sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
46fa3caf350b85955e64df11fa215a01020f30c5cbbd623ebd6640e7ce2d3f38 | MATLAB | 1,745 | 52 | function preview_all_structures_button_pushed(app)
%% First find structures for all sLambda
tempParams = app.params;
numSLambda = length(tempParams.s_lambda_sequence_glm);
wb = CmdLineProgressBar('Learning All Structures'); %feedback
fprintf('\n');
tempParams.raw_coef = cell(1,numSLambda);
tempParams.learned_struc... |
5d2f6131c15b9c78f3fef64112b511bbae77b1ce3a5b95e1a8aebdafc1e21a1b | MATLAB | 1,747 | 27 | %% Result 1 : Long-Range Connections Enhance Object Recognition in Shallow Networks (Figure 2)
% ============================================================================================================
%% Add LRC vs Add Local & Fully-connected networks (Figure 2d)
disp('======================================... |
31415ee6e9efb73dc659a6d6789a2613fb43f611f7f9f59eea479474923f7c15 | MATLAB | 1,751 | 71 | function unlock_buttons(app, stage)
if nargin == 1
stage = app.params.stage;
end
if stage >= 2
% current tab
app.PreviewPassedStructuresButton.Enable='on';
app.PreviewAllStructuresButton.Enable='on';
% next tab
app.PerformParameterEstimationButton.Enable = 'on';
app.OptimizeButton.Enable=... |
e1b67fda9c46ad97df72538184099059344ac30beeec1a67daffa0b820d77c67 | MATLAB | 1,751 | 56 | function [sensor_positions, sensor_orientations, sensor_ind, sensor_tag_cell] = zef_bst_2_zef_sensors(varargin)
sensor_counter = 0;
sensor_num = 0;
istudy = [];
sensor_type = [];
sensor_positions = [];
sensor_orientations = [];
sensor_ind = [];
sensor_tag_cell = cell(0);
scaling_constant = 1000;
if not(isempty(vararg... |
1e6183e992c32e4a850aac92ff64ad8e113ba712e08730746b46b871ef19f7d6 | MATLAB | 1,754 | 39 | function [decomposition_ind, decomposition_count, dof_positions] = zef_decompose_soure_space(source_count, center_points)
min_x = min(center_points(:,1));
max_x = max(center_points(:,1));
min_y = min(center_points(:,2));
max_y = max(center_points(:,2));
min_z = min(center_points(:,3));
max_z = max(center_points(:,3));... |
b0c828a8ad0e7cf4ed977f8bb760563a5d7d3c1ba01957f1d69a5ef635a646a8 | MATLAB | 1,755 | 58 | function [stim, noiseRMS,total_dur] = IACtSquare_ToneDetect(fs, BPfilt,WindowSize,SNRdes,StaticIAC)
%stim is 3AFC with the 3rd one being correct
%SNR in dB
%StaticIAC should be empty, 1, or 0 for this experiment
%window size in seconds
if isempty(StaticIAC)
total_dur = 0.8+WindowSize; % 400 ms of uncorrelated no... |
5de49026ae64fd168fa2d966f0ea7e44b705ad9891dfff2bf28405869063a2ac | MATLAB | 1,758 | 29 | function zef = zef_ES_optimizer_properties(zef)
if nargin == 0
zef = evalin('base','zef');
end
zef_data = zef_ES_optimizer_properties_app;
zef.h_ES_optimizer_properties_copy_all = zef_data.h_ES_optimizer_properties_copy_all;
zef.h_ES_optimizer_properties = zef_data.h_ES_optimizer_properties;
zef.h_ES_o... |
e83cfcd657bac1b2c84e4a406c8322c79f9f04da4a9d00b3121c323df79ee41f | MATLAB | 1,759 | 51 | function zef = zef_ES_optimizer_properties_show(zef)
if nargin == 0
zef = evalin('base','zef');
end
zef = zef_ES_optimizer_properties(zef);
vec = zef_ES_table(zef.y_ES_interval);
[sr, sc] = zef_ES_objective_function(zef);
for i_aux = 1:width(vec)-1 % Last one are the ES channels.
zef.h_ES_optimizer_proper... |
48654c865544ac615fc9d45949766484d9daf234de9fee94395c5bde537ebec7 | MATLAB | 1,761 | 45 | %Copyright © 2021- Sampsa Pursiainen & GPU-ToRRe-3D Development Team
%See: https://github.com/sampsapursiainen/GPU-Torre-3D
load([torre_dir '/system_data/signal_configuration.mat']);
load([torre_dir '/system_data/mesh_1.mat']);
load([torre_dir '/signal_data/simulated_data_1.mat'])
system_setting_index = 1;
paramete... |
62e4b084a3d928af341a5aefbc240dd0572171629099584c58b196a3013d549d | MATLAB | 1,762 | 40 | %Copyright © 2018- Sampsa Pursiainen & ZI Development Team
%See: https://github.com/sampsapursiainen/zeffiro_interface
if zef.mlapp == 1
zef.mesh_smoothing_on = zef.h_checkbox_mesh_smoothing_on.Value;
zef.refinement_on = zef.h_refinement_on.Value;
zef.source_interpolation_on = zef.h_source_interpolation_o... |
d68dcd960f3c80d30ba5e3f57196369159247465c4ba8ef2a17b2fe8bee265bd | MATLAB | 1,763 | 76 | % [] = XXXX()
%
% -----------------------------Definition---------------------------------%
%
% description
%
% usage :
%
% -----------------------------Input--------------------------------------%
%
% XX : description + dimension
%
% -----------------------------Output-------------------------------------%
%
% XX : d... |
21c5131111422cb69b2ff8d5fdb45449f31164fccc68658b62d36e8906c87f53 | MATLAB | 1,764 | 65 | function [p,acc,confusionmat] = networks_mvpa(nets) ;
% If multiple participtants, append
if size(nets,1)>1
for i = 1:size(nets,2)
if isempty(nets{1,i})
j = 1 ;
while isempty(nets{j,i})
j=j+1 ;
end
nets{1,i} = nets{j,i} ; nets{j,i}... |
ee3462d8aed60f09413670b6d41d6f17113327df6528e099575993f4ce1cb8cc | MATLAB | 1,769 | 48 | nodes_old = zef.nodes;
nodes = zef.nodes;
N = size(nodes,1);
taubin_lambda = 1;
taubin_mu = -1;
smoothing_param = 0.25;
[zef.sensors_attached_volume] = zef_attach_sensors_volume(zef,zef.sensors);
L = zef_electrode_struct;
if not(isempty(L))
