sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
462adb3958c9658008cced7d74d84040cd7b3597325a4b0d42bffecfbe8f158f | MATLAB | 2,398 | 85 | clear
[task_info,supPath] = loadDBAndSpecifyDataPaths('Vermis');
req_params.grade = 7;
req_params.cell_type = {'PC ss', 'PC cs', 'CRB','SNR','BG msn'};
req_params.task = 'choice';
req_params.remove_question_marks = 1;
req_params.remove_repeats = false;
req_params.num_trials = 120;
epoch = 'cue';
lines = findLinesI... |
54415e21db745244f8b150374b8575a9531563d3fd026a92a62e5826a4b41cec | MATLAB | 2,401 | 69 | function [X_RS,zX_RS,Vlow,Vhigh]= find_filter_cut_off(mypath,U)
% % =========================================================================
% % (1) In main text: The cut-off of CC eigenmodes using the graph spectrum dichotomy approach
% % =======================================================================... |
16bf26ecb00d8f1a59417927d612331383961cd5cd1111f6c0dbaeede33f8759 | MATLAB | 2,402 | 82 | function labels = load_labels(sub_id, p, sorting, includenans)
% labels = load_labels(sub_id, p, sorting, includenans)
%
% Load labels for reconstruction.
%
% Input:
% - i_sub: ID of the current subject.
% - p: Structure with analysis parameters.
% - sorting: Wheteher labels should be sorted. Set to 'sorted' if ... |
ea0090da0a12d187b8e12bf7e56d5014184c527ccd6e6568cec621203aa340fc | MATLAB | 2,402 | 78 | clear all
close all
format compact
clc
% script to calculate distances have been measured for all included scans (UsedSets)
% gt_dataPath = '/disk1/dan/datasets/mvs-data/SampleSet/MVS_Data'
gt_dataPath = '/mnt/raid/scanpb/datasets/SampleSet/MVS_Data'
% gt_dataPath = '/Users/randing/work/data_demo'
[dataPath,resultsPa... |
69c666fddcab6c65981725a4f4f108f037fbe644fe94638100796a4f044633f8 | MATLAB | 2,403 | 72 | function g = rbfbkp(net, x, z, n2, deltas)
%RBFBKP Backpropagate gradient of error function for RBF network.
%
% Description
% G = RBFBKP(NET, X, Z, N2, DELTAS) takes a network data structure NET
% together with a matrix X of input vectors, a matrix Z of hidden unit
% activations, a matrix N2 of the squared distances ... |
f83994dc64781fcb32597d24c6ae616b066b5afcf9d553f37f5ff64de1101ec3 | MATLAB | 2,403 | 75 | function bullseyeChild = createBullseye(data)
% CREATEBULLSEYE creates a bullseye, with the main function of creating
% an AHA 17 segment bullseye. Each row in "data" should have the
% following structure:
%
% [rho1, rho2, nSegs, thetaStart]
%
% where
% -rho1 is the... |
0a2c7cb1c3cf55308bccfd0fc5b48275a3405dd39188040ae02ea058486d76c9 | MATLAB | 2,404 | 102 | function [tout,rout] = rose(varargin)
%ROSE Angle histogram plot.
% ROSE(THETA) plots the angle histogram for the angles in THETA.
% The angles in the vector THETA must be specified in radians.
%
% ROSE(THETA,N) where N is a scalar, uses N equally spaced bins
% from 0 to 2*PI. The default value for N is 2... |
8371f5abc080c7277f4873ee73f85a08a2bb2e4f92917ac851a1b8798a76d20a | MATLAB | 2,413 | 65 | function directional_pie_chart_210413(areas)
% Make sure input is a cell array where cells contain a singular table with
% fields: Subregion, Electrode Pairs, Spike Area, Conduction Time, Unit
% Pairs, Direction, Recording Name, and fid. This function from that
% creates pie charts of the direction of each channel.
sub... |
c802fa1f2aecab6170a3e5f11cd276febe0e6b28f265fd74508d4e4e2ea5a249 | MATLAB | 2,413 | 51 | function [vectorField] = lie_bracket(vectorField,updateVectorField)
% LIE_BRACKET Computes the Lie bracket of vector fields
%
% [vectorField] = lie_bracket(vectorField,updateVectorField)
%
% INPUT ARGUMENTS
% vectorField - First vector field
% updateVectorField - Second vector field, assumed to be small
%... |
ef744b367346310b60ab87cb71bba286df4132cebf88e8acd36bb3d91ec4da82 | MATLAB | 2,417 | 55 | %%
%% Script to load trajectory of animal movement
%% c_ts: firing times, root.cel_x{1,1}: spike x coordinates,
%% root.cel_y{1,1}: spike y coordinates
%% references: https://github.com/hasselmonians/CMBHOME/wiki/Tutorial-2:-Apply-an-epoch-and-get-theta-signal
%% https://www.mathworks.com/matlabcentral/answers/432322-r... |
652eabff570b3d9dc8dbae3e4aa5a867b79e336ec3a61a614c1bd9a16c35257d | MATLAB | 2,419 | 58 | function links=gap_closer(A_time,segmentlists_time,segmentlists,max_interframe_distance,max_gap_closing_distance,xy_pixel_size,z_pixel_size)
xy_ratio=xy_pixel_size;
z_ratio=z_pixel_size;
% time matrix
segment_start_times=cellfun(@(cell_variable) cell_variable(1), segmentlists_time);
segment_end_times=cellfun(@(c... |
e01a1a066195f943f3eb1c2126140e54c568f8443239ab09855fbc6cc5f6fa80 | MATLAB | 2,422 | 84 | function lambda = LedoitWolfEstimate(X, form)
% Calculates the Ledoit-Wolf estimate for the shrinkage parameter used in
% the estimation of covariance matrices.
