From a46fa34b524286d805b79235de9ad41e7653276e Mon Sep 17 00:00:00 2001 From: Jan Grewe Date: Mon, 20 Jan 2020 15:55:31 +0100 Subject: [PATCH] [projects] fixes and improvements --- projects/instructions.tex | 9 +++--- .../project_adaptation_fit/adaptation_fit.tex | 2 +- .../project_photoreceptor/photoreceptor.tex | 28 +++++++++++-------- .../stimulus_reconstruction.tex | 25 ++++++----------- 4 files changed, 31 insertions(+), 33 deletions(-) diff --git a/projects/instructions.tex b/projects/instructions.tex index 2eef387..6cda593 100644 --- a/projects/instructions.tex +++ b/projects/instructions.tex @@ -12,7 +12,7 @@ The code and the presentation should be uploaded to ILIAS \textbf{at latest the night before the presentation (23:59h)}. We will store all presentations on one computer to allow fast - transitions between talks. The date of the presentations needs to be fixed. + transitions between talks. The date of the presentations will be anounced. \vspace{1ex} \textbf{Files:} @@ -21,15 +21,16 @@ everything (the pdf, the code, and the data) into a {\em single} zip-file. - Hint: make the zip file you want to upload, unpack it somewhere - else and check if your main script is still running properly. + \textbf{Hint:} create the zip file you want to upload, unpack it + somewhere else and check if your main script is still running + properly. \vspace{1ex} \textbf{Code:} The code must be executable without any further adjustments from our side. (Test it!) A single \texttt{main.m} script coordinates - the analysis by calling functions and sub-scripts and produces + the analysis by calling functions and sub-scripts which produce the {\em same} figures (\texttt{saveas()}-function, pdf or png format) that you use in your slides. The code must be comprehensible by a third person (use proper and consistent diff --git a/projects/project_adaptation_fit/adaptation_fit.tex b/projects/project_adaptation_fit/adaptation_fit.tex index 24d4355..c3bd66c 100644 --- a/projects/project_adaptation_fit/adaptation_fit.tex +++ b/projects/project_adaptation_fit/adaptation_fit.tex @@ -24,7 +24,7 @@ electroreceptors of the weakly electric fish \textit{Apteronotus \textit{spike\_times} of an P-unit electroreceptor to a stimulus of a certain intensity, i.e. the \textit{contrast} which is also stored 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 + 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. \begin{parts} diff --git a/projects/project_photoreceptor/photoreceptor.tex b/projects/project_photoreceptor/photoreceptor.tex index 350341b..e839ed5 100644 --- a/projects/project_photoreceptor/photoreceptor.tex +++ b/projects/project_photoreceptor/photoreceptor.tex @@ -12,20 +12,16 @@ %%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%% \section*{Light responses of an insect photoreceptor.} -In this project you will analyse data from intracellular recordings of +In this project you will analyze data from intracellular recordings of a fly R\,1--6 photoreceptor. These cells show graded membrane potential changes in response to a light stimulus. The membrane potential of the photoreceptor was recorded while the cell was -stimulated with a light stimulus. 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 -property. To compensate for such drifts trials are aligned to the -resting potential before stimulus onset. +stimulated with a light stimulus. \begin{questions} \question{} The accompanying dataset (photoreceptor\_data.zip) contains seven mat files. Each of these holds the data from one - stimulus intensity. In each file are three variables. (i) + stimulus intensity and contains therr variables. (i) \textit{voltage} a matrix with the recorded membrane potential from 10 consecutive trials, (ii) \textit{time} a matrix with the time-axis for each trial, and (iii) \textit{trace\_meta} a structure @@ -37,12 +33,20 @@ resting potential before stimulus onset. \begin{parts} \part Create a plot of the raw data. For each light intensity plot - the average response as a function of time. This plot should also - depict the across-trial variability in an appropriate way. + the individual responses as a function of time. - \part You will notice that the responses have three main parts, a - pre-stimulus phase, the phase in which the light was on, and - finally a post-stimulus phase. Create an characteristic curve that + \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 + property. To compensate for such drifts trials are aligned to the + resting potential before stimulus onset. + Replot the data but with the compensation for the drifts. + + \part Instead of plotting individual responses plot the average response. + This plot should also depict the across-trial variability in an appropriate way. + + \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) + a post-stimulus phase. Create an characteristic curve that plots the response strength as a function of the stimulus intensity for the ``onset'' and the ``steady state'' phases of the light response. diff --git a/projects/project_stimulus_reconstruction/stimulus_reconstruction.tex b/projects/project_stimulus_reconstruction/stimulus_reconstruction.tex index 24cb00a..d45dd7a 100644 --- a/projects/project_stimulus_reconstruction/stimulus_reconstruction.tex +++ b/projects/project_stimulus_reconstruction/stimulus_reconstruction.tex @@ -20,11 +20,11 @@ $i_{th}$ spike. $\tau$ is a temporal shift relative to the spike time. For the beginning let $\tau$ assume values in the range $\pm50$\,ms. The STA can be estimated by cutting out snippets from the stimulus that are centered on the respective spike time and by -subsequently averaging them. The STA can be used to reconstruct the -stimulus from the neuronal response. 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. +subsequently averaging these stimulus snippets. The STA can be used to +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} \question In the accompanying data files you find the spike @@ -34,22 +34,15 @@ response. stored in separate files. The neron is stimulated with an amplitude modulation of the fish's own electric field. The stored stimulus trace is the modulator that is applied to the field and is - dimensionless, i.e. it has not unit. The data is sampled with + dimensionless, i.e. it has no unit. The data is sampled with 20\,kHz temporal resolution and spike times are given in seconds. Start with the P-unit and, in the end, apply the same - analyzes/functions to the responses from the pyramidal neuron. + analyzes/functions to the pyramidal cell. \begin{parts} \part Estimate the STA and plot it. What does it tell? \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 - error using the mean-square-error and express it relative to the - variance of the original stimulus. - \begin{equation} - err = \frac{1}{N} \cdot \displaystyle\sum^{N}_{i=1}(x_i - \bar{x})^2, - \end{equation} - with $N$ the number of data points, $x_i$ the current value and - $\bar{x}$, the average of all $x$. - \part Analyze the robustness of the reconstruction: Estimate + \part Implement a function that estimates the reconstruction quality. + \part Test the robustness of the reconstruction: Estimate the STA with less and less data and estimate the reconstruction error. \part Plot the reconstruction error as a function of the amount of data