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nstColl.m
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nstColl.m
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classdef nstColl < handle
% NSTCOLL A collection of nspikeTrains
%
% <a href="matlab: methods('nstColl')">methods</a>
% <a href="matlab:web('nstCollExamples.html', '-helpbrowser')">nstColl Examples</a>
%
% see also <a href="matlab:help('CovColl')">CovColl</a>, <a
% href="matlab:help('Covariate')">Covariate</a>, <a
% href="matlab:help('SignalObj')">SignalObj</a>,<a
% href="matlab:help('nspikeTrain')">nspikeTrain</a>
%
% Reference page in Help browser
% <a href="matlab: doc('nstColl')">doc nstColl</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 (SetAccess = private)
nstrain; %An array of nspikeTrain objects
numSpikeTrains; % a running count of how many nspikeTrains are in object
minTime %Time first spike occurs in the collection
maxTime %TIme last spike occurs in the collection
sampleRate
neuronMask
neuronNames
% isSigRepBinary
neighbors %the ith row specifies neighbors of ith neuron
end
properties (Dependent = true)
uniqueNeuronNames
end
methods
function nstCollObj=nstColl(nst)
% nstCollObj=nstColl(nst)
% nst is a cell array of nspikeTrains, a single nspikeTrains
% or not specified. If not specified, an empty nstColl object is
% created.
nstCollObj.numSpikeTrains = 0;
nstCollObj.minTime=inf;
nstCollObj.maxTime=-inf;
nstCollObj.neuronMask=[];
nstCollObj.sampleRate=-inf;
nstCollObj.neighbors = [];
% nstCollObj.isSigRepBinary = nstCollObj.BinarySigRep;
if(nargin<1) %Then we were called without any data
nstCollObj.nstrain=cell(1);
else
nstCollObj.addToColl(nst);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Merge
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function nstCollObj = merge(nstCollObj, nstColl2)
nstCollObj.addToColl(nstColl2);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Get Functions
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function minTime = getFirstSpikeTime(nstCollObj)
% minTime = getFirstSpikeTime(nstCollObj)
% minTime is the time of the first spike from all of the
% nspikeTrains in the collection
minTime=nstCollObj.minTime;
end
function maxTime = getLastSpikeTime(nstCollObj)
% maxTime = getLstSpikeTime(nstCollObj)
% maxTime is the time of the last spike from all of the
% nspikeTrains in the collection
maxTime=nstCollObj.maxTime;
end
% function nstCollObj = shift(nstCollObj,deltaT)
% minTime=inf;
% maxTime=-inf;
% for i=1:nstCollObj.numSpikeTrains
% nstCollObj.getNST(i).shift(deltaT);
% minTime=min(nstCollObj.getNST(i).minTime,minTime);
% maxTime=max(nstCollObj.getNST(i).maxTime,maxTime);
% end
% nstCollObj.setMinTime(minTime);
% nstCollObj.setMaxTime(maxTime);
% end
function answer = getMaxBinSizeBinary(nstCollObj)
% answer = getMaxBinSizeBinary(nstCollObj)
% answer is the binsize above which at least one nspikeTrain
% will no longer have a binary representation
%
% Only unmasked ('visible') nspikeTrains are considered.
if(nstCollObj.isNeuronMaskSet)
selectorArray = nstCollObj.getIndFromMask;
else
selectorArray = 1:nstCollObj.numSpikeTrains;
end
val = zeros(1,length(selectorArray));
for i=1:length(selectorArray)
% i
val(i) = nstCollObj.getNST(selectorArray(i)).getMaxBinSizeBinary;
end
answer = min(val);
end
% function value = get.isSigRepBinary(nstCollObj)
% % value is 1 if all of the nspikeTrains in the collection have
% % a binary representation. value=0 otherwise.
% % value =nstCollObj.BinarySigRep;
% end
function uniqueNames = get.uniqueNeuronNames(nstCollObj)
uniqueNames=nstCollObj.getUniqueNSTnames;
end
function [n numNeighbors] = getNeighbors(nstCollObj, neuronNum)
if(length(neuronNum)==1)
%neuronNum: if neuronNum is not a scalar, a cell array is
%returned. The ith entry of the cell array is a row vector
%with the indicies of the ith neuron. If all of the rows
%have the same length, then a matrix is returned instead of
%a cell array.
