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This commit is contained in:
Fabian Sinz 2014-11-03 11:55:53 +01:00
parent 57f727ecf4
commit 29b33fdf93
3 changed files with 12 additions and 14 deletions
projects
project_fano_slope
fano_slope.tex
project_fano_time
fano_time.tex
project_isipdffit
isipdffit.tex

13
projects/project_fano_slope/fano_slope.tex
View File

@ -103,13 +103,12 @@ spikes = lifboltzmanspikes( trials, input, tmax, Dnoise, imax, ithresh, slope );
For which slopes can the two stimuli be well discriminated?
\underline{Hint:} A possible readout is to set a threshold $n_{thresh}$ for
the observed spike count. Any response smaller than the threshold
assumes that the stimulus was $I_1$, any response larger than the
threshold assumes that the stimulus was $I_2$. What is the
probability that the stimulus was indeed $I_1$ or $I_2$,
respectively? Find the threshold $n_{thresh}$ that
results in the best discrimination performance.
\underline{Hint:} A possible readout is to set a threshold
$n_{thresh}$ for the observed spike count. Any response smaller
than the threshold assumes that the stimulus was $I_1$, any
response larger than the threshold assumes that the stimulus was
$I_2$. Find the threshold $n_{thresh}$ that results in the best
discrimination performance.
\part Also plot the Fano factor as a function of the slope. How is it related to the discriminability?

11
projects/project_fano_time/fano_time.tex
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@ -97,12 +97,11 @@ input = 75.0; % I_2
For which observation times can the two stimuli perfectly discriminated?
\underline{Hint:} A possible readout is to set a threshold $n_{thresh}$ for
the observed spike count. Any response smaller than the threshold
assumes that the stimulus was $I_1$, any response larger than the
threshold assumes that the stimulus was $I_2$. What is the
probability that the stimulus was indeed $I_1$ or $I_2$,
respectively? For a given $W$ find the threshold $n_{thresh}$ that
\underline{Hint:} A possible readout is to set a threshold
$n_{thresh}$ for the observed spike count. Any response smaller
than the threshold assumes that the stimulus was $I_1$, any
response larger than the threshold assumes that the stimulus was
$I_2$. For a given $W$ find the threshold $n_{thresh}$ that
results in the best discrimination performance.
\part Also plot the Fano factor as a function of $W$. How is it related to the discriminability?

2
projects/project_isipdffit/isipdffit.tex
View File

@ -119,7 +119,7 @@ spikes = pifouspikes( trials, input, tmax, Dnoise, outau );
interspike intervals. How well do they describe the real
distributions for the different conditions?
\part Also fit eq.~(\ref{pcn}) to the data. Here you need to apply a non-linear fit algorithm.
\part Also fit eq.~(\ref{pcn}) to the data using maximum (log)-likelihood.
How well does this function describe the data?