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KernelRegressionPhotoM.m
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KernelRegressionPhotoM.m
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clear all
close all
% Jan 2019: I think this code is obsolete. we will keep it for now.
%[48, 50,51] coresponding to ALK068, 70 and 71
animal_ID = 48
load('BehPhotoM_Exp23')
RTLimit = 5.9; % in s, Dont change. excluding trials with RT longer than this
Timereso = 50; %this is in unit of 1200 in s. dont change
color = [
1 0 0 % red
1 0.5 0 % orange
1 1 0 % yellow
0.5 1 0.5 % light green
0 1 1 % light blue
0 0.5 1 % medium blue
0 0 1]; % blue
colorGray = [ 0.8 0.8 0.8
0.6 0.6 0.6
0.4 0.4 0.4
0 0 0];
colorRed = [ 1 0.8 0.8
1 0.6 0.6
1 0.4 0.4
1 0 0];
BehData = [];
BeepData = [];
StimData = [];
RewardData = [];
sessionz = 1:length(BehPhotoM(animal_ID).Session);
for iSession = sessionz
TempBehData = BehPhotoM(animal_ID).Session(iSession).TrialTimingData;
TempBeepData= BehPhotoM(animal_ID).Session(iSession).NeuronBeep;
TempStimData= BehPhotoM(animal_ID).Session(iSession).NeuronStim;
TempRewardData= BehPhotoM(animal_ID).Session(iSession).NeuronReward;
BeepData = [BeepData;TempBeepData];
StimData = [StimData;TempStimData];
RewardData = [RewardData;TempRewardData];
BehData = [BehData; TempBehData];
end
%StimData(:,5400:end) = StimData(:,5400:end) - repmat(mean(StimData (:,5400:5500),2), 1, 13100-5399);
StimData(:,5500:end) = StimData(:,5500:end) - repmat(mean(StimData (:,5200:5500),2), 1, 13100-5499);
%StimData = StimData - repmat(mean(StimData,2), 1, 13100);
% trial by trial reaction time (in fact reward stim interval)
RT = BehData(:,14) - BehData(:,13);
ToLargeR = find((BehData(:,3)==-1 & BehData(:,8)==1) | ...
(BehData(:,3)==1 & BehData(:,8)==2));
BehData(ToLargeR,16)=1;
ToSmallR = setdiff(1:size(BehData,1),ToLargeR)';
BehData(ToSmallR, 16)=-1;
abzStim = unique(abs(BehData(:,2)))';
totalSamples = size(StimData,1) * size(StimData,2);
toExclude = unique( [find(BehData(:,1)==1) ; find(RT > RTLimit)]); % exlcuding trials with very long RT
%and also the first trials of each sessnio since the beep timing in the
%very first trial is sometimes wrong
% make continous data
inverted_Matrix=StimData;
inverted_Matrix(toExclude,:) = [];
inverted_Matrix =inverted_Matrix';
invertedStimData_to_row = inverted_Matrix(:)';
% make continous time
t = 1:1:totalSamples; % time
t=reshape(t,13100,size(BehData,1))';
t(toExclude,:) = [];
t=reshape(t',length(invertedStimData_to_row),1);
% this is according to my saved data (13100 samples convering -3 to 8 s
% after the event)
stim_time = 3700 : 13100 : totalSamples;
beep_time = stim_time - 1200.* (BehData (:,13) - BehData (:,12))';
action_onsetTime = stim_time + 1200.* (BehData (:,10) - BehData (:,13))';
outcome_time = stim_time + 1200.* (BehData (:,14) - BehData (:,13))';
beep_time(toExclude) = [];
stim_time (toExclude) = [];
action_onsetTime(toExclude) = [];
outcome_time(toExclude) = [];
action_onsetTime (isnan(action_onsetTime))=outcome_time(isnan(action_onsetTime))-200 ; % very rare trials we could not measure action_onset time and we use this one
