From b35365232d83336774f3d06c9bd269171d821b02 Mon Sep 17 00:00:00 2001 From: Jan Benda Date: Tue, 3 Nov 2015 12:53:42 +0100 Subject: [PATCH] Updated projects --- projects/project_fano_slope/fano_slope.tex | 24 ++++++++++--------- projects/project_fano_time/fano_time.tex | 11 +++++++++ .../project_noiseficurves/noiseficurves.tex | 19 ++++++++++++--- 3 files changed, 40 insertions(+), 14 deletions(-) diff --git a/projects/project_fano_slope/fano_slope.tex b/projects/project_fano_slope/fano_slope.tex index 40e2946..1fdc0b4 100644 --- a/projects/project_fano_slope/fano_slope.tex +++ b/projects/project_fano_slope/fano_slope.tex @@ -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? diff --git a/projects/project_fano_time/fano_time.tex b/projects/project_fano_time/fano_time.tex index e4291a7..868b026 100644 --- a/projects/project_fano_time/fano_time.tex +++ b/projects/project_fano_time/fano_time.tex @@ -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 diff --git a/projects/project_noiseficurves/noiseficurves.tex b/projects/project_noiseficurves/noiseficurves.tex index 9259b87..b185577 100644 --- a/projects/project_noiseficurves/noiseficurves.tex +++ b/projects/project_noiseficurves/noiseficurves.tex @@ -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?