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Analysis.m
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classdef Analysis
% ANALYSIS Collection of functions (static methods) used for GLM analysis
% of point process data.
% <a href="matlab: methods('Analysis')">methods</a>
% <a href="matlab:web('AnalysisExamples.html', '-helpbrowser')">Analysis Examples</a>
%
% see also <a href="matlab:help('Trial')">Trial</a>, <a
% href="matlab:help('CovColl')">CovColl</a>, <a
% href="matlab:help('nstColl')">nstColl</a>,<a
% href="matlab:help('History')">History</a>
%
% Reference page in Help browser
% <a href="matlab: doc('Analysis')">doc Analysis</a>
%%
% nSTAT v1 Copyright (C) 2012 Masschusetts Institute of Technology
% Cajigas, I, Malik, WQ, Brown, EN
% This program is free software; you can redistribute it and/or
% modify it under the terms of the GNU General Public License as published
% by the Free Software Foundation; either version 2 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
% See the GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program; if not, write to the Free Software Foundation,
% Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
properties (Constant)
colors = {'b','g','r','c','m','y','k'};
end
methods (Static)
function fitResults =RunAnalysisForNeuron(tObj,neuronNumber,configColl,makePlot,Algorithm,DTCorrection,batchMode)
% fitResults =RunAnalysisForNeuron(tObj,neuronNumber,configColl,makePlot,Algorithm)
% tObj: Trial to be analyzed
% neuronNumber: number of the neuron to be analyzed. Can be a
% vector to specify multiple neurons to be analyzed.
% If more than one neuron specified, then
% fitResults is a cell array of fitResult
% objects. fitResults{i} will contain the
% fitResults object for neuronNum(i).
% configColl: ConfigColl object containing the different
% configurations (description of the the types of fits, eg. covariates) that correspond to each fit.
% makePlot: Set to 1 to show a summary plot for this neuron. If performing multiple neuron analysis (eg. via RunAnalysisForAllNeurons) set ths parameter to zero to avoid screen clutter.
% Algorithm: Either 'GLM' or 'BNLRCG'. Default is 'GLM'
% GLM - Standard GLM regression from matlab.
% BNLRCG - faster Truncated, L-2 Regularized,
% Binomial Logistic Regression with Conjugate
% Gradient Solver by Demba Ba ([email protected]).
% DTCorrection: 0 for no DT Correction of KS plot, 1 is the
% default.
%
% batchMode: when batchMode=1 neurons with same name are fit at once rather than separetely
if(nargin<7 || isempty(batchMode))
batchMode = 0; %default treat each spike train separately
end
if(nargin<6 || isempty(DTCorrection))
DTCorrection =1;
end
if(nargin<5 || isempty(Algorithm))
Algorithm = 'GLM';
end
if(nargin<4 || isempty(makePlot))
makePlot=1;
end
numNeurons = length(neuronNumber);
labels=cell(numNeurons,1);
lambda=cell(numNeurons,1);
b =cell(numNeurons,1);
dev =zeros(numNeurons,1);
numHist=cell(numNeurons,1);
stats =cell(numNeurons,1);
histObj =cell(numNeurons,1);
ensHistObj=cell(numNeurons,1);
AIC =zeros(numNeurons,1);
BIC =zeros(numNeurons,1);
logLL =zeros(numNeurons,1);
windowSize = .01; % 1/tObj.sampleRate;% for Residual Computation;
spikeTraining = cell(1,numNeurons);
XvalData =cell(numNeurons,1);
XvalTime =cell(numNeurons,1);
spikeValidation = cell(1,numNeurons);
%% Fit Using Training Data
if(diff(tObj.validationWindow)~=0)
tObj.setTrialTimesFor('training');
end
if(batchMode==1)
display('Running in batch mode: neurons with same name are fit simultaneously');
end
for i=1:configColl.numConfigs
configColl.setConfig(tObj,i);
% fprintf(strcat('Analyzing Configuration #',num2str(i)));
pool = gcp('nocreate');
if(isempty(pool))
pools = 0;
else
pools = pool.NumWorkers;
end%matlabpool('size');
if(pools==0)
if(batchMode==0)
fprintf(strcat('Analyzing Configuration #',num2str(i),': Neuron #'));
for j=1:numNeurons
% fprintf(strcat('Analyzing Configuration #',num2str(i),': Neuron #',num2str(neuronNumber(j))));
if(j==1)
fprintf('%d',neuronNumber(j));
