The role of the asymptotic dynamics in the design of FPGA-based hardware implementations of gIF-type neural networks

Horacio Rostro 1 Bruno Cessac 1 Bernard Girau 2 César Torres-Huitzil 2
1 NEUROMATHCOMP
CRISAM - Inria Sophia Antipolis - Méditerranée , INRIA Rocquencourt, ENS Paris - École normale supérieure - Paris, UNS - Université Nice Sophia Antipolis, CNRS - Centre National de la Recherche Scientifique : UMR8548
2 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : This paper presents a numerical analysis of the role of asymptotic dynamics in the design of hardware-based implementations of the generalised integrate-and-fire (gIF) neuron models. These proposed implementations are based on extensions of the discrete-time spiking neuron model, which was introduced by Soula et al., and have been implemented on Field Programmable Gate Array (FPGA) devices using fixed-point arithmetic. Mathematical studies conducted by Cessac have evidenced the existence of three main regimes (neural death, periodic and chaotic regimes) in the activity of such neuron models. These activity regimes are characterised in hardware by considering a precision analysis in the design of an architecture for an FPGA-based implementation. The proposed approach, although based on gIF neuron models and FPGA hardware, can be extended to more complex neuron models as well as to different in silico implementations.
Type de document :
Article dans une revue
Journal of Physiology - Paris, Elsevier, 2011, 105 (1-3), pp.91-97. 〈10.1016/j.jphysparis.2011.09.004〉
Liste complète des métadonnées

https://hal.inria.fr/hal-00642997
Contributeur : Bernard Girau <>
Soumis le : dimanche 20 novembre 2011 - 22:38:58
Dernière modification le : vendredi 12 janvier 2018 - 01:50:25

Identifiants

Collections

Citation

Horacio Rostro, Bruno Cessac, Bernard Girau, César Torres-Huitzil. The role of the asymptotic dynamics in the design of FPGA-based hardware implementations of gIF-type neural networks. Journal of Physiology - Paris, Elsevier, 2011, 105 (1-3), pp.91-97. 〈10.1016/j.jphysparis.2011.09.004〉. 〈hal-00642997〉

Partager

Métriques

Consultations de la notice

201