Fabian projects done

projects/project_numbers/Makefile

@@ -0,0 +1,10 @@
+latex:
+	pdflatex *.tex > /dev/null
+	pdflatex *.tex > /dev/null
+
+clean:
+	rm -rf *.log *.aux *.zip *.out auto
+	rm -f `basename *.tex .tex`.pdf
+
+zip: latex
+	zip `basename *.tex .tex`.zip *.pdf *.dat *.mat

projects/project_numbers/numbers.tex

@@ -0,0 +1,60 @@
+\documentclass[addpoints,10pt]{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{}{}{}
+\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 %%%%%%%%%%%%%%%%%%%%%%%%%
+
+\begin{questions}
+  \question The accompanying data {\tt Neuron22.mat} stores a single
+  data matrix {\tt data\_unsorted} containing spike from a neuron in
+  macaque prefrontal cortex. The task of the monkey was to
+  discriminate point sets with 1 to 4 points. The first column
+  contains the number of points shown plus one. The remaining columns
+  contain the spike response across 1300ms. During the first 500ms the
+  monkey was fixating a target. The next 800ms the stimulus was
+  shown. This was followed by 1000ms delay time before the monkey was
+  allowed to respond. 
+  
+  \begin{parts}
+    \part Plot the data in an appropriate way. 
+    \part Sort the trials according to the stimulus presented and
+    compute the firing rate (in Hz) in the time interval
+    500-1300ms. Plot the firing rate in an appropriate way. 
+    \part Use an appropriate test to determine whether the firing rate
+    in that interval is significantly different for 1 vs. 4 points
+    shown.
+  \end{parts}
+\end{questions}
+
+
+
+
+
+\end{document}

projects/project_spectra/spectra.tex

@@ -32,7 +32,26 @@
 %%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
 
 \begin{questions}
-  \question What was the questions for 42?
+  \question The accompanying file contains ten stimulus and response
+  sequences of a P-Unit of a weakly electric fish {\em Apteronotus
+    leptorhynchus}. Another matrix contains the corresponding {\em
+    electric organ discharge (EOD)} of the fish. The sampling rate is
+  100kHz.
+  \begin{parts}
+    \part Split the data in non-overlapping 200ms windows and plot
+    them in an appropriate way. 
+    \part Compute the autocorrelation of the spike response as well as
+    the cross-correlation between stimulus and spike response. 
+    \part Determine the fundamental stimulus frequency and the EOD
+    frequency using a Fourier transform. 
+    \part Convolve the spike responses (windows) with a Gaussian of
+    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
+    spectrum at the fundamental frequency of the EOD and/or the
+    stimulus and/or their difference. 
+  \end{parts}
 \end{questions}
 
 

statistics/lecture_statistics02.tex

@@ -369,12 +369,12 @@ incubation. The following plot depicts the mean thymus gland weights in (mg):
       the the first $80$ datapoints, and repeat the following steps
       $m=500$ times:
       \begin{enumerate}
-      \item draw $50$ data points from $x$ with replacement
+      \item draw $80$ data points from $x$ with replacement
       \item compute their mean and store it
       \end{enumerate}
       Look at the standard deviation of the computed means.
     \item Compare the result to the standard deviation of the original
-      $50$ data points and the standard error.
+      $80$ data points and the standard error.
     \end{itemize}
   \end{task}
 \end{frame}

statistics/matlab/invg_loglikelihood.m

@@ -1,4 +1,5 @@
 function ll = invg_loglikelihood(x, p)
     mu = p(1);
     lambda = p(2);
-    ll = mean(.5*(log(lambda) - log(2*pi) - 3*log(x)) - lambda*(x-mu).^2./(2*mu^2*x));
+    ll = .5*(log(lambda) - log(2*pi) - 3*log(x)) - ...
+        lambda*(x-mu).^2./(2*mu^2*x);

statistics/matlab/lserr.m

@@ -1,4 +1,4 @@
-function [err, grad] = lserr(param, x, y)
+function [err, grad] = lserr(x, y, param)
     err = mean( (param(1)*x + param(2) - y).^2 );
     
     if nargout == 2