Emergence of synchronicity in a self-organizing spiking neuron network: an approach via genetic algorithms

Authors:
Gabriela E. Soares;Henrique E. Borges;Rogério M. Gomes;Gustavo M. Zeferino;Antônio P. Braga
Affiliations:
Laboratório de Sistemas Inteligentes--LSI-CEFET-MG, Belo Horizonte, Brazil CEP 30.510-000;Laboratório de Sistemas Inteligentes--LSI-CEFET-MG, Belo Horizonte, Brazil CEP 30.510-000;Laboratório de Sistemas Inteligentes--LSI-CEFET-MG, Belo Horizonte, Brazil CEP 30.510-000;Laboratório de Sistemas Inteligentes--LSI-CEFET-MG, Belo Horizonte, Brazil CEP 30.510-000;Universidade Federal de Minas Gerais, Belo Horizonte, Brazil CEP 31270-010
Venue:
Natural Computing: an international journal
Year:
2012

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Abstract

Based on the Theory of Neuronal Group Selection (TNGS), we have investigated the emergence of synchronicity in a network composed of spiking neurons via genetic algorithm. The TNGS establishes that a neuronal group is the most basic unit in the cortical area of the brain and, as a rule, it is not formed by a single neuron, but by a cluster of tightly coupled neural cells which fire and oscillate in synchrony at a predefined frequency. Thus, this paper describes a method of tuning the parameters of the Izhikevich spiking neuron model through genetic algorithm in order to enable the self-organization of the neural network. Computational experiments were performed considering a network composed of neurons of the same type and another composed of neurons of different types.