sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
049513c662ee97a9da976c97ea70cb63f6eefb2f821d6f7a6090fc33e11784ef | MATLAB | 7,474 | 184 | function [dated_output_folder,NMI_version, folder_address, keep_times]=...
Wave_Clus_batch_220103(directory,well_dir,fs,skip_to,reuse_last_NMI)
% Reuse last NMI only works if skip_to is not 0, reduces redundancy, saves memory
%Change log starting 6/2/21
%added multiple scaling factor support
% Removed peak findin... |
d89f037df84c196a6061df4fe08d08c80033e520ad18f786e52365dd5e52aabd | MATLAB | 7,476 | 254 | %% PSTHs
clear
[task_info, supPath] = loadDBAndSpecifyDataPaths('Vermis');
req_params = reqParamsEffectSize("both");
%req_params.cell_type = {'PC cs'};
raster_params.align_to = 'cue';
raster_params.time_before = 399;
raster_params.time_after = 800;
raster_params.smoothing_margins = 100;
raster_params.SD = 20;
basel... |
e7ddb4aa7c9a2021528775d2c72c98b26e006ea5c5830340b4bd936d84af6e56 | MATLAB | 7,502 | 176 | % function design = make_design_rsa_cv(cfg)
%
% Function to generate design matrix for representational similarity
% analysis, pattern similarity analysis, or pattern component modeling. In
% contrast to the general version where all data is used for training and
% testing, here independence of data is required, i... |
ce0599e8e4ab5fcdf2a3cb2364c91d4492366441551a6ec28faded1d50edcfc0 | MATLAB | 7,508 | 215 | function varargout = no_history(varargin)
% NO_HISTORY M-file for no_history.fig
% NO_HISTORY by itself, creates a new NO_HISTORY or raises the
% existing singleton*.
%
% H = NO_HISTORY returns the handle to a new NO_HISTORY or the handle to
% the existing singleton*.
%
% NO_HISTORY('CALLBACK',... |
404c87af47942247a82c43ded2e9287444fbcd70d73c6a559795071af4319268 | MATLAB | 7,509 | 364 | function [gap,len,integral_conv,pl,ph,pc,re_spike,re_spindle,peak,s_angle,spike_count]=Spindle(data,data3)
% out put zeroing for noise channel
fc1=10;
fc2=16;
fs=25000;
spindle_only=[];
gap=[];
len=[];
spike_count=[];
conv_s=[];
integral_conv=[];
s_out=[];
re_spike=[];
re_spindle=[];
peak=[];
s_angle=[];
pc=[];
pl=[];
... |
cd1a00c889d4cb971535e58a1d64f896bf40b9bf30c396bfdfb6b2a6af0b8fb8 | MATLAB | 7,524 | 322 |
%% Load data
binsize = 0.2; % bin size for population vectors, needs to be large
thSmooth = 2; % smoothing factor of population vectors
iso = 0; % set to 1 if need to re-run Isomap
velTh = 2; % velocity threshold (2 = default)
mergeTh = 0; % threshold for merging run epochs (0 = no merging)
[~, foldername, ~] = ... |
0a383b20d84d9db971ef06715bd048a844ef4c9336dbb209ea5764a899c3f67f | MATLAB | 7,531 | 222 | % @title Test script for optogenetics stimulation
% @file test_optostim.m
% @author Romain Beaubois
% @date 11 Aug 2021
% @copyright
% SPDX-FileCopyrightText: © 2021 Romain Beaubois <refbeaubois@yahoo.com>
% SPDX-License-Identifier: GPL-3.0-or-later
%
% @brief Test script for optogenetics stimulati... |
a174765d6bacee11510050f6b09143d4ffce936bb4d4281e92fe467d6e26cc2a | MATLAB | 7,537 | 235 | function plotSignlePlane(singlePlaneFit,f,histologyFluorescenceIm,octVolumeJson,isSaveFigureAsPNG,enfaceViewIm)
%This function plots the data estimated by single plane fit & Fiducial line
%structure.
%INPUTS:
% singlePlaneFit - see alignSinglePlane
% f - Fiducial line structure
% histologyFluorescenceIm - o... |
8cbf94f64643a7ceced8213991e1e6032c2a218fc467c0ba0a5d6582c5adf181 | MATLAB | 7,540 | 256 | % @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 [HHVal] = PospischilNeuronFunc(TypeOfNeuron, Dt, HHValpre, en_stim)
Vprev = HHValpre(1);
npre = HHValpre(2);
mpre ... |
a590412960605405a072b0ed7969799c644af38473a5e66975618ebaa07233e8 | MATLAB | 7,541 | 283 | function [errmsgs warnmsgs] = unit_adj_cuboid(varargin)
% Unit tests for ADJ_CUBE.M
%
% [ERRMSGS WARNMSGS] = UNIT_ADJ_CUBE(...)
%
% Tests with all-ones mask, randomly generated mask, and a
% few hand-written scenarois (where the correct output has
% been hand-calculated in advance).
%
% Doesn't test non-cuboid windows... |
59c02337d2d8e72f30be737d772b63e9a7ee9a969f0c0cdd2770b98991341dee | MATLAB | 7,546 | 204 | function [cf,Sw,lambda,mu1,mu2] = train_lda(param,X,clabel)
% Trains a linear discriminant analysis with regularization of the
% covariance matrix.
%
% Usage:
% cf = train_lda(param,X,clabel)
%
%Parameters:
% X - [samples x features] matrix of training samples
% clabel - [samples x 1] vector of cl... |
e896126d5b4b20f38018663539edb1de5f78e0a1ef283cc56ef40b4063f63847 | MATLAB | 7,560 | 172 | % redefine stimulus classes into 6 diff. classes
% to be runned after generating stimuli marks from aux data in Homer3 GUI
% strictly for ExperimentXXGUI folder
% Old, no longer needed
function redefineStimulusClasses(sbjNum,rawDataFN,respData)
dir = ['C:\Users\mn0mn\Documents\ResearchProjects\spatailAttentionProject... |
f59cbc43f420d24b1ee42d60540e622d45b3abce2cfd88ed17c3b740161cab4a | MATLAB | 7,572 | 178 | % SYNTAX:
% [pValuesS, pValuesS_cond] = hmrS_CalcPvalue(yRuns, stimRuns, mlActRuns, baselineRange, hrfTimeWindow)
%
% UI NAME:
% Pvalues_on_Session
%
% DESCRIPTION:
% Calculate the p-value matrix for a single session.
