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Jan Benda 2015-11-03 12:53:42 +01:00
parent cdd40dc3a7
commit b35365232d
3 changed files with 40 additions and 14 deletions
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24
projects/project_fano_slope/fano_slope.tex
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@ -84,7 +84,7 @@ spikes = lifboltzmanspikes( trials, input, tmax, Dnoise, imax, ithresh, slope );
Think of calling the \texttt{lifboltzmanspikes()} function as a
simple way of doing an electrophysiological experiment. You are
presenting a stimulus of constant intensity $I$ that you set. The
presenting a stimulus with a constant intensity $I$ that you set. The
neuron responds to this stimulus, and you record this
response. After detecting the timepoints of the spikes in your
recordings you get what the \texttt{lifboltzmanspikes()} function
@ -101,20 +101,22 @@ spikes = lifboltzmanspikes( trials, input, tmax, Dnoise, imax, ithresh, slope );
differrent stimuli.
\part Measure the tuning curve of the neuron with respect to the
input. That is, compute the mean firing rate (number of spikes within the recording time \texttt{tmax} divided by \texttt{tmax}) as a function of the
input strength. Find an appropriate range of input values. Do
this for different values of the \texttt{slope} parameter (values
between 0.1 and 2.0).
\part Generate histograms of the spike counts within $W=200$\,ms
of the responses to the two differrent stimuli $I_1$ and
$I_2$. How do they depend on the slope of the tuning curve of the
neuron?
input. That is, compute the mean firing rate (number of spikes
within the recording time \texttt{tmax} divided by \texttt{tmax}
and averaged over trials) as a function of the input
strength. Find an appropriate range of input values. Do this for
different values of the \texttt{slope} parameter (values between
0.1 and 2.0).
\part For the two differrent stimuli $I_1$ and $I_2$ generate
histograms of the spike counts of the evoked responses within all
windows of $W=200$\,ms width. How do the histograms of the spike
counts depend on the slope of the tuning curve of the neuron?
\part Think about a measure based on the spike count histograms
that quantifies how well the two stimuli can be distinguished
based on the spike counts. Plot the dependence of this measure as
a function of the observation time $W$.
a function of the observation time $W$ (width of the windows).
For which slopes can the two stimuli be well discriminated?

11
projects/project_fano_time/fano_time.tex
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@ -79,6 +79,17 @@ spikes = lifadaptspikes( trials, input, tmax, Dnoise, adapttau, adaptincr );
elements, each being a vector of spike times (in seconds) computed
for a duration of \texttt{tmax} seconds.
Think of calling the \texttt{lifadaptspikes()} function as a
simple way of doing an electrophysiological experiment. You are
presenting a stimulus with a constant intensity $I$ that you set. The
neuron responds to this stimulus, and you record this
response. After detecting the timepoints of the spikes in your
recordings you get what the \texttt{lifadaptspikes()} function
returns. The advantage over real data is, that you have the
possibility to simply modify the properties of the neuron via the
\texttt{Dnoise}, \texttt{adapttau}, and
\texttt{adaptincr} parameter.
For the two inputs $I_1$ and $I_2$ use
\begin{lstlisting}
input = 65.0; % I_1

19
projects/project_noiseficurves/noiseficurves.tex
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@ -56,7 +56,7 @@
as a current $I$ injected via a patch-electrode into the neuron).
Measure the tuning curve (also called the intensity-response curve) of the
neuron. That is, what is the firing rate of the neuron's response
neuron. That is, what is the mean firing rate of the neuron's response
as a function of the input $I$. How does this depend on the level of
the intrinsic noise of the neuron?
@ -74,9 +74,22 @@ spikes = lifspikes( trials, input, tmax, Dnoise );
of spike times (in seconds) computed for a duration of \texttt{tmax} seconds.
The input is set via the \texttt{input} variable, the noise strength via \texttt{Dnoise}.
Think of calling the \texttt{lifspikes()} function as a
simple way of doing an electrophysiological experiment. You are
presenting a stimulus with a constant intensity $I$ that you set. The
neuron responds to this stimulus, and you record this
response. After detecting the timepoints of the spikes in your
recordings you get what the \texttt{lifspikes()} function
returns. The advantage over real data is, that you have the
possibility to simply modify the properties of the neuron via the
\texttt{Dnoise} parameter.
\begin{parts}
\part First set the noise \texttt{Dnoise=0} (no noise). Compute and plot the firing rate
as a function of the input for inputs ranging from 0 to 20.
\part First set the noise \texttt{Dnoise=0} (no noise). Compute
and plot the mean firing rate (number of spikes within the
recording time \texttt{tmax} divided by \texttt{tmax} and averaged
over trials) as a function of the input for inputs ranging from 0
to 20.
\part Do the same for various noise strength \texttt{Dnoise}. Use $D_{noise} = 1e-3$,
1e-2, and 1e-1. How does the intrinsic noise influence the response curve?