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[projects] minor fixes, change supervisor for some projects

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This commit is contained in:
Jan Grewe 2021-02-01 14:07:44 +01:00
parent ea8a4922a4
commit b55b5789dd
7 changed files with 39 additions and 34 deletions
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4
projects/project_adaptation_fit/adaptation_fit.tex
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@ -2,8 +2,8 @@
\newcommand{\ptitle}{Adaptation time-constant} \newcommand{\ptitle}{Adaptation time-constant}
\input{../header.tex} \input{../header.tex}
\firstpagefooter{Supervisor: Jan Grewe}{phone: 29 74588}% \firstpagefooter{Supervisor: Lukas Sonnenberg}{phone:}%
{email: jan.grewe@uni-tuebingen.de} {email: lukas.sonnenberg@uni-tuebingen.de}
\begin{document} \begin{document}

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projects/project_eod/eod.tex
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@ -56,8 +56,4 @@ multiples of the fundamental frequency).
\end{parts} \end{parts}
\end{questions} \end{questions}
\end{document} \end{document}

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projects/project_eyetracker/eyetracker.tex
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@ -24,7 +24,7 @@ The eyetracker recorded ey positions with 60\,Hz. The fixation point was shown a
\begin{questions} \begin{questions}
\question Familiarize yourself with the data. \question Familiarize yourself with the data.
\begin{parts} \begin{parts}
\part Cut the data in chunks belonging to the same period (fixation and free eye-movements). \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. \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} \end{parts}
@ -35,7 +35,7 @@ The eyetracker recorded ey positions with 60\,Hz. The fixation point was shown a
\part Detect fixation points in the "free movement" part of the data. \part Detect fixation points in the "free movement" part of the data.
\end{parts} \end{parts}
\question Compare the subject's behaviour when viewing the different scenes. \question Compare the subject's behavior when viewing the different scenes.
\end{questions} \end{questions}
\end{document} \end{document}

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projects/project_ficurves/ficurves.tex
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@ -2,8 +2,8 @@
\newcommand{\ptitle}{f-I curves} \newcommand{\ptitle}{f-I curves}
\input{../header.tex} \input{../header.tex}
\firstpagefooter{Supervisor: Jan Benda}{phone: 29 74573}% \firstpagefooter{Supervisor: Jan Grewe}{phone: 29 74588}%
{email: jan.benda@uni-tuebingen.de} {email: jan.grewe@uni-tuebingen.de}
\begin{document} \begin{document}

19
projects/project_photoreceptor/photoreceptor.tex
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@ -12,16 +12,15 @@
%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
\section*{Light responses of an insect photoreceptor.} \section*{Light responses of an insect photoreceptor.}
In this project you will analyze data from intracellular recordings of Fly R\,1--6 photoreceptors respond to light-on stimuli with graded membrane
a fly R\,1--6 photoreceptor. These cells show graded membrane potential changes. In the acompanying datasets you find the membrane
potential changes in response to a light stimulus. The membrane potential of a single R\,1-6 photoreceptor that was recorded while the receptor was
potential of the photoreceptor was recorded while the cell was stimulated with a light stimulus of different amplitudes.
stimulated with a light stimulus.
\begin{questions} \begin{questions}
\question{} The accompanying dataset (photoreceptor\_data.zip) \question{} The accompanying dataset (photoreceptor\_data.zip)
contains seven mat files. Each of these holds the data from one contains seven mat files. Each of these holds the data from one
stimulus intensity and contains therr variables. (i) stimulus intensity and contains three variables. (i)
\textit{voltage} a matrix with the recorded membrane potential from \textit{voltage} a matrix with the recorded membrane potential from
10 consecutive trials, (ii) \textit{time} a matrix with the 10 consecutive trials, (ii) \textit{time} a matrix with the
time-axis for each trial, and (iii) \textit{trace\_meta} a structure time-axis for each trial, and (iii) \textit{trace\_meta} a structure
@ -36,8 +35,8 @@ stimulated with a light stimulus.
the individual responses as a function of time. the individual responses as a function of time.
\part Intracellular recordings often suffer from drifts in the resting \part Intracellular recordings often suffer from drifts in the resting
potential. This leads to a large variability in the responses which is technical and not a cellular potential. This leads to a large variability in the responses which has technical reasons and is not a cellular
property. To compensate for such drifts trials are aligned to the property. To compensate for such drifts trials usually are aligned to the
resting potential before stimulus onset. resting potential before stimulus onset.
Replot the data but with the compensation for the drifts. Replot the data but with the compensation for the drifts.
@ -46,9 +45,9 @@ stimulated with a light stimulus.
\part You will notice that the responses have three main parts, (i) a \part You will notice that the responses have three main parts, (i) a
pre-stimulus phase, (ii) the phase in which the light was on, and (iii) pre-stimulus phase, (ii) the phase in which the light was on, and (iii)
a post-stimulus phase. Create an characteristic curve that a post-stimulus phase. The light-on phase can further be devided into two parts, the onset, and the "steady state" response part. Create an characteristic curve that
plots the response strength as a function of the stimulus plots the response strength as a function of the stimulus
intensity for the ``onset'' and the ``steady state'' intensity for ``onset'' and ``steady state''
phases of the light response. phases of the light response.
\part The light switches on at time zero. Estimate the delay \part The light switches on at time zero. Estimate the delay

