72 lines
1.6 KiB
Matlab
72 lines
1.6 KiB
Matlab
%% general settings for the model neuron:
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trials = 10;
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tmax = 50.0;
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%% generate and plot spiketrains for two inputs:
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I1 = 14.0;
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I2 = 15.0;
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spikes1 = lifspikes(trials, I1, tmax);
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spikes2 = lifspikes(trials, I2, tmax);
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subplot(1, 2, 1);
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tmin = 10.0;
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spikeraster(spikes1, tmin, tmin+2.0);
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title(sprintf('I_1=%g', I1))
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subplot(1, 2, 2);
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spikeraster(spikes2, tmin, tmin+2.0);
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title(sprintf('I_2=%g', I2))
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%savefigpdf(gcf(), 'spikeraster.pdf')
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%% spike count histograms:
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Ts = [0.01 0.1 0.3 1.0];
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cmax = 100;
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figure()
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for k = 1:length(Ts)
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T = Ts(k);
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[c1, b1] = counthist(spikes1, 0.0, tmax, T, cmax);
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[c2, b2] = counthist(spikes2, 0.0, tmax, T, cmax);
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subplot(2, 2, k)
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bar(b1, c1, 'r');
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hold on;
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bar(b2, c2, 'b');
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xlim([0 cmax])
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title(sprintf('T=%gms', 1000.0*T))
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hold off;
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end
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%% discrimination measure:
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T = 0.1;
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cmax = 15;
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[d, thresholds, true1s, false1s, true2s, false2s, pratio] = discriminability(spikes1, spikes2, tmax, T, cmax);
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figure()
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subplot(1, 3, 1);
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plot(thresholds, true1s, 'b');
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hold on;
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plot(thresholds, true2s, 'b');
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plot(thresholds, false1s, 'r');
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plot(thresholds, false2s, 'r');
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xlim([0 cmax])
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hold off;
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% Ratio:
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subplot(1, 3, 2);
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fprintf('discriminability = %g\n', d);
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plot(thresholds, pratio);
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% ROC:
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subplot(1, 3, 3);
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plot(false2s, true1s);
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%% discriminability:
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Ts = 0.01:0.01:1.0;
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cmax = 100;
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ds = zeros(length(Ts), 1);
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for k = 1:length(Ts)
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T = Ts(k);
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[c1, b1] = counthist(spikes1, 0.0, tmax, T, cmax);
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[c2, b2] = counthist(spikes2, 0.0, tmax, T, cmax);
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[d, thresholds, true1s, false1s, true2s, false2s, pratio] = discriminability(spikes1, spikes2, tmax, T, cmax);
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ds(k) = d;
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end
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figure()
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plot(Ts, ds)
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