297 lines
14 KiB
TeX
Executable File
297 lines
14 KiB
TeX
Executable File
\documentclass[12pt,a4paper,pdftex]{article}
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\usepackage[left=25mm, right=25mm, top=20mm, bottom=25mm]{geometry}
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\usepackage{graphicx}
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\usepackage{amsmath}
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\usepackage{natbib}
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\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=false,citecolor=blue]{hyperref}
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\usepackage[utf8x]{inputenc}
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\usepackage[english]{babel}
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%\usepackage{float}
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\usepackage{floatrow}
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\usepackage{listings} % für den code am Ende
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Ab hier beginnt der eigentliche Text:
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\begin{document}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Titelseite
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\begin{titlepage}
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\begin{center}
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{\Huge TITEL \par}
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\vspace{0.75cm}
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{\Large Masterthesis \par}
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\vspace{0.25cm}
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{der Mathematisch-Naturwissenschaftlichen Fakultät \par} {der Eberhard Karls Universität Tübingen \par}
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\vspace{0.75cm}
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{Erstkorrektor: \\
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Zweitkorrektor: Prof.~Dr.~Jan Benda \par}
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\vspace{0.25cm}
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{Lehrbereich für Neuroethologie}
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\vfill
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\large vorgelegt von \par
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\large Alexander Mathias Ott \par
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Abgabedatum: 30.11.2017
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\end{center}
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\end{titlepage}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Erklärung
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\section*{Eigenständigkeitserklärung}
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\vspace{0.5cm}
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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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\vspace{2mm}
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\noindent
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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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\vfill
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\begin{tabular}{ll}
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$\overline{\text{Unterschrift}\hspace{6cm}}$ & $\overline{\text{Ort, Datum}\hspace{4cm}}$ \\
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\end{tabular}
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\newpage\newpage
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Inhalsverzeichnis
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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{
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\hypersetup{linkcolor=black}
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\tableofcontents
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}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Zusammenfassung
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\section{Abstract}
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%Einleitung + Ergebnisse der Diskussion in kurz
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Einleitung
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\section{Introduction}
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\begin{enumerate}
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\item electric fish
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\begin{enumerate}
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\item general: habitat,
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\item as model animal for ethology
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\item electric organ + eod
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\item sensory neurons p- and t(?)-type
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\end{enumerate}
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\item sensory perception
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\begin{enumerate}
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\item receptor -> heterogenic population
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\item further analysis limited by what receptors code for - P-Units encoding
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\item p-type neurons code AMs
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\end{enumerate}
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\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
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\item Possible to draw representative values for model parameters to generate a population ?
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\end{enumerate}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Methoden
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\section{Materials and Methods}
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\subsection{Notes:}
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\begin{enumerate}
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\item Construction of model
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\begin{enumerate}
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\item Explain general LIF
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\item parameters explanation, dif. equations
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\item Explain addition of adaption current
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\item note addition of noise
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\item check between alpha in fire-rate model adaption and a-delta in LIFAC
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\item check for noise independence from step-size (?)
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\end{enumerate}
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\item Data generation
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\begin{enumerate}
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\item How data was measured / which data used
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\item How data was chosen -> at least 30s baseline, 7 contrasts with 7 trials
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\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)
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\end{enumerate}
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\item behavior parameters:
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\begin{enumerate}
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\item which behaviors were looked at / calculated and why (bf, vs, sc, cv, fi-curve...)
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\item how exactly were they calculated in the cell and model
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\end{enumerate}
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\item Fitting of model to data
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\begin{enumerate}
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\item which variables where determined beforehand (None, just for start parameters)
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\item which variables where fit
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\item What method was used (Nelder-Mead) and why/(how it works?)
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\item fit routine ? (currently just all at the same time)
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\end{enumerate}
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\end{enumerate}
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\section{Results}
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\begin{enumerate}
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\item how well does the fitting work?