if(~nstCollObj.areNeighborsSet)
nstCollObj.setNeighbors; %default behavior
end
availNeurons =nstCollObj.getIndFromMaskMinusOne(neuronNum);
if(isa(nstCollObj.neighbors,'cell'))
nTemp = nstCollObj.neighbors{neuronNum};
else
nTemp=nstCollObj.neighbors(neuronNum,:);
end
offset=0;
n=[];
for i=1:length(nTemp)
if(any(nTemp(i)==availNeurons))
offset=offset+1;
n(offset)=nTemp(i);
end
end
numNeighbors = length(n);
else %if more than 1 neuronNum
for i=1:length(neuronNum) % call above for each
[nTemp{i}, numNeigh(i)] = nstCollObj.getNeighbors(neuronNum(i));
end
if(min(numNeigh)==max(numNeigh)) %if all the same dimension
n=[];
for i=1:length(neuronNum)
n = [n;nTemp{i}]; %convert to a Matrix
end
else %else return as a cell of neighbors
n=nTemp;
numNeighbors=max(numNeigh);
end
end
end
function [fieldVal, neuronNumbers] = getFieldVal(nstCollObj,fieldName)
% Returns a vector of the value contained within each nspikeTrain.(fieldName)
% within the nstCollObj object. Empty values are skipped.
cnt =1;
fieldVal=[];
for i=1:nstCollObj.numSpikeTrains
currVal = nstCollObj.getNST(i).getFieldVal(fieldName);
if(~isempty(currVal))
fieldVal(cnt)=currVal;
cnt = cnt+1;
neuronNumbers(cnt) = i;
end
end
end
function nstCollObj=shiftTime(nstCollObj,timeShift)
%shifts the entire collection by timeShift. If timeShift is
%empty or not give, the entire collection is shifted so that
%minTime =0;
if(nargin<2 || isempty(timeShift))
timeShift = -nstCollObj.minTime;
end
oldnSpikes= nstCollObj.nstrain;
for i=1:length(oldnSpikes)
nNew{i} = nspikeTrain(oldnSpikes{i}.spikeTimes + timeShift);
end
newColl = nstColl(nNew);
nstCollObj = newColl;
end
function setMinTime(nstCollObj,minTime)
% setMinTime(nstCollObj,minTime)
% calls setMinTime on all nspikeTrains in the collection and
% updates the minTime of the collection
if(nargin<2)
minTime=nstCollObj.minTime;
end
for i=1:nstCollObj.numSpikeTrains
nstCollObj.nstrain{i}.setMinTime(minTime);
end
nstCollObj.minTime = minTime;
% nstCollObj.isSigRepBinary = nstCollObj.BinarySigRep;
end
function setMaxTime(nstCollObj,maxTime)
% setMaxTime(nstCollObj,maxTime)
% calls setMaxTime on all nspikeTrains in the collection and
% updates the maxTime of the collection
if(nargin<2)
maxTime=nstCollObj.maxTime;
end
for i =1:nstCollObj.numSpikeTrains
nstCollObj.nstrain{i}.setMaxTime(maxTime);
end
nstCollObj.maxTime = maxTime;
% nstCollObj.isSigRepBinary = nstCollObj.BinarySigRep;
end
function setMask(nstCollObj,mask)
if(length(mask)==nstCollObj.numSpikeTrains)
if(max(mask)>1) %then these are indices
newMask = ones(1,length(mask));
nstCollObj.setNeuronMask(newMask);
else %they are selectors of 1's and 0's
nstCollObj.setNeuronMask(mask);
end
else
nstCollObj.setNeuronMaskFromInd(mask)
end
end
function setNeuronMaskFromInd(nstCollObj,mask)
% setNeuronMaskFromInd(nstCollObj,mask)
% mask is a vector of indices into the nstColl.
% all the nspikeTrains corresponding to the indices present in the mask
% will be visible in the collection
newMask = zeros(1,nstCollObj.numSpikeTrains);
newMask(mask) = 1;
nstCollObj.setNeuronMask(newMask);
end
function setNeuronMask(nstCollObj,mask)
% setNeuronMask(nstCollObj,mask)
% mask needs to vector with binary entries
% entries marked by 1s are visible, 0's are not visible
if(length(mask)==nstCollObj.numSpikeTrains)
nstCollObj.neuronMask = mask;
end
end
function setNeighbors(nstCollObj,neighborArray)
if(nargin<2)
for i =1:nstCollObj.numSpikeTrains
neuronList = 1:nstCollObj.numSpikeTrains;
neighborArray(i,:)=find(i~=neuronList); %all but ith neuron
end
end
if(~isempty(neighborArray))
[numRows, ~]=size(neighborArray);
if(numRows==nstCollObj.numSpikeTrains)
nstCollObj.neighbors = neighborArray;
else
display('Neighbor Array is not of appropriate dimensions');
end
end
end
function ind=getIndFromMask(nstCollObj)
% ind=getIndFromMask(nstCollObj)
% ind is a row vector containing the indices of the currently
% visible nspikeTrains in the collection
ind=find(nstCollObj.neuronMask==1);
end
function ind=getIndFromMaskMinusOne(nstCollObj,neuron)
ind = nstCollObj.getIndFromMask;
ind = ind(ind~=neuron);
end
function answer = isNeuronMaskSet(nstCollObj)
% answer = isNeuronMaskSet(nstCollObj)
% answer =1 if any element of the neuronMask is set to zero.
