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2
plotting/lecture/plotting-chapter.tex
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@ -28,6 +28,8 @@
\subsection{Polar plot}
\subsection{print instead of saveas????}
\subsection{Movies and animations}
\section{TODO}

4
projects/header.tex
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@ -7,7 +7,7 @@
%%%%% layout %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
\pagestyle{headandfoot}
\header{{\bfseries\large Scientific Computing}}{{\bfseries\large Project: \ptitle}}{{\bfseries\large Januar 18th, 2018}}
\header{\textbf{\large Scientific Computing Project: \ptitle}}{}{\textbf{\large January 18th, 2018}}
\runningfooter{}{\thepage}{}
\setlength{\baselineskip}{15pt}
@ -15,6 +15,8 @@
\setlength{\parskip}{0.3cm}
\renewcommand{\baselinestretch}{1.15}
\setcounter{secnumdepth}{-1}
%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\usepackage{listings}
\lstset{

36
projects/instructions.tex
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@ -1,31 +1,31 @@
\setlength{\fboxsep}{2ex}
\fbox{\parbox{1\linewidth}{\small
\fbox{\parbox{0.95\linewidth}{\small
{\bf Evaluation criteria:}
\textbf{Evaluation criteria:}
Each project has three elements that are graded: (i) the code,
(ii) the slides/figures, and (iii) the presentation.
(ii) the quality of the figures, and (iii) the presentation (see below).
\vspace{1ex}
{\bf Dates:}
\textbf{Dates:}
The {\bf code} and the {\bf presentation} should be uploaded to
The code and the presentation should be uploaded to
ILIAS at latest on Sunday, February 4th, 23:59h. We will
store all presentations on one computer to allow fast
transitions between talks. The presentations start on Monday,
February 5th at 9:15h.
\vspace{1ex}
{\bf Files:}
\textbf{Files:}
Please hand in your presentation as a pdf file. Bundle
everything (the pdf, the code, and the data) into a {\em single}
zip-file.
\vspace{1ex}
{\bf Code:}
\textbf{Code:}
The {\bf code} should be executable without any further
The code should be executable without any further
adjustments from our side. A single \texttt{main.m} script
should coordinate the analysis by calling functions and
sub-scripts and should produce the {\em same} figures
@ -43,17 +43,15 @@
\vspace{1ex}
{\bf Presentation:}
\textbf{Presentation:}
The {\bf presentation} should be {\em at most} 10min long and be
held in English. In the presentation you should (i) briefly
describe the problem, (ii) present figures introducing, showing,
and discussing your results, and (iii) explain how you solved
the problem algorithmically (don't show your entire code). All
data-related figures you show in the presentation should be
produced by your program --- no editing or labeling by
PowerPoint or other software. It is always a good idea to
illustrate the problem with basic plots of the raw-data. Make
sure the axis labels are large enough!
The presentation should be {\em at most} 10min long and be held
in English. In the presentation you should present figures
introducing, explaining, showing, and discussing your data,
methods, and results. All data-related figures you show in the
presentation should be produced by your program --- no editing
or labeling by PowerPoint or other software. It is always a good
idea to illustrate the problem with basic plots of the
raw-data. Make sure the axis labels are large enough!
}}

