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-clear all;
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-load ('R_W.mat');
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-load ('RAW.mat');
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-
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-%get parameters
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-BinBase=R_W.Param.BinBase;
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-BinDura=R_W.Param.BinDura;
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-bins=R_W.Param.bins;
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-binint=R_W.Param.binint;
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-binstart=R_W.Param.binstart;
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-PStatBins=0.01; %using more stringent cutoff to reduce pre-delivery noise
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-
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-%which bins bound the area examined for reward-selectivity? (in seconds)
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-Time1=0.4; %seconds
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-Time2=3; %seconds
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-Bin1=(((Time1-BinDura(2)/2)-binstart)/binint); %convert to bin name
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-Bin2=(((Time2-BinDura(2)/2)-binstart)/binint); %convert to bin name
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-
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-%sorting bin -- which bin the neurons' activity is sorted on for heatmap(in seconds)
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-SortBinTime=1; %seconds
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-SortBin=round((((SortBinTime-BinDura(2)/2)-binstart)/binint)); %convert to bin name
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-
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-sucrose=[1 0.6 0.1];
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-maltodextrin=[.9 0.3 .9];
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-water=[0.00 0.75 0.75];
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-total=[0.3 0.1 0.8];
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-exc=[0 113/255 188/255];
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-inh=[240/255 0 50/255];
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-
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-%% bins analysis
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-
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-%first and last bin aren't included because you can't compare to the previous/subsequent bin
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-%this axis plots the bins on their centers
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-xaxis=linspace(binstart+binint+BinDura(2)/2,binstart+(bins-2)*binint+BinDura(2)/2,bins-2);
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-
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-for i=2:(bins-1) %no including first or last bin because can't compare to previous/subsequent bin
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-
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- %finds out whether firing is stronger (high excitation or lower inhibition) for 1 or 2
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- for k=1:length(R_W.Ninfo) %runs through neurons
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- if R_W.BinStatRwrd{i,1}(k).IntSig < PStatBins %if neuron is significant for this bin
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- if R_W.BinStatRwrd{i-1,1}(k).IntSig < PStatBins || R_W.BinStatRwrd{i+1,1}(k).IntSig < PStatBins %either previous or subsequent bin must be significant
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- if R_W.BinStatRwrd{i,1}(k).R1Mean > R_W.BinStatRwrd{i,1}(k).R2Mean %if firing is greater on sucrose trials than maltodextrin
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- R_W.BinRewPref{i,1}(k,1)=1;
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- else
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- R_W.BinRewPref{i,1}(k,1)=2; %otherwise maltodextrin must be greater than sucrose
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- end
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- else
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- R_W.BinRewPref{i,1}(k,1)=0; %if not significant in 2 consecutive bins
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- end
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- else
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- R_W.BinRewPref{i,1}(k,1)=0; %if no sig reward modulation
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- end
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-
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- end
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- %find how many NAc neurons have significant reward modulation in each bin
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- NN1perBin(i,1)=sum(R_W.BinRewPref{i,1}(:,1)==1); %sucrose pref
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- NN2perBin(i,1)=sum(R_W.BinRewPref{i,1}(:,1)==2); %malto pref
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- NNperBin(i,1)=sum(R_W.BinRewPref{i,1}(:,1)>0); %either
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- %normalize to number of neurons in population
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- NN1norm=NN1perBin./sum(length(R_W.Ninfo));
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- NN2norm=NN2perBin./sum(length(R_W.Ninfo));
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- NNnorm=NNperBin./sum(length(R_W.Ninfo));
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-
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-
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-end
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-
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-%Cumulative reward selectivity
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-cumsel=zeros(length(R_W.Ninfo),bins); %set up result matrix
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-cumsel1=zeros(length(R_W.Ninfo),bins); %set up result matrix for sucrose
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-cumsel2=zeros(length(R_W.Ninfo),bins); %set up result matrix for maltodextrin
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-%note, this has to be corrected to +1 because of the way the bin matrix is
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-%layed out (bin 0 is the first column, not the 0th column)
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-for i=1:length(R_W.Ninfo) %go through each neuron
