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Copy pathInformationPairPoissonSquareProtocol_prueba.m
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90 lines (88 loc) · 5.06 KB
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%This function computes the information of pair of cells about one behavioural aspect of
%the rat with the square protocol in it's velocity.
%The variable type is the one which determinates the behavioural aspect.
% (0) Time information
% (1) Space Information
% (2) Directional Information
% (3) Speed Information
% (4) Space Directional Information
% (5) Speed Directional Information
function recording_data=InformationPairPoissonSquareProtocol_prueba(directory,files,rat,areaname,area,list,freq)
lfp=Days(list);
for index= 9:9
index
cells=SimultaneousRecordings(area,lfp(index,1:7));
spikes_times=SpikesAssembly(files,rat,area,cells,'C');
binary_data=BinarySpikesSignal(spikes_times);
for i=1:size(cells)
order = Wished_Register_Order(area,cells(i,:));
register=Rat_Register(files,area,rat,order);
[fil col]=size(register.sessions);
for se=1:col
protocol=register.sessions{se}.sess_type;
if protocol == 'C'
spikes_times= register.sessions{se}.spike_times;
pos=Loading_Pos(register,se);
tf = isfield(pos.data, 'log');
if (tf==1 && length(spikes_times)>50)
[matrix1 matrix2]=SpikesMatrix(spikes_times,pos,freq);
matrix{:,:,i}=[matrix1,matrix2];
%[pn pnt pt dn_1]=GaussianProbability(matrix1,matrix2,times);
for j=1:i-1
aux=corrcoef(binary_data(i,:),binary_data(j,:));
if size(matrix{:,:,i})==size(matrix{:,:,j})
[pn1n2 pn1n2t pt corr_matrix dn1 dn2 times]=BidimentionalGaussianProbability_prueba(matrix{:,:,i},matrix{:,:,j});
[pn1 pn1t]=MarginalTwoDimentional(pn1n2,pn1n2t,pt,1);
[pn pnt pt dn_1]=GaussianProbability_prueba(matrix1,matrix2,times);
for aspect=1:1
[I h ht hvar]=BehaviouralInformationSquareProtocol(pn,pnt,pt,1,pos,freq,aspect-1);
recording_data{index}{i}.I(j,aspect)=I;
recording_data{index}{i}.h(j,aspect)=h;
recording_data{index}{i}.ht(j,aspect)=ht;
recording_data{index}{i}.h_var(j,aspect)=hvar;
end
%name=strcat('C:\Users\meli__000\Desktop\Melisa\Doctorado\CellsCorrelation\Pn-Pn_marginal2\',int2str(i),'_',int2str(j));
%figure(j)
%subplot(2,10,[1:9])
%imagesc(pnt)
%subplot(2,10,10)
%imagesc(pn)
%subplot(2,10,[11:19])
%imagesc(pn1t)
%subplot(2,10,20)
%imagesc(pn1')
%saveas(j,name,'png')
%close(j)
%prueba=prueba+pn1t;
recording_data{index}{i}.corr_in_time(j,:)=corr_matrix;
for aspect=1:1
[I0 h0 h0t hj]=BehaviouralPairInformationSquareProtocol(pn1n2,pn1n2t,pt,1,pos,freq,aspect-1);
[I1 h1 h1t h1j]=BehaviouralInformationSquareProtocol(pn1,pn1t,pt,1,pos,freq,aspect-1);
recording_data{index}{i}.I_marginal(j,aspect)=I1;
recording_data{index}{i}.h_marginal(j,aspect)=h1;
recording_data{index}{i}.ht_marginal(j,aspect)=h1t;
recording_data{index}{i}.h_var_marginal(j,aspect)=h1j;
recording_data{index}{i}.I_pair(j,aspect)=I0;
recording_data{index}{i}.h_pair(j,aspect)=h0;
recording_data{index}{i}.ht_pair(j,aspect)=h0t;
recording_data{index}{i}.h_var_pair(j,aspect)=hj;
end
end
recording_data{index}{i}.corr(j)=aux(1,2);
end
%figure(100)
%name=strcat('C:\Users\meli__000\Desktop\Melisa\Doctorado\CellsCorrelation\Pn-Pn_marginal\suma_',int2str(i));
%subplot(2,1,1)
%imagesc(pnt)
%subplot(2,1,2)
%imagesc(prueba)
%saveas(100,name,'png')
%close(100)
end
end
end
end
end
%name=strcat(directory,areaname);
%save(name,'recording_data','-mat')
end