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\item distribution of behavior parameters (cells and models)
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\item distributions of parameters
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\item correlations: between parameters between parameters and behavior
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\item correlation between final error and behavior parameters of the cell -> hard to fit cell types
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\item (response to SAM stimuli)
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\end{enumerate}
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\section{Discussion}
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\section{Possible Sources}
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\subsection{Henriette Walz - Thesis}
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\subsubsection{Nervous system - Signal encoding}
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\begin{enumerate}
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\item single neurons are the building blocks of the nervous system (Cajal 1899)
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\item encoding of information in spike frequency - rate code(first description(?) Adrian 1928) also find examples! (light flash intensity Barlow et al. 1971, )
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\item encoding info in inter spike intervals (Singer and Gary 1995)
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\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
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\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))
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\item noise can be beneficial to encoding -> “stochastic
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resonance” (weak stimuli on thresholding devices like neurons, noice allows coding of sub threshold stimuli) (Benzi et al., 1981)
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\item single neurons are anatomically and computationally independant units, the
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representation and processing of information in vertebrate nervous systems is distributed
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over groups or networks of cells (for a review, see Pouget et al., 2000)
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\item It has
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been shown that the synchrony among cells carries information on a very fine temporal
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scale in different modalities, from olfaction (Laurent, 1996) to vision (Dan et al., 1998)
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\item In the electrosensory system it was shown before that communica-
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tion signals change the synchrony of the receptor population (Benda et al., 2005, 2006)
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and that this is read out by cells in the successive stages of the electrosensory pathway
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(Marsat and Maler, 2010, 2012; Marsat et al., 2009).
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\item An advantage of rate coding in populations is that it is fast. The rate in
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single neurons has to be averaged over a time window, that is at least as long as the
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minimum interspike interval. In contrast, the population rate can follow the stimulus
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instantaneous, as it does not have to be averaged over time but can be averaged over
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cells (Knight, 1972a).
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\item In a population of neurons subject to neuronal noise, stochastic resonance occurs
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even if the stimulus is strong enough to trigger action potentials itself (supra-threshold
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stochastic resonance described by Stocks, 2000; see Fig. 1.1 B
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\item Cells of the same type and from the same population often vary in their stimulus sen-
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sitivity (Ringach et al., 2002) as well as in their baseline activity properties (Gussin et al., 2007; Hospedales et al., 2008)
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\item Heterogeneity has been shown to improve infor-
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mation coding in both situations, in the presence of noise correlations, for example in
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the visual system cells (Chelaru and Dragoi, 2008) or when correlations mainly originate
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from shared input as in the olfactory system (Padmanabhan and Urban, 2010)
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\item A prerequisite to a neural code thus is that it can be read out by other neurons (Perkel
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and Bullock, 1968).
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\item Developement and evolution shape the func-
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tioning of many physiological systems and there is evidence that they also shape the
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encoding mechanisms of nervous systems. For example, the development of frequency
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selectivity in the auditory cortex has been shown to be delayed in animals stimulated
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with white noise only (Chang and Merzenich, 2003). Also, several encoding mecha-
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nisms can be related to the selective pressure that the energetic consumption of the ner-
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vous system has exerted on its evolution (Laughlin, 2001; Niven and Laughlin, 2008).
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These finding conformed earlier theoretical predictions that had proposed that coding
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should be optimised to encode natural stimuli in an energy-efficient way (Barlow, 1972). -> importance of using natrual stimuli as the coding and nervous system could be optimised for unknown stimuli features not contained in the artificial stimuli like white noise.
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\end{enumerate}
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\subsubsection{electrosensory system - electric fish}
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\begin{enumerate}
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\item For decades, studies examining the neurophysiological systems of weakly electric
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fish have provided insights into how natural behaviours are generated using relatively
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simple sensorimotor circuits (for recent reviews see: Chacron et al., 2011; Fortune, 2006;
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Marsat and Maler, 2012). Further, electrocommunication signals are relatively easy to
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describe, classify and simulate, facilitating quantification and experimental manipula-
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tion. Weakly electric fish are therefore an ideal system for examining how communica-
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tion signals influence sensory scenes, drive sensory system responses, and consequently
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exert effects on conspecific behaviour.
