96 | % This script is a template that can be used for an encoding analysis on
% brain image data using cross-validated Mahalanobis distance. It is for
% people who have betas available from an SPM.mat (for AFNI, see
% decoding_tutorial) and want to automatically extract the relevant images
% used for calculation of th... |
a350c3f21874ad955ba8219e1d0609dc6504374eec7800d56900ea3d281e2992 | MATLAB | 4,923 | 138 |
%% Access data loaded from import script
nwb = nwbAll_sb{:};
tsMaint = nwb.intervals_trials.vectordata.get('timestamps_Maintenance').data.load();
electrode_labels = string(nwb.general_extracellular_ephys_electrodes.vectordata.get('location').data.load());
electrode_origChan = string(nwb.general_extracellular_ephys_... |
574dc0b693c5ff73c2669a0a58e1f820193cb0818a50179ed2857fc201ec50a1 | MATLAB | 4,931 | 156 | function [yo, fo]=mtcsd2(x1,varargin)
%function [yo, fo]=mtcsd2(x,y,nFFT,Fs,WinLength,nOverlap,NW,Detrend,nTapers);
% Multitaper Cross-Spectral Density
% function A=mtcsd2(x,y,nFFT,Fs,WinLength,nOverlap,NW,Detrend,nTapers)
% x,y : input time series
% nFFT = number of points of FFT to calculate (default 1024)
% Fs = sam... |
35cd50c53b5164fb15c0f1b5f997e9cb8e75e259d7a7fb9bcc0cabb87f9ff196 | MATLAB | 4,934 | 139 | function [h, p, stats, n, stderr1, stderr2]=total_wilcox_downreg_210630(table1, table2)
% Assumes tables 1 and 2 are already organized
% input the metadata from the spontaneous and stimulated datasets into this function
% if all(~ismember({'Electrode Pairs','Spike Area','Conduction Time','Unit Pairs','Direction'}, ta... |
2bfe00731ba3866f6a95d598b471b6d80753ccf10c3cd09be9c51ffcba566f6f | MATLAB | 4,938 | 129 | % function [Segs, Complete] = LoadSegs(FileName, StartPoints, SegLen, nChannels, Channels,
% SampleRate, ResampleCoef, Buffer, IfNotComplete)
% loads segments from FileName (full name) for selection of Channels
% StartPoints give the starting times of the segments (in samples)... |
44dd07621af25bf7f76a5dcaa6d707b91d0cf31b383ee7cd67856c5a3d8c5ddc | MATLAB | 4,945 | 143 | #import "UnityAppController.h"
#import "Unity/UnityInterface.h"
#import "UnityAds/UnityAds.h"
#import <UnityAds/UADSBanner.h>
#import "UnityAds/UADSMetaData.h"
#import "UnityAdsUtilities.h"
#import "UnityAdsPurchasingWrapper.h"
#import "UnityAdsInitializationListener.h"
#import "UnityAdsLoadListener.h"
#import "Unity... |
55dad5321a0d48aaf2a0bb6b7fa380c11d6a93f1a20c4af5f514d74793c420fb | MATLAB | 4,964 | 141 |
% Data location
data_location = TFR_Clustered_Data;
save_data_location = TFR_Clustered_Data;
% Condition name
Condition_Name = cond_labels;
% Channel list
Channels = cluster_labels;
%% PT vs. Color
% Plot contrast
figure; clf
idx_subplot = [3,11,15,7,9];
for chan=1:length(Channels)
... |
9c14ce57fe202bbfc82b356e6c8051d8f2f177687ca262607244360090d67df5 | MATLAB | 4,964 | 140 | function [h, p, stats, n, stderr1, stderr2]=total_wilcox_upreg_210630(table1, table2)
% Assumes tables 1 and 2 are already organized
% input the metadata from the spontaneous and stimulated datasets into this function
% if all(~ismember({'Electrode Pairs','Spike Area','Conduction Time','Unit Pairs','Direction'}, tabl... |
49d92234db87adefa744556d077641667404a8ab7b17d480ee5aea98a072883a | MATLAB | 4,969 | 117 | function SemiCircleBullsEye(Struct2Plot, Sig, RANGE, COLORSCHEME,...
XTICKLABELS, WEDGELABELS, param)
%SemiCircleBullsEye creates a semicircle bullseye plot with each wedge of
%the bullseye colored by a value (magnitude of change frome baseline).
%Area/time bins significantly above baseline will have an asteris... |
b4dfed056d07011aab2d9577a989fb5012060cf49a6fba042049a3b292e63f0b | MATLAB | 4,974 | 106 | function PlotMeanTSRepeatedMeasures_PercentInc_FlexibleCondInt( PerIn_ByTS_preVpost, pulse_idx, pulse_num, TITLE, YLABEL, YLIMS, XLABEL, XLIMS, XTICKS,XTICKLABELS,...
