Delete 'MatlabScripts/h_Fig4RewardHistory.m'

MatlabScripts/h_Fig4RewardHistory.m

@@ -1,522 +0,0 @@
-%Looking at the average firing rate for a given window in each of 4
-%current/previous reward conditions
-
-clear all;
-load ('R_2R.mat');
-load ('RAW.mat');
-
-%run linear model and stats? 1 is yes, 0 is no
-runanalysis=1;
-
-%divide neurons up by region
-NAneurons=strcmp(R_2R.Ninfo(:,3),'NA');
-VPneurons=strcmp(R_2R.Ninfo(:,3),'VP');
-
-%get parameters
-trialsback=6; %how many trials back to look
-Baseline=R_2R.Param.BinBase; %For normalizing activity
-Window=[0.8 1.3]; %what window of activity is analyzed
-BinDura=R_2R.Param.BinDura;
-bins=R_2R.Param.bins;
-binint=R_2R.Param.binint;
-binstart=R_2R.Param.binstart;
-
-%sorting bin -- which bin the neurons' activity is sorted on for heatmap(in seconds)
-SortBinTime=1; %seconds
-SortBin=(((SortBinTime-BinDura(2)/2)-binstart)/binint); %convert to bin name
-
-%reset
-NN=0;
-EvMeanz=0;
-
-if runanalysis==1  
-    for i=1:length(RAW) %loops through sessions
-        if strcmp('NA',RAW(i).Type(1:2)) | strcmp('VP',RAW(i).Type(1:2))
-            %events
-            EV1=strmatch('RD1P2',RAW(i).Einfo(:,2),'exact');
-            EV2=strmatch('RD1P1',RAW(i).Einfo(:,2),'exact');
-            EV3=strmatch('RD2P2',RAW(i).Einfo(:,2),'exact');
-            EV4=strmatch('RD2P1',RAW(i).Einfo(:,2),'exact');
-            RD=strmatch('RD',RAW(i).Einfo(:,2),'exact');
-            R1=strmatch('Reward1Deliv',RAW(i).Einfo(:,2),'exact');
-            R2=strmatch('Reward2Deliv',RAW(i).Einfo(:,2),'exact');
-
-            %% linear model for impact of previous rewards
-            %reset
-            X=[];
-            Y=[];
-
-            %set up the matrix with outcome identity for each session
-            rewards1=cat(2,RAW(i).Erast{R1,1}(:,1),ones(length(RAW(i).Erast{R1,1}(:,1)),1));
-            rewards2=cat(2,RAW(i).Erast{R2,1}(:,1),zeros(length(RAW(i).Erast{R2,1}(:,1)),1));
-            rewards=cat(1,rewards1,rewards2);
-            [rewards(:,1),ind]=sort(rewards(:,1));
-            rewards(:,2)=rewards(ind,2);
-
-            for k=trialsback+1:length(RAW(i).Erast{RD,1}(:,1))
-                time=RAW(i).Erast{RD,1}(k,1);
-                entry=find(rewards(:,1)==time);
-                for m=1:trialsback+1
-                    X(k-trialsback,m)=rewards(entry+1-m,2);
-                end
-            end
-
-            for j= 1:length(RAW(i).Nrast) %Number of neurons within sessions
-
-                NN=NN+1;
-                rewspk=0;
-                basespk=0;
-
-                %get mean baseline firing for all reward trials
-                [Bcell1,B1n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{RD},Baseline,{2});% makes trial by trial rasters for baseline
-                for y= 1:B1n
-                    basespk(1,y)=sum(Bcell1{1,y}>Baseline(1));
-                end
-
