CUED Publications database

Neuronal dynamics on FPGA: Izhikevich's model

La Rosa, M and Caruso, E and Fortuna, L and Frasca, M and Occhipinti, L and Rivoli, F (2005) Neuronal dynamics on FPGA: Izhikevich's model. In: UNSPECIFIED pp. 87-94..

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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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Depositing User: Cron Job
Date Deposited: 01 Feb 2019 20:35
Last Modified: 15 Apr 2021 06:02
DOI: 10.1117/12.608201