From a12c47cf6ab6814b7aebdce353d314a5b1a6d516 Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Thu, 5 Mar 2026 15:08:33 -0500 Subject: [PATCH] PR3: add paper example publishing workflow and docs outputs Generated figures/docs from repository code (modern + legacy) --- CITATION.cff | 32 + README.md | 30 +- .../paper_examples_legacy/figure_001.png | Bin 0 -> 398859 bytes .../paper_examples_modern/figure_001.png | Bin 0 -> 397187 bytes docs/figures/plot_structure_legacy.json | 162 ++ docs/figures/plot_structure_modern.json | 162 ++ docs/figures/publish_report_legacy.json | 9 + docs/figures/publish_report_modern.json | 9 + .../nSTATPaperExamples_legacy.html | 2342 +++++++++++++++++ .../nSTATPaperExamples_modern.html | 2342 +++++++++++++++++ tools/publish_examples.m | 101 + 11 files changed, 5188 insertions(+), 1 deletion(-) create mode 100644 CITATION.cff create mode 100644 docs/figures/paper_examples_legacy/figure_001.png create mode 100644 docs/figures/paper_examples_modern/figure_001.png create mode 100644 docs/figures/plot_structure_legacy.json create mode 100644 docs/figures/plot_structure_modern.json create mode 100644 docs/figures/publish_report_legacy.json create mode 100644 docs/figures/publish_report_modern.json create mode 100644 docs/figures/published_html/nSTATPaperExamples_legacy.html create mode 100644 docs/figures/published_html/nSTATPaperExamples_modern.html create mode 100644 tools/publish_examples.m diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000..05dfa81 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,32 @@ +cff-version: 1.2.0 +message: "If you use nSTAT, please cite the toolbox paper below." +title: "nSTAT: Neural Spike Train Analysis Toolbox for MATLAB" +type: software +license: GPL-2.0-only +repository-code: "https://github.com/cajigaslab/nSTAT" +authors: + - family-names: Cajigas + given-names: Iahn + email: icajigas@upenn.edu + - family-names: Malik + given-names: Wasim Q. + - family-names: Brown + given-names: Emery N. +preferred-citation: + type: article + title: "nSTAT: Open-source neural spike train analysis toolbox for Matlab" + authors: + - family-names: Cajigas + given-names: I. + - family-names: Malik + given-names: W. Q. + - family-names: Brown + given-names: E. N. + journal: "Journal of Neuroscience Methods" + year: 2012 + volume: 211 + issue: 2 + start: 245 + end: 264 + doi: "10.1016/j.jneumeth.2012.08.009" + diff --git a/README.md b/README.md index 9f813ed..ffae65f 100644 --- a/README.md +++ b/README.md @@ -37,6 +37,34 @@ nSTAT_Install('RebuildDocSearch', true, 'CleanUserPathPrefs', false) - `RebuildDocSearch` rebuilds the help search database in `helpfiles/`. - `CleanUserPathPrefs` removes stale user MATLAB path entries. +Quickstart (MATLAB 2025b): + +```matlab +cd('/path/to/nSTAT') +nSTAT_Install('RebuildDocSearch',true,'CleanUserPathPrefs',true); +addpath(fullfile(pwd,'tools')); +run_all_checks('GenerateBaseline',false,'CheckParity',true,'RunTests',true,'PublishDocs',false,'Style','legacy'); +``` + +Reproduce paper examples and export generated figures: + +```matlab +addpath(fullfile(pwd,'tools')); + +% Default docs style (readability-focused) +publish_examples('Style','modern'); + +% Strict visual reproduction mode +publish_examples('Style','legacy'); +``` + +Outputs are generated under `docs/figures/` and are created from repository +code only (no publication PDF image embedding). + +Example generated output (modern style): + +![nSTAT paper examples (modern)](docs/figures/paper_examples_modern/figure_001.png) + Rendered help documentation (GitHub Pages): - https://cajigaslab.github.io/nSTAT/ @@ -66,7 +94,7 @@ This page ties major toolbox components to the paper's workflow categories: If you use nSTAT in your work, please remember to cite the above paper in any publications. nSTAT is protected by the GPL v2 Open Source License. -The code respository for nSTAT is hosted on GitHub at https://github.com/iahncajigas/nSTAT. +The code repository for nSTAT is hosted on GitHub at https://github.com/cajigaslab/nSTAT. 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nSTAT J. Neuroscience Methods Paper Examples

Author: Iahn Cajigas
Date: 01/04/2012

Experiment 1

MINIATURE EXCITATORY POST-SYNAPTIC CURRENTS (mEPSCs) Data from Marnie Phillips marnie.a.phillips@gmail.com This analysis is based on a partial version of the dataset used in
Phillips MA, Lewis LD, Gong J, Constantine-Paton M, Brown EN. 2011 Model-based statistical analysis of miniature synaptic transmission. J Neurophys (under consideration)
Date: 03/01/2011

Constant Magnesium Concentration - Constant rate poisson

Under a constant Magnesium concentration, it is seen that the mEPSCs behave as a homogeneous poisson process (constant arrival rate).
close all; clear all;
[dataDir,mEPSCDir,explicitStimulusDir,psthDir,placeCellDataDir] = ...
getPaperDataDirs();
nSTATRootDir = fileparts(dataDir);
if exist(nSTATRootDir,'dir') == 7 && ~strcmp(pwd,nSTATRootDir)
cd(nSTATRootDir);
end
epsc2 = importdata(fullfile(mEPSCDir,'epsc2.txt'));
sampleRate = 1000;
spikeTimes = epsc2.data(:,2)*1/sampleRate; %in seconds
nstConst = nspikeTrain(spikeTimes);
time = 0:(1/sampleRate):nstConst.maxTime;
 
% Define Covariates for the analysis
baseline = Covariate(time,ones(length(time),1),'Baseline','time','s',...
'',{'\mu'});
covarColl = CovColl({baseline});
 
% Create the trial structure
spikeColl = nstColl(nstConst);
trial = Trial(spikeColl,covarColl);
 
% Define how we want to analyze the data
clear tc tcc;
tc{1} = TrialConfig({{'Baseline','\mu'}},sampleRate,[]);
tc{1}.setName('Constant Baseline');
tcc = ConfigColl(tc);
% Perform Analysis (Commented to since data already saved)
results =Analysis.RunAnalysisForAllNeurons(trial,tcc,0);
Analyzing Configuration #1: Neuron #1
% h=results.plotResults;
close all;
scrsz = get(0,'ScreenSize');
results.lambda.setDataLabels({'\lambda_{const}'});
h=figure('OuterPosition',[scrsz(3)*.01 scrsz(4)*.04 ...
scrsz(3)*.98 scrsz(4)*.95]);
subplot(2,2,1); spikeColl.plot;
title({'Neural Raster with constant Mg^{2+} Concentration'},...
'FontWeight','bold',...
'Fontsize',12,...
'FontName','Arial');
hx=xlabel('time [s]','Interpreter','none');
hy=ylabel('mEPSCs','Interpreter','none');
set(gca,'yTick',[0 1]);
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
subplot(2,2,3); results.KSPlot;
subplot(2,2,2); results.plotInvGausTrans;
subplot(2,2,4); results.lambda.plot([],{{' ''b'' ,''Linewidth'',2'}});
hx=xlabel('time [s]','Interpreter','none');
hy=get(gca,'YLabel');
set([hx hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
h_legend = legend('\lambda_{const}','Location','NorthEast');
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.05 pos(2) pos(3:4)]);
set(h_legend,'FontSize',14)

Varying Magnesium Concentration - Piecewise Constant rate poisson

When the magnesium concentration of the bath decreased (i.e. magnesium is removed), the rate of mEPSCs begin to increase in frequency. This can be modeled in a many different ways (using the change in Magnesium directly as a model covariate, etc.) Here we approximate the rate as being constant during certain portions of the experiment. These segments can in principle be estimated (using heirarchical Bayesian methods), but here we select them via visual inspection. We compare three models: a constant rate model (from above), a piecewise constant rate model, and a piecewise constant rate model with history.
close all;
% load the data;
washout1 = importdata(fullfile(mEPSCDir,'washout1.txt'));
washout2 = importdata(fullfile(mEPSCDir,'washout2.txt'));
sampleRate = 1000;
% Magnesium removed at t=0
spikeTimes1 = 260+washout1.data(:,2)*1/sampleRate; %in seconds
spikeTimes2 = sort(washout2.data(:,2))*1/sampleRate + 745;%in seconds
nst = nspikeTrain([spikeTimes1; spikeTimes2]);
time = 260:(1/sampleRate):nst.maxTime;

Data Visualization

Visual inspection of the spike train is used to pick three regions where the firing rate appears to be different. Here we do not estimate where these transitions happen but pick times in an ad-hoc manner.
scrsz = get(0,'ScreenSize');
h=figure('OuterPosition',[scrsz(3)*.01 scrsz(4)*.04 scrsz(3)*.6 ...
scrsz(4)*.9]);
subplot(2,1,1);
nstConst.plot; set(gca,'yTick',[0 1]); hy=ylabel('mEPSCs');
title({'Neural Raster with constant Mg^{2+} Concentration'},...
'FontWeight','bold',...
'Fontsize',12,...
'FontName','Arial');
hx=get(gca,'XLabel');
set([hx,hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
subplot(2,1,2);
nst.plot; set(gca,'yTick',[0 1]); hy=ylabel('mEPSCs');
title({'Neural Raster with decreasing Mg^{2+} Concentration'},...
'FontWeight','bold',...
'Fontsize',12,...
'FontName','Arial');
hx=get(gca,'XLabel');
set([hx,hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');

Define Covariates for the analysis

timeInd1 =find(time<495,1,'last'); %0-495sec first constant rate
timeInd2 =find(time<765,1,'last'); %495-765 second constant rate epoch
%765 onwards third constant rate
%epoch
constantRate = ones(length(time),1);
rate1 = zeros(length(time),1); rate1(1:timeInd1)=1;
rate2 = zeros(length(time),1); rate2((timeInd1+1):timeInd2)=1;
rate3 = zeros(length(time),1); rate3((timeInd2+1):end)=1;
baseline = Covariate(time,[constantRate,rate1, rate2, rate3],...
'Baseline','time','s','',{'\mu','\mu_{1}','\mu_{2}','\mu_{3}'});
covarColl = CovColl({baseline});
 
% Create the trial structure
spikeColl = nstColl(nst);
trial = Trial(spikeColl,covarColl);
%30ms history in logarithmic spacing (chosen after using
%Analysis.computeHistLagForAll for various window lengths)
maxWindow=.3; numWindows=20;
delta=1/sampleRate;
windowTimes =unique(round([0 logspace(log10(delta),...
log10(maxWindow),numWindows)]*sampleRate)./sampleRate);
windowTimes = windowTimes(1:11);

Define how we want to analyze the data

clear tc tcc;
tc{1} = TrialConfig({{'Baseline','\mu'}},sampleRate,[]);
tc{1}.setName('Constant Baseline');
tc{2} = TrialConfig({{'Baseline','\mu_{1}','\mu_{2}','\mu_{3}'}},...
sampleRate,[]); tc{2}.setName('Diff Baseline');
tcc = ConfigColl(tc);

Perform Analysis

We see that the piece-wise constant rate model (without history) outperforms the constant baseline model in terms of AIC, BIC, and KS-statistic.
results =Analysis.RunAnalysisForAllNeurons(trial,tcc,0);
Analyzing Configuration #1: Neuron #1 +Analyzing Configuration #2: Neuron #1
% h=results.plotResults;
% Summary = FitResSummary(results);
% h=Summary.plotSummary;
close all;
scrsz = get(0,'ScreenSize');
results.lambda.setDataLabels({'\lambda_{const}',...
'\lambda_{const-epoch}'});
h=figure('OuterPosition',[scrsz(3)*.01 scrsz(4)*.04 ...
scrsz(3)*.98 scrsz(4)*.95]);
subplot(2,2,1); spikeColl.plot;
title({'Neural Raster with decreasing Mg^{2+} Concentration'},...
'FontWeight','bold',...
'Fontsize',12,...
'FontName','Arial');
hx=xlabel('time [s]','Interpreter','none');
set(gca,'YTickLabel',[]);
set([hx],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
timeInd1 =find(time<495,1,'last'); %0-495sec first constant rate
timeInd2 =find(time<765,1,'last'); %495-765 second constant rate epoch
%765 onwards third constant rate
%epoch
plot([495;495],[0,1],'r','Linewidth',4); hold on;
plot([765;765],[0,1],'r','Linewidth',4);
 
subplot(2,2,3); results.KSPlot;
subplot(2,2,2); results.plotInvGausTrans;
subplot(2,2,4);
results.lambda.getSubSignal(1).plot([],{{' ''b'' ,''Linewidth'',2'}});
results.lambda.getSubSignal(2).plot([],{{' ''g'' ,''Linewidth'',2'}});
v=axis; axis([v(1) v(2) 0 5]);
hx=xlabel('time [s]','Interpreter','none');
hy=get(gca,'YLabel');
set([hx hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
h_legend = legend('\lambda_{const}','\lambda_{const-epoch}',...
'Location','NorthEast');
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.05 pos(2)-.01 pos(3:4)]);
set(h_legend,'FontSize',14)

Experiment 2

EXPLICIT STIMULUS EXAMPLE - WHISKER STIMULATION/THALAMIC NEURON In the worksheet with analyze the stimulus effect and history effect on the firing of a thalamic neuron under a known stimulus consisting of whisker stimulation. Data from Demba Ba (demba@mit.edu)

Load the data

clear all;
close all;
[dataDir,mEPSCDir,explicitStimulusDir,psthDir,placeCellDataDir] = ...
getPaperDataDirs();
nSTATRootDir = fileparts(dataDir);
if exist(nSTATRootDir,'dir') == 7 && ~strcmp(pwd,nSTATRootDir)
cd(nSTATRootDir);
end
 
Direction=3; Neuron=1; Stim=2;
datapath = fullfile(explicitStimulusDir,['Dir' num2str(Direction)], ...
['Neuron' num2str(Neuron)],['Stim' num2str(Stim)]);
data = load(fullfile(datapath,'trngdataBis.mat'));
 
time=0:.001:(length(data.t)-1)*.001;
stimData = data.t;
spikeTimes = time(data.y==1);
 
stim = Covariate(time,stimData./10,'Stimulus','time','s','mm',{'stim'});
baseline = Covariate(time,ones(length(time),1),'Baseline','time','s','',...
{'constant'});
 
nst = nspikeTrain(spikeTimes);
nspikeColl = nstColl(nst);
cc = CovColl({stim,baseline});
trial = Trial(nspikeColl,cc);
% trial.plot;
 
scrsz = get(0,'ScreenSize');
h=figure('Position',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.8 scrsz(4)*.8]);
subplot(3,1,1);
nst2 = nspikeTrain(spikeTimes);
nst2.setMaxTime(21);nst2.plot;
set(gca,'ytick',[0 1]);
xlabel('');
hy=ylabel('spikes');
set(hy,'FontName', 'Arial','FontSize',12,'FontWeight','bold');
title({'Neural Raster'},'FontWeight','bold','FontSize',16,'FontName','Arial');
set(gca, ...
'XTick' , 0:1:max(time), ...
'XTickLabel' , [],...
'LineWidth' , 1 );
subplot(3,1,2);
stim.getSigInTimeWindow(0,21).plot([],{{' ''k'' '}}); legend off;
set(gca,'ytick',[0 0.5 1]);
hy=ylabel('Displacement [mm]','Interpreter','none'); xlabel('');
set(hy,'FontName', 'Arial','FontSize',12,'FontWeight','bold');
title({'Stimulus - Whisker Displacement'},'FontWeight','bold',...
'FontSize',16,'FontName','Arial');
 
set(gca, ...
'XTick' , 0:1:max(time), ...
'XTickLabel' , [],...
'YTick' , 0:.25:1, ...
'LineWidth' , 1 );
 
subplot(3,1,3);
stim.derivative.getSigInTimeWindow(0,21).plot([],{{' ''k'' '}}); legend off;
set(gca,'ytick',[-80 0 80]);
axis([0 21 -80 80]);
hy=ylabel('Displacement Velocity [mm/s]','Interpreter','none');
hx= xlabel('time [s]','Interpreter','none');
set([hx hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
title({'Displacement Velocity'},'FontWeight','bold',...
'FontSize',16,'FontName','Arial');
 
set(gca, ...
'XTick' , 0:1:max(time), ...
'YTick' , -80:40:80, ...
'LineWidth' , 1 );
Fit a constant baseline and Find Stimulus Lag We fit a constant rate (Poisson) model to the data and use the look at the cross-covariance function of between the stimulus and the fit residual to determine the appropriate lag for the stimulus.
clear c; close all;
selfHist = [] ; NeighborHist = []; sampleRate = 1000;
c{1} = TrialConfig({{'Baseline','constant'}},sampleRate,selfHist,NeighborHist);
c{1}.setName('Baseline');
cfgColl= ConfigColl(c);
results = Analysis.RunAnalysisForAllNeurons(trial,cfgColl,0);
Analyzing Configuration #1: Neuron #1
 
% Find Stimulus Lag (look for peaks in the cross-covariance function less
% than 1 second
scrsz = get(0,'ScreenSize');
h=figure('Position',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.8 scrsz(4)*.8]);
subplot(7,2,[1 3 5])
results.Residual.xcov(stim).windowedSignal([0,1]).plot;
 
ylabel('');
[m,ind,ShiftTime] = max(results.Residual.xcov(stim).windowedSignal([0,1]));
title(['Cross Correlation Function - Peak at t=' num2str(ShiftTime) ' sec'],'FontWeight','bold',...
'FontSize',12,...
'FontName','Arial');
hold on;
h=plot(ShiftTime,m,'ro','Linewidth',3);
set(h, 'MarkerFaceColor',[1 0 0], 'MarkerEdgeColor',[1 0 0]);
hx=xlabel('Lag [s]','Interpreter','none');
set(hx,'FontName', 'Arial','FontSize',12,'FontWeight','bold');
 
%Allow for shifts of less than 1 second
stim = Covariate(time,stimData,'Stimulus','time','s','V',{'stim'});
stim = stim.shift(ShiftTime);
baseline = Covariate(time,ones(length(time),1),'Baseline','time','s','',...
{'\mu'});
 
nst = nspikeTrain(spikeTimes);
nspikeColl = nstColl(nst);
cc = CovColl({stim,baseline});
trial2 = Trial(nspikeColl,cc);

Compare constant rate model with model including stimulus effect

Addition of the stimulus improves the fits in terms of the KS plot and the making the rescaled ISIs less correlated. The Point Process Residula also looks more "white"
clear c;
selfHist = [] ; NeighborHist = []; sampleRate = 1000;
c{1} = TrialConfig({{'Baseline','\mu'}},sampleRate,selfHist,...
NeighborHist);
c{1}.setName('Baseline');
c{2} = TrialConfig({{'Baseline','\mu'},{'Stimulus','stim'}},...
sampleRate,selfHist,NeighborHist);
c{2}.setName('Baseline+Stimulus');
cfgColl= ConfigColl(c);
results = Analysis.RunAnalysisForAllNeurons(trial2,cfgColl,0);
Analyzing Configuration #1: Neuron #1 +Analyzing Configuration #2: Neuron #1
% results.plotResults;

History Effect

Determine the best history effect model using AIC, BIC, and KS statistic
sampleRate=1000;
delta=1/sampleRate*1;
maxWindow=1; numWindows=32;
windowTimes =unique(round([0 logspace(log10(delta),...
log10(maxWindow),numWindows)]*sampleRate)./sampleRate);
results =Analysis.computeHistLagForAll(trial2,windowTimes,...
{{'Baseline','\mu'},{'Stimulus','stim'}},'BNLRCG',0,sampleRate,0);
Analyzing Configuration #1: Neuron #1 +Analyzing Configuration #2: Neuron #1 +Analyzing Configuration #3: Neuron #1 +Analyzing Configuration #4: Neuron #1 +Analyzing Configuration #5: Neuron #1 +Analyzing Configuration #6: Neuron #1 +Analyzing Configuration #7: Neuron #1 +Analyzing Configuration #8: Neuron #1 +Analyzing Configuration #9: Neuron #1 +Analyzing Configuration #10: Neuron #1 +Analyzing Configuration #11: Neuron #1 +Analyzing Configuration #12: Neuron #1 +Analyzing Configuration #13: Neuron #1 +Analyzing Configuration #14: Neuron #1 +Analyzing Configuration #15: Neuron #1 +Analyzing Configuration #16: Neuron #1 +Analyzing Configuration #17: Neuron #1 +Analyzing Configuration #18: Neuron #1 +Analyzing Configuration #19: Neuron #1 +Analyzing Configuration #20: Neuron #1 +Analyzing Configuration #21: Neuron #1 +Analyzing Configuration #22: Neuron #1 +Analyzing Configuration #23: Neuron #1 +Analyzing Configuration #24: Neuron #1 +Analyzing Configuration #25: Neuron #1 +Analyzing Configuration #26: Neuron #1 +Analyzing Configuration #27: Neuron #1 +Analyzing Configuration #28: Neuron #1 +Analyzing Configuration #29: Neuron #1 +Analyzing Configuration #30: Neuron #1
 
KSind = find(results{1}.KSStats.ks_stat == min(results{1}.KSStats.ks_stat));
AICind = find((results{1}.AIC(2:end)-results{1}.AIC(1))== ...
min(results{1}.AIC(2:end)-results{1}.AIC(1))) +1;
BICind = find((results{1}.BIC(2:end)-results{1}.BIC(1))== ...
min(results{1}.BIC(2:end)-results{1}.BIC(1))) +1;
if(AICind==1)
AICind=inf;
end
if(BICind==1)
BICind=inf; %sometime BIC is non-decreasing and the index would be 1
end
windowIndex = min([AICind,BICind]) %use the minimum order model
windowIndex = 10
Summary = FitResSummary(results);
% Summary.plotSummary;
 
 
clear c;
if(windowIndex>1)
selfHist = windowTimes(1:windowIndex+1);
else
selfHist = [];
end
NeighborHist = []; sampleRate = 1000;
%
% figure;
subplot(7,2,2);
x=0:length(windowTimes)-1;
plot(x,results{1}.KSStats.ks_stat,'.-'); axis tight; hold on;
plot(x(windowIndex),results{1}.KSStats.ks_stat(windowIndex),'r*');
 
set(gca,'XTick', 0:5:results{1}.numResults-1,'XTickLabel',[],...
'TickLength', [.02 .02] , ...
'XMinorTick', 'on','LineWidth' , 1);
 
hy=ylabel('KS Statistic');
set(hy,'FontName', 'Arial','FontSize',12,'FontWeight','bold');
dAIC = results{1}.AIC-results{1}.AIC(1);
title({'Model Selection via change'; 'in KS Statistic, AIC, and BIC'},...
'FontWeight','bold',...
'FontSize',12,...
'FontName','Arial');
subplot(7,2,4); plot(x,dAIC,'.-');
set(gca,'XTick', 0:5:results{1}.numResults-1,'XTickLabel',[],...
'TickLength', [.02 .02] , ...
'XMinorTick', 'on','LineWidth' , 1);
hy=ylabel('\Delta AIC');axis tight; hold on;
set(hy,'FontName', 'Arial','FontSize',12,'FontWeight','bold');
plot(x(windowIndex),dAIC(windowIndex),'r*');
dBIC = results{1}.BIC-results{1}.BIC(1);
 
subplot(7,2,6); plot(x,dBIC,'.-');
hy=ylabel('\Delta BIC'); axis tight; hold on;
 
plot(x(windowIndex),dBIC(windowIndex),'r*');
hx=xlabel('# History Windows, Q');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
set(gca, ...
'TickLength' , [.02 .02] , ...
'XMinorTick' , 'on' , ...
'XTick' , 0:5:results{1}.numResults-1, ...
'LineWidth' , 1 );
 
 
 
% Compare Baseline, Baseline+Stimulus Model, Baseline+History+Stimulus
% Addition of the history effect yields a model that falls within the 95%
% CI of the KS plot.
 
c{1} = TrialConfig({{'Baseline','\mu'}},sampleRate,[],NeighborHist);
c{1}.setName('Baseline');
c{2} = TrialConfig({{'Baseline','\mu'},{'Stimulus','stim'}},...
sampleRate,[],[]);
c{2}.setName('Baseline+Stimulus');
c{3} = TrialConfig({{'Baseline','\mu'},{'Stimulus','stim'}},...
sampleRate,windowTimes(1:windowIndex),[]);
c{3}.setName('Baseline+Stimulus+Hist');
cfgColl= ConfigColl(c);
results = Analysis.RunAnalysisForAllNeurons(trial2,cfgColl,0);
Analyzing Configuration #1: Neuron #1 +Analyzing Configuration #2: Neuron #1 +Analyzing Configuration #3: Neuron #1
%results.plotResults;
%
results.lambda.setDataLabels({'\lambda_{const}','\lambda_{const+stim}',...
'\lambda_{const+stim+hist}'});
subplot(7,2,[9 11 13]); results.KSPlot;
subplot(7,2,[10 12 14]); results.plotCoeffs; legend off;

