Alexander/P-unit_model
2
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

start of discussion

Browse Source
This commit is contained in:
a.ott 2020-09-17 12:11:52 +02:00
parent b4e1a8663e
commit dc6adfbd1c
2 changed files with 11 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

BIN
thesis/Masterthesis.pdf
View File

Binary file not shown.

20
thesis/Masterthesis.tex
View File

@ -437,6 +437,7 @@ All errors were then summed up for the full error. The fits were done with the N
\caption{\label{tab:scaling_factors} Scaling factors for fitting errors.}
\end{table}
\todo{Fitting more in detail number of start parameters the start parameters themselves}
\todo{explain removal of bad models! Filter criteria how many filtered etc.}
@ -526,27 +527,29 @@ All errors were then summed up for the full error. The fits were done with the N
\section{Discussion}
In this thesis a simple model based on the leaky integrate-and-fire (LIF) model was built to allow the simulation of a neuron population correctly representing the heterogeneity of P-units in the electrosensory pathway of the electric fish \textit{A. leptorhynchus}. The LIF model was extended by an adaption current, a refractory period and simulated the input synapses by rectifying and low pass filtering the input current. This model was then fit to single in vivo recordings of P-units characterized by seven behavior parameters and the resulting models compared to the reference cell. Additionally estimations of the model parameter distributions and their covariances were used to draw random parameter sets and the generated population of P-units compared to the data set.
\subsection*{Fitting quality}
\begin{itemize}
\item many bursty cells not well fitted
\item bursties might need more data to be well defined (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 but some with weird structure in ISI hist are not possible
\end{itemize}
\subsection*{Behavior correlations}
\subsection*{Fitting quality}
\begin{itemize}
\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}
@ -555,7 +558,6 @@ All errors were then summed up for the full error. The fits were done with the N
\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}
\newpage