Alexander/P-unit_model
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a.ott
2020-09-20 13:35:28 +02:00
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@@ -38,6 +38,8 @@ Bastian 1981a Electrolocation I. How the electroreceptors of Apteronotus albifro
\section{Introduction}
\begin{enumerate}
\item sensory input important for all life etc.
\item electric fish
\begin{enumerate}
\item general: habitat,
@@ -59,6 +61,7 @@ Bastian 1981a Electrolocation I. How the electroreceptors of Apteronotus albifro
\end{enumerate}
\subsection{Apteronotus leptorynchus}
\begin{itemize}
@@ -227,11 +230,38 @@ FI-Curve:
\section{Discussion}
\subsection{Fitting quality}
\begin{itemize}
\item todo
\item strongly bursty cells not well fitted with a gap between first EOD and the gauss distribution, but this is possible in the model just seems to be a "hard to reach" parameter combination. more start parameters changes in cost function.
\item additionally bursties might need more data to be "well defined" because of the more difficult pre-analysis of the cell itself (high variance in rate traces even with a mean of 7-10 trials)
\item different fitting routine / weights might also improve consistency of the fitting
\item Model CAN fit most types of cells except for double burst spikes in ISI hist (1st and 2nd EOD have high probability) and some with higher level structure in ISI hist are probably not possible with the current model
\item f-I curve works but is still not fully consistent slope of $f_0$ strongly affected by miss detections so not the best way of validating the $f_0$ response.
\item b-correlations:
\item Data correlation between base rate and VS unexpected and missing in the model.
\item data correlation between base rate and $f_0$ expected as $f_0$ ~ $f_\infty$ and $f_\infty$ ~ base rate (appeared in the model)
\item Model correlation between base rate and burstiness probably error because of the problems to "fit the gap" between burst and other ISIs. (appeared in model)
\item above probably causes base rate - SC correlation and this causes the additional SC - $f_\infty$ correlation over $f_\infty$ ~ base rate
\item In total it can deliver good models over a large space of the heterogeneity especially with the addition burstiness, but it is not yet verified with a different stimulus type like RAM or SAM and also need further investigation to it's quality for example because of the mismatched correlations $\rightarrow$ robustness analysis should be done
\end{itemize}
\subsection{random models}
\begin{itemize}
\item Gauss fits are in some cases questionable at best
\item resulting parameter distributions as such also not really that good
\item Parameter correlations have 4--5 correlations whose significance is strongly inconsistent/random even when using 1000 drawn models (while compensating for higher power): thus acceptable result??
\item behavior distribution not perfect by any means but quite alright except for the VS. Which definitely needs improvement! Maybe possible with more tweaking of the gauss fits.
\end{itemize}