Modeling Surround Suppression in V1 Neurons with a
Statistically-Derived Normalization Model
Published in:
Advances in Neural Information Processing Systems 11
ed. M.S. Kearns, S.A. Solla and D.A. Cohn, pp. 153-159, May 1999.
© MIT Press, Cambridge, MA.
Presented at:
Neural Information Processing Systems, Denver CO, 1-3 Dec 1998.
We examine the statistics of natural monochromatic images decomposed
using a multi-scale wavelet basis. Although the coefficients of this
representation are nearly decorrelated, they exhibit important
higher-order statistical dependencies that cannot be eliminated with
purely linear processing. In particular, rectified coefficients
corresponding to basis functions at neighboring spatial positions,
orientations and scales are highly correlated. A method of removing
these dependencies is to divide each coefficient by a weighted
combination of its rectified neighbors. Several successful models of
the steady-state behavior of neurons in primary visual cortex are
based on such ``divisive normalization'' computations, and thus our
analysis provides a theoretical justification for these models.
Perhaps more importantly, the statistical measurements explicitly
specify the weights that should be used in computing the normalization
signal. We demonstrate that this weighting is qualitatively
consistent with recent physiological experiments that characterize the
suppressive effect of stimuli presented outside of the classical
receptive field. Our observations thus provide evidence for the
hypothesis that early visual neural processing is well
matched to these statistical properties of images.
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