else
fprintf(',%d',neuronNumber(j));
end
%clear tempLabels;
%tObj.setCurrentNeuron(neuronNumber);
otherLabels = tObj.getLabelsFromMask(neuronNumber(j));
% labels{j}{i} = horzcat('Baseline',otherLabels); % Labels change depending on presence/absense of History or ensCovHist
labels{j}{i} = otherLabels; % Labels change depending on presence/absense of History or ensCovHist
numHist{j}{i} = tObj.getNumHist;
histObj{j}{i} = tObj.history;
ensHistObj{j}{i} = tObj.ensCovHist;
[lambdaTemp, bTemp, devTemp, statsTemp,AICTemp,BICTemp,logLLTemp, distribTemp] = Analysis.GLMFit(tObj,neuronNumber(j),i,Algorithm);
lambda{j}{i} = lambdaTemp; b{j}{i} = bTemp; stats{j}{i} = statsTemp;
dev(j,i) = devTemp; AIC(j,i)= AICTemp; BIC(j,i)= BICTemp; logLL(j,i) = logLLTemp;
distrib{j}{i} =distribTemp;
spikeTraining{j} = tObj.nspikeColl.getNST(neuronNumber(j));%.nstCopy;
spikeTraining{j}.setName(num2str(neuronNumber(j)));
%% Collect the validation Data
if(diff(tObj.validationWindow)~=0)
tObj.setTrialTimesFor('validation');
XvalData{j}{i}=tObj.getDesignMatrix(neuronNumber(j));
XvalTime{j}{i}=tObj.covarColl.getCov(1).time;
spikeValidation{j} = tObj.nspikeColl.getNST(neuronNumber(j));%.nstCopy;
spikeTraining{j}.setName(num2str(neuronNumber(j)));
tObj.setTrialTimesFor('training')
end
end
elseif(batchMode==1)
neuronNames=neuronNumber; % This is an index of names in the batchMode case
fprintf(strcat('Analyzing Configuration #',num2str(i),': Neuron #'));
for j=1:numNeurons
% if(isa(neuronNames,'cell'))
% fprintf(strcat('Analyzing Configuration #',num2str(i),': Neuron #'));
% display(strcat('Analyzing Configuration #',num2str(i),': Neuron #',neuronNames{j}));
% elseif(isa(neuronNames,'char'))
% display(strcat('Analyzing Configuration #',num2str(i),': Neuron #',neuronNames));
% elseif(isa(neuronNames,'double'))
% display(strcat('Analyzing Configuration #',num2str(i),': Neuron #',num2str(neuronNames)));
% end
if(isa(neuronNames,'cell'))
if(j==1)
fprintf('%s',neuronNames{j});
else
fprintf(',%s',neuronNames{j});
end
elseif(isa(neuronNames,'char'))
if(j==1)
fprintf('%s',neuronNames);
else
fprintf(',%s',neuronNames);
end
elseif(isa(neuronNames,'double'))
if(j==1)
fprintf('%d',neuronNames);
else
fprintf(',%d',neuronNames);
end
end
%clear tempLabels;
%tObj.setCurrentNeuron(neuronNumber);
otherLabels = tObj.getLabelsFromMask(neuronNumber(j));
% labels{j}{i} = horzcat('Baseline',otherLabels); % Labels change depending on presence/absense of History or ensCovHist
labels{j}{i} = otherLabels; % Labels change depending on presence/absense of History or ensCovHist
numHist{j}{i} = tObj.getNumHist;
histObj{j}{i} = tObj.history;
ensHistObj{j}{i} = tObj.ensCovHist;
if(isa(neuronNames,'cell'))
[lambdaTemp, bTemp, devTemp, statsTemp,AICTemp,BICTemp,logLLTemp,distribTemp] = Analysis.GLMFit(tObj,neuronNumber{j},i,Algorithm);
elseif(isa(neuronNames,'char'))
[lambdaTemp, bTemp, devTemp, statsTemp,AICTemp,BICTemp,logLLTemp,distribTemp] = Analysis.GLMFit(tObj,neuronNumber,i,Algorithm);
else
[lambdaTemp, bTemp, devTemp, statsTemp,AICTemp,BICTemp,logLLTemp,distribTemp] = Analysis.GLMFit(tObj,neuronNumber(j),i,Algorithm);
end
lambda{j}{i} = lambdaTemp; b{j}{i} = bTemp; stats{j}{i} = statsTemp;
dev(j,i) = devTemp; AIC(j,i)= AICTemp; BIC(j,i)= BICTemp; logLL(j,i) = logLLTemp;
distrib{j}{i} =distribTemp;
if(isa(neuronNames,'cell'))
currSpikes=tObj.nspikeColl.getNST(tObj.getNeuronIndFromName(neuronNames{j}));
elseif(isa(neuronNames,'char'))
currSpikes=tObj.nspikeColl.getNST(tObj.getNeuronIndFromName(neuronNames));
else
currSpikes=tObj.nspikeColl.getNST(neuronNames(j));
end
for n=1:length(currSpikes)
if(isa(currSpikes,'cell'))
currSpikes{n} = currSpikes{n}.nstCopy;
if(isa(neuronNames,'cell'))
currSpikes{n}.setName(neuronNames{j});
elseif(isa(neuronNames,'char'))
currSpikes{n}.setName(neuronNames);
else
currSpikes{n}.setName(neuronNames(j));
end
else
currSpikes = currSpikes.nstCopy;
if(isa(neuronNames,'cell'))
currSpikes.setName(neuronNames{j});
elseif(isa(neuronNames,'char'))