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projects/project_random_walk/random_walk.tex
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@ -2,8 +2,8 @@
\newcommand{\ptitle}{Random walk} \newcommand{\ptitle}{Random walk}
\input{../header.tex} \input{../header.tex}
\firstpagefooter{Supervisor: Jan Grewe}{phone: 29 74588}% \firstpagefooter{Supervisor: Lukas Sonnenberg}{phone:}%
{email: jan.grewe@uni-tuebingen.de} {email: lukas.sonnenberg@uni-tuebingen.de}
\begin{document} \begin{document}

32
projects/project_stimulus_reconstruction/stimulus_reconstruction.tex
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@ -11,27 +11,29 @@
%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
\section*{Reverse reconstruction of the stimulus that evoked a neuronal response.} \section*{Reverse reconstruction of the stimulus that evoked a neuronal response.}
To analyse the encoding properties of a neuron one often calculates the When analyzing neuronal responses we want to figure out which aspects of the stimulus are actually encoded in the neuronal response.
Spike-Triggered-Average (STA). The STA is the average stimulus that One way to do this is to calculate the
Spike-Triggered-Average (STA) and use it to reversely estimate which aspects of the stimulus are encoded in the resopnse.
The STA is the average stimulus that
led to a spike in the neuron: led to a spike in the neuron:
\[ STA(\tau) = \frac{1}{n} \displaystyle\sum_{i=1}^{n}{s(t_i - \tau)} \] \[ STA(\tau) = \frac{1}{n} \displaystyle\sum_{i=1}^{n}{s(t_i - \tau)} \]
where $n$ is the number of spikes and $t_i$ is the time of the where $n$ is the number of spikes and $t_i$ is the time of the
$i_{th}$ spike. $\tau$ is a temporal shift relative to the spike $i_{th}$ spike. $\tau$ is a temporal shift relative to the spike
time. For the beginning let $\tau$ assume values in the range time.
$\pm50$\,ms. The STA can be estimated by cutting out snippets from the
Another approach to understand the equation is to cut out snippets from the
stimulus that are centered on the respective spike time and by stimulus that are centered on the respective spike time and by
subsequently averaging these stimulus snippets. The STA can be used to subsequently averaging these stimulus snippets.
reconstruct the stimulus from the neuronal response (reverse
reconstruction). The reconstructed stimulus can then be compared to
the original stimulus and provides a good impression about the
features that are encoded in the neuronal response.
\begin{questions} \begin{questions}
\question In the accompanying data files you find the spike \question In the accompanying data files you find the spike
responses of a p-type electroreceptor afferent (P-unit) and a responses of a p-type electroreceptor afferent (P-unit) and a
pyramidal neuron recorded in the hindbrain of the weakly electric pyramidal neuron recorded in the hindbrain of the weakly electric
fish \textit{Apteronotus leptorhynchus}. The respective stimuli are fish \textit{Apteronotus leptorhynchus}. The respective stimuli are
stored in separate files. The neron is stimulated with an amplitude stored in separate files. The neuron is stimulated with an amplitude
modulation of the fish's own electric field. The stored stimulus modulation of the fish's own electric field. The stored stimulus
trace is the modulator that is applied to the field and is trace is the modulator that is applied to the field and is
dimensionless, i.e. it has no unit. The data is sampled with dimensionless, i.e. it has no unit. The data is sampled with
@ -39,7 +41,15 @@ features that are encoded in the neuronal response.
seconds. Start with the P-unit and, in the end, apply the same seconds. Start with the P-unit and, in the end, apply the same
analyzes/functions to the pyramidal cell. analyzes/functions to the pyramidal cell.
\begin{parts} \begin{parts}
\part Estimate the STA and plot it. What does it tell? \part Familiarize yourself with the cellular responses and the stimulus.
\part Estimate the STA and plot it. For the beginning let $\tau$ assume values in the range
$\pm50$\,ms. What does it tell?
\end{parts}
\question The STA can be used to reconstruct the stimulus from the neuronal response (reverse
reconstruction) by convolution of the spiking response with the STA. The reconstructed stimulus can then be compared to
the original stimulus and provides a good impression about the
features that are encoded in the neuronal response.
\begin{parts}
\part Implement a function that does the reverse reconstruction and uses the STA to reconstruct the stimulus. \part Implement a function that does the reverse reconstruction and uses the STA to reconstruct the stimulus.
\part Implement a function that estimates the reconstruction quality. \part Implement a function that estimates the reconstruction quality.
\part Test the robustness of the reconstruction: Estimate \part Test the robustness of the reconstruction: Estimate