DATALABELS, DATACOLORS, stdOrsem, cmap, cmap_num)
% Michelle Cahill 20201006, updated 20201019
%PlotMeanTSRepeatedMeasures_PercentInc_FlexibleCond... |
5f120b36eb3f3ffb270dcd2d8b8eab6ed8c181524da61c6f021c87f58754491f | MATLAB | 4,976 | 182 | function [samples, energies, diagn] = metrop(f, x, options, gradf, varargin)
%METROP Markov Chain Monte Carlo sampling with Metropolis algorithm.
%
% Description
% SAMPLES = METROP(F, X, OPTIONS) uses the Metropolis algorithm to
% sample from the distribution P ~ EXP(-F), where F is the first
% argument to METROP. T... |
2a9d176ed3b1a8bbad7593c98778cb58ac2190b536edeb74a641c35d3381a3e7 | MATLAB | 4,977 | 109 | clear () ; close ()
data_path='/Volumes/LASA/TBI_OpenNeuro/TBI_openneuro/timeseries/outputs/Turbulence_30_06_2022/';
data_path_rev='/Volumes/LASA/TBI_OpenNeuro/TBI_openneuro/timeseries/outputs/Turbulence_20_06_2023/';
% Data from controls
cd(data_path)
load('turbu_all_measurements_openneuro_controls_con1.mat')
Turbu... |
8d73a960eebb20262e4fa0d52aaefaf7453ae7bb08557245014241265d320387 | MATLAB | 4,988 | 116 | function outHandle = barWhiskerBridge(inBar,inWhisker,inBridge)
%BARWHISKERBRIDGE creates a bar-and-whisker plot with bridges connecting
%related bars
% OUTHANDLE = BARWHISKERBRIDGE( INBAR , INWHISKER , INBRIDGE ) outputs
% the handle to an 'axes' containing a bar-and-whisker plot based on the
% information in... |
c1531ab546d1d5928eb62d514eace47ca8feb7d1df4eb607a1655646730020bc | MATLAB | 4,988 | 120 | function [class_low_sig,class_high_sig] = System_permutation_test(zX_RS,U,Vlow,Vhigh,mean_low,mean_high)
% To examine the significance of system-level concentration of low- and high-frequency signals,
% we performed a non-parametric permutation test for each cortical class
% % ==================================... |
8f09533e3162d0aaff9ce318954a41c72a99c9d88b552a1713c0b57b84b0cb8b | MATLAB | 4,993 | 137 | function this = m10l5Analysis(this)
%M10L5ANALYSIS Perform nonparametric M10 and L5 analysis on an ACTI object
%
% $Author: Peng Li
% $Date: Jun 15, 2021
% $Modif.: Jun 25, 2021
% instead of calculating per day, average the days first
% see comment where revised
% Feb... |
a0c11dc2970a7de954d9476237e14f4c90352d112fb009e08d149232247207f0 | MATLAB | 4,996 | 143 | function [OCTFolders,folderNames] = yOCTGetOCTFoldersInPath (p)
%This function returns all OCT folders that are found in path p.
%If p is an OCT folder it will return it, if p has sub-folders, this
%function will search in subfolders.
%INPUTS:
% p - root path to search in. Can be on local disk or Amazon S3
%OUT... |
55e5e040f204c8c49b9453c751e469860a0cb1ea29e5b9f6f94bc2991e211d02 | MATLAB | 4,997 | 181 | function [mix, options, errlog] = gmmem(mix, x, options)
%GMMEM EM algorithm for Gaussian mixture model.
%
% Description
% [MIX, OPTIONS, ERRLOG] = GMMEM(MIX, X, OPTIONS) uses the Expectation
% Maximization algorithm of Dempster et al. to estimate the parameters
% of a Gaussian mixture model defined by a data structure... |
b408eebee39e4ba6fb9db2fa51204e3cfd0fedf6a978a15f23afe5a01280d030 | MATLAB | 4,999 | 106 | % For validation of createSingleTrialHRF_Updated
function plotAvgOfSingleTrialHRF(sbjNum)
saveDir = ['C:\Users\mn0mn\Documents\ResearchProjects\spatailAttentionProject\ProcessedDatafNIRS\Experiment' num2str(sbjNum)];
saveDir2 = ['C:\Users\mn0mn\Documents\ResearchProjects\spatailAttentionProject\RawDatafNIRS\Experiment'... |
5d1e32767fa1c431d556231d8aca7e2a6520a4281694476427b3dbfe77cd75cb | MATLAB | 5,000 | 179 | clear all
[task_info,supPath,MaestroPath] = ...