%
% INPUTS:
% yRuns:
% stimRuns:
% mlActRuns:
% baselineRange:
% hrfTimeWindow:
%
% OUTPUTS:
% pValue... |
d9626c0657210cc80d8dc8f8095b7af2e3f6312be89cadc52685181231e727d2 | MATLAB | 7,580 | 191 | function varargout = DSC_mri_aif_slice_selection_figure(varargin)
% Visualizza l'immagine del volume cerebrale e chiede all'operatore di
% selezionare la slice sulla quale individuare la slice.
% DSC_MRI_AIF_SLICE_SELECTION_FIGURE M-file for DSC_mri_aif_slice_selection_figure.fig
% DSC_MRI_AIF_SLICE_SELECTION_FIG... |
55a7bf9194193bf20c9574febba5ac14ee22da0f0ea90d665b2d9f28b7c2f96f | MATLAB | 7,583 | 198 | % STATUS: experimental.
% Not for publication.
%
% SYNTAX:
% preprocessFNIRS06_CV_ssBeta(sbjNum,rawDataFN,respData)
%
% DESCRIPTION:
% Set SS Beta a priori to 1. No GLM fitting here.
%
% RESTRICTION:
% Only for sbj 12 and afterward.
%
% INPUTS:
% sbjNum - subject ID. 2-digits string. Ex: '08'.
% rawDataFN - 1xN cel... |
f1764fa9446aed8f43243e36430328ce13e23576b6573d29168d0a1ff7a09292 | MATLAB | 7,586 | 222 | % SYNTAX:
% [data_yc, svs, nSV] = hmrR_PCAFilter( data_y, mlAct, tInc, nSV )
%
% UI NAME:
% PCA_Filter
%
% DESCRIPTION:
% Perform a PCA filter on the data matrix y.
%
% INPUT:
% data_y: SNIRF object containing data matrix where rows are time points. If y is wavelength
% data, then the columns are channels as descri... |
bb1eae559e372c96c19c36654d23d146ab1bca8a9eedf354559176a3721b3de7 | MATLAB | 7,592 | 162 | function varargout = Screen(varargin)
% Screen is a MEX file for precise control of the video display. Screen has
% many functions; type "Screen" for a list:
% Screen
%
% For explanation of any particular screen function, just add a question
% mark "?". E.g. for 'OpenWindow', try either of these equivalent forms:
% S... |
cfc898e6f95f89228c1a906d37926c0afdfdc74a8a379986c3a8db7487a60e6d | MATLAB | 7,595 | 132 | clear all;
addpath /comp_neuro/Software/Github/CMBHOME_github/
[filename,f_length]=get_filename(1);
spk_hz_group = [];
for i=1:f_length
[filename,f_length]=get_filename(i);
cell_selection=get_cell_selection(i);
spk_hz=find_hz(filename,cell_selection);
spk_hz_group=[spk_hz_group;spk_hz];
if i==1 fprintf("Pro... |
3b56a57a245a19d8d77ecd4e3eaba3a4c9ac34d28dca537ade1c0dffc781fd19 | MATLAB | 7,599 | 212 | function choose_pos_neg_peak_211025(folder_address,electrodes)
%looks at tihe positive and negative peaks from detection and determines if
%there are spikes within 1.5 ms dead time, tallest spike is kept others are
%discarded
%% Positive spikes
disp('Getting Low Spikes (Pos)...')
%set new parameters for low clusterin... |
75c0c8f41c0f939be300c12540f3575bdaf570e8e026e2eeca75b96e516c82ce | MATLAB | 7,603 | 128 | function [ Res ] = classifyCellType( Res, ResShuf, varargin )
% This is a wrapper for running the BVC, BVC (in CCE) and BC classification on whole dataset.
%
% Calls 'shufThrCellClassifer' three types, with parameters set to classify each
% of the three categories. Also constructs a general filter index, stored as Res... |
497ba14839f06ba564f2d2b752169ad088c0d9f9e0e41457d55fc4b6ff7272b5 | MATLAB | 7,611 | 245 | % run @NaturalMovieHeterogeneityEncodingMapping first
% when it stops, go to @GetPairedPathDistance
% first time:
%%
figure; imagesc(cmap); colorbar;
%%
% Representative for each BC types for the heterogeity responses across
% terminals
% (1). Show the align functional images on top of the structure and note th... |
c4b2c56a6f913d9bb207b9f2f41a1dd5e9ae42edc19f0c50df6f0876ca0b4455 | MATLAB | 7,612 | 174 | function [pparam, X, clabel] = mv_preprocess_impute_nan(pparam, X, clabel)
% Imputes nan and inf values in the data, so that downstream train functions
% return numeric results.
%
%Usage:
% [pparam, X, clabel] = mv_preprocess_impute_nan(pparam, X, clabel)
%
%Parameters:
% X - [samples x ... x ...] data ma... |
19925c694aa770614ac7a79bba2210090b4f06bd619a154606c0a34fdbfcd76a | MATLAB | 7,620 | 382 | %%%% Training Mixed and Seperate Channels Reinforcement Learning Models based on Experiment 1 and ...
% Experiment 2 subjects' choice data (refer to the paper for more info).
% This script calls for several functions (all can be found in this
% RL_model directory). These functions are concise and self-explanatory
%... |
81991fd3bc5cae7f0115633969072829aeff8940c5b33e562d6c9d2e43ffcc40 | MATLAB | 7,626 | 179 | % function design = make_design_rsa(cfg)
%
% Function to generate design matrix for representational similarity
% analysis, pattern simlarity analysis, or pattern component modeling. This
% is the most general version where all data is used for training and
% testing, i.e. without cross-validation. If cfg.files.co... |
bb661360e2509d6cf31a40504c02cdf48e00815252cf3139c9ffaedf7c12725f | MATLAB | 7,628 | 262 | function fw_singletrial_sambrook_gamblearn(dir,fin,model,regtype,varargin)
%define options
options.model = model;
options.regtype = regtype;
options.inprefix = '';
options.outprefix = '';
options.srate = [];
options.channels = [];
options.length_codes = 7; %length of behavioral codes from EEG preprocessing
myparts = ... |
c5da3425fe3b66e983846cac743875298998499cd2f67584f5c234fe4ee9f091 | MATLAB | 7,639 | 200 | function varargout = struct2xml( s, varargin )
%Convert a MATLAB structure into a xml file
% [ ] = struct2xml( s, file )
% xml = struct2xml( s )
%
% A structure containing:
% s.XMLname.Attributes.attrib1 = "Some value";
% s.XMLname.Element.Text = "Some text";
% s.XMLname.DifferentElement{1}.Attributes.attrib2 = "2";
%... |
4017814ee76bcdb4da33472bfb2cf96129834e4dedd3e7cf174f3c109513d8a9 | MATLAB | 7,647 | 232 |
% %%%%%% STARTING with just SST data!