% answer =0 if all elements are set to 1.
if(any(nstCollObj.neuronMask==0))
answer =1;
else
answer=0;
end
end
function answer = areNeighborsSet(nstCollObj)
answer=~isempty(nstCollObj.neighbors);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Utility Functions
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function restoreToOriginal(nstCollObj,rMask)
% restoreToOriginal(nstCollObj,rMask)
% restores all elements of the nstColl to their original
% states. If rMask is not specified then the neuronMask is
% retained. If rMask =1, then the neuronMask is reset as well.
if(nargin<2)
rMask=0;
end
minTime=inf;
maxTime=-inf;
for i=1:nstCollObj.numSpikeTrains
nstCollObj.getNST(i).restoreToOriginal;
minTime=min(nstCollObj.getNST(i).minTime,minTime);
maxTime=max(nstCollObj.getNST(i).maxTime,maxTime);
end
nstCollObj.setMinTime(minTime);
nstCollObj.setMaxTime(maxTime);
nstCollObj.sampleRate = nstCollObj.findMaxSampleRate;
nstCollObj.enforceSampleRate;
if(rMask==1)
nstCollObj.resetMask;
end
% nstCollObj.isSigRepBinary = nstCollObj.BinarySigRep;
end
function mSR= findMaxSampleRate(nstCollObj)
mSR=-inf;
for i=1:nstCollObj.numSpikeTrains
mSR=max(nstCollObj.getNST(i).sampleRate,mSR);
end
end
function resetMask(nstCollObj)
%resetMask(nstCollObj)
% resets the neuronMask to all 1's (All data visible)
nstCollObj.neuronMask=ones(1,nstCollObj.numSpikeTrains);
% nstCollObj.isSigRepBinary = nstCollObj.BinarySigRep;
end
function addToColl(nstCollObj,nst)
% addToColl(nstCollObj,nst)
% adds the nspikeTrain nst to the collection nstCollObj
% nst: can be a single nspikeTrain or a cell array of
% nspikeTrains
if(isa(nst,'cell'))
numElements=length(nst);
for i=1:numElements
if(isa(nst{i},'nspikeTrain'))
nstCollObj.addSingleSpikeToColl(nst{i})
else
error('nstColl requires a cell array of nspikeTrain class elements');
end
end
elseif(isa(nst,'nspikeTrain'))
nstCollObj.addSingleSpikeToColl(nst);
elseif(isa(nst,'nstColl'))
newSpikes = nst.nstrain;
nstCollObj.addToColl(newSpikes);
else
error('Can only add single spikes or cells with spikes');
end
% nstCollObj.isSigRepBinary = nstCollObj.BinarySigRep;
end
function uniqueNames = getUniqueNSTnames(nstCollObj,selectorArray)
if(nargin<2)
selectorArray = find(nstCollObj.neuronMask);
end
uniqueNames = unique(nstCollObj.neuronNames(selectorArray));
end
function names = getNSTnames(nstCollObj,selectorArray)
if(nargin<2)
selectorArray = find(nstCollObj.neuronMask);
end
names=nstCollObj.neuronNames(selectorArray);
end
function indices = getNSTIndicesFromName(nstCollObj,neuronName)
if(nargin<2)
neuronName = nstCollObj.getUniqueNSTnames;
end
if(isa(neuronName,'char'))
names = nstCollObj.getNSTnames;
indices = find(strcmp(names,neuronName));
elseif(isa(neuronName,'cell'))
for i=1:length(neuronName)
indices{i} = nstCollObj.getNSTIndicesFromName(neuronName{i});
end
end
end
function name = getNSTnameFromInd(nstCollObj,ind)
if(ind>0 && nstCollObj.numSpikeTrains)
name = nstCollObj.neuronNames(ind);
else
error('Index is out of bounds!');
end
end
function nst = getNSTFromName(nstCollObj,neuronName)
if(nargin<2)
neuronName = nstCollObj.getUniqueNSTnames;
end
indices =nstCollObj.getNSTIndicesFromName(neuronName);
nst = nstCollObj.getNST(indices);
end
function nst = getNST(nstCollObj,index)
% nst = getNST(nstCollObj,index)
% if a nspikeTrain exists in the collection with the specified
% index, it is returned in nst.