%
% Usage: lambda = LedoitWolfEstimate(X)
%
% X - [samples x features] matrix of training samples
% form - 'primal' uses the standard formul... |
9f73fc6a55116c99a9e85299fcc01015a77267a0173f67a39e059e1e544cf5e7 | MATLAB | 2,424 | 67 | function [null_W_energy, null_W_FC_r]= CC_rewired_digraphs(W,zX_RS,null_graph)
% =========================================================================
% Generate 1000 random digraphs (degree-preserving surrogate connectoms) further calculate reconstruction accuracy
% ==========================================... |
b9637c4069b06f48ab64ebd7159485e5db771b30040cdeb460d13c25e9482e05 | MATLAB | 2,426 | 59 | function display_results_registration3d(moving,fixed,...
displacementField,originalSize,gamma)
% DISPLAY_RESULTS_REGISTRATION3D Displays registration results for 3D
%
% display_results_registration3d(moving,fixed,displacementField,originalSize,gamma)
%
% INPUT ARGUMENTS
% moving - Moving image
% fixe... |
4e42341d34efed93bcae57ad02b5904d5c0fdea3c9012acc6847a081823f04f1 | MATLAB | 2,427 | 110 | function Y = SIMLR_embedding_tsne(P, do_init,DD, Y0)
%%%compute 2-D coordinates by approximae t-SNE
%
% Y = SIMLR_embedding_tsne(P, do_init)
%
% Input:
% P: N x N, pairwise (sparse) similarities or network (weighted) adjacency matrix
% do_init: boolean, do over-attraction initialization or not, default=false
... |
5cc828687fc49d86b4aed2d80dd299a92d24c6eaa043158542b723f3715d27b2 | MATLAB | 2,427 | 80 | function [transformationMatrix] = ...
compute_transformation_matrix_from_coordinates(newCoord,oldCoord,varargin)
% COMPUTE_TRANSFORMATION_MATRIX_FROM_COORDINATES
%
% Computes a transformation matrix that that transforms old coordinates to new
% coordinates
%
% [transformationMatrix] = ...
% compute_transformati... |
9a2b0e33cffb088d7669ee0b61cdab6cb6064413dce3edbcf54d9a0c33f72865 | MATLAB | 2,428 | 77 | function Experiment = set_up_timing(Experiment)
% Set Up Timing
% Input: Struct 'Experiment' with fields 'Display'
% Output: Struct 'Experiment' with field 'Timing'
% 'Timing' contains fields: 'waitSecs' - number of seconds to wait
% after certain operations 'frameFeq' - screen refresh frequency
% 'imageSe... |
ec0cb521ef41843e119bd3eea5882105cb0612fc6c423c2c1d4cc53b6cef95ab | MATLAB | 2,430 | 74 | function neurons_bv=create_neurons_bv(coords_t,reconstructed_links)
new_tracks{1}=[];
if ~isempty(reconstructed_links)
new_tracks{numel(reconstructed_links)}=[];
for i=1:numel(reconstructed_links)
new_tracks{i}.coords=[];
for j=1:numel(reconstructed_links{i})
segment=squeeze(c... |
82b05ad4d6d43bf5048d8dc09b3d9f77dc8f8a1eadcb636870e584cd4fb5a83f | MATLAB | 2,432 | 70 | function [recon_energy, recon_FC_r]= SC_rewired_undigraphs(W,zX_RS,n_null);
% =========================================================================
% Generate 1000 random undigraphs£º[W0, R] = null_model_und_sign(W)
% further calculate reconstruction accuracy
% ==============================================... |
e6245b23b3a28873af4a7a21a881b22c6d3cf035cee9df8561ca21d095a37458 | MATLAB | 2,432 | 128 | function O = dictArray(varargin)
% O = dictArray(inp) constructor for the dictArray class, inspired to
% Python dict class.
%
% Input could either be another dictArray object (copy constructor) or a
% cell array like the following
% { { Key1, Value1},
% { Key2, Value2},
% Key3,
% ...
% { KeyN, ValueN} }
% ... |
b97ec2bf8f3e9d2b175cfa765e0507e0dac9d8544895201426b3b4ea79c9250d | MATLAB | 2,436 | 75 | %function [y, ARmodel] = WhitenSignal(x, window,CommonAR,ARmodel, ARorder)
% whitens the signal
% if window specified will recompute the model in each window of that size
% (window is in samples,e,g, 300sec*1250 samples
% if CommonAR is set to 1, then will use model from first channel for all
% if ARmodel is specified... |
3055b3ba5db6c018d2bcb50255f1754f286f3160866fb9e88ac2f87d9d151d9d | MATLAB | 2,440 | 102 | % Probability Tuning curves
clear; clc; close all
[task_info, supPath ,~,task_DB_path] = loadDBAndSpecifyDataPaths('Vermis');
req_params.grade = 7;
req_params.cell_type = 'PC cs';
req_params.task = 'saccade_8_dir_75and25';
req_params.num_trials = 100;
req_params.remove_question_marks = 1;
req_params.ID = 4000:6000;
... |
b488bfe60343f468dce9c4cfaa62a4a513de0a1debd4ccb0d5b545738732cc3d | MATLAB | 2,440 | 59 | function [I_cr] = TrackWorms()
prompts = {'Min Size','Max Size','Background Probability','Min Run Length','Motion Threshold'};
prompts = inputdlg(prompts);
MinSize = str2double(prompts{1});
MaxSize = str2double(prompts{2});
bkgProb = str2double(prompts{3});
MinRun = str2double(prompts{4});
MotionThresh = str2double(pro... |
30804c520d5b541b739be797c4c9b480371ec0328a340094b578dca4b7ec337f | MATLAB | 2,443 | 71 | % load path file
% nex_idx = 8;
%
mat_idx = 7;
t1_idx = 2;
t2_idx = 4;
t3_idx = 5;
t4_idx = 6;
%
summary_co = [];
summary_ex = [];
toplot_co = [];
toplot_ex = [];
for fn = 1:size(fn_idx1,1)
%load
load(fn_idx1{fn,mat_idx});
dff = 25/size(recordingFile.eeg,1);
idx_theta = round([6 10]/... |
5c0580b43ac3c27c9d042675ee1832594fa7cbf9f0b57f0f2755ef7679061172 | MATLAB | 2,443 | 70 | function [acts,scratch] = test_gnb(testpats,testtargs, scratch)
% Use a Gaussian Naive Bayes classifier to predict regressors.
%
% [ACTS, SCRATCHPAD] = TEST_GNB(TESTPATS, TESTTARGS, SCRATCH)
%
% See TRAIN_GNB for more info.
% License:
%=====================================================================
%
% This is... |
be851cb271ed317bae5338fc43b22a11d8234f7621e60e19ab4c54d6dcc86f9f | MATLAB | 2,443 | 59 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This script is meant to be used as part of a wrokflow aimed to calculate yap overal expression and YAP
% nuc/cytoplasm ratio in fixed cells seeded on PAA gels.