-                Bhz=basespk/(Baseline(1,2)-Baseline(1,1));
-                Bmean=nanmean(Bhz);
-                Bstd=nanstd(Bhz);
-
-                %get trial by trial firing rate for all reward trials
-                [EVcell,EVn]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{RD},Window,{2});% makes trial by trial rasters for event
-                for y= 1:EVn
-                    rewspk(y,1)=sum(EVcell{1,y}>Window(1));
-                end       
-                Y=((rewspk(trialsback+1:end,1)/(Window(1,2)-Window(1,1)))-Bmean)/Bstd; %normalize the activity to baseline
-
-                %true data
-                linmdl{NN,1}=fitlm(X,Y);
-                R_2R.RewHist.LinMdlWeights(NN,1:trialsback+1)=linmdl{NN,1}.Coefficients.Estimate(2:trialsback+2)';
-                R_2R.RewHist.LinMdlPVal(NN,1:trialsback+1)=linmdl{NN,1}.Coefficients.pValue(2:trialsback+2)';
-
-                %shuffled
-                YSh=Y(randperm(length(Y)));
-                linmdlSh{NN,1}=fitlm(X,YSh);
-                R_2R.RewHist.LinMdlWeightsSh(NN,1:trialsback+1)=linmdlSh{NN,1}.Coefficients.Estimate(2:trialsback+2)';
-                R_2R.RewHist.LinMdlPValSh(NN,1:trialsback+1)=linmdlSh{NN,1}.Coefficients.pValue(2:trialsback+2)';            
-
-                %% stats comparing effect of current and previous reward
-                %resetting
-                Bcell=0;
-                EV1spk=0;
-                EV2spk=0;
-                EV3spk=0;
-                EV4spk=0;
-                EV1norm=0;
-                EV2norm=0;
-                EV3norm=0;
-                EV4norm=0;
-
-                %get mean baseline firing for all reward trials
-                [Bcell1,B1n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{EV1},Baseline,{2});% makes trial by trial rasters for baseline
-                for y= 1:B1n
-                    Bcell(1,y)=sum(Bcell1{1,y}>Baseline(1));
-                end
-                [Bcell2,B2n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{EV2},Baseline,{2});% makes trial by trial rasters for baseline
-                for z= 1:B2n
-                    Bcell(1,z+B1n)=sum(Bcell2{1,z}>Baseline(1));
-                end
-                [Bcell3,B3n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{EV3},Baseline,{2});% makes trial by trial rasters for baseline
-                for z= 1:B3n
-                    Bcell(1,z+B1n+B2n)=sum(Bcell3{1,z}>Baseline(1));
-                end
-                [Bcell4,B4n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{EV4},Baseline,{2});% makes trial by trial rasters for baseline
-                for z= 1:B4n
-                    Bcell(1,z+B1n+B2n+B3n)=sum(Bcell4{1,z}>Baseline(1));
-                end
-
-                Bhz=Bcell/(Baseline(1,2)-Baseline(1,1));
-                Bmean=nanmean(Bhz);
-                Bstd=nanstd(Bhz);
-
-                %get trial by trial firing rate for suc/mal trials