C = [];
for electrode_ind = 1 : length(L)
C_sparse = sparse(... |
257b9dc8df419bb42c616cfc3c329b133831922de736f0c35b4e396dabb7ce88 | MATLAB | 1,773 | 37 | function zef = zef_strip_tool_add_contacts(zef)
points_1 = zef.([zef.current_sensors '_points']);
impedance_1 = zef.([zef.current_sensors '_electrode_impedance']);
inner_radius_1 = zef.([zef.current_sensors '_electrode_inner_radius']);
outer_radius_1 = zef.([zef.current_sensors '_electrode_outer_radius']);
strip_stru... |
59eb6267ed22b4e22f6a5a446419c5367c31607334cdde57115ba729d09aadf8 | MATLAB | 1,773 | 63 | % For the real data experiments in:
% Vidaurre et al (2018). Temporally unconstrained decoding reveals consistent
% but time-varying stages of stimulus processing. Cerebral Cortex
% check:
% https://github.com/vidaurre/CC2018
%% Example for a continuous response that varies across time
addpath(genpath('.'))... |
3b0190343a07f2de8f2c0b05e2fe9e14ce8c37dca4c2a000f39d85f6ffdd4d8d | MATLAB | 1,780 | 52 | function covmats_unflipped = getAllCovMats(data,T,options)
if ~isfield(options,'maxlag'), options.maxlag = 10; end
if ~isfield(options,'partial'), options.partial = 0; end
if ~isfield(options,'standardise'), options.standardise = 1; end
N = length(T);
if iscell(data)
for j = 1:N
if ischar(data{j})
... |
d60f3308989be57177ecdb2ebe4611b4631ac9a1df4eb34b52831497ebc18a7a | MATLAB | 1,780 | 63 | function [P_store, z_inverse] = triple_kf_sL(m,P,A,Q,L,R,timeSteps, number_of_frames, smoothing, sL,standardization_exponent,burn_in)
P_store = cell(0);
z_inverse = cell(0);
h = zef_waitbar(0,1, 'Filtering');
zero_m=zeros(size(A,1));
A=cell2mat({A,(1/number_of_frames)*A,(1/2)*((1/number_of_frames)^2)*A;zero_m,A,(1/nu... |
93b92566b1134f7b891f6531ad237607c8a17844efba33d505ba155adb19e983 | MATLAB | 1,781 | 55 | function [z_inverse] = EnKF(m, A, P, Q, L, R, timeSteps, number_of_frames, n_ensembles)
%ENKF Summary of this function goes here
%x_ensemble = mvnrnd(zeros(size(m)), 100* ones(size(m,1)), n_ensembles)';
x_ensemble = mvnrnd(zeros(size(m)), P, n_ensembles)';
z_inverse = cell(0);
h = zef_waitbar(0,1, 'EnKF Filtering');
fo... |
184b56aea208b8864e9d33dcd7ac17ce66af5a83991b21f2aa2901fc4cb1a137 | MATLAB | 1,782 | 58 | function rids = realign_all_niftis_nacc(outputfolder, suffix)
%% main
if nargin < 1
outputfolder = '/data2/MRI_PET_DATA/processed_images_final';
end
if nargin < 2
suffix = '';
end
MRI_folder_init = '/data2/MRI_PET_DATA/raw_data/NACC_MRI';
fnames = dir(MRI_folder_init);
fnames = {fnames.name};
rids = arrayfun(@... |
63921fca237e650edf17a9bfb003e48472c26fe9d15a9f8c08b74670fcf0e0d6 | MATLAB | 1,782 | 39 | %% Result 2 : Long-Range Connections Integrate Global Features (Figure 3)
%% Classification performance for each dataset with a variation of the LRC ratio (Figure 3c)
disp('=================================================================')
disp('Result2 : Accuracy for ''Shape'',''Position'',''Shape Position''')... |
1b1f65f24f098efaf445ef6c697921a1f6a97f7bc430f0336532c087e70adf09 | MATLAB | 1,784 | 90 | function [vertex,face] = read_vtk(filename, verbose)
% read_vtk - read data from VTK file.
%
% [vertex,face] = read_vtk(filename, verbose);
%
% 'vertex' is a 'nb.vert x 3' array specifying the position of the vertices.
% 'face' is a 'nb.face x 3' array specifying the connectivity of the mesh.
%
% Copy... |
ca598a062093afc1f47a5716f481b3b076d3530bd23397b857560fd8913d968b | MATLAB | 1,784 | 60 | function [vecTime,vecIdx,vecDuration,vecEnergy,vecSlope] = findsustainedpeaks(vecData,vecT,intMaxPeaks,dblCutOffZ)
%findsustainedpeaks Returns the left-hand onset time of sustained peaks. Syntax:
% [vecTime,vecIdx,vecDurations,vecEnergy,vecSlope] = findsustainedpeaks(vecData,vecT,intMaxPeaks,dblCutOffZ)
%
%Requi... |
96e0c56e20385fe7dec3942477a6bfdf9f7e847e722e566d9c8c83acc96028de | MATLAB | 1,786 | 62 | function [D] = wishart_kl (arg1,arg2,arg3,arg4)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% [D] = wishart_kl (B_q,B_p,alpha_q,alpha_p)
%
% computes the divergence
% /
% D(q||p) = | q(x)*log(q(x)/p(x)) dx
% /
% between two k-dimensional Wishart propability densi... |
ac40f4630a43a5605186e20ba5b43ada3441931b4af4da206c783b94acb85d3e | MATLAB | 1,787 | 46 | function [model_collection, params] = estimate_parameters_parallel_implementation(params, models)
fprintf('\n');
fprintf('Parameter Estimation...\n');
num_models = length(models);
pbar = parwaitbar(num_models,'WaitMessage','Estimating Parameters','FinalMessage','Parameter Estimation Learning Complete');
parfor singl... |
69eb93814e9c588c1eeb9528cfd7a70081ca0b5d469c7112899cdd1ad7aee7c0 | MATLAB | 1,793 | 34 | if zef.nse_field.solver_type == 1
zef = zef_nse_tool_update(zef);
zef.nse_field.microcirculation_model = 0;
zef.nse_field = zef_nse_poisson(zef.nse_field,zef.nodes,zef.tetra,zef.domain_labels,zef.mvd_length);
elseif zef.nse_field.solver_type == 2
zef = zef_nse_tool_update(zef);
zef.nse_field.microci... |
adf05207480694b90d8bbda80bc7b4226607749601493145b3e27451d60c95e7 | MATLAB | 1,794 | 40 | function [Pss_single,Pss_compare]=bsliang_getPvalues(Pstructure)
Pss_single=nan(4,4);
Pss_compare=nan(4,2);
% left_clear
Pss_single(1,:) = [Pstructure.left_clear.tone_iTBS ...
