Natural Signal Statistics and Sensory Gain Control

Odelia Schwartz and Eero P Simoncelli

Published in:
Nature:Neuroscience
Vol 4, num 8, pp 819-825, August, 2001.
© Macmillan Magazines Ltd.

Related publications/presentations:
  • Annual Reviews of Neuroscience (review article on efficient coding), May 2001
  • Book chapter: Modeling adaptation with statistically-derived normalization
  • NIPS, Dec 1998
  • ARVO, May 1998
  • Asilomar conference, Nov 1997


We describe a form of nonlinear decomposition that is well-suited for efficient encoding of natural signals. Signals are initially decomposed using a bank of linear filters. Each filter response is then rectified and divided by a weighted sum of rectified responses of neighboring filters. We show that this decomposition, with parameters optimized for the statistics of a generic ensemble of natural images or sounds, provides a surprisingly good characterization of the nonlinear response properties of typical neurons in primary visual cortex or auditory nerve, respectively. These results suggest that nonlinear response properties of sensory neurons are not an accident of biological implementation, but serve an important functional role.
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