Merge branch 'master' of raven:scientificComputing

projects/disclaimer.tex

@@ -8,8 +8,8 @@
       \vspace{1ex}
 
       The {\bf code} and the {\bf presentation} should be uploaded to
-      ILIAS at latest on Thursday, November 6th, 12:00h.
+      ILIAS at latest on Thursday, November 6th, 10:00h.
-      The presentations start on Thursday 13:00h. Please hand in
+      The presentations start on Thursday 11:00h. Please hand in
       your presentation as a pdf file. Bundle everything into a
       {\em single} zip-file.
 
@@ -26,7 +26,7 @@
       variable names).
 
       \vspace{1ex} \textbf{Please write your name and matriculation
-      number as a comment at the top of a script called \texttt{main.m}!}
+      number as a comment at the top of a script called \texttt{main.m}.}
       The \texttt{main.m} script then should call all your scripts.
 
       \vspace{1ex}

projects/project_adaptation_fit/adaptation_fit.tex

@@ -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 Grewe}
 \runningfooter{}{}{}
 \pointsinmargin
 \bracketedpoints
@@ -30,8 +30,8 @@
 \end{center}
 
 %%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
-\section*{Estimating the time-constant of adaptation.}
+\section*{Estimating the adaptation time-constant.}
-Stimulating a neuron with a constant stimulus for an extended time
+Stimulating a neuron with a constant stimulus for an extended period of time
 often leads to a strong initial response that relaxes over time. This
 process is called adaptation and is ubiquitous. Your task here is to
 estimate the time-constant of the firing-rate adaptation in P-unit
@@ -41,8 +41,8 @@ electroreceptors of the weakly electric fish \textit{Apteronotus
 \begin{questions}
   \question In the accompanying datasets you find the
   \textit{spike\_times} of an P-unit electrorecptor to a stimulus of a
-  certain intensity, i.e. the \textit{contrast}. The contrast is also
+  certain intensity, i.e. the \textit{contrast} which is also stored
-  part of the file name itself.
+  in the file.
   \begin{parts}
     \part Estimate for each stimulus intensity the  
     PSTH and plot it. You will see that there are three parts.  (i)

projects/project_eod/eod.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:} Fabian Sinz}
 \runningfooter{}{}{}
 \pointsinmargin
 \bracketedpoints
@@ -46,10 +46,12 @@
     $\sin(2\pi j\omega_0\cdot t + \varphi_j )$ are called {\em
       harmonic components}. The variables $\varphi_j$ are called {\em
       phases}. For the beginning choose $n=3$. 
-    \part Play around with $n$ and see how the fit changes. Plot the
+    \part Try different choices of $n$ and see how the fit
-    fits and the original curve for different choices of $n$. If you
+    changes. Plot the fits and the original curve for different
-    want you can also play the different fits and the original as
+    choices of $n$. Also plot the fitting error as a function of
-    sound.
+    $n$. 
+    \part (optional) If you want you can also play the different fits
+    and the original as sound.
     
   \end{parts}
 \end{questions}

projects/project_eyetracker/eyetracker.tex

@@ -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 Grewe}
 \runningfooter{}{}{}
 \pointsinmargin
 \bracketedpoints
@@ -31,10 +31,10 @@
 
 %%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
 \section*{Analysis of eye trajectories.}
-In this project you will analyse eye-tracking data provided by the
+In this project you will analyse eye-tracking data (courtesy of the
-Mallot-Group. In this task the subject had to memorize the positions
+Mallot department). In this task the subject had to memorize the
-of targets that can be only learned with active gaze shifts. The eye
+positions of targets that can be only learned with active gaze
-movements during training and test are recorded.
+shifts. The eye movements during training and test are recorded.
 