currSpikes.setName(neuronNames);
else
currSpikes.setName(neuronNames(j));
end
end
end
spikeTraining{j} = currSpikes;
%% Collect the validation Data
if(diff(tObj.validationWindow)~=0)
tObj.setTrialTimesFor('validation');
tempIndices=tObj.getNeuronIndFromName(neuronNames{j});
currSpikes=tObj.nspikeColl.getNST(tempIndices);
tempX = [];
tempTime=[];
for n=1:length(tempIndices)
currSpikes{n} = currSpikes{n}.nstCopy;
currSpikes{n}.setName(neuronNames{j});
if(n==1)
tempX =tObj.getDesignMatrix(tempIndices(n));
tempTime =tObj.covarColl.getCov(1).time;
else
tempX = [tempX; tObj.getDesignMatrix(tempIndices(n))];
offset = max(tempTime)+1/tObj.sampeRate;
tempTime = [tempTime;(tObj.covarColl.getCov(1).time+offset)];
end
end
spikeValidation{j} = currSpikes;
XvalData{j}{i}=tempX;
XvalTime{j}{i}=tempTime;
tObj.setTrialTimesFor('training')
end
end
end
fprintf('\n');
else %use parallel toolbox
if(batchMode==0)
fprintf(strcat('Analyzing Configuration #',num2str(i),': Neuron #',num2str(neuronNumber)));
parfor j=1:numNeurons
%clear tempLabels;
%tObj.setCurrentNeuron(neuronNumber);
otherLabels = tObj.getLabelsFromMask(neuronNumber(j));
% labels{j}{i} = horzcat('Baseline',otherLabels); % Labels change depending on presence/absense of History or ensCovHist
labels{j}{i} = otherLabels; % Labels change depending on presence/absense of History or ensCovHist
numHist{j}{i} = tObj.getNumHist;
histObj{j}{i} = tObj.history;
ensHistObj{j}{i} = tObj.ensCovHist;
[lambdaTemp, bTemp, devTemp, statsTemp,AICTemp,BICTemp,logLLTemp,distribTemp] = Analysis.GLMFit(tObj,neuronNumber(j),i,Algorithm);
lambda{j}{i} = lambdaTemp; b{j}{i} = bTemp; stats{j}{i} = statsTemp;
dev(j,i) = devTemp; AIC(j,i)= AICTemp; BIC(j,i)= BICTemp; logLL(j,i)=logLLTemp;
distrib{j}{i} =distribTemp;
spikeTraining{j} = tObj.nspikeColl.getNST(neuronNumber(j));%.nstCopy;
spikeTraining{j}.setName(num2str(neuronNumber(j)));
%% Collect the validation Data
if(diff(tObj.validationWindow)~=0)
tObj.setTrialTimesFor('validation');
XvalData{j}{i}=tObj.getDesignMatrix(neuronNumber(j));
XvalTime{j}{i}=tObj.covarColl.getCov(1).time;
spikeValidation{j} = tObj.nspikeColl.getNST(neuronNumber(j));%.nstCopy;
spikeTraining{j}.setName(num2str(neuronNumber(j)));
tObj.setTrialTimesFor('training')
end
end
elseif(batchMode==1)
neuronNames=neuronNumber; % This is an index of names in the batchMode case
fprintf(strcat('Analyzing Configuration #',num2str(i),': Neuron #',num2str(neuronNames)));
parfor j=1:numNeurons
%clear tempLabels;
%tObj.setCurrentNeuron(neuronNumber);
otherLabels = tObj.getLabelsFromMask(neuronNumber(j));
% labels{j}{i} = horzcat('Baseline',otherLabels); % Labels change depending on presence/absense of History or ensCovHist
labels{j}{i} = otherLabels; % Labels change depending on presence/absense of History or ensCovHist
numHist{j}{i} = tObj.getNumHist;
histObj{j}{i} = tObj.history;
ensHistObj{j}{i} = tObj.ensCovHist;
if(isa(neuronNames,'cell'))
[lambdaTemp, bTemp, devTemp, statsTemp,AICTemp,BICTemp,logLLTemp,distribTemp] = Analysis.GLMFit(tObj,neuronNumber{j},i,Algorithm);
elseif(isa(neuronNames,'char'))
[lambdaTemp, bTemp, devTemp, statsTemp,AICTemp,BICTemp,logLLTemp,distribTemp] = Analysis.GLMFit(tObj,neuronNumber,i,Algorithm);
else
[lambdaTemp, bTemp, devTemp, statsTemp,AICTemp,BICTemp,logLLTemp,distribTemp] = Analysis.GLMFit(tObj,neuronNumber(j),i,Algorithm);
end
lambda{j}{i} = lambdaTemp; b{j}{i} = bTemp; stats{j}{i} = statsTemp;
dev(j,i) = devTemp; AIC(j,i)= AICTemp; BIC(j,i)= BICTemp; logLL(j,i) = logLLTemp;
distrib{j}{i} =distribTemp;
if(isa(neuronNames,'cell'))
currSpikes=tObj.nspikeColl.getNST(tObj.getNeuronIndFromName(neuronNames{j}));
elseif(isa(neuronNames,'char'))
currSpikes=tObj.nspikeColl.getNST(tObj.getNeuronIndFromName(neuronNames));
else
currSpikes=tObj.nspikeColl.getNST(neuronNames(j));
end
for n=1:length(currSpikes)
if(isa(currSpikes,'cell'))
currSpikes{n} = currSpikes{n}.nstCopy;
if(isa(neuronNames,'cell'))
currSpikes{n}.setName(neuronNames{j});
elseif(isa(neuronNames,'char'))