Pstructure.left_clear.tone_cTBS ...
Pstructure.left_clear.phon_iTBS ... |
a1e2dd4875221e65544e037d8040d0f9b84374645a8e7ce207ab36247af2e694 | MATLAB | 1,795 | 75 | function x = EM_Lasso(L,sigma,y,gamma,x0)
%MAP estimate using EM algorithm
%L1 norm minimization problem (LASSO):
%0.5|1/sigma(Lx-y)\|_2^2+0.5*\sum_i [gamma_i |x_i|]
% L : lead field matrix
% sigma: noise variance
% y: measurements
%for gamma->0 we have less sparse solutions...
%Output: x: dipole amplitude
%This co... |
0bdcbc5975a79504fecc1a4ee406ad9e9d7426d7609ad2ec271031cd633ab0b5 | MATLAB | 1,799 | 61 | function mess = CheckRealNumber(x, Bounds, MaxSize)
% CheckRealNumber - checks if the arg is a real number of correct size and value
% SYNTAX:
% mess = CheckRealNumber(x, Bounds, MaxSize);
% where x is the value to be tested;
% Bounds = [lowerBound, higherBound]
% (single value-> [0 Bounds]; not s... |
33e37130c86c9a8ac430824d86afdcbe73dd2a881f27ababb0b3a64cf8082af4 | MATLAB | 1,799 | 62 | loop_indx = 1;
ms_after_stimulus = [14 16 20 22 30];
for frame = [21 31 51 61 101]
frame = num2str(frame);
ms = num2str(ms_after_stimulus(loop_indx));
full_address = 'exportImage';
file_name = ['mneSL' ms 'ms'];
file_full = fullfile(full_address, file_name);
%%
zef.h_reconstruction_type.Va... |
4e5d95c0a30fbc41086494010a192dc5bb724611a27c17d4beef898a612874a7 | MATLAB | 1,805 | 62 | function output = ERBFilterBank(x, fcoefs)
% function output = ERBFilterBank(x, fcoefs)
% Process an input waveform with a gammatone filter bank. This function
% takes a single sound vector, and returns an array of filter outputs, one
% channel per row.
%
% The fcoefs parameter, which completely specifies the Gammato... |
97f34df2c7552ff4baad78cf579dd0c5ec742e5019cabbe5ef9fe5059de13ad4 | MATLAB | 1,805 | 55 | function [hmm] = updatePsi(hmm,Gamma,X,Y)
% Computes the parameter psi for tuning the approximation to the
% logistic function.
[T,ndim]=size(X);
% Gamma = hmm.Gamma; % warning: this is not guaranteed to be the updated Gamma!
K = hmm.train.K;
if length(unique(Y(:)))<3
binomial=true;
else
binomial=false;
end
f... |
01843912bf435b5a060de123951c1923939867b93421d5bff59a6903003dde3a | MATLAB | 1,807 | 38 | zef_init_gaussian_prior_options;
zef_data = zef_gaussian_prior_options;
zef.fieldnames = fieldnames(zef_data);
for zef_i = 1:length(zef.fieldnames)
zef.(zef.fieldnames{zef_i}) = zef_data.(zef.fieldnames{zef_i});
if find(ismember(properties(zef.(zef.fieldnames{zef_i})),'ValueChangedFcn'))
zef.(zef.fiel... |
2902784fb609df5efa9ccaaff6866548deb318b16d14499953ffc10ce5b41130 | MATLAB | 1,807 | 72 |
%--------------------------------------------------------------------------------
% qd_matrix.m
%
% This demo is included in
% Canonical neural networks perform active inference
% Takuya Isomura, Hideaki Shimazaki, Karl J. Friston
%
% The MATLAB scripts are available at
% https://github.com/takuyaisomura/reverse_engin... |
b6201a8ada28def903c56612b2cc390070fd651c36f1a3292f86540b4d2eb858 | MATLAB | 1,809 | 52 | function [z,mu,sigma] = zscore(x,mask,sigma,dim)
%ZSCORE Standardized z score.
% altered to accept external SD for rectified csc/EEG,
% UB 10/11/2014
% Z = ZSCORE(X) returns a centered, scaled version of X, the same size as X.
% For vector input X, Z is the vector of z-scores (X-MEAN(X)) ./ STD(X). For
% ma... |
cb701ca783b2351b72d1628524d1c386ae24c4b3bd042af73ab42883ee10188c | MATLAB | 1,809 | 51 | % import data from *_Igor.mat files (contains ROI data)
% returns penumbra subtracted dF/F0!
function ROIdataStruct = loadROIdata2( path ) % use full path now! localpath )
% get the list of file names
%upath = userpath; % default user directory "MATLAB" contains data directory "ROI data"
if ismac
ext = '*_Igor*.ma... |
ac67bc9cfc31526c885bdcec72e9ba681bb7c103021e7f8ca0f717ba8053c157 | MATLAB | 1,810 | 61 | function [node_performance] = calculate_node_performance(params, log_likelihood_by_frame, best_model, include_testing_in_identify)
if nargin <4
include_testing_in_identify = 1;
end
[~, num_udf] = size(params.udf);
num_node = size(best_model.structure,1);%graph to structure
if include_testing_in_identify == 1
... |
2c6a1579e1ba0ab57d71a54fb2bdcb85937350bbcece2943b84c01449506bdaa | MATLAB | 1,812 | 63 | function sensitivity_map = zef_sensitivity_map_mne( ...
project_struct, ...
weighting_type, ...
n_reconstructions, ...
noise_level, ...
diff_type, ...
dispersion_radius ...