loadDBAndSpecifyDataPaths('Vermis');
req_params.grade = 7;
req_params.cell_type = 'PC ss|CRB|SNR|BG';
req_params.task = 'choice';
req_params.num_trials = 120;
req_params.remove_question_marks = 0;
req_params.remove_repeats = 0;
req_params.ID = 5000:6000;
lines = fin... |
8ac9cc1ffc1f87924ec66fc49bff3a22d0def702806bdd73136dc7bd9031b7e5 | MATLAB | 5,000 | 105 | function vidObj = msFindBrightSpots(vidObj,stepSize, frameLimits,dFFTresh, backgroundTresh,plotting)
%MSFINDBRIGHTSPOTS Function will identify the frame and pixel location of
%local maxima of dF/F across entire video. msFindBrightSpots will step
%through the video detecting locations that exceed dFFThresh and
%backgrou... |
cc43f775b3acdec6ad2d04ac627febfc593dc338a339b57e82fb59b685ae825f | MATLAB | 5,008 | 143 | clear; close all; clc;
%% Analysis Parameters
Day = '111522';
Deinterleave = 4;
ChGFP = 1;
Rig = 2; % 1: old scientifica, 2: hyperscope
%% Load Data
PathName = uigetdir(pwd, 'Select a folder');
FileNames = dir(fullfile(PathName, '*.tif'));
FileIds = find(contains({FileNames.name},Day));
nFile = length(FileI... |
b38e2cf584ca62b04c43368797c94f958fef8716bd684bde94a1cd39b92da70e | MATLAB | 5,013 | 125 | %% TWO CLUSTERS ON TWO LEVELS (transient VHFOs, 1400Hz)
% SETTING
clear
rng(3) % 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 = 700; % time span
dt = 0.01;
% COUPLING
eps1 = 0.05; % within a cluster
... |
6bffe0d938217d26d00bfe62b9183da387aef8aac5d17c14e7f921933d0dbf3f | MATLAB | 5,016 | 156 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function [Cm, I_stim, Vinit, GVal, EVal, TauMax, Sm, VTraub] = NeuronTypeV2(TypeOfNeuron)
if strcmp(TypeOfNeuron,'FS')
... |
6e5aa58d4e0f8cb6cb00192b7e6d8d2d667fe8fe5266ec90547bb193386fcb4f | MATLAB | 5,021 | 167 |
%%% Add analysis scripts and required toolboxes to the search path %%%
addpath('/.../VisualImagery_paper/extended/fss_simulation/');
addpath('/.../VisualImagery_paper/analysis/general_purpose/');
% 1. The Decoding Toolbox (TDT), version 3.999E or higher
% https://sites.google.com/site/tdtdecodingtoolbox/
addpath('/.... |
258c9fcdfe2f4293d2ce8e9efa75c66583f96b36b8c23142b60bb126f25e5bb4 | MATLAB | 5,022 | 114 | function [r, cout] = normconv(signal, certainty, basis, applicability, ...
region_of_interest, options)
% R = NORMCONV(SIGNAL, CERTAINTY, BASIS, APPLICABILITY, ...
% REGION_OF_INTEREST, OPTIONS)
% or
% [R COUT] = NORMCONV(SIGNAL, CERTAINTY, BASIS, APPLICABILITY, ...
% REGION_OF... |
bcf3f722020320e099bbcee0235e51f50b3cb42320cf4411e8880aa23c41fc22 | MATLAB | 5,028 | 176 | function [L, model] = pure_mb_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... |
cfc7f7167144094421b72a1a90428cac8ee78c0b419a937cd5baaadcc464b8b7 | MATLAB | 5,040 | 122 | %Complex wheel kinematics script
%Update v3
%C Mount July 2018
clear all;
close all;
%complex wheel data processing
%handle = uigetfile('*.csv'); %identify data file
%wheel parameters
numRungs = 21; %number of rungs on wheel
wheelR = 6; %wheel radius in cm
smoothRevs = 100; %number of revolutions o... |
0c1b9682390e809b2b0f6975677c5eef95474e3dbaf9d12442c92b41ea2f95fa | MATLAB | 5,043 | 177 | % plot histograms
job.plot_GFXhistograms=1; % MS ver
job.plot_GFXhistograms_cdf=1; % review ver
%% set up some directories.