currSpikes{n}.setName(neuronNames);
else
currSpikes{n}.setName(neuronNames(j));
end
else
currSpikes = currSpikes.nstCopy;
if(isa(neuronNames,'cell'))
currSpikes.setName(neuronNames{j});
elseif(isa(neuronNames,'char'))
currSpikes.setName(neuronNames);
else
currSpikes.setName(neuronNames(j));
end
end
end
spikeTraining{j} = currSpikes;
%% Collect the validation Data
if(diff(tObj.validationWindow)~=0)
tObj.setTrialTimesFor('validation');
tempIndices=tObj.getNeuronIndFromName(neuronNames{j});
currSpikes=tObj.nspikeColl.getNST(tempIndices);
tempX = [];
tempTime=[];
for n=1:length(tempIndices)
currSpikes{n} = currSpikes{n}.nstCopy;
currSpikes{n}.setName(neuronNames{j});
if(n==1)
tempX =tObj.getDesignMatrix(tempIndices(n));
tempTime =tObj.covarColl.getCov(1).time;
else
tempX = [tempX; tObj.getDesignMatrix(tempIndices(n))];
offset = max(tempTime)+1/tObj.sampeRate;
tempTime = [tempTime;(tObj.covarColl.getCov(1).time+offset)];
end
end
spikeValidation{j} = currSpikes;
XvalData{j}{i}=tempX;
XvalTime{j}{i}=tempTime;
tObj.setTrialTimesFor('training')
end
end
end
fprintf('\n');
end
end
% %% Collect the validation Data
%
% if(diff(tObj.validationWindow)~=0)
% tObj.setTrialTimesFor('validation');
% for i=1:configColl.numConfigs
% configColl.setConfig(tObj,i);
% for j=1:numNeurons
% XvalData{j,i}=tObj.getDesignMatrix(neuronNumber(j));
% XvalTime{j,i}=tObj.covarColl.getCov(1).time;
% spikeValidation{j} = tObj.nspikeColl.getNST(neuronNumber(j)).nstCopy;
% spikeTraining{j}.setName(num2str(neuronNumber(j)));
% end
% end
%
% %tObj.setTrialTimesFor('training');
% end
%
%% Store the results
fitResults =cell(length(neuronNumber),1);
for j=1:numNeurons
fitResults{j}=FitResult(spikeTraining{j},labels{j},numHist{j},histObj{j},ensHistObj{j},lambda{j},b{j}, dev(j,:), stats{j},AIC(j,:),BIC(j,:),logLL(j,:),configColl,XvalData{j},XvalTime{j},distrib{j});
if(diff(tObj.validationWindow)~=0)
tObj.setTrialTimesFor('validation');
[lambdaValidation, logLLValidation] = fitResults{j}.computeValLambda;
ValResults = FitResult(spikeValidation{j},labels{j},numHist{j},histObj{j},ensHistObj{j},lambdaValidation,b{j}, dev(j,:), stats{j},AIC(j,:),BIC(j,:),logLLValidation,configColl,XvalData{j},XvalTime{j},distrib);
fitResults{j}.validation = ValResults; %validation field is actually another fitResults object but with the validation data
end
%% Process the results and compute further parameters
if(makePlot==1)
scrsz = get(0,'ScreenSize');
figure('Position',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.8 scrsz(4)*.8]);
subplot(2,4,[1 2]); Analysis.KSPlot(fitResults{j},DTCorrection,makePlot); %make the plot
hold on; text(.45, .95,strcat('Neuron:',num2str(neuronNumber(j))));
subplot(2,4,3); Analysis.plotInvGausTrans(fitResults{j},makePlot);
subplot(2,4,4); Analysis.plotSeqCorr(fitResults{j});
subplot(2,4,[7 8]); Analysis.plotFitResidual(fitResults{j},windowSize,makePlot);
subplot(2,4,[5 6]); Analysis.plotCoeffs(fitResults{j});
else
Analysis.KSPlot(fitResults{j},DTCorrection,makePlot);
Analysis.plotInvGausTrans(fitResults{j},makePlot);
Analysis.plotFitResidual(fitResults{j},windowSize,makePlot);
%fitResults.computePlotParams;
end
end
if(length(neuronNumber)==1)
fitResults = fitResults{1};
end
end
function fitResults = RunAnalysisForAllNeurons(tObj,configs,makePlot,Algorithm,DTCorrection,batchMode)
% fitResults = RunAnalysisForAllNeurons(tObj,configs,makePlot,Algorithm)
% Runs the fits specifed by configs (a ConfigColl object) on
% all the neurons that are unmasked in the trial tObj.
% tObj - trial to be analyzed
% configs - ConfigColl object specifying the types of fits to
% be performed.
% makePlot - Set to 1 to generate a summary plot for each
% neuron.
% Algorithm: Either 'GLM' or 'BNLRCG'. Default is 'GLM'
% GLM - Standard GLM regression from matlab.
% BNLRCG - faster Truncated, L-2 Regularized,
% Binomial Logistic Regression with Conjugate
% Gradient Solver by Demba Ba ([email protected]).
% DTCorrection: 0 for no DT Correction of KS plot, 1 is the
% default.
