Jan und Fabian spellchecker

projects/project_isicorrelations/isicorrelations.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}
  }
@@ -59,25 +59,25 @@
   Explore the dependence of interspike interval correlations on the firing rate,
   adaptation time constant and noise level.
 
-  \begin{parts}
-    \part The neuron is a neuron with an adaptation current. 
+The neuron is a neuron with an adaptation current. 
     It is implemented in the file \texttt{lifadaptspikes.m}.  Call it
     with the following parameters:
     \begin{lstlisting}
-      trials = 10;
-      tmax = 50.0;
-      input = 10.0;  % the input I
-      Dnoise = 1e-2;  % noise strength
-      adapttau = 0.1;  % adaptation time constant in seconds
-      adaptincr = 0.5;  % adaptation strength
+trials = 10;
+tmax = 50.0;
+input = 10.0;  % the input I
+Dnoise = 1e-2;  % noise strength
+adapttau = 0.1;  % adaptation time constant in seconds
+adaptincr = 0.5;  % adaptation strength
 
-      spikes = lifadaptspikes( trials, input, tmax, Dnoise, adapttau, adaptincr );
+spikes = lifadaptspikes( trials, input, tmax, Dnoise, adapttau, adaptincr );
     \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.
     The input is set via the \texttt{input} variable, the noise strength via \texttt{Dnoise},
     and the adaptation time constant via \texttt{adapttau}.
 
+  \begin{parts}
     \part Measure the intensity-response curve of the neuron, that is the mean firing rate 
     as a function of the input for a range of inputs from 0 to 120.