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[pointprocesses] improved spike count and fano factor exercises

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This commit is contained in:
Jan Benda
2021-01-18 23:03:30 +01:00
parent 753d756409
commit 7ec4cddd62
14 changed files with 109 additions and 220 deletions
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37
pointprocesses/code/counthist.m
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@@ -1,37 +0,0 @@
function counthist(spikes, w)
% Plot histogram of spike counts.
%
% counthist(spikes, w)
%
% Arguments:
% spikes: a cell array of vectors of spike times in seconds
% w: duration of window in seconds for computing the counts
% 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];
end
% histogram of spike counts:
maxn = max(n);
[counts, bins] = hist(n, 0:1:maxn+10);
% normalize to probabilities:
counts = counts / sum(counts);
% plot:
bar(bins, counts);
xlabel('counts k');
ylabel('P(k)');
end

39
pointprocesses/code/fano.m
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@@ -1,39 +0,0 @@
function fano( spikes )
% computes fano factor as a function of window size
% spikes: a cell array of vectors of spike times
tmax = spikes{1}(end);
windows = 0.01:0.05:0.01*tmax;
mc = windows;
vc = windows;
ff = windows;
fs = windows;
for j = 1:length(windows)
w = windows( j );
% collect counts:
n = [];
for k = 1:length(spikes)
for tk = 0:w:tmax-w
nn = sum( ( spikes{k} >= tk ) & ( spikes{k} < tk+w ) );
%nn = length( find( ( spikes{k} >= tk ) & ( spikes{k} < tk+w ) ) );
n = [ n nn ];
end
end
% statistics for current window:
mc(j) = mean( n );
vc(j) = var( n );
ff(j) = vc( j )/mc( j );
fs(j) = sqrt(vc( j )/mc( j ));
end
subplot( 1, 2, 1 );
scatter( mc, vc, 'filled' );
xlabel( 'Mean count' );
ylabel( 'Count variance' );
subplot( 1, 2, 2 );
scatter( 1000.0*windows, fs, 'filled' );
xlabel( 'Window W [ms]' );
ylabel( 'Fano factor' );
end

33
pointprocesses/code/fanoplot.m
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@@ -1,33 +0,0 @@
function fanoplot(spikes, titles)
% computes and plots fano factor as a function of window size
% spikes: a cell array of vectors of spike times
% titles: string that is used as a title for the plots
windows = logspace(-3.0, -0.5, 100);
mc = windows;
vc = windows;
ff = windows;
for j = 1:length(windows)
w = windows(j);
counts = spikecounts(spikes, w);
% statistics for current window:
mc(j) = mean(counts);
vc(j) = var(counts);
ff(j) = vc(j)/mc(j);
end
subplot(1, 2, 1);
scatter(mc, vc, 'filled');
title(titles);
xlabel('Mean count');
ylabel('Count variance');
subplot(1, 2, 2);
scatter(1000.0*windows, ff, 'filled');
title(titles);
xlabel('Window [ms]');
ylabel('Fano factor');
xlim(1000.0*[windows(1) windows(end)])
ylim([0.0 1.1]);
set(gca, 'XScale', 'log');
end

9
pointprocesses/code/fanoplots.m
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@@ -1,9 +0,0 @@
spikes{1} = poissonspikes;
spikes{2} = pifouspikes;
spikes{3} = lifadaptspikes;
idents = {'poisson', 'pifou', 'lifadapt'};
for k = 1:3
figure(k)
fanoplot(spikes{k}, titles{k});
savefigpdf(gcf, sprintf('fanoplots%s.pdf', idents{k}), 20, 7);
end

3
pointprocesses/code/hompoissonspikes.m
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@@ -2,8 +2,6 @@ function spikes = hompoissonspikes(rate, trials, tmax)
% Generate spike times of a homogeneous poisson process
% using the exponential interspike interval distribution.
%
% spikes = hompoissonspikes(rate, trials, tmax)
%
% Arguments:
% rate: the rate of the Poisson process in Hertz
% trials: number of trials that should be generated
@@ -11,7 +9,6 @@ function spikes = hompoissonspikes(rate, trials, tmax)
%
% Returns:
% spikes: a cell array of vectors of spike times in seconds
spikes = cell(trials, 1);
mu = 1.0/rate;
nintervals = 2*round(tmax/mu);

14
pointprocesses/code/isihist.m
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@@ -1,25 +1,13 @@
function [pdf, centers] = isihist(isis, binwidth)
% Compute normalized histogram of interspike intervals.
%
% [pdf, centers] = isihist(isis, binwidth)
%
% Arguments:
% isis: vector of interspike intervals in seconds
% binwidth: optional width in seconds to be used for the isi bins
% binwidth: width in seconds to be used for the ISI bins
%
% Returns:
% pdf: vector with pdf of interspike intervals in Hertz
% centers: vector with centers of interspikeintervalls in seconds
if nargin < 2
% compute good binwidth:
nperbin = 200; % average number of data points per bin
bins = length(isis)/nperbin; % number of bins
binwidth = max(isis)/bins;
if binwidth < 5e-4 % half a millisecond
binwidth = 5e-4;
end
end
bins = 0.5*binwidth:binwidth:max(isis);
% histogram data:
[nelements, centers] = hist(isis, bins);