BehData(toExclude,:) = [];
BeepData(toExclude,:) = [];
StimData(toExclude,:) = [];
RewardData(toExclude,:) = [];
% moving average and downsampling
%invertedStimData_to_row = movingmean(invertedStimData_to_row',100)';
%inverted_Matrix_to_row = (inverted_Matrix_to_row -
%movmean(inverted_Matrix_to_row,3000)); this was a bad idea
% bpFilt = designfilt('highpassfir','FilterOrder',20,'CutoffFrequency',50, 'SampleRate', 1200);
% inverted_Matrix_to_row = smooth(invertedStimData_to_row,300);
% StimDataSmooth = reshape(inverted_Matrix_to_row', 13100,size(BehData,1))';
% StimDataSmoothDownSample=downsample(StimDataSmooth',Timereso)';
%
% [inverted_Matrix_to_row, s] = deconvolveCa(inverted_Matrix_to_row,'ar2');
%
%
% inverted_Matrix_to_row = downsample(inverted_Matrix_to_row,Timereso);
inverted_Matrix_to_row = invertedStimData_to_row;
inverted_Matrix_to_row = downsample(inverted_Matrix_to_row,Timereso);
StimDataSmooth2Visualise = reshape(inverted_Matrix_to_row', 262,size(BehData,1))';
[inverted_Matrix_to_row, s] = deconvolveCa(inverted_Matrix_to_row,'ar2');
StimDataSmoothDownSample = reshape(inverted_Matrix_to_row', 262,size(BehData,1))';
t=downsample(t,Timereso);
% kernle regression
%%
% full
eventTimes = {beep_time;stim_time; action_onsetTime; outcome_time};
eventValues = {[];[];[];[]};
windows = {[-400 800];[-200 1600];[-600 200];[200 2000]}; %
%windows = {[-400 800];[-400 2600];[-1000 200];[-400 2000]}; %
%windows = {[-1000 2000];[-1000 3000];[-2000 600];[-800 2000]}; %
% eventTimes = {beep_time; action_onsetTime; outcome_time};
% eventValues = {[];[];[]};
% % % % windows = {[-400 800];[-600 200];[200 2000]}; %
% windows = {[-400 800];[-1000 200];[-400 2000]}; %
% % %
% % % %
% eventTimes = {beep_time;stim_time; outcome_time};
% eventValues = {[];[];[]};
% % % % windows = {[-400 800];[-200 1600];[200 2000]}; %
% windows = {[-400 800];[-400 2600];[-400 2000]}; %
% % %
% % %
% eventTimes = {beep_time;stim_time; action_onsetTime};
% eventValues = {[];[];[]};
% % %
% windows = {[-400 800];[-400 2600];[-1000 200]}; %
% %
% %
% eventTimes = {stim_time; action_onsetTime; outcome_time};
% % eventValues = {[];[];[]};
% %windows = {[-200 1600];[-600 200];[200 2000]};
% windows = {[-400 2600];[-1000 200];[-400 2000]}; %
[fitKernels, predictedSignals,cvErr] = kernelRegression(inverted_Matrix_to_row', t', eventTimes, eventValues, windows , [0], [0 0]);
% explained Variance
EV = 1-mean(mean((predictedSignals-inverted_Matrix_to_row').^2))/mean(mean(inverted_Matrix_to_row.^2));
for iter = 1 : length(stim_time) -1 % we ingore the last trial
[diff_recover i_recover] = min (abs(t - stim_time(iter)));
Sliced_PredictedSignalStim (iter,:) =predictedSignals(i_recover-10 : i_recover+200);
[diff_recover i_recover] = min (abs(t - outcome_time(iter)));
Sliced_PredictedSignalRew (iter,:) =predictedSignals(i_recover-10 : i_recover+200);
end
figure
subplot(1,4,1)
plot(fitKernels{1})
subplot(1,4,2)
plot(fitKernels{2})
subplot(1,4,3)
plot(fitKernels{3})
subplot(1,4,4)
plot(fitKernels{4})
%%
% find trial by trail coefficents (regression)
