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fixes in project description, exchange data for eod project

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This commit is contained in:
Jan Grewe 2015-11-06 11:04:05 +01:00
parent d126c785fe
commit 566d82cd6b
3 changed files with 7 additions and 5 deletions
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10
projects/project_adaptation_fit/adaptation_fit.tex
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@ -40,10 +40,12 @@ electroreceptors of the weakly electric fish \textit{Apteronotus
\begin{questions} \begin{questions}
\question In the accompanying datasets you find the \question In the accompanying datasets you find the
\textit{spike\_times} of an P-unit electrorecptor to a stimulus of a \textit{spike\_times} of an P-unit electroreceptor to a stimulus of
certain intensity, i.e. the \textit{contrast} which is also stored a certain intensity, i.e. the \textit{contrast} which is also stored
in the file. The data is sampled with 20\,kHz sampling frequency and in the file. The contrast of the stimulus is a measure relative to
spike times are given in milliseconds relative to the stimulus onset. the amplitude of fish's field, it has no unit. The data is sampled
with 20\,kHz sampling frequency and spike times are given in
milliseconds relative to the stimulus onset.
\begin{parts} \begin{parts}
\part Estimate for each stimulus intensity the PSTH and plot \part Estimate for each stimulus intensity the PSTH and plot
it. You will see that there are three parts. (i) The first it. You will see that there are three parts. (i) The first

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projects/project_eod/EOD_data.mat
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2
projects/project_stimulus_reconstruction/stimulus_reconstruction.tex
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@ -57,7 +57,7 @@ reconstruct the stimulus a neuron has been stimulated with.
error using the mean-square-error and express it relative to the error using the mean-square-error and express it relative to the
variance of the original stimulus. variance of the original stimulus.
\begin{equation} \begin{equation}
err = \frac{1}{N} \cdot \displaystyle\sum^{N}_{i=1}(x_i - \bar{x}), err = \frac{1}{N} \cdot \displaystyle\sum^{N}_{i=1}(x_i - \bar{x})^2,
\end{equation} \end{equation}
with $N$ the number of data points, $x_i$ the current value and with $N$ the number of data points, $x_i$ the current value and
$\bar{x}$, the average of all $x$. $\bar{x}$, the average of all $x$.