Example 3 - PSTH Data

% Generate a known Conditional Intensity Function
% We generated a known conditional intensity function (rate function) and
% generate distinct realizations of point processes consistent with this
% rate function. We use the method of thinning to simulate a point process.
clear all;
[dataDir,mEPSCDir,explicitStimulusDir,psthDir,placeCellDataDir] = ...
getPaperDataDirs();
close all;
delta = 0.001;
Tmax = 1;
time = 0:delta:Tmax;
f=2;
mu = -3;
 
tempData = 1*sin(2*pi*f*time)+mu; %lambda >=0
lambdaData = exp(tempData)./(1+exp(tempData))*(1/delta);
lambda = Covariate(time,lambdaData, '\lambda(t)','time','s',...
'spikes/sec',{'\lambda_{1}'},{{' ''b'', ''LineWidth'' ,2'}});
numRealizations = 20;
spikeCollSim = CIF.simulateCIFByThinningFromLambda(lambda,numRealizations);
 
 
scrsz = get(0,'ScreenSize');
h=figure('Position',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.8 scrsz(4)*.8]);
 
subplot(2,2,3);spikeCollSim.plot;
set(gca,'YTick',0:5:numRealizations,'YTickLabel',0:5:numRealizations);
title({[num2str(numRealizations) ' Simulated Point Process Sample Paths']},...
'FontWeight','bold','Fontsize',14,'FontName','Arial');
xlabel('time [s]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
ylabel('Trial [k]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
 
subplot(2,2,1);lambda.plot;
title({'Simulated Conditional Intensity Function (CIF)'},...
'FontWeight','bold','FontSize',14,'FontName','Arial');
xlabel('time [s]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
hy=get(gca,'YLabel');
set(hy,'FontName', 'Arial','FontSize',14,'FontWeight','bold');
x = load(fullfile(psthDir,'Results.mat'));
numTrials = x.Results.Data.Spike_times_STC.balanced_SUA.Nr_trials;
cellNum=6; clear nst;
for i=1:numTrials
spikeTimes{i}=x.Results.Data.Spike_times_STC.balanced_SUA.spike_times{1,i,cellNum};
nst{i} = nspikeTrain(spikeTimes{i});
nst{i}.setName(num2str(cellNum));
end
 
spikeCollReal1=nstColl(nst);
spikeCollReal1.setMinTime(0); spikeCollReal1.setMaxTime(2);
subplot(2,2,2);spikeCollReal1.plot; set(gca,'YTick',0:2:numTrials,...
'YTickLabel',0:2:numTrials);
%set(gca,'xtick',[0:.5:2],'xtickLabel',{'0','0.5','1','1.5','2'});
xlabel('time [s]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
ylabel('Trial [k]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
title('Response to Moving Visual Stimulus (Neuron 6)',...
'FontWeight','bold','Fontsize',14,'FontName','Arial');
 
cellNum=1; clear nst;
for i=1:numTrials
spikeTimes{i}=x.Results.Data.Spike_times_STC.balanced_SUA.spike_times{1,i,cellNum};
nst{i} = nspikeTrain(spikeTimes{i});
nst{i}.setName(num2str(cellNum));
end
 
spikeCollReal2=nstColl(nst);
spikeCollReal2.setMinTime(0); spikeCollReal2.setMaxTime(2);
subplot(2,2,4);spikeCollReal2.plot;
set(gca,'YTick',0:2:numTrials,'YTickLabel',0:2:numTrials);
%set(gca,'xtick',[0:.5:2],'xtickLabel',{'0','0.5','1','1.5','2'});
xlabel('time [s]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
ylabel('Trial [k]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
title('Response to Moving Visual Stimulus (Neuron 1)','FontWeight',...
'bold','Fontsize',14,'FontName','Arial');

Estimate the PSTH with 50ms windows

close all;
 
scrsz = get(0,'ScreenSize');
h=figure('Position',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.8 scrsz(4)*.8]);
 
binsize = .05; %50ms window
psth = spikeCollSim.psth(binsize);
psthGLM = spikeCollSim.psthGLM(binsize);
Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1
true = lambda; %rate*delta = expected number of arrivals per bin
subplot(2,3,4);
 
h1=true.plot([],{{' ''b'',''Linewidth'',4'}});
h3=psthGLM.plot([],{{' ''k'',''Linewidth'',4'}});
h2=psth.plot([],{{' ''rx'',''Linewidth'',4'}});
 
xlabel('time [s]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
ylabel('[spikes/sec]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
 
legend off;
h_legend=legend([h1(1) h2(1) h3(1)],'true','PSTH','PSTH_{glm}');
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.005 pos(2)+.095 pos(3:4)]);
 
 
%
subplot(2,3,1);spikeCollSim.plot;
set(gca,'YTick',0:2:spikeCollSim.numSpikeTrains,'YTickLabel',0:2:spikeCollSim.numSpikeTrains);
xlabel('time [s]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
ylabel('Trial [k]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
 
subplot(2,3,5);
binsize = .05; %50ms window
psthReal1 = spikeCollReal1.psth(binsize);
psthGLMReal1 = spikeCollReal1.psthGLM(binsize);%,[],[],[],[],[],1000);
Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #6
 
h3=psthGLMReal1.plot([],{{' ''k'',''Linewidth'',4'}});
h2=psthReal1.plot([],{{' ''rx'',''Linewidth'',4'}});
xlabel('time [s]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
ylabel('[spikes/sec]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
 
h_legend=legend([h2(1) h3(1)],'PSTH','PSTH_{glm}');
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.005 pos(2)+.07 pos(3:4)]);
subplot(2,3,2); spikeCollReal1.plot;
set(gca,'YTick',0:2:spikeCollReal2.numSpikeTrains,'YTickLabel',0:2:spikeCollReal2.numSpikeTrains);
xlabel('time [s]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
ylabel('Trial [k]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
subplot(2,3,6);
psthReal2 = spikeCollReal2.psth(binsize);
psthGLMReal2 = spikeCollReal2.psthGLM(binsize);%,[],[],[],[],[],1000);
Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1
h3=psthGLMReal2.plot([],{{' ''k'',''Linewidth'',4'}});
h2=psthReal2.plot([],{{' ''rx'',''Linewidth'',4'}});
xlabel('time [s]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
ylabel('[spikes/sec]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
 
 
h_legend=legend([h2(1) h3(1)],'PSTH','PSTH_{glm}');
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.005 pos(2)+.07 pos(3:4)]);
subplot(2,3,3); spikeCollReal2.plot;
set(gca,'YTick',0:2:spikeCollReal2.numSpikeTrains,'YTickLabel',0:2:spikeCollReal2.numSpikeTrains);
xlabel('time [s]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
ylabel('Trial [k]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');

Example 3b - SSGLM Example

Example of estimating with-in and across trial dynamics Methods from: G. Czanner, U. T. Eden, S. Wirth, M. Yanike, W. A. Suzuki, and E. N. Brown, "Analysis of between-trial and within-trial neural spiking dynamics.," Journal of neurophysiology, vol. 99, no. 5, pp. 2672?2693, May. 2008.
close all;
clear all;
[dataDir,mEPSCDir,explicitStimulusDir,psthDir,placeCellDataDir] = ...
getPaperDataDirs();
% set(0,'DefaultFigureRenderer','ZBuffer')
delta = 0.001; Tmax = 1;
time = 0:delta:Tmax;
Ts=.001;
numRealizations = 50; %Each realization corresponds to a distinct trial
 
for i=1:numRealizations
% The within trial dynamics are sinusoidal
% For each trial the stimulus effect increases
f=2; b1(i)=3*((i)/numRealizations);b0=-3;
u = sin(2*pi*f*time);
e = zeros(length(time),1); %No Ensemble input
 
stim=Covariate(time',u,'Stimulus','time','s','Voltage',{'sin'});
ens =Covariate(time',e,'Ensemble','time','s','Spikes',{'n1'});
 
mu=b0;
histCoeffs=[-4 -1 -.5];
H=tf(histCoeffs,[1],Ts,'Variable','z^-1');
S=tf([b1(i)],1,Ts,'Variable','z^-1');
E=tf([0],1,Ts,'Variable','z^-1');
simTypeSelect='binomial'; %Parameters are used to compute
%binomial conditional intensity function
%
 
% Obtain a realization of the point process with the current
% stimulus and history effect
[sC, lambdaTemp]=CIF.simulateCIF(mu,H,S,E,stim,ens,1,simTypeSelect);
 
if(i==1)
lambda=lambdaTemp; %Store the conditional intensity function
else
lambda = lambda.merge(lambdaTemp); %Add it to the other realizations
end
nst{i} = sC.getNST(1); %get the neural spikeTrain from the collection
nst{i} = nst{i}.resample(1/delta); %make sure that it is sampled at the current samplerate
end
 
spikeColl = nstColl(nst); %Create a collection of the spike trains across trials

Summarize Simulated Data

close all;
scrsz = get(0,'ScreenSize');
h=figure('Position',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.8 scrsz(4)*.8]);
 
%Plot the raster
subplot(3,2,[3 4]); spikeColl.plot;
set(gca,'ytick',0:10:numRealizations,'ytickLabel',0:10:numRealizations);
set(gca,'xtick',0:.1:Tmax,'xtickLabel',0:.1:Tmax); xlabel('');
xlabel('time [s]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
ylabel('Trial [k]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
title('Simulated Neural Raster','Interpreter','none','FontName', 'Arial',...
'Fontsize',14,'FontWeight','bold');
 
% Plot the actual stimulus effect (not including history)
% The CIF including the history effect is stored in the lambda Covariate
% above
 
 
stimData = exp(b0 + u'*b1);
if(strcmp(simTypeSelect,'binomial'))
stimData = stimData./(1+stimData);
end
 
%Plot the trial dependence
subplot(3,2,1); plot(time,u,'k','LineWidth',3);
% xlabel('time [s]');ylabel('stimulus');
xlabel('time [s]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
ylabel('Stimulus','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
title('Within Trial Stimulus','Interpreter','none','FontName', 'Arial',...
'Fontsize',14,'FontWeight','bold');
 
subplot(3,2,2); plot(1:length(b1),b1,'k','LineWidth',3);
xlabel('Trial [k]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
ylabel('Stimulus Gain','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
title('Across Trial Stimulus Gain','Interpreter','none','FontName',...
'Arial','Fontsize',14,'FontWeight','bold');
 
subplot(3,2,[5 6]);
imagesc(stimData'./delta); set(gca, 'YDir','normal');
set(gca,'xtick',0:100:Tmax/delta,'xtickLabel',0:.1:Tmax);
set(gca,'ytick',0:10:numRealizations,'ytickLabel',0:10:numRealizations);
xlabel('time [s]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
ylabel('Trial [k]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
title('True Conditional Intensity Function','Interpreter',...
'none','FontName', 'Arial','Fontsize',14,'FontWeight','bold');
 
 
axis tight;

Estimation of the Stimulus Response

% Create the covariates that will be used for the GLM regression
stim = Covariate(time,sin(2*pi*f*time),'Stimulus','time','s','V',{'stim'});
baseline = Covariate(time,ones(length(time),1),'Baseline','time','s','',...
{'constant'});
 
% Specify the windows of the history coefficients to be estimated
windowTimes=[0:.001:.003];
% Number of bins to discrtize time into (used both for the PSTH and for
% thec
% SSGLM model.
numBasis = 25;
 
spikeColl.resample(1/delta); % Enforce sampleRate
spikeColl.setMaxTime(Tmax); % Make all spikeTrains end at time Tmax
 
 
dN=spikeColl.dataToMatrix'; % Convert the spikeTrains into a matrix
% of 1's and 0's corresponding to the presence
% or absense of a spike in each time window.
dN(dN>1)=1; % One should sample finely enough so there is
% one spike per bin. Here we make sure that
% this is the case regardless of the
% sampleRate
% The width of each rectangular basis pulse is determined by Tmax and by the
% number of basis pulses to use.
basisWidth=(spikeColl.maxTime-spikeColl.minTime)/numBasis;
 
if(simTypeSelect==0)
fitType='binomial';
else
fitType='poisson';
end
if(strcmp(fitType,'binomial'))
Algorithm = 'BNLRCG'; % BNLRCG - faster Truncated, L-2 Regularized,
% Binomial Logistic Regression with Conjugate
% Gradient Solver by Demba Ba (demba@mit.edu).
else
Algorithm = 'GLM'; % Standard Matlab GLM (Can be used for binomial or
% or Poisson CIFs
end
% Use the values obtained from a PSTH to initialize the SSGLM filter
[psthSig, ~, psthResult] =spikeColl.psthGLM(basisWidth,windowTimes,fitType);
Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1
gamma0=psthResult.getHistCoeffs';%+.1*randn(size(histCoeffs));
gamma0(isnan(gamma0))=-5; % Depending on the amount of data the
% the psth may not identify all parameters
% Just make sure that the estimates are real
% numbers
x0=psthResult.getCoeffs; %The initial estimate for the SSGLM model
 
% Estimate the variance within each time bin across trials
numVarEstIter=10;
Q0 = spikeColl.estimateVarianceAcrossTrials(numBasis,windowTimes,...
numVarEstIter,fitType);
Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1 +Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1 +Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1 +Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1 +Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1 +Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1 +Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1 +Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1 +Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1 +Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1
A=eye(numBasis,numBasis);
delta = 1/spikeColl.sampleRate;

Run the SSGLM Filter

CompilingHelpFile=1;
% Commented out to speed up help file creation ...
if(~CompilingHelpFile)
Q0d=diag(Q0);
neuronName = psthResult.neuronNumber;
[xK,WK, WkuFinal,Qhat,gammahat,fitResults,stimulus,stimCIs,logll,...
QhatAll,gammahatAll,nIter]=DecodingAlgorithms.PPSS_EMFB(A,Q0d,x0,...
dN,fitType,delta,gamma0,windowTimes, numBasis,neuronName);
 
fR = fitResults.toStructure;
psthR = psthResult.toStructure;
end
% save SSGLMExampleData psthR fR xK WK WkuFinal Qhat gammahat fitResults stimulus stimCIs logll QhatAll gammahatAll nIter;
installPath = which('nSTAT_Install');
if isempty(installPath)
error('nSTATPaperExamples:MissingInstallPath', ...
'Could not locate nSTAT_Install.m on MATLAB path.');
end
nstatRoot = fileparts(installPath);
if exist(nstatRoot,'dir') == 7 && ~strcmp(pwd,nstatRoot)
cd(nstatRoot);
end
addpath(nstatRoot,'-begin');
load(fullfile(nstatRoot,'data','SSGLMExampleData.mat'));
fitResults = FitResult.fromStructure(fR);
psthResult = FitResult.fromStructure(psthR);
t=psthResult.mergeResults(fitResults);
%t.plotResults; %Compare the results with the PSTH Model
t.lambda.setDataLabels({'\lambda_{PSTH}','\lambda_{SSGLM}'});
scrsz = get(0,'ScreenSize');
h=figure('Position',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.8 scrsz(4)*.8]);
subplot(2,2,1); t.KSPlot;
subplot(2,2,2); t.plotResidual;
subplot(2,2,3); t.plotInvGausTrans;
subplot(2,2,4); t.plotSeqCorr;
 
S=FitResSummary(t);
dAIC=diff(S.AIC)
dAIC = -1.7978e+04
dBIC=diff(S.BIC)
dBIC = -6.9523e+03
dKS =diff(S.KSStats);
close all;
% Generate the actual stimulus effect
minTime=0; maxTime = Tmax;
stimData = stim.data*b1;
if(strcmp(fitType,'poisson'))
actStimEffect=exp(stimData + b0)./delta;
elseif(strcmp(fitType,'binomial'))
actStimEffect=exp(stimData + b0)./(1+exp(stimData + b0))./delta;
end
%
 
% Generate the basis function so that the estimated effect can be plotted
% at the same temporal resolution as the theoretical effect
if(~isempty(numBasis))
basisWidth = (maxTime-minTime)/numBasis;
sampleRate=1/delta;
unitPulseBasis=nstColl.generateUnitImpulseBasis(basisWidth,minTime,...
maxTime,sampleRate);
basisMat = unitPulseBasis.data;
end
 
% Generate the estimated stimulus effect
if(strcmp(fitType,'poisson'))
estStimEffect=exp(basisMat*xK)./delta;
elseif(strcmp(fitType,'binomial'))
estStimEffect=exp(basisMat*xK)./(1+exp(basisMat*xK))./delta;
end
 
 
scrsz = get(0,'ScreenSize');
h=figure('OuterPosition',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.4 scrsz(4)*.8]);
 
% Plot the actual and estimated stimulus effect as a function of trial and
% time
subplot(3,1,[1 2 3]);
lighting gouraud
surf((1:length(b1))',stim.time,actStimEffect,'FaceAlpha',0.1,...
'EdgeAlpha',0.1,'AlphaData',0.1);
hx=xlabel('Trial [k]'); hy=ylabel('time [s]');
hz=zlabel('Stimulus Effect [spikes/sec]'); hold all;
set([hx hy hz],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
 
surf((1:length(b1))',stim.time,estStimEffect(:,1:length(b1)),...
'FaceAlpha',0.5,'EdgeAlpha',0.1,'AlphaData',0.5); %xlabel('Trial [k]'); ylabel('time [s]'); zlabel('Stimulus Effect');
set(gca,'YDir','reverse');
set(gca,'ytick',0:.1:Tmax,'ytickLabel',0:.1:Tmax);
 
title('SSGLM Estimated vs. Actual Stimulus Effect','FontWeight','bold',...
'Fontsize',14,...
'FontName','Arial');
 
close all;
h=figure('OuterPosition',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.4 scrsz(4)*.8]);
 
% The actual stimulus effect
subplot(3,1,1);
lighting gouraud
mesh((1:length(b1))',stim.time,actStimEffect);
hx=xlabel('Trial [k]'); hy=ylabel('time [s]');
zlabel('Stimulus Effect [spikes/sec]'); hold all;
set([hx hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
% title('True Stimulus Effect');
title('True Stimulus Effect','FontWeight','bold',...
'Fontsize',14,...
'FontName','Arial');
set(gca,'xtick',[],'xtickLabel',[]);
set(gca,'ytick',[],'ytickLabel',[]);
CLIM = [min(min(stimData./delta)) max(max(stimData./delta))];
view(gca,[90 -90]);
 
 
 
% The PSTH estimate
subplot(3,1,2);
lighting gouraud
mesh((1:length(b1))',stim.time,repmat(psthSig.data, [1 numRealizations]));
hx=xlabel('Trial [k]'); hy=ylabel('time [s]');
hz=zlabel('Stimulus Effect [spikes/sec]'); hold all;
set([hx hy hz],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
% title('PSTH Estimated Stimulus Effect');
title('PSTH Estimated Stimulus Effect','FontWeight','bold',...
'Fontsize',14,...
'FontName','Arial');
 
set(gca,'xtick',[],'xtickLabel',[]);
set(gca,'ytick',[],'ytickLabel',[]);
CLIM = [min(min(stimData./delta)) max(max(stimData./delta))];
view(gca,[90 -90]);
 
% The SSGLM estimated stimulus effect
subplot(3,1,3);
lighting gouraud
mesh((1:length(b1))',stim.time,estStimEffect);
xlabel('Trial [k]'); ylabel('time [s]');
zlabel('Stimulus Effect [spikes/sec]'); hold all;
hx=get(gca,'XLabel'); hy=get(gca,'YLabel'); hz=get(gca,'ZLabel');
set([hx hy hz],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
% title('SSGLM Estimated Stimulus Efferct');
title('SSGLM Estimated Stimulus Effect','FontWeight','bold',...
'Fontsize',14,...
'FontName','Arial');
set(gca,'xtick',[],'xtickLabel',[]);
set(gca,'ytick',[],'ytickLabel',[]);
set(gca, 'YDir','normal')
view(gca,[90 -90]);

Compare differences across trials

close all;
minTime=0; maxTime = Tmax;
% Generate the basis function so that the estimated effect can be plotted
% at the same temporal resolution as the theoretical effect
if(~isempty(numBasis))
basisWidth = (maxTime-minTime)/numBasis;
sampleRate=1/delta;
unitPulseBasis=nstColl.generateUnitImpulseBasis(basisWidth,...
minTime,maxTime,sampleRate);
basisMat = unitPulseBasis.data;
end
 
 
% close all;
 
t0=0; tf=Tmax;
[spikeRateBinom, ProbMat,sigMat]=DecodingAlgorithms.computeSpikeRateCIs(xK,...
WkuFinal,dN,t0,tf,fitType,delta,gammahat,windowTimes);
 
lt=find(sigMat(1,:)==1,1,'first');
display(['The learning trial (compared to the first trial) is trial #' ...
num2str(find(sigMat(1,:)==1,1,'first'))]);
The learning trial (compared to the first trial) is trial #6
scrsz = get(0,'ScreenSize');
h=figure('OuterPosition',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.8 scrsz(4)*.8]);
 
subplot(2,3,1);
spikeRateBinom.setName(['(' num2str(Tmax) '-0)^-1*\Lambda(0,' ...
num2str(Tmax) ')']);
spikeRateBinom.plot([],{{' ''k'',''Linewidth'',4'}});
% e = Events(lt,{''});
% e.plot;
v=axis;
plot(lt*[1;1],v(3:4),'r','Linewidth',2);
hx=xlabel('Trial [k]','Interpreter','none'); hold all;
hy=ylabel('Average Firing Rate [spikes/sec]','Interpreter','none');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
title(['Learning Trial:' num2str(lt)],'FontWeight','bold',...
'Fontsize',12,...
'FontName','Arial');
 
 
h=subplot(2,3,[2 3 5 6]);
K=size(dN,1);
colormap(flipud(gray));
imagesc(ProbMat); hold on;
for k=1:K
for m=(k+1):K
if(sigMat(k,m)==1)
plot3(m,k,1,'r*'); hold on;
end
end
end
%
set(h,'XAxisLocation','top','YAxisLocation','right');
hx=xlabel('Trial Number','Interpreter','none'); hold all;
hy=ylabel('Trial Number','Interpreter','none');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
 
subplot(2,3,4)
stim1 = Covariate(time, basisMat*stimulus(:,1),'Trial1','time','s',...
'spikes/sec');
temp = ConfidenceInterval(time, basisMat*squeeze(stimCIs(:,1,:)));
stim1.setConfInterval(temp);
stimlt = Covariate(time, basisMat*stimulus(:,lt),'Trial1','time','s',...
'spikes/sec');
temp = ConfidenceInterval(time, basisMat*squeeze(stimCIs(:,lt,:)));
temp.setColor('r');
stimlt.setConfInterval(temp);
stimltm1 = Covariate(time, basisMat*stimulus(:,lt-1),'Trial1','time','s',...
'spikes/sec');
temp = ConfidenceInterval(time, basisMat*squeeze(stimCIs(:,lt-1,:)));
temp.setColor('r');
stimltm1.setConfInterval(temp);
 
% figure;
h1=stim1.plot([],{{' ''k'',''Linewidth'',4'}}); hold all;
h2=stimlt.plot([],{{' ''r'',''Linewidth'',4'}});
hx=xlabel('time [s]','Interpreter','none'); hold all;
hy=ylabel('Firing Rate [spikes/sec]','Interpreter','none');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
 
title({'Learning Trial Vs. Baseline Trial';'with 95% CIs'},'FontWeight','bold',...
'Fontsize',12,...
'FontName','Arial');
h_legend=legend([h1(1) h2(1)],'\lambda_{1}(t)',['\lambda_{' num2str(lt) '}(t)']);
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.03 pos(2)+.01 pos(3:4)]);

Example 4 - HIPPOCAMPAL PLACE CELL - RECEPTIVE FIELD ESTIMATION

Estimation of receptive fields of neurons is a very common data analysis problem in neuroscience. Here we use the nSTAT software to perform an estimation of the receptive fields of hippocampal place cells using a bivariate Gaussian model and Zernike polynomials. The number of zernike polynomials is based on "An Analysis of Hippocampal Spatio-Temporal Representations Using a Bayesian Algorithm for Neural Spike Train Decoding" Barbieri et. al 2005. The data used herein in was provided by Dr. Ricardo Barbieri on 2/28/2011.
Author: Iahn Cajigas
Date: 3/1/2011

