title: Hardware implementation of stochastic spiking neural networks.
abstract: 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.
authors: Physics Department, Universitat de les Illes Balears, Cra. Valldemossa km. 7.5, Palma de Mallorca, Balears, 07122,  Spain.
authors: Josep L
authors: JL
authors: Rosselló
authors: Vincent
authors: V
authors: Canals
authors: Antoni
authors: A
authors: Morro
authors: Antoni
authors: A
authors: Oliver
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D003198:Computer Simulation; D003201:Computers; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation; D013269:Stochastic Processes; D013997:Time Factors