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- for j=2:bins-1 %look in each bin we did analysis on
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- if R_W.BinRewPref{j,1}(i,1)>0 || cumsel(i,j-1)==1
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- cumsel(i,j) = 1;
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- end
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- if R_W.BinRewPref{j,1}(i,1)==1 || cumsel1(i,j-1)==1
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- cumsel1(i,j) = 1;
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- end
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- if R_W.BinRewPref{j,1}(i,1)==2 || cumsel2(i,j-1)==1
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- cumsel2(i,j) = 1;
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- end
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- end
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-end
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-
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-%plotting number of significantly modulated neurons across time
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-subplot(3,2,2);
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-hold on;
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-plot(xaxis,NNnorm(2:bins-1),'Color', total,'linewidth',1.5);
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-plot(xaxis,NN1norm(2:bins-1),'Color',water,'linewidth',1.5);
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-plot(xaxis,NN2norm(2:bins-1),'Color',maltodextrin,'linewidth',1.5);
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-plot([Time1 Time1],[-1 1],':','color','k','linewidth',0.75);
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-plot([Time2 Time2],[-1 1],':','color','k','linewidth',0.75);
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-axis([xaxis(1) xaxis(end) 0 0.7]);
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-legend('Total','Wat > mal','Mal > wat','Location','northeast')
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-ylabel('Fraction of population');
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-title('Reward-selective neurons');
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-xlabel('Seconds from RD');
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-
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-
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-
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-%% plotting maltodextrin-selective neurons
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-
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-%color map
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-[magma,inferno,plasma,viridis]=colormaps;
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-colormap(plasma);
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-c=[-100 2000];ClimE=sign(c).*abs(c).^(1/4);%colormap
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-
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-%events we're looking at
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-ev1=strcmp('RD1', R_W.Erefnames);
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-ev2=strcmp('RD2', R_W.Erefnames);
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-
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-%setting up parameters
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-Xaxis=[-2 5];
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-inttime=find(R_W.Param.Tm>=Xaxis(1) & R_W.Param.Tm<=Xaxis(2));
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-paramtime=R_W.Param.Tm(inttime);
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-
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-%find all neurons with greater firing for maltodextrin
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-for i = 1:(Bin2-Bin1+1)
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- %the added +1 is necessary because bin 20 is the 21st entry in the matrix
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- Pref1(:,i)=R_W.BinRewPref{Bin1+i}==2; %get neurons that have greater firing for water in any of the bins bounded above
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- Resp11(:,i)=Pref1(:,i)&cat(1,R_W.BinStatRwrd{Bin1+i,1}.WatRespDir)==-1; %get neurons with inhibition to water
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- Resp12(:,i)=Pref1(:,i)&cat(1,R_W.BinStatRwrd{Bin1+i,1}.MalRespDir)==1;%get neurons with excitation to maltodextrin
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-end
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-
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-Mel=sum(Pref1,2)>0; %all neurons selective for mal in any bin
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-Mel2=sum(Resp11,2)>0; %all selective neurons water inhibited in any bin
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-Mel3=sum(Resp12,2)>0; %all selective neurons malto excited in any bin
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-
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-subplot(9,40,[(15:23)+120 (15:23)+160]); %heatmap of maltodextrin preferring neurons' response to water
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-Neurons=R_W.Ev(ev1).PSTHz(Mel,inttime); %get the firing rates of neurons of interest
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-MalResp=cat(1,R_W.BinStatRwrd{SortBin+1,1}.R2Mean); %water responses
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-TMP=MalResp(Mel); %find the magnitude of the excitations for water for this bin
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-TMP(isnan(TMP))=0; %To place the neurons with no onset/duration/peak at the top of the color-coded map
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-[~,SORTimg]=sort(TMP);
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-Neurons=Neurons(SORTimg,:); %sort the neurons by magnitude
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-imagesc(paramtime,[1,sum(Mel,1)],Neurons,ClimE);
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-title(['Water trials']);
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-ylabel('Individual neurons');
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-hold on;
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-plot([0 0],[0 sum(Mel)],':','color','k','linewidth',0.75);
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-
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-subplot(9,40,[(27:35)+120 (27:35)+160]); %heatmap of maltodextrin preferring neurons' response to maltodextrin
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-Neurons=R_W.Ev(ev2).PSTHz(Mel,inttime); %get the firing rates of neurons of interest
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-Neurons=Neurons(SORTimg,:); %sort the neurons same as before