-                [EV1cell,EV1n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{EV1},Window,{2});% makes trial by trial rasters for event
-                for y= 1:EV1n
-                    EV1spk(1,y)=sum(EV1cell{1,y}>Window(1));
-                end       
-                EV1hz=EV1spk/(Window(1,2)-Window(1,1));
-                for x= 1:EV1n
-                    EV1norm(1,x)=((EV1hz(1,x)-Bmean)/Bstd);
-                end
-
-                %get trial by trial firing rate for suc/suc trials
-                [EV2cell,EV2n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{EV2},Window,{2});% makes trial by trial rasters for event
-                for y= 1:EV2n
-                    EV2spk(1,y)=sum(EV2cell{1,y}>Window(1));
-                end       
-                EV2hz=EV2spk/(Window(1,2)-Window(1,1));
-                for x= 1:EV2n
-                    EV2norm(1,x)=((EV2hz(1,x)-Bmean)/Bstd);
-                end
-
-                %get trial by trial firing rate for mal/mal trials
-                [EV3cell,EV3n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{EV3},Window,{2});% makes trial by trial rasters for event
-                for y= 1:EV3n
-                    EV3spk(1,y)=sum(EV3cell{1,y}>Window(1));
-                end       
-                EV3hz=EV3spk/(Window(1,2)-Window(1,1));
-                for x= 1:EV3n
-                    EV3norm(1,x)=((EV3hz(1,x)-Bmean)/Bstd);
-                end
-
-                %get trial by trial firing rate for mal/suc trials
-                [EV4cell,EV4n]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{EV4},Window,{2});% makes trial by trial rasters for event
-                for y= 1:EV4n
-                    EV4spk(1,y)=sum(EV4cell{1,y}>Window(1));
-                end       
-                EV4hz=EV4spk/(Window(1,2)-Window(1,1));
-                for x= 1:EV4n
-                    EV4norm(1,x)=((EV4hz(1,x)-Bmean)/Bstd);
-                end
-
-                EvMeanz(NN,1)=nanmean(EV1norm);
-                EvMeanz(NN,2)=nanmean(EV2norm);
-                EvMeanz(NN,3)=nanmean(EV3norm);
-                EvMeanz(NN,4)=nanmean(EV4norm);
-
-                R_2R.RewHist.PrevRewMeanz=EvMeanz;
-
-                fprintf('Neuron # %d\n',NN);
-            end
-        end
-
-    end
-end
-
-%% which neurons to look at for stats and plotting?
-
-% Sel=R_2R.SucN | R_2R.MalN; %only look at reward-selective neurons
-Sel=NAneurons | VPneurons; %look at all neurons
-%Sel=R_2R.RewHist.LinMdlPVal(:,2)<0.1; %only neurons with significant impact of previous trial
-%Sel=R_2R.RewHist.LinMdlWeights(:,2)<-1; %only neurons with strong negative impact of previous trial
-
-%% ANOVAs
-
-%setup and run ANOVA for effects of current reward, previous reward, and region on reward firing
-CurrRew=cat(2,zeros(length(NAneurons),2),ones(length(NAneurons),2));
