CodingProject/InformationPairPoissonSquareProtocol.m at master · melisamc89/CodingProject · GitHub

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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(directory,files,rat,areaname,area,list,freq)

    lfp=Days(list);
    for index= 9:9
        cells=SimultaneousRecordings(area,lfp(index,1:7));
        [spikes_times session_number]=SpikesAssembly(files,rat,area,cells,'C');
        binary_data=BinarySpikesSignal(spikes_times);
        [ncells nse]=size(session_number);
        matrix=[];
        for i=1:ncells
            order = Wished_Register_Order(area,cells(i,:));
            register=Rat_Register(files,area,rat,order);
            for se=1:nse
                cell_spikes_times{se}=spikes_times{i,se};
            end
               [matrix1 matrix2]=SpikesMatrix(cell_spikes_times,register,session_number(i,:),freq);
               matrix(:,:,i)=[matrix1,matrix2];
                %[pn pnt pt dn_1]=GaussianProbability(matrix(:,:,i));
                %for aspect=1:1
                 %  [I h ht hvar]=BehaviouralInformationSquareProtocol(pn,pnt,pt,1,pos,freq,aspect-1);
                  % recording_data{index}{i}.I(aspect)=I;
                   %recording_data{index}{i}.h(aspect)=h;
                   %recording_data{index}{i}.ht(aspect)=ht;
                   %recording_data{index}{i}.h_var(aspect)=hvar;
                %end
        end
        for i=1:ncells
            for j=1:ncells
                aux=corrcoef(binary_data(i,:),binary_data(j,:));
                if i~=j
                    if size(matrix(:,:,i))==size(matrix(:,:,j))
                        [pn1n2 pn1n2t pt corr_matrix dn1 dn2]=BidimentionalGaussianProbability(matrix(:,:,i),matrix(:,:,j));
                        [pn1 pn1t]=MarginalTwoDimentional(pn1n2,pn1n2t,pt,1);
                        [pn pnt pt dn_1]=GaussianProbability(matrix(:,:,i));
                        name=strcat('C:\Users\meli__000\Desktop\Melisa\Doctorado\CellsCorrelation\Pn-Pn_marginal3\',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)
                        end
                        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
            end
        end
    name=strcat(directory,areaname);
    save(name,'recording_data','-mat')
end