### 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 language | English (US) |
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Title of host publication | Advances in Neural Information Processing Systems |

Publisher | Neural information processing systems foundation |

Pages | 153-154 |

Number of pages | 2 |

ISBN (Print) | 0262112450, 9780262112451 |

State | Published - 1999 |

Externally published | Yes |

Event | 12th Annual Conference on Neural Information Processing Systems, NIPS 1998 - Denver, CO, United States Duration: Nov 30 1998 → Dec 5 1998 |

### Other

Other | 12th Annual Conference on Neural Information Processing Systems, NIPS 1998 |
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Country | United States |

City | Denver, CO |

Period | 11/30/98 → 12/5/98 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Networks and Communications
- Information Systems
- Signal Processing

### Cite this

*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.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*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.

}

TY - GEN

T1 - Modeling surround suppression in V1 neurons with a statistically-derived normalization model

AU - Simoncelli, Eero P.

AU - Schwartz, Odelia

PY - 1999

Y1 - 1999

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84898956385&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84898956385&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84898956385

SN - 0262112450

SN - 9780262112451

SP - 153

EP - 154

BT - Advances in Neural Information Processing Systems

PB - Neural information processing systems foundation

ER -