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-imagesc(paramtime,[1,sum(Mel,1)],Neurons,ClimE);
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-title(['Maltodextrin trials']);
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-xlabel('Seconds from RD');
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-hold on;
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-plot([0 0],[0 sum(Mel)],':','color','k','linewidth',0.75);
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-
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-%plot water preferring neurons to water
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-psth1=nanmean(R_W.Ev(ev1).PSTHz(Mel,inttime),1);
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-sem1=nanste(R_W.Ev(ev1).PSTHz(Mel,inttime),1); %calculate standard error of the mean
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-up1=psth1+sem1;
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-down1=psth1-sem1;
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-
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-%plot water preferring neurons to malt
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-psth2=nanmean(R_W.Ev(ev2).PSTHz(Mel,inttime),1);
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-sem2=nanste(R_W.Ev(ev2).PSTHz(Mel,inttime),1); %calculate standard error of the mean
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-up2=psth2+sem2;
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-down2=psth2-sem2;
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-
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-%plotting
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-subplot(9,3,[10 13]);
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-title(['Mal>wat (n=' num2str(sum(Mel)) ' of ' num2str(length(Mel)) ')'])
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-hold on;
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-plot(paramtime,psth1,'Color',water,'linewidth',1);
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-plot(paramtime,psth2,'Color',maltodextrin,'linewidth',1);
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-patch([paramtime,paramtime(end:-1:1)],[up1,down1(end:-1:1)],water,'EdgeColor','none');alpha(0.5);
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-patch([paramtime,paramtime(end:-1:1)],[up2,down2(end:-1:1)],maltodextrin,'EdgeColor','none');alpha(0.5);
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-plot([-2 5],[0 0],':','color','k','linewidth',0.75);
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-plot([0 0],[-2 10],':','color','k','linewidth',0.75);
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-axis([-2 5 -1.5 3.5]);
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-ylabel('Mean firing (z-score)');
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-legend('Wat trials','Mal trials');
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-xlabel('Seconds from RD');
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-
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-
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-%display inhibitions and excitations
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-both = sum(Mel2&Mel3);
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-excite = sum(Mel3)-both;
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-inhib = sum(Mel2)-both;
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-
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-subplot(9,40,[160 200]);
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-b = bar([inhib both excite; 0 0 0],'stacked');
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-b(1,1).FaceColor=water;
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-b(1,2).FaceColor=total;
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-b(1,3).FaceColor=maltodextrin;
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-axis([1 1.2 0 both+excite+inhib]);
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-ylabel('Inh / Both / Excited');
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-set(gca,'xtick',[])
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-
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-
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-%% emergence of maltodextrin and water responses
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-
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-%select which time point we're examining
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-Window=[0.8 1.8]; %For looking at emergence of excitation
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-NN=0; %neuron counter
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-Sess=0; %session counter
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-
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-%plotting
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-sucrose=[1 0.6 0.1];
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-maltodextrin=[.9 0.3 .9];
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-water=[0.00 0.75 0.75];
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-total=[0.3 0.1 0.8];
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-exc=[0 113/255 188/255];
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-inh=[240/255 0 50/255];
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-
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-%get firing rates on each trial
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-for i=1:length(RAW) %loops through sessions
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- if strcmp('WA',RAW(i).Type(1:2))
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- Sess=Sess+1; %session counter
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- SN{Sess}=0; %selective neuron counter
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- ev1=strmatch('RD1',RAW(i).Einfo(:,2),'exact');
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- ev2=strmatch('RD2',RAW(i).Einfo(:,2),'exact');
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- for j= 1:size(RAW(i).Nrast,1) %Number of neurons within sessions
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- NN=NN+1;
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- if Mel(NN)==1
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-
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- SN{Sess}=SN{Sess}+1;
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- Bcell1=0;
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- Bcell2=0;
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- Bcell=0;
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- ev1spk=0;
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- Ev2spk=0;
