ranking the projects, todos and fixes

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).
+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

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).

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

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}