fixed initial condition of IF models
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instructions.tex
project_fano_slope
project_fano_time
project_isicorrelations
project_isipdffit
project_noiseficurves
@ -1,5 +1,5 @@
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\setlength{\fboxsep}{2ex}
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\fbox{\parbox{1\linewidth}{ \small
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\fbox{\parbox{1\linewidth}{\small
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{\bf Evaluation criteria:}
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@ -48,7 +48,9 @@
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and discussing your results, and (iii) explain how you solved
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the problem algorithmically (don't show your entire code). All
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data-related figures you show in the presentation should be
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produced by your program. It is always a good idea to illustrate
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the problem with basic plots of the raw-data. Make sure the axis
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labels are large enough!
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}}
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produced by your program --- no editing or labeling by
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PowerPoint or other software. It is always a good idea to
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illustrate the problem with basic plots of the raw-data. Make
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sure the axis labels are large enough!
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}}
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@ -20,7 +20,7 @@ function spikes = lifboltzmannspikes(trials, input, tmax, gain)
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for k=1:trials
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times = [];
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j = 1;
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v = vreset;
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v = vreset + (vthresh - vreset) * rand();
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noise = sqrt(2.0*D)*randn(n, 1)/sqrt(dt);
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for i=1:n
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v = v + (- v + noise(i) + inb)*dt/tau;
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@ -76,7 +76,8 @@ plot(false2s, true1s);
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T = 0.1;
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gains = 0.01:0.01:1.0;
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cmax = 100;
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ds = zeros(length(gains), 1);
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dstt = zeros(length(gains), 1);
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dsff = zeros(length(gains), 1);
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for k = 1:length(gains)
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gain = gains(k);
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spikes1 = lifboltzmannspikes(trials, I1, tmax, gain);
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@ -84,9 +85,11 @@ for k = 1:length(gains)
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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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dstt(k) = d;
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dsff(k) = min(false1s + false2s);
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end
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figure()
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plot(gains, ds)
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plot(gains, dstt);
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hold on;
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plot(gains, dsff);
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hold off;
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@ -20,7 +20,7 @@ function spikes = lifboltzmannspikes(trials, input, tmax, gain)
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for k=1:trials
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times = [];
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j = 1;
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v = vreset;
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v = vreset + (vthresh - vreset) * rand();
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noise = sqrt(2.0*D)*randn(n, 1)/sqrt(dt);
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for i=1:n
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v = v + (- v + noise(i) + inb)*dt/tau;
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@ -19,7 +19,7 @@ function spikes = lifspikes(trials, input, tmax)
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for k=1:trials
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times = [];
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j = 1;
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v = vreset + (vthresh-vreset)*rand(1);
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v = vreset + (vthresh-vreset)*rand();
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noise = sqrt(2.0*D)*randn(n, 1)/sqrt(dt);
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for i=1:length(noise)
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v = v + (- v + noise(i) + input)*dt/tau;
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@ -19,7 +19,7 @@ function spikes = lifspikes(trials, input, tmax)
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for k=1:trials
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times = [];
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j = 1;
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v = vreset + (vthresh-vreset)*rand(1);
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v = vreset + (vthresh-vreset)*rand();
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noise = sqrt(2.0*D)*randn(n, 1)/sqrt(dt);
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for i=1:length(noise)
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v = v + (- v + noise(i) + input)*dt/tau;
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@ -32,7 +32,7 @@ function spikes = lifadaptspikes( trials, input, tmaxdt, D, tauadapt, adaptincr
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for k=1:trials
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times = [];
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j = 1;
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v = vreset;
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v = vreset + (vthresh-vreset)*rand();
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a = 0.0;
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noise = sqrt(2.0*D)*randn( length( input ), 1 )/sqrt(dt);
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for i=1:length( noise )
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@ -28,7 +28,7 @@ function spikes = lifouspikes( trials, input, tmaxdt, D, outau )
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times = [];
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j = 1;
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n = 0.0;
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v = vreset;
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v = vreset + (vthresh-vreset)*rand();
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noise = sqrt(2.0*D)*randn( length( input ), 1 )/sqrt(dt);
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for i=1:length( noise )
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n = n + ( - n + noise(i))*dt/outau;
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@ -28,7 +28,7 @@ function spikes = pifouspikes( trials, input, tmaxdt, D, outau )
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times = [];
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j = 1;
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n = 0.0;
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v = vreset;
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v = vreset + (vthresh-vreset)*rand();
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noise = sqrt(2.0*D)*randn( length( input ), 1 )/sqrt(dt);
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for i=1:length( noise )
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n = n + ( - n + noise(i))*dt/outau;
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@ -18,7 +18,7 @@ function spikes = lifspikes(trials, input, tmax, D)
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for k=1:trials
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times = [];
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j = 1;
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v = vreset;
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v = vreset + (vthresh-vreset)*rand();
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noise = sqrt(2.0*D)*randn(n, 1)/sqrt(dt);
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for i=1:n
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v = v + (- v + noise(i) + input)*dt/tau;
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@ -18,7 +18,7 @@ function spikes = lifspikes(trials, input, tmax, D)
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for k=1:trials
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times = [];
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j = 1;
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v = vreset;
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v = vreset + (vthresh-vreset)*rand();
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noise = sqrt(2.0*D)*randn(n, 1)/sqrt(dt);
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for i=1:n
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v = v + (- v + noise(i) + input)*dt/tau;
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