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-
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- %get mean baseline firing for all reward trials
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- [Bcell1,B1n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{ev1},BinBase,{2});% makes trial by trial rasters for baseline
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- for y= 1:B1n
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- Bcell(1,y)=sum(Bcell1{1,y}>BinBase(1));
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- end
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- [Bcell2,B2n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{ev2},BinBase,{2});% makes trial by trial rasters for baseline
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- for z= 1:B2n
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- Bcell(1,z+B1n)=sum(Bcell2{1,z}>BinBase(1));
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- end
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- Bhz=Bcell/(BinBase(1,2)-BinBase(1,1));
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- Bmean=nanmean(Bhz);
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- Bstd=nanstd(Bhz);
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-
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- %get trial by trial firing rate for water trials
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- [ev1cell,ev1n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{ev1},Window,{2});% makes trial by trial rasters for event
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- for y= 1:ev1n
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- ev1spk(SN{Sess},y)=sum(ev1cell{1,y}>Window(1));
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- end
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- ev1hz=ev1spk(SN{Sess},:)/(Window(1,2)-Window(1,1));
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- for x= 1:ev1n
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- ev1norm{Sess}(SN{Sess},x)=((ev1hz(1,x)-Bmean)/Bstd);
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- end
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-
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- %get trial by trial firing rate for maltodextrin trials
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- [ev2cell,ev2n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{ev2},Window,{2});% makes trial by trial rasters for event
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- for y= 1:ev2n
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- ev2spk(1,y)=sum(ev2cell{1,y}>Window(1));
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- end
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- ev2hz=ev2spk/(Window(1,2)-Window(1,1));
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- for x= 1:ev2n
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- ev2norm{Sess}(SN{Sess},x)=((ev2hz(1,x)-Bmean)/Bstd);
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- end
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- end
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- end %neurons in each session
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-
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- trialmeanz1{Sess}=NaN(2,1);
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- trialmeanz2{Sess}=NaN(2,1);
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- ind=0;
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- order=0;
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-
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- [~,ind]=sort(cat(1,RAW(i).Erast{ev1},RAW(i).Erast{ev2}));
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- for j=1:length(ind)
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- order(j,1)=find(ind==j);
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- end
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- xaxis1{Sess}=order(1:length(ev1norm{Sess}(1,:)));
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- xaxis2{Sess}=order(1+length(ev1norm{Sess}(1,:)):end);
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-
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- for k=1:length(ev1norm{Sess}(1,:))
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- trialmeanz1{Sess}(1,k)=nanmean(ev1norm{Sess}(:,k));
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- trialmeanz1{Sess}(2,k)=nanste(ev1norm{Sess}(:,k),1);
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- end
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-
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- for k=1:length(ev2norm{Sess}(1,:))
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- trialmeanz2{Sess}(1,k)=nanmean(ev2norm{Sess}(:,k));
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- trialmeanz2{Sess}(2,k)=nanste(ev2norm{Sess}(:,k),1);
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- end
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-
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- %stats checking for interaction between # of reward presentations
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- %and reward on firing rate
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- numtrials=min([length(xaxis1{Sess}) length(xaxis2{Sess})]); %number of trials for stats (fewest number of trials for the 2 outcomes)
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- trials1=repmat(1:numtrials,length(ev1norm{Sess}(:,1)),1);
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- trials2=repmat(1:numtrials,length(ev2norm{Sess}(:,1)),1);
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- reward1=zeros(size(trials1));
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- reward2=ones(size(trials2));
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- data1=ev1norm{Sess}(:,1:numtrials);
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- data2=ev2norm{Sess}(:,1:numtrials);
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-
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- [~,R_W.EmergenceStats{Sess,1},R_W.EmergenceStats{Sess,2}]=anovan(cat(1,data1(:),data2(:)),{cat(1,trials1(:),trials2(:)),cat(1,reward1(:),reward2(:))},'varnames',{'trials','reward'},'display','off','model','full');
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-
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- end %checking if a water session
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-
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-
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-
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-end %session
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-
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-%plotting
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-for i=1:Sess