% It is the first one to use in order to seg... |
c784f922253884b5e54d0bdbceff69760c2db621fd7f40fbce16306c181026b3 | MATLAB | 2,447 | 94 |
%% Add path
addpath(genpath('../Ionnic Channel'),genpath('../Tools'), genpath('../Synapses'), genpath('../Datas'));
%% Import data from neuron (FS)
dt = 0.01;
Vmem = -90:dt:60;
l_V = length(Vmem);
%% Matrix Initialization
rinf = zeros(1, l_V);
taur = zero... |
d33fd8852c07bd6c0a30f58d54d1b9024cc854cc7eef1193c106679ee6645639 | MATLAB | 2,447 | 94 |
%% Add path
addpath(genpath('../../Ionnic Channel'),genpath('../../Tools'), genpath('../../Synapses'), genpath('../../Datas'));
%% Import data from neuron (FS)
dt = 0.01;
Vmem = -50:dt:50;
l_V = length(Vmem);
%% Matrix Initialization
qinf = zeros(1, l_V);
tauq ... |
1d8299787f52199fb0f9c9187299606e9486000e4854656529cfec4399a3c205 | MATLAB | 2,448 | 61 | %initialize matlab
close all
clearvars
clc
% Determine where your m-file's folder is.
folder = fileparts(which(mfilename));
% Add that folder plus all subfolders to the path.
addpath(genpath(folder));
%user input
selpath = uigetdir('Select MINFLUX directory');
if isequal(selpath,0)
disp('User se... |
3269df392720fbb514ef29458cc42f59be9a72ed0c5810973ae7c41e210098d0 | MATLAB | 2,449 | 95 | %DEMGLM1 Demonstrate simple classification using a generalized linear model.
%
% Description
% The problem consists of a two dimensional input matrix DATA and a
% vector of classifications T. The data is generated from two
% Gaussian clusters, and a generalized linear model with logistic
% output is trained using it... |
45ee88730490f2b327275c819d97a2b6bd1245468cd3f5beee2461d8f71e7834 | MATLAB | 2,451 | 103 | %DEMKNN1 Demonstrate nearest neighbour classifier.
%
% Description
% The problem consists of data in a two-dimensional space. The data is
% drawn from three spherical Gaussian distributions with priors 0.3,
% 0.5 and 0.2; centres (2, 3.5), (0, 0) and (0,2); and standard
% deviations 0.2, 0.5 and 1.0. The first figure ... |
fde7f13d3e6b43555df23760bb517489abad861214bc657f9c4dd9d687adcfa8 | MATLAB | 2,454 | 77 | function [subj] = separate_regressors(subj, regsname, varargin)
% Separate regressors matrix into row vectors
%
% [SUBJ] = SEPARATE_REGRESSORS(SUBJ, REGSNAME, ...)
%
% Given a regressors matrix, creates new regressors objects with
% appropriate names (from 'condnames' property) that consist of the
% individual rows of... |
60802f041f0432918f254a89e419745c090bc00a908d19c97e13450392b6ef34 | MATLAB | 2,455 | 77 | clear all;
addpath('/Volumes/LASA/TBI_OpenNeuro/TBI_openneuro/timeseries/inputs/');
addpath('/Volumes/LASA/TBI_OpenNeuro/Shared_code/Hopf_TBI_ON_personalized/');
load tseries_openneuro_tbi_tp123_sch1000.mat; % Load your data
% Parameters of the data
TR=2; % Repetition Time (seconds)
NSUB=12;
NPARCELLS=1000;
Tmax=144;... |
570223556e7306e483012a1b794d9c7fabff2898f05667a0cea4eb4e3f5e2f01 | MATLAB | 2,459 | 81 | function [subj] = initset_object(subj,objtype,objname,newmat,varargin)
% Calls init_object then set_mat (and set_objfield optionally)
%
% [SUBJ] = INITSET_OBJECT(SUBJ,OBJTYPE,OBJNAME,NEWMAT,...)
%
% This is for lazy people who want to init their object and then
% put the mat inside it, all in one go
%
% You can also s... |
069ed82b8cc65e39bd43733dd9a4952017f91482af2c22cb401bb39e0a211a33 | MATLAB | 2,464 | 97 | function [pat] = get_full_pattern(subj, patname, varargin)
% Retrieves a pattern in volume or padded matrix form.
%
% This is useful for saving patterns to AFNI or for computing
% statistics for patterns in +tlrc space, where voxels have a 1:1
% correspondence.
%
% Usage:
%
% [PAT] = GET_FULL_PATTERN(SUBJ, PATNAME, ..... |
f8af63bb26fa501e370a8d64806c2e4a9c5caecd829089b35426b397722c89db | MATLAB | 2,464 | 83 | function pSVR_plot_bfca_vviq_correlation(p)
% Get relevant variables from 'p'
delay = p.psvr.delay;
p.psvr.label = 'target';
%%% Prepare data %%%
% Load results
stats_file = dir(fullfile(p.dirs.data, 'analysis', 'all', 'results', ['pSVR_' p.psvr.event '_' p.psvr.label '_' p.psvr.roi '_stats.mat']));
if isempty(stats... |
6aea3996e434bcea057bf75ed0d273d8b208f5b91a2957a0f77837f87b9443b7 | MATLAB | 2,465 | 53 | function [FirstFrame_AboveBelowBaseline, IncDecNoChange] = FirstFrame_AboveBelowBaselineMean(FluorescenceTrace, BaselinePeriod, z, STDorSEM, SearchAfterFrameX)
%FirstFrame_AboveBaselineMean finds the first frame greater than or equal
%to Z STD or SEM above the baseline mean
% Michelle Cahill 202111202 adapted from F... |
1904405c8579020f2d8d865696cb50b238d9e18e5fadf9c19c3f6ba8836c1d23 | MATLAB | 2,466 | 75 | function [Xavecorr, Tavecorr]=removeShamBF(data_corr, iR, iRsham, iRcov, lambda, GaussEnv, widthCov, widthArtifact, U)
%
% This function removes the from one condition (active TMS) the signal
% produced by the sources activated by sham condition (the second
% condition). Data from both condtions are given as input ... |
b764cf80374bab6ecd1481c9856252335a10b9d155a4d25336ccd529614c49e8 | MATLAB | 2,468 | 77 | function [subj] = load_pattern_from_hd(subj,patname,varargin)
% Reverse of MOVE_PATTERN_TO_HD
%
% [SUBJ] = LOAD_PATTERN_FROM_HD(SUBJ,PATNAME,...)