% If length(index)>1 then get a cell-array of nspikeTrains.
% Care should be taken because it is
% the actual nst and not a copy (recall handle behavior of
% nspikeTrains)
if(all(index>0) && all(index<=nstCollObj.numSpikeTrains))
if(length(index)>1)
for i=1:length(index)
sampleRate = nstCollObj.sampleRate;
nst{i} = nstCollObj.nstrain{index(i)};%.nstCopy;
if(nst{i}.sampleRate~=sampleRate)
nst{i} = nst{i}.resample(sampleRate);
end
end
else
sampleRate = nstCollObj.sampleRate;
nst = nstCollObj.nstrain{index};%.nstCopy;
if(nst.sampleRate~=sampleRate)
nst = nst.resample(sampleRate);
end
end
else
error(['Index', num2str(index), ' out of bounds']);
end
end
function resample(nstCollObj,sampleRate)
%resample(nstCollObj,sampleRate)
%resamples all the nspikeTrains in the collection at the
%speciied sampleRate.
%Updates teh sampleRate of the collection to the specified
%value.
if(nstCollObj.sampleRate~=sampleRate)
for i=1:nstCollObj.numSpikeTrains
tempNST = nstCollObj.getNST(i);
tempNST.resample(sampleRate);
tempNST.setMinTime(nstCollObj.minTime);
tempNST.setMaxTime(nstCollObj.maxTime);
end
nstCollObj.sampleRate=sampleRate;
end
% nstCollObj.isSigRepBinary = nstCollObj.BinarySigRep;
end
function answer=isSigRepBinary(nstCollObj)
answer = nstCollObj.BinarySigRep;
end
function answer=BinarySigRep(nstCollObj)
% answer=BinarySigRep(nstCollObj)
% answer=1 if the all of the SignalObj representations of each
% of the nspikeTrains in the collection have a binary
% representation with the current collection parameters.
% answer=0 if at least one nspikeTrain does not.
tempAns = zeros(1,nstCollObj.numSpikeTrains);
for i =1:nstCollObj.numSpikeTrains
tempAns(i) = nstCollObj.getNST(i).isSigRepBinary;
end
answer=all(tempAns);
end
function ensembleCovariates = getEnsembleNeuronCovariates(nstCollObj,neuronNum,neighborIndex,windowTimes)
% ensembleCovariates = getEnsembleNeuronCovariates(nstCollObj,neuronNum,neighborIndex,windowTimes)
% returns a collection of covariates. Each covariate has number
% of dimensions same as the number of history windows. There
% will be the same number of covariates as neighbors.
if(nargin<4 || isempty(windowTimes))
windowTimes = [0 0.001];
end
if(nargin<3 || isempty(neighborIndex))
allNeighbors=nstCollObj.getNeighbors(neuronNum);
else
allNeighbors=neighborIndex;
end
if(isa(windowTimes,'History'))
histObj = windowTimes; %we were passed an actual history object;
elseif(isa(windowTimes,'double'))
histObj = History(windowTimes); % we got windowTimes;
end
ensembleCovariates = histObj.computeHistory(nstCollObj.getNST(1:nstCollObj.numSpikeTrains));
ensembleCovariates.maskAwayAllExcept(allNeighbors);
nstCollObj.addNeuronNamesToEnsCovColl(ensembleCovariates);
end
function addNeuronNamesToEnsCovColl(nstCollObj,ensembleCovariates)
% addNeuronNamesToEnsCovColl(nstCollObj,ensembleCovariates)
% Given a covariate collection of the ensemble effects, adds
% the neuron number to each data dimension of each ensembleCovariate element.
