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