%
% Reloads a pattern that was saved to the harddisk. Uses the MOVEHD
% field stored in the pattern to retrieve things, and then removes it
% afterwards
%
% LEAVE_ON_HD (opt... |
3e00bde1a7a65685795e19ebae77c41212a8d06f43be97c7a5dd473568de9fc8 | MATLAB | 2,469 | 53 | function ec2RunStructure = awsEC2BuildRunStructure ...
(ami,pemFilePath,IAMRole,securityGroup,region,userName)
%This function builds a structure used to initialize EC2 instance
%To initialize EC2 instance we will need to define:
%INPUTS:
% - AMI (image id, pem name, pem file path) that defines: operational s... |
fc89bf90e7ea4d7f30c853c6b10364a7e2fe66d7d42c43067ee538c0e9d2823d | MATLAB | 2,474 | 77 | function [combined_table]=combine_similar_times_nowobble_210719(table)
if all(~ismember({'Subregion','Electrode Pairs','Spike Area','Conduction Time','Unit Pairs','Direction'}, table.Properties.VariableNames))
error('This table does not have the appropriate variables: Electrode Pairs,Spike Area,Conduction Time,Uni... |
3361f898abdc8fe63da9c6c370f6cd75b5c2960b2d9aa7550d9d8d79215880db | MATLAB | 2,476 | 72 | % SYNTAX:
% [stim, tRange] = hmrR_StimRejection(data, stim, tIncAuto, tIncMan, tRange)
%
% UI NAME:
% Stim_Exclude
%
% DESCRIPTION:
% Excludes stims that fall within the time points identified as
% motion artifacts from HRF calculation.
%
% INPUT:
% data: SNIRF data object
% stim: SNIRF stim object
% tIncAu... |
a41c49791cdc3870711ca6efb57e3174fcbc301de84d970727d09e6db75660b1 | MATLAB | 2,477 | 70 | function model = lvl2_2samplettest_specify(p)
% Exptract relevant variables from p
data_dir = p.dirs.data;
title = p.lvl1.title;
% Initialize output variable
model = {};
files = {};
for i_sub = 1:numel(p.subjects)
sub_str = num2str(p.subjects(i_sub),'%02i');
list = spm_select... |
c7371a5d23dccfb5ca0abf834d8198f6dad8b4e6cb64ecf73b33adf166ff17cb | MATLAB | 2,477 | 104 | % Figure S1 Van den Berg*, Wong* et al, 2024, iScience
% FigS1A - Salicylate Concentration vs Time
close all; clear; clc;
datapaths
load([ConcPath,filesep,'salicylate_concentration_data.mat']);
% F = figure;
% a=gca;
% plot(a,T,C,'ko','MarkerFaceColor','k');
p = polyfit(T,C,1);
% [fitresult, gof] = expfit(T, C);
a=... |
3711eddacdcb7e35d6d03e43ec2e2fe58d80997d65ab95f4f9a3127395a7ff48 | MATLAB | 2,479 | 89 | function Par = LoadPar(FileName, varargin)
% LoadPar(FileName)
% loads the specified par file and returns a structure with these elements:
%
% .FileName -> name of file loaded from
% .nChannels -> number of total channels
% .nBits -> number of bits of the file
% .SampleTime -> time, in microseconds,... |
108a40c3abeeb2612c7ebf39b7b16d616d64100579364c24ae7ec65bbe50a4c1 | MATLAB | 2,480 | 67 | function [actives_bal] = balance_active_selector(conds,actives)
% [ACTIVES_BAL] = BALANCE_ACTIVE_SELECTOR(CONDS,ACTIVES)
%
% Takes in a single boolean actives selector ACTIVES, as well as the
% conditions regressor CONDS, and returns a whittled actives selector
% ACTIVES_BAL that balances the number of timepoints in e... |
b8906eabef14ac81a6464c73d71291082bcdfa99e447a60c37b9baf607cfe167 | MATLAB | 2,480 | 77 | % Create a table that will mean the EC activation across iterations and
% fold
%% Set Up the environment
clear all
clc
%% Add the pm_data path
pmDataPath='/Users/federica/data/GridMagination/3Iso/Script/00_pm_data';
addpath(pmDataPath)
WorkingDirectory='/Users/federica/data/GridMagination/3Iso/CONT';
%Subject Lsi... |
d788a0c734b6e317b0c805944bf0b1edd8c338938dfeb9848eb10dec9f0fc345 | MATLAB | 2,480 | 93 | function CalibrateCamera(varargin)
% CalibrateCamera - Calibrate camera, from pixel to cms
%
% USAGE
%
% CalibrateCamera(<options>)
%
% <options> optional list of property-value pairs (see table below)
%
% =========================================================================
% Properties Valu... |
5b77aaa1b559b63b201544c70682ca2d6e3ec946fc7313001b71f5d4cca0da30 | MATLAB | 2,481 | 76 | function [fx] = f_Hampton_2games_cinq(x,P,u,in)
if VBA_isWeird (u) % e.g., 1st trial
fx = x;
return;
end
o = u(1); % opponent's last choice (o)
a = u(2); % agent's last choice (a)
p0_game1 = VBA_sigmoid(x(1)); % previous estimate of P(o=1|game1)
p0_game2 = VBA_sigmoid(x(2)); % previous estimate of P... |
9dc4197f1d89a494c3bb738883e76abfcb1e2a4121ecee919f03061d832eedcc | MATLAB | 2,484 | 88 | %% Movement
clear
[task_info,supPath] = loadDBAndSpecifyDataPaths('Vermis');
req_params.grade = 7;
req_params.cell_type = {'PC ss', 'PC cs', 'CRB','SNR','BG msn'};
req_params.task = 'speed_2_dir_0,50,100';
req_params.ID = 4000:6000;
req_params.num_trials = 70;
req_params.remove_question_marks = 1;
raster_params.ali... |
4bb0ce074fccd315e410bf5633618df7b2feb62f08f2900d30ad56aca193a5a8 | MATLAB | 2,485 | 56 | function [spike_burst_dyn_table]=well_compute_spike_burst_dynamics_axons_240703(allregion_unit_matched)
%despite the name, this function computes spike dynamics for the wells
% This folder contains the contains the mea structs
%load(strcat(parent_dir,'\','allregion_unit_matched'))
% Parameters
fs = 25e3;
% Initial... |
5765bf6780cd1bf007d222ba627a10d298dc4b2cd4df1c14a53a64b5d4c30019 | MATLAB | 2,488 | 89 | clear
[task_info,supPath,MaestroPath] = ...