%PC
homedir = 'C:\Users\mdav0285\OneDrive - The University of Sydney (Staff)\Desktop\tracking-CFS';
%macbook:
% homedir= '/Users/matthewdavidson/Documents/GitHub/tCFS-ver1/';
cd(homedi... |
3ac9ae7b6f551696e3674ecb284fe5493dca3b42d5d151329a9b7090f1b24a13 | MATLAB | 5,047 | 146 | % run CircleAnalysis_BipolarCellTerminal.m
options.IsPCA = 1;
options.IsDebug = 0;
options.EstimationType = 'DoG';
%[Params, ~, Costv] = estimateCenterSurround(ROIFeatures, options);
options.EstimationType = 'Amp';
[SimpleCSRio, Curves] = estimateCenterSurround(ROIFeatures, options);
options.EstimationType... |
0c2a84e8d3c6a5eede39e65e26f9988a23cccb635de8d6b4899a4e645131e0e5 | MATLAB | 5,051 | 122 | % function [passed_data,cfg] = fill_passed_data(passed_data,cfg,label,chunk)
%
% This function fills in the fields of passed_data that can (in principle)
% automatically be determined. IMPORTANT: There are no checks that the
% fields which are filled would have been required for your decoding. This
% means please... |
b23c5ec6298329b2462ae7e9c456f4e58b12316755a52d3af3390e4de18e442f | MATLAB | 5,051 | 237 | clear
[task_info,supPath,~,task_DB_path] = loadDBAndSpecifyDataPaths('Vermis');
EPOCH = 'targetMovementOnset';
TIME_BEFORE = 0;
TIME_AFTER = 800;
FEILD = 'directions';
req_params = reqParamsEffectSize("both");
req_params.cell_type = {'CRB'};
req_params.remove_question_marks = true;
lines = findLinesInDB (task_info, ... |
1b54ea24e6f6b2071d3f3713d524aa15a90d49788614bbbd4c0556f66bc2f84b | MATLAB | 5,053 | 175 | function [L, model] = hybrid_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 ne... |
d6acc684abfa08e00be14c15263cc9ed2fc2d33de0280aef51443db340a9ba1a | MATLAB | 5,053 | 166 | clear all
supPath = 'C:\noga\TD complex spike analysis\Data\albert\pursuit_8_dir_75and25';
MaestroPath = 'C:\Users\Owner\Desktop\DATA\albert\';
load ('C:\noga\TD complex spike analysis\task_info');
req_params.grade = 7;
req_params.cell_type = 'PC ss';
req_params.task = 'pursuit_8_dir_75and25';
req_params.ID = 4000:500... |
780f0eb0ea73185a4dee59a036a5cb074c9ec591a47526f881f9313b646eaaf4 | MATLAB | 5,054 | 161 | % function fig_handle = display_volume(vol, mode, title_str, fig_handle, cfg)
%
% Show all slices of a volume in a new figure. Slices are along the 3rd
% dimension of the input volume (z-direction).
%
% IN
% vol: 3d matrix or name of 3d image (2d will also work, then it plots one slice)
% OPTIONAL
% title_... |
006006deb920243ac619f3a90a5a3961d24184cd31334bc4f8209933abe92ab0 | MATLAB | 5,059 | 153 | close all
clearvars
clc
%get user input
selpath=uigetdir('*.*','select MINFLUX directory for a single construct')
%find result files
files=dir(fullfile(selpath,'**','*reeval.xls'));
%define edges for histograms
edgeX=-0.5:24.5;ctrsX=(edgeX(2:end)+edgeX(1:end-1))./2;
Tall=[];
for k=1:length(files)
... |
0bc88cba11f18f546f1fafd05432865cd0efad4f84677808b65667061f1240c3 | MATLAB | 5,076 | 167 | function net = vbhafa(Y, nStates, maxdim, nIter,tol,pcaflag,approach, net)
global pa pb alpha alphaa alphapi psii u stran sprior Lm Wa Wpi Lcov...
Xm Xcov Qns Qnss QnsCell a b logOutProbs Fhist F iter
Ycell = Y;
Y = cell2mat(Y);
[p n] = size(Y);
if nargin<2
error('at least two inputes are expected: data, n... |
a5071affa3b54f5aceadd0f44e5fcd1d0e8ad65c471084198076359bc3fdf6b1 | MATLAB | 5,079 | 91 | %Simulate 40Hz flicker data based on superposition hypothesis, and run PSD analysis.
%2024/02/25
%% Set dataset directory and dependent repos:
[root_dir,repo]=define_flicker_root_dir;
for field_name=fieldnames(repo)'
addpath(genpath(repo.(field_name{:})));
end
%note: can use function fetch_flicker_subjectIDs to g... |
f58c9631dbe0a3d83169c3e9df9cc06e6b0e36ca2ab3a60d9671fb3b8e7ccbfe | MATLAB | 5,079 | 147 | % for sbj 08 and 10
function [mlActAuto_Unused, allCues] = getPrunedChns_Special(s,snrThresh)
sbjNum = s.name;
rawDataFN = s.fName;
movieList = s.movieList;
%behData = s.resp;
startT = s.startT;
endT = s.endT;
fs = 50;
saveDir = ['C:\Users\mn0mn\Documents\ResearchProjects\spatailAttentionProject\RawDatafNIRS\Experim... |
650739191c3a4482715b508dd15621e33c40b1161e5151da25c9bfcb3136d602 | MATLAB | 5,087 | 121 | function [update] = symmetric_optical_flow_registration(...
moving, fixed, movingDeformed, fixedDeformed, ...
transformationModel, multiModal,varargin)
% OPTICAL_FLOW_REGISTRATION Estimates a displacement field using optical flow
%
% [update] = symmetric_optical_flow_registration(...
% moving, fixed, moving... |
db5cdc58aa7413fe93ef915758668a6610bb7b00da670a1fd1a579ae1716f939 | MATLAB | 5,089 | 185 | function [timescale,f,windownum,alpha]=DFA(data,dfaorder,segments,outfilename,figurenum)
%The program performed the detrended fluctuation analysis (DFA) on the provided
%signal.