% batchMode: when batchMode=1 neurons with same name are fit at once rather than separetely
if(nargin<6 || isempty(batchMode))
batchMode = 0; %default treat each spike train separately
end
if(nargin<5 || isempty(DTCorrection))
DTCorrection =1;
end
if(nargin<4 || isempty(Algorithm))
Algorithm = 'GLM';
end
if(nargin<3 || isempty(makePlot))
makePlot=1; %default to plotting results
end
if(batchMode==0)
neuronIndex=tObj.getNeuronIndFromMask;
elseif(batchMode==1)
neuronIndex=tObj.getUniqueNeuronNames;
end
% numLoops = floor(length(neuronIndex)/4);
% loopArray = cell(1,numLoops);
% for k=1:numLoops
% if(k==numLoops)
% loopArray{k} = neuronIndex((4*(k-1)+1):end);
% else
% loopArray{k} = neuronIndex((4*(k-1)+1):4*k);
% end
% end
% parfor i=1:length(neuronIndex)
fitResults = Analysis.RunAnalysisForNeuron(tObj,neuronIndex,configs,makePlot,Algorithm,DTCorrection,batchMode);
%end
end
function [lambda,b, dev, stats,AIC, BIC,logLL, distribution] = GLMFit(tObj,neuronNumber,lambdaIndex,Algorithm)
% [lambda,b, dev, stats,AIC, BIC] = GLMFit(tObj,neuronNumber,lambdaIndex,Algorithm)
% Given a trial, tObj, and a neuronNumber specifying a neuron
% from the trial, extracts the design matrix X from the current
% covariate masks, history, and ensemble history in the trial,
% and the observation vector,Y, and performs the GLM regression
% using the specified algorithm. lambdaIndex: is used to
% labeling the returned lambda with the number of the
% configuration that it corresponds to.
% Algorithm: Either 'GLM' or 'BNLRCG'. Default is 'GLM'
% GLM - Standard GLM regression from matlab.
% BNLRCG - faster Truncated, L-2 Regularized,
% Binomial Logistic Regression with Conjugate
% Gradient Solver by Demba Ba ([email protected]).
% Returns:
% lambda - Covariate containing the resulting conditional
% intensity function evaluated with the design matrix data.
% b - the GLM regression coefficients. Constant term is
% first followed by the components in X.
%
% dev - deviance for the this regression.
% stats - stats structure from the GLM regression
% (p-values,std dev, etc.)
% AIC - Akaike's information criteria for this regression.
% BIC - Bayes Information Criteria for this regression.
% logLL - Log Likelihood evaluated with the fit parameters.
if(nargin<4)
Algorithm='GLM';
end
if(isa(neuronNumber,'double'))
binaryRep=tObj.nspikeColl.getNST(neuronNumber).isSigRepBinary;
indices=neuronNumber;
elseif(isa(neuronNumber,'char'))
indices=tObj.getNeuronIndFromName(neuronNumber);
binRep=zeros(size(indices));
for i=1:length(indices)
binRep(i)=tObj.nspikeColl.getNST(indices(i)).isSigRepBinary;
end
binaryRep=prod(binRep);
elseif(isa(neuronNumber,'cell'))
indices=tObj.getNeuronIndFromName(neuronNumber{1});
binRep=zeros(size(indices));
for i=1:length(indices)
binRep(i)=tObj.nspikeColl.getNST(indices(i)).isSigRepBinary;
end
binaryRep=prod(binRep);
end
if(strcmp(Algorithm,'BNLRCG') && ~binaryRep)
error('To use BNLRCG Algorithm, spikeTrain must have a binary representation. Increase sampleRate and try again');
end
%If performing batchMode analysis, this stacks up the
%corresponding spike vectors and the design matrices
for i=1:length(indices)
if(i==1)
y=tObj.getSpikeVector(indices(i));
X=tObj.getDesignMatrix(indices(i));
lambdaTime = tObj.getCov(1).time;
else
y=[y; tObj.getSpikeVector(indices(i))];
X=[X; tObj.getDesignMatrix(indices(i))];
offset = max(lambdaTime)+1/tObj.sampleRate;
lambdaTime = [lambdaTime; (tObj.getCov(1).time +offset)];
end
end
%For a single neuron given covariates,perform the GLM fit.
%
% if(binaryRep)
% distribution = 'binomial';
% linkfunction = 'logit';
% else
% distribution = 'poisson';
% linkfunction = 'log';
% end
% size(X)
% size(y)
if(strcmp(Algorithm,'GLM'))
distribution = 'poisson';
linkfunction = 'log';
[b,dev,stats] = glmfit(X,y,distribution, 'link', linkfunction,'constant','off');
elseif(strcmp(Algorithm,'BNLRCG'))
distribution = 'binomial';
linkfunction = 'logit';
rrflag=0; %ML estimation
[b,dev,stats] = bnlrCG(X,y,rrflag);
else
error('Algorithm not supported!');
end
b=real(b); %make sure to avoid complex coefficients ... sometimes algorithms return
%complex b with the imaginary part near zero.
%Need to explore why. For now just keep the real
%part.
if(length(b)>=1)
if(strcmp(distribution,'binomial'))
data = exp(X*b(1:end));
data = (data./(1+data)).*tObj.sampleRate;
elseif(strcmp(distribution,'poisson'));
data = exp(X*b(1:end)).*tObj.sampleRate;
%
end
end
lambdaIndexStr = num2str(lambdaIndex);
lambda=Covariate(lambdaTime,data,...
