Updated projects

projects/project_noiseficurves/noiseficurves.tex

@@ -56,7 +56,7 @@
   as a current $I$ injected via a patch-electrode into the neuron).
 
   Measure the tuning curve (also called the intensity-response curve) of the
-  neuron.  That is, what is the firing rate of the neuron's response
+  neuron.  That is, what is the mean firing rate of the neuron's response
   as a function of the input $I$. How does this depend on the level of
   the intrinsic noise of the neuron?
 
@@ -74,9 +74,22 @@ spikes = lifspikes( trials, input, tmax, Dnoise );
     of spike times (in seconds) computed for a duration of \texttt{tmax} seconds.
     The input is set via the \texttt{input} variable, the noise strength via \texttt{Dnoise}.
 
+    Think of calling the \texttt{lifspikes()} function as a
+    simple way of doing an electrophysiological experiment. You are
+    presenting a stimulus with a constant intensity $I$ that you set. The
+    neuron responds to this stimulus, and you record this
+    response. After detecting the timepoints of the spikes in your
+    recordings you get what the \texttt{lifspikes()} function
+    returns. The advantage over real data is, that you have the
+    possibility to simply modify the properties of the neuron via the
+    \texttt{Dnoise} parameter.
+
   \begin{parts}
-    \part First set the noise \texttt{Dnoise=0} (no noise). Compute and plot the firing rate
-    as a function of the input for inputs ranging from 0 to 20.
+    \part First set the noise \texttt{Dnoise=0} (no noise). Compute
+    and plot the mean firing rate (number of spikes within the
+    recording time \texttt{tmax} divided by \texttt{tmax} and averaged
+    over trials) as a function of the input for inputs ranging from 0
+    to 20.
 
     \part Do the same for various noise strength \texttt{Dnoise}. Use $D_{noise} = 1e-3$,
     1e-2, and 1e-1. How does the intrinsic noise influence the response curve?