Statistically efficient estimation using cortical lateral connection

Alexandre Pouget, Kechen Zhang

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

Abstract

Coarse codes are widely used throughout the brain to encode sensory and motor variables. Methods designed to interpret these codes, such as population vector analysis, are either inefficient, i.e., the variance of the estimate is much larger than the smallest possible variance, or biologically implausible, like maximum likelihood. Moreover, these methods attempt to compute a scalar or vector estimate of the encoded variable. Neurons are faced with a similar estimation problem. They must read out the responses of the presynaptic neurons, but, by contrast, they typically encode the variable with a further population code rather than as a scalar. We show how a non-linear recurrent network can be used to perform these estimation in an optimal way while keeping the estimate in a coarse code format. This work suggests that lateral connections in the cortex may be involved in cleaning up uncorrelated noise among neurons representing similar variables.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 9 - Proceedings of the 1996 Conference, NIPS 1996
PublisherNeural information processing systems foundation
Pages97-103
Number of pages7
ISBN (Print)0262100657, 9780262100656
StatePublished - Jan 1 1997
Externally publishedYes
Event10th Annual Conference on Neural Information Processing Systems, NIPS 1996 - Denver, CO, United States
Duration: Dec 2 1996Dec 5 1996

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other10th Annual Conference on Neural Information Processing Systems, NIPS 1996
CountryUnited States
CityDenver, CO
Period12/2/9612/5/96

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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