)
arguments
project_struct (1,1) struct
weighting_type (1,:) char { mustBeMember(weighting_type, ['... |
321c543d45f986b48c7a40a1f0cb05c89c649790c48c19d620c3ed841d459ae4 | MATLAB | 1,812 | 66 | function obj = stats_autoinformation(obj,maxlag)
% Calculate autoinformation of microstate sequence
% Check inputs
if isempty(obj.label)
error('To calculation microstate autoinformation, property label is required')
end
if nargin < 2
maxlag = 500 ; % 500 samples
end
if m... |
5b9f18c28939ce20e1f2c362a45f83b65f609ab5c56d2106e528bf395eec6c9f | MATLAB | 1,815 | 52 | function zef_set_sliders_print(mode,h_axes_image)
h_figure = h_axes_image.Parent;
if mode == 1
h_axes_image.Colormap = zef_colormap(evalin('base','zef.h_update_colormap.Value'));
zef_update_ambience(h_figure);
zef_update_diffusion(h_figure);
zef_update_specular(h_figure);
zef_update_colorscale(h... |
26605d34e07a1263e0779235b38281a09e60c6621397bcd19193ebafb6768319 | MATLAB | 1,816 | 66 | function [bcR, p, T, df] = bcdistcorr(x, y)
% BCDISTCORR computes the bias corrected distance correlation
%
% [BCR,P,T,DF] = BCDISTCORR(X,Y), where X (size n-by-p) and Y (size
% n-by-q) are n random samples of observation. The function returns the
% bias corrected distance correlation BCR and the correspond... |
08fba1b23324b51f099a0da112af35c856abeb0e8f913ac4eb49563ee3ecc8cc | MATLAB | 1,817 | 27 | %Copyright © 2018- Sampsa Pursiainen & ZI Development Team
%See: https://github.com/sampsapursiainen/zeffiro_interface
if not(isfield(zef,'synth_source_ROI'));
zef.synth_source_ROI = [1 0 0 0 1 1 0 0 0 0 0 1 1 0 0 10 0 1 1 3];
end
set(zef.h_synth_source_ROI_style,'value',zef.synth_source_ROI(1,1));
set(zef.h_... |
540bb794f9fd2e88deeb77c4e293160576b3a8ee23e3aee2b33172f09e49c154 | MATLAB | 1,825 | 70 | % Generate the (1) UI and (2) wall-clock computation time vectors for comparing four different methods:
% adm, fmincon with gradient and Hessian, fimincon with gradient only, and fmincon blackbox.
% The admUI_mex executable is to be generated using Matlab 2017a.
% Data for Fig. 2 (top panel)
clear all;
addpath(genpa... |
c79aa0af6898a75fe1a21d02ae9867106d2c522ee2e1b34a1c0d7f1ae3ef9033 | MATLAB | 1,825 | 68 | function ETC_KeyPress(hMain,eventdata)
%dummy for testing
global sFigETC;
if ~exist('eventdata','var')
eventdata = struct;
eventdata.Key = 'r';
eventdata.Modifier = [];
end
% Get guidata
if toc(sFigETC.lastPress) < 0.1;return;end
sFigETC.lastPress = tic;
if strcmp(eventdata.Key,'rightarrow') || strc... |
55c528a5ab000511c3ab10c43245b99b2c99f760306a6f982d75559182d228ff | MATLAB | 1,826 | 72 | % Generate the (1) UI and (2) wall-clock computation time vectors for comparing four different methods:
% adm, fmincon with gradient and Hessian, fimincon with gradient only, and fmincon blackbox.
% The admUI_mex executable is to be generated using Matlab 2017a.
% Data for Fig. 2 (middle panel)
clear all;
addpath(ge... |
8f998bd48bc9fae87764a5ea001d773ec34d3865141b008313d7e70b8462a2f3 | MATLAB | 1,827 | 60 | function stats = extract_stats_segmentation(net, stats)
sel = find(cellfun(@(x) isa(x,'dagnn.LossSemantic'), {net.layers.block})) ;
obj = net.vars(net.layers(sel).outputIndexes(1)).value;
predictions = net.vars(net.layers(sel).inputIndexes(1)).value;
labels = net.vars(net.layers(sel).inputIndexes(2)).value;
L ... |
4d1f4da804415e61c56fe1b7dc5d1187122519e00d53cc38eb21c927ccc74697 | MATLAB | 1,829 | 53 | function Y = vl_nnlossDice(in, out, weights, dzdy)
% VL_NNLOSS CNN log-loss
% Y = VL_NNLOSS(X, C) applies the the logistic loss to the data
% X. X has dimension H x W x D x N, packing N arrays of W x H
% D-dimensional vectors.
%
% C contains the class labels, which should be integers in the range
% 1 to... |
b736ae9e162c07ba9f73c14f882b75059d3499ab73ab50e0909f357272462033 | MATLAB | 1,829 | 47 | function h = structuredROIax( x, y, im, fax, r, deadzone, penumbra, expand )
% x, y position of click, im is an image (usually max projection),
% fax is the target axes for drawing polygons,
% r is radius, offset the gap between ROI and deadzone, and deadzone and
% penumbra
% expand (pixels) expands/shrinks the F... |
f3a92e4ba63c0cbf108893ad3d403160a2b1b53030884c0d6b2725b133e86c1f | MATLAB | 1,829 | 56 | function zef = zef_create_sensors(zef,sensor_tag,field_cell_update)
if isequal(sensor_tag,'s') || isequal(sensor_tag,'s1')
eval(['zef.current_sensors =''' sensor_tag ''';']);
end
zef_struct_name = 'zef';
if nargin == 2
field_cell_update = cell(0);
end
field_cell_default = {{'on', '1'}
{'color', '[0.1 0.1... |
465717de08904429aaf3b4a51acfef8aa3a3fcba17169728d5aa3a7dd63f828f | MATLAB | 1,832 | 71 | % Generate the (1) UI and (2) wall-clock computation time vectors for comparing four different methods:
% adm, fmincon with gradient and Hessian, fimincon with gradient only, and fmincon blackbox.
% The admUI_mex executable is to be generated using Matlab 2017a.
% Data for Fig. 2 (bottom panel)
% ns=ny=nz
clear all;
... |
9410255cec98dd79891f5279760f141bb9a57b3eb646f085a87c81293b90d780 | MATLAB | 1,834 | 18 |
zef_create_dipolar_pair_update_struct;
zef.aux_field = sqrt(zef.create_dipolar_pair_ori_x.^2 + zef.create_dipolar_pair_ori_y.^2 + zef.create_dipolar_pair_ori_z.^2);
zef.create_dipolar_pair_ori_x = zef.create_dipolar_pair_ori_x/zef.aux_field;
zef.create_dipolar_pair_ori_y = zef.create_dipolar_pair_ori_y/zef.aux_field;... |
5cd4db3825324c99a6be5b87a7bbd2519e0111b7aa67aa89b32cbfbfee97aa08 | MATLAB | 1,836 | 42 | function nd = zef_init_jr_nodedata()
%ZEF_INIT_JR_NODEDATA Default NodeData for Jansen–Rit node (descriptive names).
