From ae51f8c3e16be6cf152aad0e6cc8194e906de3f2 Mon Sep 17 00:00:00 2001 From: Jan Grewe Date: Fri, 11 Jan 2019 11:57:14 +0100 Subject: [PATCH] ranking the projects, todos and fixes --- projects/README | 61 +++++++++++++------ projects/project_eod/eod.tex | 14 ++++- projects/project_eyetracker/eyetracker.tex | 2 +- .../stimulus_reconstruction.tex | 32 +++++----- 4 files changed, 72 insertions(+), 37 deletions(-) diff --git a/projects/README b/projects/README index beca3f5..b232b14 100644 --- a/projects/README +++ b/projects/README @@ -1,70 +1,95 @@ project_adaptation_fit -OK +OK, medium +Add plotting of cost function project_eod -Needs to be checked! +OK, medium - difficult project_eyetracker -OK +OK, difficult +no statistics, but kmeans project_fano_slope -OK +OK, difficult +Add t-test project_fano_test -OK +OK - project_fano_time -OK +OK, difficult +Add t-test project_ficurves -OK +OK, medium +Maybe add correlation test or fit statistics project_input_resistance -What is the problem with this project? +medium +What is the problem with this project? --> No difference between segments +Improve questions project_isicorrelations +medium-difficult +Add statistical test for dependence on adapttau! Need to program a solution! project_isipdffit Too technical project_lif -OK +OK, difficult +no statistics project_mutualinfo -OK +OK, medium project_noiseficurves -OK +OK, simple-medium +no statistics project_numbers +simple We might add some more involved statistical analysis project_pca_natural_images -Needs PCA... +medium +Make a solution (->Lukas) project_photoreceptor -OK - text needs to be improved -Maybe also add how responses are influenced by unstable resting potential -Maybe more cells... +OK, simple project_populationvector +difficult OK project_qvalues +- Interesting! But needs solution. project_random_walk +simple-medium Improve it! Provide code exmaples for plotting world and making movies project_serialcorrelation -OK +OK, simple-medium project_spectra +- Needs improvements and a solution project_stimulus_reconstruction -OK Fix equation? +OK, difficult +Add specific hints for statistics project_vector_strength -OK Maybe add something for explainiong the vector strength (average unit vector). \ No newline at end of file +OK, medium-difficult +Maybe add something for explaining the vector strength (average unit vector). +Check text (d) +Add statisitcs for (e) + +Peter power: +medium + +Marius monkey data: +medium-difficult \ No newline at end of file diff --git a/projects/project_eod/eod.tex b/projects/project_eod/eod.tex index e225546..59b7254 100644 --- a/projects/project_eod/eod.tex +++ b/projects/project_eod/eod.tex @@ -37,8 +37,18 @@ multiples of the fundamental frequency). the beginning choose $n=3$. \part Try different choices of $n$ and see how the fit changes. Plot the fits and the section of the original curve that - you used for fitting for different choices of $n$. Also plot the - fitting error as a function of $n$. + you used for fitting for different choices of $n$. + \part \label{fiterror} Plot the fitting error as a function of $n$. + What do you observe? + \part Another way to quantify the quality of the fit is to compute + the correlation coefficient between the fit and the + data. Illustrate this correlation for a few values of $n$. Plot + the correlation coefficient as a function of $n$. What is the + minimum $n$ needed for a good fit? How does this compare to the + results from (\ref{fiterror})? + \part Plot the amplitudes $\alpha_j$ and phases $\varphi_j$ as a + function of the frequencies $\omega_j$ --- the amplitude and phase + spectra, also called ``Bode plot''. \part Why does the fitting fail when you try to fit the entire recording? \part (optional) If you want you can also play the different fits and the original as sound (check the help). diff --git a/projects/project_eyetracker/eyetracker.tex b/projects/project_eyetracker/eyetracker.tex index 9ad9afb..017c368 100644 --- a/projects/project_eyetracker/eyetracker.tex +++ b/projects/project_eyetracker/eyetracker.tex @@ -33,7 +33,7 @@ shifts. The eye movements during training and test are recorded. speed and/or accelerations. \part Detect and correct the eye traces for instances in which the eye was not correctly detected. Interpolate linearily in these sections. - \part Create a 'heatmap' plot that shows the eye trajectories + \part Create a 'heatmap' plot of the eye-positions for one or two (nice) trials. \part Use the \verb+kmeans+ clustering function to identify fixation points. Manually select a good number of cluster diff --git a/projects/project_stimulus_reconstruction/stimulus_reconstruction.tex b/projects/project_stimulus_reconstruction/stimulus_reconstruction.tex index 0730ab0..eae2513 100644 --- a/projects/project_stimulus_reconstruction/stimulus_reconstruction.tex +++ b/projects/project_stimulus_reconstruction/stimulus_reconstruction.tex @@ -10,29 +10,29 @@ \input{../instructions.tex} %%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%% -\section*{Reverse reconstruction of the stimulus evoking neuronal responses.} +\section*{Reverse reconstruction of the stimulus that evoked a neuronal response.} To analyse encoding properties of a neuron one often calculates the -Spike-Triggered-Average (STA). -\[ STA(\tau) = \frac{1}{\langle n \rangle} \left\langle - \displaystyle\sum_{i=1}^{n}{s(t_i - \tau)} \right\rangle \] - -The STA is the average stimulus that led to a spike in the neuron and -is calculated by cutting out snippets form the stimulus centered on -the respective spike time. The Spike-Triggered-Average can be used to -reconstruct the stimulus a neuron has been stimulated with. +Spike-Triggered-Average (STA). The STA is the average stimulus that +led to a spike in the neuron and is calculated by cutting out snippets +form the stimulus centered on the respective spike time: +\[ STA(\tau) = \frac{1}{n} \displaystyle\sum_{i=1}^{n}{s(t_i - \tau)} \], +where $n$ is the number of trials and $t_i$ is the time of the +$i_{th}$ spike. The Spike-Triggered-Average can be used to reconstruct +the stimulus from the neuronal response. The reconstructed stimulus +can then be compared to the original stimulus. \begin{questions} \question In the accompanying files you find the spike responses of - P-units and pyramidal neurons of the weakly electric fish + a p-type electroreceptor afferent (P-unit) and a pyramidal neurons + recorded in the hindbrain of the weakly electric fish \textit{Apteronotus leptorhynchus}. The respective stimuli are stored in separate files. 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 functions to the pyramidal - data. + P-unit and, in the end, apply the same analyzes/functions to the + pyramidal data. \begin{parts} \part Estimate the STA and plot it. - \part Implement a function that does the reconstruction of the - stimulus using the STA. + \part Implement a function that does the reverse reconstruction and uses the STA to recopnstruct 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. @@ -44,8 +44,8 @@ reconstruct the stimulus a neuron has been stimulated with. \part Analyze 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 data - amount used to estimate the STA. + \part Plot the reconstruction error as a function of the amount of data + used to estimate the STA. \part Repeat the above steps for the pyramidal neuron, what do you observe? \end{parts}