'\lambda(t)',tObj.getCov(1).xlabelval,...
tObj.getCov(1).xunits,'Hz',strcat('\lambda_{',lambdaIndexStr,'}'));
mu=b;
s=stats.se;
% Mc=30;
% for c=1:Mc
% z=normrnd(0,1,length(s),1);
% bKDraw(:,c)=mu+(s.*z);
% end
% if(strcmp(distribution,'poisson'))
% lambdaDraw=exp(X*bKDraw)*(tObj.sampleRate);
% else
% lambdaDraw=exp(X*bKDraw)./(1+exp(X*bKDraw))*(tObj.sampleRate);
% end
% lambdaDraw(isinf(lambdaDraw))=0;
% alphaVal=.05;
% for k=1:length(lambdaDraw)
% [f,x] = ecdf(squeeze(lambdaDraw(k,:)));
% CIs(k,1) = x(find(f<alphaVal/2,1,'last'));
% CIs(k,2) = x(find(f>(1-alphaVal/2),1,'first'));
% end
%
%
% ciPSTHGLM = ConfidenceInterval(lambdaTime,CIs,'CI_{psth_GLM}',lambda.xlabelval,lambda.xunits,lambda.yunits);
% lambda.setConfInterval(ciPSTHGLM);
%The deviance should be real since it a probability measure
%and therefore any imaginary part is ignored.
AIC = 2*length(b)+real(dev);
BIC = length(b)*log(length(y))+real(dev);
delta = 1/tObj.sampleRate;
logLL =sum(y.*log(data*delta)+(1-y).*(1-data*delta));
end
function handle = plotInvGausTrans(fitResults,makePlot)
% handle = plotInvGausTrans(fitResults,makePlot)
% Given the CDF of the rescaled spike times (the u'js) computes
% the auto-correlation function inverse gaussian tranformated
% u'js and the 95% confidence interval that they are distinct
% from zero.
% Idea: if gaussian RVs are uncorrelated, they are indep., then
% this suggest independence of the uj's and of the zj's
% from the time-rescaling theorem. If zj's are
% independent and KS plot is within 95% confidence
% interval suggests that candidate lambda is close to the
% true lambda.
if(nargin<2)
makePlot=0;
end
[X,rhoSig,confBoundSig] = Analysis.computeInvGausTrans(fitResults.Z);
fitResults.setInvGausStats(X,rhoSig,confBoundSig);
if(fitResults.isValDataPresent)
[X,rhoSig,confBoundSig] = Analysis.computeInvGausTrans(fitResults.validation.Z);
fitResults.validation.setInvGausStats(X,rhoSig,confBoundSig);
end
if(makePlot==1)
handle=fitResults.plotInvGausTrans;
end
end
function plotFitResidual(fitResults,windowSize,makePlot)
% plotFitResidual(fitResults,windowSize,makePlot)
% computes the point process residual between the true spike
% train and that predicted by the candidate conditional
% intensity function.
% The result is stored in fitResult.
%
if(nargin<3 || isempty(makePlot))
makePlot=1;
end
if(nargin<2 || isempty(windowSize))
windowSize=.01;
end
M = Analysis.computeFitResidual(fitResults.neuralSpikeTrain,fitResults.lambda,windowSize);
fitResults.setFitResidual(M);
if(fitResults.isValDataPresent)
M = Analysis.computeFitResidual(fitResults.validation.neuralSpikeTrain,fitResults.validation.lambda,windowSize);
fitResults.validation.setFitResidual(M);
end
if(makePlot)
fitResults.plotResidual;
end
end
function handle = KSPlot(fitResults,DTCorrection,makePlot)
%handle = KSPlot(fitResults,makePlot)
% Computes the KS statistics and makes the plot. Stores
% appropriate parameters in fitResults.
% If validation data is also available, it does the same for
% the validation data.
% DTCorrection: 0 for no DT Correction of KS plot, 1 is the
% default.
if(nargin <3)
makePlot =1; %By default make the plot
end
if(nargin<2)
DTCorrection = 1;
end
[Z, U, xAxis, KSSorted, ks_stat] = Analysis.computeKSStats(fitResults.neuralSpikeTrain,fitResults.lambda,DTCorrection);
fitResults.setKSStats(Z,U, xAxis, KSSorted, ks_stat);
if(fitResults.isValDataPresent)
%make sure nst is in appropriate window
[Z, U, xAxis, KSSorted, ks_stat] = Analysis.computeKSStats(fitResults.validation.neuralSpikeTrain,fitResults.validation.lambda,DTCorrection);
fitResults.validation.setKSStats(Z, U, xAxis, KSSorted, ks_stat);
end
if(makePlot)
handle = fitResults.KSPlot;
else
handle = [];
end
end
function handle = plotSeqCorr(fitResults)
% handle = plotSeqCorr(fitResults)
% Plots the sequential correlation coefficients of the rescaled
% ISIs. zj vs. zj-1
handle = fitResults.plotSeqCorr;
end
function handle = plotCoeffs(fitResults)
% handle = plotCoeffs(fitResults)
% Plots the regression coefficients for all the different fits.
handle = fitResults.plotCoeffs;
end
function [X,rhoSig,confBoundSig] = computeInvGausTrans(Z)
% [U,X,rhoSig,confBoundSig] = computeInvGausTrans(Z)
% Take rescaled spikeTimes, zjs, transforms them to
% uniform(0,1), then computes the inverse gaussian
% transformation of these to xj's. rhoSig is the
% auto-correlation funcion of these xj's and is used to test
% for independence of the xj's. Independence of the xj's
% suggests indepence of the uj's and zj's (a condition
% necessary for the Time Rescaling Theorem).
