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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 Think of calling the \texttt{lifboltzmanspikes()} function as a
simple way of doing an electrophysiological experiment. You are 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 neuron responds to this stimulus, and you record this
response. After detecting the timepoints of the spikes in your response. After detecting the timepoints of the spikes in your
recordings you get what the \texttt{lifboltzmanspikes()} function recordings you get what the \texttt{lifboltzmanspikes()} function
@ -101,20 +101,22 @@ spikes = lifboltzmanspikes( trials, input, tmax, Dnoise, imax, ithresh, slope );
differrent stimuli. differrent stimuli.
\part Measure the tuning curve of the neuron with respect to the \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. That is, compute the mean firing rate (number of spikes
input strength. Find an appropriate range of input values. Do within the recording time \texttt{tmax} divided by \texttt{tmax}
this for different values of the \texttt{slope} parameter (values and averaged over trials) as a function of the input
between 0.1 and 2.0). strength. Find an appropriate range of input values. Do this for
different values of the \texttt{slope} parameter (values between
\part Generate histograms of the spike counts within $W=200$\,ms 0.1 and 2.0).
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 \part For the two differrent stimuli $I_1$ and $I_2$ generate
neuron? 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 \part Think about a measure based on the spike count histograms
that quantifies how well the two stimuli can be distinguished that quantifies how well the two stimuli can be distinguished
based on the spike counts. Plot the dependence of this measure as 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? 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 elements, each being a vector of spike times (in seconds) computed
for a duration of \texttt{tmax} seconds. 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 For the two inputs $I_1$ and $I_2$ use
\begin{lstlisting} \begin{lstlisting}
input = 65.0; % I_1 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). as a current $I$ injected via a patch-electrode into the neuron).
Measure the tuning curve (also called the intensity-response curve) of the 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 as a function of the input $I$. How does this depend on the level of
the intrinsic noise of the neuron? 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. 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}. 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} \begin{parts}
\part First set the noise \texttt{Dnoise=0} (no noise). Compute and plot the firing rate \part First set the noise \texttt{Dnoise=0} (no noise). Compute
as a function of the input for inputs ranging from 0 to 20. 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 $D_{noise} = 1e-3$, \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? 1e-2, and 1e-1. How does the intrinsic noise influence the response curve?