for i=1:ensembleCovariates.numCov
tempCov = ensembleCovariates.covArray{i};
dataLabels = cell(1,tempCov.dimension);
for j=1:tempCov.dimension;
name = nstCollObj.getNST(i).name;
if(isnumeric(name))
if(i>0 && i<10)
name = strcat(num2str(0),name);
end
dataLabels{j} = strcat('N',name, ':', tempCov.dataLabels{j});
else
dataLabels{j} = strcat(name, ':', tempCov.dataLabels{j});
end
end
tempCov.setDataLabels(dataLabels);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Change of Representation Functions
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function dataMat = dataToMatrix(nstCollObj,selectorArray, binwidth,minTime,maxTime)
% dataMat = dataToMatrix(nstCollObj,selectorArray, binwidth,minTime,maxTime)
% dataMat is a matrix with number of rows corresponding to the
% sampleRate of the nstColl and the minTime & maxTime values
% that are currently set. The number of columns defaults to the
% number of visible nspikeTrains as determined by the
% neuronMask. This default behavior can be changed by specifyin
% values for: selectorArray, binwidth, minTime, and maxTime
% when calling the function
if(nargin<5)
maxTime=nstCollObj.maxTime;
end
if(nargin<4)
minTime=nstCollObj.minTime;
end
if(nargin<3)
binwidth=1/nstCollObj.sampleRate;
end
if(nargin<2)
if(nstCollObj.isNeuronMaskSet)
selectorArray = nstCollObj.getIndFromMask;
else
selectorArray = 1:nstCollObj.numSpikeTrains;
end
end
if(isa(selectorArray,'double'))
if(any(selectorArray > nstCollObj.numSpikeTrains) || any(selectorArray < 1))
error('Neuron index is out of bounds');
end
elseif(isa(selectorArray,'cell')||isa(selectorArray,'char'));
selectorArray = nstCollObj.getNSTIndicesFromName(selectorArray);
if(isa(selectorArray,'cell'))
tempSelect = [];
for i=1:length(selectorArray)
tempSelect = [tempSelect selectorArray{i}];
end
end
end
%minTime and maxTime can be any values. We return a
%a matrix that starts at t=minTime and ends at t=maxTime;
% maxTime
% minTime
% binwidth
dataMat=zeros(floor(abs(maxTime-minTime)/binwidth)+1,length(selectorArray));
testSig = nstCollObj.getNST(1).getSigRep(binwidth,minTime, maxTime);
dataMat=zeros(length(testSig.dataToMatrix),length(selectorArray));
for i=1:length(selectorArray)
nspikeSigRep=nstCollObj.getNST(selectorArray(i)).getSigRep(binwidth,minTime, maxTime);
% size(nspikeSigRep.dataToMatrix)
% size(dataMat)
%A=nspikeSigRep.dataToMatrix;
%dataMat(:,i)=A(1:length(dataMat),:);
% sum(nspikeSigRep.data)==size(nstCollObj.getNST(selectorArray(i)).spikeTimes,2)
dataMat(:,i)=nspikeSigRep.dataToMatrix;
d(i)=length(nstCollObj.getNST(selectorArray(i)).spikeTimes)-sum(dataMat(:,i));
end
end
function spikeTrainObj = toSpikeTrain(nstCollObj,selectorArray,minTime,maxTime,windowTimes)
if(nargin<5)
windowTimes=[];
end
if(nargin<4 || isempty(maxTime))
maxTime=nstCollObj.maxTime;
end
if(nargin<3 || isempty(minTime))
minTime=nstCollObj.minTime;
end
if(nargin<2 || isempty(selectorArray))
if(nstCollObj.isNeuronMaskSet)
selectorArray = nstCollObj.getIndFromMask;
else
selectorArray = 1:nstCollObj.numSpikeTrains;
end
end
spikeTimes=[];
offset=0;
delta = 1/nstCollObj.sampleRate;
nst=cell(1,length(selectorArray));
if(isempty(windowTimes))
for i=1:length(selectorArray)
if(i==1)
nst{i}= nstCollObj.getNST(selectorArray(i));
spikeTimes = nst{i}.spikeTimes;
name = nst{i}.name;
else
offset = offset+nst{i-1}.maxTime + delta;
nst{i}= nstCollObj.getNST(selectorArray(i));
if(~isempty(nst{i}.spikeTimes))
spikeTimes = [spikeTimes, nst{i}.spikeTimes+offset];
end
end
end
else
if(length(selectorArray)==length(windowTimes)-1)
for i=1:length(selectorArray)
minTime=windowTimes(i);
deltaTW=windowTimes(i+1)-minTime;
if(i==1)
nst{i}= nstCollObj.getNST(selectorArray(i));
spikeTimes = nst{i}.spikeTimes*deltaTW+minTime;
name = nst{i}.name;
else
% offset = offset+nst{i-1}.maxTime + delta;
nst{i}= nstCollObj.getNST(selectorArray(i));
spikeTimes = [spikeTimes, nst{i}.spikeTimes*deltaTW+minTime];
end
end
else
error('Window Times must be 1 row longer than selectorArray');
end
end
spikeTrainObj = nspikeTrain(spikeTimes);
spikeTrainObj.setName(name);
maxTimeTot = maxTime*length(selectorArray);
spikeTrainObj.setMinTime(minTime);
spikeTrainObj.setMaxTime(maxTimeTot);
spikeTrainObj.resample(1/delta);
end
function psthSignal = psth(nstCollObj,binwidth,selectorArray, minTime,maxTime)
% psthSignal = psth(nstCollObj,selectorArray, binwidth,minTime,maxTime)
% Given a collection a neural spike trains, sums the activity
% in each time bin across all the neurons to generate a time
% histogram of the neural firing.
