Optimal quantization for energy-efficient information transfer in a population of neuron-like devices

Abstract

Suprathreshold Stochastic Resonance (SSR) is a recently discoveredform 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 haspreviously been shown that information transmission through such asystem is optimized by nonzero internal noise. It is also clearthat it is desirable for the brain to transfer information in anenergy efficient manner. In this paper we discuss the energy efficient maximization of information transmission for the case ofvariable 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.