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Fabian Sinz
2014-11-03 11:28:54 +01:00
parent 11564e16a1
commit 57f727ecf4
22 changed files with 210 additions and 132 deletions
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35
projects/project_fano_time/fano_time.tex
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@@ -1,4 +1,4 @@
\documentclass[addpoints,10pt]{exam}
\documentclass[addpoints,11pt]{exam}
\usepackage{url}
\usepackage{color}
\usepackage{hyperref}
@@ -9,7 +9,7 @@
\firstpageheader{Scientific Computing}{Project Assignment}{11/05/2014
-- 11/06/2014}
%\runningheader{Homework 01}{Page \thepage\ of \numpages}{23. October 2014}
\firstpagefooter{}{}{}
\firstpagefooter{}{}{{\bf Supervisor:} Jan Benda}
\runningfooter{}{}{}
\pointsinmargin
\bracketedpoints
@@ -31,7 +31,7 @@
% captionpos=t,
xleftmargin=2em,
xrightmargin=1em,
% aboveskip=10pt,
% aboveskip=11pt,
%title=\lstname,
% title={\protect\filename@parse{\lstname}\protect\filename@base.\protect\filename@ext}
}
@@ -62,32 +62,33 @@
duration $W$ of the observation time? How is this related to the fano factor
(the ratio between the variance and the mean of the spike counts)?
\begin{parts}
\part The neuron is implemented in the file \texttt{lifadaptspikes.m}.
The neuron is implemented in the file \texttt{lifadaptspikes.m}.
Call it with the following parameters:
\begin{lstlisting}
trials = 10;
tmax = 50.0;
input = 65.0;
Dnoise = 0.1;
adapttau = 0.2;
adaptincr = 0.5;
trials = 10;
tmax = 50.0;
input = 65.0;
Dnoise = 0.1;
adapttau = 0.2;
adaptincr = 0.5;
spikes = lifadaptspikes( trials, input, tmax, Dnoise, adapttau, adaptincr );
spikes = lifadaptspikes( trials, input, tmax, Dnoise, adapttau, adaptincr );
\end{lstlisting}
The returned \texttt{spikes} is a cell array with \texttt{trials} elements, each being a vector
of spike times (in seconds) computed for a duration of \texttt{tmax} seconds.
For the two inputs $I_1$ and $I_2$ use
\begin{lstlisting}
input = 65.0; % I_1
input = 75.0; % I_2
input = 65.0; % I_1
input = 75.0; % I_2
\end{lstlisting}
Show two raster plots for the responses to the two differrent stimuli.
\begin{parts}
\part
Show two raster plots for the responses to the two different stimuli.
\part Generate histograms of the spike counts within $W$ of the
responses to the two differrent stimuli. How do they depend on the observation time $W$
responses to the two different stimuli. How do they depend on the observation time $W$
(use values between 1\,ms and 1\,s)?
\part Think about a measure based on the spike count histograms that quantifies how well
@@ -96,7 +97,7 @@
For which observation times can the two stimuli perfectly discriminated?
Hint: A possible readout is to set a threshold $n_{thresh}$ for
\underline{Hint:} A possible readout is to set a threshold $n_{thresh}$ for
the observed spike count. Any response smaller than the threshold
assumes that the stimulus was $I_1$, any response larger than the
threshold assumes that the stimulus was $I_2$. What is the