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projects/project_adaptation_fit/adaptation_fit.tex
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@ -11,10 +11,10 @@
%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
\section*{Estimating the adaptation time-constant.}
\section{Estimating the adaptation time-constant}
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
process is called adaptation. Your task here is to
estimate the time-constant of the firing-rate adaptation in P-unit
electroreceptors of the weakly electric fish \textit{Apteronotus
leptorhynchus}.
@ -26,27 +26,41 @@ electroreceptors of the weakly electric fish \textit{Apteronotus
in the file. The contrast of the stimulus is a measure relative to
the amplitude of fish's field, it has no unit. The data is sampled
with 20\,kHz sampling frequency and spike times are given in
milliseconds relative to the stimulus onset.
milliseconds (not seconds!) relative to the stimulus onset.
\begin{parts}
\part Estimate for each stimulus intensity the 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
next 1000\,ms the stimulus was switched on. (iii) After stimulus
offset the neuronal activity was recorded for further 825\,ms.
\part Estimate for each stimulus intensity the PSTH. You will see
that there are three parts: (i) 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. Find an appropriate bin-width
for the PSTH.
\part Estimate the adaptation time-constant for both the stimulus
on- and offset. To do this fit an exponential function to the
data. For the decay use:
\begin{equation}
f_{A,\tau,y_0}(t) = y_0 + A \cdot e^{-\frac{t}{\tau}},
\end{equation}
where $y_0$ the offset, $A$ the amplitude, $t$ the time, $\tau$
the time-constant.
For the increasing phases use an exponential of the form:
on- and offset. To do this fit an exponential function
$f_{A,\tau,y_0}(t)$ to appropriate regions of the data:
\begin{equation}
f_{A,\tau, y_0}(t) = y_0 + A \cdot \left(1 - e^{-\frac{t}{\tau}}\right ),
f_{A,\tau,y_0}(t) = A \cdot e^{-\frac{t}{\tau}} + y_0,
\end{equation}
\part Plot the best fits into the data.
\part Plot the estimated time-constants as a function of stimulus intensity.
where $t$ is time, $A$ the (positive or negative) amplitude of the
exponential decay, $\tau$ the adaptation time-constant, and $y_0$
an offset.
Before you do the fitting, familiarize yourself with the three
parameter of the exponential function. What is the value of
$f_{A,\tau,y_0}(t)$ at $t=0$? What is the value for large times? How does
$f_{A,\tau,y_0}(t)$ change if you change either of the parameter?
Which of the parameter could you directly estimate from the data
(without fitting)?
How could you get good estimates for the other parameter?
Do the fit and show the resulting exponential function together
with the data.
\part Do the estimated time-constants depend on stimulus intensity?
Use an appropriate statistical test to support your observation.
\end{parts}
\end{questions}

2
projects/project_onset_fi/onset_fi.tex
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@ -11,7 +11,7 @@
%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
\section*{Quantifying the responsiveness of a neuron using the F-I curve.}
\section{Quantifying the responsiveness of a neuron by its F-I curve}
The responsiveness of a neuron is often quantified using an F-I
curve. The F-I curve plots the \textbf{F}iring rate of the neuron as a
function of the stimulus \textbf{I}ntensity.

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projects/project_serialcorrelation/serialcorrelation.tex
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@ -16,10 +16,9 @@
\question P-unit electroreceptor afferents of the gymnotiform weakly
electric fish \textit{Apteronotus leptorhynchus} are spontaneously
active when the fish is not electrically stimulated.
\begin{itemize}
\item How do the firing rates and the serial correlations of the
interspike intervals vary between different cells?
\end{itemize}
How do the firing rates and the serial correlations of the
interspike intervals vary between different cells?
In the file \texttt{baselinespikes.mat} you find two variables:
\texttt{cells} is a cell-array with the names of the recorded cells
@ -34,7 +33,7 @@
this project.
By just looking on the spike rasters, what are the differences
betwen the cells?
between the cells?
\part Compute the firing rate of each cell, i.e. number of spikes per time.
@ -46,15 +45,18 @@
correlations similar betwen the cells? How do they differ?
\part Implement a permutation test for computing the significance
at a 1\,\% level of the serial correlations. Illustrate for a few
cells the computed serial correlations and the 1\,\% and 99\,\%
percentile from the permutation test. At which lag are the serial
correlations clearly significant?
at an appropriate significance level of the serial
correlations. Keep in mind that you test the correlations at 10
different lags. At which lags are the serial correlations
statistically significant?
\part Are the serial correlations somehow dependent on the firing rate?
Plot the significant correlations against the firing rate. Do you
observe any dependence?
Use an appropriate statistical test to support your observation.
\end{parts}
\end{questions}

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regression/lecture/regression-chapter.tex
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@ -16,8 +16,25 @@
\input{regression}
Example for fit with matlab functions lsqcurvefit, polyfit
\section{Fitting in practice}
Fit with matlab functions lsqcurvefit, polyfit
\subsection{Non-linear fits}
\begin{itemize}
\item Example that illustrates the Nebenminima Problem (with error surface)
\item You need got initial values for the parameter!
\item Example that fitting gets harder the more parameter yuo have.
\item Try to fix as many parameter before doing the fit.
\item How to test the quality of a fit? Residuals. $\Chi^2$ test. Run-test.
\end{itemize}
\subsection{Linear fits}
\begin{itemize}
\item Polyfit is easy: unique solution!
\item Example for overfitting with polyfit of a high order (=number of data points)
\end{itemize}
Example for overfitting with polyfit of a high order (=number of data points)
\end{document}