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@@ -1,199 +0,0 @@
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-%decodes trial identity from spiking across bins for neurons pooled across sessions
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-%also does this for only selective and non-selective neurons
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-
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-%need to run A, B, and D first
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-clear all;
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-load ('R_2R.mat');
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-load ('RAW.mat');
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-
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-%NAc is 1, VP is 2
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-region={'NA';'VP'};
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-
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-%decoding parameters
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-NumNeurons=[10 25 50 100 150]; %matrix of how many neurons to use on each iteration
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-repetitions=20; %how many times to run the analysis
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-folds = 5; %number of times cross-validated:
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-shuffs = 1; %number of shuffled models created
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-
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-%load parameters
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-BinDura=R_2R.Param.BinDura;
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-bins=R_2R.Param.bins;
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-binint=R_2R.Param.binint;
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-binstart=R_2R.Param.binstart;
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-
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-%Need to keep number of trials used in VP and NAc constant, so have to pick
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-%minimum number across all sessions in both regions. Right now with
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-%existing sessions it's 20 Ev1s and 20 Ev2s
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-Ev1s=20;
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-Ev2s=20;
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-
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-
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-
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-%find number of trials in each session
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-for i=1:length(RAW)
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-
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- %events being compared
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- Ev1=strcmp('RD1', RAW(i).Einfo(:,2));
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- Ev2=strcmp('RD2', RAW(i).Einfo(:,2));
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-
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- Ev1perSess(i)=length(RAW(i).Erast{Ev1,1});
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- Ev2perSess(i)=length(RAW(i).Erast{Ev2,1});
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-end
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-
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-%setup variables
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-PoolDec=[];
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-NN = 0;
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-
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-for e=1:3 %different selections of neurons
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-
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- %pick which set of neurons: all, reward-specific, or non-reward specific
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- if e==1 selection=R_2R.Ninfo; end %all neurons
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- if e==2 selection=R_2R.RSinfo; end %reward-selective neurons
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- if e==3 selection=R_2R.RNSinfo; end %reward-nonselective neurons
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-
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- for f=1:2 %region
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-
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- TotalNeurons = 0;
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-
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- %total number of neurons in all sessions and events per session
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- for i=1:length(RAW)
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- if strcmp(region(f),RAW(i).Type(1:2))
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- TotalNeurons=TotalNeurons+size(RAW(i).Nrast,1);
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- end
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- end
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-
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- for l=1:bins
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- DecodeSpikes=NaN(1,1);
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- AllSpikes=NaN(1,1);
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- NN = 0;
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- for i=1:length(RAW) %loops through sessions
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- if strcmp(region(f),RAW(i).Type(1:2)) && Ev1perSess(i)>=20 && Ev2perSess(i)>=20
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-
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- %events being compared
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- Ev1=strcmp('RD1', RAW(i).Einfo(:,2));
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- Ev2=strcmp('RD2', RAW(i).Einfo(:,2));
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-
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- for j= 1:size(RAW(i).Nrast,1) %Number of neurons per session
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- if sum(strcmp(RAW(i).Ninfo(j,1),selection(:,1)) & strcmp(RAW(i).Ninfo(j,2),selection(:,2)))==1 %check if this neuron is on the selection list
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- NN=NN+1; %neuron counter
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-
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- Ev1Spikes=NaN(1,1);
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- Ev2Spikes=NaN(1,1);
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-
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- %get number of spikes for this neuron in this bin for all
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- %Ev1 trials
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- [PSR1,N1]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{Ev1},[(BinDura(1)+(binstart - binint)+l*binint) (BinDura(2)+(binstart - binint)+l*binint)],{2});% makes trial by trial rasters. PSR1 is a cell(neurons, trials)
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- for m=1:length(PSR1)
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- Ev1Spikes(m,1)=sum(PSR1{1,m}>(binstart));
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- end
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-
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- %pick which trials get used for decoding
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- SetupTrials=cat(1,ones(Ev1s,1),zeros(length(PSR1)-Ev1s,1));
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- Trials=(SetupTrials(randperm(length(SetupTrials)))==1);
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-
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- %put spikes from those trials in a matrix
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- AllSpikes(1:Ev1s,NN)=Ev1Spikes(Trials,1);
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-
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- %get all the spikes from reward 1p2 trials
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- [PSR2,N2]=MakePSR04(RAW(i).Nrast(j),RAW(i).Erast{Ev2},[(BinDura(1)+(binstart - binint)+l*binint) (BinDura(2)+(binstart - binint)+l*binint)],{2});% makes trial by trial rasters. PSR1 is a cell(neurons, trials)
