Merge branch 'master' of raven.am28.uni-tuebingen.de:scientificComputing

Well, I just fiddeled around in fano.tex and forgot to make a git pull
before....
This commit is contained in:
Jan Benda 2014-11-01 16:05:29 +01:00
commit fac4ed2d48
10 changed files with 177 additions and 35 deletions

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@ -99,14 +99,6 @@ Cross-Correlation, Spike--Triggered--Average and Reverse Reconstruction}
\end{frame}
\begin{frame}[plain]
\frametitle{Rekapitulation}
\begin{enumerate}
\item PSTH\pause
\end{enumerate}
\end{frame}
\begin{frame}
\frametitle{Introduction to scientific computing}
\frametitle{Menue}
@ -119,7 +111,7 @@ Cross-Correlation, Spike--Triggered--Average and Reverse Reconstruction}
\begin{frame}[plain]
\huge{1. Recapitulation: PSTH}
\huge{1. Recapitulation: Plotting neuronal activity as a function of time.}
\end{frame}
@ -142,6 +134,17 @@ Cross-Correlation, Spike--Triggered--Average and Reverse Reconstruction}
\end{frame}
\begin{frame}
\frametitle{Relating stimulus and response}
\framesubtitle{Displaying the neuronal response over time - PSTH}
\begin{itemize}
\item What does this tell us? \pause
\item Wouldn't it be more interesting to relate the response to the stimulus?!
\end{itemize}
\end{frame}
\begin{frame}[plain]
\huge{2. Relating stimulus and response}
\end{frame}
@ -152,7 +155,7 @@ Cross-Correlation, Spike--Triggered--Average and Reverse Reconstruction}
\framesubtitle{How can we relate the response to the stimulus?}
\begin{figure}
\centering
\includegraphics[height=0.9\textheight]{images/conv_stim}
\includegraphics[height=0.75\textheight]{images/conv_stim}
\end{figure}
\end{frame}
@ -201,20 +204,34 @@ ylabel('correlation')
\frametitle{Relating stimulus and response}
\framesubtitle{Cross--correlation - Exercises}
\begin{enumerate}
\item calculate the cross-correlation between two vectors of random
\item Calculate the cross-correlation between two vectors of random
numbers.
\item Calculate the cross-correlation between one of these vectors
and itself (auto-correlation).
\item Calculate the cross-correlation between one vector and a
time-shifted version of itself (use \verb+circshift+ to do this).
\item Generate two vectors of random numbers, one having a (slight)
correlation with the other.
\item Calculate the correlation coefficient (\verb+corrcoef+).
\item Calculate the cross-correlation.
\item Calculate the the correlation coefficient between the one
vector and a \verb+circshif+ted version of the other.
\item Calculate the cross-correlation of these.
\item Find out the maximum correlation and its position.
\end{enumerate}
\textbf{Note:} Select max\_lag to be less than 10\% of the length of
your vectors!
\end{frame}
\begin{frame}[fagile]
\frametitle{Relating stimulus and response}
\framesubtitle{Cross--correlation - Exercises}
\begin{enumerate}
\item Create the cross correlation of the p-unit data and stimulus.
\item \textbf{Note:} you have to convert the spike\_times to a PSTH!
\item Find out the position of the correlation peak.
\item What does this tell you?
\item What does it tell you?
\end{enumerate}
\end{frame}
@ -273,8 +290,8 @@ ylabel('correlation')
\begin{enumerate}
\item Write a function \verb+sta(x, y, count, sample_rate)+ that
takes the stimulus (x), the response (y, as spike times), the
number (count) of sampling points it should cut out from the
stimulus and the sampling\_rate to convert from times to
number (count) of sample points it should cut out from the
stimulus and the sample\_rate to convert from times to
indices.
\item \textbf{Beware:} sometimes the spike\_time may be too close
to the beginning or the end of the stimulus to cut out enough
@ -284,23 +301,6 @@ ylabel('correlation')
\end{frame}
\begin{frame}[fragile]
\frametitle{Relating stimulus and response}
\framesubtitle{Spike--Triggered--Average -- STA}
What does the \textbf{STA} tell us?
\begin{figure}
\centering
\includegraphics[width=0.25\columnwidth]{images/sta}
\end{figure}
\begin{enumerate}
\item Is there a relation between stimulus and response?\pause
\item Is there a lag between them and how large is it?\pause
\item How far in the past does a neuron encode?\pause
\item Can it see into the future?
\end{enumerate}
\end{frame}
\begin{frame}[plain]
\huge{3. Reverse reconstruction using the \textbf{STA}}
\end{frame}

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@ -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

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@ -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}

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@ -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

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@ -0,0 +1,61 @@
\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 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}
\end{document}

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@ -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}

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@ -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);

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@ -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