 \begin{questions}
   \question In the accompanying dataset you find six variables. (i)
@@ -48,15 +48,15 @@ movements during training and test are recorded.
   the same marker belong to the same trial.
   \begin{parts}
     \part Cut the data in chunks belonging to the same trial.
-    \part Characterize the eye movements statistically; eye
+    \part Characterize the eye movements statistically, e.g. with eye
-    velocity, accelerations.
+    speed and/or accelerations.
     \part Detect and correct the eye traces for instances in which the 
     eye was not correctly detected. Interpolate linearily in these sections. 
     \part Create a 'heatmap' plot that shows the eye trajectories 
     for one or two (nice) trials.
     \part Use the \verb+kmeans+ clustering function to 
-    discriminate different types of eye-movements. Try clustering
+    identify fixation points. Manually select a good number of cluster
-    using eye velocitiy and acceleration.
+    centroids. 
   \end{parts}
 \end{questions}
 

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}
+The neuron is implemented in the file \texttt{lifboltzmanspikes.m}.
-    \part The neuron is implemented in the file \texttt{lifboltzmanspikes.m}.
     Call it with the following parameters:
-    \begin{lstlisting}
+\begin{lstlisting}
-      trials = 10;
+trials = 10;
-      tmax = 50.0;
+tmax = 50.0;
-      Dnoise = 1.0;
+Dnoise = 1.0;
-      imax = 25.0;
+imax = 25.0;
-      ithresh = 10.0;
+ithresh = 10.0;
-      slope=0.2;
+slope=0.2;
-      input = 10.0;
+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,
@@ -99,15 +101,14 @@
     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
-    the observed spike count.  Any response smaller than the threshold
+    $n_{thresh}$ for the observed spike count.  Any response smaller
-    assumes that the stimulus was $I_1$, any response larger than the
+    than the threshold assumes that the stimulus was $I_1$, any
-    threshold assumes that the stimulus was $I_2$. What is the
+    response larger than the threshold assumes that the stimulus was
-    probability that the stimulus was indeed $I_1$ or $I_2$,
+    $I_2$. Find the threshold $n_{thresh}$ that results in the best
-    respectively? Find the threshold $n_{thresh}$ that
+    discrimination performance.
-    results in the best discrimination performance.
 
     \part Also plot the Fano factor as a function of the slope. How is it related to the discriminability?
 

projects/project_fano_test/fano.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:} Fabian Sinz}
 \runningfooter{}{}{}
 \pointsinmargin
 \bracketedpoints
@@ -39,25 +39,16 @@
   process---for which each spike is independent of every other---has a
   Fano factor of one.
   
-  The table contains spike counts from a neuron measured in twelve
+  The accompanying file contains two vectors with spike counts from
-  trials.
+  two neurons each measured in a time window of 1s.
-
-  \begin{center}
-    \begin{tabular}{cccc}
-      \multicolumn{4}{c}{\bf number of spikes} \\ \hline\\
-      36 & 28 & 38 & 35\\
-      32 & 30 & 35 & 29\\
-      29 & 24 & 26 & 34
-    \end{tabular}
-  \end{center}
 
   \begin{parts}
+    \part Plot the spike counts of both neurons appropriately.
     \part Use {\em Eden, U. T., \& Kramer, M. (2010). Drawing
       inferences from Fano factor calculations. Journal of
       neuroscience methods, 190(1), 149--152} to construct a test that
     uses the Fano factor as test statistic and tests against the Null
     hypothesis that the spike counts come from a Poisson process.
-    \part Plot the spike counts appropriately. 
     \part Implement the test and use it on the data above.
   \end{parts}
 

projects/project_fano_time/fano_time.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}
  }
@@ -62,32 +62,33 @@
   duration $W$ of the observation time? How is this related to the fano factor
   (the ratio between the variance and the mean of the spike counts)?
 
-  \begin{parts}
+The neuron is implemented in the file \texttt{lifadaptspikes.m}.
-    \part The neuron is implemented in the file \texttt{lifadaptspikes.m}.
     Call it with the following parameters:
     \begin{lstlisting}
-      trials = 10;
+trials = 10;
-      tmax = 50.0;
+tmax = 50.0;
-      input = 65.0; 
+input = 65.0; 
-      Dnoise = 0.1;
+Dnoise = 0.1;
-      adapttau = 0.2;
+adapttau = 0.2;
-      adaptincr = 0.5;
+adaptincr = 0.5;
-
+
-      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.
 
     For the two inputs $I_1$ and $I_2$ use
     \begin{lstlisting}
-      input = 65.0; % I_1
+input = 65.0; % I_1
-      input = 75.0; % I_2
+input = 75.0; % I_2
     \end{lstlisting}
 
-    Show two raster plots for the responses to the two differrent stimuli.
+  \begin{parts}
+    \part 
+    Show two raster plots for the responses to the two different stimuli.
 
     \part Generate histograms of the spike counts within $W$ of the
-    responses to the two differrent stimuli. How do they depend on the observation time $W$
+    responses to the two different stimuli. How do they depend on the observation time $W$
     (use values between 1\,ms and 1\,s)?
 
     \part Think about a measure based on the spike count histograms that quantifies how well
@@ -96,12 +97,11 @@
 
     For which observation times can the two stimuli perfectly discriminated?
 
-    Hint: A possible readout is to set a threshold $n_{thresh}$ for
+    \underline{Hint:} A possible readout is to set a threshold
-    the observed spike count.  Any response smaller than the threshold
+    $n_{thresh}$ for the observed spike count.  Any response smaller
-    assumes that the stimulus was $I_1$, any response larger than the
+    than the threshold assumes that the stimulus was $I_1$, any
-    threshold assumes that the stimulus was $I_2$. What is the
+    response larger than the threshold assumes that the stimulus was
-    probability that the stimulus was indeed $I_1$ or $I_2$,
+    $I_2$. For a given $W$ find the threshold $n_{thresh}$ that
-    respectively? For a given $W$ find the threshold $n_{thresh}$ that
     results in the best discrimination performance.
 
     \part Also plot the Fano factor as a function of $W$. How is it related to the discriminability?

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}
+The neuron is a neuron with an adaptation current. 
-    \part 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;
+trials = 10;
-      tmax = 50.0;
+tmax = 50.0;
-      input = 10.0;  % the input I
+input = 10.0;  % the input I
-      Dnoise = 1e-2;  % noise strength
+Dnoise = 1e-2;  % noise strength
-      adapttau = 0.1;  % adaptation time constant in seconds
+adapttau = 0.1;  % adaptation time constant in seconds
-      adaptincr = 0.5;  % adaptation strength
+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.
 

projects/project_isipdffit/isipdffit.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}
  }
@@ -72,7 +72,8 @@
     p_\mathrm{ig}(T) = \frac{1}{\sqrt{4 \pi D T^{3}}} \exp \left[ - \frac{(T - \mu)^{2} }{4 D T \mu^{2}} \right]
   \end{equation}
   where $\mu$ is the mean interspike interval and
-  $D=\textrm{var}(T)/(2\mu^3)$ is the so called diffusion coefficient.
+  % $D=\textrm{var}(T)/(2\mu^3)$ 
+  $D$ is the so called diffusion coefficient.
 
   The third one was derived for neurons driven with colored noise:
   \begin{equation}\label{pcn}
@@ -91,35 +92,34 @@
   \end{equation}
   with $\delta=\mu/\tau$. 
 
-  \begin{parts}
+ The two neurons are implemented in the files \texttt{pifouspikes.m}
-    \part The two neurons are implemented in the files \texttt{pifouspikes.m}
     and \texttt{lifouspikes.m}.
     Call them with the following parameters:
     \begin{lstlisting}
-      trials = 10;
+trials = 10;
-      tmax = 50.0;
+tmax = 50.0;
-      input = 10.0;  % the input I
+input = 10.0;  % the input I
-      Dnoise = 1.0;  % noise strength
+Dnoise = 1.0;  % noise strength
-      outau = 1.0;   % correlation time of the noise in seconds
+outau = 1.0;   % correlation time of the noise in seconds
 
-      spikes = pifouspikes( trials, input, tmax, Dnoise, outau );
+spikes = pifouspikes( trials, input, tmax, Dnoise, outau );
     \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.
-
+  \begin{parts}
-    \part Find for both model neurons the inputs $I_i$ required to make the fire with a mean rate
+    \part For both model neurons find the inputs $I_i$ required to
-    of 10, 20, 50, and 100\,Hz. 
+    make them fire with a mean rate of 10, 20, 50, and 100\,Hz.
 
     \part Compute interspike interval distributions of the two model neurons for these inputs $I_i$.
 
     \part Compare the interspike interval distributions with the exponential 
     distribution eq.~(\ref{exppdf}) and the inverse Gaussian
     eq.~(\ref{invgauss}) by measuring their parameters from the
-    interspike intervals. How well do theu describe the real
+    interspike intervals. How well do they describe the real
     distributions for the different conditions?
 
-    \part Also fit eq.~(\ref{pcn}) to the data. Here you need to apply a non-linear fit algorithm.
+    \part Also fit eq.~(\ref{pcn}) to the data using maximum (log)-likelihood. 
 
     How well does this function describe the data?
 

projects/project_mutualinfo/mutualinfo.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:} Fabian Sinz}
 \runningfooter{}{}{}
 \pointsinmargin
 \bracketedpoints
@@ -51,8 +51,8 @@
     \log_2\frac{P(x,y)}{P(x)P(y)}$$ that the answers provide about the
     actually presented object. 
     \part What is the maximally achievable mutual information (try to
-    find out by generating your own dataset; the situation in which
+    find out by generating your own dataset which naturally should
-    the information is maximal is pretty straightforward)?
+    yield maximal information)?
     \part Use bootstrapping to compute the $95\%$ confidence interval
     for the mutual information estimate in that dataset. 
   \end{parts}

projects/project_noiseficurves/noiseficurves.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}
  }
@@ -60,21 +60,21 @@
   as a function of the input $I$. How does this depend on the level of
   the intrinsic noise of the neuron?
 
-  \begin{parts}
+The neuron is implemented in the file \texttt{lifspikes.m}.
-    \part The neuron is implemented in the file \texttt{lifspikes.m}.
     Call it with the following parameters:
     \begin{lstlisting}
-      trials = 10;
+trials = 10;
-      tmax = 50.0;
+tmax = 50.0;
-      input = 10.0;  % the input I
+input = 10.0;  % the input I
-      Dnoise = 1.0;  % noise strength
+Dnoise = 1.0;  % noise strength
 
-      spikes = lifspikes( trials, input, tmax, Dnoise );
+spikes = lifspikes( trials, input, tmax, Dnoise );
     \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}.
 
+  \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.
 

projects/project_numbers/numbers.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:} Fabian Sinz}
 \runningfooter{}{}{}
 \pointsinmargin
 \bracketedpoints
@@ -33,14 +33,14 @@
 
 \begin{questions}
   \question The accompanying data {\tt Neuron22.mat} stores a single
-  data matrix {\tt data\_unsorted} containing spike from a neuron in
+  data matrix {\tt data\_unsorted} containing spikes from a neuron in
-  macaque prefrontal cortex. The task of the monkey was to
+  macaque prefrontal cortex (data courtesy of Prof. Nieder). The task
-  discriminate point sets with 1 to 4 points. The first column
+  of the monkey was to discriminate point-sets with 1 to 4 points. The
-  contains the number of points shown plus one. The remaining columns
+  first column contains the number of points shown plus one. The
-  contain the spike response across 1300ms. During the first 500ms the
+  remaining columns contain the spike response across 1300ms. During
-  monkey was fixating a target. The next 800ms the stimulus was
+  the first 500ms the monkey was fixating a target. The next 800ms the
-  shown. This was followed by 1000ms delay time before the monkey was
+  stimulus was shown. This was followed by 1000ms delay time before
-  allowed to respond. 
+  the monkey was allowed to respond.
   
   \begin{parts}
     \part Plot the data in an appropriate way. 

projects/project_onset_fi/onset_fi.tex

@@ -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 Grewe}
 \runningfooter{}{}{}
 \pointsinmargin
 \bracketedpoints
@@ -39,25 +39,25 @@ of the stimulus \textbf{I}ntensity.
   \question In the accompanying datasets you find the
   \textit{spike\_times} of an P-unit electrorecptor of the weakly
   electric fish \textit{Apteronotus leptorhynchus} to a stimulus of a
-  certain intensity, i.e. the \textit{contrast}. The contrast is also
+  certain intensity, i.e. the \textit{contrast}. 
-  part of the file name itself.
   \begin{parts}
-    \part Estimate for each stimulus intensity the average response 
+    \part For each stimulus intensity estimate the average response
     (PSTH) and plot it. You will see that there are three parts.  (i)
-    The first 200 ms is the baseline (no stimulus) activity. (ii) During
+    The first 200 ms is the baseline (no stimulus) activity. (ii)
-    the next 1000 ms the stimulus was switched on. (iii) After stimulus
+    During the next 1000 ms the stimulus was switched on. (iii) After
-    offset the neuronal activity was recorded for further 825 ms.
+    stimulus offset the neuronal activity was recorded for further 825
+    ms.
     \part Extract the neuron's activity in the first 50 ms after stimulus onset
     and plot it against the stimulus intensity, respectively the
     contrast, in an appropriate way.
     \part Fit a Boltzmann function to the FI-curve. The Boltzmann function
     is defined as:
     \begin{equation}
-       y=\frac{\alpha-\beta}{1+e^{(x-x_0)/dx}}+\beta,
+       y=\frac{\alpha-\beta}{1+e^{(x-x_0)/\Delta x}}+\beta,
     \end{equation}
     where $\alpha$ is the starting firing rate, $\beta$ the saturation
     firing rate, $x$ the current stimulus intensity, $x_0$ starting
-    stimulus intensity, and $dx$ a measure of the slope.
+    stimulus intensity, and $\Delta x$ a measure of the slope.
     \part Plot the fit into the data.
   \end{parts}
 \end{questions}

projects/project_pca_natural_img/Makefile

@@ -0,0 +1,11 @@
+latex:
+	pdflatex *.tex > /dev/null
+	pdflatex *.tex > /dev/null
+	pdflatex *.tex > /dev/null
+
+clean:
+	rm -rf *.log *.aux *.zip *.out auto *.bbl *.blg
+	rm -f `basename *.tex .tex`.pdf
+
+zip: latex
+	zip `basename *.tex .tex`.zip *.pdf *.jpg

projects/project_pca_natural_img/pca_natural_images.tex

@@ -0,0 +1,61 @@
+\documentclass[addpoints,11pt]{exam}
+\usepackage{url}
+\usepackage{color}
+\usepackage{hyperref}
+
+\pagestyle{headandfoot}
+\runningheadrule
+\firstpageheadrule
+\firstpageheader{Scientific Computing}{Project Assignment}{11/05/2014
+  -- 11/06/2014}
+%\runningheader{Homework 01}{Page \thepage\ of \numpages}{23. October 2014}
+\firstpagefooter{}{}{{\bf  Supervisor:} Fabian Sinz}
+\runningfooter{}{}{}
+\pointsinmargin
+\bracketedpoints
+
+%\printanswers
+%\shadedsolutions
+
+
+\begin{document}
+%%%%%%%%%%%%%%%%%%%%% Submission instructions %%%%%%%%%%%%%%%%%%%%%%%%%
+\sffamily
+% \begin{flushright}
+% \gradetable[h][questions]
+% \end{flushright}
+
+\begin{center}
+  \input{../disclaimer.tex}
+\end{center}
+
+%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
+
+In you zip file you find a natural image called {\tt natimg.jpg}. 
+\begin{questions}
+
+  \question Load the image and extract all pixels as three dimensional
+  vectors (red, green, and blue channel). 
+  
+  \question Perform a principal component analysis on these
+  three-dimensional vectors.
+
+  \question Try to find an interpretation of the principal components
+  you find in terms of colors. Find a good way to visualize this. 
+
+  \question What could be the biological significance of that (\cite{BG} can
+  give you a clue)?
+
+\end{questions}
+
+\begin{thebibliography}{1}
+\bibitem{BG} Buchsbaum, G., \& Gottschalk, A. (1983). Trichromacy,
+  opponent colours coding and optimum colour information transmission
+  in the retina. Proceedings of the Royal Society of London. Series B,
+  Containing Papers of a Biological Character. Royal Society (Great
+  Britain), 220(1218), 89–113.
+\end{thebibliography}
+
+
+
+\end{document}

projects/project_q-values/qvalues.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:} Fabian Sinz}
 \runningfooter{}{}{}
 \pointsinmargin
 \bracketedpoints

projects/project_spectra/spectra.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:} Fabian Sinz}
 \runningfooter{}{}{}
 \pointsinmargin
 \bracketedpoints
@@ -48,7 +48,7 @@
     appropriate size and compute the average Fourier amplitude
     spectrum of the spike response. Plot the result in an appropriate
     way. 
-    \part Determine whether you can find peas in the amplitude
+    \part Determine whether you can find peak in the amplitude
     spectrum at the fundamental frequency of the EOD and/or the
     stimulus and/or their difference. 
   \end{parts}

projects/project_steady_fi/steady_state_fi.tex

@@ -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 Grewe}
 \runningfooter{}{}{}
 \pointsinmargin
 \bracketedpoints
@@ -47,17 +47,17 @@ of the stimulus \textbf{I}ntensity.
     The first 200 ms is the baseline (no stimulus) activity. (ii) During
     the next 1000 ms the stimulus was switched on. (iii) After stimulus
     offset the neuronal activity was recorded for further 825 ms.
-    \part Extract the neuron's activity in the first 50 ms after stimulus onset
+    \part Extract the neuron's activity in the last 200 ms before
-    and plot it against the stimulus intensity, respectively the
+    stimulus offset and plot it against the stimulus intensity or the
-    contrast, in an appropriate way.
+    contrast, respectively.
     \part Fit a Boltzmann function to the FI-curve. The Boltzmann function
     is defined as:
     \begin{equation}
-       y=\frac{\alpha-\beta}{1+e^{(x-x_0)/dx}}+\beta,
+       y=\frac{\alpha-\beta}{1+e^{(x-x_0)/\Delta x}}+\beta,
     \end{equation}
     where $\alpha$ is the starting firing rate, $\beta$ the saturation
     firing rate, $x$ the current stimulus intensity, $x_0$ starting
-    stimulus intensity, and $dx$ a measure of the slope.
+    stimulus intensity, and $\Delta x$ a measure of the slope.
   \end{parts}
 \end{questions}
 

projects/project_stimulus_reconstruction/stimulus_reconstruction.tex

@@ -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 Grewe}
 \runningfooter{}{}{}
 \pointsinmargin
 \bracketedpoints
@@ -49,12 +49,13 @@ reconstruct the stimulus a neuron has been stimulated with.
     \end{equation}
     with $N$ the number of data points, $x_i$ the current value and
     $\bar{x}$, the average of all $x$.
-    \part Analyze the robustness of the reconstruction. Estimate 
+    \part Analyze the robustness of the reconstruction: Estimate 
     the STA with less and less data and estimate the reconstruction
     error.
     \part Plot the reconstruction error as a function of the data 
     amount used to estimate the STA.
-    \part Do the same for the pyramidal neuron, what do you observe?
+    \part Repeat the above steps for the pyramidal neuron, what do you
+    observe?
   \end{parts}
 \end{questions}
 

projects/project_template/template.tex

@@ -1,4 +1,4 @@
-\documentclass[addpoints,10pt]{exam}
+\documentclass[addpoints,11pt]{exam}
 \usepackage{url}
 \usepackage{color}
 \usepackage{hyperref}

projects/project_vector_strength/vector_strength.tex

@@ -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 Grewe}
 \runningfooter{}{}{}
 \pointsinmargin
 \bracketedpoints
@@ -35,8 +35,8 @@ P-unit electrorecptors are driven by the fish's self-generated field,
 the EOD. In this project you have to quantify the strength of this
 coulpling using the \textbf{vector strength}:
 \begin{equation}
- VS = \sqrt{\left(\frac{1}{n}\sum_{i=1}^{n}cos
+ VS = \sqrt{\left(\frac{1}{n}\sum_{i=1}^{n}\cos
- \alpha_i\right)^2 + \left(\frac{1}{n}\sum_{i = 1}^{n} sin \alpha_i
+ \alpha_i\right)^2 + \left(\frac{1}{n}\sum_{i = 1}^{n} \sin \alpha_i
  \right)^2},
 \end{equation}
 with $n$ the number of spikes and $\alpha_i$ the timing of the each