Optimal coding of a random stimulus by a population of parallel neuron models

Abstract

Copyright © 2007 SPIE - The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. Copyright 2007 Society of Photo-Optical Instrumentation Engineers. This paper was published in Noise and Fluctuations in Biological, Biophysical, and Biomedical Systems, edited by Sergey M. Bezrukov, Proc. of SPIE Vol. 6602, 66020R and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.We examine the question of how a population of independently noisy sensory neurons should be configured to optimize the encoding of a random stimulus into sequences of neural action potentials. For the case where firing rates are the same in all neurons, we consider the problem of optimizing the noise distribution for a known stimulus distribution, and the converse problem of optimizing the stimulus for a given noise distribution. This work is related to suprathreshold stochastic resonance (SSR). It is shown that, for a large number of neurons, the SSR model is equivalent to a single rate-coding neuron with multiplicative output noise.Mark D. McDonnell, Nigel G. Stocks and Derek Abbot