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figure1j.m
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figure1j.m
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function hfig=figure1j
% This software is part of the supplementary material of the publication:
%
% Eyherabide HG, Samengo I, When and why noise correlations are important in
% neural decoding, J Neurosci (2013), 33(45): 17921-17936; doi: 10.1523/JNEUROSCI.0357-13.2013
%
% Should you use this code, we kindly request you to cite the aforementioned publication.
%
% DESCRIPTION:
%
% Constructs Figure 1J.
%
% VERSION CONTROL
%
% V1.000 Hugo Gabriel Eyherabide, University of Helsinki (20 Nov 2013)
%
% Should you find bugs, please contact either Prof. Inés Samengo (samengo at
% cab.cnea.gov.ar) or Hugo Gabriel Eyherabide (hugo.eyherabide at helsinki.fi)
%
% LICENSE
%
% Copyright 2013 Hugo Gabriel Eyherabide
%
% This program is free software: you can redistribute it and/or modify it under
% the terms of the GNU General Public License as published by the Free Software
% Foundation, either version 3 of the License, or (at your option) any later version.
%
% This program is distributed in the hope that it will be useful, but WITHOUT ANY
% WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
% PARTICULAR PURPOSE. See the GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License along with this
% program. If not, see http://www.gnu.org/licenses/.
% Response probabilities conditioned to each of two stimuli.
ex.pr1r2ds=zeros(2,3,3);
ex.pr1r2ds(1,:,:)=[0 ,.5 ,0;
.5 ,0 ,0;
0 ,0 ,0];
% Stimulus probabilities
ex.ps=[.25,.75];
ex.pos=2; % Places legends in the upper left corner
ex.title='Figure 1J - J Neurosci (2013), 33(45): 17921-17936';
% Construct panel with estimations of minimum information loss attainable by
% NI decoders.
hfig=plotf1pllds2(ex);
end