-PrevRew=cat(2,zeros(length(NAneurons),1),ones(length(NAneurons),1),zeros(length(NAneurons),1),ones(length(NAneurons),1));
-Region=cat(2,NAneurons,NAneurons,NAneurons,NAneurons);
-
-%to look at a specific selection of cells
-EvMeanz=R_2R.RewHist.PrevRewMeanz(Sel,:);
-CurrRew=CurrRew(Sel,:);
-PrevRew=PrevRew(Sel,:);
-Region=Region(Sel,:);
-
-[~,R_2R.RewHist.PrevRewStats{1,1},R_2R.RewHist.PrevRewStats{1,2}]=anovan(EvMeanz(:),{CurrRew(:),PrevRew(:),Region(:)},'varnames',{'Current Reward','Previous Reward','Region'},'display','off','model','full');
-
-%setup and run ANOVA for effects of shuffle, trial, and region on coefficient
-Trial=[];
-Region=[];
-for i=1:trialsback+1
-    Trial=cat(2,Trial,i*ones(length(NAneurons),1));
-    Region=cat(2,Region,NAneurons);
-end
-Trial=cat(2,Trial,Trial);
-Region=cat(2,Region,Region);
-Shuffd=cat(2,zeros(length(NAneurons),trialsback+1),ones(length(NAneurons),trialsback+1));
-Coeffs=cat(2,R_2R.RewHist.LinMdlWeights(:,1:trialsback+1),R_2R.RewHist.LinMdlWeightsSh(:,1:trialsback+1));
-
-[~,R_2R.RewHist.LinCoeffStats{1,1},R_2R.RewHist.LinCoeffStats{1,2}]=anovan(Coeffs(:),{Shuffd(:),Region(:),Trial(:)},'varnames',{'Shuffd','Region','Trial'},'display','off','model','full');
-
-%% plotting
-Xaxis=[-0.5 3];
-Ishow=find(R_2R.Param.Tm>=Xaxis(1) & R_2R.Param.Tm<=Xaxis(2));
-time1=R_2R.Param.Tm(Ishow);
-
-%color map
-[magma,inferno,plasma,viridis]=colormaps;
-colormap(plasma);
-c=[-100 2000];ClimE=sign(c).*abs(c).^(1/4);%colormap
-
-%colors
-sucrose=[.95  0.55  0.15];
-maltodextrin=[.9  0.3  .9];
-water=[0.00  0.75  0.75];
-total=[0.3  0.1  0.8];
-inh=[0.1 0.021154 0.6];
-exc=[0.9 0.75 0.205816];
-NAc=[0.5  0.1  0.8];
-VP=[0.3  0.7  0.1];
-
-%extra colors to make a gradient
-sucrosem=[.98  0.8  0.35];
-sucrosel=[1  1  0.4];
-maltodextrinm=[1  0.75  1];
-maltodextrinl=[1  0.8  1];
-
-RD1P1=strcmp('RD1P1', R_2R.Erefnames);
-RD1P2=strcmp('RD1P2', R_2R.Erefnames);
-RD2P1=strcmp('RD2P1', R_2R.Erefnames);
-RD2P2=strcmp('RD2P2', R_2R.Erefnames);
-
-%% Get mean firing according to previous trial and then plot
-
-%NAc
-
-%plot suc after suc
-psth1=nanmean(R_2R.Ev(RD1P1).PSTHz(Sel&NAneurons,Ishow),1); 
-sem1=nanste(R_2R.Ev(RD1P1).PSTHz(Sel&NAneurons,Ishow),1); %calculate standard error of the mean
-up1=psth1+sem1;
-down1=psth1-sem1;
-
-%plot suc after malt
-psth2=nanmean(R_2R.Ev(RD1P2).PSTHz(Sel&NAneurons,Ishow),1); 
-sem2=nanste(R_2R.Ev(RD1P2).PSTHz(Sel&NAneurons,Ishow),1); %calculate standard error of the mean
-up2=psth2+sem2;
-down2=psth2-sem2;
-
-
-%plot malt after suc
-psth3=nanmean(R_2R.Ev(RD2P1).PSTHz(Sel&NAneurons,Ishow),1); 
-sem3=nanste(R_2R.Ev(RD2P1).PSTHz(Sel&NAneurons,Ishow),1); %calculate standard error of the mean
-up3=psth3+sem3;
-down3=psth3-sem3;
-
-%plot malt after malt
-psth4=nanmean(R_2R.Ev(RD2P2).PSTHz(Sel&NAneurons,Ishow),1); 
-sem4=nanste(R_2R.Ev(RD2P2).PSTHz(Sel&NAneurons,Ishow),1); %calculate standard error of the mean
-up4=psth4+sem4;