participant_SST_3_mo_dir = '/Users/tawnytsang/Desktop/Projects/ETdataFiles/infantSibs/CB-SST Data/matlab/SST/test/';
participant_SST_6_mo_dir = '/Users/tawnytsang/Desktop/Projects/ETdataFiles/infantSibs/CB-SST Data/matlab/SST/old/new6/';
participant_SST_9_mo_dir = '/Users/tawnytsa... |
66fa6cc1d59a6efc4f6e7b0721d722e0b4c42b63e35325350b30e816cbb27514 | MATLAB | 7,652 | 177 | #import <UnityAds/UnityMonetization.h>
#import <Unity/UnityInterface.h>
#import "UnityAdsUtilities.h"
#import "UnityJsonAdditions.h"
typedef void (*UnityMonetizationShowAdStartCallback)();
typedef void (*UnityMonetizationShowAdFinishCallback)(int finishState);
@interface UnityMonetizationUnityShowAdDelegate : NSObjec... |
4325f280de9c6e60655ebe4a5eca09478e6e5fd336f32bbeb6443e32066796e5 | MATLAB | 7,666 | 184 | function PatternChange_Regression_Behav_1st()
cfg = load('neurovar_cfg.mat').cfg;
trial_cutoff = 1;
pe_type = 'spe'; % 'spe' OR 'rpe'
output_dir = fullfile(cfg.global.deriv_dir, 'PatternChange_Regression_Behav', 'rpe_PatternChange_ActionChange');
for i_sub = 1:44
fprintf(strcat('PPI Sub_', string(i_sub),'\n'))... |
9340b0013a105cb228dffb4eb6fcfd854a6dfcd7f4911580ad0d96819a1791cc | MATLAB | 7,666 | 164 | % ----------------------------------------------------------------------- %
% M U L T I P L E T E S T I N G %
% ----------------------------------------------------------------------- %
% Function 'fdr_storey' computes the Storey correction of the False %
% Discovery ... |
40295e2c259826003f71a421a74660adc0ede51a36f44fe3c7b9a4dbbd791ebe | MATLAB | 7,668 | 140 | classdef Sharedpaths
%SHAREDPATHS Class to hold all relevant paths
% Class to hold all relevant paths. Not sure whether this is common practice, but seems useful up to now. Used so that
% ABFBATCH and ABFFILE objects, which inherit from SHAREDPATHS know where to retrieve / store data.
%
% ... |
722a3dd3a3febb870e019a2baaeff58b0c07bb9081517ae2f50b72ef0453bc06 | MATLAB | 7,668 | 222 | % FindSynapse - find putatitve monosynaptic connections based on
% cross-correlograms
%
% USAGE
% [synLat,synStrZ,synStrR,bounds] = FindSynapse(cch,options)
%
% cch: a matrix of cross-correlograms in columns (expressed in number of events)
%
% Returns:
% synLat: two column vectors of synaptic latencies:
% ... |
2b34c8c7d2a6e189ef63e08569740de35fb041ee8f142b69268fc56f2e1afe12 | MATLAB | 7,673 | 212 | function h = mv_plot_topography(cfg, topo, pos)
%Plots a topography
%
%Usage:
% ax = mv_plot_topography(cfg, topo, pos)
%
%Parameters:
% topo - [C x 1] vector with specifying a topography, where C
% is the number of channels.
% Alternatively, if a matrix [C x M] is... |
be5ef7de11c8c16b008d49595d51cd5d51167a355313327992c16027ffa95369 | MATLAB | 7,680 | 233 | function [anova_subregion,anova_ff,anova_fb]=network_stats_211110(ff_table,fb_table)
%get number of subregions
subregions=unique(ff_table{1}.Subregion,'stable');
for i=1:length(subregions)
current_subregion=subregions(i);
num_axons=0;
axons_per_tunnel=[];
conduction_times=[];
for j=1:length(ff_tab... |
79a9cb75fb6604089a616ad424c441b253f1666eecd9fec3e9d2e7815e9ae1ec | MATLAB | 7,683 | 281 | function [samples, energies, diagn] = hmc(f, x, options, gradf, varargin)
%HMC Hybrid Monte Carlo sampling.
%
% Description
% SAMPLES = HMC(F, X, OPTIONS, GRADF) uses a hybrid Monte Carlo
% algorithm to sample from the distribution P ~ EXP(-F), where F is the
% first argument to HMC. The Markov chain starts at the poi... |
c9c19ebadaef1284aeb7ea2dd7b4f33de3484bf1ae82383bc766692733f4e0e7 | MATLAB | 7,685 | 189 | function param = mv_get_hyperparameter(model, param)
% Returns a struct with default hyperparameters for a given
% classifier or regression model.
%
%Usage:
% param = mv_get_hyperparameter(model, <param>)
%
%Parameters:
% model - [string] the classifier or regression model (eg 'lda')
% param - [str... |
6d095fd7674afd42d0cb89a6974474a65c654d71405cd078d9bc05a1e67ee44d | MATLAB | 7,690 | 185 | function [pparam, X, clabel] = mv_preprocess_mnn(pparam, X, clabel)
% Applies Multivariate Noise Normalization (MNN) (Guggenmos et al., 2018).
