Natural Sound Statistics and Divisive Normalization in the Auditory System

Abstract

We explore the statistical properties of natural sound stimuli pre(cid:173) processed with a bank of linear filters. The responses of such filters exhibit a striking form of statistical dependency, in which the response variance of each filter grows with the response amplitude of filters tuned for nearby frequencies. These dependencies may be substantially re(cid:173) duced using an operation known as divisive normalization, in which the response of each filter is divided by a weighted sum of the recti(cid:173) fied responses of other filters. The weights may be chosen to maximize the independence of the normalized responses for an ensemble of natu(cid:173) ral sounds. We demonstrate that the resulting model accounts for non(cid:173) linearities in the response characteristics of the auditory nerve, by com(cid:173) paring model simulations to electrophysiological recordings. In previous work (NIPS, 1998) we demonstrated that an analogous model derived from the statistics of natural images accounts for non-linear properties of neurons in primary visual cortex. Thus, divisive normalization appears to be a generic mechanism for eliminating a type of statistical dependency that is prevalent in natural signals of different modalities.