% selectoryArray: can be used specify a subset of all the
% neurons in the collection
% binwidth: the size of the time bins used to sum the neural
% activity. Default is 1ms if not specified.
% minTime: time to start the psth. Default is the minTime of
% the nstCollObj.
% maxTime: time to end the psth. Degault to maxTime of
% nstCollObj.
if(nargin<5)
maxTime=nstCollObj.maxTime;
end
if(nargin<4)
minTime=nstCollObj.minTime;
end
if(nargin<3 || isempty(selectorArray))
if(nstCollObj.isNeuronMaskSet)
selectorArray = nstCollObj.getIndFromMask;
else
selectorArray = 1:nstCollObj.numSpikeTrains;
end
end
if(nargin<2)
binwidth=.100; %100ms
end
windowTimes = minTime:binwidth:maxTime;
if(~any(windowTimes==maxTime))
windowTimes=[windowTimes, maxTime];
end
psthData=zeros(1,length(windowTimes));
%dataMat = nstCollObj.dataToMatrix(nstCollObj,selectorArray,binwidth,minTime,maxTime);
for i=1:length(selectorArray)
spikeTimes = nstCollObj.getNST(selectorArray(i)).getSpikeTimes;
if(~isempty(spikeTimes))
psthData = psthData+histc(spikeTimes,windowTimes);
end
end
%due to how histc works add the last observation to the
%previous one;
tempPsth = psthData(1:end-1);
% tempPsth(end) = tempPsth(end)+psthData(end);
psthData = tempPsth;
% unitPulseBasis=nstCollObj.generateUnitImpulseBasis(binwidth,minTime,maxTime);
% psthData = (unitPulseBasis.data*psthData')./binwidth;
psthData = psthData./binwidth./length(selectorArray);
% unitPulseBasis=nstCollObj.generateUnitImpulseBasis(binwidth,minTime,maxTime,nstCollObj.sampleRate);
% psthData = unitPulseBasis.data*psthData';
% time = unitPulseBasis.time;
time = (windowTimes(2:end)+windowTimes(1:end-1))/2;
psthSignal = SignalObj(time, psthData, 'PSTH','time','s','Hz');
% psthSignal.setMinTime(minTime);
% psthSignal.setMaxTime(maxTime);
% figure; bar(time,psth); ylabel('Spikes'); xlabel('time [sec]');
end
function psthSignal = psthBars(nstCollObj,binwidth,selectorArray, minTime,maxTime)
% psthSignal = psthBars(nstCollObj,selectorArray, binwidth,minTime,maxTime)
% Given a collection a neural spike trains, sums the activity
% in each time bin across all the neurons to generate a time
% histogram of the neural firing. Uses the implementation Baysian Adaptive Regression
% Splines (developed by Wallstrom, Leibner and Kass) available
% on Ryan C. Kelly's website:
% <a href="matlab:web('http://www.cnbc.cmu.edu/~rkelly/code.html', '-helpbrowser')">Ryan C. Kelly's BARS for Matlab</a>
% Requires that: barsP.m, defaultParams.m, and nlsd_mex.c be in
% the Matlab path and that nlsd_mex.c be compiled to the
% corresponding mex file.
%
%
% selectoryArray: can be used specify a subset of all the
% neurons in the collection
% binwidth: the size of the time bins used to sum the neural
% activity. Default is 1ms if not specified.
% minTime: time to start the psth. Default is the minTime of
% the nstCollObj.