%Inputs:
% data (signal data points)
% segments (segments of data to be analyzed)
% dfaorder (order of the DFA for the... |
c734c9e95fe6ded518bcd0530ec1fed1282961664dad215ad258b160e40ab50b | MATLAB | 5,100 | 154 | function plot_post_scan_questionnaires(p)
% Load post-scan questionnaire data
vviq_file = dir(fullfile(p.dirs.data, 'questionnaire', 'post_scan.mat'));
if isempty(vviq_file)
warning('vviq.mat not found.');
return;
end
load(fullfile(vviq_file.folder, vviq_file.name), 'post_scan')
vviq_post = post_scan.('VVIQ po... |
4e5b5fda08d9c12a291b9e6acee8a8ecab9b1c4451c8bd13079ee86a8e3adf28 | MATLAB | 5,102 | 123 | function plot_tunnels_in_graph_220309(unit_match_struct, endNodes, isSlope)
get_well_node_no = @(NodeTable, well_name) find(strcmp(NodeTable.Name, well_name));
tunnelEndNodes=string(endNodes.Edges.EndNodes);
if isSlope
max_lwidth = 2;
else
max_lwidth = 1;
end
%% Create NodeTable, and EdgeTable
NodeTable = c... |
166c7ea22177074d53cee9177ddd20aac12a6d705f274f34de03d24855a29b37 | MATLAB | 5,104 | 120 | function [T,Y]=msfm(F, SourcePoints, UseSecond, UseCross)
% This function MSFM calculates the shortest distance from a list of
% points to all other pixels in an image volume, using the
% Multistencil Fast Marching Method (MSFM). This method gives more accurate
% distances by using second order derivatives and c... |
6c1dbe553ab580a5c56b0848dd81b3a754941699418943b3783bd7e7e4118ccf | MATLAB | 5,107 | 189 | clear
[task_info,supPath,~,task_DB_path] = loadDBAndSpecifyDataPaths('Vermis');
EPOCH = 'targetMovementOnset';
req_params = reqParamsEffectSize("saccade");
%req_params.ID = 5666;
lines = findLinesInDB (task_info, req_params);
cells = findPathsToCells (supPath,task_info,lines);
for ii = 1:length(cells)
... |
e8d086b9730ffa22867618bf4d8ce060dcaf24fa1954f869f9d3cf950ff16bc8 | MATLAB | 5,115 | 161 | function QA = getImagePairVisualQualityMetric(QA)
% This function will prompt user to answer a few questions about image pair
% quality matric.
% QA - default or current QA metric
if ~exist('QA','var')
QA = [];
end
% set default answers if exist
answer1 = [];
answer2 = [];
answer3 = [];
answer4 = []... |
3e3d65ec1ce1d4d556620e1cbc17c01b606f4d9c6a4247f3964f1f1db7a751e7 | MATLAB | 5,118 | 152 | function h = mv_plot_1D(varargin)
%Plots 1D results, e.g. classification across time. If a 2D matrix is given
%several plots are created.
%
%Usage: Two possible usages, either giving additional parameters in a cfg
% struct (with cfg.key1 = value1) or directly giving the key-value
% pairs as parameters:
% ... |
0b05084a1d283801c4c995db08de7396937b36f5a0fd2c53c652bee52a61f4d2 | MATLAB | 5,122 | 123 | function trial_wise_pattern_betas_1st()
cfg = load('neurovar_cfg.mat').cfg;
for i_sub = 1:44
fprintf(strcat('Sub_', string(i_sub),'\n'));
%% Prepare folder
sub_dir = fullfile(cfg.global.deriv_dir, 'trial_wise_pattern_betas',...
strcat('sub-',sprintf('%02d',i_sub)));
mult_con_dir = fullfil... |
8fe6f81a2e86a3442a01af3555a8acff860091c39bb3c1dd783dc8faf484f354 | MATLAB | 5,122 | 102 | function MachSetTimeConstraintPriority(periodSecs,computationSecs, constraintSecs, preemptibleFlag)
% MachSetTimeConstraintPriority(periodSecs,computationSecs, constraintSecs, preemptibleFlag)
%
% Assign the main MATLAB thread "time constraint" status, giving it super
% priority over other threads on the system. A t... |
6d8583329b7974cb02858e8eb39b940d0706d9e6eab52a14d8fecc00930f304f | MATLAB | 5,130 | 136 |
%%
X = Dfull([(1:nBC)'; remids(:)+nBC], :);
options.EstimationType = 'surround';
[SimpleCSRio, Curves] = estimateCenterSurround(X, options);
options.EstimationType = 'linearity';
[SimpleONOFFRio, Curves] = estimateCenterSurround(X, options);
% SimpleCSRio(SimpleCSRio<-0.4) = -0.4;
options.EstimationType = 'tra... |
c8afcc8e0b2d33250d3da9fa303b6ed7b2d5867f573925465f92517fb7ba277f | MATLAB | 5,131 | 97 |
function model = BayesianSwitchingDynamicalSystems(data, max_nstates, max_ldim, opt)
%Inputs:
% data : {data_subj1, data_subj2,...,data_subjS}, data_subj1 is a D-by-N matrix where D is dimension and N is the number of samples
% max_nstates : maximum number of states (set ... |
5a77bc5f20a68685522953e9285690fd057125e7171eb878bec47739a6359c2d | MATLAB | 5,132 | 143 | function [res, fmt] = detUPMEMD(actigraphy, epoch, varargin)
%DETUPMEMD do uniform phase masked empirical mode decomposition and calculate
% circadian metrics
%
% [RES, FMT] = DETUPMEMD(ACTIGRAPHY, EPOCH, CYCLELENGTH, FILENAME, STARTTIME, FILEPATH, QUALITY, MINCYCLE)
% performs uniform phase masked empi... |
3a95d8cabed8f2e383bbde53da9060056345c8d3d6f3fb18157337d6e32fcf90 | MATLAB | 5,137 | 105 | function [a11, a12, a13, a22, a23, a33, b1, b2 b3] = ...