% Data is denoised with an estimate of the noise covariance:
%
% X_denoised = C^(-1/2) * X
%
% where C is the covariance matrix and C^(-1/2) is its inverse square-... |
125d3706c490eac27bea05a9bc9989d5db695f8f3ed2c172afb727c0b29dcc53 | MATLAB | 7,720 | 244 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%
% Optimized vs Original Pospischil equations
%
% Authors : KHOYRATEE Farad
% Date : 25/01/2017
% Update by : KHOYRATEE Farad
% Update : 16/04/2019
%
% Version : V1.0
%
% Descritption: Optimized Hodgkin-Huxley from Pos... |
40721ae3b2702acdfc47c4144fc0d5cedf6c0eb1424b076a34896807254bc1c5 | MATLAB | 7,724 | 260 | function DifferenceImageMovies(fileParams)
%% File Parameters
secBtwFrame = fileParams.secBtwFrames;
pxPerMicron = fileParams.pxPerMicron;
exportFolder = fileParams.exportFolder;
thetaInDegrees = fileParams.thetaInDegrees;
thetaInRadians = fileParams.thetaInRadians;
%% Input Parameters
numMicrons = 10;
%% Define Fold... |
3a4bb3d577a71449806b240128d14ad8be24ff575de4996569f84d8732647556 | MATLAB | 7,725 | 309 | function varargout = file_utils(str, varargin)
%
% Character array (or cell array of char) handling facility
%
% USAGE:
%
% To list files or directories (with fullpath if necessary)::
%
% [files, dirs] = bids.internal.file_utils('List', directory, regexp)
% [files, dirs] = bids.internal.f... |
bcee688ec72b470cf8942beab7ade83cffa23c83b7b27d48b0195374e77d59b4 | MATLAB | 7,728 | 215 | function varargout = not_loaded(varargin)
% NOT_LOADED M-file for not_loaded.fig
% NOT_LOADED by itself, creates a new NOT_LOADED or raises the
% existing singleton*.
%
% H = NOT_LOADED returns the handle to a new NOT_LOADED or the handle to
% the existing singleton*.
%
% NOT_LOADED('CA... |
bdd208036855c33f4166fbdfc69c716e78850ce3821ff025a98d6eefa7b4ae71 | MATLAB | 7,728 | 204 | %% Create mea tables
% Creates tables compatible with
% compute_allregion_AFR_table_at_P1s_203011.m ultimately for creation of
% connectivity movie.
% In order to use, directories must be populated with matlab variables
% conaining wave_clus cluster_class variables. different files need to be
% created for feedforward ... |
d74bdbbbdac23f79453f3f06c700b40dd23b6c7986561825af1844e61b2bdf3b | MATLAB | 7,735 | 224 | function [errs warns] = unit_afni_cellarrayfilename_load()
% [ERRS WARNS] = UNIT_TEMPLATE()
%
% This is the template for a unit test. Unit tests should
% take in no arguments (or only varargin optional
% arguments), and return ERRS and WARNS cell arrays of
% error messages. If both are empty, the test has passe... |
d754c4123f870a31582e7d74180e2f4c8c976c477f3b2f5200d40217d69d36af | MATLAB | 7,741 | 222 | % Analyze sensitivity to parameters
%
% Hannah Payne 2020
%
% Abbreviations for conditions:
% d = VORD, p = smooth pursuit, s = stim
%
% Abbreviations for inputs:
% H = Head, T = Target, L = Light (1 or 0), E = Eye, P = Purkinje, Z = stim
% PARAMS
stim_on = 0; % Include stim condition
complex_on = 0; % Incl... |
748e13ede873cba395c9155987eaca096c56c68434a98c6024d80b92e083be9c | MATLAB | 7,745 | 277 | % references: https://www.mathworks.com/matlabcentral/answers/180778-plotting-a-3d-gaussian-function-using-surf
% https://www.mathworks.com/help/symbolic/rotation-matrix-and-transformation-matrix.html
% https://www.mathworks.com/matlabcentral/answers/430093-rotation-about-a-point
% run options
sample_matrix = 1;
write... |
e44d960e9ad702efa6db6c0a1be5fd4be86b837031a8afb444ed050401e46490 | MATLAB | 7,750 | 245 | function output = model_postCAT(x,data)
% Likelihood function for Q-learning on experiment eegCATrandom with
% parameters (1)learning rate, (2)inverse temperature, (3)
% perseveration (4)assignment rate
%
% USAGE: lik = model_postCAT(x,data)
%
% INPUTS:
% x - parameters:
% x(1) - learning rate
% x(2) - i... |
01dfbd770f0540fd8f2699935bf2e2320ad11f7f5c672325283f7da8a22334f3 | MATLAB | 7,753 | 209 | function tunnel_merge_211124(folder_name,length_rec_sec)
cd(char(folder_name))
fs=40e3; %change samplig freq if needed
try
load('matching_table_wNMI.mat')
catch
error('Matching_table_wNMI.mat not found. Please run compute_NMI_200701 on the wave... |
32db1c9df59730979901bd7be9ebf7ca0d2c5e4bdac5cb5f1a7dcfe33c224c18 | MATLAB | 7,753 | 235 | function [forward_t_table,backward_t_table]=pre_post_stim_paired_ttest_210225(table1ff,table1fb,table2ff,table2fb)
% Assumes tables 1 and 2 are already organized
if all(~ismember({'Electrode Pairs','Spike Area','Conduction Time','Unit Pairs','Direction'}, table1ff.Properties.VariableNames))...