nd = struct();
nd.Model = struct('Name','Model','Value','JansenRit');
nd.OutputGain = struct('Name','Position','Value',0);
nd.SourcePosition = struct('Name','Position','Value',[0 0 0... |
a2e66d662caf49e3521a270ad19f12e0ab17d10fff075182109978bb3e4eb745 | MATLAB | 1,837 | 71 | function [sl_indices, sl_dists, sl_lengths] = zef_dti_nearest_streamlines(points, dti_streamlines, roi_radius)
n_points = size(points,1);
n_streams = numel(dti_streamlines);
total_pts = 0;
for s = 1:n_streams
sl = dti_streamlines{s};
if ~isempty(sl)
total_pts = total_pts +... |
14f74aea2fc7155ed7bc34ae89f54fa76243d254b0bf14435774ee5e7fa4b6b0 | MATLAB | 1,843 | 66 | function g = spike_func_evaluate2(tau, data)
% spike_func_evaluate
% ---
%
% --- data in spike window
% data.y : y(t) (1 x Nt)
% data.Dt : spike onset (# of pattarn) x (# of spike) : (Nconf x Ns)
% data.dt : sampling time step [sec]
% --- time constant of spike response
% tau = [Ą1; Ą2] ... |
af6e2cd50265dac441bd965d9551a00c71667f9e6411e62e3ae93c1eff657df8 | MATLAB | 1,843 | 79 | function hmm = versCompatibilityFix(hmm)
if ~isfield(hmm.train,'orders')
hmm.train.orders = formorders(hmm.train.order,hmm.train.orderoffset,...
hmm.train.timelag,hmm.train.exptimelag);
end
if isfield(hmm,'cache')
hmm = rmfield(hmm,'cache');
end
if isfield(hmm.train,'state')
hmm.train = rmfield(h... |
037fea713a6d94ead312f2d5874b12c417af9b0e3676106582691295e5cdcae9 | MATLAB | 1,844 | 51 | function [binary_structure] = process_structure(coefficients, density, absolute, mode, neighborhoods)
num_nodes = size(coefficients, 1);
if absolute
AbsMat = abs(coefficients+coefficients');
else
AbsMat = coefficients+coefficients';
end
possible_edges = zeros(num_nodes... |
bfbc059fd59a627ebec392fda56b5ae5f15a884286c6ca09bef6296ae26a5dc9 | MATLAB | 1,846 | 56 | function vecReorder = sortmat(matIn,intMaxIter)
%sortmat Sort [n x p] matIn matrix
% vecReorder = sortmat(matIn,[intMaxIter=100])
%
%matIn must be [n x p] and contain more observations (n) than predictors (p)
%uses 2nd PC as initial global ordering, then refines by swapping entries to maximize
%inter-neighbor c... |
6d695cdd23d158b90664d401131a28c0d76292fc7f834458999dc1d1f1c97b20 | MATLAB | 1,852 | 78 | function C = centerOfMass(A,intDim)
% CENTEROFMASS finds the center of mass of the N-dimensional input array
%
% CENTEROFMASS(A) finds the gray-level-weighted center of mass of the
% N-dimensional numerical array A. A must be real and finite. A warning
% is issued if A contains any negative values. Any NaN elemen... |
d2b56bf3812927f127d8614dd515cf34f7bbd1d13a95b183c1852bec1fa0ff61 | MATLAB | 1,853 | 42 | function plot_ensemble_eval_coordinates_style_2(app)
v = app.StimulusEV.Value;
ROI_contours = app.rois;
%ROI_bounds = ROI_contours.bounds;
num_rois = size(ROI_contours.xpix,1);
%xrange = ROI_contours.xrange;
%yrange = ROI_contours.yrange;
%ensemble_colors = jet(numel(app.ensemble_nodes{v}));
newcolors = app.newcolors... |
342307779495f2fe3a746cbf5927d36a9347618a04e6b0d36c9f37c50c53b4c6 | MATLAB | 1,854 | 35 | if not(evalin('base','zef.lock_sensor_names_on'))
if ismember(evalin('base',['size(zef.' zef.current_sensors '_points,2)']),[6,0])
evalin('base',['zef.' zef.current_sensors '_points = [zef.' zef.current_sensors '_points; [0 0 0 0 1 ' num2str(evalin('base','zef.default_impedance_value')) ' ]];']);
else
... |
99b5a7e5c4ac07336459224cea3a53141e397845e5e5d04f93117792ddfa401f | MATLAB | 1,854 | 84 | clear all
close all
clc
addpath('./tools/')
bias_set = linspace(0, 3, 7);
time_today = datestr(now, 'mmddyyyy');
reset_t = 80;
noise_level = 2.0 * 10 ^ (-2);
n=200;
train_t = 150000;
iteration = 10;
rmse_set_lorenz = zeros(length(bias_set), iteration);
rmse_set_circle = zeros(length(bias_set), i... |
acfd5511090af17dd82464118031362332c2912b54503099995fa65b8f5fe91d | MATLAB | 1,855 | 75 | function [pxy,info] = normalizeposition(xy,margin)
%NORMALIZEPOSITION Rescale an NPOINTS×2 array of (X,Y) points to [0,1)²
% [PXY,INFO] = NORMALIZEPOSITION(XY,MARGIN) Expects an NPOINTS×2 array of
% (X,Y) point data as input, and rescales all points to lie in [0,1)²
% with padding fraction MARGIN (If MARGIN is ... |
9080c7f0616d7d5a9b97195244a14d0d9e96ac518cb1120512a6295e44934383 | MATLAB | 1,856 | 57 | function [multigrid_dec, multigrid_ind, multigrid_perm] = zef_make_multigrid_dec(center_points,n_subset,n_decs,n_levels)
multigrid_dec = cell(0);
multigrid_ind = cell(0);
c_ind = [1 : size(center_points,1)]';
multigrid_perm_1 = zeros(size(center_points,1),1);
multigrid_perm_2 = zeros(n_decs*n_levels*n_subset,1);
ind_c... |
1c9bdb379969fd0be27df626eddb2e276205381af0ad0c585c7551371678f9ec | MATLAB | 1,857 | 56 | function [ hex ] = rgb2hex(rgb)
% rgb2hex converts rgb color values to hex color format.
%
% This function assumes rgb values are in [r g b] format on the 0 to 1
% scale. If, however, any value r, g, or b exceed 1, the function assumes
% [r g b] are scaled between 0 and 255.
%
% * * * * * * * * * * * * * * ... |
20ecc76419409d326946f77120e9b42c24f5ca62b6674233b994bfb20ea3cfc0 | MATLAB | 1,857 | 33 | function h2 = drawROI( h, axe )
% h is a handle structure containing AT LEAST .ROI, .deadzone and .penumbra
% these contain coordinates of the actual ROI, deadzone and penumbra
% (NOT THE POLYGON HANDLES!)