Example Data

The x and y coordinates of a freely foraging rat in a circular environment (70cm in diameter and 30cm high walls) and a fixed visual cue. The x and y coordinates at the time when a spike was observed are marked in red. The position coordinates have been normalized to be between -1 and 1 to allow to simplify the analysis.
close all;
load(fullfile(placeCellDataDir,'PlaceCellDataAnimal1.mat'));
exampleCell = [2 21 25 49];
% exampleCell = 1:length(neuron);
% figure(1);
scrsz = get(0,'ScreenSize');
h=figure('OuterPosition',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.6 scrsz(4)*.9]);
 
for i=1:length(exampleCell)
subplot(2,2,i);
h1=plot(x,y,'b','Linewidth',.5); hold on;
h2=plot(neuron{exampleCell(i)}.xN,neuron{exampleCell(i)}.yN,'r.',...
'MarkerSize',7);
hx=xlabel('X Position'); hy=ylabel('Y Position');
% title(['Animal#1, Cell#' num2str(exampleCell(i))]);
title(['Cell#' num2str(exampleCell(i))],'FontWeight','bold',...
'Fontsize',12,'FontName','Arial');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
set(gca,'xTick',-1:.5:1,'yTick',-1:.5:1); axis square;
if(i==4)
h_legend = legend([h1 h2],'Animal Path',...
'Location at time of spike');
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.09 pos(2)+.06 pos(3:4)]);
end
end

Analyze All Cells

numAnimals=2;
CompilingHelpFile=1;
if(~CompilingHelpFile)
for n=1:numAnimals
% load the data
clear x y neuron time nst tc tcc z;
load(fullfile(placeCellDataDir,['PlaceCellDataAnimal' num2str(n) '.mat']));
 
% Create the spikeTrains for each cell
for i=1:length(neuron)
nst{i} = nspikeTrain(neuron{i}.spikeTimes);
end
 
 
% Convert to polar coordinates
[theta,r] = cart2pol(x,y);
 
 
% Evaluate the Zernike Polynomials
% Number of polynomials from "An Analysis of Hippocampal
% Spatio-Temporal Representations Using a Bayesian Algorithm for Neural
% Spike Train Decoding" Barbieri et. al 2005
cnt=0;
for l=0:3
for m=-l:l
if(~any(mod(l-m,2))) % otherwise the polynomial = 0
cnt = cnt+1;
z(:,cnt) = zernfun(l,m,r,theta,'norm');
% zernfun by Paul Fricker
% http://www.mathworks.com/matlabcentral/fileexchange/7687
end
end
end
 
% Data sampled at 30 Hz but just to be sure
delta=min(diff(time));
sampleRate = round(1/delta);
% Define Covariates for the analysis
baseline = Covariate(time,ones(length(x),1),'Baseline','time','s','',...
{'mu'});
zernike = Covariate(time,z,'Zernike','time','s','m',{'z1','z2','z3',...
'z4','z5','z6','z7','z8','z9','z10'});
gaussian = Covariate(time,[x y x.^2 y.^2 x.*y],'Gaussian','time',...
's','m',{'x','y','x^2','y^2','x*y'});
covarColl = CovColl({baseline,gaussian,zernike});
 
% Create the trial structure
spikeColl = nstColl(nst);
trial = Trial(spikeColl,covarColl);
 
 
% Define how we want to analyze the data
tc{1} = TrialConfig({{'Baseline','mu'},{'Gaussian',...
'x','y','x^2','y^2','x*y'}},sampleRate,[]);
tc{1}.setName('Gaussian');
tc{2} = TrialConfig({{'Zernike' 'z1','z2','z3','z4','z5','z6',...
'z7','z8','z9','z10'}},sampleRate,[]);
tc{2}.setName('Zernike');
tcc = ConfigColl(tc);
 
% Perform Analysis (Commented to since data already saved)
results =Analysis.RunAnalysisForAllNeurons(trial,tcc,0);
 
% Save results
resStruct =FitResult.CellArrayToStructure(results);
filename = fullfile(dataDir,['PlaceCellAnimal' num2str(n) 'Results']);
save(filename,'resStruct');
end
end

View Summary Statistics

Note the Zernike Polynomials yield better fits in terms of decreased KS Statistics (less deviation from the 45 degree line), reduced AIC and reduced BIC across the majority of cells and for both animals
clear Summary;
numAnimals =2;
for n=1:numAnimals
resData = load(fullfile(dataDir,['PlaceCellAnimal' num2str(n) 'Results.mat']));
results = FitResult.fromStructure(resData.resStruct);
Summary{n} = FitResSummary(results);
% Summary{n}.plotSummary;
end
close all;
scrsz = get(0,'ScreenSize');
h=figure('OuterPosition',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.6 scrsz(4)*.5]);
subplot(1,3,1);
maxLength = max([Summary{1}.numNeurons,Summary{2}.numNeurons]);
dKS = nan(maxLength, 2);
dKS(1:Summary{1}.numNeurons,1) = (Summary{1}.KSStats(:,1)-Summary{1}.KSStats(:,2)) ;
dKS(1:Summary{2}.numNeurons,2) = (Summary{2}.KSStats(:,1)-Summary{2}.KSStats(:,2)) ;
 
boxplot(dKS ,{'Animal 1', 'Animal 2'},'labelorientation','inline');
title('\Delta KS Statistic','FontWeight','bold','FontSize',14,...
'FontName','Arial');
 
subplot(1,3,2);
dAIC = nan(maxLength, 2);
dAIC(1:Summary{1}.numNeurons,1) = Summary{1}.getDiffAIC(1);
dAIC(1:Summary{2}.numNeurons,2) = Summary{2}.getDiffAIC(1);
 
boxplot(dAIC ,{'Animal 1', 'Animal 2'},'labelorientation','inline');
title('\Delta AIC','FontWeight','bold','FontSize',14,'FontName','Arial');
 
subplot(1,3,3);
dBIC = nan(maxLength, 2);
dBIC(1:Summary{1}.numNeurons,1) = Summary{1}.getDiffBIC(1);
dBIC(1:Summary{2}.numNeurons,2) = Summary{2}.getDiffBIC(1);
 
boxplot(dBIC ,{'Animal 1', 'Animal 2'},'labelorientation','inline'); %ylabel('\Delta BIC'); %xticklabel_rotate([],45,[],'Fontsize',6);
title('\Delta BIC','FontWeight','bold','FontSize',14,'FontName','Arial');
 
% close all;

Visualize the results

close all;
% Define a grid
[x_new,y_new]=meshgrid(-1:.01:1); %define new x and y
y_new = flipud(y_new); x_new = fliplr(x_new);
[theta_new,r_new] = cart2pol(x_new,y_new);
 
%Data for the gaussian fit
newData{1} =ones(size(x_new));
newData{2} =x_new; newData{3} =y_new;
newData{4} =x_new.^2; newData{5} =y_new.^2;
newData{6} =x_new.*y_new;
 
 
% Zernike polynomials only defined on the unit disk
idx = r_new<=1;
zpoly = cell(1,10);
cnt=0;
for l=0:3
for m=-l:l
if(~any(mod(l-m,2)))
cnt = cnt+1;
temp = nan(size(x_new));
temp(idx) = zernfun(l,m,r_new(idx),theta_new(idx),'norm');
zpoly{cnt} = temp;
end
end
end
 
 
 
for n=1:numAnimals
clear lambdaGaussian lambdaZernike;
load(fullfile(placeCellDataDir,['PlaceCellDataAnimal' num2str(n) '.mat']));
resData = load(fullfile(dataDir,['PlaceCellAnimal' num2str(n) 'Results.mat']));
results = FitResult.fromStructure(resData.resStruct);
for i=1:length(neuron)
% Evaluate our fits using the new data and the estimated parameters
lambdaGaussian{i} = results{i}.evalLambda(1,newData);
lambdaZernike{i} = results{i}.evalLambda(2,zpoly);
end
 
% Plot the receptive fields
for i=1:length(neuron)
% 3d plot of an example place field
 
% 2d plot of all the cell's fields
if(n==1)
h4=figure(4);
colormap('jet');
if(i==1)
tb=annotation(h4,'textbox',...
[0.283261904761904 0.928571428571418 ...
0.392857142857143 0.0595238095238095],...
'String',{['Gaussian Place Fields - Animal#' ...
num2str(n)]},'FitBoxToText','on','Fontsize',11,...
'FontName','Arial','FontWeight','bold','LineStyle',...
'none','HorizontalAlignment','center'); hold on;
end
subplot(7,7,i);
elseif(n==2)
h6=figure(6);
colormap('jet');
if(i==1)
annotation(h6,'textbox',...
[0.283261904761904 0.928571428571418 ...
0.392857142857143 0.0595238095238095],...
'String',{['Gaussian Place Fields - Animal#' ...
num2str(n)]},'FitBoxToText','on','Fontsize',11,...
'FontName','Arial','FontWeight','bold','LineStyle',...
'none','HorizontalAlignment','center'); hold on;
end
subplot(6,7,i);
end
pcolor(x_new,y_new,lambdaGaussian{i}), shading interp
axis square; set(gca,'xtick',[],'ytick',[]);
set(gca, 'Box' , 'off');
 
if(n==1)
h5=figure(5);
colormap('jet');
if(i==1)
annotation(h5,'textbox',...
[0.303261904761904 0.928571428571418 ...
0.392857142857143 0.0595238095238095],...
'String',{['Zernike Place Fields - Animal#' ...
num2str(n)]},'FitBoxToText','on','Fontsize',11,...
'FontName','Arial','FontWeight','bold','LineStyle','none'); hold on;
end
subplot(7,7,i);
elseif(n==2)
h7=figure(7);
colormap('jet');
if(i==1)
annotation(h7,'textbox',...
[0.303261904761904 0.928571428571418 ...
0.392857142857143 0.0595238095238095],...
'String',{['Zernike Place Fields - Animal#' ...
num2str(n)]},'FitBoxToText','on','Fontsize',11,...
'FontName','Arial','FontWeight','bold','LineStyle',...
'none','HorizontalAlignment','center'); hold on;
end
subplot(6,7,i);
end
pcolor(x_new,y_new,lambdaZernike{i}), shading interp
axis square;
set(gca,'xtick',[],'ytick',[]);
set(gca, 'Box' , 'off');
end
 
end
clear lambdaGaussian lambdaZernike;
load(fullfile(placeCellDataDir,'PlaceCellDataAnimal1.mat'));
resData = load(fullfile(dataDir,'PlaceCellAnimal1Results.mat'));
results = FitResult.fromStructure(resData.resStruct);
for i=1:length(neuron)
% Evaluate our fits using the new data and the estimated parameters
lambdaGaussian{i} = results{i}.evalLambda(1,newData);
lambdaZernike{i} = results{i}.evalLambda(2,zpoly);
end
 
% h1=plot(x,y,'b');
% h2=plot(x,y,'g');
%
exampleCell = 25;
% figure(8);
% plot(x,y,'b',neuron{exampleCell}.xN,neuron{exampleCell}.yN,'r.');
% xlabel('x'); ylabel('y');
% title(['Animal#1, Cell#' num2str(exampleCell)]);
%
close all;
h9=figure(9);
h_mesh = mesh(x_new,y_new,lambdaGaussian{exampleCell},'AlphaData',0);
get(h_mesh,'AlphaData');
set(h_mesh,'FaceAlpha',0.2,'EdgeAlpha',0.2,'EdgeColor','b');
hold on;
h_mesh = mesh(x_new,y_new,lambdaZernike{exampleCell},'AlphaData',0);
get(h_mesh,'AlphaData');
set(h_mesh,'FaceAlpha',0.2,'EdgeAlpha',0.2,'EdgeColor','g');
 
% h_legend=legend('\lambda_{Gaussian}','\lambda_{Zernike}');
% set(h_legend,'FontSize',20);
plot(x,y,neuron{exampleCell}.xN,neuron{exampleCell}.yN,'r.');
axis tight square;
xlabel('x position'); ylabel('y position');
title(['Animal#1, Cell#' num2str(exampleCell)],'FontWeight','bold',...
'Fontsize',12,'FontName','Arial');
hx=get(gca,'XLabel'); hy=get(gca,'YLabel');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
 
 

Example 5 - STIMULUS DECODING

In this example we show how to decode a univariate and a bivariate stimulus based on a point process observations using nSTAT. Even though due to the simulated nature of the data, we know the exact condition intensity function, we estimate the parameters before moving on to the decoding stage.

Generate the conditional Intensity Function

close all; clear all;
[dataDir,mEPSCDir,explicitStimulusDir,psthDir,placeCellDataDir] = ...
getPaperDataDirs();
delta = 0.001; Tmax = 1;
time = 0:delta:Tmax;
numRealizations = 20;
f=2; b1=randn(numRealizations,1);b0=log(10*delta)+randn(numRealizations,1);
x = sin(2*pi*f*time);
clear nst;
for i=1:numRealizations
expData = exp(b1(i)*x+b0(i));
lambdaData = expData./(1+expData);
 
if(i==1)
lambda = Covariate(time,lambdaData./delta, ...
'\Lambda(t)','time','s','spikes/sec',{'\lambda_{1}'},...
{{' ''b'', ''LineWidth'' ,2'}});
else
tempLambda = Covariate(time,lambdaData./delta, ...
'\Lambda(t)','time','s','spikes/sec',{'\lambda_{1}'},...
{{' ''b'', ''LineWidth'' ,2'}});
lambda = lambda.merge(tempLambda);
end
spikeColl = CIF.simulateCIFByThinningFromLambda(...
lambda.getSubSignal(i),1);
nst{i} = spikeColl.getNST(1);
end
spikeColl = nstColl(nst);scrsz = get(0,'ScreenSize');
h=figure('Position',[scrsz(3)*.1 scrsz(4)*.1 ...
scrsz(3)*.6 scrsz(4)*.8]);
% figure;
subplot(3,1,1); plot(time,x,'k');
set(gca,'xtick',[],'xtickLabel',[]); ylabel('Stimulus');
hx=get(gca,'XLabel'); hy=get(gca,'YLabel');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
title('Driving Stimulus','FontWeight','bold',...
'FontSize',14,'FontName','Arial');
subplot(3,1,2); lambda.plot([],{{' ''k'',''Linewidth'',1'}});
legend off;
hy=ylabel('Firing Rate [spikes/sec]', 'Interpreter','none');
hx=xlabel('','Interpreter','none');
set([hx, hy],'FontName', 'Arial','FontSize',12,...
'FontWeight','bold');
set(gca,'xtickLabel',[]);
title('Conditional Intensity Functions','FontWeight',...
'bold','FontSize',14,'FontName','Arial');
subplot(3,1,3); spikeColl.plot;
set(gca,'ytick',0:10:numRealizations,'ytickLabel',...
0:10:numRealizations);
xlabel('time [s]','Interpreter','none');
ylabel('Cell Number','Interpreter','none');
hx=get(gca,'XLabel'); hy=get(gca,'YLabel');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
title('Point Process Sample Paths','FontWeight',...
'bold','FontSize',14,'FontName','Arial');
 
stim = Covariate(time,sin(2*pi*f*time),'Stimulus','time','s','V',{'stim'});
baseline = Covariate(time,ones(length(time),1),'Baseline','time','s','',...
{'constant'});
 
% close all;
close all;
 
clear lambdaCIF;
spikeColl.resample(1/delta);
dN=spikeColl.dataToMatrix;
 
% Make noise according to the dynamic range of the stimulus
Q=std(stim.data(2:end)-stim.data(1:end-1));
Px0=.1; A=1;
x0 = x(:,1); yT=x(:,end);
Pi0 = eps*eye(size(x0,1),size(x0,1));
PiT = eps*eye(size(x0,1),size(x0,1));
 
 
[x_p, W_p, x_u, W_u] = DecodingAlgorithms.PPDecodeFilterLinear(A, ...
Q, dN',b0,b1','binomial',delta);
%
h=figure('Position',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.8 scrsz(4)*.6]);
zVal=1.96;
ciLower = min(x_u(1:end)-zVal*sqrt(squeeze(W_u(1:end)))',...
x_u(1:end)+zVal*sqrt(squeeze(W_u(1:end))'));
ciUpper = max(x_u(1:end)-zVal*sqrt(squeeze(W_u(1:end)))',...
x_u(1:end)+zVal*sqrt(squeeze(W_u(1:end))'));
 
estimatedStimulus = Covariate(time,x_u(1:end),'\hat{x}(t)','time','s','');
CI= ConfidenceInterval(time,[ciLower', ciUpper'],'\hat{x}(t)','time','s','');
estimatedStimulus.setConfInterval(CI);
 
% hold all;
% hEst=plot(time,x_u(1:end),'b','Linewidth',2); hold on;
% plot(time, [ciUpper', ciLower'],'b');
 
hEst = estimatedStimulus.plot([],{{' ''k'',''Linewidth'',4'}});
hStim=stim.plot([],{{' ''b'',''Linewidth'',4'}});
legend off;
h_legend=legend([hEst(1) hStim],'Decoded','Actual');
set(h_legend,'Interpreter','none');
set(h_legend,'FontSize',22);
title(['Decoded Stimulus +/- 95% CIs with ' num2str(numRealizations) ' cells'],...
'FontWeight','bold','Fontsize',22,'FontName','Arial');
xlabel('time [s]','Interpreter','none');
ylabel('Stimulus','Interpreter','none');
hx=get(gca,'XLabel'); hy=get(gca,'YLabel');
set([hx, hy],'FontName', 'Arial','FontSize',22,'FontWeight','bold');
 
 

Example 5b - Arm reaching to target Simulation

See L. Srinivasan, U. T. Eden, A. S. Willsky, and E. N. Brown, "A state-space analysis for reconstruction of goal-directed movements using neural signals.," Neural computation, vol. 18, no. 10, pp. 2465?2494, Oct. 2006.
close all;
clear all;
[dataDir,mEPSCDir,explicitStimulusDir,psthDir,placeCellDataDir] = ...
getPaperDataDirs();
%Process noise covariance only drives the movement velocity
q=1e-4;
Q=diag([1e-12 1e-12 q q]);
 
delta = .001; % Time increment
r=1e-6; % in meters^2
p=1e-6; % in meters^2/s^2
PiT=diag([r r p p]); % Uncertainty in the target state
Pi0=PiT;
T=2; % Reach Duration
 
x0 = [0;0;0;0]; % Initial Position and velocities (states)
xT = [-.35;.2; 0;0];% Final Target
time=0:delta:T; % time vector
 
A=[1 0 delta 0 ; %State transition matrix
0 1 0 delta;
0 0 1 0 ;
0 0 0 1 ];
 
x=zeros(4,length(time));
 
 
% Simulate a reach trajectory
% Differs from reference by multiplication by delta instead of division so
% that the velocity has units of meters per second
R=chol(Q);
L=chol(PiT);
for k=1:length(time)
if(k==1)
x(:,k)=x0;
else
x(:,k)=A*x(:,k-1)+...
delta/(2)*(pi/T)^2*cos(pi*time(k)/T)*[0;0;...
xT(1)-x0(1);xT(2)-x0(2)]; %Reach to target model
%x(:,k)=A*x(:,k-1)+R*randn(size(x,1),1); %Random walk model
end
 
end
xT =x(:,end); % The target generated by the model
yT=xT; % Assume we have observed the actual target position with uncertainty PiT
 
%Define Q according to the dynamic range of the movement above
Q=diag(var(diff(x,[],2),[],2))*100;
 
% Plot the movement trajectories and the hand path
scrsz = get(0,'ScreenSize');
fig1=figure('OuterPosition',[scrsz(3)*.1 scrsz(4)*.1 ...
scrsz(3)*.8 scrsz(4)*.8]);
%Plot The movement path
subplot(4,2,[1 3]);
plot(100*x(1,:),100*x(2,:),'k','Linewidth',2);
xlabel('X Position [cm]'); ylabel('Y Position [cm]');
hx=get(gca,'XLabel'); hy=get(gca,'YLabel');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
title('Reach Path','FontWeight','bold','Fontsize',14,'FontName','Arial');
hold on;
axis([sort([100*x0(1)+5, 100*xT(1)-5]), sort([100*x0(2)-5, 100*xT(2)+5])]);
h1=plot(100*x(1,1),100*x(2,1),'bo','MarkerSize',14);
h2=plot(100*x(1,end),100*x(2,end),'ro','MarkerSize',14);
legend([h1 h2],'Start','Finish','Location','NorthEast');
 
 
subplot(4,2,5); h1=plot(time,100*x(1,:),'k','Linewidth',2); hold on;
h2=plot(time,100*x(2,:),'k-.','Linewidth',2);
h_legend=legend([h1,h2],'x','y','Location','NorthEast');
set(h_legend,'FontSize',14)
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.06 pos(2)+.01 pos(3:4)]);
hx=xlabel('time [s]'); hy=ylabel('Position [cm]');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
% Plot the velocity profiles
 
subplot(4,2,7);
h1=plot(time,100*x(3,:),'k','Linewidth',2); hold on;
h2=plot(time,100*x(4,:),'k-.','Linewidth',2);
h_legend=legend([h1 h2],'v_x','v_y','Location','NorthEast');
xlabel('time [s]');
set(h_legend,'FontSize',14);
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.06 pos(2)+.01 pos(3:4)]);
hx=xlabel('time [s]'); hy=ylabel('Velocity [cm/s]');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
%
 
gamma=0;
windowTimes=[0, 0.001];
 
 
% Simulate neural responses
% logit(lambda_i*delta) = mu_i + b_x_i*v_x + b_y_i*v_y
% logit(lambda_i*delta) = X_i*beta_i;
numCells = 20;
bCoeffs=10*(rand(numCells,2)-.5); % b_i = [b_x_i b_y_i] ~ U(-5, 5);
phiMax = atan2(bCoeffs(:,2),bCoeffs(:,1)); % Maximal firing direction of cell
phiMaxNorm = (phiMax+pi)./(2*pi);
meanMu = log(10*delta); % baseline firing rate -10Hz
MuCoeffs = meanMu+randn(numCells,1); % mu_i ~ G(meanMu,1)
 
dataMat = [ones(length(time),1) x(3,:)' x(4,:)']; % design matrix: X (
coeffs = [MuCoeffs bCoeffs]; % coefficient vector: beta
fitType='binomial';
clear nst;
for i=1:numCells
tempData = exp(dataMat*coeffs(i,:)');
 
if(strcmp(fitType,'poisson'))
lambdaData = tempData;
else
lambdaData = tempData./(1+tempData); % Conditional Intensity Function for ith cell
end
lambda{i}=Covariate(time,lambdaData./delta, ...
'\Lambda(t)','time','s','spikes/sec',...
{strcat('\lambda_{',num2str(i),'}')},{{' ''b'' '}});
lambda{i}=lambda{i}.resample(1/delta);
% Generate CIF representation in case we want to use the symbolic
% versions of the PPDecodeFilter (i.e. not PPDecodeFilterLinear
lambdaCIF{i} = CIF([MuCoeffs(i) 0 0 bCoeffs(i,:)],...
{'1','x','y','vx','vy'},{'x','y','vx','vy'},fitType);
% generate one realization for each cell
tempSpikeColl{i} = CIF.simulateCIFByThinningFromLambda(lambda{i},1); nst{i} = tempSpikeColl{i}.getNST(1); % grab the realization
nst{i}.setName(num2str(i)); % give each cell a unique name
subplot(4,2,[6 8]);
h2=lambda{i}.plot([],{{' ''k'', ''LineWidth'' ,.5'}});
legend off; hold all; % Plot the CIF
end
title('Neural Conditional Intensity Functions','FontWeight',...
'bold','Fontsize',14,'FontName','Arial');
hx=xlabel('time [s]','Interpreter','none');
hy=ylabel('Firing Rate [spikes/sec]','Interpreter','none');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
spikeColl = nstColl(nst); % Create a neural spike train collection
subplot(4,2,[2,4]); spikeColl.plot;
set(gca,'xtick',[],'xtickLabel',[]);
title('Neural Raster','FontWeight','bold','Fontsize',14,...
'FontName','Arial');
hx=xlabel('time [s]','Interpreter','none');
hy=ylabel('Cell Number','Interpreter','none');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
 
% close all;
 
close all;
numExamples=20;
scrsz = get(0,'ScreenSize');
fig1=figure('OuterPosition',[scrsz(3)*.1 scrsz(4)*.1 ...
scrsz(3)*.6 scrsz(4)*.9]);
for k=1:numExamples
bCoeffs=10*(rand(numCells,2)-.5); % b_i = [b_x_i b_y_i] ~ U(-5, 5);
phiMax = atan2(bCoeffs(:,2),bCoeffs(:,1)); % Maximal firing direction of cell
phiMaxNorm = (phiMax+pi)./(2*pi);
meanMu = log(10*delta); % baseline firing rate
MuCoeffs = meanMu+randn(numCells,1); % mu_i ~ G(meanMu,1)
 
dataMat = [ones(length(time),1) x(3,:)' x(4,:)']; % design matrix: X (
coeffs = [MuCoeffs bCoeffs]; % coefficient vector: beta
fitType='binomial';
clear nst lambda;
for i=1:numCells
tempData = exp(dataMat*coeffs(i,:)');
if(strcmp(fitType,'poisson'))
lambdaData = tempData;
else
% Conditional Intensity Function for ith cell
lambdaData = tempData./(1+tempData);
end
lambda{i}=Covariate(time,lambdaData./delta, ...
'\Lambda(t)','time','s','spikes/sec',...
{strcat('\lambda_{',num2str(i),'}')},{{' ''b'' '}});
lambda{i}=lambda{i}.resample(1/delta);
% Generate CIF representation in case we want to use the symbolic
% versions of the PPDecodeFilter (i.e. not PPDecodeFilterLinear
% generate one realization for each cell
tempSpikeColl{i} = CIF.simulateCIFByThinningFromLambda(lambda{i},1);
nst{i} = tempSpikeColl{i}.getNST(1); % grab the realization
nst{i}.setName(num2str(i)); % give each cell a unique name
end
 
