From 29b33fdf93ccb72ab70e216f494e49e2166a1232 Mon Sep 17 00:00:00 2001 From: Fabian Sinz Date: Mon, 3 Nov 2014 11:55:53 +0100 Subject: [PATCH] small changes --- projects/project_fano_slope/fano_slope.tex | 13 ++++++------- projects/project_fano_time/fano_time.tex | 11 +++++------ projects/project_isipdffit/isipdffit.tex | 2 +- 3 files changed, 12 insertions(+), 14 deletions(-) diff --git a/projects/project_fano_slope/fano_slope.tex b/projects/project_fano_slope/fano_slope.tex index 7d2fe2d..ebb30c0 100644 --- a/projects/project_fano_slope/fano_slope.tex +++ b/projects/project_fano_slope/fano_slope.tex @@ -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? diff --git a/projects/project_fano_time/fano_time.tex b/projects/project_fano_time/fano_time.tex index 2c893b1..761561d 100644 --- a/projects/project_fano_time/fano_time.tex +++ b/projects/project_fano_time/fano_time.tex @@ -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? diff --git a/projects/project_isipdffit/isipdffit.tex b/projects/project_isipdffit/isipdffit.tex index fc48361..7ffc3a1 100644 --- a/projects/project_isipdffit/isipdffit.tex +++ b/projects/project_isipdffit/isipdffit.tex @@ -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?