%StimDataSmoothDownSample = smooth2a(StimDataSmoothDownSample,10,0);
scaling =1;
Coefiz = nan(size(StimData,1),4); % full
%Coefiz = nan(size(StimData,1),3); % reduced, no action
for itrial = 1: size(StimData,1)
% 72 is the stim onset (for 262) - full model
zeroPadBeep = zeros(1,262);
zeroPadAction = zeros(1,262);
zeroPadStim = zeros(1,262);
zeroPadOutcome = zeros(1,262);
zeroPadStim (71 -4:71+35-4) = scaling .* (fitKernels{2}');
%zeroPadStim (71 -8:71+59-8) = scaling .* (fitKernels{2}');
t_beep = floor((BehData(itrial, 13 ) - BehData(itrial, 12))*24); % 11 s is 262 samples, thus 1 s is 24
zeroPadBeep(71-t_beep -8 : 71 -t_beep+ 23-8) = scaling.*( fitKernels{1}');
t_act = floor((BehData(itrial, 10 ) - BehData(itrial, 13))*24); % 11 s is 328 samples, thus 1 s is 30
t_act (isnan(t_act))=floor((BehData(itrial, 14 ) - BehData(itrial, 13))*24) - 4; % very rare trials we could not measure action_onset time and we use this one
zeroPadAction(71+t_act-12 : 71+t_act-12 +15) = scaling .*(fitKernels{3}');
%zeroPadAction(71+t_act-20 : 71+t_act-20 +23) = scaling .*(fitKernels{3}');
t_out = floor((BehData(itrial, 14 ) - BehData(itrial, 13))*24); % 11 s is 328 samples, thus 1 s is 30
zeroPadOutcome(71+t_out+4 : 71+t_out+4 +35) = scaling .*(fitKernels{4}');
% zeroPadOutcome(71+t_out-8: 71+t_out-8 +47) = scaling .*(fitKernels{4}');
[B] = regress(StimDataSmoothDownSample(itrial,:)',[zeroPadBeep',zeroPadStim',zeroPadAction',zeroPadOutcome']);
% reduced model with good EV
% zeroPadBeep = zeros(1,262);
% zeroPadStim = zeros(1,262);
% zeroPadOutcome = zeros(1,262);
%
% zeroPadStim (72 -4:72+35-4) = scaling .* (fitKernels{2}');
%
% t_beep = floor((BehData(itrial, 13 ) - BehData(itrial, 12))*24); % 11 s is 262 samples, thus 1 s is 24
% zeroPadBeep(72-t_beep -8 : 72 -t_beep+ 23-8) = scaling.*( fitKernels{1}');
%
% t_out = floor((BehData(itrial, 14 ) - BehData(itrial, 13))*24); % 11 s is 328 samples, thus 1 s is 30
% zeroPadOutcome(72+t_out+4 : 72+t_out+4 +35) = scaling .*(fitKernels{3}');
%
%
% [B] = regress(StimDataSmoothDownSample(itrial,:)',[zeroPadBeep',zeroPadStim',zeroPadOutcome']);
Coefiz(itrial,:)= B';
% plotting example trials:
% if ismember(itrial,[400:430])
%
% figure
% plot(smooth(sum([B(1) .* zeroPadBeep',B(2) .*zeroPadStim',B(3) .*zeroPadAction',B(4) .*zeroPadOutcome']'),20) )
% hold on
% plot(smooth(StimDataSmooth2Visualise(itrial,:)',20))
%
%
%
% ax = gca;
%
% InterStimAction = 71 + t_act;
% h=rectangle(ax, 'Position',[InterStimAction 0 0.1 5],'EdgeColor',[1 0 0]);
%
%
% InterBeepStim = 71 - t_beep ;
% h=rectangle(ax, 'Position',[InterBeepStim 0 0.1 5],'EdgeColor',[0 1 0]);
%
%
% InterStimRew = 71 + t_out ;
% h=rectangle(ax, 'Position',[InterStimRew 0 0.1 5],'EdgeColor',[0 0 1]);
%
% end
end
%%
%Coefiz(Coefiz > 3) = nan;
%Coefiz(Coefiz < -2) = nan;
%Coefiz = zscore(Coefiz')';
StimsAllowed = unique(BehData(:,2))';
Stimz = BehData(:,2);
block = BehData(:,8);
correct = BehData(:,9);
BehDataAbs = BehData;
BehDataAbs(:,2) = abs(BehDataAbs(:,2));
for i=abzStim
tempIndex = find(abs(Stimz)==i);
[i j]=ismember(i, abzStim);
% correct large