% Plot the neural raster across all the cells
spikeColl = nstColl(nst); % Create a neural spike train collection
 
% Based on the temporal resolution defined by delta, bin the data and get
% a matrix representation of the neural firing
dN=spikeColl.dataToMatrix';
dN(dN>1)=1; % more than one spike per bin will be treated as one spike. In
% general we should pick delta small enough so that there is
% only one spike per bin
 
[C,N] = size(dN); % N time samples, C cells
 
beta=[zeros(2,numCells); bCoeffs'];
 
%Use the Goal Directed Movement Version of the Point Process adaptive
%Filter
[x_p, W_p, x_u, W_u,x_uT,W_uT,x_pT,W_pT] = ...
DecodingAlgorithms.PPDecodeFilterLinear(A, Q, dN,...
MuCoeffs,beta,fitType,delta,gamma,windowTimes,x0, Pi0, yT,PiT,0);
 
%Use the Free Movement Version of the Point Process adaptive
%Filter
[x_pf, W_pf, x_uf, W_uf] = ...
DecodingAlgorithms.PPDecodeFilterLinear(A, Q, dN,...
MuCoeffs,beta,fitType,delta,gamma,windowTimes,x0);
 
 
if(k==numExamples)
subplot(4,2,1:4);h1=plot(100*x(1,:),100*x(2,:),'k','LineWidth',3);
hold on;
axis([sort([100*x0(1)+5, 100*xT(1)-5]), ...
sort([100*x0(2)-5, 100*xT(2)+5])]);
title('Estimated vs. Actual Reach Paths',...
'FontWeight','bold','Fontsize',12,'FontName','Arial');
end
subplot(4,2,1:4);h2=plot(100*x_u(1,:)',100*x_u(2,:)','b'); hold all;
subplot(4,2,1:4);h3=plot(100*x_uf(1,:)',100*x_uf(2,:)','g');
hx=xlabel('x [cm]'); hy=ylabel('y [cm]');
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
h1=plot(100*x0(1),100*x0(2),'bo','MarkerSize',10); hold on;
h2=plot(100*xT(1),100*xT(2),'ro','MarkerSize',10);
legend([h1 h2],'Start','Finish','Location','NorthEast');
 
 
subplot(4,2,5);
h1=plot(time,100*x(1,:),'k','LineWidth',3); hold on;
h2=plot(time,100*x_u(1,:)','b');
h3=plot(time,100*x_uf(1,:)','g');
hy=ylabel('x(t) [cm]'); hx=xlabel('time [s]');
set(gca,'xtick',[],'xtickLabel',[]);
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
title('X Position','FontWeight','bold','Fontsize',12,'FontName','Arial');
 
subplot(4,2,6);
h1=plot(time,100*x(2,:),'k','LineWidth',3); hold on;
h2=plot(time,100*x_u(2,:)','b');
h3=plot(time,100*x_uf(2,:)','g');
h_legend=legend([h1(1) h2(1) h3(1)],'Actual','PPAF+Goal',...
'PPAF','Location','SouthEast');
hy=ylabel('y(t) [cm]'); hx=xlabel('time [s]');
set(gca,'xtick',[],'xtickLabel',[]);
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
title('Y Position','FontWeight','bold','Fontsize',12,'FontName','Arial');
set(h_legend,'FontSize',10)
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)-.63 pos(2)+.23 pos(3:4)]);
 
subplot(4,2,7);
h1=plot(time,100*x(3,:),'k','LineWidth',3); hold on;
h2=plot(time,100*x_u(3,:)','b');
h3=plot(time,100*x_uf(3,:)','g');
hy=ylabel('v_{x}(t) [cm/s]'); hx=xlabel('time [s]');
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
title('X Velocity','FontWeight','bold','Fontsize',12,'FontName','Arial');
 
subplot(4,2,8);
h1=plot(time,100*x(4,:),'k','LineWidth',3); hold on;
h2=plot(time,100*x_u(4,:)','b');
h3=plot(time,100*x_uf(4,:)','g');
hy=ylabel('v_{y}(t) [cm/s]'); hx=xlabel('time [s]');
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
title('Y Velocity','FontWeight','bold','Fontsize',12,'FontName','Arial');
 
end
 
% close all;

Experiment 6 - Hybrid Point Process Filter Example

NOTE THIS EXAMPLE WAS NOT INCLUDED IN THE FINAL VERSION OF THE PAPER This example is based on an implementation of the Hybrid Point Process filter described in General-purpose filter design for neural prosthetic devices by Srinivasan L, Eden UT, Mitter SK, Brown EN in J Neurophysiol. 2007 Oct, 98(4):2456-75.

Problem Statement

Suppose that a process of interest can be modeled as consisting of several discrete states where the evolution of the system under each state can be modeled as a linear state space model. The observations of both the state and the continuous dynamics are not direct, but rather observed through how the continuous and discrete states affect the firing of a population of neurons. The goal of the hybrid filter is to estimate both the continuous dynamics and the underlying system state from only the neural population firing (point process observations).
To illustrate the use of this filter, we consider a reaching task. We assume two underlying system states s=1="Not Moving"=NM and s=2="Moving"=M. Under the "Not Moving" the position of the arm remain constant, whereas in the "Moving" state, the position and velocities evolved based on the arm acceleration that is modeled as a gaussian white noise process.
Under both the "Moving" and "Not Moving" states, the arm evolution state vector is

Generated Simulated Arm Reach

clear all;
[dataDir,mEPSCDir,explicitStimulusDir,psthDir,placeCellDataDir] = ...
getPaperDataDirs();
close all;
delta=0.001;
Tmax=2;
time=0:delta:Tmax;
A{2} = [1 0 delta 0 delta^2/2 0;
0 1 0 delta 0 delta^2/2;
0 0 1 0 delta 0;
0 0 0 1 0 delta;
0 0 0 0 1 0;
0 0 0 0 0 1];
A{1} = [1 0 0 0 0 0;
0 1 0 0 0 0;
0 0 0 0 0 0;
0 0 0 0 0 0;
0 0 0 0 0 0;
0 0 0 0 0 0];
A{1} = [1 0;
0 1];
Px0{2} =1e-6*eye(6,6);
Px0{1} =1e-6*eye(2,2);
 
minCovVal = 1e-12;
covVal = 1e-3;
 
 
 
Q{2}=[minCovVal 0 0 0 0 0;
0 minCovVal 0 0 0 0;
0 0 minCovVal 0 0 0;
0 0 0 minCovVal 0 0;
0 0 0 0 covVal 0;
0 0 0 0 0 covVal];
 
Q{1}=minCovVal*eye(2,2);
 
mstate = zeros(1,length(time));
ind{1}=1:2;
ind{2}=1:6;
 
% Acceleration model
X=zeros(max([size(A{1},1),size(A{2},1)]),length(time));
p_ij = [.998 .002;
.001 .999];
 
for i = 1:length(time)
if(i==1)
mstate(i) = 1;
else
if(rand(1,1)<p_ij(mstate(i-1),mstate(i-1)))
mstate(i) = mstate(i-1);
else
if(mstate(i-1)==1)
mstate(i) = 2;
else
mstate(i) = 1;
end
end
end
st = mstate(i);
R=chol(Q{st});
if(i<length(time))
X(ind{st},i+1) = A{st}*X(ind{st},i) + R*randn(length(ind{st}),1);
end
 
end
%save paperHybridFilterExample time Tmax delta mstate X p_ij ind A Q Px0
load(fullfile(fileparts(which('nSTATPaperExamples')),'paperHybridFilterExample.mat'));
Q{1}=minCovVal*eye(2,2);
numCells=40;
close all;
scrsz = get(0,'ScreenSize');
fig1=figure('OuterPosition',[scrsz(3)*.1 scrsz(4)*.1 ...
scrsz(3)*.8 scrsz(4)*.9]);
subplot(4,2,[1 3]);
plot(100*X(1,:),100*X(2,:),'k','Linewidth',2); hx=xlabel('X [cm]');
hy=ylabel('Y [cm]'); hold on;
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
title('Reach Path','FontWeight','bold','Fontsize',14,'FontName','Arial');
hold on;
h1=plot(100*X(1,1),100*X(2,1),'bo','MarkerSize',16);
h2=plot(100*X(1,end),100*X(2,end),'ro','MarkerSize',16);
legend([h1 h2],'Start','Finish','Location','NorthEast');
 
 
 
subplot(4,2,[6 8]);
plot(time,mstate,'k','Linewidth',2); axis tight; v=axis;
axis([v(1) v(2) 0 3]);
hx=xlabel('time [s]'); hy=ylabel('state');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
set(gca,'YTick',[1 2],'YTickLabel',{'N','M'})
title('Discrete Movement State','FontWeight','bold','Fontsize',14,...
'FontName','Arial');
 
subplot(4,2,5);
h1=plot(time,100*X(1,1:end),'k','Linewidth',2); hold on;
h2=plot(time,100*X(2,1:end),'k-.','Linewidth',2);
hx=xlabel('time [s]'); hy=ylabel('Position [cm]');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
h_legend=legend([h1,h2],'x','y','Location','NorthEast');
set(h_legend,'FontSize',14)
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.06 pos(2)+.01 pos(3:4)]);
 
 
subplot(4,2,7);
h1=plot(time,100*X(3,1:end),'k','Linewidth',2); hold on;
h2=plot(time,100*X(4,1:end),'k-.','Linewidth',2);
hx=xlabel('time [s]'); hy=ylabel('Velocity [cm/s]');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
h_legend=legend([h1,h2],'v_{x}','v_{y}','Location','NorthEast');
set(h_legend,'FontSize',14)
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.06 pos(2)+.01 pos(3:4)]);
 
meanMu = log(10*delta); % baseline firing rate
MuCoeffs = meanMu+randn(numCells,1); % mu_i ~ G(meanMu,1)
coeffs = [MuCoeffs 0*randn(numCells,2) 10*(rand(numCells,2)-.5) ...
0*randn(numCells,2)];
%Add realization by thinning with history
dataMat = [ones(size(X,2),1),X(:,1:end)'];
% Generate M1 cells
clear lambda tempSpikeColl lambdaCIF n;
fitType ='binomial';
% matlabpool open;
for i=1:numCells
tempData = exp(dataMat*coeffs(i,:)');
if(strcmp(fitType,'binomial'));
lambdaData = tempData./(1+tempData);
else
lambdaData = tempData;
end
lambda{i}=Covariate(time,lambdaData./delta, ...
'\Lambda(t)','time','s','spikes/sec',...
{strcat('\lambda_{',num2str(i),'}')},{{' ''b'', ''LineWidth'' ,2'}});
maxTimeRes = 0.001;
tempSpikeColl{i} = CIF.simulateCIFByThinningFromLambda(lambda{i},1,[]);
n{i} = tempSpikeColl{i}.getNST(1);
n{i}.setName(num2str(i));
end
spikeColl = nstColl(n);
subplot(4,2,[2 4]);
spikeColl.plot;
set(gca,'xtick',[],'xtickLabel',[],'ytickLabel',[]);
title('Neural Raster','FontWeight','bold','Fontsize',14,'FontName','Arial');
hx=xlabel('time [s]','Interpreter','none');
hy=ylabel('Cell Number','Interpreter','none');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
 
% close all;

Simulate Neural Firing

We simulate a population of neurons that fire in response to the movement velocity (x and y coorinates)
%Use the data to estimate the process noise for the moving case and
%non-moving case
 
nonMovingInd = intersect(find(X(5,:)==0),find(X(6,:)==0));
movingInd=setdiff(1:size(X,2),nonMovingInd);
Q{2}=diag(var(diff(X(:,movingInd),[],2),[],2));
Q{2}(1:4,1:4)=0;
varNV=diag(var(diff(X(:,nonMovingInd),[],2),[],2));
Q{1} = varNV(1:2,1:2);
close all; clear S_est X_est MU_est S_estNT X_estNT MU_estNT;
numExamples = 20;
numCells=40;
scrsz = get(0,'ScreenSize');
fig1=figure('OuterPosition',[scrsz(3)*.1 scrsz(4)*.1 ...
scrsz(3)*.9 scrsz(4)*.9]);
 
for n=1:numExamples
meanMu = log(10*delta); % baseline firing rate
MuCoeffs = meanMu+randn(numCells,1); % mu_i ~ G(meanMu,1)
coeffs = [MuCoeffs 0*randn(numCells,2) 10*(rand(numCells,2)-.5) ...
0*randn(numCells,2)];
 
 
 
%Add realization by thinning with history
dataMat = [ones(size(X,2),1),X(:,1:end)'];
% Generate M1 cells
clear lambda tempSpikeColl lambdaCIF nst;
fitType ='binomial';
% matlabpool open;
for i=1:numCells
tempData = exp(dataMat*coeffs(i,:)');
if(strcmp(fitType,'binomial'))
lambdaData = tempData./(1+tempData);
else
lambdaData = tempData;
end
lambda{i}=Covariate(time,lambdaData./delta, ...
'\Lambda(t)','time','s','spikes/sec',...
{strcat('\lambda_{',num2str(i),'}')},{{' ''b'', ''LineWidth'' ,2'}});
maxTimeRes = 0.001;
tempSpikeColl{i} = ...
CIF.simulateCIFByThinningFromLambda(lambda{i},1,[]);
nst{i} = tempSpikeColl{i}.getNST(1);
nst{i}.setName(num2str(i));
end
 
% Decode the x-y trajectory
 
% Enforce that the maximum time resolution is delta
spikeColl = nstColl(nst);
spikeColl.resample(1/delta);
dN = spikeColl.dataToMatrix;
dN(dN>1)=1; %Avoid more than 1 spike per bin.
 
% Starting states are equally probable
Mu0=.5*ones(size(p_ij,1),1);
clear x0 yT clear Pi0 PiT;
x0{1} = X(ind{1},1);
yT{1} = X(ind{1},end);
Pi0 = Px0;
PiT{1} = 1e-9*eye(size(x0{1},1),size(x0{1},1));
 
x0{2} = X(ind{2},1);
yT{2} = X(ind{2},end);
PiT{2} = 1e-9*eye(size(x0{2},1),size(x0{2},1));
 
 
% Run the Hybrid Point Process Filter
[S_est, X_est, W_est, MU_est, X_s, W_s,pNGivenS]=...
DecodingAlgorithms.PPHybridFilterLinear(A, Q, p_ij,Mu0, dN',...
coeffs(:,1),coeffs(:,2:end)',fitType,delta,[],[],x0,Pi0, yT,PiT);
[S_estNT, X_estNT, W_estNT, MU_estNT, X_sNT, W_sNT,pNGivenSNT]=...
DecodingAlgorithms.PPHybridFilterLinear(A, Q, p_ij,Mu0, dN',...
coeffs(:,1),coeffs(:,2:end)',fitType,delta,[],[],x0,Pi0);
%Store the results for computing relevant statistics later
X_estAll(:,:,n) = X_est;
X_estNTAll(:,:,n) = X_estNT;
S_estAll(n,:)=S_est;
S_estNTAll(n,:)=S_estNT;
MU_estAll(:,:,n)=MU_est;
MU_estNTAll(:,:,n) = MU_estNT;
 
%State Estimate
subplot(4,3,[1 4]);
plot(time,mstate,'k','LineWidth',3); hold all;
plot(time,S_est,'b-.','Linewidth',.5);
plot(time,S_estNT,'g-.','Linewidth',.5); axis tight; v=axis;
axis([v(1) v(2) 0.5 2.5]);
 
%Movement State Probability (Non-movement State probability is 1-Pr(Movement))
subplot(4,3,[7 10]);
plot(time,MU_est(2,:),'b-.','Linewidth',.5); hold on;
plot(time,MU_estNT(2,:),'g-.','Linewidth',.5); hold on;
axis([min(time) max(time) 0 1.1]);
 
%The movement path
subplot(4,3,[2 3 5 6]);
h1=plot(100*X(1,:)',100*X(2,:)','k'); hold all;
h2=plot(100*X_est(1,:)',100*X_est(2,:)','b-.'); hold all;
h3=plot(100*X_estNT(1,:)',100*X_estNT(2,:)','g-.');
%X-Position
subplot(4,3,8);
h1=plot(time,100*X(1,:),'k','LineWidth',3); hold on;
h2=plot(time,100*X_est(1,:)','b-.');
h3=plot(time,100*X_estNT(1,:)','g-.');
 
%Y-Position
subplot(4,3,9);
h1=plot(time,100*X(2,:),'k','LineWidth',3); hold on;
h2=plot(time,100*X_est(2,:)','b-.');
h3=plot(time,100*X_estNT(2,:)','g-.');
 
%X-Velocity
subplot(4,3,11);
h1=plot(time,100*X(3,:),'k','LineWidth',3); hold on;
h2=plot(time,100*X_est(3,:)','b-.');
h3=plot(time,100*X_estNT(3,:)','g-.');
 
subplot(4,3,12);
h1=plot(time,100*X(4,:),'k','LineWidth',3); hold on;
h2=plot(time,100*X_est(4,:)','b-.');
h3=plot(time,100*X_estNT(4,:)','g-.');
 
 
end
 
%
% Save all the example Data
% save Experiment6ReachExamples X_estAll X_estNTAll S_estAll ...
% S_estNTAll MU_estAll MU_estNTAll;
%
% load Experiment6ReachExamples;
 
% Mean Discrete State Estimate
subplot(4,3,[1 4]);
hold all;
plot(time,mstate,'k','LineWidth',3);
plot(time,mean(S_estAll),'b','LineWidth',3);
plot(time,mean(S_estNTAll),'g','LineWidth',3);
set(gca,'xtick',[],'YTick',[1 2.1],'YTickLabel',{'N','M'});
hy=ylabel('state'); hx=xlabel('time [s]');
set([hy hx],'FontName', 'Arial','FontSize',10,'FontWeight','bold',...
'Interpreter','none');
title('Estimated vs. Actual State','FontWeight','bold','Fontsize',...
12,'FontName','Arial');
 
 
% Mean State Movement State Probability
subplot(4,3,[7 10]);
plot(time, mean(squeeze(MU_estAll(2,:,:)),2),'b','LineWidth',3);
hold on;
plot(time,mean(squeeze(MU_estNTAll(2,:,:)),2),'g','LineWidth',3);
hold on;
axis([min(time) max(time) 0 1.1]);
hx=xlabel('time [s]'); hy=ylabel('P(s(t)=M | data)');
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
title('Probability of State','FontWeight','bold','Fontsize',12,...
'FontName','Arial');
% Mean movement path
subplot(4,3,[2 3 5 6]);
h1=plot(100*X(1,:)',100*X(2,:)','k'); hold all;
mXestAll=mean(100*X_estAll,3);
mXestNTAll=mean(100*X_estNTAll,3);
plot(mXestAll(1,:),mXestAll(2,:),'b','Linewidth',3);
plot(mXestNTAll(1,:),mXestNTAll(2,:),'g','Linewidth',3);
hx=xlabel('x [cm]'); hy=ylabel('y [cm]');
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
 
h1=plot(100*X(1,1),100*X(2,1),'bo','MarkerSize',14); hold on;
h2=plot(100*X(1,end),100*X(2,end),'ro','MarkerSize',14);
legend([h1 h2],'Start','Finish','Location','NorthEast');
title('Estimated vs. Actual Reach Path','FontWeight','bold',...
'Fontsize',12,'FontName','Arial');
 
% Mean X-Positon
subplot(4,3,8);
h1=plot(time,100*X(1,:),'k','LineWidth',3); hold on;
h2=plot(time,mXestAll(1,:),'b','LineWidth',3); hold on;
h3=plot(time,mXestNTAll(1,:),'g','LineWidth',3); hold on;
hy=ylabel('x(t) [cm]'); hx=xlabel('time [s]');
set(gca,'xtick',[],'xtickLabel',[]);
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
title('X Position','FontWeight','bold','Fontsize',12,'FontName','Arial');
% Mean Y-Position
subplot(4,3,9);
h1=plot(time,100*X(2,:),'k','LineWidth',3); hold on;
h2=plot(time,mXestAll(2,:),'b','LineWidth',3); hold on;
h3=plot(time,mXestNTAll(2,:),'g','LineWidth',3); hold on;
h_legend=legend([h1(1) h2(1) h3(1)],'Actual','PPAF+Goal',...
'PPAF','Location','SouthEast');
hy=ylabel('y(t) [cm]'); hx=xlabel('time [s]');
set(gca,'xtick',[],'xtickLabel',[]);
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
title('Y Position','FontWeight','bold','Fontsize',12,'FontName','Arial');
set(h_legend,'FontSize',10)
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)-.40 pos(2)+.51 pos(3:4)]);
 
% Mean X-Velocity
subplot(4,3,11);
h1=plot(time,100*X(3,:),'k','LineWidth',3); hold on;
h2=plot(time,mXestAll(3,:),'b','LineWidth',3); hold on;
h3=plot(time,mXestNTAll(3,:),'g','LineWidth',3); hold on;
hy=ylabel('v_{x}(t) [cm/s]'); hx=xlabel('time [s]');
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
title('X Velocity','FontWeight','bold','Fontsize',12,'FontName','Arial');
 
% Mean Y-Velocity
subplot(4,3,12);
h1=plot(time,100*X(4,:),'k','LineWidth',3); hold on;
h2=plot(time,mXestAll(4,:),'b','LineWidth',3); hold on;
h3=plot(time,mXestNTAll(4,:),'g','LineWidth',3); hold on;
hy=ylabel('v_{y}(t) [cm/s]'); hx=xlabel('time [s]');
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
title('Y Velocity','FontWeight','bold','Fontsize',12,'FontName','Arial');
 
function [dataDir,mEPSCDir,explicitStimulusDir,psthDir,placeCellDataDir] = ...
getPaperDataDirs()
% Resolve local data folders robustly when Live Editor executes from a temp
% location (e.g., /private/var/.../T).
candidateRoots = {};
 
scriptPath = mfilename('fullpath');
if ~isempty(scriptPath)
candidateRoots = appendCandidateRoot(candidateRoots, fileparts(fileparts(scriptPath)));
end
 
paperPath = which('nSTATPaperExamples');
if ~isempty(paperPath)
candidateRoots = appendCandidateRoot(candidateRoots, fileparts(fileparts(paperPath)));
end
 
installPath = which('nSTAT_Install');
if ~isempty(installPath)
candidateRoots = appendCandidateRoot(candidateRoots, fileparts(installPath));
end
 
try
activeFile = matlab.desktop.editor.getActiveFilename;
catch
activeFile = '';
end
if ~isempty(activeFile)
candidateRoots = appendCandidateRoot(candidateRoots, fileparts(fileparts(activeFile)));
end
 
candidateRoots = appendCandidateRoot(candidateRoots, pwd);
 
nSTATDir = '';
for iRoot = 1:numel(candidateRoots)
candidateDataDir = fullfile(candidateRoots{iRoot}, 'data');
if exist(candidateDataDir, 'dir') == 7
nSTATDir = candidateRoots{iRoot};
break;
end
end
 
if isempty(nSTATDir)
error('nSTATPaperExamples:MissingInstallPath', ...
['Could not resolve the nSTAT root path. Checked roots derived from ', ...
'mfilename, which(''nSTATPaperExamples''), which(''nSTAT_Install''), ', ...
'the active editor file, and pwd.']);
end
 
dataDir = fullfile(nSTATDir,'data');
mEPSCDir = fullfile(dataDir,'mEPSCs');
explicitStimulusDir = fullfile(dataDir,'Explicit Stimulus');
psthDir = fullfile(dataDir,'PSTH');
placeCellDataDir = fullfile(dataDir,'Place Cells');
 
if exist(dataDir,'dir') ~= 7
error('nSTATPaperExamples:MissingDataDir', ...
'Could not find local nSTAT data folder at %s', dataDir);
end
end
 
function roots = appendCandidateRoot(roots, startDir)
if isempty(startDir)
return;
end
 
thisDir = startDir;
while true
if ~any(strcmp(roots, thisDir))
roots{end+1} = thisDir; %#ok<AGROW>
end
parentDir = fileparts(thisDir);
if strcmp(parentDir, thisDir)
break;
end
thisDir = parentDir;
end
end
+
+ +
\ No newline at end of file diff --git a/docs/figures/published_html/nSTATPaperExamples_modern.html b/docs/figures/published_html/nSTATPaperExamples_modern.html new file mode 100644 index 0000000..2a0d61b --- /dev/null +++ b/docs/figures/published_html/nSTATPaperExamples_modern.html @@ -0,0 +1,2342 @@ + +nSTAT J. Neuroscience Methods Paper Examples

nSTAT J. Neuroscience Methods Paper Examples

Author: Iahn Cajigas
Date: 01/04/2012

Experiment 1

MINIATURE EXCITATORY POST-SYNAPTIC CURRENTS (mEPSCs) Data from Marnie Phillips marnie.a.phillips@gmail.com This analysis is based on a partial version of the dataset used in
Phillips MA, Lewis LD, Gong J, Constantine-Paton M, Brown EN. 2011 Model-based statistical analysis of miniature synaptic transmission. J Neurophys (under consideration)
Date: 03/01/2011