loadDBAndSpecifyDataPaths('Vermis');
%load('sessionMap.mat')
K_FOLD = 10;
EPOCH = 'targetMovementOnset';
req_params = reqParamsEffectSize("saccade");
raster_params.align_to = EPOCH;
raster_params.time_before = 0;
raster_params.time_after = 800;
raster_params.smoothing_... |
593da9f742dac55bdcac6d819052dd14e25d65075a3eb60989943c7589269b30 | MATLAB | 2,489 | 90 | %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 create... |
301aa0cf404f384996944a3ba0d2efc10cf77c8cb72fc87088787e7b9d4f260d | MATLAB | 2,492 | 89 | function [hf, hp] = plotFilters(K)
% function [hf, hp] = plotFilters(K)
plot_all = 1;
color = 'k';
nCols = 3;
dt = K.dt;
%% 5. Plot resulting filter
hp = [];
hf = figure;
tf = (0:length(K.PH)-1)*dt;
tf_EP = (0:length(K.EP)-1)*dt;
tf_vis = (0:length(K.PR_vel)-1)*dt;
tf_tar_sine = (0:length(K.PT_vel)-1); % TODO: Ch... |
6e8f7914a70f612d744bbcd5b1a9443b73475255bf7e789f50c43930435f50c5 | MATLAB | 2,492 | 70 | function chnName = getChnName(numChn)
if numChn == 30
chnName = {'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',...
'S3D3 Ant... |
9aef8b8cc0e54c2eae9a488e14c4d7334793aeba83ec6b6a65427734805fb89f | MATLAB | 2,492 | 53 | function vidObj = msAlignment(vidObj)
%MSALIGNMENT Aligns frames from a single experiment. Aligns using the
%middle frame as reference.
%First click and drag to select ROI.
%Algorithm runs in a parfor loop to speed up alignment
hShift = nan(vidObj.numFrames,size(vidObj.alignmentROI,2));
wShift = nan(vidObj.nu... |
06beba62cf459fe0b71b08c564a8d6523f1ec9baed61dede9e3b9ebb57266f06 | MATLAB | 2,494 | 113 | function [out1] = getAppDir(inp1)
out1 = [];
ns = getNamespace();
if isempty(ns)
return;
end
if strcmp(ns, 'AtlasViewerGUI')
if nargin == 0
[out1] = getAppDir_AtlasViewerGUI();
elseif nargin == 1
[out1] = getAppDir_AtlasViewerGUI(inp1);
end
elseif strcmp(ns, 'Homer3')
if nargin == 0
... |
49ad4cd00bd53bc201651c2b34b70c300ca837731fa0adcd1bb1d28ac01b6b4b | MATLAB | 2,494 | 63 | %% Plot indirect pathway nuclei coloured according to fMRI-MIC LME coefficients (Figure 4B)
repo_path = 'Path_to\Manuscript_repository';
funcs_fpath = fullfile(repo_fpath, 'plotting\matlab_funcs');
addpath(funcs_fpath);
addpath('Path_to\wjn_toolbox_tsbinns');
addpath(genpath('Path_to\spm12'));
addpath(genpath('Path_to... |
0248fbde60a8f97e7b719eed15b6f9b0c68523d85d398ad617edb9758bb075ad | MATLAB | 2,496 | 67 | function [displacementField] = create_nonrigid_displacement_field3d(fieldSize,varargin)
% CREATE_NON_RIGID_DISPLACEMENT_FIELD3D Creates a 3D non-rigid displacement field
%
% [displacementField] = create_nonrigid_displacement_field3d(fieldSize)
%
% INPUT ARGUMENTS
% fieldSize - [rowSize colSize rodSize], ass... |
b956489197194a6fc644b87f71b860968e38b8b5dc542c97c6fcec0ee213c2bd | MATLAB | 2,501 | 84 | function [binActivity] = difine_bin_activity(NeuronSL,traj,Lap,binNum,thetaR)
%DIFINE_BIN_ACTIVITY
% Default bin number: 48
% Default starting postition(theta) 0.8634
% if nargin <=4
% thetaR = 0.8634;
% if nargin <=3
% binNum = 48;
% end
% end
if floor(binNum)~=ceil(binNum)
di... |
29ad86870ca6c6ac9e782589d720171f4ae54fd45a2fcf08fc46f93f9a886eb3 | MATLAB | 2,502 | 88 | %DEMMLP1 Demonstrate simple regression using a multi-layer perceptron
%
% Description
% The problem consists of one input variable X and one target variable
% T with data generated by sampling X at equal intervals and then
% generating target data by computing SIN(2*PI*X) and adding Gaussian
% noise. A 2-layer network ... |
443e3896582fc8aeaa709e17e29353255d477673c438c1a0a328adddc7092eaa | MATLAB | 2,505 | 82 | function epoch_movie( ...
x, row, col, color_axis, plot_axis, Fs, save_option, normalized_flag, bound )
% *WAVE*
%
% EPOCH MOVIE image/surface animation of a wave propagation epoch
%
% INPUT:
% x - datacube (r,c,t)
% row - row in x for channel plot
% col - column in x for channel plot
% color_axis - caxis for im... |
b9e41c928171a5eeb47d9f8432a1a4038216480ffbc0139209efbc9783e41254 | MATLAB | 2,507 | 112 | %function [ptab,map] = MultimodalTest(data, max_k, Verbose)
%Silverman's method for testing succesive hypotheses
%that a dataset has 1, 2, 3,..., max_k modes.