% axe is axes handle
msize = 0.1;
lsize = 0.1;
nROIs = size(h,2);
for i = 1:nROIs
h2(i).... |
eb4bc041459c1ce371286f9f2542f2cb2d8d7fadb2fa4eba4663799cb27aad38 | MATLAB | 1,857 | 26 | %Copyright © 2018- Sampsa Pursiainen & ZI Development Team
%See: https://github.com/sampsapursiainen/zeffiro_interface
zef.ramus_multires_n_levels = str2num(get(zef.h_ramus_multires_n_levels,'string'));
zef.ramus_multires_sparsity = str2num(get(zef.h_ramus_multires_sparsity,'string'));
zef.ramus_multires_n_decompositio... |
57deb556dc11273dd51600ede1be52314e6c25cf5ae954d710b8f2fbb416d2f4 | MATLAB | 1,858 | 65 |
%--------------------------------------------------------------------------------
% number_to_hand_digit.m
%
% This demo is included in
% Triple equivalence for the emergence of biological intelligence
% Takuya Isomura
%
% The MATLAB scripts are available at
% https://github.com/takuyaisomura/reverse_engineering
%
% C... |
30c15a1275d4c46717bf2fb2b20339b38f0f40414787e709b653573a062194bb | MATLAB | 1,861 | 64 | %function [vecMuFit,vecSigmaFit,resnorm,residual,exitflag,matFit] = getFitGaussMV(matDataGrid,vecMu0,matSigma0,boolUniform)
%getFitGaussMV Fits multivariate Gaussian to input matrix. Syntax:
% [vecMuFit,vecSigmaFit,resnorm,residual,exitflag,matFit] = getFitGaussMV(matDataGrid,vecMu0,matSigma0,boolUniform)
%
%When... |
07b91e03176cdda30bbbb1d407980de9876a7455ce73355cd07f008e55920125 | MATLAB | 1,864 | 42 | function zef = zef_strip_tool_embed(zef)
cell_aux = zef.([zef.current_sensors '_strip_cell']);
strip_struct = zef_get_strip_parameters(cell_aux{zef.strip_tool.current_strip});
[strip_struct] = zef_create_strip(strip_struct);
points = zef_strip_coordinate_transform(strip_struct,'forward');
strip_struct.compartment_ta... |
1b7338f19fbb94c7efd41582784207d1f4d6b6821624a961f4300eec5d3b3373 | MATLAB | 1,864 | 67 | %Generating IAC(t) & ITD(t) stim for EEG
clear all
fs = 48828.125;
%load('Mseq_IAC_ITD.mat'); %loads Mseq
load('Mseq_9bit.mat')
load('Seed16.mat'); %load s
rng(s);
M_dur = .050;%50 ms
M_samps = round(M_dur*fs); %number of samples for each point in Mseq
flow = 200;
fhigh =1.5e3;
Trials = 300;
%windw = hann(round(M_... |
7e254fd8bef3ce6c3b17a1be84c491d205f1273b67e8f93b33d4dc1d62045a1a | MATLAB | 1,864 | 57 | %% initialize
clear; clc;
currentPath = pwd;
cd ../temp
load('tempFileForAnalysis.mat');
cd(pathBIDS);
cd derivatives
pathDeriv = pwd;
cd(pathDeriv);
cd('spm-preproc'); % For saving preprocessed file from SPM
pathPrepFile = pwd;
prepSubjFolders = dir('sub*');
cd(pathDeriv);
cd('behavior');
pathBehav = pwd;
load('be... |
9bac43a9133d054f197b6de04b3851845c05e6d2870bf9361bd0335fad3b17e2 | MATLAB | 1,864 | 52 | function S = zef_hdiv_stencil(tetrahedra,sub_ind,varargin)
K = length(sub_ind);
K2 = size(tetrahedra,1);
if not(isempty(varargin))
value_vec = varargin{1};
else
value_vec = ones(K2,1);
end
%An auxiliary matrix for picking up the correct nodes from tetrahedra
ind_m = [ 2 3 4 ;
3 4 1 ;
4 1 2 ;
1 2... |
731fccd6978aa3a83ef2512e497be6aa1cd73e172002e36c418f0e5d8cd8ed04 | MATLAB | 1,866 | 58 | %% initialize
clear; clc;
currentPath = pwd;
cd ../temp
load('tempFileForAnalysis.mat');
cd(pathBIDS);
cd derivatives
pathDeriv = pwd;
cd(pathDeriv);
cd('spm-preproc'); % For saving preprocessed file from SPM
pathPrepFile = pwd;
prepSubjFolders = dir('sub*');
cd(pathDeriv);
cd('behavior');
pathBehav = pwd;
load('be... |
873c984c2bb20e1ee7bd97ae01bf49b0c0e01a666cb2a7c489a2895364077e83 | MATLAB | 1,867 | 67 | %function [] = AnalyzeNerveMseq(date)
clear all
addpath('../../Neuron Analysis Functions')
addpath('StimFuncs')
data_date = '10.12.18';
load(['AN_' data_date '.mat']); %loads 'data'
if strcmp(data_date,'10.01.18')
data{35} = []; %garbage tuning curve, getting rid of on front end for now
data{37} = []; %also ... |
a453505a3fa6ec61485d2d157cbf2e1b5360514f69548afce1ed365ef4aec35b | MATLAB | 1,867 | 44 | function estimate_parameters_implementation_specific_call(app)
%sort models so the faster ones are first
[app.models] = sort_models(app.models);
app.params.stage=3; %parameter estimation in progress
update_log(app, 'Beginning Parameter Estimation');
% match to the appropriate function
switch app.params.implementatio... |
c4b598b945c8ea070898cc941ebe173701b0b84d52b04e2c8e6060c04979fef3 | MATLAB | 1,867 | 79 | load data/ary_model/ary_model.mat;
%% 1
load zeffiro_projects/pallomalli_pem.mat;
[mag_v_1, rdm_v_1, La, Lfem] = mag_rdm_fn(ary_model, zef_data);
s_p_1 = zef_data.source_positions;
% %% 2
%
% load zeffiro_projects/pallomalli_pem.mat;
%
% [mag_v_2, rdm_v_2, La, Lfem] = mag_rdm_fn(ary_model, zef_data);
% s_p_2 = zef_... |
d641b8315949781d605e028b29c1a0da65805b7c903ab02c916a41b0a7394671 | MATLAB | 1,868 | 57 | %% initialize
clear; clc;
currentPath = pwd;
cd ../temp
load('tempFileForAnalysis.mat');
cd(pathBIDS);
cd derivatives
pathDeriv = pwd;
cd(pathDeriv);
cd('spm-preproc'); % For saving preprocessed file from SPM
pathPrepFile = pwd;