-down4=psth4-sem4;
-
-%make the plot
-subplot(2,4,1);
-hold on;
-title(['Reward response, current/prev reward'])
-plot(time1,psth2,'Color',sucrosem,'linewidth',1);
-plot(time1,psth1,'Color',sucrose,'linewidth',1);
-plot(time1,psth4,'Color',maltodextrinm,'linewidth',1);
-plot(time1,psth3,'Color',maltodextrin,'linewidth',1);
-patch([time1,time1(end:-1:1)],[up2,down2(end:-1:1)],sucrosem,'EdgeColor','none');alpha(0.5);
-patch([time1,time1(end:-1:1)],[up1,down1(end:-1:1)],sucrose,'EdgeColor','none');alpha(0.5);
-patch([time1,time1(end:-1:1)],[up3,down3(end:-1:1)],maltodextrin,'EdgeColor','none');alpha(0.5);
-patch([time1,time1(end:-1:1)],[up4,down4(end:-1:1)],maltodextrinm,'EdgeColor','none');alpha(0.5);
-plot([-2 5],[0 0],':','color','k','linewidth',0.75);
-plot([0 0],[-2 8],':','color','k','linewidth',0.75);
-plot([Window(1) Window(1)],[-2 8],'color','b','linewidth',0.85);
-plot([Window(2) Window(2)],[-2 8],'color','b','linewidth',0.85);
-axis([Xaxis(1) Xaxis(2) min(down3)-0.15 max(up2)+0.2]);
-ylabel('Mean firing (z-score)');
-xlabel('Seconds from RD');
-legend('Suc/mal','Suc/suc','Mal/mal','Mal/suc','location','northeast');
-
-if cell2mat(R_2R.RewHist.PrevRewStats{1,1}(3,7))<0.05
-    plot(Window(1)+(Window(2)-Window(1))/4,max(up2)+0.1,'*','color','b','markersize',13);
-end
-
-%VP
-
-%plot suc after suc
-psth1=nanmean(R_2R.Ev(RD1P1).PSTHz(Sel&VPneurons,Ishow),1); 
-sem1=nanste(R_2R.Ev(RD1P1).PSTHz(Sel&VPneurons,Ishow),1); %calculate standard error of the mean
-up1=psth1+sem1;
-down1=psth1-sem1;
-
-%plot suc after malt
-psth2=nanmean(R_2R.Ev(RD1P2).PSTHz(Sel&VPneurons,Ishow),1); 
-sem2=nanste(R_2R.Ev(RD1P2).PSTHz(Sel&VPneurons,Ishow),1); %calculate standard error of the mean
-up2=psth2+sem2;
-down2=psth2-sem2;
-
-%plot malt after suc
-psth3=nanmean(R_2R.Ev(RD2P1).PSTHz(Sel&VPneurons,Ishow),1); 
-sem3=nanste(R_2R.Ev(RD2P1).PSTHz(Sel&VPneurons,Ishow),1); %calculate standard error of the mean
-up3=psth3+sem3;
-down3=psth3-sem3;
-
-%plot malt after malt
-psth4=nanmean(R_2R.Ev(RD2P2).PSTHz(Sel&VPneurons,Ishow),1); 
-sem4=nanste(R_2R.Ev(RD2P2).PSTHz(Sel&VPneurons,Ishow),1); %calculate standard error of the mean
-up4=psth4+sem4;
-down4=psth4-sem4;
-
-%make the plot
-subplot(2,4,5);
-hold on;
-title(['Reward response, current/prev reward'])
-plot(time1,psth2,'Color',sucrosem,'linewidth',1);
-plot(time1,psth1,'Color',sucrose,'linewidth',1);
-plot(time1,psth4,'Color',maltodextrinm,'linewidth',1);
-plot(time1,psth3,'Color',maltodextrin,'linewidth',1);
-patch([time1,time1(end:-1:1)],[up2,down2(end:-1:1)],sucrosem,'EdgeColor','none');alpha(0.5);
-patch([time1,time1(end:-1:1)],[up1,down1(end:-1:1)],sucrose,'EdgeColor','none');alpha(0.5);
-patch([time1,time1(end:-1:1)],[up3,down3(end:-1:1)],maltodextrin,'EdgeColor','none');alpha(0.5);
-patch([time1,time1(end:-1:1)],[up4,down4(end:-1:1)],maltodextrinm,'EdgeColor','none');alpha(0.5);