estimate_displacement_based_upon_least_square_solution3d(solution, variates, ...
sizeImage, ...
dk, ck, ...
filterDirection, ...
numberOfFilterDirections, ...
t11, t12, t13, t22, t23, t33)
% ESTIMATE_DISPLACEMENT_BASED_UPON_LEAST_SQUARE_SO... |
cf07c6c2fe4518382325ba4b05ebdab1c876717452e0f0b9cfe0e5433fc302fd | MATLAB | 5,137 | 167 | celltype = [50 51 57 58 6 7 89];
BCTypelabels = {'BC5o', 'BC5i', 'BC5t', 'XBC', 'BC6', 'BC7', 'BC89'};
%% ON
cts = 1;
celltype = [50 51 57 58 6 7 89];
peakv = [];
T = (-BasDurFrm:StmDurFrm)/Fz;
Twin = T>0.1 & T<1.5;
ii = 0;
jj = [];
for t = 1:7
ctyp = celltype(t);
rois = find(Typ == ctyp);
Vars... |
9a224d2176bb543a6582f19d5ee25bfd88c8e1ef05de94414158c04f7d5ba703 | MATLAB | 5,141 | 167 | clear,clc
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% This script is used to evaluate the spatial correlation between
% CCA-weighted brain map and math-related meta maps
%
% Jin
% 7/21/2022
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% set the... |
aeac94c82d6cf652a64a7a0bd50d63835e6f611c2d15423b07e9be0ca3717326 | MATLAB | 5,142 | 141 | % CCH_CONV predictor and p-values for CCH using convolution
%
% CALL [ PVALS, PRED, QVALS ] = CCH_CONV( CCH, W )
%
% GETS CCH vector (a single CCH) or matrix (CCHs in columns)
% has to be non-negative integ... |
b5c09caeabc7102f1fe3da2c607e861b6b872fe8cc7bc19569f6e1e6b3b0fd45 | MATLAB | 5,146 | 107 | % different from redefineStimulusClassesUpdated02.
% removed prune channels step to observe auditory cortical regions.
% This is for sbj 08 and 10
% For later sbjs, use plotSingleTrialHRF_UpdatedDCNew.m
function plotSingleTrialHRF_UpdatedDCNew_Special(sbjNum)
saveDir = ['C:\Users\mn0mn\Documents\ResearchProjects\spatai... |
fdb5b7b38d5560a6460a0bccd903c41ba12d25d08a6f707ac34145b6a1937ca7 | MATLAB | 5,151 | 130 | Condition_Name = cond_labels;
Channels = cluster_labels;
chan=3; % right temporal parietal
%% Load all data
for sub = 1:length(subject_list)
subject = subject_list{sub};
load([TFR_Data subject, '_tf_data_' int2str(1)])
% initialize matrices on 1st subject
if sub == 1
tf_a... |
d6d15653ef8192062e079c4fe8050856b3b2a9775b29708eccab5b1c6c947b9e | MATLAB | 5,152 | 176 | function [x, options, flog, pointlog] = quasinew(f, x, options, gradf, ...
varargin)
%QUASINEW Quasi-Newton optimization.
%
% Description
% [X, OPTIONS, FLOG, POINTLOG] = QUASINEW(F, X, OPTIONS, GRADF) uses a
% quasi-Newton algorithm to find a local minimum of the function F(X)
% wh... |
75588ebd5745543a0b23dd5753f24307c39aeea4aec04ac3e8130d59f252acd7 | MATLAB | 5,153 | 152 | % STATUS: Inactive(?)
%
% SYNTAX:
% performanceLDA = calcCumSum_DiffStartT_SingleChnHbX_NoSave(sbjNum,...
% trialsTrHbO,trialsTstHbO,trialsTrHbR,trialsTstHbR,trialsTrHbT,trialsTstHbT,...
% movieListTrain,movieListTest,numClasses,mlActAuto)
%
% DESCRIPTION:
% Perform cross Validation where SS beta coefficients fro... |
fedb8a9dd35e3c8469fdd072339bbb11188ebf30e2fb4692fad7349fc4aefb8c | MATLAB | 5,155 | 163 | function [chnList, LSChnList] = getChnList(isNew)
% all 36 channels for fNIRS
if isNew
chnList = {'S1D1 Middle Posterior FEF Left',...