Constant Magnesium Concentration - Constant rate poisson

Under a constant Magnesium concentration, it is seen that the mEPSCs behave as a homogeneous poisson process (constant arrival rate).
close all; clear all;
[dataDir,mEPSCDir,explicitStimulusDir,psthDir,placeCellDataDir] = ...
getPaperDataDirs();
nSTATRootDir = fileparts(dataDir);
if exist(nSTATRootDir,'dir') == 7 && ~strcmp(pwd,nSTATRootDir)
cd(nSTATRootDir);
end
epsc2 = importdata(fullfile(mEPSCDir,'epsc2.txt'));
sampleRate = 1000;
spikeTimes = epsc2.data(:,2)*1/sampleRate; %in seconds
nstConst = nspikeTrain(spikeTimes);
time = 0:(1/sampleRate):nstConst.maxTime;
 
% Define Covariates for the analysis
baseline = Covariate(time,ones(length(time),1),'Baseline','time','s',...
'',{'\mu'});
covarColl = CovColl({baseline});
 
% Create the trial structure
spikeColl = nstColl(nstConst);
trial = Trial(spikeColl,covarColl);
 
% Define how we want to analyze the data
clear tc tcc;
tc{1} = TrialConfig({{'Baseline','\mu'}},sampleRate,[]);
tc{1}.setName('Constant Baseline');
tcc = ConfigColl(tc);
% Perform Analysis (Commented to since data already saved)
results =Analysis.RunAnalysisForAllNeurons(trial,tcc,0);
Analyzing Configuration #1: Neuron #1
% h=results.plotResults;
close all;
scrsz = get(0,'ScreenSize');
results.lambda.setDataLabels({'\lambda_{const}'});
h=figure('OuterPosition',[scrsz(3)*.01 scrsz(4)*.04 ...
scrsz(3)*.98 scrsz(4)*.95]);
subplot(2,2,1); spikeColl.plot;
title({'Neural Raster with constant Mg^{2+} Concentration'},...
'FontWeight','bold',...
'Fontsize',12,...
'FontName','Arial');
hx=xlabel('time [s]','Interpreter','none');
hy=ylabel('mEPSCs','Interpreter','none');
set(gca,'yTick',[0 1]);
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
subplot(2,2,3); results.KSPlot;
subplot(2,2,2); results.plotInvGausTrans;
subplot(2,2,4); results.lambda.plot([],{{' ''b'' ,''Linewidth'',2'}});
hx=xlabel('time [s]','Interpreter','none');
hy=get(gca,'YLabel');
set([hx hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
h_legend = legend('\lambda_{const}','Location','NorthEast');
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.05 pos(2) pos(3:4)]);
set(h_legend,'FontSize',14)

Varying Magnesium Concentration - Piecewise Constant rate poisson

When the magnesium concentration of the bath decreased (i.e. magnesium is removed), the rate of mEPSCs begin to increase in frequency. This can be modeled in a many different ways (using the change in Magnesium directly as a model covariate, etc.) Here we approximate the rate as being constant during certain portions of the experiment. These segments can in principle be estimated (using heirarchical Bayesian methods), but here we select them via visual inspection. We compare three models: a constant rate model (from above), a piecewise constant rate model, and a piecewise constant rate model with history.
close all;
% load the data;
washout1 = importdata(fullfile(mEPSCDir,'washout1.txt'));
washout2 = importdata(fullfile(mEPSCDir,'washout2.txt'));
sampleRate = 1000;
% Magnesium removed at t=0
spikeTimes1 = 260+washout1.data(:,2)*1/sampleRate; %in seconds
spikeTimes2 = sort(washout2.data(:,2))*1/sampleRate + 745;%in seconds
nst = nspikeTrain([spikeTimes1; spikeTimes2]);
time = 260:(1/sampleRate):nst.maxTime;

Data Visualization

Visual inspection of the spike train is used to pick three regions where the firing rate appears to be different. Here we do not estimate where these transitions happen but pick times in an ad-hoc manner.
scrsz = get(0,'ScreenSize');
h=figure('OuterPosition',[scrsz(3)*.01 scrsz(4)*.04 scrsz(3)*.6 ...
scrsz(4)*.9]);
subplot(2,1,1);
nstConst.plot; set(gca,'yTick',[0 1]); hy=ylabel('mEPSCs');
title({'Neural Raster with constant Mg^{2+} Concentration'},...
'FontWeight','bold',...
'Fontsize',12,...
'FontName','Arial');
hx=get(gca,'XLabel');
set([hx,hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
subplot(2,1,2);
nst.plot; set(gca,'yTick',[0 1]); hy=ylabel('mEPSCs');
title({'Neural Raster with decreasing Mg^{2+} Concentration'},...
'FontWeight','bold',...
'Fontsize',12,...
'FontName','Arial');
hx=get(gca,'XLabel');
set([hx,hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');

Define Covariates for the analysis

timeInd1 =find(time<495,1,'last'); %0-495sec first constant rate
timeInd2 =find(time<765,1,'last'); %495-765 second constant rate epoch
%765 onwards third constant rate
%epoch
constantRate = ones(length(time),1);
rate1 = zeros(length(time),1); rate1(1:timeInd1)=1;
rate2 = zeros(length(time),1); rate2((timeInd1+1):timeInd2)=1;
rate3 = zeros(length(time),1); rate3((timeInd2+1):end)=1;
baseline = Covariate(time,[constantRate,rate1, rate2, rate3],...
'Baseline','time','s','',{'\mu','\mu_{1}','\mu_{2}','\mu_{3}'});
covarColl = CovColl({baseline});
 
% Create the trial structure
spikeColl = nstColl(nst);
trial = Trial(spikeColl,covarColl);
%30ms history in logarithmic spacing (chosen after using
%Analysis.computeHistLagForAll for various window lengths)
maxWindow=.3; numWindows=20;
delta=1/sampleRate;
windowTimes =unique(round([0 logspace(log10(delta),...
log10(maxWindow),numWindows)]*sampleRate)./sampleRate);
windowTimes = windowTimes(1:11);

Define how we want to analyze the data

clear tc tcc;
tc{1} = TrialConfig({{'Baseline','\mu'}},sampleRate,[]);
tc{1}.setName('Constant Baseline');
tc{2} = TrialConfig({{'Baseline','\mu_{1}','\mu_{2}','\mu_{3}'}},...
sampleRate,[]); tc{2}.setName('Diff Baseline');
tcc = ConfigColl(tc);

Perform Analysis

We see that the piece-wise constant rate model (without history) outperforms the constant baseline model in terms of AIC, BIC, and KS-statistic.
results =Analysis.RunAnalysisForAllNeurons(trial,tcc,0);
Analyzing Configuration #1: Neuron #1 +Analyzing Configuration #2: Neuron #1
% h=results.plotResults;
% Summary = FitResSummary(results);
% h=Summary.plotSummary;
close all;
scrsz = get(0,'ScreenSize');
results.lambda.setDataLabels({'\lambda_{const}',...
'\lambda_{const-epoch}'});
h=figure('OuterPosition',[scrsz(3)*.01 scrsz(4)*.04 ...
scrsz(3)*.98 scrsz(4)*.95]);
subplot(2,2,1); spikeColl.plot;
title({'Neural Raster with decreasing Mg^{2+} Concentration'},...
'FontWeight','bold',...
'Fontsize',12,...
'FontName','Arial');
hx=xlabel('time [s]','Interpreter','none');
set(gca,'YTickLabel',[]);
set([hx],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
timeInd1 =find(time<495,1,'last'); %0-495sec first constant rate
timeInd2 =find(time<765,1,'last'); %495-765 second constant rate epoch
%765 onwards third constant rate
%epoch
plot([495;495],[0,1],'r','Linewidth',4); hold on;
plot([765;765],[0,1],'r','Linewidth',4);
 
subplot(2,2,3); results.KSPlot;
subplot(2,2,2); results.plotInvGausTrans;
subplot(2,2,4);
results.lambda.getSubSignal(1).plot([],{{' ''b'' ,''Linewidth'',2'}});
results.lambda.getSubSignal(2).plot([],{{' ''g'' ,''Linewidth'',2'}});
v=axis; axis([v(1) v(2) 0 5]);
hx=xlabel('time [s]','Interpreter','none');
hy=get(gca,'YLabel');
set([hx hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
h_legend = legend('\lambda_{const}','\lambda_{const-epoch}',...
'Location','NorthEast');
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.05 pos(2)-.01 pos(3:4)]);
set(h_legend,'FontSize',14)

Experiment 2

EXPLICIT STIMULUS EXAMPLE - WHISKER STIMULATION/THALAMIC NEURON In the worksheet with analyze the stimulus effect and history effect on the firing of a thalamic neuron under a known stimulus consisting of whisker stimulation. Data from Demba Ba (demba@mit.edu)

Load the data

clear all;
close all;
[dataDir,mEPSCDir,explicitStimulusDir,psthDir,placeCellDataDir] = ...
getPaperDataDirs();
nSTATRootDir = fileparts(dataDir);
if exist(nSTATRootDir,'dir') == 7 && ~strcmp(pwd,nSTATRootDir)
cd(nSTATRootDir);
end
 
Direction=3; Neuron=1; Stim=2;
datapath = fullfile(explicitStimulusDir,['Dir' num2str(Direction)], ...
['Neuron' num2str(Neuron)],['Stim' num2str(Stim)]);
data = load(fullfile(datapath,'trngdataBis.mat'));
 
time=0:.001:(length(data.t)-1)*.001;
stimData = data.t;
spikeTimes = time(data.y==1);
 
stim = Covariate(time,stimData./10,'Stimulus','time','s','mm',{'stim'});
baseline = Covariate(time,ones(length(time),1),'Baseline','time','s','',...
{'constant'});
 
nst = nspikeTrain(spikeTimes);
nspikeColl = nstColl(nst);
cc = CovColl({stim,baseline});
trial = Trial(nspikeColl,cc);
% trial.plot;
 
scrsz = get(0,'ScreenSize');
h=figure('Position',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.8 scrsz(4)*.8]);
subplot(3,1,1);
nst2 = nspikeTrain(spikeTimes);
nst2.setMaxTime(21);nst2.plot;
set(gca,'ytick',[0 1]);
xlabel('');
hy=ylabel('spikes');
set(hy,'FontName', 'Arial','FontSize',12,'FontWeight','bold');
title({'Neural Raster'},'FontWeight','bold','FontSize',16,'FontName','Arial');
set(gca, ...
'XTick' , 0:1:max(time), ...
'XTickLabel' , [],...
'LineWidth' , 1 );
subplot(3,1,2);
stim.getSigInTimeWindow(0,21).plot([],{{' ''k'' '}}); legend off;
set(gca,'ytick',[0 0.5 1]);
hy=ylabel('Displacement [mm]','Interpreter','none'); xlabel('');
set(hy,'FontName', 'Arial','FontSize',12,'FontWeight','bold');
title({'Stimulus - Whisker Displacement'},'FontWeight','bold',...
'FontSize',16,'FontName','Arial');
 
set(gca, ...
'XTick' , 0:1:max(time), ...
'XTickLabel' , [],...
'YTick' , 0:.25:1, ...
'LineWidth' , 1 );
 
subplot(3,1,3);
stim.derivative.getSigInTimeWindow(0,21).plot([],{{' ''k'' '}}); legend off;
set(gca,'ytick',[-80 0 80]);
axis([0 21 -80 80]);
hy=ylabel('Displacement Velocity [mm/s]','Interpreter','none');
hx= xlabel('time [s]','Interpreter','none');
set([hx hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
title({'Displacement Velocity'},'FontWeight','bold',...
'FontSize',16,'FontName','Arial');
 
set(gca, ...
'XTick' , 0:1:max(time), ...
'YTick' , -80:40:80, ...
'LineWidth' , 1 );
Fit a constant baseline and Find Stimulus Lag We fit a constant rate (Poisson) model to the data and use the look at the cross-covariance function of between the stimulus and the fit residual to determine the appropriate lag for the stimulus.
clear c; close all;
selfHist = [] ; NeighborHist = []; sampleRate = 1000;
c{1} = TrialConfig({{'Baseline','constant'}},sampleRate,selfHist,NeighborHist);
c{1}.setName('Baseline');
cfgColl= ConfigColl(c);
results = Analysis.RunAnalysisForAllNeurons(trial,cfgColl,0);
Analyzing Configuration #1: Neuron #1
 
% Find Stimulus Lag (look for peaks in the cross-covariance function less
% than 1 second
scrsz = get(0,'ScreenSize');
h=figure('Position',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.8 scrsz(4)*.8]);
subplot(7,2,[1 3 5])
results.Residual.xcov(stim).windowedSignal([0,1]).plot;
 
ylabel('');
[m,ind,ShiftTime] = max(results.Residual.xcov(stim).windowedSignal([0,1]));
title(['Cross Correlation Function - Peak at t=' num2str(ShiftTime) ' sec'],'FontWeight','bold',...
'FontSize',12,...
'FontName','Arial');
hold on;
h=plot(ShiftTime,m,'ro','Linewidth',3);
set(h, 'MarkerFaceColor',[1 0 0], 'MarkerEdgeColor',[1 0 0]);
hx=xlabel('Lag [s]','Interpreter','none');
set(hx,'FontName', 'Arial','FontSize',12,'FontWeight','bold');
 
%Allow for shifts of less than 1 second
stim = Covariate(time,stimData,'Stimulus','time','s','V',{'stim'});
stim = stim.shift(ShiftTime);
baseline = Covariate(time,ones(length(time),1),'Baseline','time','s','',...
{'\mu'});
 
nst = nspikeTrain(spikeTimes);
nspikeColl = nstColl(nst);
cc = CovColl({stim,baseline});
trial2 = Trial(nspikeColl,cc);

Compare constant rate model with model including stimulus effect

Addition of the stimulus improves the fits in terms of the KS plot and the making the rescaled ISIs less correlated. The Point Process Residula also looks more "white"
clear c;
selfHist = [] ; NeighborHist = []; sampleRate = 1000;
c{1} = TrialConfig({{'Baseline','\mu'}},sampleRate,selfHist,...
NeighborHist);
c{1}.setName('Baseline');
c{2} = TrialConfig({{'Baseline','\mu'},{'Stimulus','stim'}},...
sampleRate,selfHist,NeighborHist);
c{2}.setName('Baseline+Stimulus');
cfgColl= ConfigColl(c);
results = Analysis.RunAnalysisForAllNeurons(trial2,cfgColl,0);
Analyzing Configuration #1: Neuron #1 +Analyzing Configuration #2: Neuron #1
% results.plotResults;

History Effect

Determine the best history effect model using AIC, BIC, and KS statistic
sampleRate=1000;
delta=1/sampleRate*1;
maxWindow=1; numWindows=32;
windowTimes =unique(round([0 logspace(log10(delta),...
log10(maxWindow),numWindows)]*sampleRate)./sampleRate);
results =Analysis.computeHistLagForAll(trial2,windowTimes,...
{{'Baseline','\mu'},{'Stimulus','stim'}},'BNLRCG',0,sampleRate,0);
Analyzing Configuration #1: Neuron #1 +Analyzing Configuration #2: Neuron #1 +Analyzing Configuration #3: Neuron #1 +Analyzing Configuration #4: Neuron #1 +Analyzing Configuration #5: Neuron #1 +Analyzing Configuration #6: Neuron #1 +Analyzing Configuration #7: Neuron #1 +Analyzing Configuration #8: Neuron #1 +Analyzing Configuration #9: Neuron #1 +Analyzing Configuration #10: Neuron #1 +Analyzing Configuration #11: Neuron #1 +Analyzing Configuration #12: Neuron #1 +Analyzing Configuration #13: Neuron #1 +Analyzing Configuration #14: Neuron #1 +Analyzing Configuration #15: Neuron #1 +Analyzing Configuration #16: Neuron #1 +Analyzing Configuration #17: Neuron #1 +Analyzing Configuration #18: Neuron #1 +Analyzing Configuration #19: Neuron #1 +Analyzing Configuration #20: Neuron #1 +Analyzing Configuration #21: Neuron #1 +Analyzing Configuration #22: Neuron #1 +Analyzing Configuration #23: Neuron #1 +Analyzing Configuration #24: Neuron #1 +Analyzing Configuration #25: Neuron #1 +Analyzing Configuration #26: Neuron #1 +Analyzing Configuration #27: Neuron #1 +Analyzing Configuration #28: Neuron #1 +Analyzing Configuration #29: Neuron #1 +Analyzing Configuration #30: Neuron #1
 
KSind = find(results{1}.KSStats.ks_stat == min(results{1}.KSStats.ks_stat));
AICind = find((results{1}.AIC(2:end)-results{1}.AIC(1))== ...
min(results{1}.AIC(2:end)-results{1}.AIC(1))) +1;
BICind = find((results{1}.BIC(2:end)-results{1}.BIC(1))== ...
min(results{1}.BIC(2:end)-results{1}.BIC(1))) +1;
if(AICind==1)
AICind=inf;
end
if(BICind==1)
BICind=inf; %sometime BIC is non-decreasing and the index would be 1
end
windowIndex = min([AICind,BICind]) %use the minimum order model
windowIndex = 10
Summary = FitResSummary(results);
% Summary.plotSummary;
 
 
clear c;
if(windowIndex>1)
selfHist = windowTimes(1:windowIndex+1);
else
selfHist = [];
end
NeighborHist = []; sampleRate = 1000;
%
% figure;
subplot(7,2,2);
x=0:length(windowTimes)-1;
plot(x,results{1}.KSStats.ks_stat,'.-'); axis tight; hold on;
plot(x(windowIndex),results{1}.KSStats.ks_stat(windowIndex),'r*');
 
set(gca,'XTick', 0:5:results{1}.numResults-1,'XTickLabel',[],...
'TickLength', [.02 .02] , ...
'XMinorTick', 'on','LineWidth' , 1);
 
hy=ylabel('KS Statistic');
set(hy,'FontName', 'Arial','FontSize',12,'FontWeight','bold');
dAIC = results{1}.AIC-results{1}.AIC(1);
title({'Model Selection via change'; 'in KS Statistic, AIC, and BIC'},...
'FontWeight','bold',...
'FontSize',12,...
'FontName','Arial');
subplot(7,2,4); plot(x,dAIC,'.-');
set(gca,'XTick', 0:5:results{1}.numResults-1,'XTickLabel',[],...
'TickLength', [.02 .02] , ...
'XMinorTick', 'on','LineWidth' , 1);
hy=ylabel('\Delta AIC');axis tight; hold on;
set(hy,'FontName', 'Arial','FontSize',12,'FontWeight','bold');
plot(x(windowIndex),dAIC(windowIndex),'r*');
dBIC = results{1}.BIC-results{1}.BIC(1);
 
subplot(7,2,6); plot(x,dBIC,'.-');
hy=ylabel('\Delta BIC'); axis tight; hold on;
 
plot(x(windowIndex),dBIC(windowIndex),'r*');
hx=xlabel('# History Windows, Q');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
set(gca, ...
'TickLength' , [.02 .02] , ...
'XMinorTick' , 'on' , ...
'XTick' , 0:5:results{1}.numResults-1, ...
'LineWidth' , 1 );
 
 
 
% Compare Baseline, Baseline+Stimulus Model, Baseline+History+Stimulus
% Addition of the history effect yields a model that falls within the 95%
% CI of the KS plot.
 
c{1} = TrialConfig({{'Baseline','\mu'}},sampleRate,[],NeighborHist);
c{1}.setName('Baseline');
c{2} = TrialConfig({{'Baseline','\mu'},{'Stimulus','stim'}},...
sampleRate,[],[]);
c{2}.setName('Baseline+Stimulus');
c{3} = TrialConfig({{'Baseline','\mu'},{'Stimulus','stim'}},...
sampleRate,windowTimes(1:windowIndex),[]);
c{3}.setName('Baseline+Stimulus+Hist');
cfgColl= ConfigColl(c);
results = Analysis.RunAnalysisForAllNeurons(trial2,cfgColl,0);
Analyzing Configuration #1: Neuron #1 +Analyzing Configuration #2: Neuron #1 +Analyzing Configuration #3: Neuron #1
%results.plotResults;
%
results.lambda.setDataLabels({'\lambda_{const}','\lambda_{const+stim}',...
'\lambda_{const+stim+hist}'});
subplot(7,2,[9 11 13]); results.KSPlot;
subplot(7,2,[10 12 14]); results.plotCoeffs; legend off;

Example 3 - PSTH Data

% Generate a known Conditional Intensity Function
% We generated a known conditional intensity function (rate function) and
% generate distinct realizations of point processes consistent with this
% rate function. We use the method of thinning to simulate a point process.
clear all;
[dataDir,mEPSCDir,explicitStimulusDir,psthDir,placeCellDataDir] = ...
getPaperDataDirs();
close all;
delta = 0.001;
Tmax = 1;
time = 0:delta:Tmax;
f=2;
mu = -3;
 
tempData = 1*sin(2*pi*f*time)+mu; %lambda >=0
lambdaData = exp(tempData)./(1+exp(tempData))*(1/delta);
lambda = Covariate(time,lambdaData, '\lambda(t)','time','s',...
'spikes/sec',{'\lambda_{1}'},{{' ''b'', ''LineWidth'' ,2'}});
numRealizations = 20;
spikeCollSim = CIF.simulateCIFByThinningFromLambda(lambda,numRealizations);
 
 
scrsz = get(0,'ScreenSize');
h=figure('Position',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.8 scrsz(4)*.8]);
 
subplot(2,2,3);spikeCollSim.plot;
set(gca,'YTick',0:5:numRealizations,'YTickLabel',0:5:numRealizations);
title({[num2str(numRealizations) ' Simulated Point Process Sample Paths']},...
'FontWeight','bold','Fontsize',14,'FontName','Arial');
xlabel('time [s]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
ylabel('Trial [k]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
 
subplot(2,2,1);lambda.plot;
title({'Simulated Conditional Intensity Function (CIF)'},...
'FontWeight','bold','FontSize',14,'FontName','Arial');
xlabel('time [s]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
hy=get(gca,'YLabel');
set(hy,'FontName', 'Arial','FontSize',14,'FontWeight','bold');
x = load(fullfile(psthDir,'Results.mat'));
numTrials = x.Results.Data.Spike_times_STC.balanced_SUA.Nr_trials;
cellNum=6; clear nst;
for i=1:numTrials
spikeTimes{i}=x.Results.Data.Spike_times_STC.balanced_SUA.spike_times{1,i,cellNum};
nst{i} = nspikeTrain(spikeTimes{i});
nst{i}.setName(num2str(cellNum));
end
 
spikeCollReal1=nstColl(nst);
spikeCollReal1.setMinTime(0); spikeCollReal1.setMaxTime(2);
subplot(2,2,2);spikeCollReal1.plot; set(gca,'YTick',0:2:numTrials,...
'YTickLabel',0:2:numTrials);
%set(gca,'xtick',[0:.5:2],'xtickLabel',{'0','0.5','1','1.5','2'});
xlabel('time [s]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
ylabel('Trial [k]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
title('Response to Moving Visual Stimulus (Neuron 6)',...
'FontWeight','bold','Fontsize',14,'FontName','Arial');
 
cellNum=1; clear nst;
for i=1:numTrials
spikeTimes{i}=x.Results.Data.Spike_times_STC.balanced_SUA.spike_times{1,i,cellNum};
nst{i} = nspikeTrain(spikeTimes{i});
nst{i}.setName(num2str(cellNum));
end
 
spikeCollReal2=nstColl(nst);
spikeCollReal2.setMinTime(0); spikeCollReal2.setMaxTime(2);
subplot(2,2,4);spikeCollReal2.plot;
set(gca,'YTick',0:2:numTrials,'YTickLabel',0:2:numTrials);
%set(gca,'xtick',[0:.5:2],'xtickLabel',{'0','0.5','1','1.5','2'});
xlabel('time [s]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
ylabel('Trial [k]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
title('Response to Moving Visual Stimulus (Neuron 1)','FontWeight',...
'bold','Fontsize',14,'FontName','Arial');

Estimate the PSTH with 50ms windows

close all;
 
scrsz = get(0,'ScreenSize');
h=figure('Position',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.8 scrsz(4)*.8]);
 
binsize = .05; %50ms window
psth = spikeCollSim.psth(binsize);
psthGLM = spikeCollSim.psthGLM(binsize);
Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1
true = lambda; %rate*delta = expected number of arrivals per bin
subplot(2,3,4);
 
h1=true.plot([],{{' ''b'',''Linewidth'',4'}});
h3=psthGLM.plot([],{{' ''k'',''Linewidth'',4'}});
h2=psth.plot([],{{' ''rx'',''Linewidth'',4'}});
 
xlabel('time [s]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
ylabel('[spikes/sec]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
 
legend off;
h_legend=legend([h1(1) h2(1) h3(1)],'true','PSTH','PSTH_{glm}');
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.005 pos(2)+.095 pos(3:4)]);
 