&& all(~ismembe... |
e59699080df3e9b99d9a85cac187f43124c315a1bffda151e13fbe8e374226a1 | MATLAB | 7,756 | 269 | %% Behavior figure
clear
[task_info,supPath] = loadDBAndSpecifyDataPaths('Vermis');
SINGLE_SESSION = false;
PROBABITIES = [25,75];
COL ={'r','b'}';
SCATTER_TIME = 200:250;
MONKEY = "both";
req_params = reqParamsEffectSize("pursuit",MONKEY);
behavior_params.time_after = 1000;
behavior_params.time_before = 0;
behavi... |
4ad6f07a678b694b9c0324c45fd93bd10ead8de7bc0ce982044423b777877731 | MATLAB | 7,762 | 264 | % Joint reconstruction for multii-band multi-shot diffusion MRI (on Matlab R2018b)
% (c) Erpeng Dai, Stanford University
% Reference: Dai E, et al. Multi-band Multi-shot Diffusion MRI
% Reconstruction with Joint Usage of Structured Low-rank Constraints and Explicit Phase Mapping (JULEP)
close all
clear
clc
%% Input p... |
e842b248679cb3da720ee50bdff934c0c26d7951f35eac77a4b44a859dca26e9 | MATLAB | 7,774 | 224 | % FindSynapse - find putatitve monosynaptic connections based on
% cross-correlograms
%
% USAGE
% [synLat,synStrZ,synStrR,bounds] = FindSynapse(cch,options)
%
% cch: a matrix of cross-correlograms in columns (expressed in number of events)
%
% Returns:
% synLat: two column vectors of synaptic latencies:
% ... |
baceb66cb086e6fba9681ac24a9d8a25195f50366fe383617fd2ad7dbe1a3606 | MATLAB | 7,778 | 257 | clear all
load ('C:\noga\TD complex spike analysis\task_info');
tasks = {'saccade_8_dir_75and25'}
req_params.grade = 7;
req_params.num_trials = 50;
req_params.ID = 4000:5000;
req_params.remove_question_marks = 1;
cell_type1 = 'CRB|PC ss';
cell_type2 = 'CRB|PC ss';
pairs = [];
counter = 0;
for t = 1:length(tasks)
... |
e4329749c4b13769f5cc82e60711c7cf527fe32442ada2adf630204e8c758b80 | MATLAB | 7,778 | 282 | function [F0,Fn,Zn,F] = EPGX_TSE_BM(theta,ESP,T1x,T2x,f,ka,varargin)
%%%% [F0,Fn,Zn,F] = EPGX_TSE_BM(theta,ESP,T1x,T2x,f,ka,varargin)
%
% EPG-X for Bloch McConnell coupled systems w/ TSE sequences
%
% arguments:
% theta: vector of flip angles (rad) - length = #pulses
% ESP: ... |
5c125f740790636e18da86bbbb31783d34840630d6d1e8b0bd4404a4faf63cab | MATLAB | 7,779 | 142 | % Inactive
% Plot HRF using outputs from scripting
% plotHRF.m plot HRF using outputs from group.mat from Homer3 GUI
% Completed
% This plot all channels from one source, divide into 6 subplots, one for
% each condition
function plotHRF3(sbjNum)
figSaveDir = ['C:\Users\mn0mn\Documents\ResearchProjects\spatailAttentio... |
98d4ca56f928380435dc3ae1567cf577c5370d2877cb548bebcdddef77329a25 | MATLAB | 7,786 | 96 | function [A,h] = build_A_h_3d(dphi,cert,phiGrad,transformatioinModel)
% BUILD_A_H_LINEAR3D Builds the equation system A*p = h
%
% INPUT ARGUMENTS
% dphi - Phase-difference
% cert - Certainty
% phiGrad - Phase gradient
% transformationModel - Transformation model (transl... |
4aa5e379f426baef87bd8dacd249b2d9361030d58d78802f226955cb4d652449 | MATLAB | 7,791 | 244 | function PLS_summary_component_stats(GENEdata_root, MRIdata_root,...
ncomp, nperm, perm_type, working_dir, coords)
% PLS component summary statistics
%% ------------------------- SCRIPT INFORMATION ---------------------------
%
% This script will assess if each individual component in a model explains
% more or les... |
91f28caf5b7ddc9f4e4fe246924d36e00d55dbfa4fcc5782908e7744d6be4977 | MATLAB | 7,792 | 244 | function PLS_summary_component_stats(GENEdata_root, MRIdata_root,...
ncomp, nperm, perm_type, working_dir, coords)
% PLS component summary statistics
%% ------------------------- SCRIPT INFORMATION ---------------------------
%
% This script will assess if each individual component in a model explains
% more or les... |
a87add2508e0cb857e07d6d6d3af4f59373d3fe1d7da6287a5d69be01f29ee24 | MATLAB | 7,808 | 225 | function test_yosemite
% TEST_YOSEMITE
%
% This function reproduces the results in the last five rows of table 6.2,
% page 102, of Gunnar Farnebäck's thesis "Polynomial Expansion for
% Orientation and Motion Estimation". The results in table 7.1, page 126,
% are also reproduced.
%
% Author: Gunnar Farnebäck
% C... |
914e1230fba322a4592a1ba3ded97550271848f67dcfdd69478be6fe8c158681 | MATLAB | 7,818 | 216 | function varargout = not_calculated(varargin)
% NOT_CALCULATED M-file for not_calculated.fig
% NOT_CALCULATED by itself, creates a new NOT_CALCULATED or raises the
% existing singleton*.
%
% H = NOT_CALCULATED returns the handle to a new NOT_CALCULATED or the handle to
% the existing singleton... |
de07a2e1ea6846d4b1d48ea7aa6039a9bdf19bc423f7558f97ec859b8c54b25c | MATLAB | 7,844 | 196 | % Import Wizard
%
% Descriptions tba
%
% $Author: Peng Li, Ph.D.
% Division of Sleep Medicine, Brigham & Women's Hospital
% Division of Sleep Medicine, Harvard Medical School
% $Date: Dec 23, 2019
%
% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++... |
3fd176ecb6fccbdd52c0da4992dcba10a64444a5abbe7dc0f669fd2ad650f138 | MATLAB | 7,872 | 195 | function [pparam, X, clabel] = mv_preprocess_csp(pparam, X, clabel)
% Calculates Common Spatial Patterns (CSP) by projecting the data onto
% n leading (largest eigenvalues) and n trailing (smallest eigenvalues)
% eigenvectors for the generalized eigenvalue problem
%
% lambda = (w' * C1 * w) / (w' * C2 * w)
%
% where ... |
a0dffedb5bf1fb4a0dfd486a92115ea530fac1c50ec7a0dd57f1fbc32218204c | MATLAB | 7,874 | 188 | function [cfg, Y, nmetrics, Y2] = mv_check_inputs_for_regression(cfg, X, Y, X2, Y2)
% For regression: Performs some sanity checks and sets some defaults for
% input parameters cfg, X, and y.
% Also checks whether external toolboxes (LIBSVM and LIBLINEAR) are
% available if required.
if ~iscell(cfg.metric)
cfg.met... |
04ce1f6bc50d8b2ca0975b42770b0191834233d9d577b4820b44f42a9cebdb80 | MATLAB | 7,885 | 264 | function [F,fieldSynthesis,dithered,Q,T] = FieldSynthesisTheorem(F,L)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Small program to illustrate a new Field Synthesis Theorem.
