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The rate in +single neurons has to be averaged over a time window, that is at least as long as the +minimum interspike interval. In contrast, the population rate can follow the stimulus +instantaneous, as it does not have to be averaged over time but can be averaged over +cells (Knight, 1972a). +\item In a population of neurons subject to neuronal noise, stochastic resonance occurs +even if the stimulus is strong enough to trigger action potentials itself (supra-threshold +stochastic resonance described by Stocks, 2000; see Fig. 1.1 B +\item Cells of the same type and from the same population often vary in their stimulus sen- +sitivity (Ringach et al., 2002) as well as in their baseline activity properties (Gussin et al., 2007; Hospedales et al., 2008) +\item Heterogeneity has been shown to improve infor- +mation coding in both situations, in the presence of noise correlations, for example in +the visual system cells (Chelaru and Dragoi, 2008) or when correlations mainly originate +from shared input as in the olfactory system (Padmanabhan and Urban, 2010) +\item A prerequisite to a neural code thus is that it can be read out by other neurons (Perkel +and Bullock, 1968). +\item Developement and evolution shape the func- +tioning of many physiological systems and there is evidence that they also shape the +encoding mechanisms of nervous systems. For example, the development of frequency +selectivity in the auditory cortex has been shown to be delayed in animals stimulated +with white noise only (Chang and Merzenich, 2003). Also, several encoding mecha- +nisms can be related to the selective pressure that the energetic consumption of the ner- +vous system has exerted on its evolution (Laughlin, 2001; Niven and Laughlin, 2008). +These finding conformed earlier theoretical predictions that had proposed that coding +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. \end{enumerate} +\subsubsection{electrosensory system - electric fish} +\begin{enumerate} +\item For decades, studies examining the neurophysiological systems of weakly electric +fish have provided insights into how natural behaviours are generated using relatively +simple sensorimotor circuits (for recent reviews see: Chacron et al., 2011; Fortune, 2006; +Marsat and Maler, 2012). Further, electrocommunication signals are relatively easy to +describe, classify and simulate, facilitating quantification and experimental manipula- +tion. Weakly electric fish are therefore an ideal system for examining how communica- +tion signals influence sensory scenes, drive sensory system responses, and consequently +exert effects on conspecific behaviour. +\item The weakly electric fish use active electroreception to navigate and communicate under +low light conditions (Zupanc et al., 2001). +\item In active electroreception, animals produce +an electric field using and electric organ (and this electric field is therefore called the +electric organ discharge, EOD) and infer, from changes of the EOD, information about +the location and identification of objects and conspecifics in their vicinity (e.g. Kelly +et al., 2008; MacIver et al., 2001). However, perturbations result not only from objects +and other fish, but also from self-motion and other factors. All of these together make +up the electrosensory scene. The perturbed version of the fish’s own field on its skin +is called the electric image (Caputi and Budelli, 2006), which is sensed via specialised +receptors distributed over the body surface (Carr et al., 1982). +\item In A. leptorhynchus, the +dipole-like electric field (electric organ discharge, EOD) oscillates in a quasi-sinusoidal +fashion at frequencies from 700 to 1100 Hz (Zakon et al., 2002) with males emitting at +higher frequencies than females (Meyer et al., 1987). +\item The EOD of each individual fish +has a specific frequency (the EOD frequency, EODf) that remains stable in time (exhibit- +ing a coefficient of variation of the interspikes intervals as low as $2 ∗ 10^{−4} $; Moortgat et al., 1998). +\item During social encounters, wave-type fish often modulate the frequency as +well as the amplitude of their field to communicate (Hagedorn and Heiligenberg, 1985). +\item Communication signals in A. leptorhynchus have been clas- +sified into two classes: (i) chirps are transient and stereotyped EODf excursions over +tens of milliseconds (Zupanc et al., 2006), while (ii) rises are longer duration and more +variable modulations of EODf, typically lasting for hundreds of milliseconds to sec- +onds (Hagedorn and Heiligenberg, 1985; Tallarovic and Zakon, 2002). (OLD INFO ? RISES NOW OVER MINUTES/HOURS) +\end{enumerate} + +\subsubsection{P-Units encoding} + +\begin{enumerate} +\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) +\item Since tuberous receptors are distributed over the whole body and the EOD spans the +whole surrounding, all P-units of a given animal are stimulated with a similar stimulus +(see Kelly et al. (2008) for an exact model of the EOD). Their noise sources are, however, +uncorrelated (Chacron et al., 2005b). +\item In response to a step increase in EOD amplitude, P-units exhibit pronounced spike frequency +adaptation (Benda et al., 2005; Chacron et al., 2001b; Nelson et al., 1997; Xu et al., 1996). +\end{enumerate} + +\subsubsection{Chapter 4 - other models} +\begin{enumerate} +\itemKashimori et al. +(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. +\item Nelson +et al. (1997) constrained a stochastically spiking model by linear filters of the previously determined P-unit frequency tuning. +\item Kreiman et al. (2000) used the same frequency +filters to stimulate a noisy perfect integrate-and-fire neuron with which they investi- +gated the variability of cell responses to random amplitude modulations (RAMs). +\item To reproduce the probabilistic phase-locked firing and the correlations of the ISIs, Chacron +et al. (2000) used a noisy leaky integrate-and-fire model with refractoriness as well as a +dynamical threshold. +\item Benda et al. (2005) used a firing rate model with a negative adap- +tation current to reproduce the high-pass behaviour of P-units. +\end{enumerate} \end{document} \ No newline at end of file diff --git a/thesis/Masterthesis.toc b/thesis/Masterthesis.toc index d142f2e..748391c 100755 --- a/thesis/Masterthesis.toc +++ b/thesis/Masterthesis.toc @@ -5,3 +5,7 @@ \contentsline {subsection}{\numberline {3.1}Notes:}{2}{subsection.3.1} \contentsline {section}{\numberline {4}Results}{3}{section.4} \contentsline {section}{\numberline {5}Discussion}{3}{section.5} +\contentsline {section}{\numberline {6}Possible Sources}{3}{section.6} +\contentsline {subsection}{\numberline {6.1}Henriette Walz - Thesis}{3}{subsection.6.1} +\contentsline {subsubsection}{\numberline {6.1.1}Nervous system - Signal encoding}{3}{subsubsection.6.1.1} +\contentsline {subsubsection}{\numberline {6.1.2}electrosensory system - electric fish}{5}{subsubsection.6.1.2}