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Proceedings Paper

Neuronal dynamics on FPGA: Izhikevich's model
Author(s): M. La Rosa; E. Caruso; L. Fortuna; M. Frasca; L. Occhipinti; F. Rivoli
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Paper Abstract

The study of spatio-temporal patterns generation and processing in systems with high parallelism like biological neuronal networks gives birth to a new technology able to realize architectures with robust performance even in noisy environments. The behavioural properties of neural assemblies warrant an effective exchange and use of information in presence of high-level neuronal noise. Neuron population processing and self-organization have been reproduced by connecting several neuron through synaptic connections, which can be either electrical or chemical, in artificial information processing architectures based on Field Programmable Gate Arrays (FPGA). The adopted neuron model is based on Izhikevich’s description of cortical neuron dynamics [1]. The development of biological neuronal network models has been focused on architecture features like changes over time of topologies, uniformity of the connections, node diversity, etc. The hardware reproduction of neuron dynamical behaviour, by giving high computation performance, allows the development of innovative computational methods and models based on self-organizing nonlinear architectures.

Paper Details

Date Published: 29 June 2005
PDF: 8 pages
Proc. SPIE 5839, Bioengineered and Bioinspired Systems II, (29 June 2005); doi: 10.1117/12.608201
Show Author Affiliations
M. La Rosa, STMicroelectronics (Italy)
E. Caruso, Univ. degli Studi di Catania (Italy)
L. Fortuna, Univ. degli Studi di Catania (Italy)
M. Frasca, Univ. degli Studi di Catania (Italy)
L. Occhipinti, STMicroelectronics (Italy)
F. Rivoli, STMicroelectronics (Italy)


Published in SPIE Proceedings Vol. 5839:
Bioengineered and Bioinspired Systems II
Ricardo A. Carmona; Gustavo Linan-Cembrano, Editor(s)

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