U=1-exp(-Z);
U(U>=.999999)=.999999; %Prevent any 1 values which lead to infinity in X
U(U==0)=.000001;
X = norminv(U,0,1);
%X=erfinv(U);
[~,colm] = size(X);
if(~isempty(X))
for i=1:colm
[c(:,i),lags] = xcov(X(:,i));
end
else
[c,lags] = xcov(X);
end
index=find(lags==1);
lags=lags(index:end);
rho=c(index:end,:)./repmat(c(index-1,:),length(lags),1);
n=length(X);
% Defaults to the 95% confidence intervals
% Can extend to allow selection of 95% or 99% CI
confBound = 1.96/sqrt(n)*ones(length(lags),1);
% size(lags)
% size(rho)
confBoundSig = SignalObj(lags,[confBound -confBound],'ACF[ \Phi^{-1}(u_i) ]','\Delta \tau','sec');
confBoundSig.setPlotProps({' ''r'', ''LineWidth'' ,3'},1);
confBoundSig.setPlotProps({' ''r'', ''LineWidth'' ,3'},2);
handle=[];
rhoSig = SignalObj(lags,rho, 'ACF[ \Phi^-1(u_i) ]','Lag \Delta \tau','sec');
plotProps = cell(1,colm);
if(~isempty(X))
for i=1:colm
plotProps{i}=strcat('''', '.',Analysis.colors{mod(i-1,length(Analysis.colors))+1},'''');
end
else
plotProps=strcat('''', '.',Analysis.colors{1},'''');
end
rhoSig.setPlotProps(plotProps);
end
function [Z,U,xAxis,KSSorted, ks_stat] = computeKSStats(nspikeObj,lambdaInput,DTCorrection)
% [Z,U,xAxis,KSSorted, ks_stat] = computeKSStats(nspikeTrain,lambdaInput)
% Given a neural spike train (a sequence of spike times) and a
% conditional intensity function, computes the rescaled ISIs
% according to the time-rescaling theorem in Z. The Uj are
% returned in U and correspond to a transformation fo the Zjs
% (exponential rate 1 (according to T-R theorem) to be
% uniform(0,1).
%
% DTCorrection: 0 for no DT Correction of KS plot, 1 is the
% default.
% nspikeTrain: a nspikeTrain object
% lambdaInput: candidate conditional intensity function (a Covariate)
% Z: rescaled spike times
% U: Zjs tranformed to be uniform(0,1)
% xAxis: x-axis of the KS plot
% KSSorted: y-axis of KS plot
% ks_stat: the KS statistic. Maximum deviations from the 45
% degree line for each conditional intensity function.
%get the relevant spike train
if(nargin<3)
DTCorrection =1;
end
if(length(nspikeObj)>1) %in batch analysis we get multiple trials
nstCollObj = nstColl(nspikeObj);
nCopy = nstCollObj.toSpikeTrain;
else
nCopy =nspikeObj.nstCopy;
% nCopy =nspikeObj;
end
% minTime = nCopy.minTime;
% maxTime = nCopy.maxTime;
nCopy.resample(lambdaInput.sampleRate);
nCopy.setMinTime(lambdaInput.minTime);
nCopy.setMaxTime(lambdaInput.maxTime);
repBin = nCopy.isSigRepBin;
if(~repBin)
lambdaInput=lambdaInput.resample(2*lambdaInput.sampleRate);
nCopy.resample(lambdaInput.sampleRate);
end
if(DTCorrection==1 && repBin)
% Use DT Correction for Time Rescaling Theorem - Haslinger, Pipa and Brown (2010)
pkSignal=lambdaInput;
pk = pkSignal.data.*(1/lambdaInput.sampleRate);
pk = max(pk,1e-10);
spikeTrain = nCopy.getSigRep.data;
minDim = min(size(pk,1),size(spikeTrain,1));
pk=pk(1:minDim,:);
spikeTrain=spikeTrain(1:minDim,:);
intValues=zeros(length(nCopy.getSpikeTimes)-1,lambdaInput.dimension);
for i=1:lambdaInput.dimension
pk(:,i) = nanmin(nanmax(pk(:,i),0),1);
temp = ksdiscrete(pk(:,i),spikeTrain,'spiketrain');
% length(temp)
% length(intValues(:,i))
%sometimes ksdiscrete returns 1 less spike train than
%expected ... need to debug .... for now just fix
%using length(temp) to index into intValues;
intValues(1:length(temp),i) = temp;
end
else % do not correct for discrete time effects
tempLambda = lambdaInput;
% tempLambda = tempLambda.resample(tempLambda.sampleRate*4);
% lambda=tempLambda.getSigInTimeWindow(minTime,maxTime);%.dataToMatrix;
lambdaPosdata = max(tempLambda.data,0);
lambda = Covariate(tempLambda.time,lambdaPosdata,tempLambda.name,tempLambda.xlabelval,tempLambda.xunits,tempLambda.yunits,tempLambda.dataLabels);
lambdaInt = lambda.integral;
if(nCopy.isSigRepBin)
spikeTimes = nCopy.getSpikeTimes;
spikeTimes = [0 spikeTimes];
else
% spikeTimes = nCopy.getSpikeTimes;
% maxBinSize=nCopy.getMaxBinSizeBinary;
% lambdaInt = lambda.resample(1/maxBinSize).integral;
nstSignal = nCopy.getSigRep;
spikeTimes=nstSignal.time(nstSignal.data~=0);
spikeTimes = [0 spikeTimes'];
end
if(~isempty(spikeTimes))
tempVals = lambdaInt.getValueAt(spikeTimes);
intValues= tempVals(2:end,:)-tempVals(1:end-1,:);
else
intValues = 0;
end
% intValues=2*intValues;
% lambdaInt.plot; hold all;
% vals =lambdaInt.getValueAt(spikeTimes);
% plot(spikeTimes,vals,'.')