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\item The weakly electric fish use active electroreception to navigate and communicate under
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low light conditions (Zupanc et al., 2001).
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\item In active electroreception, animals produce
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an electric field using and electric organ (and this electric field is therefore called the
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electric organ discharge, EOD) and infer, from changes of the EOD, information about
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the location and identification of objects and conspecifics in their vicinity (e.g. Kelly
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et al., 2008; MacIver et al., 2001). However, perturbations result not only from objects
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and other fish, but also from self-motion and other factors. All of these together make
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up the electrosensory scene. The perturbed version of the fish’s own field on its skin
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is called the electric image (Caputi and Budelli, 2006), which is sensed via specialised
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receptors distributed over the body surface (Carr et al., 1982).
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\item In A. leptorhynchus, the
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dipole-like electric field (electric organ discharge, EOD) oscillates in a quasi-sinusoidal
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fashion at frequencies from 700 to 1100 Hz (Zakon et al., 2002) with males emitting at
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higher frequencies than females (Meyer et al., 1987).
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\item The EOD of each individual fish
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has a specific frequency (the EOD frequency, EODf) that remains stable in time (exhibit-
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ing a coefficient of variation of the interspikes intervals as low as $2 ∗ 10^{−4} $; Moortgat et al., 1998).
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\item During social encounters, wave-type fish often modulate the frequency as
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well as the amplitude of their field to communicate (Hagedorn and Heiligenberg, 1985).
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\item Communication signals in A. leptorhynchus have been clas-
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sified into two classes: (i) chirps are transient and stereotyped EODf excursions over
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tens of milliseconds (Zupanc et al., 2006), while (ii) rises are longer duration and more
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variable modulations of EODf, typically lasting for hundreds of milliseconds to sec-
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onds (Hagedorn and Heiligenberg, 1985; Tallarovic and Zakon, 2002). (OLD INFO ? RISES NOW OVER MINUTES/HOURS)
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\end{enumerate}
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\subsubsection{P-Units encoding}
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\begin{enumerate}
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\item In baseline conditions (stimulus only own EOD), they fire irregularly at a certain baseline rate. Action potentials occur approximately at a certain phase of the EOD cycle, they are phase-locked to the EOD, but only with a certain probability to each cycle. The baseline rate differs from cell to cell (compare the two example cells in Fig. 2.2 A and B, Gussin et al., 2007)
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\item Since tuberous receptors are distributed over the whole body and the EOD spans the
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whole surrounding, all P-units of a given animal are stimulated with a similar stimulus
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(see Kelly et al. (2008) for an exact model of the EOD). Their noise sources are, however,
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uncorrelated (Chacron et al., 2005b).
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\item In response to a step increase in EOD amplitude, P-units exhibit pronounced spike frequency
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adaptation (Benda et al., 2005; Chacron et al., 2001b; Nelson et al., 1997; Xu et al., 1996).
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\end{enumerate}
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\subsubsection{Chapter 4 - other models}
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\begin{enumerate}
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\itemKashimori et al.
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(1996) built a conductance-based model of the whole electroreceptor unit and were able to qualitatively reproduce the behaviour of different types of tuberous units.
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\item Nelson
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et al. (1997) constrained a stochastically spiking model by linear filters of the previously determined P-unit frequency tuning.
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\item Kreiman et al. (2000) used the same frequency
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filters to stimulate a noisy perfect integrate-and-fire neuron with which they investi-
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gated the variability of cell responses to random amplitude modulations (RAMs).
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\item To reproduce the probabilistic phase-locked firing and the correlations of the ISIs, Chacron
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et al. (2000) used a noisy leaky integrate-and-fire model with refractoriness as well as a
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dynamical threshold.
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\item Benda et al. (2005) used a firing rate model with a negative adap-
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tation current to reproduce the high-pass behaviour of P-units.
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\end{enumerate}
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\end{document} |