Modeling surround suppression in V1 neurons with a statistically-derived normalization model

Eero P. Simoncelli, Odelia Schwartz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

59 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems
PublisherNeural information processing systems foundation
Pages153-154
Number of pages2
ISBN (Print)0262112450, 9780262112451
StatePublished - 1999
Externally publishedYes
Event12th Annual Conference on Neural Information Processing Systems, NIPS 1998 - Denver, CO, United States
Duration: Nov 30 1998Dec 5 1998

Other

Other12th Annual Conference on Neural Information Processing Systems, NIPS 1998
CountryUnited States
CityDenver, CO
Period11/30/9812/5/98

Fingerprint

Neurons
Processing
Statistics
Experiments

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Simoncelli, E. P., & Schwartz, O. (1999). Modeling surround suppression in V1 neurons with a statistically-derived normalization model. In Advances in Neural Information Processing Systems (pp. 153-154). Neural information processing systems foundation.

Modeling surround suppression in V1 neurons with a statistically-derived normalization model. / Simoncelli, Eero P.; Schwartz, Odelia.

Advances in Neural Information Processing Systems. Neural information processing systems foundation, 1999. p. 153-154.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Simoncelli, EP & Schwartz, O 1999, Modeling surround suppression in V1 neurons with a statistically-derived normalization model. in Advances in Neural Information Processing Systems. Neural information processing systems foundation, pp. 153-154, 12th Annual Conference on Neural Information Processing Systems, NIPS 1998, Denver, CO, United States, 11/30/98.
Simoncelli EP, Schwartz O. Modeling surround suppression in V1 neurons with a statistically-derived normalization model. In Advances in Neural Information Processing Systems. Neural information processing systems foundation. 1999. p. 153-154
Simoncelli, Eero P. ; Schwartz, Odelia. / Modeling surround suppression in V1 neurons with a statistically-derived normalization model. Advances in Neural Information Processing Systems. Neural information processing systems foundation, 1999. pp. 153-154
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