-plot([-2 5],[0 0],':','color','k','linewidth',0.75);
-plot([0 0],[-2 8],':','color','k','linewidth',0.75);
-plot([Window(1) Window(1)],[-2 8],'color','b','linewidth',0.85);
-plot([Window(2) Window(2)],[-2 8],'color','b','linewidth',0.85);
-axis([Xaxis(1) Xaxis(2) min(down3)-0.3 max(up2)+0.3]);
-ylabel('Mean firing (z-score)');
-xlabel('Seconds from RD');
-legend('Suc/mal','Suc/suc','Mal/mal','Mal/suc','location','northeast');
-
-if cell2mat(R_2R.RewHist.PrevRewStats{1,1}(3,7))<0.05
-    plot(Window(1)+(Window(2)-Window(1))/4,max(up2)+0.15,'*','color','b','markersize',13);
-end
-
-if cell2mat(R_2R.RewHist.PrevRewStats{1,1}(7,7))<0.05
-    plot(Window(1)+3*(Window(2)-Window(1))/4,max(up2)+0.15,'*','color','r','markersize',13);
-end
-
-%% Plotting heatmaps of each condition
-
-%NAc
-
-%sucrose responses following sucrose
-subplot(2,24,[10 11]); %heatmap of water preferring neurons' response to water
-Neurons=R_2R.Ev(RD1P1).PSTHz(Sel&NAneurons,Ishow); %get the firing rates of neurons of interest
-SucResp=cat(1,R_2R.BinStatRwrd{SortBin+1,1}.R1Mean); %sucrose responses
-TMP=SucResp(Sel&NAneurons); %find the magnitude of the excitations for sucrose for this bin
-TMP(isnan(TMP))=0; %To place the neurons with no onset/duration/peak at the top of the color-coded map
-[~,SORTimg]=sort(TMP);
-Neurons=Neurons(SORTimg,:); %sort the neurons by magnitude
-imagesc(time1,[1,sum(Sel&NAneurons,1)],Neurons,ClimE);
-title(['Suc after suc']);
-xlabel('Seconds from RD');
-hold on;
-plot([0 0],[0 sum(Sel)],':','color','k','linewidth',0.75);
-
-%following maltodextrin
-subplot(2,24,[7 8]); %heatmap of sucrose preferring neurons' response to maltodextrin
-Neurons=R_2R.Ev(RD1P2).PSTHz(Sel&NAneurons,Ishow); %get the firing rates of neurons of interest
-Neurons=Neurons(SORTimg,:); %sort the neurons same as before
-imagesc(time1,[1,sum(Sel&NAneurons,1)],Neurons,ClimE);
-title(['Suc after mal']);
-ylabel('All neurons plotted individually');
-hold on;
-plot([0 0],[0 sum(Sel)],':','color','k','linewidth',0.75);
-
-%maltodextrin responses following sucrose
-subplot(2,24,[16 17]); %heatmap of water preferring neurons' response to water
-Neurons=R_2R.Ev(RD2P1).PSTHz(Sel&NAneurons,Ishow); %get the firing rates of neurons of interest
-Neurons=Neurons(SORTimg,:); %sort the neurons as before
-imagesc(time1,[1,sum(Sel&NAneurons,1)],Neurons,ClimE);
-title(['Mal after suc']);
-hold on;
-plot([0 0],[0 sum(Sel)],':','color','k','linewidth',0.75);
-
-%following maltodextrin
-subplot(2,24,[13 14]); %heatmap of sucrose preferring neurons' response to maltodextrin
-Neurons=R_2R.Ev(RD2P2).PSTHz(Sel&NAneurons,Ishow); %get the firing rates of neurons of interest
-Neurons=Neurons(SORTimg,:); %sort the neurons same as before
-imagesc(time1,[1,sum(Sel&NAneurons,1)],Neurons,ClimE);
-title(['Mal after mal']);
-hold on;
-plot([0 0],[0 sum(Sel)],':','color','k','linewidth',0.75);
-
-%VP
-
-%sucrose responses following sucrose
