Real-time simulation of biologically realistic stochastic neurons in VLSI

Authors:
Hsin Chen;Sylvain Saïghi;Laure Buhry;Sylvie Renaud
Affiliations:
Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan;IMS Lab, University of Bordeaux, France;IMS Lab, University of Bordeaux, France;IMS Lab, University of Bordeaux, France
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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Abstract

Neuronal variability has been thought to play an important role in the brain. As the variability mainly comes from the uncertainty in biophysical mechanisms, stochastic neuron models have been proposed for studying how neurons compute with noise. However, most papers are limited to simulating stochastic neurons in a digital computer. The speed and the efficiency are thus limited especially when a large neuronal network is of concern. This brief explores the feasibility of simulating the stochastic behavior of biological neurons in a very large scale integrated (VLSI) system, which implements a programmable and configurable Hodgkin-Huxley model. By simply injecting noise to the VLSI neuron, various stochastic behaviors observed in biological neurons are reproduced realistically in VLSI. The noise-induced variability is further shown to enhance the signal modulation of a neuron. These results point toward the development of analog VLSI systems fOlr exploring the stochastic behaviors of biological neuronal networks in large scale.