[pointprocesses] moved exercise codes to exercise folder

pointprocesses/code/counts.m

@@ -0,0 +1,25 @@
+function n = counts(spikes, w)
+% Count spikes in time windows.
+%
+% Arguments:
+%   spikes: a cell array of vectors of spike times in seconds
+%   w: duration of window in seconds for computing the counts
+%
+% Returns:
+%   n: vector with spike counts
+    tmax = spikes{1}(end);
+    n = [];
+    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
+end

pointprocesses/code/plotcounthist.m

@@ -1,4 +1,4 @@
-function counthist(spikes, w)
+function plotcounthist(spikes, w)
 % Plot histogram of spike counts.
 %
 % Arguments:

pointprocesses/code/plotisihs.m

@@ -1,19 +0,0 @@
-maxisi = 300.0;
-subplot(1, 3, 1);
-poissonisis = isis(poissonspikes);
-plotisihist(poissonisis, 0.001);
-xlim([0, maxisi])
-title('Poisson');
-
-subplot(1, 3, 2);
-pifouisis = isis(pifouspikes);
-plotisihist(pifouisis, 0.001);
-xlim([0, maxisi])
-title('PIF OU');
-
-subplot(1, 3, 3);
-lifadaptisis = isis(lifadaptspikes);
-plotisihist(lifadaptisis, 0.001);
-xlim([0, maxisi])
-title('LIF adapt');
-savefigpdf(gcf, 'isihist.pdf', 20, 7);

pointprocesses/code/plotserialcorr.m

@@ -1,17 +0,0 @@
-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');
-savefigpdf(gcf, 'serialcorr.pdf', 20, 7);

pointprocesses/code/plotspikeraster.m

@@ -1,13 +0,0 @@
-subplot(1, 3, 1);
-rasterplot(poissonspikes, 1.0);
-title('Poisson');
-
-subplot(1, 3, 2);
-rasterplot(pifouspikes, 1.0);
-title('PIF OU');
-
-subplot(1, 3, 3);
-rasterplot(lifadaptspikes, 1.0);
-title('LIF adapt');
-
-savefigpdf(gcf, 'spikeraster.pdf', 15, 5);

pointprocesses/code/spikecounts.m

@@ -1,30 +0,0 @@
-function counts = spikecounts(spikes, w)
-% Compute vector of spike counts.
-%
-% counts = spikecounts(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: vector of spike counts
-
-    % collect spike counts:
-    tmax = spikes{1}(end);
-    counts = [];
-    for k = 1:length(spikes)
-        times = spikes{k};
-%       method 1: count the number of spikes in each window:
-%         for tk = 0:w:tmax-w
-%             nn = sum((times >= tk) & (times < tk+w));
-%             %nn = length(times((times >= tk) & (times < tk+w)));
-%             %nn = length(find((times >= tk) & (times < tk+w)));
-%             counts = [counts nn];
-%         end
-%     method 2: use the hist() function to do that!
-        tbins = 0.5*w:w:tmax-0.5*w;
-        nn = hist(times, tbins);
-        counts = [counts nn];
-    end
-end

pointprocesses/code/spikecountshists.m

@@ -1,17 +0,0 @@
-w = 0.1;
-bins = 0.0:1.0:10.0;
-
-counts{1} = spikecounts(poissonspikes, w);
-counts{2} = spikecounts(pifouspikes, w);
-counts{3} = spikecounts(lifadaptspikes, w);
-titles = {'Poisson', 'PIF OU', 'LIF adapt'};
-for k = 1:3
-    subplot(1, 3, k);
-    [h, b] = hist(counts{k}, bins);
-    bar(b, h/sum(h));
-    title(titles{k})
-    xlabel('Spike count');
-    xlim([-0.5, 8.5]);
-    ylim([0.0 0.8]);
-end
-savefigpdf(gcf, 'spikecountshists.pdf', 20, 7);