%
% In essence it says that the projection of the absolute modulus of a
% complex field is the same as... |
dcee27d50a11c84cb0a41e84d2fc03f3b4a634dbd01e2eb6f6db36e094f659d4 | MATLAB | 7,889 | 264 | % Probability Tuning curves - regardles of direction
clear
[task_info, supPath ,~,task_DB_path] = ...
loadDBAndSpecifyDataPaths('Vermis');
velocities = [15, 25];
req_params.grade = 7;
req_params.cell_type = 'CRB';
req_params.task = 'speed_2_dir_0,50,100';
req_params.ID = 4000:6000;
req_params.num_trials = 60;
re... |
12f590ce6f7f403f2c189e3f1a7d9f719b50ca474aa9ec422408c9d2d890f4c3 | MATLAB | 7,902 | 237 | % function design = make_design_cv(cfg)
%
%
% Function to generate design matrix for cross validation using the
% decoding toolbox. This function uses a "leave one run out" cross
% validation method.
%
% IN
% cfg.files.chunk: a vector, one chunk number for each file in
% cfg.files.name. Chunks can be used to ke... |
855f57b966fc5a3254b14e897588ee97d429437efc395f15d4b27470a480b55b | MATLAB | 7,903 | 231 | % calcPhase compute single-frequency phase
%
% [ phs, xf ] = calcPhase( x, fir, method, usf, graphics )
%
% ARGUMENTS:
% x signal (vector or matrix). this can be:
% -the actual data (then any method can be applied)
% -a list of sample numbers corresponding to extr... |
df90e65570e096a65853d4ddd63152afc19ebbb3e2af951e2086f13fae4f3e9b | MATLAB | 7,911 | 225 | function progress(varargin)
% PROGRESS shows a graphical or non-graphical progress indication similar
% to the standard Matlab WAITBAR function, but with the extra option of
% printing it in the command window as a plain text string or as a rotating
% dial. Alternatively, you can also specify it not to give feedback o... |
1df1b063b97909e2139b497075a881764c016c22e460f554c933286012d46720 | MATLAB | 7,923 | 251 | %% DLC data processing pipeline
% RD fall 2019
%% Get list of randomly selected videos for dlc training/generating
videoDir = '';
dlcdata.sampleVids (sesData, mainPath);
%% Create dlcdata objects and analysis files for each session
clear
% Read session data from Excel file
mainPath = 'E:\high_speed_video\... |
20cdd8e394f474105be0f9f7406e21085f584e6e3ceea68ea2579cd49e0a1c2e | MATLAB | 7,943 | 216 | function varargout = load_data_vars(varargin)
% LOAD_DATA_VARS M-file for load_data_vars.fig
% LOAD_DATA_VARS, by itself, creates a new LOAD_DATA_VARS or raises the existing
% singleton*.
%
% H = LOAD_DATA_VARS returns the handle to a new LOAD_DATA_VARS or the handle to
% the existing singleto... |
3145f8765c0b990f97943c5a2c558794aa3644e35afde4cd207595f8104f8b91 | MATLAB | 7,947 | 255 | % This script generates post processed images including OCT slides,
% Histology sections and a mask to highlight good parts from bad ones.
%% Inputs
subjectPath = s3SubjectPath('42');
sectionName = 'Slide05_Section03';
% How many y planes to take around the center OCT plane +-yPlanesAroundCenter
yPlanesAround... |
e8eb4e0e58f2a9be4d03ff008dd0dab4bc7074cf51b17be562a32af6590d3ab5 | MATLAB | 7,950 | 246 | function PLS_summary_component_stats(GENEdata_root, MRIdata_root,...
ncomp, nperm, perm_type, working_dir, coords)
% PLS component summary statistics
%% ------------------------- SCRIPT INFORMATION ---------------------------
%
% This script will assess if each individual component in a model explains
% more or les... |
08469587ccddbc7360a8ac4c111c79d38c8fd9f47851f4ce910e26032e661bd9 | MATLAB | 7,962 | 273 | %% load data
clear; close all; clc;
datapaths;
AMResPath = [PopPath,'\AM_results_salicylate.mat'];
load(AMResPath);
MPlotLabels = [0.06,0.93,0.045,0.03; ...
0.51,0.93,0.045,0.03; ...
0.06,0.63,0.045,0.03; ...
0.51,0.63,0.045,0.03; ...
0.06,0.3,0.045,0.03; ...
0.51,0.3,0.045,0.03];
% plot VSpp afte... |
d3dd87037a9da178bae6c1dd0c683b2a1cabf46133c48f054c85f2b9329ed830 | MATLAB | 7,965 | 279 | function [F0,Fn,Zn,F] = EPGX_TSE_MT(theta,B1SqrdTau,ESP,T1x,T2a,f,ka,G,varargin)
%%%% [F0,Fn,Zn,F] = EPGX_TSE_MT(theta,B1SqrdTau,ESP,T1x,T2a,f,ka,G,varargin)
%
% EPG-X for Pulsed MT systems w/ gradient echo sequences
%
% arguments:
% theta: vector of flip angles (rad) - length = #pulses
% ... |
1736f2b8affce6ff9b2519f03ca1962c9d2e9c4fd1fa3a36a7d47d84f1a0ce96 | MATLAB | 7,972 | 241 | % This script creates envelope of AM stimulus in van den Berg*, Wong* et al
% the final output is the vector total_mag, with timing in TT (or x)
%% Generation of envelope
% display
startT = -0.2; endT = 2.2;
Fs = 1/5.12e-6; % sampling rate
showGates = 1; % whether the different envelopes/gates are shown
show... |
842481816c45767d0e7005f3848897ec0bcbe564512badcbedce519a0250384e | MATLAB | 7,984 | 233 | close all; clear; clc;
%%
CellStr = {'122718_2', '10119_4', '11919_1', '11019_3', '10519_3', '12119_3', '11819_1',...
