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fixed many index entries

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This commit is contained in:
Jan Benda
2019-12-09 20:01:27 +01:00
parent f24c14e6f5
commit bf52536b7b
12 changed files with 332 additions and 306 deletions
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30
pointprocesses/lecture/pointprocesses.tex
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@@ -73,10 +73,10 @@ number of observed events within a certain time window $n_i$
(\figref{pointprocessscetchfig}).
\begin{exercise}{rasterplot.m}{}
Implement a function \code{rasterplot()} that displays the times of
action potentials within the first \code{tmax} seconds in a raster
Implement a function \varcode{rasterplot()} that displays the times of
action potentials within the first \varcode{tmax} seconds in a raster
plot. The spike times (in seconds) recorded in the individual trials
are stored as vectors of times within a \codeterm{cell-array}.
are stored as vectors of times within a \codeterm{cell array}.
\end{exercise}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -95,10 +95,10 @@ describing the statistics of stochastic real-valued variables:
\end{figure}
\begin{exercise}{isis.m}{}
Implement a function \code{isis()} that calculates the interspike
Implement a function \varcode{isis()} that calculates the interspike
intervals from several spike trains. The function should return a
single vector of intervals. The spike times (in seconds) of each
trial are stored as vectors within a \codeterm{cell-array}.
trial are stored as vectors within a cell-array.
\end{exercise}
%\subsection{First order interval statistics}
@@ -117,7 +117,7 @@ describing the statistics of stochastic real-valued variables:
\end{itemize}
\begin{exercise}{isihist.m}{}
Implement a function \code{isiHist()} that calculates the normalized
Implement a function \varcode{isiHist()} that calculates the normalized
interspike interval histogram. The function should take two input
arguments; (i) a vector of interspike intervals and (ii) the width
of the bins used for the histogram. It further returns the
@@ -126,7 +126,7 @@ describing the statistics of stochastic real-valued variables:
\begin{exercise}{plotisihist.m}{}
Implement a function that takes the return values of
\code{isiHist()} as input arguments and then plots the data. The
\varcode{isiHist()} as input arguments and then plots the data. The
plot should show the histogram with the x-axis scaled to
milliseconds and should be annotated with the average ISI, the
standard deviation and the coefficient of variation.
@@ -167,7 +167,7 @@ $\rho_k$ is usually plotted against the lag $k$
with itself and is always 1.
\begin{exercise}{isiserialcorr.m}{}
Implement a function \code{isiserialcorr()} that takes a vector of
Implement a function \varcode{isiserialcorr()} that takes a vector of
interspike intervals as input argument and calculates the serial
correlation. The function should further plot the serial
correlation. \pagebreak[4]
@@ -213,12 +213,12 @@ time interval , \determ{Feuerrate}) that is given in Hertz
% \end{figure}
\begin{exercise}{counthist.m}{}
Implement a function \code{counthist()} that calculates and plots
Implement a function \varcode{counthist()} that calculates and plots
the distribution of spike counts observed in a certain time
window. The function should take two input arguments: (i) a
\codeterm{cell-array} of vectors containing the spike times in
seconds observed in a number of trials, and (ii) the duration of the
time window that is used to evaluate the counts.\pagebreak[4]
cell-array of vectors containing the spike times in seconds observed
in a number of trials, and (ii) the duration of the time window that
is used to evaluate the counts.\pagebreak[4]
\end{exercise}
@@ -244,7 +244,7 @@ In an \enterm[Poisson process!inhomogeneous]{inhomogeneous Poisson
\lambda(t)$.
\begin{exercise}{poissonspikes.m}{}
Implement a function \code{poissonspikes()} that uses a homogeneous
Implement a function \varcode{poissonspikes()} that uses a homogeneous
Poisson process to generate events at a given rate for a certain
duration and a number of trials. The rate should be given in Hertz
and the duration of the trials is given in seconds. The function
@@ -293,7 +293,7 @@ The homogeneous Poisson process has the following properties:
\end{itemize}
\begin{exercise}{hompoissonspikes.m}{}
Implement a function \code{hompoissonspikes()} that uses a
Implement a function \varcode{hompoissonspikes()} that uses a
homogeneous Poisson process to generate spike events at a given rate
for a certain duration and a number of trials. The rate should be
given in Hertz and the duration of the trials is given in
@@ -422,7 +422,7 @@ potentials (\figref{binpsthfig} top). The resulting histogram is then
normalized with the bin width $W$ to yield the firing rate shown in
the bottom trace of figure \ref{binpsthfig}. The above sketched
process is equivalent to estimating the probability density. It is
possible to estimate the PSTH using the \code{hist()} method
possible to estimate the PSTH using the \code{hist()} function
\sindex[term]{Feuerrate!Binningmethode}
The estimated firing rate is valid for the total duration of each