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- subplot(3,2,4+i);
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- hold on;
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- errorbar(xaxis1{i},trialmeanz1{i}(1,:),trialmeanz1{i}(2,:),'*','Color',water,'linewidth',1.5);
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- errorbar(xaxis2{i},trialmeanz2{i}(1,:),trialmeanz2{i}(2,:),'*','Color',maltodextrin,'linewidth',1.5);
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- %plot(xaxis1,(cell2mat(ev1stat{i}(1,:))*13-16.9),'*','Color',water); %(12.5-trialmeanz242(1,1:trials1)-trialmeanz242(2,1:trials1))
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- %plot(xaxis2,(cell2mat(ev2stat{i}(1,:))*13-16.7),'*','Color',maltodextrin); %(12.5-trialmeanz242(1,1:trials1)-trialmeanz242(2,1:trials1))
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- %plot(xaxis1,(cell2mat(trialstat42(1,1:trials1))*13-17.1),'*','Color','k'); %(12.5-trialmeanz242(1,1:trials1)-trialmeanz242(2,1:trials1))
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- plot([0 60],[0 0],':','color','k','linewidth',0.75);
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- ylabel(['Mean firing ' num2str(Window(1,1)) '-' num2str(Window(1,2)) 's (z-score)']);
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- title(['Rat ' num2str(i) ' (n=' num2str(SN{i}) ')']);
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- axis([1 max([xaxis1{i};xaxis2{i}]) min(trialmeanz1{i}(1,:))*1.3 max(trialmeanz2{i}(1,:))*1.3]);
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- xlabel('Trial #');
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- legend('Wat','Mal','location','northwest');
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-
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-end
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-
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-
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-%% conduct lick analysis
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-global Dura Tm BSIZE Tbin
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-path='C:\Users\dottenh2\Documents\MATLAB\David\2Rewards Nex Files\2RLick_paper.xls';
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-
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-%Main settings
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-BSIZE=0.01; %Do not change
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-Dura=[-22 20]; Tm=Dura(1):BSIZE:Dura(2);
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-Tbin=-0.5:0.005:0.5; %window used to determine the optimal binsize
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-MinNumTrials=5;
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-Lick=[];Lick.Ninfo={};LL=0;Nlick=0;
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-
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-%Smoothing
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-Smoothing=1; %0 for raw and 1 for smoothing
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-SmoothTYPE='lowess'; %can change this between lowess and rlowess (more robust, ignores outliers more)
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-SmoothSPAN=50; %percentage of total data points
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-if Smoothing~=1, SmoothTYPE='NoSmoothing';SmoothSPAN=NaN; end
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-
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-% List of events to analyze and analysis windows EXTRACTED from excel file
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-[~,Erefnames]=xlsread(path,'Windows','a3:a10'); % cell that contains the event names
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-
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-%Finds the total number of sessions
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-for i=1:length(RAW)
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- if strcmp('WA',RAW(i).Type(1:2))
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- Nlick=Nlick+1;
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- Lick.Linfo(i,1)=RAW(i).Ninfo(1,1);
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- end
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-end
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-Lick.Erefnames= Erefnames;
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-
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-%preallocating the result matrix
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-for k=1:length(Erefnames)
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- Lick.Ev(k).PSTHraw(1:Nlick,1:length(Tm))=NaN(Nlick,length(Tm));
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- Lick.Ev(k).BW(1:Nlick,1)=NaN;
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- Lick.Ev(k).NumberTrials(1:Nlick,1)=NaN;
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-end
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-
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-for i=1:length(RAW) %loops through sessions
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- if strcmp('WA',RAW(i).Type(1:2))
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- LL=LL+1; %lick session counter
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- for k=1:length(Erefnames) %loops thorough the events
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- EvInd=strcmp(Erefnames(k),RAW(i).Einfo(:,2)); %find the event id number from RAW
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- LickInd=strcmp('Licks',RAW(i).Einfo(:,2)); %find the event id number from RAW
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- if sum(EvInd)==0
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- fprintf('HOWDY, CANT FIND EVENTS FOR ''%s''\n',Erefnames{k});
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- end
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-
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- Lick.Ev(k).NumberTrials(LL,1)=length(RAW(i).Erast{EvInd});
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- if ~isempty(EvInd) && Lick.Ev(k).NumberTrials(LL,1)>MinNumTrials %avoid analyzing sessions where that do not have enough trials
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- [PSR1,N1]=MakePSR04(RAW(i).Erast(LickInd),RAW(i).Erast{EvInd},Dura,{1});% makes collpased rasters. PSR1 is a cell(neurons)
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- if ~isempty(PSR1{1}) %to avoid errors, added on 12/28 2011
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- %forcing 100ms bin size to keep it consistent across
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- %sessions (no reason is should be different for licks)