'12019_4', '53018_2', '10119_2', '61018_1', '53018_1', '92418_2', '12019_7', '10919_3'};
nCell = length(CellStr);
qualthr = 0.2;
%%
FolderPath = '\\storage1.ris.wustl.edu\kerschensteinerd\Act... |
2a25b1218199d1fd3b06a7b0c84429c19958773960365b308bc600b970eb93fe | MATLAB | 7,999 | 231 | % This script imports SP5 Fluorescence images
% This is similar to SVS5Import but for SP5
%% Inputs
%Where to upload data
s3Dir = s3SubjectPath('01');
% Not used, algorithm figures out itself
%slideNumber = 1;
%sectionNumber = 1;
%Pointer to the folder structure, expecting [folderStructurePath '\slideX... |
dbdb7e926d7b4ab9e0e9fd5c4dd166415b9512b0f58c6afbddb967725295c89d | MATLAB | 7,999 | 318 | function [errmsgs warnmsgs] = unit_set_mat()
% USAGE :[ERRMSGS WARNMSGS] = TEST_SET_MAT()
%
% This is a script that tests the test_sets_mats function. It
% does negative and positive tests on the pattern, selectors and
% the actives selectors.
%
% ERRMSGS = cell array holding the error strings
% describing any tests ... |
6cedea26dee56ca52987cff6f66cf52f5eea63dcf067b55dd11100f0b6120201 | MATLAB | 8,004 | 221 | % run CircleAnalysis_ResponseScreening072122_MorphSeg.m or
% CircleAnalysis_ResponseScreening071222_MorphSeg.m first
clear all; clc;
GroupData.Days = {...
% 1 2 3 4 5 6 7 8 9 10
'021818', '021918', '022218', '022718', '030618', '030918', '031... |
d9e96ecbd1650c0e0e469135841dd91d0dcf58aed9378eecc374907338ee15f0 | MATLAB | 8,016 | 231 | % function dodSpline = hmrR_MotionCorrectSpline_Nirs(dod, t, SD, tInc, p)
%
% UI NAME:
% Spline_Motion_Correction
%
% dodSpline = hmrR_MotionCorrectSpline_Nirs(dod, t, SD, tInc, p)
% Perform a cubic spline correction of the motion artifacts identified in
% tInc. The algorithm follows the procedure describe by
% Scholkm... |
c7337ccd1467f96017e02d312925044da9fb89c1e01aebbe0b80047147d73e5b | MATLAB | 8,021 | 177 | function [fs_index,n_vox_steps,output] = feature_selection_embedded(cfg,labels,data_scaled,n_vox,nested_n_vox,i_train)
% Sorry for the somewhat convoluted code (mainly caused by forward and
% backward selection and several ways in which nested_n_vox and n_vox can
% be entered). I just think it would not be much ea... |
0abd509e335817f4f727384dbb5a9e994e6424b09fcdbc0c6cc436e19c070533 | MATLAB | 8,022 | 196 | clear ()
close ()
code_path='/Volumes/LASA/TBI_OpenNeuro/Shared_code/TBI_ON_Lesions/';
addpath(code_path)
data_path='/Volumes/LASA/TBI_OpenNeuro/TBI_openneuro/data/lesions/overlap_Schaefer/Schaefer1000_wotbi09/';
cd (data_path)
table_overlap_volume_tbi02=import_overlap_volume_Sch1000_tbi02('overlap_volume_combined_wsub... |
8f9055f7fd8c04ab827b38fef590c563767f58d7cc7aeb02d0d2e303835307d6 | MATLAB | 8,022 | 234 | function [varargout] = FieldSynthesisVersusLattice(n,w,r,offset,dispRange)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
%Simulation for field synthesis
%
% compares field synthesis vs square lattice
%
%
% Reto, May 2017
% Mark Kittisopikul, August 2018
%
% INPUT
% n - Defines... |
be6af08309a1ad8c399b2ee9ebe93dad4a1676c4bb7b6be1f0f77bc1035ae840 | MATLAB | 8,027 | 203 | function varargout = CellSizeCutoffGUI(varargin)
% CELLSIZECUTOFFGUI MATLAB code for CellSizeCutoffGUI.fig
% CELLSIZECUTOFFGUI, by itself, creates a new CELLSIZECUTOFFGUI or raises the existing
% singleton*.
%
% H = CELLSIZECUTOFFGUI returns the handle to a new CELLSIZECUTOFFGUI or the handle to
% t... |
bd7277fcc97c0422144d5c3bcb42113952fe4d4895fad71c38ba491ae7322353 | MATLAB | 8,036 | 189 | function output_variable=neuron_reconstruction(wholestack,to_be_added,source_variable,outputpath)
% wholestack: the recorded stacked
% to_be_added: the neurons to be added
% source_variable: neurons already tracked
% outputpath: outputfolder
figure
id_added(size(to_be_added,1),1)=0;
for i=1:size(to_be_added,1)... |
3b60dcc8edb516f837fbffe9bef1768dff13a8f7b0bda6dc440c05b94ac965fe | MATLAB | 8,037 | 65 | % Look-up table for Turbo colormap.
% Includes example usage and comparison with other colormaps.
% Adapted for Matlab from https://gist.github.com/mikhailov-work/ee72ba4191942acecc03fe6da94fc73f
% Nikolas Karalis, 21 August 2019
% Turned into function Hannah Payne April 2020
function map = turbo(m, limits)
turbomap ... |
da646df38339e17ace1d8f0dbcae8a612ede12b9bb9a2ac7674bf3f98a714a49 | MATLAB | 8,041 | 210 |
%% CLEAR AND INITIALIZE
clear;close all;clc;tic;
%% DEFINE THE STRUCTURE THAT WILL HOLD ALL THE PARAMETERS
params = [];
%% DEFINE INPUTS AND OUTPUTS
params.data_folder = 'E:\NAS_SD\SuiviClient\Triffilieff\2023\Data\RWD_Enrica\cohort1';
params.analysis_folder = 'E:\NAS_SD\SuiviClient\Triffilieff\2023\Analyses\RWD_En... |
131d489d26741a0e3a0734f580d153731950db47b9fe9539d242f5b5915a5e7b | MATLAB | 8,044 | 179 | function [M] = makeCanonicalModelMaps_v2(varargin)
% Make the complete set of BVC model maps (for given phi and d ranges) for
% 'canonical' environment shapes, as follows:
%
% 'hp' - Square environment 25x25 pixels
% 'barrier N-S'
% 'barrier W-E'
%
% etc .. FINISH ME!