-subplot(2,24,[34 35]); %heatmap of water preferring neurons' response to water
-Neurons=R_2R.Ev(RD1P1).PSTHz(Sel&VPneurons,Ishow); %get the firing rates of neurons of interest
-SucResp=cat(1,R_2R.BinStatRwrd{SortBin+1,1}.R1Mean); %sucrose responses
-TMP=SucResp(Sel&VPneurons); %find the magnitude of the excitations for sucrose for this bin
-TMP(isnan(TMP))=0; %To place the neurons with no onset/duration/peak at the top of the color-coded map
-[~,SORTimg]=sort(TMP);
-Neurons=Neurons(SORTimg,:); %sort the neurons by magnitude
-imagesc(time1,[1,sum(Sel&VPneurons,1)],Neurons,ClimE);
-title(['Suc after suc']);
-xlabel('Seconds from RD');
-hold on;
-plot([0 0],[0 sum(Sel)],':','color','k','linewidth',0.75);
-
-%following maltodextrin
-subplot(2,24,[31 32]); %heatmap of sucrose preferring neurons' response to maltodextrin
-Neurons=R_2R.Ev(RD1P2).PSTHz(Sel&VPneurons,Ishow); %get the firing rates of neurons of interest
-Neurons=Neurons(SORTimg,:); %sort the neurons same as before
-imagesc(time1,[1,sum(Sel&VPneurons,1)],Neurons,ClimE);
-title(['Suc after mal']);
-ylabel('All neurons plotted individually');
-hold on;
-plot([0 0],[0 sum(Sel)],':','color','k','linewidth',0.75);
-
-%maltodextrin responses following sucrose
-subplot(2,24,[40 41]); %heatmap of water preferring neurons' response to water
-Neurons=R_2R.Ev(RD2P1).PSTHz(Sel&VPneurons,Ishow); %get the firing rates of neurons of interest
-Neurons=Neurons(SORTimg,:); %sort the neurons as before
-imagesc(time1,[1,sum(Sel&VPneurons,1)],Neurons,ClimE);
-title(['Mal after suc']);
-hold on;
-plot([0 0],[0 sum(Sel)],':','color','k','linewidth',0.75);
-
-%following maltodextrin
-subplot(2,24,[37 38]); %heatmap of sucrose preferring neurons' response to maltodextrin
-Neurons=R_2R.Ev(RD2P2).PSTHz(Sel&VPneurons,Ishow); %get the firing rates of neurons of interest
-Neurons=Neurons(SORTimg,:); %sort the neurons same as before
-imagesc(time1,[1,sum(Sel&VPneurons,1)],Neurons,ClimE);
-title(['Mal after mal']);
-hold on;
-plot([0 0],[0 sum(Sel)],':','color','k','linewidth',0.75);
-
-%% plot linear model coefficients
-
-%Plot all neurons
-Sel=NAneurons<2;
-
-%coefficients for each trial
-subplot(2,4,4);
-hold on;
-errorbar(0:trialsback,nanmean(R_2R.RewHist.LinMdlWeights(Sel&NAneurons,1:trialsback+1),1),nanste(R_2R.RewHist.LinMdlWeights(Sel&NAneurons,1:trialsback+1),1),'color',NAc,'linewidth',1);
-errorbar(0:trialsback,nanmean(R_2R.RewHist.LinMdlWeights(Sel&VPneurons,1:trialsback+1),1),nanste(R_2R.RewHist.LinMdlWeights(Sel&VPneurons,1:trialsback+1),1),'color',VP,'linewidth',1);
-errorbar(0:trialsback,nanmean(R_2R.RewHist.LinMdlWeightsSh(Sel&NAneurons,1:trialsback+1),1),nanste(R_2R.RewHist.LinMdlWeights(Sel&NAneurons,1:trialsback+1),1),'color','k','linewidth',1);
-errorbar(0:trialsback,nanmean(R_2R.RewHist.LinMdlWeightsSh(Sel&VPneurons,1:trialsback+1),1),nanste(R_2R.RewHist.LinMdlWeights(Sel&VPneurons,1:trialsback+1),1),'color','k','linewidth',1);
