Jan und Fabian spellchecker

projects/project_fano_slope/fano_slope.tex

@@ -1,4 +1,4 @@
-\documentclass[addpoints,10pt]{exam}
+\documentclass[addpoints,11pt]{exam}
 \usepackage{url}
 \usepackage{color}
 \usepackage{hyperref}
@@ -9,7 +9,7 @@
 \firstpageheader{Scientific Computing}{Project Assignment}{11/05/2014
   -- 11/06/2014}
 %\runningheader{Homework 01}{Page \thepage\ of \numpages}{23. October 2014}
-\firstpagefooter{}{}{}
+\firstpagefooter{}{}{{\bf Supervisor:} Jan Benda}
 \runningfooter{}{}{}
 \pointsinmargin
 \bracketedpoints
@@ -31,7 +31,7 @@
 % captionpos=t,
  xleftmargin=2em,
  xrightmargin=1em,
-% aboveskip=10pt,
+% aboveskip=11pt,
  %title=\lstname,
 % title={\protect\filename@parse{\lstname}\protect\filename@base.\protect\filename@ext}
  }
@@ -63,19 +63,18 @@
   fano factor (the ratio between the variance and the mean of the
   spike counts)?
 
-  \begin{parts}
-    \part The neuron is implemented in the file \texttt{lifboltzmanspikes.m}.
+The neuron is implemented in the file \texttt{lifboltzmanspikes.m}.
     Call it with the following parameters:
-    \begin{lstlisting}
-      trials = 10;
-      tmax = 50.0;
-      Dnoise = 1.0;
-      imax = 25.0;
-      ithresh = 10.0;
-      slope=0.2;
-      input = 10.0;
+\begin{lstlisting}
+trials = 10;
+tmax = 50.0;
+Dnoise = 1.0;
+imax = 25.0;
+ithresh = 10.0;
+slope=0.2;
+input = 10.0;
 
-      spikes = lifboltzmanspikes( trials, input, tmax, Dnoise, imax, ithresh, slope );
+spikes = lifboltzmanspikes( trials, input, tmax, Dnoise, imax, ithresh, slope );
     \end{lstlisting}
     The returned \texttt{spikes} is a cell array with \texttt{trials} elements, each being a vector
     of spike times (in seconds) computed for a duration of \texttt{tmax} seconds.
@@ -83,6 +82,9 @@
 
     For the two inputs use $I_1=10$ and $I_2=I_1 + 1$.
 
+
+  \begin{parts}
+    \part 
     First, show two raster plots for the responses to the two differrent stimuli.
 
     \part Measure the tuning curve of the neuron with respect to the input. That is,
@@ -99,9 +101,9 @@
     the two stimuli can be distinguished based on the spike
     counts. Plot the dependence of this measure as a function of the observation time $W$.
 
-    For which slopes can the two stimuli perfectly discriminated?
+    For which slopes can the two stimuli be well discriminated?
 
-    Hint: A possible readout is to set a threshold $n_{thresh}$ for
+    \underline{Hint:} A possible readout is to set a threshold $n_{thresh}$ for
     the observed spike count.  Any response smaller than the threshold
     assumes that the stimulus was $I_1$, any response larger than the
     threshold assumes that the stimulus was $I_2$. What is the