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- for n=1:length(PSR2)
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- Ev2Spikes(n,1)=sum(PSR2{1,n}>(binstart));
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- end
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-
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- %pick which trials get used for decoding
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- SetupTrials2=cat(1,ones(Ev2s,1),zeros(length(PSR2)-Ev2s,1));
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- Trials2=(SetupTrials2(randperm(length(SetupTrials2)))==1);
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-
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- %put spikes from those trials in a matrix
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- AllSpikes((Ev1s+1):(Ev1s+Ev2s),NN)=Ev2Spikes(Trials2,1);
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- end
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- end %neurons
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- end %checking if enough events in that session
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- end %sessions
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-
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- TotalNeurons=NN;
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-
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- %do the decoding
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- for v = 1:length(NumNeurons)
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- if TotalNeurons>NumNeurons(v)
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- for u=1:repetitions
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- LowHz=zeros(1,NumNeurons(v)); %reset the lowHz identifier
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-
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- %pick which neurons to use
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- SetupSel=cat(1,ones(NumNeurons(v),1),zeros(TotalNeurons-NumNeurons(v),1));
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- Sel=(SetupSel(randperm(length(SetupSel)))==1);
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-
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- %setup the event identity matrix for decoding
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- %DecodeRs=zeros(Ev1s+Ev2s,NumNeurons(v));
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- DecodeRs(1:Ev1s,1)=1;
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- %DecodeRs(1:Ev1s,end)=1;
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- %DecodeRs((Ev1s+1):(Ev1s+Ev2s),2:end-1)=1;
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- DecodeRs((Ev1s+1):(Ev1s+Ev2s),1)=0;
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-
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- %setup decode spike matrix
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- DecodeSpikes=AllSpikes(:,Sel);
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-
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- %find neurons with too few spikes
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-
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- for t=1:NumNeurons(v)
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- if sum(DecodeSpikes(1:Ev1s,t))<7 || sum(DecodeSpikes((1+Ev1s):(Ev1s+Ev2s),t))<7
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- LowHz(1,t)=1;
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- end
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- end
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-
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- %remove neurons with too few spikes
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- if sum(LowHz)>0
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- Sel2=LowHz<1;
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- DecodeSpikes=DecodeSpikes(:,Sel2);
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- end
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-
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- %set up validation result matrices
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- CVacc = NaN(folds,1);
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- CVaccSh = NaN(folds,1);
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-
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- %normal model
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- for r = 1:folds
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-
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- Partitions = cvpartition(DecodeRs,'KFold',folds);
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- SVMModel = fitcdiscr(DecodeSpikes(Partitions.training(r),:),DecodeRs(Partitions.training(r)));
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- prediction = predict(SVMModel,DecodeSpikes(Partitions.test(r),:));
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- actual = DecodeRs(Partitions.test(r));
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- correct = prediction - actual;
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- CVacc(r) = sum(correct==0) / length(correct);
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-
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- end
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-
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- %shuffled model
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- for q=1:shuffs
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- DecodeRsSh=DecodeRs(randperm(length(DecodeRs)));
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- PartitionsSh = cvpartition(DecodeRsSh,'KFold',folds);
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- for s = 1:folds
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- SVMModelSh = fitcdiscr(DecodeSpikes(PartitionsSh.training(s),:),DecodeRsSh(PartitionsSh.training(s)));
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- predictionSh = predict(SVMModelSh,DecodeSpikes(PartitionsSh.test(s),:));
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- actualSh = DecodeRsSh(PartitionsSh.test(s));
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- correctSh = predictionSh - actualSh;
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- CVaccSh(s) = sum(correctSh==0) / length(correctSh);
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- end
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- AccShuff(q,1) = nanmean(CVaccSh);
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- end
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-
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- PoolDec{e,f}.True{v,1}(u,l) = nanmean(CVacc);
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- PoolDec{e,f}.Shuff{v,1}(u,l) = nanmean(AccShuff);
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-
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-
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- end %repetitions
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- fprintf(['Selection #' num2str(e) ', Region #' num2str(f) ', Bin #' num2str(l) ', Condition #' num2str(v) '\n']);
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- else
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- PoolDec{e,f}.True{v,1} = NaN;
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- PoolDec{e,f}.Shuff{v,1} = NaN;
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- fprintf(['Selection #' num2str(e) ', Region #' num2str(f) ', Bin #' num2str(l) ', Condition #' num2str(v) '\n']);
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- end %checking if there are enough neurons
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- end %conditions (number of neurons)
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- end %bins
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- end %region
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-end %selections
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-
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-save('PoolDec_2R.mat','PoolDec');
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-
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-R_2R.Param.NumNeurons=NumNeurons;
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-save('R_2R.mat','R_2R');
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