Neural Comput - A bayesian model of polychronicity.

Tópicos

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Resumo

A significant feature of spiking neural networks with varying connection delays, such as those in the brain, is the existence of strongly connected groups of neurons known as polychronous neural groups (PNGs). Polychronous groups are found in large numbers in these networks and are proposed by Izhikevich (2006a) to provide a neural basis for representation and memory. When exposed to a familiar stimulus, spiking neural networks produce consistencies in the spiking output data that are the hallmarks of PNG activation. Previous methods for studying the PNG activation response to stimuli have been limited by the template-based methods used to identify PNG activation. In this letter, we outline a new method that overcomes these difficulties by establishing for the first time a probabilistic interpretation of PNG activation. We then demonstrate the use of this method by investigating the claim that PNGs might provide the foundation of a representational system.

Resumo Limpo

signific featur spike neural network vari connect delay brain exist strong connect group neuron known polychron neural group pngs polychron group found larg number network propos izhikevich provid neural basi represent memori expos familiar stimulus spike neural network produc consist spike output data hallmark png activ previous method studi png activ respons stimuli limit templatebas method use identifi png activ letter outlin new method overcom difficulti establish first time probabilist interpret png activ demonstr use method investig claim pngs might provid foundat represent system

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