end
Z = intValues; % rescales spike times - exponential rate 1
U = 1-exp(-Z); % store the rescaled spike times - uniform(0,1)
KSSorted = sort( U,'ascend' );
N = size(KSSorted,1);
if(N~=0)
xAxis=(([1:N]-.5)/N)'*ones(1,lambdaInput.dimension);
ks_stat = max(abs(KSSorted - (([1:N]-.5)/N)'*ones(1,lambdaInput.dimension)));
else
ks_stat=1;
xAxis=[];
end
end
function M=computeFitResidual(nspikeObj,lambda,windowSize)
% M=computeFitResidual(nspikeTrain,lambda,windowSize)
% Computes the Point Process residual defined in
% 'A point process framework for relating neural spiking
% activity to spiking history, W Truccolo, UT Eden, MR Fellows,
% JP Donoghue and EN. Brown. Journal of Neurophysiology 2005.
%
% nspikeTrain: nspikeTrain object
% lambda: candidate conditional intensity function evaluated on the time
% interval of the spike train.
% windowSize: the size of the window over which to compute the
% residual.
% M: the point process residual (a Covariate object).
%
if(nargin<3 || isempty(windowSize))
windowSize=.01;
end
if(length(nspikeObj)>1) %in batch analysis we get multiple trials
nstCollObj = nstColl(nspikeObj);
nCopy = nstCollObj.toSpikeTrain;
else
nCopy =nspikeObj.nstCopy;
% nCopy =nspikeObj;
end
nCopy.resample(lambda.sampleRate);
nCopy.setMinTime(lambda.minTime);
nCopy.setMaxTime(lambda.maxTime);
sumSpikes=nCopy.getSigRep(windowSize);%tObj.getNeuron(fitResults.neuronNumber).nstCopy;
% sumSpikesOverWindow = sumSpikes.data(1:end);
% windowTimes = nCopy.minTime:windowSize:lambda.maxTime;
windowTimes = linspace(nCopy.minTime,nCopy.maxTime,length(sumSpikes.time));
lambdaInt = lambda.integral;
lambdaIntVals = lambdaInt.getValueAt(windowTimes(2:end))-lambdaInt.getValueAt(windowTimes(1:(end-1)));
if(length(lambdaIntVals)==length(sumSpikes.data))
sumSpikesOverWindow = sumSpikes.data(1:end);
elseif(length(lambdaIntVals)<length(sumSpikes.data))
sumSpikesOverWindow = sumSpikes.data(2:end);
end
Mdata=repmat(sumSpikesOverWindow,[1 lambdaInt.dimension])-lambdaIntVals;
dataLabels = cell(1,lambdaInt.dimension);
for i=1:lambdaInt.dimension
dataLabels{i} = lambda.dataLabels{i};
end
M=Covariate(windowTimes(1:end),[zeros(1,size(Mdata,2));Mdata],'M(t_k)',lambdaInt.xlabelval, ...
lambdaInt.xunits,lambdaInt.yunits,dataLabels);
end
function [fitResults,ensembleCovariate,tcc] = compHistEnsCoeffForAll(tObj,history,makePlot)
% [fitResults,ensembleCovariate,tcc] = compHistEnsCoeffForAll(tObj,history,makePlot)
% runs Analysis.compHistEnsCoff for each neuron that is not masked.
if(nargin<3 || isempty(makePlot))
makePlot=1;
end
neuronIndex=tObj.getNeuronIndFromMask;
fitResults = cell(1,length(neuronIndex));
tcc = cell(1,length(neuronIndex));
ensembleCovariate = tObj.getEnsembleNeuronCovariates(neuronIndex(1),[],history);
[fitResults{1},tcc{1}] = Analysis.compHistEnsCoeff(tObj,history,neuronIndex(1),tObj.getNeuronNeighbors(1),ensembleCovariate,makePlot);
for i=2:length(neuronIndex)
ensembleCovariate.maskAwayAllExcept(tObj.getNeuronNeighbors(neuronIndex(i)));
[fitResults{i},tcc{i}] = Analysis.compHistEnsCoeff(tObj,history,neuronIndex(i),tObj.getNeuronNeighbors(neuronIndex(i)),ensembleCovariate,makePlot);
end
end
function [fitResults,ensembleCov,tcc] = compHistEnsCoeff(tObj,history,neuronNum,neighbors,ensembleCov,makePlot)
% [fitResults,ensembleCov,tcc] = compHistEnsCoeff(tObj,history,neuronNum,neighbors,ensembleCov,makePlot)
% Given a trial, a history object compute the history time
% series for the ensemble of neighboring neurons. This is done for all neurons and the result is returned in
% ensembleCov as a covariate collection. This collection is
% then used as the design matrix and the analysis performed for
% each neuron. The results are returned in fitResults.
%
%
if(nargin<6 || isempty(makePlot))
makePlot=1;
end
if(nargin<3 || isempty(neuronNum))
neuronNum=tObj.getNeuronIndFromMask;
end
if(nargin<4 || isempty(neighbors))
neighbors=tObj.getNeuronNeighbors(neuronNum); %every other neuron
end