'S1D2 Posterior to sPCS/tgPSC Left',...
'S1D3 Middle FEF Left',...
'S1D4 sPCS/tgPCS Left',...
'S2D1 Posterior FEF Left',...
'S2D2 Inferior to FEF Left',...
'SS1... |
0ef58e52d86487a05b1904fb5f5100c2f22dd11ef52252da7838b703ac6e44de | MATLAB | 5,159 | 103 | function [mat2d_T2_mapscan, dmat2d_coeffvals, k, dvec, dvec1, dvec2,cj] = curve_fitting21(dmat2d_coeffvals, k, dmat4d_ROI_T2, dcount, cj, dmat3d_cropped_label, mat2d_T2_mapscan, dvec, dvec1, dvec2);
%Input: T2 scan multiplied with ROI mask for a given ROI for 15 TE's
%Output: Monoexponential curve fitted T2 co-effe... |
42691ea74478908c44082c652ec4edfeb42c78d2d643f50de5e8cd743969e44c | MATLAB | 5,160 | 139 | % 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 subset of channels
% DiffTLen!!!
% STATUS: Active(?)
%
% SYNTAX:
% [performanc... |
d3d3880406bfdb02c733bb98cebcea256ca0ef3f6d3e72e47113f90a288628c5 | MATLAB | 5,160 | 141 | function Y = runmean(X, m, dim, modestr)
% RUNMEAN - Very fast running mean (aka moving average) filter
% For vectors, Y = RUNMEAN(X,M) computes a running mean (also known as
% moving average) on the elements of the vector X. It uses a window of
% 2*M+1 datapoints. M an positive integer defining (... |
e723d1acc819e9d0d6cc76e55c0c7eb1524a99de8d9903c0fc1019769f68ef81 | MATLAB | 5,163 | 134 | function [transformationMatrix] = ...
phase_difference_linear_registration3d(moving, movingCertainty, ...
fixed, fixedCertainty, varargin)
% PHASE_DIFFERENCE_LINEAR_REGISTRATION3D Estimates a transformation matrix using phase-difference
%
% [transformationMatrix] = ...
% phase_difference_linear_registration... |
508c7b885c888a277379eab75c3ab43ea4b65ea718077cffec1a97d30cfcb3b9 | MATLAB | 5,166 | 160 | function [OUT] = getBehavior_vp_eegCAT(IN,locking,trialnums,set_fb,pos,set_stim,pos_stim,rt_stim)
%aggregate variables
valueP1 = [IN.value1_p1 IN.value2_p1];
valueP2 = [IN.value1_p2 IN.value2_p2];
difficultyP1 = [IN.diff1_p1 IN.diff2_p1];
difficultyP2 = [IN.diff1_p2 IN.diff2_p2];
valueP1_old = [IN.value1_p1_old ... |
b693b7cfc7a263afa3a2bc2d78cd32fc597fff6ea1104053b31b9864f7a501a1 | MATLAB | 5,173 | 153 | %To use after THexporttraces_xlsx.m in order to record maximum amplitudes
%of the dff files. Also important for making and updating sheetlist
%variable for legend/options box
prompt='How many traces?';
blank=input(prompt); %decide how many traces you want to analyze (1-4)
sheetlist=string(sheets); %makes sheetlist int... |
94e1a6a66d38458604d1f7a4000f47648127c9ae46d9ca24c29a3e7fb28d3ea6 | MATLAB | 5,175 | 133 | %chdata=load('C:\Users\DBHeyer\Documents\PhD\China\Djai\Recordings\mats\stim\180621_012.mat');
chdata=load('C:\Users\DBHeyer\Documents\PhD\China\Djai\Recordings\mats\stim\180705_002.mat');
%settings
ch='Ch1';
secch='Ch2';
stimch='Ch3';
StringCh='Ch31';
CCStepkey='B';
stopkey='Z';
%sweeplength=10001.3425; %length of ea... |
471ef67fce341ef0bc6cd36f2e665b94ebe75e5d0909b28885359884cf892dd5 | MATLAB | 5,179 | 117 | function [figOut] = NWB_samplePAC_SU(nwbAll, all_units, paths)
% - nwbAll: a cell array of all nwb files imported in NWB_SBCAT_import_main
% operation:
% - paths: Paths defined in NWB_SBCAT_import_main. The path of interest is
% the code path, which is used to load the dataset of the trials selected
% for the computat... |
6bb157749c5e6ae47bf2a146d5357a7c6d4983fd1fac77a16ab41bc5cdc67ee3 | MATLAB | 5,188 | 145 | % For sbj 12 and afterward
function [mlActAuto_Unused, allCues] = getPrunedChns(s,snrThresh)
sbjNum = s.name;
rawDataFN = s.fName;
movieList = s.movieList;
%behData = s.resp;
startT = s.startT;
endT = s.endT;
fs = 50;
saveDir = ['C:\Users\mn0mn\Documents\ResearchProjects\spatailAttentionProject\RawDatafNIRS\Experime... |
d262bf4754d127ca1e3d341d91337a6c92e362630f61100721a6f7f4c4374f1a | MATLAB | 5,200 | 125 | function [select] = select_channel_list(label, select, titlestr);
% SELECT_CHANNEL_LIST presents a dialog for selecting multiple elements
% from a cell array with strings, such as the labels of EEG channels.
