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[projects] little updates

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This commit is contained in:
Jan Benda
2019-01-11 14:07:34 +01:00
parent ae51f8c3e1
commit e826428446
7 changed files with 38 additions and 27 deletions
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16
projects/project_fano_time/fano_time.tex
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@@ -1,6 +1,6 @@
\documentclass[a4paper,12pt,pdftex]{exam}
\newcommand{\ptitle}{Stimulus discrimination}
\newcommand{\ptitle}{Stimulus discrimination: time}
\input{../header.tex}
\firstpagefooter{Supervisor: Jan Benda}{phone: 29 74573}%
{email: jan.benda@uni-tuebingen.de}
@@ -87,10 +87,11 @@ input = 15.0; % I_2
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
based on the spike counts. Plot the dependence of this measure as
a function of the observation time $T$.
\part \label{discrmeasure} 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
$T$.
For which observation times can the two stimuli perfectly
discriminated?
@@ -103,6 +104,11 @@ input = 15.0; % I_2
results in the best discrimination performance. How can you
quantify ``best discrimination'' performance?
\part Another way to quantify the discriminability of the spike
counts in response to the two stimuli is to apply an appropriate
statistical test and check for significant differences. How does
this compare to your findings from (\ref{discrmeasure})?
\end{parts}
\end{questions}