Int J Neural Syst - Hardware implementation of stochastic spiking neural networks.

Tópicos

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Resumo

Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.

Resumo Limpo

spike neural network last generat artifici neural network character bioinspir natur higher comput capac respect neural model real biolog neuron stochast process repres import mechan neural behavior respons special arithmet capabl work present simpl hardwar implement spike neuron consid probabilist natur advantag propos implement fulli digit therefor can massiv implement field programm gate array high comput capabl propos model demonstr studi feedforward recurr network abl implement highspe signal filter solv complex system linear equat

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