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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.
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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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