Statistically Efficient Estimation Using Population Coding

Alexandre Pouget, Kechen Zhang, Sophie Deneve, Peter E. Latham

Research output: Contribution to journalArticlepeer-review

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 (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 nonlinear recurrent network can be used to perform estimation in a near-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)
Pages (from-to)373-401
Number of pages29
JournalNeural Computation
Volume10
Issue number2
DOIs
StatePublished - Feb 15 1998
Externally publishedYes

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience

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