 
%
subplot(2,3,1);spikeCollSim.plot;
set(gca,'YTick',0:2:spikeCollSim.numSpikeTrains,'YTickLabel',0:2:spikeCollSim.numSpikeTrains);
xlabel('time [s]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
ylabel('Trial [k]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
 
subplot(2,3,5);
binsize = .05; %50ms window
psthReal1 = spikeCollReal1.psth(binsize);
psthGLMReal1 = spikeCollReal1.psthGLM(binsize);%,[],[],[],[],[],1000);
Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #6
 
h3=psthGLMReal1.plot([],{{' ''k'',''Linewidth'',4'}});
h2=psthReal1.plot([],{{' ''rx'',''Linewidth'',4'}});
xlabel('time [s]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
ylabel('[spikes/sec]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
 
h_legend=legend([h2(1) h3(1)],'PSTH','PSTH_{glm}');
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.005 pos(2)+.07 pos(3:4)]);
subplot(2,3,2); spikeCollReal1.plot;
set(gca,'YTick',0:2:spikeCollReal2.numSpikeTrains,'YTickLabel',0:2:spikeCollReal2.numSpikeTrains);
xlabel('time [s]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
ylabel('Trial [k]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
subplot(2,3,6);
psthReal2 = spikeCollReal2.psth(binsize);
psthGLMReal2 = spikeCollReal2.psthGLM(binsize);%,[],[],[],[],[],1000);
Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1
h3=psthGLMReal2.plot([],{{' ''k'',''Linewidth'',4'}});
h2=psthReal2.plot([],{{' ''rx'',''Linewidth'',4'}});
xlabel('time [s]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
ylabel('[spikes/sec]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
 
 
h_legend=legend([h2(1) h3(1)],'PSTH','PSTH_{glm}');
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.005 pos(2)+.07 pos(3:4)]);
subplot(2,3,3); spikeCollReal2.plot;
set(gca,'YTick',0:2:spikeCollReal2.numSpikeTrains,'YTickLabel',0:2:spikeCollReal2.numSpikeTrains);
xlabel('time [s]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
ylabel('Trial [k]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');

Example 3b - SSGLM Example

Example of estimating with-in and across trial dynamics Methods from: G. Czanner, U. T. Eden, S. Wirth, M. Yanike, W. A. Suzuki, and E. N. Brown, "Analysis of between-trial and within-trial neural spiking dynamics.," Journal of neurophysiology, vol. 99, no. 5, pp. 2672?2693, May. 2008.
close all;
clear all;
[dataDir,mEPSCDir,explicitStimulusDir,psthDir,placeCellDataDir] = ...
getPaperDataDirs();
% set(0,'DefaultFigureRenderer','ZBuffer')
delta = 0.001; Tmax = 1;
time = 0:delta:Tmax;
Ts=.001;
numRealizations = 50; %Each realization corresponds to a distinct trial
 
for i=1:numRealizations
% The within trial dynamics are sinusoidal
% For each trial the stimulus effect increases
f=2; b1(i)=3*((i)/numRealizations);b0=-3;
u = sin(2*pi*f*time);
e = zeros(length(time),1); %No Ensemble input
 
stim=Covariate(time',u,'Stimulus','time','s','Voltage',{'sin'});
ens =Covariate(time',e,'Ensemble','time','s','Spikes',{'n1'});
 
mu=b0;
histCoeffs=[-4 -1 -.5];
H=tf(histCoeffs,[1],Ts,'Variable','z^-1');
S=tf([b1(i)],1,Ts,'Variable','z^-1');
E=tf([0],1,Ts,'Variable','z^-1');
simTypeSelect='binomial'; %Parameters are used to compute
%binomial conditional intensity function
%
 
% Obtain a realization of the point process with the current
% stimulus and history effect
[sC, lambdaTemp]=CIF.simulateCIF(mu,H,S,E,stim,ens,1,simTypeSelect);
 
if(i==1)
lambda=lambdaTemp; %Store the conditional intensity function
else
lambda = lambda.merge(lambdaTemp); %Add it to the other realizations
end
nst{i} = sC.getNST(1); %get the neural spikeTrain from the collection
nst{i} = nst{i}.resample(1/delta); %make sure that it is sampled at the current samplerate
end
 
spikeColl = nstColl(nst); %Create a collection of the spike trains across trials

Summarize Simulated Data

close all;
scrsz = get(0,'ScreenSize');
h=figure('Position',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.8 scrsz(4)*.8]);
 
%Plot the raster
subplot(3,2,[3 4]); spikeColl.plot;
set(gca,'ytick',0:10:numRealizations,'ytickLabel',0:10:numRealizations);
set(gca,'xtick',0:.1:Tmax,'xtickLabel',0:.1:Tmax); xlabel('');
xlabel('time [s]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
ylabel('Trial [k]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
title('Simulated Neural Raster','Interpreter','none','FontName', 'Arial',...
'Fontsize',14,'FontWeight','bold');
 
% Plot the actual stimulus effect (not including history)
% The CIF including the history effect is stored in the lambda Covariate
% above
 
 
stimData = exp(b0 + u'*b1);
if(strcmp(simTypeSelect,'binomial'))
stimData = stimData./(1+stimData);
end
 
%Plot the trial dependence
subplot(3,2,1); plot(time,u,'k','LineWidth',3);
% xlabel('time [s]');ylabel('stimulus');
xlabel('time [s]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
ylabel('Stimulus','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
title('Within Trial Stimulus','Interpreter','none','FontName', 'Arial',...
'Fontsize',14,'FontWeight','bold');
 
subplot(3,2,2); plot(1:length(b1),b1,'k','LineWidth',3);
xlabel('Trial [k]','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
ylabel('Stimulus Gain','Interpreter','none','FontName', 'Arial','Fontsize',...
12,'FontWeight','bold');
title('Across Trial Stimulus Gain','Interpreter','none','FontName',...
'Arial','Fontsize',14,'FontWeight','bold');
 
subplot(3,2,[5 6]);
imagesc(stimData'./delta); set(gca, 'YDir','normal');
set(gca,'xtick',0:100:Tmax/delta,'xtickLabel',0:.1:Tmax);
set(gca,'ytick',0:10:numRealizations,'ytickLabel',0:10:numRealizations);
xlabel('time [s]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
ylabel('Trial [k]','Interpreter','none','FontName', 'Arial',...
'Fontsize',12,'FontWeight','bold');
title('True Conditional Intensity Function','Interpreter',...
'none','FontName', 'Arial','Fontsize',14,'FontWeight','bold');
 
 
axis tight;

Estimation of the Stimulus Response

% Create the covariates that will be used for the GLM regression
stim = Covariate(time,sin(2*pi*f*time),'Stimulus','time','s','V',{'stim'});
baseline = Covariate(time,ones(length(time),1),'Baseline','time','s','',...
{'constant'});
 
% Specify the windows of the history coefficients to be estimated
windowTimes=[0:.001:.003];
% Number of bins to discrtize time into (used both for the PSTH and for
% thec
% SSGLM model.
numBasis = 25;
 
spikeColl.resample(1/delta); % Enforce sampleRate
spikeColl.setMaxTime(Tmax); % Make all spikeTrains end at time Tmax
 
 
dN=spikeColl.dataToMatrix'; % Convert the spikeTrains into a matrix
% of 1's and 0's corresponding to the presence
% or absense of a spike in each time window.
dN(dN>1)=1; % One should sample finely enough so there is
% one spike per bin. Here we make sure that
% this is the case regardless of the
% sampleRate
% The width of each rectangular basis pulse is determined by Tmax and by the
% number of basis pulses to use.
basisWidth=(spikeColl.maxTime-spikeColl.minTime)/numBasis;
 
if(simTypeSelect==0)
fitType='binomial';
else
fitType='poisson';
end
if(strcmp(fitType,'binomial'))
Algorithm = 'BNLRCG'; % BNLRCG - faster Truncated, L-2 Regularized,
% Binomial Logistic Regression with Conjugate
% Gradient Solver by Demba Ba (demba@mit.edu).
else
Algorithm = 'GLM'; % Standard Matlab GLM (Can be used for binomial or
% or Poisson CIFs
end
% Use the values obtained from a PSTH to initialize the SSGLM filter
[psthSig, ~, psthResult] =spikeColl.psthGLM(basisWidth,windowTimes,fitType);
Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1
gamma0=psthResult.getHistCoeffs';%+.1*randn(size(histCoeffs));
gamma0(isnan(gamma0))=-5; % Depending on the amount of data the
% the psth may not identify all parameters
% Just make sure that the estimates are real
% numbers
x0=psthResult.getCoeffs; %The initial estimate for the SSGLM model
 
% Estimate the variance within each time bin across trials
numVarEstIter=10;
Q0 = spikeColl.estimateVarianceAcrossTrials(numBasis,windowTimes,...
numVarEstIter,fitType);
Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1 +Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1 +Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1 +Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1 +Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1 +Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1 +Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1 +Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1 +Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1 +Running in batch mode: neurons with same name are fit simultaneously +Analyzing Configuration #1: Neuron #1
A=eye(numBasis,numBasis);
delta = 1/spikeColl.sampleRate;

Run the SSGLM Filter

CompilingHelpFile=1;
% Commented out to speed up help file creation ...
if(~CompilingHelpFile)
Q0d=diag(Q0);
neuronName = psthResult.neuronNumber;
[xK,WK, WkuFinal,Qhat,gammahat,fitResults,stimulus,stimCIs,logll,...
QhatAll,gammahatAll,nIter]=DecodingAlgorithms.PPSS_EMFB(A,Q0d,x0,...
dN,fitType,delta,gamma0,windowTimes, numBasis,neuronName);
 
fR = fitResults.toStructure;
psthR = psthResult.toStructure;
end
% save SSGLMExampleData psthR fR xK WK WkuFinal Qhat gammahat fitResults stimulus stimCIs logll QhatAll gammahatAll nIter;
installPath = which('nSTAT_Install');
if isempty(installPath)
error('nSTATPaperExamples:MissingInstallPath', ...
'Could not locate nSTAT_Install.m on MATLAB path.');
end
nstatRoot = fileparts(installPath);
if exist(nstatRoot,'dir') == 7 && ~strcmp(pwd,nstatRoot)
cd(nstatRoot);
end
addpath(nstatRoot,'-begin');
load(fullfile(nstatRoot,'data','SSGLMExampleData.mat'));
fitResults = FitResult.fromStructure(fR);
psthResult = FitResult.fromStructure(psthR);
t=psthResult.mergeResults(fitResults);
%t.plotResults; %Compare the results with the PSTH Model
t.lambda.setDataLabels({'\lambda_{PSTH}','\lambda_{SSGLM}'});
scrsz = get(0,'ScreenSize');
h=figure('Position',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.8 scrsz(4)*.8]);
subplot(2,2,1); t.KSPlot;
subplot(2,2,2); t.plotResidual;
subplot(2,2,3); t.plotInvGausTrans;
subplot(2,2,4); t.plotSeqCorr;
 
S=FitResSummary(t);
dAIC=diff(S.AIC)
dAIC = -1.7978e+04
dBIC=diff(S.BIC)
dBIC = -6.9523e+03
dKS =diff(S.KSStats);
close all;
% Generate the actual stimulus effect
minTime=0; maxTime = Tmax;
stimData = stim.data*b1;
if(strcmp(fitType,'poisson'))
actStimEffect=exp(stimData + b0)./delta;
elseif(strcmp(fitType,'binomial'))
actStimEffect=exp(stimData + b0)./(1+exp(stimData + b0))./delta;
end
%
 
% Generate the basis function so that the estimated effect can be plotted
% at the same temporal resolution as the theoretical effect
if(~isempty(numBasis))
basisWidth = (maxTime-minTime)/numBasis;
sampleRate=1/delta;
unitPulseBasis=nstColl.generateUnitImpulseBasis(basisWidth,minTime,...
maxTime,sampleRate);
basisMat = unitPulseBasis.data;
end
 
% Generate the estimated stimulus effect
if(strcmp(fitType,'poisson'))
estStimEffect=exp(basisMat*xK)./delta;
elseif(strcmp(fitType,'binomial'))
estStimEffect=exp(basisMat*xK)./(1+exp(basisMat*xK))./delta;
end
 
 
scrsz = get(0,'ScreenSize');
h=figure('OuterPosition',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.4 scrsz(4)*.8]);
 
% Plot the actual and estimated stimulus effect as a function of trial and
% time
subplot(3,1,[1 2 3]);
lighting gouraud
surf((1:length(b1))',stim.time,actStimEffect,'FaceAlpha',0.1,...
'EdgeAlpha',0.1,'AlphaData',0.1);
hx=xlabel('Trial [k]'); hy=ylabel('time [s]');
hz=zlabel('Stimulus Effect [spikes/sec]'); hold all;
set([hx hy hz],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
 
surf((1:length(b1))',stim.time,estStimEffect(:,1:length(b1)),...
'FaceAlpha',0.5,'EdgeAlpha',0.1,'AlphaData',0.5); %xlabel('Trial [k]'); ylabel('time [s]'); zlabel('Stimulus Effect');
set(gca,'YDir','reverse');
set(gca,'ytick',0:.1:Tmax,'ytickLabel',0:.1:Tmax);
 
title('SSGLM Estimated vs. Actual Stimulus Effect','FontWeight','bold',...
'Fontsize',14,...
'FontName','Arial');
 
close all;
h=figure('OuterPosition',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.4 scrsz(4)*.8]);
 
% The actual stimulus effect
subplot(3,1,1);
lighting gouraud
mesh((1:length(b1))',stim.time,actStimEffect);
hx=xlabel('Trial [k]'); hy=ylabel('time [s]');
zlabel('Stimulus Effect [spikes/sec]'); hold all;
set([hx hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
% title('True Stimulus Effect');
title('True Stimulus Effect','FontWeight','bold',...
'Fontsize',14,...
'FontName','Arial');
set(gca,'xtick',[],'xtickLabel',[]);
set(gca,'ytick',[],'ytickLabel',[]);
CLIM = [min(min(stimData./delta)) max(max(stimData./delta))];
view(gca,[90 -90]);
 
 
 
% The PSTH estimate
subplot(3,1,2);
lighting gouraud
mesh((1:length(b1))',stim.time,repmat(psthSig.data, [1 numRealizations]));
hx=xlabel('Trial [k]'); hy=ylabel('time [s]');
hz=zlabel('Stimulus Effect [spikes/sec]'); hold all;
set([hx hy hz],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
% title('PSTH Estimated Stimulus Effect');
title('PSTH Estimated Stimulus Effect','FontWeight','bold',...
'Fontsize',14,...
'FontName','Arial');
 
set(gca,'xtick',[],'xtickLabel',[]);
set(gca,'ytick',[],'ytickLabel',[]);
CLIM = [min(min(stimData./delta)) max(max(stimData./delta))];
view(gca,[90 -90]);
 
% The SSGLM estimated stimulus effect
subplot(3,1,3);
lighting gouraud
mesh((1:length(b1))',stim.time,estStimEffect);
xlabel('Trial [k]'); ylabel('time [s]');
zlabel('Stimulus Effect [spikes/sec]'); hold all;
hx=get(gca,'XLabel'); hy=get(gca,'YLabel'); hz=get(gca,'ZLabel');
set([hx hy hz],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
% title('SSGLM Estimated Stimulus Efferct');
title('SSGLM Estimated Stimulus Effect','FontWeight','bold',...
'Fontsize',14,...
'FontName','Arial');
set(gca,'xtick',[],'xtickLabel',[]);
set(gca,'ytick',[],'ytickLabel',[]);
set(gca, 'YDir','normal')
view(gca,[90 -90]);

Compare differences across trials

close all;
minTime=0; maxTime = Tmax;
% Generate the basis function so that the estimated effect can be plotted
% at the same temporal resolution as the theoretical effect
if(~isempty(numBasis))
basisWidth = (maxTime-minTime)/numBasis;
sampleRate=1/delta;
unitPulseBasis=nstColl.generateUnitImpulseBasis(basisWidth,...
minTime,maxTime,sampleRate);
basisMat = unitPulseBasis.data;
end
 
 
% close all;
 
t0=0; tf=Tmax;
[spikeRateBinom, ProbMat,sigMat]=DecodingAlgorithms.computeSpikeRateCIs(xK,...
WkuFinal,dN,t0,tf,fitType,delta,gammahat,windowTimes);
 
lt=find(sigMat(1,:)==1,1,'first');
display(['The learning trial (compared to the first trial) is trial #' ...
num2str(find(sigMat(1,:)==1,1,'first'))]);
The learning trial (compared to the first trial) is trial #14
scrsz = get(0,'ScreenSize');
h=figure('OuterPosition',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.8 scrsz(4)*.8]);
 
subplot(2,3,1);
spikeRateBinom.setName(['(' num2str(Tmax) '-0)^-1*\Lambda(0,' ...
num2str(Tmax) ')']);
spikeRateBinom.plot([],{{' ''k'',''Linewidth'',4'}});
% e = Events(lt,{''});
% e.plot;
v=axis;
plot(lt*[1;1],v(3:4),'r','Linewidth',2);
hx=xlabel('Trial [k]','Interpreter','none'); hold all;
hy=ylabel('Average Firing Rate [spikes/sec]','Interpreter','none');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
title(['Learning Trial:' num2str(lt)],'FontWeight','bold',...
'Fontsize',12,...
'FontName','Arial');
 
 
h=subplot(2,3,[2 3 5 6]);
K=size(dN,1);
colormap(flipud(gray));
imagesc(ProbMat); hold on;
for k=1:K
for m=(k+1):K
if(sigMat(k,m)==1)
plot3(m,k,1,'r*'); hold on;
end
end
end
%
set(h,'XAxisLocation','top','YAxisLocation','right');
hx=xlabel('Trial Number','Interpreter','none'); hold all;
hy=ylabel('Trial Number','Interpreter','none');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
 
subplot(2,3,4)
stim1 = Covariate(time, basisMat*stimulus(:,1),'Trial1','time','s',...
'spikes/sec');
temp = ConfidenceInterval(time, basisMat*squeeze(stimCIs(:,1,:)));
stim1.setConfInterval(temp);
stimlt = Covariate(time, basisMat*stimulus(:,lt),'Trial1','time','s',...
'spikes/sec');
temp = ConfidenceInterval(time, basisMat*squeeze(stimCIs(:,lt,:)));
temp.setColor('r');
stimlt.setConfInterval(temp);
stimltm1 = Covariate(time, basisMat*stimulus(:,lt-1),'Trial1','time','s',...
'spikes/sec');
temp = ConfidenceInterval(time, basisMat*squeeze(stimCIs(:,lt-1,:)));
temp.setColor('r');
stimltm1.setConfInterval(temp);
 
% figure;
h1=stim1.plot([],{{' ''k'',''Linewidth'',4'}}); hold all;
h2=stimlt.plot([],{{' ''r'',''Linewidth'',4'}});
hx=xlabel('time [s]','Interpreter','none'); hold all;
hy=ylabel('Firing Rate [spikes/sec]','Interpreter','none');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
 
title({'Learning Trial Vs. Baseline Trial';'with 95% CIs'},'FontWeight','bold',...
'Fontsize',12,...
'FontName','Arial');
h_legend=legend([h1(1) h2(1)],'\lambda_{1}(t)',['\lambda_{' num2str(lt) '}(t)']);
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.03 pos(2)+.01 pos(3:4)]);

Example 4 - HIPPOCAMPAL PLACE CELL - RECEPTIVE FIELD ESTIMATION

Estimation of receptive fields of neurons is a very common data analysis problem in neuroscience. Here we use the nSTAT software to perform an estimation of the receptive fields of hippocampal place cells using a bivariate Gaussian model and Zernike polynomials. The number of zernike polynomials is based on "An Analysis of Hippocampal Spatio-Temporal Representations Using a Bayesian Algorithm for Neural Spike Train Decoding" Barbieri et. al 2005. The data used herein in was provided by Dr. Ricardo Barbieri on 2/28/2011.
Author: Iahn Cajigas
Date: 3/1/2011

Example Data

The x and y coordinates of a freely foraging rat in a circular environment (70cm in diameter and 30cm high walls) and a fixed visual cue. The x and y coordinates at the time when a spike was observed are marked in red. The position coordinates have been normalized to be between -1 and 1 to allow to simplify the analysis.
close all;
load(fullfile(placeCellDataDir,'PlaceCellDataAnimal1.mat'));
exampleCell = [2 21 25 49];
% exampleCell = 1:length(neuron);
% figure(1);
scrsz = get(0,'ScreenSize');
h=figure('OuterPosition',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.6 scrsz(4)*.9]);
 
for i=1:length(exampleCell)
subplot(2,2,i);
h1=plot(x,y,'b','Linewidth',.5); hold on;
h2=plot(neuron{exampleCell(i)}.xN,neuron{exampleCell(i)}.yN,'r.',...
'MarkerSize',7);
hx=xlabel('X Position'); hy=ylabel('Y Position');
% title(['Animal#1, Cell#' num2str(exampleCell(i))]);
title(['Cell#' num2str(exampleCell(i))],'FontWeight','bold',...
'Fontsize',12,'FontName','Arial');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
set(gca,'xTick',-1:.5:1,'yTick',-1:.5:1); axis square;
if(i==4)
h_legend = legend([h1 h2],'Animal Path',...
'Location at time of spike');
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.09 pos(2)+.06 pos(3:4)]);
end
end

Analyze All Cells

numAnimals=2;
CompilingHelpFile=1;
if(~CompilingHelpFile)
for n=1:numAnimals
% load the data
clear x y neuron time nst tc tcc z;
load(fullfile(placeCellDataDir,['PlaceCellDataAnimal' num2str(n) '.mat']));
 
% Create the spikeTrains for each cell
for i=1:length(neuron)
nst{i} = nspikeTrain(neuron{i}.spikeTimes);
end
 
 
% Convert to polar coordinates
[theta,r] = cart2pol(x,y);
 
 
% Evaluate the Zernike Polynomials
% Number of polynomials from "An Analysis of Hippocampal
% Spatio-Temporal Representations Using a Bayesian Algorithm for Neural
% Spike Train Decoding" Barbieri et. al 2005
cnt=0;
for l=0:3
for m=-l:l
if(~any(mod(l-m,2))) % otherwise the polynomial = 0
cnt = cnt+1;
z(:,cnt) = zernfun(l,m,r,theta,'norm');
% zernfun by Paul Fricker
% http://www.mathworks.com/matlabcentral/fileexchange/7687
end
end
end
 
% Data sampled at 30 Hz but just to be sure
delta=min(diff(time));
sampleRate = round(1/delta);
% Define Covariates for the analysis
baseline = Covariate(time,ones(length(x),1),'Baseline','time','s','',...
{'mu'});
zernike = Covariate(time,z,'Zernike','time','s','m',{'z1','z2','z3',...
'z4','z5','z6','z7','z8','z9','z10'});
gaussian = Covariate(time,[x y x.^2 y.^2 x.*y],'Gaussian','time',...
's','m',{'x','y','x^2','y^2','x*y'});
covarColl = CovColl({baseline,gaussian,zernike});
 
% Create the trial structure
spikeColl = nstColl(nst);
trial = Trial(spikeColl,covarColl);
 
 
% Define how we want to analyze the data
tc{1} = TrialConfig({{'Baseline','mu'},{'Gaussian',...
'x','y','x^2','y^2','x*y'}},sampleRate,[]);
tc{1}.setName('Gaussian');
tc{2} = TrialConfig({{'Zernike' 'z1','z2','z3','z4','z5','z6',...
'z7','z8','z9','z10'}},sampleRate,[]);
tc{2}.setName('Zernike');
tcc = ConfigColl(tc);
 
% Perform Analysis (Commented to since data already saved)
results =Analysis.RunAnalysisForAllNeurons(trial,tcc,0);
 
% Save results
resStruct =FitResult.CellArrayToStructure(results);
filename = fullfile(dataDir,['PlaceCellAnimal' num2str(n) 'Results']);
save(filename,'resStruct');
end
end

