[pointprocesses] cleaned up code

pointprocesses/code/colorednoisepdf.m

@@ -1,10 +0,0 @@
-function pcn = colorednoisepdf( x, misi, epsilon, tau )
-% returns the ISI pdf for PIF with colored noise drive
-% x: the input ISI
-% misis: the mean isi
-% epsilon: a parameter
-% tau: the correlation time of the noise
-    gamma1 = x/tau+exp(-x/tau)-1.0;
-    gamma2 = 1.0-exp(-x/tau);
-    pcn=exp(-(x-misi).^2./(4.0*epsilon*tau.^2.*gamma1)).*(((misi-x).*gamma2+2*gamma1*tau).^2./(2*gamma1*tau^2)-epsilon*(gamma2.^2-2*gamma1.*exp(-x/tau))) ./ (2*tau*sqrt(4*pi*epsilon*gamma1.^3));
-end

pointprocesses/code/colorednoisepdfisifit.m

@@ -1,24 +0,0 @@
-% misi = 0.02;
-% epsilon = 1.0;
-% tau = 0.1;
-x=0:0.002:0.1;
-% pcn = colorednoisepdf( x, misi, epsilon, tau )+10.0*randn( size( x ) );
-% plot( x, pcn );
-
-spikes = lifouspikes( 10, 15, 50.0, 1.0, 1.0 );
-isivec = isis( spikes );
-misi = mean( isivec );
-1.0/misi
-isibins = 0:0.0005:0.1;
-[ n, c ] = hist( isivec, isibins );
-n = n / sum(n)/(isibins(2)-isibins(1));
-bar( c, n );
-
-beta0 = [ 1.0, 0.01 ];
-b = nlinfit(c(1:end-2), n(1:end-2), @(b,x)(colorednoisepdf(x, misi, b(1), b(2))), beta0)
-
-pcn = colorednoisepdf( x, misi, b(1), b(2) );
-hold on
-plot( x, pcn, 'r', 'LineWidth', 3 );
-hold off
-

pointprocesses/code/counthistpoisson.m

@@ -1,55 +0,0 @@
-function [counts, bins] = counthist(spikes, w)
-% computes count histogram and compare with Poisson distribution
-%
-% [counts, bins] = counthist(spikes, w)
-%
-% Arguments:
-%   spikes: a cell array of vectors of spike times in seconds
-%   w: observation window duration in seconds for computing the counts
-%
-% Returns:
-%   counts: the histogram of counts normalized to probabilities
-%   bins: the bin centers for the histogram
-
-    % collect spike counts:
-    tmax = spikes{1}(end);
-    n = [];
-    r = [];
-    for k = 1:length(spikes)
-        times = spikes{k};
-%       alternative 1: count the number of spikes in each window:
-%         for tk = 0:w:tmax-w
-%             nn = sum( ( times >= tk ) & ( times < tk+w ) );
-%             %nn = length( find( ( times >= tk ) & ( times < tk+w ) ) );
-%             n = [ n nn ];
-%         end
-%     alternative 2: use the hist function to do that!
-        tbins = 0.5*w:w:tmax-0.5*w;
-        nn = hist(times, tbins);
-        n = [ n nn ];
-        % the rate of the spikes:
-        rate = (length(times)-1)/(times(end) - times(1));
-        r = [ r rate ];
-    end
-
-    % histogram of spike counts:
-    maxn = max( n );
-    [counts, bins ] = hist( n, 0:1:maxn+10 );
-    % normalize to probabilities:
-    counts = counts / sum( counts );
-
-    % plot:
-    if nargout == 0
-        bar( bins, counts );
-        hold on;
-        % Poisson distribution:
-        rate = mean( r );
-        x = 0:1:maxn+10;
-        a = rate*w;
-        y = a.^x.*exp(-a)./factorial(x);
-        plot( x, y, 'r', 'LineWidth', 3 );
-        hold off;
-        xlabel( 'counts k' );
-        ylabel( 'P(k)' );
-    end
-end

