fixed initial condition of IF models

projects/instructions.tex

@@ -1,5 +1,5 @@
 \setlength{\fboxsep}{2ex}
-\fbox{\parbox{1\linewidth}{ \small 
+\fbox{\parbox{1\linewidth}{\small 
 
       {\bf Evaluation criteria:}
 
@@ -48,7 +48,9 @@
       and discussing your results, and (iii) explain how you solved
       the problem algorithmically (don't show your entire code). All
       data-related figures you show in the presentation should be
-      produced by your program. It is always a good idea to illustrate
-      the problem with basic plots of the raw-data. Make sure the axis
-      labels are large enough!
-    }}
+      produced by your program --- no editing or labeling by
+      PowerPoint or other software. It is always a good idea to
+      illustrate the problem with basic plots of the raw-data. Make
+      sure the axis labels are large enough!
+
+}}

projects/project_fano_slope/lifboltzmannspikes.m

@@ -20,7 +20,7 @@ function spikes = lifboltzmannspikes(trials, input, tmax, gain)
     for k=1:trials
         times = [];
         j = 1;
-        v = vreset;
+        v = vreset + (vthresh - vreset) * rand();
         noise = sqrt(2.0*D)*randn(n, 1)/sqrt(dt);
         for i=1:n
             v = v + (- v + noise(i) + inb)*dt/tau;

projects/project_fano_slope/solution/fanoslope.m

@@ -76,7 +76,8 @@ plot(false2s, true1s);
 T = 0.1;
 gains = 0.01:0.01:1.0;
 cmax = 100;
-ds = zeros(length(gains), 1);
+dstt = zeros(length(gains), 1);
+dsff = zeros(length(gains), 1);
 for k = 1:length(gains)
     gain = gains(k);
 	spikes1 = lifboltzmannspikes(trials, I1, tmax, gain);
@@ -84,9 +85,11 @@ for k = 1:length(gains)
     [c1, b1] = counthist(spikes1, 0.0, tmax, T, cmax);
     [c2, b2] = counthist(spikes2, 0.0, tmax, T, cmax);
     [d, thresholds, true1s, false1s, true2s, false2s, pratio] = discriminability(spikes1, spikes2, tmax, T, cmax);
-    ds(k) = d;
+    dstt(k) = d;
+    dsff(k) = min(false1s + false2s);
 end
 figure()
-plot(gains, ds)
-
-
+plot(gains, dstt);
+hold on;
+plot(gains, dsff);
+hold off;

projects/project_fano_slope/solution/lifboltzmannspikes.m

@@ -20,7 +20,7 @@ function spikes = lifboltzmannspikes(trials, input, tmax, gain)
     for k=1:trials
         times = [];
         j = 1;
-        v = vreset;
+        v = vreset + (vthresh - vreset) * rand();
         noise = sqrt(2.0*D)*randn(n, 1)/sqrt(dt);
         for i=1:n
             v = v + (- v + noise(i) + inb)*dt/tau;

projects/project_fano_time/lifspikes.m

@@ -19,7 +19,7 @@ function spikes = lifspikes(trials, input, tmax)
     for k=1:trials
         times = [];
         j = 1;
-        v = vreset + (vthresh-vreset)*rand(1);
+        v = vreset + (vthresh-vreset)*rand();
         noise = sqrt(2.0*D)*randn(n, 1)/sqrt(dt);
         for i=1:length(noise)
             v = v + (- v + noise(i) + input)*dt/tau;

projects/project_fano_time/solution/lifspikes.m

@@ -19,7 +19,7 @@ function spikes = lifspikes(trials, input, tmax)
     for k=1:trials
         times = [];
         j = 1;
-        v = vreset + (vthresh-vreset)*rand(1);
+        v = vreset + (vthresh-vreset)*rand();
         noise = sqrt(2.0*D)*randn(n, 1)/sqrt(dt);
         for i=1:length(noise)
             v = v + (- v + noise(i) + input)*dt/tau;

projects/project_isicorrelations/lifadaptspikes.m

@@ -32,7 +32,7 @@ function spikes = lifadaptspikes( trials, input, tmaxdt, D, tauadapt, adaptincr
     for k=1:trials
         times = [];
         j = 1;
-        v = vreset;
+        v = vreset + (vthresh-vreset)*rand();
         a = 0.0;
         noise = sqrt(2.0*D)*randn( length( input ), 1 )/sqrt(dt);
         for i=1:length( noise )

projects/project_isipdffit/lifouspikes.m

@@ -28,7 +28,7 @@ function spikes = lifouspikes( trials, input, tmaxdt, D, outau )
         times = [];
         j = 1;
         n = 0.0;
-        v = vreset;
+        v = vreset + (vthresh-vreset)*rand();
         noise = sqrt(2.0*D)*randn( length( input ), 1 )/sqrt(dt);
         for i=1:length( noise )
             n = n + ( - n + noise(i))*dt/outau;

projects/project_isipdffit/pifouspikes.m

@@ -28,7 +28,7 @@ function spikes = pifouspikes( trials, input, tmaxdt, D, outau )
         times = [];
         j = 1;
         n = 0.0;
-        v = vreset;
+        v = vreset + (vthresh-vreset)*rand();
         noise = sqrt(2.0*D)*randn( length( input ), 1 )/sqrt(dt);
         for i=1:length( noise )
             n = n + ( - n + noise(i))*dt/outau;

projects/project_noiseficurves/lifspikes.m

@@ -18,7 +18,7 @@ function spikes = lifspikes(trials, input, tmax, D)
     for k=1:trials
         times = [];
         j = 1;
-        v = vreset;
+        v = vreset + (vthresh-vreset)*rand();
         noise = sqrt(2.0*D)*randn(n, 1)/sqrt(dt);
         for i=1:n
             v = v + (- v + noise(i) + input)*dt/tau;

projects/project_noiseficurves/solution/lifspikes.m

@@ -18,7 +18,7 @@ function spikes = lifspikes(trials, input, tmax, D)
     for k=1:trials
         times = [];
         j = 1;
-        v = vreset;
+        v = vreset + (vthresh-vreset)*rand();
         noise = sqrt(2.0*D)*randn(n, 1)/sqrt(dt);
         for i=1:n
             v = v + (- v + noise(i) + input)*dt/tau;