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fixed fano slop eproject

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Jan Benda
2017-01-22 15:31:40 +01:00
parent 1e2ce84104
commit 5d2beb12eb
11 changed files with 354 additions and 159 deletions
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projects/project_fano_time/fano_time.tex
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@@ -52,58 +52,64 @@
\begin{questions}
\question An important property of sensory systems is their ability
to discriminate similar stimuli. For example, to discriminate two
colors, light intensities, pitch of two tones, sound intensity, etc.
to discriminate similar stimuli. For example, discrimination of two
colors, light intensities, pitch of two tones, sound intensities, etc.
Here we look at the level of a single neuron. What does it mean that
two similar stimuli can be discriminated given the spike train
responses that have been evoked by the two stimuli?
You are recording the activity of a neuron in response to two
different stimuli $I_1$ and $I_2$ (think of them, for example, of
two light intensities with different intensities $I_1$ and $I_2$ and
the activity of a ganglion cell in the retina). The neuron responds
to a stimulus with a number of spikes. You (an upstream neuron) can
count the number of spikes of this response within an observation
time of duration $T$. For perfect discrimination, the number of
spikes evoked by the stronger stimulus within $T$ is larger than for
two different light intensities, $I_1$ and $I_2$, and the spiking
activity of a ganglion cell in the retina). The neuron responds to a
stimulus with a number of spikes. You (an upstream neuron) can count
the number of spikes of this response within an observation time of
duration $T$. For perfect discrimination, the number of spikes
evoked by the stronger stimulus within $T$ is always larger than for
the smaller stimulus. The situation is more complicated, because the
number of spikes evoked by one stimulus is not fixed but varies.
How well can an upstream neuron discriminate the two
stimuli based on the spike counts $n$? How does this depend on the
duration $T$ of the observation time?
number of spikes evoked by one stimulus is not fixed but varies,
such that the number of spikes evoked by the stronger stimulus could
happen to be lower than the number of spikes evoked by the smaller
stimulus.
The central questions of this project are:
\begin{itemize}
\item How can an upstream neuron discriminate two stimuli based
on the spike counts $n$?
\item How does this depend on the duration $T$ of the observation
time?
\end{itemize}
The neuron is implemented in the file \texttt{lifspikes.m}.
Call it like this:
\begin{lstlisting}
\begin{lstlisting}
trials = 10;
tmax = 50.0;
input = 15.0;
spikes = lifspikes(trials, input, tmax);
\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. The intensity of the stimulus
is given by \texttt{input}.
\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. The intensity of the
stimulus is given by \texttt{input}.
Think of calling the \texttt{lifspikes()} function as a
simple way of doing an electrophysiological experiment. You are
presenting a stimulus with an intensity $I$ that you set. The
neuron responds to this stimulus, and you record this
response. After detecting the time points of the spikes in your
recordings you get what the \texttt{lifspikes()} function
returns.
Think of calling the \texttt{lifspikes()} function as a simple way
of doing an electrophysiological experiment. You are presenting a
stimulus with an intensity $I$ that you set. The neuron responds to
this stimulus, and you record this response. After detecting the
time points of the spikes in your recordings you get what the
\texttt{lifspikes()} function returns.
For the two inputs $I_1$ and $I_2$ use
\begin{lstlisting}
For the two inputs $I_1$ and $I_2$ to be discriminated use
\begin{lstlisting}
input = 14.0; % I_1
input = 15.0; % I_2
\end{lstlisting}
\end{lstlisting}
\begin{parts}
\part
Show two raster plots for the responses to the two different
stimuli. Find an appropriate time window and an appropriate
stimuli. Use an appropriate time window and an appropriate
number of trials for the spike raster.
Just by looking at the raster plots, can you discriminate the two
@@ -111,12 +117,13 @@ input = 15.0; % I_2
responses?
\part Generate properly normalized histograms of the spike counts
within $T$ (use $T=100$\,ms) of the responses to the two different
stimuli. Do the two histograms overlap? What does this mean for
the discriminability of the two stimuli?
within $T$ (use $T=100$\,ms) of the spike responses to the two
different stimuli. Do the two histograms overlap? What does this
mean for the discriminability of the two stimuli?
How do the histograms depend on the observation time $T$ (use
values of 10\,ms, 100\,ms, 300\,ms and 1\,s)?
How do the histograms of the spike counts depend on the
observation time $T$? Plot them for four different values of $T$
(use values of 10\,ms, 100\,ms, 300\,ms and 1\,s).
\part Think about a measure based on the spike-count histograms
that quantifies how well the two stimuli can be distinguished