prepSubjFolders = dir('sub*');
cd(pathDeriv);
cd('behavior');
pathBehav = pwd;
load('be... |
48383305c225a1dd419c338ea11d975cc10cc8a1810f181b8666fb5271968f22 | MATLAB | 1,870 | 57 | %% initialize
clear; clc;
currentPath = pwd;
cd ../temp
load('tempFileForAnalysis.mat');
cd(pathBIDS);
cd derivatives
pathDeriv = pwd;
cd(pathDeriv);
cd('spm-preproc'); % For saving preprocessed file from SPM
pathPrepFile = pwd;
prepSubjFolders = dir('sub*');
cd(pathDeriv);
cd('behavior');
pathBehav = pwd;
load('be... |
1513128af5078bf2654e95be85f5f8f644e26f5977dfe0e28d5c0c49679b7973 | MATLAB | 1,871 | 57 | %% initialize
clear; clc;
currentPath = pwd;
cd ../temp
load('tempFileForAnalysis.mat');
cd(pathBIDS);
cd derivatives
pathDeriv = pwd;
cd(pathDeriv);
cd('spm-preproc'); % For saving preprocessed file from SPM
pathPrepFile = pwd;
prepSubjFolders = dir('sub*');
cd(pathDeriv);
cd('behavior');
pathBehav = pwd;
load('be... |
cda385b5db526d6ceaa692efd09a407cafa514327a2a94d8ade8e9185c4558d5 | MATLAB | 1,875 | 52 | function varZ = get2DGauss(vecParams,matGridXY)
%% get2DGauss build 2D Gaussian based on X and Y coordinates
% Syntax: varZ = get2DGauss(vecParams,matGridXY)
% Rotation matrix is given by Mrot = [cos(phi) -sin(phi); sin(phi) cos(phi)]
%
% Inputs:
% vecParams = [z-const, Amp, x0, wx, y0, wy, phi]
% (phi is optional fo... |
cfc19b49fc6c93af8ddd2c0a53fb27f08ebcb2836f0e46826b0c48862ad53de5 | MATLAB | 1,875 | 57 | %% initialize
clear; clc;
currentPath = pwd;
cd ../temp
load('tempFileForAnalysis.mat');
cd(pathBIDS);
cd derivatives
pathDeriv = pwd;
cd(pathDeriv);
cd('spm-preproc'); % For saving preprocessed file from SPM
pathPrepFile = pwd;
prepSubjFolders = dir('sub*');
cd(pathDeriv);
cd('behavior');
pathBehav = pwd;
load('be... |
41444df947481d11377d5cb44a2933866131bf84129ff0e6a02f135f384609f5 | MATLAB | 1,877 | 57 | %% initialize
clear; clc;
currentPath = pwd;
cd ../temp
load('tempFileForAnalysis.mat');
cd(pathBIDS);
cd derivatives
pathDeriv = pwd;
cd(pathDeriv);
cd('spm-preproc'); % For saving preprocessed file from SPM
pathPrepFile = pwd;
prepSubjFolders = dir('sub*');
cd(pathDeriv);
cd('behavior');
pathBehav = pwd;
load('be... |
db264c0f4c3c0b8f70fb2764995ec33b64be0381ea5b01930241052d4151b865 | MATLAB | 1,877 | 65 | function [] = func_plot_movie(start_step, movie_step, time_all, q1, q2, properties, line_prop)
l1=properties(3);
l2=properties(4);
value_x_test=l1*cos(q1)+l2*cos(q1+q2);
value_y_test=l1*sin(q1)+l2*sin(q1+q2);
value_x1=l1*cos(q1);
value_y1=l1*sin(q1);
if strcmp(line_prop, 'solid') == 1
line_property ... |
f08727acb0a6cf1a25628b56ce9f0b7e194582b0eefa83755139755985463245 | MATLAB | 1,878 | 57 | %% initialize
clear; clc;
currentPath = pwd;
cd ../temp
load('tempFileForAnalysis.mat');
cd(pathBIDS);
cd derivatives
pathDeriv = pwd;
cd(pathDeriv);
cd('spm-preproc'); % For saving preprocessed file from SPM
pathPrepFile = pwd;
prepSubjFolders = dir('sub*');
cd(pathDeriv);
cd('behavior');
pathBehav = pwd;
load('be... |
a8e054dc3c0253e83ab89c8bbf3b3eb99dd18f35a2a8737e480de2b086669867 | MATLAB | 1,879 | 61 | function [z_inverse] = EnKF(m, A, P, Q, L, R, timeSteps, number_of_frames, n_ensembles, q_given)
%ENKF Summary of this function goes here
%x_ensemble = mvnrnd(zeros(size(m)), 100* ones(size(m,1)), n_ensembles)';
x_ensemble = mvnrnd(zeros(size(m)), P, n_ensembles)';
z_inverse = cell(0);
if not(q_given)
q_values = Q;... |
f14cef82c651ae290f998c491d9449f729d2d59be31b42a1524f6446a7489ba1 | MATLAB | 1,880 | 76 | % zef_pbo_system
%
% A function for generating a matrix of interpolation coefficients via
% position-based optimization or PBO.
%
% Input:
%
% - arg_locs
%
% An N × 3 dipole location matrix needed in interpolation.
%
% - arg_dirs
%
% An N × 3 dipole direction matrix needed in interpolation.
%
% - arg_interp_locs
%
... |
f26a4f6f2385b961f1dd3dad615c93061653138913c9e8ae77d62cda419793da | MATLAB | 1,880 | 57 | function encmodel = standard_encoding(X,Y,T,options,binsize)
% Compute maps representing the "encoding" model for each
% time point (or window, see below) in the trial (i.e. a GML).
% The reported statistic is the explained variance from
% regressing Y on the data at this sensor/voxel.
%
% INPUT
% X: Brain data, (time... |
00989133be93909ea77dbd8ba6a5d5bb2b94d93d430a46e1d0c7e0d89e535c66 | MATLAB | 1,882 | 67 | function save_firing_rates(params)
zzz = 1;
xx = load(fullfile(params.outdirname, [params.N1spiketimes_fn{zzz}, '.mat']));
st_and_nn1_full = xx.mat;
xx = load(fullfile(params.outdirname, [params.N2spiketimes_fn{zzz}, '.mat']));
st_and_nn2_full = xx.mat;
st = 0;
ed = params.durtn;
binSz = 2; % 1 ms
[N1_totfr, N1... |
c2c3f4ee682310c82f31790b4b918cf5328349c30c72597ab9ac59362d64cfd4 | MATLAB | 1,883 | 50 | function hmm_out = logisticMarginaliseHMM(hmm_in,iY)
% This function takes a multiple logistic strcuture HMM and returns the
% equivalent HMM with only the marginalised component iY of the
% logisticYdim components.
