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.
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Acknowledgements
The authors would like to thank FAPEMIG for funding the project, the laboratory of intelligent systems of CEFET-MG for the technical support and for making available its infra-structure, which made this work possible.
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Soares, G.E., Borges, H.E., Gomes, R.M. et al. Emergence of synchronicity in a self-organizing spiking neuron network: an approach via genetic algorithms. Nat Comput 11, 405–413 (2012). https://doi.org/10.1007/s11047-011-9288-3
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DOI: https://doi.org/10.1007/s11047-011-9288-3