resp_CorrStimLarge(j) = nanmedian(Coefiz(mintersect(tempIndex,find( correct==1),find(BehData(:,16)==1)),2));
resp_ErrStimLarge(j) = nanmedian(Coefiz(mintersect(tempIndex,find( correct==0),find(BehData(:,16)==1)),2));
resp_CorrStimSmall(j) = nanmedian(Coefiz(mintersect(tempIndex,find( correct==1),find(BehData(:,16)==-1)),2));
resp_CorrActionLarge(j) = nanmedian(Coefiz(mintersect(tempIndex,find( correct==1),find(BehData(:,16)==1)),3));
resp_ErrActionLarge(j) = nanmedian(Coefiz(mintersect(tempIndex,find( correct==0),find(BehData(:,16)==1)),3));
resp_CorrActionSmall(j) = nanmedian(Coefiz(mintersect(tempIndex,find( correct==1),find(BehData(:,16)==-1)),3));
resp_CorrOutcomeLarge(j) = nanmedian(Coefiz(mintersect(tempIndex,find( correct==1),find(BehData(:,16)==1)),4));
resp_ErrOutcomeLarge(j) = nanmedian(Coefiz(mintersect(tempIndex,find( correct==0),find(BehData(:,16)==1)),4));
resp_CorrOutcomeSmall(j) = nanmedian(Coefiz(mintersect(tempIndex,find( correct==1),find(BehData(:,16)==-1)),4));
end
%
figure
subplot(2,3,1)
plot(resp_CorrStimLarge,'g')
hold on
plot(resp_ErrStimLarge,'r')
subplot(2,3,2)
plot(resp_CorrStimLarge,'g')
hold on
plot(resp_CorrStimSmall,'--g')
% plot(resp_CorrAct,'g')
% hold on
% plot(resp_ErrAct,'r')
subplot(2,3,3)
plot(resp_CorrOutcomeLarge,'g')
hold on
plot(resp_ErrOutcomeLarge,'r')
subplot(2,3,4)
plot(resp_CorrOutcomeLarge,'g')
hold on
plot(resp_CorrOutcomeSmall,'--g')
%%
StimsAllowed = unique(BehData(:,2))';
Stimz = BehData(:,2);
block = BehData(:,8);
correct = BehData(:,9);
for i=unique(BehData(:,2))'
tempIndex = find(Stimz==i);
[i j]=ismember(i, StimsAllowed);
% full
resp_CorrStim(j) = nanmedian(Coefiz(mintersect(tempIndex,find( correct==1)),2));
resp_ErrStim(j) = nanmedian(Coefiz(mintersect(tempIndex,find( correct==0)),2));
resp_CorrAct(j) = nanmedian(Coefiz(mintersect(tempIndex,find( correct==1)),3));
resp_ErrAct(j) = nanmedian(Coefiz(mintersect(tempIndex,find( correct==0)),3));
resp_CorrRew(j) = nanmedian(Coefiz(mintersect(tempIndex,find( correct==1)),4));
resp_ErrRew(j) = nanmedian(Coefiz(mintersect(tempIndex,find( correct==0)),4));
resp_Block1StimCor(j) = nanmedian(Coefiz(mintersect(tempIndex,find( block==1),find( correct==1)),2));
resp_Block2StimCor(j) = nanmedian(Coefiz(mintersect(tempIndex,find( block==2),find( correct==1)),2));
resp_Block1ActCor(j) = nanmedian(Coefiz(mintersect(tempIndex,find( block==1),find( correct==0)),3));
resp_Block2ActCor(j) = nanmedian(Coefiz(mintersect(tempIndex,find( block==2),find( correct==0)),3));
resp_Block1RewCor(j) = nanmedian(Coefiz(mintersect(tempIndex,find( block==1),find( correct==1)),4));
resp_Block2RewCor(j) = nanmedian(Coefiz(mintersect(tempIndex,find( block==2),find( correct==1)),4));
% reduced
% resp_CorrStim(j) = nanmedian(Coefiz(mintersect(tempIndex,find( correct==1)),2));
% resp_ErrStim(j) = nanmedian(Coefiz(mintersect(tempIndex,find( correct==0)),2));
%
%
% resp_CorrRew(j) = nanmedian(Coefiz(mintersect(tempIndex,find( correct==1)),3));
% resp_ErrRew(j) = nanmedian(Coefiz(mintersect(tempIndex,find( correct==0)),3));
%
% resp_Block1StimCor(j) = nanmedian(Coefiz(mintersect(tempIndex,find( block==1),find( correct==1)),2));