% maxTime: time to end the psth. Degault to maxTime of
% nstCollObj.
if(nargin<5)
maxTime=nstCollObj.maxTime;
end
if(nargin<4)
minTime=nstCollObj.minTime;
end
if(nargin<3 || isempty(selectorArray))
if(nstCollObj.isNeuronMaskSet)
selectorArray = nstCollObj.getIndFromMask;
else
selectorArray = 1:nstCollObj.numSpikeTrains;
end
end
if(nargin<2)
binwidth=.100; %100ms
end
time = minTime:binwidth:maxTime;
psthData=zeros(1,length(time));
%dataMat = nstCollObj.dataToMatrix(nstCollObj,selectorArray,binwidth,minTime,maxTime);
for i=1:length(selectorArray)
spikeTimes = nstCollObj.getNST(selectorArray(i)).getSpikeTimes;
if(~isempty(spikeTimes))
psthData = psthData+histc(spikeTimes,time);
end
end
psthData = psthData./binwidth;
psthData = psthData/length(selectorArray);
%due to how histc works add the last observation to the
%previous one;
bp = defaultParams;
bp.prior_id = 'POISSON';
bp.dparams = 4;
numTrials = length(selectorArray);
fit = barsP(psthData,[minTime maxTime],numTrials);
psthSignal = SignalObj(time, [fit.mode fit.mean fit.confBands], 'PSTH_{bars}','time','s','Hz',{'mode','mean','ciLower','ciUpper'});
end
function [xK,WK, Qhat,gammahat,logll,fitResults] = ssglm(nstCollObj,windowTimes,numBasis,numVarEstIter,fitType)
if(nargin<5 || isempty(fitType))
fitType='poisson';%'binomial';
end
if(nargin<4 || isempty(numVarEstIter))
numVarEstIter=10;
end
if(nargin<3 || isempty(numBasis))
basisWidth=.02;
numBasis = (nstCollObj.maxTime-nstCollObj.minTime)./basisWidth;
end
if(nargin<2)
windowTimes = [];
end
dN=nstCollObj.dataToMatrix';
dN(dN>1)=1;
basisWidth=(nstCollObj.maxTime-nstCollObj.minTime)/numBasis;
[~, ~, psthResult] =nstCollObj.psthGLM(basisWidth,windowTimes,fitType);
gamma0=psthResult.getHistCoeffs';%+.1*randn(size(histCoeffs));
gamma0(isnan(gamma0))=-5;
x0=psthResult.getCoeffs;
Q0 = nstCollObj.estimateVarianceAcrossTrials(numBasis,windowTimes,numVarEstIter,fitType);
if(any(diag(Q0)==0))
Q0=Q0+0.001*diag(rand(numBasis,1));
end
A=eye(numBasis,numBasis);
% fitType='poisson';
delta = 1/nstCollObj.sampleRate;
[xK,WK, Qhat,gammahat,logll]=DecodingAlgortihms.PPSS_EM(A,Q0,x0,dN,fitType,delta,gamma0,windowTimes, numBasis);
minTime=nstCollObj.minTime; maxTime = nstCollObj.maxTime;
if(~isempty(numBasis))
basisWidth = (maxTime-minTime)/numBasis;
sampleRate=1/delta;
unitPulseBasis=nstCollObj.generateUnitImpulseBasis(basisWidth,minTime,maxTime,sampleRate);
basisMat = unitPulseBasis.data;
end
nCopy = nstCollObj.toSpikeTrain;
histObj = History(windowTimes);
cnt=1; [K,N]=size(dN);
R=size(xK,1);
% R=numBasis;
clear beta otherLabels;
lambdaData=[];
nst=nstCollObj.nstrain;
for k=1:K
Hk{k}=histObj.computeHistory(nst{k}).dataToMatrix';
stimK=basisMat*xK(:,k);
histEffect=exp(gammahat(end,:)*Hk{k})';
stimEffect=exp(stimK);
lambdaDelta = histEffect.*stimEffect;
lambdaData = [lambdaData;lambdaDelta/delta];
for r=1:R
otherLabels{cnt} = ['b_{' num2str(r) ',' num2str(k) '}'];
beta(cnt) = xK(r,k);
cnt=cnt+1;
end
end
lambdaTime = minTime:delta:(length(lambdaData)-1)*delta;
nCopy.setMaxTime(max(lambdaTime));
nCopy.setMinTime(min(lambdaTime));
% otherLabels = tObj.getLabelsFromMask(neuronNumber);
numLabels = length(otherLabels);
histLabels = histObj.computeHistory(nst{1}).getCovLabelsFromMask;
otherLabels((numLabels+1):(numLabels+length(histLabels)))=histLabels;
clear labels distrib stats b XvalData XvalTime;
labels{1} = otherLabels; % Labels change depending on presence/absense of History or ensCovHist
numHist = length(histObj.windowTimes)-1;
% histObj = tObj.history;
ensHistObj = [];
% [lambdaTemp, bTemp, devTemp, statsTemp,AICTemp,BICTemp,distribTemp] = Analysis.GLMFit(tObj,neuronNumber,i,Algorithm);
lambdaIndexStr=1;
lambda=Covariate(lambdaTime,lambdaData,...