4
pointprocesses/code/isis.m
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@@ -1,13 +1,11 @@
function isivec = isis(spikes)
% returns a single list of isis computed from all trials in spikes
%
% isivec = isis(spikes)
%
% Arguments:
% spikes: a cell array of vectors of spike times in seconds
%
% Returns:
% isivec: a column vector with all the interspike intervalls
% isivec: a column vector with all the interspike intervals
isivec = [];
for k = 1:length(spikes)
difftimes = diff(spikes{k});

5
pointprocesses/code/isiserialcorr.m
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@@ -1,8 +1,6 @@
function [isicorr, lags] = isiserialcorr(isivec, maxlag)
% serial correlation of interspike intervals
%
% isicorr = isiserialcorr(isivec, maxlag)
%
% Arguments:
% isivec: vector of interspike intervals in seconds
% maxlag: the maximum lag
@@ -16,8 +14,7 @@ function [isicorr, lags] = isiserialcorr(isivec, maxlag)
lag = lags(k);
if length(isivec) > lag+10 % ensure "enough" data
% NOTE: the arguments to corr must be column vectors!
% We insure this in the isis() function that
% generates the isivec.
% We insure this already in the isis() function.
isicorr(k) = corr(isivec(1:end-lag), isivec(lag+1:end));
end
end

38
pointprocesses/code/plotcounthist.m
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@@ -1,24 +1,14 @@
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);
function counthist(spikes, w)
% Plot histogram of spike counts.
%
% Arguments:
% spikes: a cell array of vectors of spike times in seconds
% w: duration of window in seconds for computing the counts
n = counts(spikes, w);
maxn = max(n);
[counts, bins] = hist(n, 0:1:maxn+10);
counts = counts / sum(counts);
bar(bins, counts);
xlabel('counts k');
ylabel('P(k)');
end

31
pointprocesses/code/plotfanofactor.m Normal file
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@@ -0,0 +1,31 @@
function plotfanofactor(spikes, wmin, wmax)
% Compute and plot Fano factor as a function of window size.
%
% Arguments:
% spikes: a cell array of vectors of spike times in seconds
% wmin: minimum window size in seconds
% wmax: maximum window size in seconds
windows = logspace(log10(wmin), log10(wmax), 100);
mc = zeros(1, length(windows));
vc = zeros(1, length(windows));
for k = 1:length(windows)
w = windows(k);
n = counts(spikes, w);
mc(k) = mean(n);
vc(k) = var(n);
end
subplot(1, 2, 1);
scatter(mc, vc, 'filled');
xlabel('Mean count');
ylabel('Count variance');
subplot(1, 2, 2);
scatter(1000.0*windows, vc ./ mc, 'filled');
xlabel('Window [ms]');
ylabel('Fano factor');
xlim(1000.0*[windows(1) windows(end)])
ylim([0.0 1.1]);
set(gca, 'XScale', 'log');
end

15
pointprocesses/code/plotisihist.m
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@@ -1,24 +1,13 @@
function plotisihist(isis, binwidth)
% Plot and annotate histogram of interspike intervals.
%
% plotisihist(isis, binwidth)
%
% Arguments:
% isis: vector of interspike intervals in seconds
% binwidth: optional width in seconds to be used for the isi bins
% compute normalized histogram:
if nargin < 2
[pdf, centers] = isihist(isis);
else
[pdf, centers] = isihist(isis, binwidth);
end
% plot:
% binwidth: width in seconds to be used for the ISI bins
[pdf, centers] = isihist(isis, binwidth);
bar(1000.0*centers, pdf); % milliseconds on x-axis
xlabel('ISI [ms]')
ylabel('p(ISI) [1/s]')
% annotation:
misi = mean(isis);
sdisi = std(isis);
text(0.95, 0.8, sprintf('mean=%.1f ms', 1000.0*misi), ...

2
pointprocesses/code/plotisiserialcorr.m
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@@ -1,8 +1,6 @@
function isicorr = plotisiserialcorr(isivec, maxlag)
% plot serial correlation of interspike intervals
%
% plotisiserialcorr(isivec, maxlag)
%
% Arguments:
% isivec: vector of interspike intervals in seconds
% maxlag: the maximum lag

4
pointprocesses/code/rasterplot.m
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@@ -1,8 +1,6 @@
function rasterplot(spikes, tmax)
% Display a spike raster of the spike times given in spikes.
%
% rasterplot(spikes, tmax)
%
% Arguments:
% spikes: a cell array of vectors of spike times in seconds
% tmax: plot spike raster up to tmax seconds
@@ -21,7 +19,7 @@ function rasterplot(spikes, tmax)
ones(1, length(times)) * (k+0.4); ...
ones(1, length(times)) * nan]];
end
% convert matrices into simple vectors of (x,y) pairs:
% convert matrices into column vectors of (x,y) pairs:
spiketimes = spiketimes(:);
trials = trials(:);
% plotting this is lightning fast: