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[projects] checked Jan B and Lukas projects

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2
pointprocesses/exercises/pointprocesses-1.tex
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@ -173,7 +173,7 @@
In addition, compare the distributions with the Poisson
distribution expected for a Poisson spike train:
\[ P(k) = \frac{(\lambda W)^ke^{\lambda W}}{k!} \; , \] where
\[ P(k) = \frac{(\lambda W)^ke^{-\lambda W}}{k!} \; , \] where
$\lambda$ is the rate of the spike train that you should estimate
from the data.
\begin{solution}

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projects/instructions.tex
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@ -1,6 +1,8 @@
\vspace*{\fill}
\setlength{\fboxsep}{2ex}
\fbox{\parbox{0.95\linewidth}{\small
\vspace{1ex}
This is your project assignment. The project applies
topics from the course on real or simulated data, or is about
concepts we haven't covered yet. Work yourself into the data and
@ -14,21 +16,21 @@
\vspace{1ex}
Happy hacking!
\vspace{3ex}
\vspace{5ex}
\textbf{Evaluation criteria:}
For your grade we mainly evaluate the technical aspects of your
code and figures. You can view the evaluation criteria in
\emph{SciCompScoreSheet.pdf} on Ilias.
\vspace{3ex}
\vspace{5ex}
\textbf{Dates:}
Deadline for uploading the code and the presentation on ILIAS is\\
\centerline{\textbf{Sunday, February 21st, 2021, 23:59h}.}
Deadline for uploading the code and the presentation on ILIAS is\\[2ex]
\centerline{\textbf{Sunday, February 21st, 2021, 23:59h}.}\vspace{2ex}
The presentations are on Monday February 22nd, 09:30--12:00, Tuesday
February 23rd, 9:30--11:00 and Wednesday 24th, 09:30--12:00.
\vspace{3ex}
\vspace{5ex}
\textbf{Files:}
Bundle everything (the code, the data, and the pdf of the
presentation) into a {\em single} zip-file named with your last
@ -38,7 +40,7 @@
somewhere else on your computer and check if your main script
is still running properly.
\vspace{3ex}
\vspace{5ex}
\textbf{Code:}
The code must be executable without any further adjustments from
our side --- test it! A single \texttt{main.m} script
@ -57,7 +59,7 @@
\emph{Please note your name and matriculation number as a
comment at the top of the \texttt{main.m} script.}
\vspace{3ex}
\vspace{5ex}
\textbf{Presentation:}
Hand in your presentation as a pdf file.
@ -69,6 +71,11 @@
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!
\vspace{1ex}
}}
\vspace*{\fill}
\vspace*{\fill}
\newpage

10
projects/project_activation_curve/activation_curve.tex
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@ -9,9 +9,6 @@
\input{../instructions.tex}
%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
\section{Estimation of activation curves of sodium channels}
Mutations in genes encoding ion channels can result in a variety of
neurological diseases like epilepsy, autism, or intellectual
disability. One way to find a possible treatment is to compare the
@ -20,9 +17,10 @@ corresponding wild-type (non-mutated channel). Voltage-clamp
experiments are used to measure and describe the kinetics.
In the project you will compute and compare the activation curves of
the Nav1.6 wild-type (WT) channel and the A1622D mutation (the amino
acid Alanine (A) at the 1622nd position is replaced by Aspartic acid
(D)) that causes intellectual disability in humans.
sodium channel, in particular the Nav1.6 wild-type (WT) channel and
the A1622D mutation (the amino acid Alanine (A) at the 1622nd position
is replaced by Aspartic acid (D)) that causes intellectual disability
in humans.
\begin{questions}
\question In the accompanying datasets you find recordings of both

18
projects/project_adaptation_fit/adaptation_fit.tex
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@ -9,15 +9,11 @@
\input{../instructions.tex}
%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
\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. 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}.
Stimulating a neuron with a constant stimulus for an extended period
of time often results in a decay of an initially strong response. This
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}.
\begin{questions}
\question In the accompanying datasets you find the
@ -26,8 +22,10 @@ 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 and is given in percent. The data is sampled
with 20\,kHz sampling frequency and spike times are given in
milliseconds (not seconds!) relative to the stimulus onset.
milliseconds (not seconds!) relative to stimulus onset.
\begin{parts}
\part Plot spike rasters of the data.
\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

