added README to the projects
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projects/README
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projects/README
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project_adaptation_fit
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OK
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project_eod
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Needs to be checked!
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project_eyetracker
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OK
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project_fano_slope
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OK
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project_fano_test
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OK
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project_fano_time
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OK
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project_ficurves
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OK
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project_input_resistance
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What is the problem with this project?
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project_isicorrelations
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Need to program a solution!
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project_isipdffit
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Too technical
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project_lif
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OK
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project_mutualinfo
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OK
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project_noiseficurves
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OK
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project_numbers
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We might add some more involved statistical analysis
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project_pca_natural_images
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Needs PCA...
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project_photoreceptor
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OK - text needs to be improved
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Maybe also add how responses are influenced by unstable resting potential
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Maybe more cells...
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project_populationvector
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OK
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project_qvalues
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Interesting! But needs solution.
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project_random_walk
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Improve it! Provide code exmaples for plotting world and making movies
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project_serialcorrelation
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OK
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project_spectra
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Needs improvements and a solution
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project_stimulus_reconstruction
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OK Fix equation?
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project_vector_strength
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OK Maybe add something for explainiong the vector strength (average unit vector).
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@ -7,7 +7,7 @@
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%%%%% layout %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
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\pagestyle{headandfoot}
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\header{\textbf{\large Scientific Computing Project: \ptitle}}{}{\textbf{\large January 18th, 2018}}
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\header{\textbf{\large Scientific Computing Project:}}{}{\textbf{\large \ptitle}}
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\runningfooter{}{\thepage}{}
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\setlength{\baselineskip}{15pt}
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@ -36,7 +36,6 @@
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stimulus could happen to be lower than the number of spikes evoked
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by the smaller stimulus.
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The central questions of this project are:
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\begin{itemize}
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\item How can an upstream neuron discriminate two stimuli based
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@ -45,7 +44,7 @@
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\end{itemize}
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The neuron is implemented in the file \texttt{lifboltzmannspikes.m}.
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Call it with the following parameters:
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Call it with the following parameters:\vspace{-5ex}
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\begin{lstlisting}
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trials = 10;
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tmax = 50.0;
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@ -76,9 +75,7 @@ spikes = lifboltzmanspikes(trials, input, tmax, gain);
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Plot the tuning curve for four different neurons that differ in
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their \texttt{gain} property. Use 0.1, 0.2, 0.5 and 1 as values
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for the \texttt{gain} parameter.
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Why is this parameter called 'gain'?
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for the \texttt{gain} parameter. Why is this parameter called 'gain'?
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\part Show two raster plots for the responses to two different
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stimuli with $I_1=10$ and $I_2=11$. Set the gain of the neuron to
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@ -102,7 +99,7 @@ spikes = lifboltzmanspikes(trials, input, tmax, gain);
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that quantifies how well the two stimuli can be distinguished
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based on the spike counts. Plot the dependence of this measure as
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a function of the gain of the neuron.
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%
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For which gains can the two stimuli perfectly discriminated?
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\underline{Hint:} A possible readout is to set a threshold
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@ -25,7 +25,7 @@
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\part Plot the spike counts of both neurons appropriately.
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\part Use {\em Eden, U. T., \& Kramer, M. (2010). Drawing
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inferences from Fano factor calculations. Journal of
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neuroscience methods, 190(1), 149--152} to construct a test that
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Neuroscience Methods, 190(1), 149--152} to construct a test that
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uses the Fano factor as test statistic and tests against the Null
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hypothesis that the spike counts come from a Poisson process.
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\part Implement the test and use it on the data above.
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@ -115,7 +115,8 @@ time = [0.0:dt:tmax]; % t_i
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r = \frac{n-1}{t_n - t_1}
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\end{equation}
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What do you observe? Does the firing rate encode the frequency of
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the stimulus?
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the stimulus? Look at the spike trains in response the sine waves
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to figure out what is going on.
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\end{parts}
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\end{questions}
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