View Summary Statistics

Note the Zernike Polynomials yield better fits in terms of decreased KS Statistics (less deviation from the 45 degree line), reduced AIC and reduced BIC across the majority of cells and for both animals
clear Summary;
numAnimals =2;
for n=1:numAnimals
resData = load(fullfile(dataDir,['PlaceCellAnimal' num2str(n) 'Results.mat']));
results = FitResult.fromStructure(resData.resStruct);
Summary{n} = FitResSummary(results);
% Summary{n}.plotSummary;
end
close all;
scrsz = get(0,'ScreenSize');
h=figure('OuterPosition',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.6 scrsz(4)*.5]);
subplot(1,3,1);
maxLength = max([Summary{1}.numNeurons,Summary{2}.numNeurons]);
dKS = nan(maxLength, 2);
dKS(1:Summary{1}.numNeurons,1) = (Summary{1}.KSStats(:,1)-Summary{1}.KSStats(:,2)) ;
dKS(1:Summary{2}.numNeurons,2) = (Summary{2}.KSStats(:,1)-Summary{2}.KSStats(:,2)) ;
 
boxplot(dKS ,{'Animal 1', 'Animal 2'},'labelorientation','inline');
title('\Delta KS Statistic','FontWeight','bold','FontSize',14,...
'FontName','Arial');
 
subplot(1,3,2);
dAIC = nan(maxLength, 2);
dAIC(1:Summary{1}.numNeurons,1) = Summary{1}.getDiffAIC(1);
dAIC(1:Summary{2}.numNeurons,2) = Summary{2}.getDiffAIC(1);
 
boxplot(dAIC ,{'Animal 1', 'Animal 2'},'labelorientation','inline');
title('\Delta AIC','FontWeight','bold','FontSize',14,'FontName','Arial');
 
subplot(1,3,3);
dBIC = nan(maxLength, 2);
dBIC(1:Summary{1}.numNeurons,1) = Summary{1}.getDiffBIC(1);
dBIC(1:Summary{2}.numNeurons,2) = Summary{2}.getDiffBIC(1);
 
boxplot(dBIC ,{'Animal 1', 'Animal 2'},'labelorientation','inline'); %ylabel('\Delta BIC'); %xticklabel_rotate([],45,[],'Fontsize',6);
title('\Delta BIC','FontWeight','bold','FontSize',14,'FontName','Arial');
 
% close all;

Visualize the results

close all;
% Define a grid
[x_new,y_new]=meshgrid(-1:.01:1); %define new x and y
y_new = flipud(y_new); x_new = fliplr(x_new);
[theta_new,r_new] = cart2pol(x_new,y_new);
 
%Data for the gaussian fit
newData{1} =ones(size(x_new));
newData{2} =x_new; newData{3} =y_new;
newData{4} =x_new.^2; newData{5} =y_new.^2;
newData{6} =x_new.*y_new;
 
 
% Zernike polynomials only defined on the unit disk
idx = r_new<=1;
zpoly = cell(1,10);
cnt=0;
for l=0:3
for m=-l:l
if(~any(mod(l-m,2)))
cnt = cnt+1;
temp = nan(size(x_new));
temp(idx) = zernfun(l,m,r_new(idx),theta_new(idx),'norm');
zpoly{cnt} = temp;
end
end
end
 
 
 
for n=1:numAnimals
clear lambdaGaussian lambdaZernike;
load(fullfile(placeCellDataDir,['PlaceCellDataAnimal' num2str(n) '.mat']));
resData = load(fullfile(dataDir,['PlaceCellAnimal' num2str(n) 'Results.mat']));
results = FitResult.fromStructure(resData.resStruct);
for i=1:length(neuron)
% Evaluate our fits using the new data and the estimated parameters
lambdaGaussian{i} = results{i}.evalLambda(1,newData);
lambdaZernike{i} = results{i}.evalLambda(2,zpoly);
end
 
% Plot the receptive fields
for i=1:length(neuron)
% 3d plot of an example place field
 
% 2d plot of all the cell's fields
if(n==1)
h4=figure(4);
colormap('jet');
if(i==1)
tb=annotation(h4,'textbox',...
[0.283261904761904 0.928571428571418 ...
0.392857142857143 0.0595238095238095],...
'String',{['Gaussian Place Fields - Animal#' ...
num2str(n)]},'FitBoxToText','on','Fontsize',11,...
'FontName','Arial','FontWeight','bold','LineStyle',...
'none','HorizontalAlignment','center'); hold on;
end
subplot(7,7,i);
elseif(n==2)
h6=figure(6);
colormap('jet');
if(i==1)
annotation(h6,'textbox',...
[0.283261904761904 0.928571428571418 ...
0.392857142857143 0.0595238095238095],...
'String',{['Gaussian Place Fields - Animal#' ...
num2str(n)]},'FitBoxToText','on','Fontsize',11,...
'FontName','Arial','FontWeight','bold','LineStyle',...
'none','HorizontalAlignment','center'); hold on;
end
subplot(6,7,i);
end
pcolor(x_new,y_new,lambdaGaussian{i}), shading interp
axis square; set(gca,'xtick',[],'ytick',[]);
set(gca, 'Box' , 'off');
 
if(n==1)
h5=figure(5);
colormap('jet');
if(i==1)
annotation(h5,'textbox',...
[0.303261904761904 0.928571428571418 ...
0.392857142857143 0.0595238095238095],...
'String',{['Zernike Place Fields - Animal#' ...
num2str(n)]},'FitBoxToText','on','Fontsize',11,...
'FontName','Arial','FontWeight','bold','LineStyle','none'); hold on;
end
subplot(7,7,i);
elseif(n==2)
h7=figure(7);
colormap('jet');
if(i==1)
annotation(h7,'textbox',...
[0.303261904761904 0.928571428571418 ...
0.392857142857143 0.0595238095238095],...
'String',{['Zernike Place Fields - Animal#' ...
num2str(n)]},'FitBoxToText','on','Fontsize',11,...
'FontName','Arial','FontWeight','bold','LineStyle',...
'none','HorizontalAlignment','center'); hold on;
end
subplot(6,7,i);
end
pcolor(x_new,y_new,lambdaZernike{i}), shading interp
axis square;
set(gca,'xtick',[],'ytick',[]);
set(gca, 'Box' , 'off');
end
 
end
clear lambdaGaussian lambdaZernike;
load(fullfile(placeCellDataDir,'PlaceCellDataAnimal1.mat'));
resData = load(fullfile(dataDir,'PlaceCellAnimal1Results.mat'));
results = FitResult.fromStructure(resData.resStruct);
for i=1:length(neuron)
% Evaluate our fits using the new data and the estimated parameters
lambdaGaussian{i} = results{i}.evalLambda(1,newData);
lambdaZernike{i} = results{i}.evalLambda(2,zpoly);
end
 
% h1=plot(x,y,'b');
% h2=plot(x,y,'g');
%
exampleCell = 25;
% figure(8);
% plot(x,y,'b',neuron{exampleCell}.xN,neuron{exampleCell}.yN,'r.');
% xlabel('x'); ylabel('y');
% title(['Animal#1, Cell#' num2str(exampleCell)]);
%
close all;
h9=figure(9);
h_mesh = mesh(x_new,y_new,lambdaGaussian{exampleCell},'AlphaData',0);
get(h_mesh,'AlphaData');
set(h_mesh,'FaceAlpha',0.2,'EdgeAlpha',0.2,'EdgeColor','b');
hold on;
h_mesh = mesh(x_new,y_new,lambdaZernike{exampleCell},'AlphaData',0);
get(h_mesh,'AlphaData');
set(h_mesh,'FaceAlpha',0.2,'EdgeAlpha',0.2,'EdgeColor','g');
 
% h_legend=legend('\lambda_{Gaussian}','\lambda_{Zernike}');
% set(h_legend,'FontSize',20);
plot(x,y,neuron{exampleCell}.xN,neuron{exampleCell}.yN,'r.');
axis tight square;
xlabel('x position'); ylabel('y position');
title(['Animal#1, Cell#' num2str(exampleCell)],'FontWeight','bold',...
'Fontsize',12,'FontName','Arial');
hx=get(gca,'XLabel'); hy=get(gca,'YLabel');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
 
 

Example 5 - STIMULUS DECODING

In this example we show how to decode a univariate and a bivariate stimulus based on a point process observations using nSTAT. Even though due to the simulated nature of the data, we know the exact condition intensity function, we estimate the parameters before moving on to the decoding stage.

Generate the conditional Intensity Function

close all; clear all;
[dataDir,mEPSCDir,explicitStimulusDir,psthDir,placeCellDataDir] = ...
getPaperDataDirs();
delta = 0.001; Tmax = 1;
time = 0:delta:Tmax;
numRealizations = 20;
f=2; b1=randn(numRealizations,1);b0=log(10*delta)+randn(numRealizations,1);
x = sin(2*pi*f*time);
clear nst;
for i=1:numRealizations
expData = exp(b1(i)*x+b0(i));
lambdaData = expData./(1+expData);
 
if(i==1)
lambda = Covariate(time,lambdaData./delta, ...
'\Lambda(t)','time','s','spikes/sec',{'\lambda_{1}'},...
{{' ''b'', ''LineWidth'' ,2'}});
else
tempLambda = Covariate(time,lambdaData./delta, ...
'\Lambda(t)','time','s','spikes/sec',{'\lambda_{1}'},...
{{' ''b'', ''LineWidth'' ,2'}});
lambda = lambda.merge(tempLambda);
end
spikeColl = CIF.simulateCIFByThinningFromLambda(...
lambda.getSubSignal(i),1);
nst{i} = spikeColl.getNST(1);
end
spikeColl = nstColl(nst);scrsz = get(0,'ScreenSize');
h=figure('Position',[scrsz(3)*.1 scrsz(4)*.1 ...
scrsz(3)*.6 scrsz(4)*.8]);
% figure;
subplot(3,1,1); plot(time,x,'k');
set(gca,'xtick',[],'xtickLabel',[]); ylabel('Stimulus');
hx=get(gca,'XLabel'); hy=get(gca,'YLabel');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
title('Driving Stimulus','FontWeight','bold',...
'FontSize',14,'FontName','Arial');
subplot(3,1,2); lambda.plot([],{{' ''k'',''Linewidth'',1'}});
legend off;
hy=ylabel('Firing Rate [spikes/sec]', 'Interpreter','none');
hx=xlabel('','Interpreter','none');
set([hx, hy],'FontName', 'Arial','FontSize',12,...
'FontWeight','bold');
set(gca,'xtickLabel',[]);
title('Conditional Intensity Functions','FontWeight',...
'bold','FontSize',14,'FontName','Arial');
subplot(3,1,3); spikeColl.plot;
set(gca,'ytick',0:10:numRealizations,'ytickLabel',...
0:10:numRealizations);
xlabel('time [s]','Interpreter','none');
ylabel('Cell Number','Interpreter','none');
hx=get(gca,'XLabel'); hy=get(gca,'YLabel');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
title('Point Process Sample Paths','FontWeight',...
'bold','FontSize',14,'FontName','Arial');
 
stim = Covariate(time,sin(2*pi*f*time),'Stimulus','time','s','V',{'stim'});
baseline = Covariate(time,ones(length(time),1),'Baseline','time','s','',...
{'constant'});
 
% close all;
close all;
 
clear lambdaCIF;
spikeColl.resample(1/delta);
dN=spikeColl.dataToMatrix;
 
% Make noise according to the dynamic range of the stimulus
Q=std(stim.data(2:end)-stim.data(1:end-1));
Px0=.1; A=1;
x0 = x(:,1); yT=x(:,end);
Pi0 = eps*eye(size(x0,1),size(x0,1));
PiT = eps*eye(size(x0,1),size(x0,1));
 
 
[x_p, W_p, x_u, W_u] = DecodingAlgorithms.PPDecodeFilterLinear(A, ...
Q, dN',b0,b1','binomial',delta);
%
h=figure('Position',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.8 scrsz(4)*.6]);
zVal=1.96;
ciLower = min(x_u(1:end)-zVal*sqrt(squeeze(W_u(1:end)))',...
x_u(1:end)+zVal*sqrt(squeeze(W_u(1:end))'));
ciUpper = max(x_u(1:end)-zVal*sqrt(squeeze(W_u(1:end)))',...
x_u(1:end)+zVal*sqrt(squeeze(W_u(1:end))'));
 
estimatedStimulus = Covariate(time,x_u(1:end),'\hat{x}(t)','time','s','');
CI= ConfidenceInterval(time,[ciLower', ciUpper'],'\hat{x}(t)','time','s','');
estimatedStimulus.setConfInterval(CI);
 
% hold all;
% hEst=plot(time,x_u(1:end),'b','Linewidth',2); hold on;
% plot(time, [ciUpper', ciLower'],'b');
 
hEst = estimatedStimulus.plot([],{{' ''k'',''Linewidth'',4'}});
hStim=stim.plot([],{{' ''b'',''Linewidth'',4'}});
legend off;
h_legend=legend([hEst(1) hStim],'Decoded','Actual');
set(h_legend,'Interpreter','none');
set(h_legend,'FontSize',22);
title(['Decoded Stimulus +/- 95% CIs with ' num2str(numRealizations) ' cells'],...
'FontWeight','bold','Fontsize',22,'FontName','Arial');
xlabel('time [s]','Interpreter','none');
ylabel('Stimulus','Interpreter','none');
hx=get(gca,'XLabel'); hy=get(gca,'YLabel');
set([hx, hy],'FontName', 'Arial','FontSize',22,'FontWeight','bold');
 
 

Example 5b - Arm reaching to target Simulation

See L. Srinivasan, U. T. Eden, A. S. Willsky, and E. N. Brown, "A state-space analysis for reconstruction of goal-directed movements using neural signals.," Neural computation, vol. 18, no. 10, pp. 2465?2494, Oct. 2006.
close all;
clear all;
[dataDir,mEPSCDir,explicitStimulusDir,psthDir,placeCellDataDir] = ...
getPaperDataDirs();
%Process noise covariance only drives the movement velocity
q=1e-4;
Q=diag([1e-12 1e-12 q q]);
 
delta = .001; % Time increment
r=1e-6; % in meters^2
p=1e-6; % in meters^2/s^2
PiT=diag([r r p p]); % Uncertainty in the target state
Pi0=PiT;
T=2; % Reach Duration
 
x0 = [0;0;0;0]; % Initial Position and velocities (states)
xT = [-.35;.2; 0;0];% Final Target
time=0:delta:T; % time vector
 
A=[1 0 delta 0 ; %State transition matrix
0 1 0 delta;
0 0 1 0 ;
0 0 0 1 ];
 
x=zeros(4,length(time));
 
 
% Simulate a reach trajectory
% Differs from reference by multiplication by delta instead of division so
% that the velocity has units of meters per second
R=chol(Q);
L=chol(PiT);
for k=1:length(time)
if(k==1)
x(:,k)=x0;
else
x(:,k)=A*x(:,k-1)+...
delta/(2)*(pi/T)^2*cos(pi*time(k)/T)*[0;0;...
xT(1)-x0(1);xT(2)-x0(2)]; %Reach to target model
%x(:,k)=A*x(:,k-1)+R*randn(size(x,1),1); %Random walk model
end
 
end
xT =x(:,end); % The target generated by the model
yT=xT; % Assume we have observed the actual target position with uncertainty PiT
 
%Define Q according to the dynamic range of the movement above
Q=diag(var(diff(x,[],2),[],2))*100;
 
% Plot the movement trajectories and the hand path
scrsz = get(0,'ScreenSize');
fig1=figure('OuterPosition',[scrsz(3)*.1 scrsz(4)*.1 ...
scrsz(3)*.8 scrsz(4)*.8]);
%Plot The movement path
subplot(4,2,[1 3]);
plot(100*x(1,:),100*x(2,:),'k','Linewidth',2);
xlabel('X Position [cm]'); ylabel('Y Position [cm]');
hx=get(gca,'XLabel'); hy=get(gca,'YLabel');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
title('Reach Path','FontWeight','bold','Fontsize',14,'FontName','Arial');
hold on;
axis([sort([100*x0(1)+5, 100*xT(1)-5]), sort([100*x0(2)-5, 100*xT(2)+5])]);
h1=plot(100*x(1,1),100*x(2,1),'bo','MarkerSize',14);
h2=plot(100*x(1,end),100*x(2,end),'ro','MarkerSize',14);
legend([h1 h2],'Start','Finish','Location','NorthEast');
 
 
subplot(4,2,5); h1=plot(time,100*x(1,:),'k','Linewidth',2); hold on;
h2=plot(time,100*x(2,:),'k-.','Linewidth',2);
h_legend=legend([h1,h2],'x','y','Location','NorthEast');
set(h_legend,'FontSize',14)
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.06 pos(2)+.01 pos(3:4)]);
hx=xlabel('time [s]'); hy=ylabel('Position [cm]');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
% Plot the velocity profiles
 
subplot(4,2,7);
h1=plot(time,100*x(3,:),'k','Linewidth',2); hold on;
h2=plot(time,100*x(4,:),'k-.','Linewidth',2);
h_legend=legend([h1 h2],'v_x','v_y','Location','NorthEast');
xlabel('time [s]');
set(h_legend,'FontSize',14);
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.06 pos(2)+.01 pos(3:4)]);
hx=xlabel('time [s]'); hy=ylabel('Velocity [cm/s]');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
%
 
gamma=0;
windowTimes=[0, 0.001];
 
 
% Simulate neural responses
% logit(lambda_i*delta) = mu_i + b_x_i*v_x + b_y_i*v_y
% logit(lambda_i*delta) = X_i*beta_i;
numCells = 20;
bCoeffs=10*(rand(numCells,2)-.5); % b_i = [b_x_i b_y_i] ~ U(-5, 5);
phiMax = atan2(bCoeffs(:,2),bCoeffs(:,1)); % Maximal firing direction of cell
phiMaxNorm = (phiMax+pi)./(2*pi);
meanMu = log(10*delta); % baseline firing rate -10Hz
MuCoeffs = meanMu+randn(numCells,1); % mu_i ~ G(meanMu,1)
 
dataMat = [ones(length(time),1) x(3,:)' x(4,:)']; % design matrix: X (
coeffs = [MuCoeffs bCoeffs]; % coefficient vector: beta
fitType='binomial';
clear nst;
for i=1:numCells
tempData = exp(dataMat*coeffs(i,:)');
 
if(strcmp(fitType,'poisson'))
lambdaData = tempData;
else
lambdaData = tempData./(1+tempData); % Conditional Intensity Function for ith cell
end
lambda{i}=Covariate(time,lambdaData./delta, ...
'\Lambda(t)','time','s','spikes/sec',...
{strcat('\lambda_{',num2str(i),'}')},{{' ''b'' '}});
lambda{i}=lambda{i}.resample(1/delta);
% Generate CIF representation in case we want to use the symbolic
% versions of the PPDecodeFilter (i.e. not PPDecodeFilterLinear
lambdaCIF{i} = CIF([MuCoeffs(i) 0 0 bCoeffs(i,:)],...
{'1','x','y','vx','vy'},{'x','y','vx','vy'},fitType);
% generate one realization for each cell
tempSpikeColl{i} = CIF.simulateCIFByThinningFromLambda(lambda{i},1); nst{i} = tempSpikeColl{i}.getNST(1); % grab the realization
nst{i}.setName(num2str(i)); % give each cell a unique name
subplot(4,2,[6 8]);
h2=lambda{i}.plot([],{{' ''k'', ''LineWidth'' ,.5'}});
legend off; hold all; % Plot the CIF
end
title('Neural Conditional Intensity Functions','FontWeight',...
'bold','Fontsize',14,'FontName','Arial');
hx=xlabel('time [s]','Interpreter','none');
hy=ylabel('Firing Rate [spikes/sec]','Interpreter','none');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
spikeColl = nstColl(nst); % Create a neural spike train collection
subplot(4,2,[2,4]); spikeColl.plot;
set(gca,'xtick',[],'xtickLabel',[]);
title('Neural Raster','FontWeight','bold','Fontsize',14,...
'FontName','Arial');
hx=xlabel('time [s]','Interpreter','none');
hy=ylabel('Cell Number','Interpreter','none');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
 
% close all;
 
close all;
numExamples=20;
scrsz = get(0,'ScreenSize');
fig1=figure('OuterPosition',[scrsz(3)*.1 scrsz(4)*.1 ...
scrsz(3)*.6 scrsz(4)*.9]);
for k=1:numExamples
bCoeffs=10*(rand(numCells,2)-.5); % b_i = [b_x_i b_y_i] ~ U(-5, 5);
phiMax = atan2(bCoeffs(:,2),bCoeffs(:,1)); % Maximal firing direction of cell
phiMaxNorm = (phiMax+pi)./(2*pi);
meanMu = log(10*delta); % baseline firing rate
MuCoeffs = meanMu+randn(numCells,1); % mu_i ~ G(meanMu,1)
 
dataMat = [ones(length(time),1) x(3,:)' x(4,:)']; % design matrix: X (
coeffs = [MuCoeffs bCoeffs]; % coefficient vector: beta
fitType='binomial';
clear nst lambda;
for i=1:numCells
tempData = exp(dataMat*coeffs(i,:)');
if(strcmp(fitType,'poisson'))
lambdaData = tempData;
else
% Conditional Intensity Function for ith cell
lambdaData = tempData./(1+tempData);
end
lambda{i}=Covariate(time,lambdaData./delta, ...
'\Lambda(t)','time','s','spikes/sec',...
{strcat('\lambda_{',num2str(i),'}')},{{' ''b'' '}});
lambda{i}=lambda{i}.resample(1/delta);
% Generate CIF representation in case we want to use the symbolic
% versions of the PPDecodeFilter (i.e. not PPDecodeFilterLinear
% generate one realization for each cell
tempSpikeColl{i} = CIF.simulateCIFByThinningFromLambda(lambda{i},1);
nst{i} = tempSpikeColl{i}.getNST(1); % grab the realization
nst{i}.setName(num2str(i)); % give each cell a unique name
end
 
% Plot the neural raster across all the cells
spikeColl = nstColl(nst); % Create a neural spike train collection
 
% Based on the temporal resolution defined by delta, bin the data and get
% a matrix representation of the neural firing
dN=spikeColl.dataToMatrix';
dN(dN>1)=1; % more than one spike per bin will be treated as one spike. In
% general we should pick delta small enough so that there is
% only one spike per bin
 
[C,N] = size(dN); % N time samples, C cells
 
beta=[zeros(2,numCells); bCoeffs'];
 
%Use the Goal Directed Movement Version of the Point Process adaptive
%Filter
[x_p, W_p, x_u, W_u,x_uT,W_uT,x_pT,W_pT] = ...
DecodingAlgorithms.PPDecodeFilterLinear(A, Q, dN,...
MuCoeffs,beta,fitType,delta,gamma,windowTimes,x0, Pi0, yT,PiT,0);
 
%Use the Free Movement Version of the Point Process adaptive
%Filter
[x_pf, W_pf, x_uf, W_uf] = ...
DecodingAlgorithms.PPDecodeFilterLinear(A, Q, dN,...
MuCoeffs,beta,fitType,delta,gamma,windowTimes,x0);
 
 
if(k==numExamples)
subplot(4,2,1:4);h1=plot(100*x(1,:),100*x(2,:),'k','LineWidth',3);
hold on;
axis([sort([100*x0(1)+5, 100*xT(1)-5]), ...
sort([100*x0(2)-5, 100*xT(2)+5])]);
title('Estimated vs. Actual Reach Paths',...
'FontWeight','bold','Fontsize',12,'FontName','Arial');
end
subplot(4,2,1:4);h2=plot(100*x_u(1,:)',100*x_u(2,:)','b'); hold all;
subplot(4,2,1:4);h3=plot(100*x_uf(1,:)',100*x_uf(2,:)','g');
hx=xlabel('x [cm]'); hy=ylabel('y [cm]');
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
h1=plot(100*x0(1),100*x0(2),'bo','MarkerSize',10); hold on;
h2=plot(100*xT(1),100*xT(2),'ro','MarkerSize',10);
legend([h1 h2],'Start','Finish','Location','NorthEast');
 
 
subplot(4,2,5);
h1=plot(time,100*x(1,:),'k','LineWidth',3); hold on;
h2=plot(time,100*x_u(1,:)','b');
h3=plot(time,100*x_uf(1,:)','g');
hy=ylabel('x(t) [cm]'); hx=xlabel('time [s]');
set(gca,'xtick',[],'xtickLabel',[]);
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
title('X Position','FontWeight','bold','Fontsize',12,'FontName','Arial');
 
subplot(4,2,6);
h1=plot(time,100*x(2,:),'k','LineWidth',3); hold on;
h2=plot(time,100*x_u(2,:)','b');
h3=plot(time,100*x_uf(2,:)','g');
h_legend=legend([h1(1) h2(1) h3(1)],'Actual','PPAF+Goal',...
'PPAF','Location','SouthEast');
hy=ylabel('y(t) [cm]'); hx=xlabel('time [s]');
set(gca,'xtick',[],'xtickLabel',[]);
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
title('Y Position','FontWeight','bold','Fontsize',12,'FontName','Arial');
set(h_legend,'FontSize',10)
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)-.63 pos(2)+.23 pos(3:4)]);
 
subplot(4,2,7);
h1=plot(time,100*x(3,:),'k','LineWidth',3); hold on;
h2=plot(time,100*x_u(3,:)','b');
h3=plot(time,100*x_uf(3,:)','g');
hy=ylabel('v_{x}(t) [cm/s]'); hx=xlabel('time [s]');
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
title('X Velocity','FontWeight','bold','Fontsize',12,'FontName','Arial');
 
subplot(4,2,8);
h1=plot(time,100*x(4,:),'k','LineWidth',3); hold on;
h2=plot(time,100*x_u(4,:)','b');
h3=plot(time,100*x_uf(4,:)','g');
hy=ylabel('v_{y}(t) [cm/s]'); hx=xlabel('time [s]');
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
title('Y Velocity','FontWeight','bold','Fontsize',12,'FontName','Arial');
 
end
 
% close all;

Experiment 6 - Hybrid Point Process Filter Example

NOTE THIS EXAMPLE WAS NOT INCLUDED IN THE FINAL VERSION OF THE PAPER This example is based on an implementation of the Hybrid Point Process filter described in General-purpose filter design for neural prosthetic devices by Srinivasan L, Eden UT, Mitter SK, Brown EN in J Neurophysiol. 2007 Oct, 98(4):2456-75.

