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Proceedings Paper

Optimal quantization for energy-efficient information transfer in a population of neuron-like devices
Author(s): Mark D. McDonnell; Nigel G. Stocks; Charles E. M. Pearce; Derek Abbott
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Paper Abstract

Suprathreshold Stochastic Resonance (SSR) is a recently discovered form of stochastic resonance that occurs in populations of neuron-like devices. A key feature of SSR is that all devices in the population possess identical threshold nonlinearities. It has previously been shown that information transmission through such a system is optimized by nonzero internal noise. It is also clear that it is desirable for the brain to transfer information in an energy efficient manner. In this paper we discuss the energy efficient maximization of information transmission for the case of variable thresholds and constraints imposed on the energy available to the system, as well as minimization of energy for the case of a fixed information rate. We aim to demonstrate that under certain conditions, the SSR configuration of all devices having identical thresholds is optimal. The novel feature of this work is that optimization is performed by finding the optimal threshold settings for the population of devices, which is equivalent to solving a noisy optimal quantization problem.

Paper Details

Date Published: 25 May 2004
PDF: 11 pages
Proc. SPIE 5471, Noise in Complex Systems and Stochastic Dynamics II, (25 May 2004); doi: 10.1117/12.546934
Show Author Affiliations
Mark D. McDonnell, Univ. of Adelaide (Australia)
Nigel G. Stocks, Univ. of Warwick (United Kingdom)
Charles E. M. Pearce, Univ. of Adelaide (Australia)
Derek Abbott, Univ. of Adelaide (Australia)


Published in SPIE Proceedings Vol. 5471:
Noise in Complex Systems and Stochastic Dynamics II
Zoltan Gingl, Editor(s)

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