% resp_Block2StimCor(j) = nanmedian(Coefiz(mintersect(tempIndex,find( block==2),find( correct==1)),2));
%
% resp_Block1RewCor(j) = nanmedian(Coefiz(mintersect(tempIndex,find( block==1),find( correct==1)),3));
% resp_Block2RewCor(j) = nanmedian(Coefiz(mintersect(tempIndex,find( block==2),find( correct==1)),3));
%
end
figure
subplot(2,3,1)
plot(resp_CorrStim,'g')
hold on
plot(resp_ErrStim,'r')
subplot(2,3,2)
% plot(resp_CorrAct,'g')
% hold on
% plot(resp_ErrAct,'r')
subplot(2,3,3)
plot(resp_CorrRew,'g')
hold on
plot(resp_ErrRew,'r')
subplot(2,3,4)
plot(resp_Block1StimCor,'k')
hold on
plot(resp_Block2StimCor,'-.k')
subplot(2,3,5)
% plot(resp_Block1ActCor,'k')
% hold on
% plot(resp_Block2ActCor,'-.k')
subplot(2,3,6)
plot(resp_Block1RewCor,'k')
hold on
plot(resp_Block2RewCor,'-.k')
%save
BehPhotoM(animal_ID).KernelSummary.Kernels = fitKernels;
BehPhotoM(animal_ID).KernelSummary.resp_CorrStim = resp_CorrStim;
BehPhotoM(animal_ID).KernelSummary.resp_ErrStim = resp_ErrStim;
BehPhotoM(animal_ID).KernelSummary.resp_CorrAct=resp_CorrAct;
BehPhotoM(animal_ID).KernelSummary.resp_ErrAct=resp_ErrAct;
BehPhotoM(animal_ID).KernelSummary.resp_CorrRew=resp_CorrRew;
BehPhotoM(animal_ID).KernelSummary.resp_ErrRew=resp_ErrRew;
BehPhotoM(animal_ID).KernelSummary.resp_Block1StimCor=resp_Block1StimCor;
BehPhotoM(animal_ID).KernelSummary.resp_Block2StimCor=resp_Block2StimCor;
BehPhotoM(animal_ID).KernelSummary.resp_Block1ActCor=resp_Block1ActCor;
BehPhotoM(animal_ID).KernelSummary.resp_Block2ActCor=resp_Block2ActCor;
BehPhotoM(animal_ID).KernelSummary.resp_Block1RewCor=resp_Block1RewCor;
BehPhotoM(animal_ID).KernelSummary.resp_Block2RewCor=resp_Block2RewCor;
%%
StimzAbs = abs(BehData(1:end-1,2));
StimsAllowed = unique(StimzAbs');
for i=unique(StimzAbs')
tempIndex = find(StimzAbs==i);
[i j]=ismember(i, StimsAllowed);
PredictedPopStimAlign(j,:)=nanmean(Sliced_PredictedSignalStim(tempIndex,:));
end
figure;
coefStim = [0.6 1 2 3]
for i=1:4
PredictedPopStimAlign(i,15:end)= coefStim(i) .* PredictedPopStimAlign(i,15:end);
hold on
plot(PredictedPopStimAlign(i,:),'color',colorGray(i,:))
end
BehData(end,:)=[];
IndexLargeRew = [find(BehData(:,9)==1 & BehData(:,3)==-1 & BehData(:,8)==1 ) ; find(BehData(:,9)==1 & BehData(:,3)==1 & BehData(:,8)==2) ];
IndexSmallRew = [find(BehData(:,9)==1 & BehData(:,3)==-1 & BehData(:,8)==2 ) ; find(BehData(:,9)==1 & BehData(:,3)==1 & BehData(:,8)==1) ];
IndexNoRew = find(BehData(:,9)==1);
figure; hold on
plot(nanmean(Sliced_PredictedSignalRew(IndexLargeRew,:)));
plot(nanmean(0.5 * Sliced_PredictedSignalRew(IndexSmallRew,:)));
plot(nanmean(-0.2 * Sliced_PredictedSignalRew(IndexNoRew,:)));
BehPhotoM(animal_ID).KernelSummary.PredictedPopStimAlign=PredictedPopStimAlign;
BehPhotoM(animal_ID).KernelSummary.PredictedPopRewAlignLarge=nanmean(Sliced_PredictedSignalRew(IndexLargeRew,:));
BehPhotoM(animal_ID).KernelSummary.PredictedPopRewAlignSmall=nanmean(0.5 * Sliced_PredictedSignalRew(IndexSmallRew,:));
BehPhotoM(animal_ID).KernelSummary.PredictedPopRewAlignNoRew=nanmean(-0.2 *Sliced_PredictedSignalRew(IndexNoRew,:));