14
projects/project_eyetracker/eyetracker.tex
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@ -9,12 +9,10 @@
\input{../instructions.tex}
%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
\section*{Analysis of eye trajectories.}
In this project you will analyze eye-tracking data (courtesy of Gregor Hardiess,
Cognitive Neuroscience, Uni-T\"ubingen). In this task subjects were viewing
biblical images while their eye movements were recorded.
In this project you will analyze eye-tracking data (courtesy of Gregor
Hardiess, Cognitive Neuroscience, Uni-T\"ubingen). In this task
subjects were viewing biblical images while their eye movements were
recorded.
In the accompanying datasets you find a subject's eye tracking data when viewing two different images
(\emph{Genesis\_VIII.png} and \emph{Genesis\_XXXIX.png}, files \verb+1_1.mat+ and \verb+1_2.mat+, respectively). Each \verb+mat+-file contains five variables: \verb+frame_index+, the \verb+gaze_x+ and \verb+gaze_y+ position (in pixel on the screen), a boolean vector \verb+eye_found+ telling whether the tracker could actually estimate the eye position, and a vector \verb+marker+. The \verb+marker+ is used to indicate sections in the data. 0 can be ignored, 1 marks the fixation period and 2 indicates the acutal trial.
@ -25,13 +23,17 @@ The eyetracker recorded ey positions with 60\,Hz. The fixation point was shown a
\question Familiarize yourself with the data.
\begin{parts}
\part Cut the data into chunks belonging to the same period (fixation and free eye-movements).
\part Detect problems in the data (e.g. the eye was not found) and correct the eye traces. Interpolate linearily in these sections.
\end{parts}
\question Characterize the eye movements statistically.
\begin{parts}
\part Calculate with eye speed and/or accelerations.
\part Create a 'heatmap' plot of the eye-positions.
\part Detect fixation points in the "free movement" part of the data.
\end{parts}

1
projects/project_lif/lif.tex
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@ -85,7 +85,6 @@ potentials $V_i$ for successive time points $t_i$ according to
How does the filter function depend on the membrane time constant?
\end{parts}
\continue
\question Leaky integrate-and-fire neuron
The passive neuron can be turned into a spiking neuron by

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projects/project_random_walk/random_walk.tex
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@ -9,10 +9,6 @@
\input{../instructions.tex}
%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
\section*{Random walk with memory.}
The movement pattern of some animals can be described as a random walk when
searching for food. In some cases this random walk is not completely
random. In fact, sometimes there is some memory involved. Whenever
@ -21,7 +17,6 @@ animal will continue in the very same direction as in the step before. When the
next step leads to a decrease in food gain the animal switches back to
a random walk and changes directions randomly.
\begin{questions}
\question{} The accompanying dataset (random\_world.mat) contains a
single variable. This is the world (10000\,m$^2$ area with
@ -29,25 +24,33 @@ a random walk and changes directions randomly.
food sources (Gaussian blotches of food).
\begin{parts}
\part{} Create a plot of the world using \code{imshow}.\\[0.5ex]
\part{} Create a model animal (agent) that performs a pure random walk. The
agent can walk in eight different directions (the cardinal and
diagonal directions) with a stepsize of 10\,cm
\part Create a plot of the world using \code{imshow()}.
\part Create a model animal (agent) that performs a pure random
walk. The agent can walk in eight different directions (the
cardinal and diagonal directions) with a stepsize of 10\,cm
(approximately). Let the agent start at a random location in the
world and count how much food it eats in 10000 steps (eaten food
disappears from the world, of course). If the agent bumps into the
borders of the world choose a different direction.\\[0.5ex]
\part{} Plot a typical example walk. (You can also make an animation
with MATLAB, see plotting chapter in the script).\\[0.5ex]
\part{} Same as above, but create a model animal that has some memory,
i.e. the direction is kept constant as long as there is a positive
gradient in the food gain. Otherwise, a random walk is performed.\\[0.5ex]
\part{} Plot a typical example walk also for this agent.\\[0.5ex]
\part{} Compare the performance of the two agents. Create
borders of the world choose a different direction.
\part Plot a typical example walk. (You can also make an animation
with MATLAB, see plotting chapter in the script).
\part Same as above, but create a model animal that has some
memory, i.e. the direction is kept constant as long as there is a
positive gradient in the food gain. Otherwise, a random walk is
performed.
\part Plot a typical example walk also for this agent.
\part Compare the performance of the two agents. Create
appropriate plots and apply statistical methods. You will need to
run the simulations several times to get a good estimate of the
neumbers.
\part{} Can you think about better search strategies?
numbers.
\part Can you think about better search strategies?
\end{parts}
\end{questions}