% from A Sirota toolbox, changed by Adrien Peyrache, 2011
function [ptab,htab] = MultimodalTest(data,max_k)
hrange = [0.001:0.001:1];
boot_iterations=200;
%ma... |
f15acef9cf447b4be8b43f23f9e1cb7082525e9e0c4825d687a01ca2b833d322 | MATLAB | 2,509 | 99 | %% Figure 1A - Parametric stimulus sheet
% - Size: 12x9
%
% - Horizontal grating background
% - Target rotates from 0 to 90 deg in phase in 15 deg steps
TCONT = 12/100;
BGCONT = 12/100;
BGCONT2 = 24/100;
TORI = [0 15 30 45 60 90];
yy = (0:360*8)';
canvas = repmat(BGCONT *cosd(yy),1,numel(yy)*num... |
e362df299f5c494a724b1e66fb30b87a98f101ac31214f3b08cc6eb820f9286e | MATLAB | 2,511 | 78 | function [d, state, num, val, mindist] = analyze_cluster(clus, dim, inside, targetindx, sgn)
% ANALYZE_CLUSTER analyzes the array of clusters clus and returns for each element
% the following quantities:
% d = the euclidean distance between the pair of voxels closest to the target pair,
% and the target pai... |
014f719c61745907057694c32f8af7f278c6c1e88804a6d2527d30c4fb84d6f5 | MATLAB | 2,515 | 101 | function [err,v, typ] = BrikInfo_SectionValue (BRIKinfo, sSection)
%
% [err,s, typ] = BrikInfo_SectionValue (sHead, sSection)
%
%Purpose:
% Get the values of a section in the .HEAD string
% This function is dedicated for BrikInfo function
%
%Input Parameters:
% sHead, a string vector containing the .HEAD inf... |
0c0d85ded7502e0afc90847d1f3a21b6b991fb311e49619433ee927e9127e646 | MATLAB | 2,515 | 80 | function [R,EX,EY,EZ] = fiff_getpos(file,coord_system,calfile,z_offset)
% Copyright (c) 2016, Elekta Oy
% ---------------------------------------
%
% Redistribution and use of the Software in source and binary forms, with or without
% modification, are permitted for non-commercial use.
%
% The Software is provided "... |
a85e6d2c668232aef8503a50174574ecbebcdcd9eaaac18fe04fac0017e2de52 | MATLAB | 2,515 | 81 | % @title Generate optogenetic stimulation pattern
% @file gen_opto_stim.m
% @author Romain Beaubois
% @date 14 Aug 2021
% @copyright
% SPDX-FileCopyrightText: © 2021 Romain Beaubois <refbeaubois@yahoo.com>
% SPDX-License-Identifier: GPL-3.0-or-later
%
% @details
% > **06 Aug 2021** : file creation... |
9e42ab3787803b20fe5d0242e30ace79e3bd828be6fa1713aa9fe8fb4869d76d | MATLAB | 2,521 | 82 |
function [groups, group_names] = createGroups(data,epoch,ind,...
prevOut,velocityInsteadReward,numCorrectiveSaccadesInsteadOfReward)
PROBABILITIES = 0:25:100;
if strcmp(data.info.task,'rwd_direction_tuning') % FLOCCULUS TASK!
[~,match_d] = getDirections (data,ind,'omitNonIndexed',true);
[~,match_p] = get... |
a93d4b635ad21ce6984ac0cadec0f0fc51baaad626883e03b0832f70fcb74876 | MATLAB | 2,521 | 90 |
clear
[task_info,supPath,~,task_DB_path] = loadDBAndSpecifyDataPaths('Vermis');
WAVEFORMS_PATH = [supPath '\waveforms'];
WINDOW_SIZE_BEFORE_PEAK = 40;
WINDOW_SIZE_AFTER_PEAK = 40;
BASELINE = 10; % 10 sample from min point
Fs = 40; % kHz
PLOT_CELL = 0;
req_params = reqParamsEffectSize("both");
req_params.cell_type =... |
cd830c4aa32792c4bbecc3463df5a94d8226de7b790f9ff46138026ff1a005f6 | MATLAB | 2,521 | 80 | function [R,EX,EY,EZ] = origheader_getpos(info,coord_system,calfile,z_offset)
% Copyright (c) 2016, Elekta Oy
% ---------------------------------------
%
% Redistribution and use of the Software in source and binary forms, with or without
% modification, are permitted for non-commercial use.
%
% The Software is prov... |
65c9a294b7cf1ca84ef8199a5248e8a9a762e2d21b5c44e431b93d2648c2b06c | MATLAB | 2,523 | 56 | % Extract single HRF from dcNew variable, the output of GLM fitting function
% save to mat file.
% For experiment 08 and 10
% For allSSingle and allSMultiple, numClasses is predetermined.
% stimTr
% stimTr(1,1) = leftOnsetSTr;
% stimTr(1,2) = rightOnsetSTr;
% stimTr(1,3) = centerOnsetSTr;
% stimTr(1,4)... |
d4f56e3185a621119adfe89efd186add498247f37872cdfe5527c9d40b7dd7f3 | MATLAB | 2,524 | 84 | function [guess, combinations]=find_bpn1(sv_var,coords,C,final_time)
% OPTION 2:
% std of the whole trace
% for i=1:size(coords,1)
% stds(i)=std(sv_var(i).coords(:,5));
% end
% [~, sorted_ids]=sort(stds,'descend');
%
% % sort according to highest stds
% guess=[];
% selection=sorted_ids(1:60);
% sort a... |
d61a4fbec4bfa0259db144af772cf34b476f57f21c6bcecc6ddac23e5e647873 | MATLAB | 2,529 | 47 | function [cTable,angles] = generate_cTab_loc(cData)
%% NOTE: This version is after correcting the error in classifying invalid responses
angles = unique(cData(:,6));
if angles(1,1) == 0
angles = angles(2:end,1);
end
cTable = zeros(5,5,size(angles,1));
resp = [3 4 1 2];
for tr = 1:size(cData,1)
... |
a64ac60e6ef04cf8de9df1a9b8d0558930dfeb752db7700137f1ca2ec5f0fb4f | MATLAB | 2,532 | 143 | function [xy, c_lo, c_hi] = nancorrcoef(varargin)
%[xy, clo, chi] = nancorrcoef(x,y, alpha) Correlation coefficients.
% NANCORRCOEF(X) is a matrix of correlation coefficients formed
% from array X whose each row is an observation, and each
% column is a variable.