pointprocesses/code/isireturnmap.m

@@ -1,24 +0,0 @@
-function isireturnmap( isis, lag2 )
-% plot return maps for lag 1 and lag lag2
-
-    clf;
-    subplot( 1, 2, 1 );
-    lag = 1;
-    scatter( 1000.0*isis(1:end-lag)', 1000.0*isis(1+lag:end)', 'b', 'filled', 'MarkerEdgeColor', 'white' );
-    xlabel( 'ISI T_i [ms]' );
-    ylabel( 'ISI T_{i+1} [ms]' );
-    maxisi = max( isis );
-    maxy = ceil(maxisi/10)*10.0;
-    xlim( [0 1.5*maxy ])
-    ylim( [0 maxy ])
-
-    subplot( 1, 2, 2 );
-    lag = lag2;
-    scatter( 1000.0*isis(1:end-lag)', 1000.0*isis(1+lag:end)', 'b', 'filled', 'MarkerEdgeColor', 'white' );
-    xlabel( 'ISI T_i [ms]' );
-    ylabel( 'ISI T_{i+2} [ms]' );
-    xlim( [0 1.5*maxy ])
-    ylim( [0 maxy ])
-
-end
-

pointprocesses/code/plotspikestats.m

@@ -1,100 +0,0 @@
-%% load data:
-clear all
-% alternative 1:
-% pro: no structs. contra: global unknown variables
-load poisson.mat
-whos
-poissonspikes = spikes;
-load pifou.mat;
-pifouspikes = spikes;
-load lifadapt.mat;
-lifadaptspikes = spikes;
-clear spikes;
-% alternative 2:
-% pro: clean code. contra: structs that we do not really know yet
-clear all
-x = load( 'poisson.mat' );
-poissonspikes = x.spikes;
-x = load( 'pifou.mat' );
-pifouspikes = x.spikes;
-x = load( 'lifadapt.mat' );
-lifadaptspikes = x.spikes;
-
-%% spike raster plots:
-tmax = 1.0;
-subplot(1, 3, 1);
-spikeraster(poissonspikes, tmax);
-title('Poisson');
-
-subplot(1, 3, 2);
-spikeraster(pifouspikes, tmax);
-title('PIF OU');
-
-subplot(1, 3, 3);
-spikeraster(lifadaptspikes, tmax);
-title('LIF adapt');
-
-%% isi histograms:
-maxisi = 300.0;
-binwidth = 0.002;
-subplot(1, 3, 1);
-poissonisis = isis(poissonspikes);
-isihist(poissonisis, binwidth);
-xlim([0, maxisi])
-title('Poisson');
-
-subplot(1, 3, 2);
-pifouisis = isis(pifouspikes);
-isihist(pifouisis, binwidth);
-xlim([0, maxisi])
-title('PIF OU');
-
-subplot(1, 3, 3);
-lifadaptisis = isis(lifadaptspikes);
-isihist(lifadaptisis, binwidth);
-xlim([0, maxisi])
-title('LIF adapt');
-
-%% serial correlations:
-maxlag = 10;
-rrange = [-0.5, 1.05];
-subplot(1, 3, 1);
-isiserialcorr(poissonisis, maxlag);
-ylim(rrange)
-title('Poisson');
-
-subplot(1, 3, 2);
-isiserialcorr(pifouisis, maxlag);
-ylim(rrange)
-title('PIF OU');
-
-subplot(1, 3, 3);
-isiserialcorr(lifadaptisis, maxlag);
-ylim(rrange)
-title('LIF adapt');
-
-%% spike counts:
-w = 0.1;
-cmax = 8;
-pmax = 0.5;
-subplot(1, 3, 1);
-counthist(poissonspikes, w);
-xlim([0 cmax])
-set(gca, 'XTick', 0:2:cmax)
-ylim([0 pmax])
-title('Poisson');
-
-subplot(1, 3, 2);
-counthist(pifouspikes, w);
-xlim([0 cmax])
-set(gca, 'XTick', 0:2:cmax)
-ylim([0 pmax])
-title('PIF OU');
-
-subplot(1, 3, 3);
-counthist(lifadaptspikes, w);
-xlim([0 cmax])
-set(gca, 'XTick', 0:2:cmax)
-ylim([0 pmax])
-title('LIF adapt');
-savefigpdf(gcf, 'counthist.pdf', 20, 7);

