期刊
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
卷 22, 期 4, 页码 -出版社
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0129065712500141
关键词
Stochastic spiking neural networks; neural networks; signal processing; hardware implementation; Gabor filters
资金
- European Development Funds (FEDER)
- Spanish Ministry of Economy and Competitiveness [TEC2011-23113]
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.
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