Natural Signal Statistics and Sensory Gain Control
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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