pointprocesses/code/poissonisih.m

@@ -1,27 +0,0 @@
-rate = 100.0;
-trials = 50;
-tmax = 100.0;
-
-% generate spikes:
-spikes = poissonspikes( trials, rate, tmax );
-% interspike intervals:
-isivec = isis( spikes );
-% histogram
-f = figure( 1 );
-isihist( isivec );
-hold on
-% theoretical density:
-xmax = 5.0/rate;
-x = 0:0.0001:xmax;
-y = rate*exp(-rate*x);
-plot( 1000.0*x, y, 'r', 'LineWidth', 3 );
-% plot details:
-title( sprintf( 'Poisson spike trains, rate=%g Hz, nisi=%d', rate, length( isivec ) ) )
-xlim( [ 0.0 1000.0*xmax ] )
-ylim( [ 0.0 1.1*rate ] )
-legend( 'data', 'poisson' )
-hold off
-
-% serial correlations:
-f = figure( 2 );
-isiserialcorr( isivec, 10 );

pointprocesses/code/poissonisistats.m

@@ -1,46 +0,0 @@
-rates = 1:1:100;
-avisi = [];
-sdisi = [];
-cvisi = [];
-
-for rate = rates
-    spikes = poissonspikes( 10, rate, 100.0 );
-    isivec = isis( spikes );
-    av = mean( isivec );
-    sd = std( isivec );
-    cv = sd/av;
-    avisi = [ avisi av ];
-    sdisi = [ sdisi sd ];
-    cvisi = [ cvisi cv ];
-end
-
-f = figure;
-subplot( 1, 3, 1 );
-scatter( rates, 1000.0*avisi, 'b', 'filled' );
-hold on;
-plot( rates, 1000.0./rates, 'r' );
-hold off;
-xlabel( 'Rate \lambda [Hz]' );
-ylim( [ 0 1000 ] );
-title( 'Mean ISI [ms]' );
-legend( 'simulation', 'theory 1/\lambda' );
-
-subplot( 1, 3, 2 );
-scatter( rates, 1000.0*sdisi, 'b', 'filled' );
-hold on;
-plot( rates, 1000.0./rates, 'r' );
-hold off;
-xlabel( 'Rate \lambda [Hz]' );
-ylim( [ 0 1000 ] )
-title( 'Standard deviation ISI [ms]' );
-legend( 'simulation', 'theory 1/\lambda' );
-
-subplot( 1, 3, 3 );
-scatter( rates, cvisi, 'b', 'filled' );
-hold on;
-plot( rates, ones( size( rates ) ), 'r' );
-hold off;
-xlabel( 'Rate \lambda [Hz]' );
-ylim( [ 0 2 ] )
-title( 'CV' );
-legend( 'simulation', 'theory' );

pointprocesses/code/psth.m

@@ -1,14 +0,0 @@
-function p = psth(spikes, dt, tmax)
-% plots a PSTH of the spikes with binwidth dt
-    t = 0.0:dt:tmax+dt;
-    p = zeros(1, length(t));
-    for k=1:length(spikes)
-        times = spikes{k};
-        [h, b] = hist(times, t);
-        p = p + h;
-    end
-    p = p/length(spikes)/dt;
-    t(end) = [];
-    p(end) = [];
-    plot(t, p);
-end

pointprocesses/code/spikerate.m

@@ -1,12 +0,0 @@
-function r = spikerate(spikes, duration)
-% returns the average spike rate of the spikes
-% for the first duration seconds
-% spikes: a cell array of vectors of spike times
-
-    rates = zeros(length(spikes),1);
-    for k = 1:length(spikes)
-        times = spikes{k};
-        rates(k) = sum(times<duration)/duration;
-    end
-    r = mean(rates);
-end

pointprocesses/lecture/pointprocesses-chapter.tex

@@ -20,7 +20,6 @@
 
 \section{TODO}
 \begin{itemize}
-\item Explain difference stationary versus non-stationary point process
 \item Show different types of ISI histograms (regular, noisy, poisson, bursty, locking)
 \item Multitrial firing rates
 \item Better explain difference between ISI method and PSTHes. The