P-unit_model/thesis/Masterthesis.tex

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		\begin{center}
		{\Huge TITEL \par}
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		{\Large Masterthesis \par}
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		{der Mathematisch-Naturwissenschaftlichen Fakultät \par} {der Eberhard Karls Universität Tübingen \par}
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		{Erstkorrektor: \\
		Zweitkorrektor: Prof.~Dr.~Jan Benda \par}
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		{Lehrbereich für Neuroethologie}
		
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		\large vorgelegt von \par
		\large Alexander Mathias Ott \par
		Abgabedatum: 30.11.2017
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\section*{Eigenständigkeitserklärung}

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Hiermit erkläre ich, dass ich die vorgelegte Arbeit selbstständig verfasst habe und keine anderen als die angegebenen Quellen und Hilfsmittel benutzt habe. 

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Außerdem erkläre ich, dass die eingereichte Arbeit weder vollständig noch in wesentlichen Teilen Gegenstand eines anderen Prüfungsverfahrens gewesen ist.






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\section{Abstract}
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\section{Introduction}

\begin{enumerate}
	\item electric fish
	\begin{enumerate}
		\item general: habitat, 
		\item as model animal for ethology
		\item electric organ + eod
		\item sensory neurons p- and t(?)-type
	\end{enumerate}
	\item sensory perception
	\begin{enumerate}
		\item receptor -> heterogenic population
		\item further analysis limited by what receptors code for
		\item p-type neurons code AMs
	\end{enumerate}
	
	\item goal be able to simulate heterogenic population to analyse full coding properties -> many cells at the same time needed -> only possible in vitro/ with model simulations
	
	\item Possible to draw representative values for model parameters to generate a population ?
	

\end{enumerate}

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\section{Materials and Methods}

\subsection{Notes:}

\begin{enumerate}
	
	\item Construction of model
	\begin{enumerate}
		\item Explain general LIF
		\item parameters explanation, dif. equations
		\item Explain addition of adaption current
		\item note addition of noise
		\item check between alpha in fire-rate model adaption and a-delta in LIFAC
		\item check for noise independence from step-size (?)
	\end{enumerate}
	
	\item Data generation
	\begin{enumerate}
		\item How data was measured / which data used
		\item How data was chosen -> at least 30s baseline, 7 contrasts with 7 trials
		\item experimental protocells were allowed by XYZ (before 2012: All experimental protocols were approved and complied with national and regional laws (file no. 55.2-1-54-2531-135-09). between 2013-2016 ZP 1/13 Regierungspräsidium Tübingen and after 2016 ZP 1/16 Regierungspräsidium Tübingen)
	\end{enumerate}
	
	\item behavior parameters:
	\begin{enumerate}
		\item which behaviors were looked at / calculated and why (bf, vs, sc, cv, fi-curve...)
		\item how exactly were they calculated in the cell and model
	\end{enumerate}		
	
	\item Fitting of model to data
	\begin{enumerate}
		\item which variables where determined beforehand (None, just for start parameters)
		\item which variables where fit 
		\item What method was used (Nelder-Mead) and why/(how it works?)
		\item fit routine ? (currently just all at the same time)
	\end{enumerate}
\end{enumerate}


\section{Results}

\begin{enumerate}

\item how well does the fitting work? 

\item distribution of behavior parameters (cells and models)

\item distributions of parameters

\item correlations: between parameters between parameters and behavior

\item correlation between final error and behavior parameters of the cell -> hard to fit cell types

\item (response to SAM stimuli)
\end{enumerate}


\section{Discussion}



\section{Possible Sources}

\subsection{Henriette Walz - Thesis}
\subsubsection{Nervous system - Signal encoding}
\begin{enumerate}
\item single neurons are the building blocks of the nervous system (Cajal 1899)
\item encoding of information in spike frequency - rate code(first description(?) Adrian 1928) also find examples! (light flash intensity Barlow et al. 1971, )
\item encoding info in inter spike intervals (Singer and Gary 1995)
\item encoding time window (Theunissen and Miller 1995) "This time window is the time scale in which the encoding is assumed to take placewithin the nervous system
\item encoding is noisy (Mainen and Sejnowski 1995, Tolhurst et al 1983, Tomko and Crapper 1974 -> review Faisal et al 2008) in part because of stimulus properties but also cell properties (Ion channel stochasticity (van Rossum et al.,2003))
\item noise can be beneficial to encoding -> “stochastic
resonance” (weak stimuli on thresholding devices like neurons, noice allows coding of sub threshold stimuli) (Benzi et al., 1981)
\end{enumerate}


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