improved noiseficurves projects

projects/project_noiseficurves/solution/ficurve.m

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+function rates = ficurve(trials, inputs, tmax, D)
+% compute f-I curve.
+rates = zeros(length(inputs), 1);
+for k=1:length(inputs)
+	spikes = lifspikes(trials, inputs(k), tmax, D);
+	rates(k) = firingrate(spikes, 0.0, tmax);
+end
+end

projects/project_noiseficurves/solution/firingrate.m

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+function rate = firingrate(spikes, tmin, tmax)
+% mean firing rate between tmin and tmax.
+rates = zeros(length(spikes), 1);
+for i = 1:length(spikes)
+	times= spikes{i};
+	rates(i) = length(times((times>=tmin)&(times<=tmax)))/(tmax-tmin);
+end
+rate = mean(rates);
+end

projects/project_noiseficurves/solution/isih.m

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+function isih(spikes, bins)
+isis = [];
+for i = 1:length(spikes)
+	times= spikes{i};
+	isis = [isis; diff(times(:))];
+end
+[h, b] = hist(isis, bins);
+h = h / sum(h) / (bins(2)-bins(1));
+bar(1000.0*b, h);
+xlim([0 1000.0*b(end)])
+end

projects/project_noiseficurves/solution/lifspikes.m

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+function spikes = lifspikes(trials, input, tmax, D)
+% Generate spike times of a leaky integrate-and-fire neuron
+% trials: the number of trials to be generated
+% input: the stimulus either as a single value or as a vector
+% tmax: duration of a trial
+% D: the strength of additive white noise
+    
+    tau = 0.01;
+    if nargin < 4
+        D = 1e0;
+    end
+    vreset = 0.0;
+    vthresh = 10.0;
+    dt = 1e-4;
+    
+    n = ceil(tmax/dt);
+    spikes = cell(trials, 1);
+    for k=1:trials
+        times = [];
+        j = 1;
+        v = vreset;
+        noise = sqrt(2.0*D)*randn(n, 1)/sqrt(dt);
+        for i=1:n
+            v = v + (- v + noise(i) + input)*dt/tau;
+            if v >= vthresh
+                v = vreset;
+                times(j) = i*dt;
+                j = j + 1;
+            end
+        end
+        spikes{k} = times;
+    end
+end

projects/project_noiseficurves/solution/noiseficurves.m

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+%% general settings for the model neuron:
+trials = 10;
+tmax = 50.0;
+
+%% f-I curves:
+figure()
+Ds = [0, 0.001, 0.01, 0.1];
+for j = 1:length(Ds)
+	D = Ds(j);
+	inputs = 0.0:0.5:20.0;
+	rates = ficurve(trials, inputs, tmax, D);
+	plot(inputs, rates);
+	hold on;
+end
+hold off;
+
+%% spike raster and CVs
+input = 12.0;
+for j = 1:length(Ds)
+	D = Ds(j);
+	spikes = lifspikes(trials, input, tmax, D);
+	subplot(4, 2, 2*j-1);
+	spikeraster(spikes, 0.0, 1.0);
+	subplot(4, 2, 2*j);
+	isih(spikes, [0:0.001:0.04]);
+end

projects/project_noiseficurves/solution/spikeraster.m

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+function spikeraster(spikes, tmin, tmax)
+% Display a spike raster of the spike times given in spikes.
+%
+% spikeraster(spikes, tmax)
+%   spikes: a cell array of vectors of spike times in seconds
+%   tmin: plot spike raster starting at tmin seconds
+%   tmax: plot spike raster upto tmax seconds
+
+ntrials = length(spikes);
+for k = 1:ntrials
+    times = spikes{k};
+    times = times((times>=tmin) & (times<=tmax));
+    if tmax < 1.5
+        times = 1000.0*times; % conversion to ms
+    end
+    for i = 1:length( times )
+      line([times(i) times(i)],[k-0.4 k+0.4], 'Color', 'k'); 
+    end
+end
+if (tmax-tmin) < 1.5
+    xlabel('Time [ms]');
+    xlim([1000.0*tmin 1000.0*tmax]);
+else
+    xlabel('Time [s]');
+    xlim([tmin tmax]);
+end
+ylabel('Trials');
+ylim([0.3 ntrials+0.7 ]);
+end
+