Questo sito utilizza cookie di terze parti per inviarti pubblicità in linea con le tue preferenze. Se vuoi saperne di più clicca QUI 
Chiudendo questo banner, scorrendo questa pagina, cliccando su un link o proseguendo la navigazione in altra maniera, acconsenti all'uso dei cookie. OK

A Computational Approach to the Study of an In-Vitro Population of Spontaneously Reverberating Neurons: Biological Models of Neuronal Networks and Statistical Methods for the Analysis of Their Ensemble Spiking Activity

In neuroscience, the computational approach is frequently used to study natural phenomena that would be too difficult to analyse, if not impossible, via direct experimentation. The role played by external stimuli in influencing neural activity, has a great interest in the neurophysiologic field. In particular the spontaneously reverberating activity, a collective oscillation of the consecutive firing and resting exhibited by the network, is widely analysed and existing studies are not yet able to provide an unified analytical model capable of describing completely and exhaustively the cognitive phenomenon. Each existing computational models is in fact characterised by the introduction of a certain degree of approximation.
The goal of this work was to study and compare the performances of the main neuronal network models both in terms of computational potential and calculating speed. The most important and widely spread analytical models were analysed in order to examine, from a statistical point of view, the spiking activity of a population of spontaneously reverberating neurons, a probabilistic phenomenon that occurs in correspondence of an external stimulus and it is manifested as a peak in the function that describes the membrane potential.

Mostra/Nascondi contenuto.
Chapter 1 Introduction 1.1 State−of−the−art In neuroscience, the computational approach is frequently used to study natural phenomena that would be too difficult to analyse, if not impossible, via direct experimentation. The higher and higher level of detail and realism represents the driving factor in the increasing spread of computational neuroscience, which will surely play a crucial role in the future of scientific research. The role played by external stimuli in influencing neural activity, has a great interest in the neurophysiologic field. In particular the spontaneously rever- berating activity, a collective oscillation of the consecutive firing and resting exhibited by the network, is widely analysed and existing studies are not yet able to provide an unified analytical model capable of describing completely and exhaustively the cognitive phenomenon. Each existing computational models is in fact characterised by the introduc- tion of a certain degree of approximation. The adopted simplifications penalise their versatility in a way that these turn out to be differently suited to simulat- ing specific aspects of the network, requiring the joint use of multiple analysis in order to obtain an organic description of the phenomenon. 1.2 Goals The goal of this work is to study and compare the performances of the main neuronal network models both in terms of computational potential and calculating speed. The issues already addressed in Dayan and Abbott [1] are 1

Laurea liv.II (specialistica)

Facoltà: Scienze Matematiche, Fisiche e Naturali

Autore: Alessandra Paladini Contatta »

Composta da 169 pagine.

 

Questa tesi ha raggiunto 104 click dal 26/11/2009.

Disponibile in PDF, la consultazione è esclusivamente in formato digitale.