### Abstract

We explore the statistical properties of natural sound stimuli pre-processed with a bank of linear filters. The responses of such filters exhibit a striking form of statistical dependency, in which the response variance of each filter grows with the response amplitude of filters tuned for nearby frequencies. These dependencies may be substantially reduced using an operation known as divisive normalization, in which the response of each filter is divided by a weighted sum of the rectified responses of other filters. The weights may be chosen to maximize the independence of the normalized responses for an ensemble of natural sounds. We demonstrate that the resulting model accounts for non-linearities in the response characteristics of the auditory nerve, by comparing model simulations to electrophysiological recordings. In previous work (NIPS, 1998) we demonstrated that an analogous model derived from the statistics of natural images accounts for non-linear properties of neurons in primary visual cortex. Thus, divisive normalization appears to be a generic mechanism for eliminating a type of statistical dependency that is prevalent in natural signals of different modalities.

Original language | English (US) |
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Title of host publication | Advances in Neural Information Processing Systems 13 - Proceedings of the 2000 Conference, NIPS 2000 |

Publisher | Neural information processing systems foundation |

ISBN (Print) | 0262122413, 9780262122412 |

State | Published - 2001 |

Event | 14th Annual Neural Information Processing Systems Conference, NIPS 2000 - Denver, CO, United States Duration: Nov 27 2000 → Dec 2 2000 |

### Other

Other | 14th Annual Neural Information Processing Systems Conference, NIPS 2000 |
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Country | United States |

City | Denver, CO |

Period | 11/27/00 → 12/2/00 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Networks and Communications
- Information Systems
- Signal Processing

### Cite this

*Advances in Neural Information Processing Systems 13 - Proceedings of the 2000 Conference, NIPS 2000*Neural information processing systems foundation.

**Natural sound statistics and divisive normalization in the auditory system.** / Schwartz, Odelia; Simoncelli, Eero.

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

*Advances in Neural Information Processing Systems 13 - Proceedings of the 2000 Conference, NIPS 2000.*Neural information processing systems foundation, 14th Annual Neural Information Processing Systems Conference, NIPS 2000, Denver, CO, United States, 11/27/00.

}

TY - GEN

T1 - Natural sound statistics and divisive normalization in the auditory system

AU - Schwartz, Odelia

AU - Simoncelli, Eero

PY - 2001

Y1 - 2001

N2 - We explore the statistical properties of natural sound stimuli pre-processed with a bank of linear filters. The responses of such filters exhibit a striking form of statistical dependency, in which the response variance of each filter grows with the response amplitude of filters tuned for nearby frequencies. These dependencies may be substantially reduced using an operation known as divisive normalization, in which the response of each filter is divided by a weighted sum of the rectified responses of other filters. The weights may be chosen to maximize the independence of the normalized responses for an ensemble of natural sounds. We demonstrate that the resulting model accounts for non-linearities in the response characteristics of the auditory nerve, by comparing model simulations to electrophysiological recordings. In previous work (NIPS, 1998) we demonstrated that an analogous model derived from the statistics of natural images accounts for non-linear properties of neurons in primary visual cortex. Thus, divisive normalization appears to be a generic mechanism for eliminating a type of statistical dependency that is prevalent in natural signals of different modalities.

AB - We explore the statistical properties of natural sound stimuli pre-processed with a bank of linear filters. The responses of such filters exhibit a striking form of statistical dependency, in which the response variance of each filter grows with the response amplitude of filters tuned for nearby frequencies. These dependencies may be substantially reduced using an operation known as divisive normalization, in which the response of each filter is divided by a weighted sum of the rectified responses of other filters. The weights may be chosen to maximize the independence of the normalized responses for an ensemble of natural sounds. We demonstrate that the resulting model accounts for non-linearities in the response characteristics of the auditory nerve, by comparing model simulations to electrophysiological recordings. In previous work (NIPS, 1998) we demonstrated that an analogous model derived from the statistics of natural images accounts for non-linear properties of neurons in primary visual cortex. Thus, divisive normalization appears to be a generic mechanism for eliminating a type of statistical dependency that is prevalent in natural signals of different modalities.

UR - http://www.scopus.com/inward/record.url?scp=84898932123&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84898932123&partnerID=8YFLogxK

M3 - Conference contribution

SN - 0262122413

SN - 9780262122412

BT - Advances in Neural Information Processing Systems 13 - Proceedings of the 2000 Conference, NIPS 2000

PB - Neural information processing systems foundation

ER -