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- [PTH1,BW1,~]=MakePTH07(PSR1,repmat(N1, size(RAW(i).Erast{LickInd},1),1),{2,0,0.1});%these values force bin size to be 100ms
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- PTH1=smooth(PTH1,SmoothSPAN,SmoothTYPE)';
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-
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- %------------- Fills the R.Ev(k) fields --------------
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- Lick.Ev(k).BW(LL,1)=BW1;
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- Lick.Ev(k).PSTHraw(LL,1:length(Tm))=PTH1;
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- end
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- end
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- end
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- end
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-end
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-
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-Xaxis=[-2 12];
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-Ishow=find(Tm>=Xaxis(1) & Tm<=Xaxis(2));
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-time1=Tm(Ishow);
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-c=[-1000 7500];ClimE=sign(c).*abs(c).^(1/4);%ColorMapExc
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-colormap(jet);
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-sucrose=[1 0.6 0.1];
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-maltodextrin=[.9 0.3 .9];
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-water=[0.00 0.75 0.75];
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-total=[0.3 0.1 0.8];
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-exc=[0 113/255 188/255];
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-inh=[240/255 0 50/255];
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-
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-Ev1=strcmp('RD1', Lick.Erefnames);
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-Ev2=strcmp('RD2', Lick.Erefnames);
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-
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-psth1=nanmean(Lick.Ev(Ev1).PSTHraw(:,Ishow),1);
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-sem1=nanste(Lick.Ev(Ev1).PSTHraw(:,Ishow),1); %calculate standard error of the mean
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-up1=psth1+sem1;
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-down1=psth1-sem1;
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-
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-psthE=nanmean(Lick.Ev(Ev2).PSTHraw(:,Ishow),1);
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-semE=nanste(Lick.Ev(Ev2).PSTHraw(:,Ishow),1); %calculate standard error of the mean
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-upE=psthE+semE;
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-downE=psthE-semE;
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-
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-%plotting
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-subplot(3,2,1);
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-hold on;
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-plot(time1,psth1,'Color',water,'linewidth',1);
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-plot(time1,psthE,'Color',maltodextrin,'linewidth',1);
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-patch([time1,time1(end:-1:1)],[up1,down1(end:-1:1)],water,'EdgeColor','none');alpha(0.5);
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-patch([time1,time1(end:-1:1)],[upE,downE(end:-1:1)],maltodextrin,'EdgeColor','none');alpha(0.5);
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-title('Mean lick rate');
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-%plot([-2 10],[0 0],':','color','k');
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-plot([0 0],[-2 8],':','color','k','linewidth',0.75);
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-axis([-2 12 0 7.5]);
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-xlabel('Seconds from reward delivery');
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-ylabel('Licks/s');
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-legend('Water trials','Maltodextrin trials');
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-%color map
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-[magma,inferno,plasma,viridis]=colormaps;
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-colormap(plasma);
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|
-c=[-100 2000];ClimE=sign(c).*abs(c).^(1/4);%colormap
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-
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-%% creating and saving new variables
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-R_W.MalN=Mel;
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-
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-% proportion summary and chi squared test for reward selective neurons in
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|
-% sucrose and water sessions in VP
|
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-
|
|
|
-%find all reward-selective neurons
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|
|
-for i = 1:(Bin2-Bin1+1)
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|
- %the added +1 is necessary because bin 20 is the 21st entry in the matrix
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|
|
- Selective(:,i)=R_W.BinRewPref{Bin1+i}>0; %get neurons that have greater firing for water in any of the bins bounded above
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-end
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|
-WSel=sum(Pref1,2)>0; %all neurons selective for mal in any bin
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|
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-
|
|
|
-%get reward selective neurons from these two rats
|
|
|
-load ('R_2R.mat');
|
|
|
-selection=(R_2R.SucN | R_2R.MalN); %selective neurons in suc vs mal sessions
|
|
|
-SSel=selection(strcmp('VP2',R_2R.Ninfo(:,4)) | strcmp('VP5',R_2R.Ninfo(:,4)),1);
|
|
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-
|
|
|
-%get proportion and stats
|
|
|
-R_W.SucvsWatX2(1,1)=sum(SSel)/length(SSel); %selective neurons in maltodextrin sessions
|
|
|
-R_W.SucvsWatX2(1,2)=sum(WSel)/length(WSel); %how many selective neurons in water sessions
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|
-[~,R_W.SucvsWatX2(2,1),R_W.SucvsWatX2(2,2)]=crosstab(cat(1,SSel,WSel),cat(1,ones(length(SSel),1),zeros(length(WSel),1))); %find chi squared value for this distribution
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-
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|
-save('R_W.mat','R_W');
|