teaching/scientificComputing
Archived
3
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

small changes

Browse Source
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
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

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? For which slopes can the two stimuli be well discriminated?
\underline{Hint:} A possible readout is to set a threshold $n_{thresh}$ for \underline{Hint:} A possible readout is to set a threshold
the observed spike count. Any response smaller than the threshold $n_{thresh}$ for the observed spike count. Any response smaller
assumes that the stimulus was $I_1$, any response larger than the than the threshold assumes that the stimulus was $I_1$, any
threshold assumes that the stimulus was $I_2$. What is the response larger than the threshold assumes that the stimulus was
probability that the stimulus was indeed $I_1$ or $I_2$, $I_2$. Find the threshold $n_{thresh}$ that results in the best
respectively? Find the threshold $n_{thresh}$ that discrimination performance.
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? \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
View File

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