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projects/project_vector_strength/vector_strength.tex
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@ -9,18 +9,18 @@
\input{../instructions.tex}
%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
\section*{Quantifying the coupling of action potentials to the EOD.}
Phase coupling of neuronal activity is observed in several
system. This means that the action potentials fired by a neuron occur
with specific phase relation to the driving periodic signal. For example sensory
neurons in the auditory system and the electrosensory system fire in
close phase relation to the stimulus frequncy. P-type electroreceptor
afferents (P-units) of the weakly electric fish \emph{Apteronotus
leptorhynchus} are driven by the fish's self-generated field, the
EOD and fire action potentials phase locked to it. In this project you
have to quantify the strength of this coulpling using the
\textbf{vector strength}:
Phase coupling of neuronal activity is observed in many systems. This
means that the action potentials fired by a neuron occur with a
specific phase relation to a driving periodic signal. For example,
sensory neurons in auditory systems and electrosensory systems fire in
close phase relation to the stimulus frequency. P-type
electroreceptor afferents (P-units) of the weakly electric fish
\emph{Apteronotus leptorhynchus} are driven by the fish's
self-generated field, the electric organ discharge (EOD), and fire
action potentials phase locked to it.
In this project you quantify the strength of the coupling of P-unit
spikes to the EOD using the \textbf{vector strength}:
\begin{equation}
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
@ -28,27 +28,38 @@ have to quantify the strength of this coulpling using the
\end{equation}
with $n$ the number of spikes and $\alpha_i$ the timing of the each
spike expressed as the phase relative to the EOD. The vector strength
varies between $0$ and $1$ for no phase locking to perfect phase
locking, respectively.
varies between $0$ for no phase locking and $1$ for perfect phase
locking.
\begin{questions}
\question In the accompanying datasets you find recordings of the
``baseline'' activity of P-unit electroreceptors (in the absence of
an external stimulus) of different weakly electric fish of the
species \textit{Apteronotus leptorhynchus}. The files further
contain respective recordings of the \textit{eod}, i.e. the fish's
contain respective recordings of the EOD, i.e. the fish's
electric field. The data is sampled with 20\,kHz and the spike times
are given in seconds.
\begin{parts}
\part Illustrate the phase locking by plotting the PSTH within the EOD cycle.
\part Plot the EOD with the evoked spikes on top.
\part Illustrate the phase locking by plotting the PSTH within the
EOD cycle.
\part Implement a function that estimates the vector strength
between the \textit{EOD} and the spikes.
between the EOD and the spikes.
\part Create a polar plot that shows the timing of the spikes
relatve to the EOD.
\part Apply an appropriate statistical test to check whether locking is statistically significant.
\part Analyze the baseline responses of each fish and extract measures as were introduced in chapter 10 of the script. Plot the results
appropriately.
\part Does the vector strength correlate with the EOD frequency or the reponse variability (CV)?
relative to the EOD.
\part Apply an appropriate statistical test to check whether
locking is statistically significant.
\part Analyze the baseline responses of each fish and extract
measures as were introduced in chapter 10 of the script. Plot the
results appropriately.
\part Does the vector strength correlate with the EOD frequency or
the reponse variability (CV)?
\end{parts}
\end{questions}