-xlabel('Trials back');
-ylabel('Mean coefficient weight');
-title('Linear model coefficients');
-legend('NAc','VP','Shuff');
-
-axis([-1 trialsback+1 -1 2.8]);
-plot([-1 trialsback+1],[0 0],':','color','k','linewidth',0.75);
-
-%stats to check if VP is greater than NAc
-R_2R.RewHist.LinCoeffMultComp=[];
-[c,~,~,~]=multcompare(R_2R.RewHist.LinCoeffStats{1,2},'dimension',[1,2,3],'display','off');
-for i=1:trialsback+1
-
-    %NAc vs VP
-    Cel=c(:,1)==4*(i-1)+1 & c(:,2)==4*(i-1)+3;
-    if c(Cel,6)<0.05 R_2R.RewHist.LinCoeffMultComp(i,1)=1; else R_2R.RewHist.LinCoeffMultComp(i,1)=0; end
-    R_2R.RewHist.LinCoeffMultComp(i,2)=c(Cel,6);
-
-    %VP vs shuff
-    Cel=c(:,1)==4*(i-1)+1 & c(:,2)==4*(i-1)+2;
-    if c(Cel,6)<0.05 R_2R.RewHist.LinCoeffMultComp(i,3)=1; else R_2R.RewHist.LinCoeffMultComp(i,3)=0; end
-    R_2R.RewHist.LinCoeffMultComp(i,4)=c(Cel,6);
-
-    %NAc vs shuff
-    Cel=c(:,1)==4*(i-1)+3 & c(:,2)==4*(i-1)+4;
-    if c(Cel,6)<0.05 R_2R.RewHist.LinCoeffMultComp(i,5)=1; else R_2R.RewHist.LinCoeffMultComp(i,5)=0; end
-    R_2R.RewHist.LinCoeffMultComp(i,6)=c(Cel,6);
-end
-subplot(2,4,4);
-hold on;
-plot([0:trialsback]-0.15,(R_2R.RewHist.LinCoeffMultComp(:,3)-0.5)*5,'*','color',VP); %VP vs shuff
-plot([0:trialsback],(R_2R.RewHist.LinCoeffMultComp(:,5)-0.5)*5,'*','color',NAc); %NAc vs shuff
-plot([0:trialsback]+0.15,(R_2R.RewHist.LinCoeffMultComp(:,1)-0.5)*5,'*','color','k'); %VP vs NAc
-
-
-%number of neurons with significant weights
-subplot(2,4,8);
-hold on;
-plot(0:trialsback,sum(R_2R.RewHist.LinMdlPVal(Sel&NAneurons,1:trialsback+1)<0.05,1)/sum(Sel&NAneurons),'color',NAc,'linewidth',1);
-plot(0:trialsback,sum(R_2R.RewHist.LinMdlPVal(Sel&VPneurons,1:trialsback+1)<0.05,1)/sum(Sel&VPneurons),'color',VP,'linewidth',1);
-plot(0:trialsback,sum(R_2R.RewHist.LinMdlPValSh(Sel&NAneurons,1:trialsback+1)<0.05,1)/sum(Sel&NAneurons),'color',NAc/3,'linewidth',1);
-plot(0:trialsback,sum(R_2R.RewHist.LinMdlPValSh(Sel&VPneurons,1:trialsback+1)<0.05,1)/sum(Sel&VPneurons),'color',VP/3,'linewidth',1);
-axis([-1 trialsback+1 0 0.5]);
-xlabel('Trials back');
-ylabel('Fraction of the population');
-title('Outcome-modulated neurons');
-
-%Chi squared stat for each trial
-R_2R.RewHist.LinMdlX2all=[];
-R_2R.RewHist.LinMdlX2region=[];
-
-for i=1:trialsback+1
-    [~,R_2R.RewHist.LinMdlX2all(i,1),R_2R.RewHist.LinMdlX2all(i,2)]=crosstab(cat(1,R_2R.RewHist.LinMdlPVal(Sel,i)<0.05,R_2R.RewHist.LinMdlPValSh(Sel,i)<0.05),cat(1,VPneurons,VPneurons+2));
-    [~,R_2R.RewHist.LinMdlX2region(i,1),R_2R.RewHist.LinMdlX2region(i,2)]=crosstab(R_2R.RewHist.LinMdlPVal(Sel,i)<0.05,VPneurons);
-end
-%plot([0:trialsback]-0.1,(R_2R.LinMdlX2all(:,2)<0.05)-0.52,'*','color',NAc);
-plot([0:trialsback],(R_2R.RewHist.LinMdlX2region(:,2)<0.05&R_2R.RewHist.LinMdlX2all(:,2)<0.05)-0.52,'*','color','k');
-
-save('R_2R.mat','R_2R');