'\Lambda(t)','time',...
's','Hz',strcat('\lambda_{',lambdaIndexStr,'}'));
AIC = 2*length(otherLabels)-2*logll(end);
BIC = -2*logll(end)+length(otherLabels)*log(length(lambdaData));
statsTemp{1}=[];
dev=-2*logll(end);
% lambda{i} = lambdaTemp;
b{1} = [beta';gammahat(end,:)'];
stats{1} = statsTemp;
% dev(i) = devTemp;
% AIC(i)= AICTemp;
% BIC(i)= BICTemp;
distrib{1} =fitType;
currSpikes=nst;%nspikeColl.getNST(tObj.getNeuronIndFromName(neuronNames));
for n=1:length(currSpikes)
currSpikes{n} = currSpikes{n}.nstCopy;
currSpikes{n}.setName(nCopy.name);
end
XvalData{1} = [];
XvalTime{1} = [];
spikeTraining = currSpikes;
fitResults=FitResult(nCopy,labels,numHist,histObj,ensHistObj,lambda,b, dev, stats,AIC,BIC,configColl,XvalData,XvalTime,distrib);
DTCorrection=1;
makePlot=0;
Analysis.KSPlot(fitResults,DTCorrection,makePlot);
Analysis.plotInvGausTrans(fitResults,makePlot);
Analysis.plotFitResidual(fitResults,[],makePlot);
%fitResults.computePlotParams;
end
function [psth, histSignal, psthResult] = psthGLM(nstCollObj, basisWidth,history,fitType,selectorArray,minTime,maxTime,sampleRate)
if(nargin<8)
sampleRate = 1/basisWidth;
end
if(nargin<7 || isempty(maxTime))
maxTime=nstCollObj.maxTime;
end
if(nargin<6 || isempty(minTime))
minTime=nstCollObj.minTime;
end
if(nargin<5 || isempty(selectorArray))
if(nstCollObj.isNeuronMaskSet)
selectorArray = nstCollObj.getIndFromMask;
else
selectorArray = 1:nstCollObj.numSpikeTrains;
end
end
if(nargin<4 ||isempty(fitType))
fitType = 'poisson'; %'binomial';
end
if(nargin<3)
history =[];
end
if(nargin<2)
basisWidth = .100; %100ms
end
numBasis=ceil((maxTime-minTime)/basisWidth);
alphaVal = .05;
unitPulseBasis=nstColl.generateUnitImpulseBasis(basisWidth,minTime,maxTime,sampleRate);
cc = CovColl({unitPulseBasis});
trial = Trial(nstCollObj,cc);
trial.setMinTime(minTime);
trial.setMaxTime(maxTime);
% for i=1:trial.nspikeColl.numSpikeTrains
% if(i==1)
% Y=num
%
clear c;
selfHist = history ; NeighborHist = []; sampleRate = nstCollObj.sampleRate;
LabelSelect = cell(1,unitPulseBasis.dimension+1);
LabelSelect{1} = unitPulseBasis.name;
LabelSelect(2:end) = unitPulseBasis.dataLabels(1:end);
tc{1} = TrialConfig({LabelSelect},sampleRate,selfHist,NeighborHist);
if(~isempty(selfHist))
tc{1}.setName('GLM-PSTH+Hist');
else
tc{1}.setName('GLM-PSTH');
end
cfgColl= ConfigColl(tc);
warning off;
if(strcmp(fitType,'poisson'))
Algorithm='GLM';
else
Algorithm='BNLRCG';
end
batchMode =1;
psthResult = Analysis.RunAnalysisForAllNeurons(trial,cfgColl,0,Algorithm,[],batchMode);
% cfgColl.setConfig(trial,1);
% for i=1:trial.nspikeColl.numSpikeTrains
% if(i==1)
% Y=trial.getSpikeVector(i);
% X=trial.getDesignMatrix(i);
% else
% Y=[Y;trial.getSpikeVector(i)];
% X=[X;trial.getDesignMatrix(i)];
% end
% end
%
% % y=tObj.getSpikeVector(neuronNumber);