Natural Sound Statistics and Divisive Normalization in the Auditory System
Presented (as a talk) at:
Neural Information Processing Systems, Denver CO, Dec 2000.
Published in:
Advances in Neural Information Processing Systems 13
ed. T.K. Leen, T.G. Dietterich, and V. Tresp,
To appear, May 2001.
© MIT Press, Cambridge, MA.
We explore the statistical properties of natural sound stimuli
pre-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 reduced using an
operation known as divisive normalization, in which the response
of each filter is divided by a weighted sum of the rectified
responses of other filters. The weights may be chosen to maximize
the independence of the normalized responses for an ensemble of
natural sounds. We demonstrate that the resulting model accounts
for non-linearities in the response characteristics of the
auditory nerve, by comparing 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.
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