Problem Statement

Suppose that a process of interest can be modeled as consisting of several discrete states where the evolution of the system under each state can be modeled as a linear state space model. The observations of both the state and the continuous dynamics are not direct, but rather observed through how the continuous and discrete states affect the firing of a population of neurons. The goal of the hybrid filter is to estimate both the continuous dynamics and the underlying system state from only the neural population firing (point process observations).
To illustrate the use of this filter, we consider a reaching task. We assume two underlying system states s=1="Not Moving"=NM and s=2="Moving"=M. Under the "Not Moving" the position of the arm remain constant, whereas in the "Moving" state, the position and velocities evolved based on the arm acceleration that is modeled as a gaussian white noise process.
Under both the "Moving" and "Not Moving" states, the arm evolution state vector is

Generated Simulated Arm Reach

clear all;
[dataDir,mEPSCDir,explicitStimulusDir,psthDir,placeCellDataDir] = ...
getPaperDataDirs();
close all;
delta=0.001;
Tmax=2;
time=0:delta:Tmax;
A{2} = [1 0 delta 0 delta^2/2 0;
0 1 0 delta 0 delta^2/2;
0 0 1 0 delta 0;
0 0 0 1 0 delta;
0 0 0 0 1 0;
0 0 0 0 0 1];
A{1} = [1 0 0 0 0 0;
0 1 0 0 0 0;
0 0 0 0 0 0;
0 0 0 0 0 0;
0 0 0 0 0 0;
0 0 0 0 0 0];
A{1} = [1 0;
0 1];
Px0{2} =1e-6*eye(6,6);
Px0{1} =1e-6*eye(2,2);
 
minCovVal = 1e-12;
covVal = 1e-3;
 
 
 
Q{2}=[minCovVal 0 0 0 0 0;
0 minCovVal 0 0 0 0;
0 0 minCovVal 0 0 0;
0 0 0 minCovVal 0 0;
0 0 0 0 covVal 0;
0 0 0 0 0 covVal];
 
Q{1}=minCovVal*eye(2,2);
 
mstate = zeros(1,length(time));
ind{1}=1:2;
ind{2}=1:6;
 
% Acceleration model
X=zeros(max([size(A{1},1),size(A{2},1)]),length(time));
p_ij = [.998 .002;
.001 .999];
 
for i = 1:length(time)
if(i==1)
mstate(i) = 1;
else
if(rand(1,1)<p_ij(mstate(i-1),mstate(i-1)))
mstate(i) = mstate(i-1);
else
if(mstate(i-1)==1)
mstate(i) = 2;
else
mstate(i) = 1;
end
end
end
st = mstate(i);
R=chol(Q{st});
if(i<length(time))
X(ind{st},i+1) = A{st}*X(ind{st},i) + R*randn(length(ind{st}),1);
end
 
end
%save paperHybridFilterExample time Tmax delta mstate X p_ij ind A Q Px0
load(fullfile(fileparts(which('nSTATPaperExamples')),'paperHybridFilterExample.mat'));
Q{1}=minCovVal*eye(2,2);
numCells=40;
close all;
scrsz = get(0,'ScreenSize');
fig1=figure('OuterPosition',[scrsz(3)*.1 scrsz(4)*.1 ...
scrsz(3)*.8 scrsz(4)*.9]);
subplot(4,2,[1 3]);
plot(100*X(1,:),100*X(2,:),'k','Linewidth',2); hx=xlabel('X [cm]');
hy=ylabel('Y [cm]'); hold on;
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
title('Reach Path','FontWeight','bold','Fontsize',14,'FontName','Arial');
hold on;
h1=plot(100*X(1,1),100*X(2,1),'bo','MarkerSize',16);
h2=plot(100*X(1,end),100*X(2,end),'ro','MarkerSize',16);
legend([h1 h2],'Start','Finish','Location','NorthEast');
 
 
 
subplot(4,2,[6 8]);
plot(time,mstate,'k','Linewidth',2); axis tight; v=axis;
axis([v(1) v(2) 0 3]);
hx=xlabel('time [s]'); hy=ylabel('state');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
set(gca,'YTick',[1 2],'YTickLabel',{'N','M'})
title('Discrete Movement State','FontWeight','bold','Fontsize',14,...
'FontName','Arial');
 
subplot(4,2,5);
h1=plot(time,100*X(1,1:end),'k','Linewidth',2); hold on;
h2=plot(time,100*X(2,1:end),'k-.','Linewidth',2);
hx=xlabel('time [s]'); hy=ylabel('Position [cm]');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
h_legend=legend([h1,h2],'x','y','Location','NorthEast');
set(h_legend,'FontSize',14)
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.06 pos(2)+.01 pos(3:4)]);
 
 
subplot(4,2,7);
h1=plot(time,100*X(3,1:end),'k','Linewidth',2); hold on;
h2=plot(time,100*X(4,1:end),'k-.','Linewidth',2);
hx=xlabel('time [s]'); hy=ylabel('Velocity [cm/s]');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
h_legend=legend([h1,h2],'v_{x}','v_{y}','Location','NorthEast');
set(h_legend,'FontSize',14)
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)+.06 pos(2)+.01 pos(3:4)]);
 
meanMu = log(10*delta); % baseline firing rate
MuCoeffs = meanMu+randn(numCells,1); % mu_i ~ G(meanMu,1)
coeffs = [MuCoeffs 0*randn(numCells,2) 10*(rand(numCells,2)-.5) ...
0*randn(numCells,2)];
%Add realization by thinning with history
dataMat = [ones(size(X,2),1),X(:,1:end)'];
% Generate M1 cells
clear lambda tempSpikeColl lambdaCIF n;
fitType ='binomial';
% matlabpool open;
for i=1:numCells
tempData = exp(dataMat*coeffs(i,:)');
if(strcmp(fitType,'binomial'));
lambdaData = tempData./(1+tempData);
else
lambdaData = tempData;
end
lambda{i}=Covariate(time,lambdaData./delta, ...
'\Lambda(t)','time','s','spikes/sec',...
{strcat('\lambda_{',num2str(i),'}')},{{' ''b'', ''LineWidth'' ,2'}});
maxTimeRes = 0.001;
tempSpikeColl{i} = CIF.simulateCIFByThinningFromLambda(lambda{i},1,[]);
n{i} = tempSpikeColl{i}.getNST(1);
n{i}.setName(num2str(i));
end
spikeColl = nstColl(n);
subplot(4,2,[2 4]);
spikeColl.plot;
set(gca,'xtick',[],'xtickLabel',[],'ytickLabel',[]);
title('Neural Raster','FontWeight','bold','Fontsize',14,'FontName','Arial');
hx=xlabel('time [s]','Interpreter','none');
hy=ylabel('Cell Number','Interpreter','none');
set([hx, hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
 
% close all;

Simulate Neural Firing

We simulate a population of neurons that fire in response to the movement velocity (x and y coorinates)
%Use the data to estimate the process noise for the moving case and
%non-moving case
 
nonMovingInd = intersect(find(X(5,:)==0),find(X(6,:)==0));
movingInd=setdiff(1:size(X,2),nonMovingInd);
Q{2}=diag(var(diff(X(:,movingInd),[],2),[],2));
Q{2}(1:4,1:4)=0;
varNV=diag(var(diff(X(:,nonMovingInd),[],2),[],2));
Q{1} = varNV(1:2,1:2);
close all; clear S_est X_est MU_est S_estNT X_estNT MU_estNT;
numExamples = 20;
numCells=40;
scrsz = get(0,'ScreenSize');
fig1=figure('OuterPosition',[scrsz(3)*.1 scrsz(4)*.1 ...
scrsz(3)*.9 scrsz(4)*.9]);
 
for n=1:numExamples
meanMu = log(10*delta); % baseline firing rate
MuCoeffs = meanMu+randn(numCells,1); % mu_i ~ G(meanMu,1)
coeffs = [MuCoeffs 0*randn(numCells,2) 10*(rand(numCells,2)-.5) ...
0*randn(numCells,2)];
 
 
 
%Add realization by thinning with history
dataMat = [ones(size(X,2),1),X(:,1:end)'];
% Generate M1 cells
clear lambda tempSpikeColl lambdaCIF nst;
fitType ='binomial';
% matlabpool open;
for i=1:numCells
tempData = exp(dataMat*coeffs(i,:)');
if(strcmp(fitType,'binomial'))
lambdaData = tempData./(1+tempData);
else
lambdaData = tempData;
end
lambda{i}=Covariate(time,lambdaData./delta, ...
'\Lambda(t)','time','s','spikes/sec',...
{strcat('\lambda_{',num2str(i),'}')},{{' ''b'', ''LineWidth'' ,2'}});
maxTimeRes = 0.001;
tempSpikeColl{i} = ...
CIF.simulateCIFByThinningFromLambda(lambda{i},1,[]);
nst{i} = tempSpikeColl{i}.getNST(1);
nst{i}.setName(num2str(i));
end
 
% Decode the x-y trajectory
 
% Enforce that the maximum time resolution is delta
spikeColl = nstColl(nst);
spikeColl.resample(1/delta);
dN = spikeColl.dataToMatrix;
dN(dN>1)=1; %Avoid more than 1 spike per bin.
 
% Starting states are equally probable
Mu0=.5*ones(size(p_ij,1),1);
clear x0 yT clear Pi0 PiT;
x0{1} = X(ind{1},1);
yT{1} = X(ind{1},end);
Pi0 = Px0;
PiT{1} = 1e-9*eye(size(x0{1},1),size(x0{1},1));
 
x0{2} = X(ind{2},1);
yT{2} = X(ind{2},end);
PiT{2} = 1e-9*eye(size(x0{2},1),size(x0{2},1));
 
 
% Run the Hybrid Point Process Filter
[S_est, X_est, W_est, MU_est, X_s, W_s,pNGivenS]=...
DecodingAlgorithms.PPHybridFilterLinear(A, Q, p_ij,Mu0, dN',...
coeffs(:,1),coeffs(:,2:end)',fitType,delta,[],[],x0,Pi0, yT,PiT);
[S_estNT, X_estNT, W_estNT, MU_estNT, X_sNT, W_sNT,pNGivenSNT]=...
DecodingAlgorithms.PPHybridFilterLinear(A, Q, p_ij,Mu0, dN',...
coeffs(:,1),coeffs(:,2:end)',fitType,delta,[],[],x0,Pi0);
%Store the results for computing relevant statistics later
X_estAll(:,:,n) = X_est;
X_estNTAll(:,:,n) = X_estNT;
S_estAll(n,:)=S_est;
S_estNTAll(n,:)=S_estNT;
MU_estAll(:,:,n)=MU_est;
MU_estNTAll(:,:,n) = MU_estNT;
 
%State Estimate
subplot(4,3,[1 4]);
plot(time,mstate,'k','LineWidth',3); hold all;
plot(time,S_est,'b-.','Linewidth',.5);
plot(time,S_estNT,'g-.','Linewidth',.5); axis tight; v=axis;
axis([v(1) v(2) 0.5 2.5]);
 
%Movement State Probability (Non-movement State probability is 1-Pr(Movement))
subplot(4,3,[7 10]);
plot(time,MU_est(2,:),'b-.','Linewidth',.5); hold on;
plot(time,MU_estNT(2,:),'g-.','Linewidth',.5); hold on;
axis([min(time) max(time) 0 1.1]);
 
%The movement path
subplot(4,3,[2 3 5 6]);
h1=plot(100*X(1,:)',100*X(2,:)','k'); hold all;
h2=plot(100*X_est(1,:)',100*X_est(2,:)','b-.'); hold all;
h3=plot(100*X_estNT(1,:)',100*X_estNT(2,:)','g-.');
%X-Position
subplot(4,3,8);
h1=plot(time,100*X(1,:),'k','LineWidth',3); hold on;
h2=plot(time,100*X_est(1,:)','b-.');
h3=plot(time,100*X_estNT(1,:)','g-.');
 
%Y-Position
subplot(4,3,9);
h1=plot(time,100*X(2,:),'k','LineWidth',3); hold on;
h2=plot(time,100*X_est(2,:)','b-.');
h3=plot(time,100*X_estNT(2,:)','g-.');
 
%X-Velocity
subplot(4,3,11);
h1=plot(time,100*X(3,:),'k','LineWidth',3); hold on;
h2=plot(time,100*X_est(3,:)','b-.');
h3=plot(time,100*X_estNT(3,:)','g-.');
 
subplot(4,3,12);
h1=plot(time,100*X(4,:),'k','LineWidth',3); hold on;
h2=plot(time,100*X_est(4,:)','b-.');
h3=plot(time,100*X_estNT(4,:)','g-.');
 
 
end
 
%
% Save all the example Data
% save Experiment6ReachExamples X_estAll X_estNTAll S_estAll ...
% S_estNTAll MU_estAll MU_estNTAll;
%
% load Experiment6ReachExamples;
 
% Mean Discrete State Estimate
subplot(4,3,[1 4]);
hold all;
plot(time,mstate,'k','LineWidth',3);
plot(time,mean(S_estAll),'b','LineWidth',3);
plot(time,mean(S_estNTAll),'g','LineWidth',3);
set(gca,'xtick',[],'YTick',[1 2.1],'YTickLabel',{'N','M'});
hy=ylabel('state'); hx=xlabel('time [s]');
set([hy hx],'FontName', 'Arial','FontSize',10,'FontWeight','bold',...
'Interpreter','none');
title('Estimated vs. Actual State','FontWeight','bold','Fontsize',...
12,'FontName','Arial');
 
 
% Mean State Movement State Probability
subplot(4,3,[7 10]);
plot(time, mean(squeeze(MU_estAll(2,:,:)),2),'b','LineWidth',3);
hold on;
plot(time,mean(squeeze(MU_estNTAll(2,:,:)),2),'g','LineWidth',3);
hold on;
axis([min(time) max(time) 0 1.1]);
hx=xlabel('time [s]'); hy=ylabel('P(s(t)=M | data)');
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
title('Probability of State','FontWeight','bold','Fontsize',12,...
'FontName','Arial');
% Mean movement path
subplot(4,3,[2 3 5 6]);
h1=plot(100*X(1,:)',100*X(2,:)','k'); hold all;
mXestAll=mean(100*X_estAll,3);
mXestNTAll=mean(100*X_estNTAll,3);
plot(mXestAll(1,:),mXestAll(2,:),'b','Linewidth',3);
plot(mXestNTAll(1,:),mXestNTAll(2,:),'g','Linewidth',3);
hx=xlabel('x [cm]'); hy=ylabel('y [cm]');
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
 
h1=plot(100*X(1,1),100*X(2,1),'bo','MarkerSize',14); hold on;
h2=plot(100*X(1,end),100*X(2,end),'ro','MarkerSize',14);
legend([h1 h2],'Start','Finish','Location','NorthEast');
title('Estimated vs. Actual Reach Path','FontWeight','bold',...
'Fontsize',12,'FontName','Arial');
 
% Mean X-Positon
subplot(4,3,8);
h1=plot(time,100*X(1,:),'k','LineWidth',3); hold on;
h2=plot(time,mXestAll(1,:),'b','LineWidth',3); hold on;
h3=plot(time,mXestNTAll(1,:),'g','LineWidth',3); hold on;
hy=ylabel('x(t) [cm]'); hx=xlabel('time [s]');
set(gca,'xtick',[],'xtickLabel',[]);
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
title('X Position','FontWeight','bold','Fontsize',12,'FontName','Arial');
% Mean Y-Position
subplot(4,3,9);
h1=plot(time,100*X(2,:),'k','LineWidth',3); hold on;
h2=plot(time,mXestAll(2,:),'b','LineWidth',3); hold on;
h3=plot(time,mXestNTAll(2,:),'g','LineWidth',3); hold on;
h_legend=legend([h1(1) h2(1) h3(1)],'Actual','PPAF+Goal',...
'PPAF','Location','SouthEast');
hy=ylabel('y(t) [cm]'); hx=xlabel('time [s]');
set(gca,'xtick',[],'xtickLabel',[]);
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
title('Y Position','FontWeight','bold','Fontsize',12,'FontName','Arial');
set(h_legend,'FontSize',10)
pos = get(h_legend,'position');
set(h_legend, 'position',[pos(1)-.40 pos(2)+.51 pos(3:4)]);
 
% Mean X-Velocity
subplot(4,3,11);
h1=plot(time,100*X(3,:),'k','LineWidth',3); hold on;
h2=plot(time,mXestAll(3,:),'b','LineWidth',3); hold on;
h3=plot(time,mXestNTAll(3,:),'g','LineWidth',3); hold on;
hy=ylabel('v_{x}(t) [cm/s]'); hx=xlabel('time [s]');
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
title('X Velocity','FontWeight','bold','Fontsize',12,'FontName','Arial');
 
% Mean Y-Velocity
subplot(4,3,12);
h1=plot(time,100*X(4,:),'k','LineWidth',3); hold on;
h2=plot(time,mXestAll(4,:),'b','LineWidth',3); hold on;
h3=plot(time,mXestNTAll(4,:),'g','LineWidth',3); hold on;
hy=ylabel('v_{y}(t) [cm/s]'); hx=xlabel('time [s]');
set([hx, hy],'FontName', 'Arial','FontSize',10,'FontWeight','bold');
title('Y Velocity','FontWeight','bold','Fontsize',12,'FontName','Arial');
 
function [dataDir,mEPSCDir,explicitStimulusDir,psthDir,placeCellDataDir] = ...
getPaperDataDirs()
% Resolve local data folders robustly when Live Editor executes from a temp
% location (e.g., /private/var/.../T).
candidateRoots = {};
 
scriptPath = mfilename('fullpath');
if ~isempty(scriptPath)
candidateRoots = appendCandidateRoot(candidateRoots, fileparts(fileparts(scriptPath)));
end
 
paperPath = which('nSTATPaperExamples');
if ~isempty(paperPath)
candidateRoots = appendCandidateRoot(candidateRoots, fileparts(fileparts(paperPath)));
end
 
installPath = which('nSTAT_Install');
if ~isempty(installPath)
candidateRoots = appendCandidateRoot(candidateRoots, fileparts(installPath));
end
 
try
activeFile = matlab.desktop.editor.getActiveFilename;
catch
activeFile = '';
end
if ~isempty(activeFile)
candidateRoots = appendCandidateRoot(candidateRoots, fileparts(fileparts(activeFile)));
end
 
candidateRoots = appendCandidateRoot(candidateRoots, pwd);
 
nSTATDir = '';
for iRoot = 1:numel(candidateRoots)
candidateDataDir = fullfile(candidateRoots{iRoot}, 'data');
if exist(candidateDataDir, 'dir') == 7
nSTATDir = candidateRoots{iRoot};
break;
end
end
 
if isempty(nSTATDir)
error('nSTATPaperExamples:MissingInstallPath', ...
['Could not resolve the nSTAT root path. Checked roots derived from ', ...
'mfilename, which(''nSTATPaperExamples''), which(''nSTAT_Install''), ', ...
'the active editor file, and pwd.']);
end
 
dataDir = fullfile(nSTATDir,'data');
mEPSCDir = fullfile(dataDir,'mEPSCs');
explicitStimulusDir = fullfile(dataDir,'Explicit Stimulus');
psthDir = fullfile(dataDir,'PSTH');
placeCellDataDir = fullfile(dataDir,'Place Cells');
 
if exist(dataDir,'dir') ~= 7
error('nSTATPaperExamples:MissingDataDir', ...
'Could not find local nSTAT data folder at %s', dataDir);
end
end
 
function roots = appendCandidateRoot(roots, startDir)
if isempty(startDir)
return;
end
 
thisDir = startDir;
while true
if ~any(strcmp(roots, thisDir))
roots{end+1} = thisDir; %#ok<AGROW>
end
parentDir = fileparts(thisDir);
if strcmp(parentDir, thisDir)
break;
end
thisDir = parentDir;
end
end
+
+ +
\ No newline at end of file diff --git a/tools/publish_examples.m b/tools/publish_examples.m new file mode 100644 index 0000000..11ed9bd --- /dev/null +++ b/tools/publish_examples.m @@ -0,0 +1,101 @@ +function report = publish_examples(varargin) +%PUBLISH_EXAMPLES Publish nSTAT paper examples and export generated figures. +% +% Syntax: +% report = publish_examples +% report = publish_examples('Style','legacy') +% +% Name-Value options: +% 'Style' - Plot style tag: 'modern' (default) or 'legacy'. +% 'Seed' - RNG seed for deterministic execution (default 0). +% 'PublishDir' - Output root for docs artifacts (default docs/figures). +% 'ExecuteLiveScript' - Execute/save .mlx before export (default false). +% +% Output: +% report - Struct with paths to exported figures and HTML. + +opts = parseOptions(varargin{:}); + +rootDir = detectRootDir(); +publishRoot = fullfile(rootDir, opts.PublishDir); +figureDir = fullfile(publishRoot, sprintf('paper_examples_%s', opts.Style)); +htmlDir = fullfile(publishRoot, 'published_html'); +htmlPath = fullfile(htmlDir, sprintf('nSTATPaperExamples_%s.html', opts.Style)); +reportPath = fullfile(publishRoot, sprintf('publish_report_%s.json', opts.Style)); + +ensureDir(publishRoot); +ensureDir(figureDir); +ensureDir(htmlDir); + +addpath(fullfile(rootDir, 'tools')); +capture = nstat.baseline.capture_nSTATPaperExamples( ... + 'RootDir', rootDir, ... + 'Seed', opts.Seed, ... + 'Style', opts.Style, ... + 'ExportFigures', true, ... + 'FigureDir', figureDir, ... + 'ExecuteLiveScript', opts.ExecuteLiveScript, ... + 'PublishHtml', true, ... + 'PublishHtmlPath', htmlPath); + +report = struct(); +report.generatedAt = char(datetime('now','TimeZone','local','Format','yyyy-MM-dd''T''HH:mm:ssXXX')); +report.style = opts.Style; +report.seed = opts.Seed; +report.figureCount = capture.plotStructure.figureCount; +report.figureDir = figureDir; +report.publishHtmlPath = capture.runInfo.publishHtmlPath; +report.plotStructurePath = fullfile(publishRoot, sprintf('plot_structure_%s.json', opts.Style)); + +writeJson(reportPath, report); +writeJson(report.plotStructurePath, capture.plotStructure); + +fprintf('\npublish_examples complete.\n'); +fprintf(' Style : %s\n', opts.Style); +fprintf(' Figure dir : %s\n', report.figureDir); +fprintf(' Published : %s\n', report.publishHtmlPath); +fprintf(' Report JSON : %s\n', reportPath); +end + +function opts = parseOptions(varargin) +parser = inputParser; +parser.FunctionName = 'publish_examples'; +addParameter(parser, 'Style', 'modern', @(x)ischar(x) || (isstring(x) && isscalar(x))); +addParameter(parser, 'Seed', 0, @(x)isnumeric(x) && isscalar(x) && isfinite(x)); +addParameter(parser, 'PublishDir', fullfile('docs','figures'), @(x)ischar(x) || (isstring(x) && isscalar(x))); +addParameter(parser, 'ExecuteLiveScript', false, @(x)islogical(x) || (isnumeric(x) && isscalar(x))); +parse(parser, varargin{:}); +opts = parser.Results; +opts.Style = validateStyle(opts.Style); +opts.PublishDir = char(string(opts.PublishDir)); +opts.ExecuteLiveScript = logical(opts.ExecuteLiveScript); +end + +function style = validateStyle(style) +style = lower(char(string(style))); +valid = {'legacy', 'modern'}; +if ~any(strcmp(style, valid)) + error('nstat:docs:InvalidStyle', ... + 'Invalid style "%s". Valid styles are: legacy, modern.', style); +end +end + +function rootDir = detectRootDir() +rootDir = fileparts(mfilename('fullpath')); +rootDir = fileparts(rootDir); +end + +function ensureDir(pathStr) +if exist(pathStr, 'dir') ~= 7 + mkdir(pathStr); +end +end + +function writeJson(pathStr, data) +fid = fopen(pathStr, 'w'); +if fid < 0 + error('nstat:docs:WriteJsonFailed', 'Could not open %s for writing.', pathStr); +end +cleanupObj = onCleanup(@()fclose(fid)); %#ok +fprintf(fid, '%s\n', jsonencode(data, 'PrettyPrint', true)); +end

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