Xfulldim = hmm_in.train.ndim;
Xdim = Xfulldim - hmm_in.train.logisticYdim;
Xfulldim_new=Xdim+1;
select_... |
cb960e9d9e80db611cd716d49b648de4e5e0435af82390ffad48feece7609900 | MATLAB | 1,885 | 44 | function [temporal_FC] = concatenate_voxel_based_temporal_functional_connectivity(subject,part)
% The goal of this script it to concatenate the information from the
% temporally-contiguous functional connectivity matrices into one 3d matrix
% corresponding to each part (parts defined as in the case of the static
% vo... |
94f084e1732c2032c1783f096c4acd5177b5614521e330d4257243c06085a35c | MATLAB | 1,886 | 86 | function [Points, Tri, Normals, Scalars, LUT] = VTKPolyLineReader(filename)
% Usage:
% [Points, Tri, Normals. Scalars, LUT] = VTKPolyDataReader(filename)
% Copyright modified by Jean Dumoncel
fid = fopen(filename, 'r');
if(fid==-1)
error('Error: file descriptor not valid, check the file name.\n');
return;
end... |
99ddafa258d0dfc4056c2f70a5da6db38fde9740c5c8017d6ac6d56c8ff790d4 | MATLAB | 1,893 | 50 |
%--------------------------------------------------------------------------------
% mdp_vfe.m
%
% This demo is included in
% Canonical neural networks perform active inference
% Takuya Isomura, Hideaki Shimazaki, Karl J. Friston
%
% The MATLAB scripts are available at
% https://github.com/takuyaisomura/reverse_enginee... |
fd2a1668ac2e73e5c0a8273a8f182b9bedaf298cf10f9f344edf268bbdbb7cd8 | MATLAB | 1,893 | 71 | function [post_info, spike_info] = evaluate_post_prob( ...
data, Model, parm, post_sw)
% evaluate_performance
%
% [post_info, spike_info, ypred] = evaluate_performance(data, Model, parm)
%
% post_info.Pspike(ns,n) = P(ns | yk) of n-th overlap window
% post_info.twin{n} = time index of n-th overlap window
% ... |
b6ccff874ec82433be0a1b27c3feb72a5f4d28238a7c98afcb81e6aeaad2e812 | MATLAB | 1,898 | 53 | function ETP_redraw()
%get globals
global sETP;
global sFigETP;
%% redraw image, boxes & detection
%redraw image
cla(sFigETP.ptrAxesMainVid);
sFigETP.ptrCurFrame=imshow(sFigETP.matMain,'Parent', sFigETP.ptrAxesMainVid);
hold(sFigETP.ptrAxesMainVid,'on');
%redraw boxes
vecRectSyncPix = [sETP.vecRectSync(... |
057656d447480d75e85c654cbb0dee19356b18f28d754291478f1560b0d9b04d | MATLAB | 1,900 | 55 | function [tpeak, gpeak, tdecay] = ...
spike_func_peak(tau, decayRate)
% peak of double exponential spike function
% [tpeak, gpeak, tdecay] = spike_func_peak(tau,decayRate)
%
% --- time constant of spike response
% tau = [Ą1; Ą2] or [Ą1; Ą2; Ą3] (T2 < T3)
% g : (Nconf x Nt) or (Nconf x Nt... |
66969c30e5011f47d5815ba858ff917243fe46ede9033a8af8c0cc93efdb84a7 | MATLAB | 1,900 | 71 | function [W,covm,pred,residuals,fracerr] = mlmar (X,T,Sind,maxorder,order,orderoffset,timelag,exptimelag,zeromean,W,Gamma)
%
% Estimates maximum-likelihood (ML) MAR model
%
% INPUT
% X observations
% T length of series
% order,orderoffset,timelag,exptimelag,zeromean - check documentation
% W ... |
98d97fae1cbc7557ac2c7b56bfea7299594d394f3171bf95d22479098256ede8 | MATLAB | 1,901 | 59 | function sET = ET_populateStructure(sET)
%ET_populateStructure Prepares EyeTracker parameters by loading ini file,
% or creates one with default values
%check for ini file
strPath = ET_getIniPath();
strIni = strcat(strPath,filesep,'config.ini');
%get defaults
sDET = ET_defaultValues();
%load ini
if ... |
f91dde99701970c8d93e1bdd5368554c4e8511c6926ee13af7ee04247b58da75 | MATLAB | 1,901 | 75 | %Copyright © 2018- Sampsa Pursiainen & ZI Development Team
%See: https://github.com/sampsapursiainen/zeffiro_interface
function [scale_param, snr_vec] = zef_find_gaussian_prior(snr_val, varargin)
L = [];
source_space_size = 1;
normalize_data = 'maximum';
balance_snr = 1;
w_param = 0.5;
balance_param = 0;
if length(v... |
289737f6a16354f44f7719d1a1285517cc20a521ec653dc6ac42987f3e015715 | MATLAB | 1,902 | 57 | function skew_correct_one(TiffFolder, SaveFolder, RunName, InfoFile)
%Open a dataset with volume Avg
TifName=strcat(TiffFolder,'/', RunName);
%On ouvre les fichiers .tiff
Infos=imfinfo(TifName);
NLines=Infos.Height;
NCol=Infos.Width;
NPlanes=size(Infos,1);
... |
939cce8558c6c0a2bbbf9ccdbef85fd10029d6878512214a01bb68a7979c4c57 | MATLAB | 1,902 | 79 |
%--------------------------------------------------------------------------------
% figure_output.m
%
% This demo is included in
% Canonical neural networks perform active inference
% Takuya Isomura, Hideaki Shimazaki, Karl J. Friston
%
% The MATLAB scripts are available at
% https://github.com/takuyaisomura/reverse_e... |
45bcb704256d334692cf9de4dfea25e3520d7c7aaab5f1487432e3339b95c0cc | MATLAB | 1,903 | 57 | function gradients = zef_volume_gradient(nodes, tetrahedra, node_index)
% Calculates all of the ∇𝑉 of tetrahedra in the generated finite element mesh
% as a triple product a ⋅ (b × c). See https://math.stackexchange.com/q/797845
% for the general idea.
%
% input:
%
% - nodes: a matrix that contains the 3D coordinates ... |
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