% NANCORRCOEF(X,Y), where X and Y are column vec... |
b648e7d8abc7cf197aa9a96376ab2e1053f9b7d49d71b7775854df5ec4bbbe18 | MATLAB | 2,534 | 83 | %close all; clear all; clc;
Act = @(MaxAmp, Alpha, Tau_r, Tau_f, t) MaxAmp * Alpha * (exp(- t/Tau_f) - exp(- t/Tau_r)) /(Tau_f - Tau_r);
% Act = @(MaxAmp, Alpha, Tau_r, Tau_f, t) MaxAmp * Alpha * (exp(- t/Tau_f) - exp(- t/Tau_r));
SamplingRate = 1000; % times per second
% T = (0:TimeLength-1)/SamplingRate;
T = -... |
573978c5878c6b1f230a7ac844cbcdad0bc4d3feb9c454de10f4896233eafba8 | MATLAB | 2,536 | 91 | function [nb_corr,nb_significance] = NBCorrFunction(data,...
raster_params, bin_sz, task, varargin)
DIRECTIONS = 0:45:315;
PROBABILIES = [25,75];
OUTCOMES = [0,1];
ii = inputParser;
defaultShiftControl = false;
defaultPlotZScores = false;
defaultSeperateByPrev = false;
addOptional(ii,'shiftControl',defaultShiftCo... |
dad8f9870940f859194001000fee148f7d5550242098051402546feaa064297f | MATLAB | 2,537 | 102 | % MA_CORR moving average correlation between multiple signals.
%
% CC = MA_CORR( X, M )
%
% pearson correlation computed in a moving average window of M samples
%
% 1. if input is matrix, works on columns
% 2. NaNs are NOT taken care of
% 3. zero phase lag
%
% calls firfilt
% 09-sep-13 ES
% work... |
1d2b992709c4f068b92b5a1138322059470aa0c518942b5776eed0dfd55c6327 | MATLAB | 2,542 | 96 | function PrintSystemInfo(logger, appname, varargin)
if ~exist('logger','var')
logger = [];
end
if ~exist('appname','var') || isempty(appname)
appname = {'Untitled'};
end
logger = InitLogger(logger, 'SystemInfo');
platform = ['R', version('-release')];
if ~iscell(appname)
appnames = [appname; getLibnames(a... |
6636c71f9990d3761e3c1b7d6fb2fef4450cd40262d38102cc8a729efb4a0e2c | MATLAB | 2,543 | 79 | % Runs various limited-memory solvers on 2D rosenbrock function for 25
% function evaluations
maxFunEvals = 25;
fprintf('Result after %d evaluations of limited-memory solvers on 2D rosenbrock:\n',maxFunEvals);
fprintf('---------------------------------------\n');
fprintf('x1 = %.4f, x2 = %.4f (starting point)\n',0,0)... |
0d59444171e9c931e1bccaf70d0c08f2226cd6cbbf4d4e0cb3921ce2c0d8908a | MATLAB | 2,544 | 63 | function [cfg, Xtrain, Ytrain, Xtest, Ytest] = mv_select_train_and_test_data(cfg, X, Y, train_indices, test_indices, is_kernel_matrix)
% Splits the data into training and test set.
% Also selects data provided as preprocessing parameters if the .data field
% in cfg.preprocess_param is not empty (e.g. .signal and .nois... |
c7ae70c5fa7f6a8b31ffe685d3b6fc6a20fcee9c6eb28ef1efaa388b8846dc06 | MATLAB | 2,548 | 83 | function r = MD_vmrnd(mu, ka, N)
% _
% Random Numbers from a von Mises Distribution
% FORMAT r = MD_vmrnd(mu, ka, N)
%
% mu - the mean of the von Mises distribution
% ka - the precision of the von Mises distribution
% N - the number of samples to be drawn
%
% r - an N x 1 vector of von Mises random ... |
5c2978e5d811fc33da2e224ce2ad9d7b4273faf3c209a71274d3806e030a5487 | MATLAB | 2,550 | 73 | function [analysis_complete, predictions, results, first_perm] = pSVR_check_progress_permute(sub_id, p)
% function [analysis_complete, predictions, results, first_perm, first_tr] = pSVR_check_progress_permute(sub_id, p)
%
% Checks whether a final result file or temporary result file for the
% current subject has alrea... |
42d56dce7cdacd497082da922d94e693249bee35b0ae27d7b8c80914968fa364 | MATLAB | 2,552 | 103 | %DEMGLM2 Demonstrate simple classification using a generalized linear model.
%
% Description
% The problem consists of a two dimensional input matrix DATA and a
% vector of classifications T. The data is generated from three
% Gaussian clusters, and a generalized linear model with softmax output
% is trained using i... |
d75f4a7343b7b416c7b8495fb97538952de6515bff7f55384d198466bae6dd28 | MATLAB | 2,552 | 78 | function bhvr = bhvr_analyse(bhvr, p)
% function bhvr = bhvr_analyse(bhvr, p)
%
% Runs an analysis on the behavioral data of the MRI experiment, by fitting
% a von Mises mixture model (vMMM) to the distribution of the response
% errors. The vMMM consists of 3 components: detections (response to the
% target), swap err... |
705cdda9e3ff4c5e3e0e2ce8f247c48da163dfc2e2865baa819ca3d0e8d80c0d | MATLAB | 2,554 | 66 | function varargout = iif(varargin)
% out = iif(conditions_actions)
%
% This is an inline form of "if", allowing one to use "if" as a function.
% Generally, the form looks like:
%
% iif(<if this>, <then that>, ...
% <else if this>, <then that>);
%
% That is, the odd inputs are treated as conditions and the ev... |
27b8a23f1ed1e81ec3a236a3f162fc08d9b8fc96c5739523ecb2db5b589d5ada | MATLAB | 2,563 | 77 | function [subj] = load_spm_mask(subj,new_maskname,filename,varargin)
% Loads an NIFTI dataset into the subj structure as a mask
%
% [SUBJ] = LOAD_ANALYZE_MASK(SUBJ,NEW_MASKNAME,FILENAME,...)
