A Framework for Simulation and Analysis of Dynamically Organized Distributed Neural Networks

Authors:
Vladyslav Shaposhnyk;Pierre Dutoit;Victor Contreras-Lámus;Stephen Perrig;Alessandro E. Villa
Affiliations:
Neuroheuristic Research Group, Grenoble Institute of Neuroscience, Université Joseph Fourier, Grenoble, France and Non-linear Analysis Department, Institute for Applied System Analysis, State ...;Neuroheuristic Research Group, Grenoble Institute of Neuroscience, Université Joseph Fourier, Grenoble, France and Sleep Research Laboratory, Dept. of Psychiatry Belle-Idée, Hôpitau ...;Neuroheuristic Research Group, Grenoble Institute of Neuroscience, Université Joseph Fourier, Grenoble, France;Sleep Research Laboratory, Dept. of Psychiatry Belle-Idée, Hôpitaux Universitaires de Genève, Switzerland;Neuroheuristic Research Group, Grenoble Institute of Neuroscience, Université Joseph Fourier, Grenoble, France and Sleep Research Laboratory, Dept. of Psychiatry Belle-Idée, Hôpitau ...
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Year:
2009

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Abstract

We present a framework for modelling and analyzing emerging neural activity from multiple interconnected modules, where each module is formed by a neural network. The neural network simulator operates a 2D lattice tissue of leaky integrate-and-fire neurons with genetic, ontogenetic and epigenetic features. The Java Agent DEvelopment (JADE) environment allows the implementation of an efficient automata-like virtually unbound and platform-independent system of agents exchanging hierarchically organized messages. This framework allowed us to develop linker agents capable to handle dynamic configurations characterized by the entrance and exit of additional modules at any time following simple rewiring rules. The development of a virtual electrode allows the recording of a "neural" generated signal, called electrochipogram (EChG), characterized by dynamics close to biological local field potentials and electroencephalograms (EEG). These signals can be used to compute Evoked Potentials by complex sensory inputs and comparisons with neurophysiological signals of similar kind.