% The dialog presents two columns with an add and remove mechanism.
%
% select = select_channel_list(label, in... |
710c85d5b6d571e1b8a87328d5f06037e58d1013380c7770f4f95a2025367210 | MATLAB | 5,204 | 153 | function net = gtminit(net, options, data, samp_type, varargin)
%GTMINIT Initialise the weights and latent sample in a GTM.
%
% Description
% NET = GTMINIT(NET, OPTIONS, DATA, SAMPTYPE) takes a GTM NET and
% generates a sample of latent data points and sets the centres (and
% widths if appropriate) of NET.RBFNET.
%
% I... |
77a268deddf3188f4b2b2edf0cb489c2e7be87f564248daeb701d2e0a2b892c6 | MATLAB | 5,206 | 143 | IsMakeVideo = 0;
if IsMakeVideo
FolderPath = '\\storage1.ris.wustl.edu\kerschensteinerd\Active\Emily\NaturalSceneVideo\Preprocessed\Blue\';
nClip = 11;
MovieSize = nan(nClip, 3);
SharedVar = nan(nClip, 1);
clear RFctr_L RFctr_NL
for i = 1:nClip
FileName = sprintf('%s013... |
6018acaafec8bf6e5aa568e0b92d88baffafe48de6d495153febdba44aeecdd5 | MATLAB | 5,208 | 162 | % 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 subset of channels
% DiffTLen!!!
% baselinePerf is classification accuracy for c... |
47816ac5c24011781d49b3bf1ec561fdb3b61a2f009d54c5486f0bbc4cd5a8fd | MATLAB | 5,209 | 127 |
function Y = VSH_cubic_integral(L,M,A)
% L is the L value of the matrix
% M is the M value of the matrix
% A is the side of the cubic ROI - in metres
atol = 1e-15;
rtol = 1e-10;
if isempty(L)
L = [8 8];
end
if isempty(M)
M = [5 5];
end
if isempty(A)
A = [0.1 0.9];
%A = 0.1;
end
% if isempty(l... |
b2b4eff97f709ea7b935160206f826e248f77c160733ba15adedc0687f9d20f8 | MATLAB | 5,211 | 129 | %% Artifact rejection
% Check if variable Exclusion_Info exists already. If yes, load it.
if exist([output_dir output_specific 'Exclusion_Info.mat']) == 2
load([output_dir output_specific 'Exclusion_Info'],'Exclusion_Info');
end
% Correct for DC offset, using the average of the whole epoch
EEG = eeg_check... |
96691a2d4e6fe2c618f00c4de785986c1640f2da2dfd53f4c449ba3e5df33e20 | MATLAB | 5,214 | 164 | function files = FindFiles(varargin)
% Syntax:
%
% [files, dirnameGroup] = FindFiles()
% [files, dirnameGroup] = FindFiles(dirnameGroup)
% [files, dirnameGroup] = FindFiles(dirnameGroup, fmt)
%
% Descrition:
%
% Find all files of format fmt, in the current folder.
%
% Examples:
%
% files = FindFiles... |
0ec3da25b01c156ced1742966403ee1c759a47f29913ae162c427fe356aa495c | MATLAB | 5,224 | 138 | function [subj] = ebc_tutorial_adv_preprocess(subj, default_params, varargin)
% Runs the pre-processing phase of the advanced EBC tutorial.
%
% [SUBJ] = EBC_TUTORIAL_ADV_PREPROCESS(SUBJ, DEFAULT_PARAMS, ...)
%
% Performs z-scoring, moving of the original pattern to HD, then
% creates cross validation selectors, all re... |
cd88711f14244a1df15b2abcc45bea67d4bb7d1c97bc8b8b2b416c6b1921599f | MATLAB | 5,226 | 89 | %Plots SPEP modulation on 3D model of subject's brain.
%2024/02/26
function plot_group_spep_3d(fnames,subjectIDs,ref_method)
%get mni template data:
mni=fetch_mni_anat(fnames.root_dir);
%fetch anat data:
subject=fetch_subject_data(fnames.root_dir,{fnames.subjectID},'anat');
subject_nber=1... |
5004c811c44ff05edfe013346c859247fc218dadbcb504706caf304891109d4d | MATLAB | 5,231 | 197 | clear all
supPath = 'C:\noga\TD complex spike analysis\Data\pursuit_8_dir_75and25';
load ('C:\noga\TD complex spike analysis\task_info');
req_params.grade = 7;
req_params.cell_type = 'PC ss';
req_params.task = 'pursuit_8_dir_75and25';
req_params.ID = 4000:5000;
req_params.num_trials = 50;
req_params.remove_question_m... |
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