%
% Adds the following objects:
% - mask object called NEW_MASKNAME
%
% License:
%==============================================... |
9e1e49a9cdd1f56b0216aedd289ae918fac3b58c82b08dfdeb91436555fc0cec | MATLAB | 2,563 | 82 | %% Plotting goodness-of-fit p-val, median split of sessions based on jackknife estimates of sensitivity and bias
%% Code
Trials = ["All","RF"];
split_sessions.parameter = ["sens_cued","LR_cued","sens_opp","LR_opp","sens_cued_mi","LR_cued_mi","sens_opp_mi","LR_opp_mi"];
sessions_list = {[4:15 18 20:24] , [34:45]};... |
b4f7e24129873fe6e5fa235a802b066efb0e1e29a21182bc9d3c3777af0cbf76 | MATLAB | 2,563 | 81 | function [objno] = get_number(subj,objtype,objname,varargin)
% Internal function - find the cell index of an object
%
% OBJNO = GET_NUMBER(SUBJ,OBJTYPE,OBJNAME,...)
%
% % Looks into the subj structure for the object cell called
% OBJNAME and return its index. Returns an error if no
% pattern with this name is found (u... |
c721ba19e6ff41dc1bfff3c9906695ecf03154bfe5ded4a895d32116a49a33af | MATLAB | 2,564 | 86 | function [same,errmsgs,warnmsgs] = compare_struct(s1,s2,errmsgs,warnmsgs,struct_name)
% Are two structs equal?
%
% [SAME,ERRMSGS,WARNMSGS] = COMPARE_STRUCT(S1,S2,[ERRMSGS],[WARNMSGS],[STRUCT_NAME])
%
% Feed in two structs, S1 and S2 to see if they're
% equal.
%
% ERRMSGS and WARNMSGS are cell arrays, as per standard u... |
e6279771b868f586ea9897a6029c3aedd5c4e5280c2be976620aaa0341404c6b | MATLAB | 2,564 | 66 | function ec2Instance = awsEC2RunstructureToInstance(ec2RunStructure, id, dns)
% This function converts ec2Runstructure to ec2Instance, setting up pem and
% permissions.
% USAGE:
% awsEC2RunstructureToInstance(ec2RunStructure, id, dns)
% INPUTS:
% - ec2RunStructure - see awsEC2BuildRunStructure for details
% ... |
73c6f557de1aaed2f15031052cb041ebc02cd0bf61a28dd76e01a3d61ec4814f | MATLAB | 2,566 | 68 | function skullstrip()
clear cfg
cfg = load('neurovar_cfg.mat').cfg;
for i_sub = 1:44
fprintf(strcat('Skullstrip Sub_', string(i_sub),'\n'));
%% DARTEL apply to rc1
clear matlabbatch
matlabbatch{1}.spm.tools.dartel.mni_norm.data.subj(1).flowfield = ...
{select_nii_anat(cfg, i_sub, 'u_rc1')};... |
f66bfa76d8ca7e4d30ff8ed0e2f810787a96521ddcd3e580247bf52ce5077c2b | MATLAB | 2,569 | 72 |
sbjGrp = 1;
if sbjGrp == 1
sbjGrpStr = 'HighPerf';
elseif sbjGrp == 2
sbjGrpStr = 'All';
end
% For GrpAsSbj
% number of different conditions
numCond = 3;
% number of source-detector channels
numChn = 36;
numChnWOSS = 30;
% HbO, HbR, HbT
numConc = 3;
sizeOneCond = numChn*numConc;
procDataDir = 'C:\Users\mn0... |
6abd38323b3d543fe70e3507d42d8caeeaff7353e41146f8a43f971091b80e2f | MATLAB | 2,571 | 89 | function axh = PlotISet(IS, varargin)
opt_varargin = varargin ;
defined_options = dictArray({ {'MarkerSize', {10, {'numeric'} } }
{'Marker', {'k.', {'char'} } }
{'MarkerMultiColor', {0, {'numeric'} } }
... |
99425a2f35fb7719d95c904f6809ef7acea6778fdd21cb7d58e25de7e2b66891 | MATLAB | 2,571 | 83 | function [ zfilt ] = gaussfilt( t,z,sigma)
%Apply a Gaussian filter to a time series
% Inputs: t = independent variable, z = data at points t, and
% sigma = standard deviation of Gaussian filter to be applied.
% Outputs: zfilt = filtered data.
%
% written by James Conder. Aug 22, 2013
% convolution for u... |
3aff73c8eda81b46cd1c7ac146485ddb6cd81a6263dc943bd112d31985b3e40f | MATLAB | 2,575 | 76 | function warp_rois_to_subject_space(p, roi_name)
% function warp_rois_to_subject_space(p, roi_name)
%
% Warps ROI probability maps into subject space, thresholds at 0.1 and
% creates binary masks. Please note: the output files in subject space will
% be prefixed with 'w' according to SPM convention.
%
% Input:
% -... |
4bd23d7002045535a6a3d5238dd5449b666983c560f7bbd9f86a312ce7e675bb | MATLAB | 2,575 | 57 | function [regFrame,dv0,cx] = return_offsets_phasecorr_batch(frame,ops)
% Do phase correlation registration (sub-pixel with kriging) on a single
% frame using pre-calculated registration variables (ops)
% use with setup_registration_phasecorr.m
% e.g:
% [ops] = setup_registration_phasecorr(refImg)
% [dv0,cx] = return_o... |
a9eab1103ab953a56f58b6aaf5df8f197822ef5910a7609cc65e2d260feafa50 | MATLAB | 2,575 | 91 | %function thrun = thetarun(FileBase,Periods,frin,frout,ch2use)
% runs automatic Gaussian HMM segmentation of the period of recording
% given by Periods in seconds
function thrun = thetarun(FileBase,varargin)
load([FileBase '.eegseg.mat']);
Par = LoadXml([FileBase '.xml']);
[Periods,frin,frout,ch2use] = DefaultArgs(vara... |
def450968bc33adf5ceaef280095074acbcef82123bb4895a8120e49f25d00cf | MATLAB | 2,577 | 64 | function [nwbAll, importLog] = NWB_importFromFolder_SBCAT(datasetPath, importRange)
%NWB_importFromFolder Returns a cell array containing all imported nwb
%files in the specified folder and import range.
% pathIn: Path to desired folder
% importRange: Range of SB/SC IDs to import.
%
% mkyzar 4/15/2023
%% Compil... |
3c8d85655ea8fe153aaeb320659e334895adc3f5bfa30587453c6d168e01c1f4 | MATLAB | 2,578 | 76 | % MY_XCORR compute the column-wise cross-correlation between real matrices.
%
% call CC = MY_XCORR( X, Y, MLAG, NORMF )
%
% gets X matrix, trials in columns
% Y {X}; another matrix of same dimensions; if
% l... |
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