%
% Note that all the env shape dimensions are hard... |
bc5b71b1705ff0ebff6a830dbcb40991f9a7fe63983adef49c8552e15247f2c6 | MATLAB | 8,062 | 190 | %% Cumulative well-tunnel/subregion-tunnel scatter plots
% Sam Lassers
% This program uses data from AFR tables to create a scatter plot of
% frequency responses over an entire recording either between a specified
% electrode-tunnel pair, or between an entire subergion of electrodes and
% the entirety of a set of tunne... |
72bbc46660240422bc6de813cd29b44d110380a836845d03d3b0a02802ebb3c1 | MATLAB | 8,079 | 229 | % split into single and multi groups.
% new. Use trials from dcNew var
% option to re-run training and classifying or pull from saved data
% sbjList is cell array of experiment "number". Ex: sbjList = {'01','02'};
% sbjList = {'08','10','12','13','14','15'};tInd = 8;featureName = 'Cumulative Sum';numRept = 5;numClasses... |
a51bb9aca06f0734d1ad8e79e23bcb1a8053667cdd31216609f572ab78db7e15 | MATLAB | 8,080 | 241 | function [clus] = cluster6D(cfg, dat)
% [CLUS] = CLUSTER6D(CFG, DAT)
%
% Performs a 6-dimensional clustering on a thresholded data matrix.
% The data matrix is a 2-dimensional representation of a 6-dimensional volume
% Required fields for the input configuration are:
% cfg.inside
% cfg.dim
% cfg.threshold
% Copyri... |
be1bf635bc1d363ceb7c0aa846d49923472a6fd4a3ff184cc79db085c76e1d16 | MATLAB | 8,114 | 268 | % Probability Cue Response
clear all;
supPath = 'C:\Users\Noga\Documents\Vermis Data';
load ('C:\Users\Noga\Documents\Vermis Data\task_info');
% Make list of significant cells
req_params.task = 'saccade_8_dir_75and25';
req_params.ID = 4000:5000;
req_params.remove_question_marks = 1;
req_params.grade = 10;
req_params... |
4f4e8c2a653c7965dd986e6893885c6894c57f8e5d8a2d79109a17f4ae0d35c6 | MATLAB | 8,118 | 183 | %% Turbulence all empirical measurements original
% Analyze turbulence from the TBI dataset in OpenNeuro: <https://openneuro.org/datasets/ds000220/versions/1.0.0/file-display/dataset_description.json
% https://openneuro.org/datasets/ds000220/versions/1.0.0/file-display/dataset_description.json>
% Load inputs
clear al... |
fbf14a105b5f40648dc239fd52232bc615a6e2794e0b645cf6c3d4f4ba61f718 | MATLAB | 8,118 | 183 | %% Turbulence all empirical measurements original
% Analyze turbulence from the TBI dataset in OpenNeuro: <https://openneuro.org/datasets/ds000220/versions/1.0.0/file-display/dataset_description.json
% https://openneuro.org/datasets/ds000220/versions/1.0.0/file-display/dataset_description.json>
% Load inputs
clear al... |
a0083422cf1e5a424536fe5a17a04226acb7483fc54beca91fba4db874c28389 | MATLAB | 8,123 | 183 | %% Turbulence all empirical measurements original
% Analyze turbulence from the TBI dataset in OpenNeuro: <https://openneuro.org/datasets/ds000220/versions/1.0.0/file-display/dataset_description.json
% https://openneuro.org/datasets/ds000220/versions/1.0.0/file-display/dataset_description.json>
% Load inputs
clear al... |
5bd1ad7bd94d4363eb0a1884f419269218d9f45e616124e7a04c92bbdb13a059 | MATLAB | 8,129 | 314 | %% get population list
clear; clc; close all;
% PopPath = 'F:\Maurits\Population\';
datapaths;
%% Fig2AB - FRA example plots (threshold elevation)
close all;
% Fig2A example unit 1
mouse1 = 42;
unit1 = 196;
G = figure('Position',[400,200,745,393]);
s1=subplot(1,3,[1 1.3]);
s2=subplot(1,3,[2.7 3]);
load([FRAPath,'\M... |
589f60ebe893412c005b0415777b5d8984bf1c250c9b331d7c1618f158e7e47e | MATLAB | 8,131 | 349 |
%% Load data
binsize = 0.2; % bin size for population vectors, needs to be large
thSmooth = 2; % smoothing factor of population vectors
iso = 1; % set to 1 if need to re-run Isomap
[~, foldername, ~] = fileparts(pwd);
load(fullfile('Data','SpikeData'));
load(fullfile('Data','BehavEpochs'));
load(fullfile('Data','C... |
1a171c0f0fe1c69de062394b6a94b5543df9f68120784755458f0eb9d462d4c4 | MATLAB | 8,143 | 234 | function [spfsOut,isOutlierOut,nOut,sectionDistanceToOriginOut,averagePixelSize_um] = spfComputeStackAverageAndRealign(spfs, speculatedDistanceToOrigin)
% This function takes an array of Single Plane Fits (spfs) and rectify them
% as they should all have the same alignment, as they are from the same
% stack
%
% IN... |
50c8b95202c2029a979e6f2d00cc40c8e333506ff4c980f01c3e96c52bb6956a | MATLAB | 8,145 | 262 | % Probability Tuning curves
clear
[task_info,supPath] = loadDBAndSpecifyDataPaths('Vermis');
req_params.grade = 7;
req_params.cell_type = 'SNR';
req_params.task = 'saccade_8_dir_75and25';
req_params.ID = 4000:5000;
req_params.num_trials = 50;
req_params.remove_question_marks = 1;
raster_params.align_to = 'targetMov... |
0d636afd39bf1b353d8530a85f1add13361bb4b1752c23961444e5976b770b78 | MATLAB | 8,148 | 202 | function [varargout]=errorbarxy(varargin)
linewidth = 6.0;
% ERRORBARXY is a function to generate errorbars on both x and y axes
% with specified errors modified from codes written by Nils Sj?berg
% (http://www.mathworks.com/matlabcentral/fileexchange/5444-xyerrorbar)
%
% errorbarxy(x, y, lerrx, uerrx, ler... |
736cabd5c16ffb8edf5d810e841060280c9206260cbcde8d8d4f1ff0f33ded7e | MATLAB | 8,153 | 182 | %This analysis code was used in:
%Mares I, Ewing L, Papasavva E, Ducrocq E, Smith FW, Smith MLS (in press).
%Face recognition ability is manifest in early dynamic decoding of face-orientation
%selectivity – evidence from multi-variate pattern analysis of the neural
%response. Cortex.
%The code was originally cre... |
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