The neural costs of optimal control

Samuel J. Gershman, Robert C. Wilson

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

10 Scopus citations

Abstract

Optimal control entails combining probabilities and utilities. However, for most practical problems, probability densities can be represented only approximately. Choosing an approximation requires balancing the benefits of an accurate approximation against the costs of computing it. We propose a variational framework for achieving this balance and apply it to the problem of how a neural population code should optimally represent a distribution under resource constraints. The essence of our analysis is the conjecture that population codes are organized to maximize a lower bound on the log expected utility. This theory can account for a plethora of experimental data, including the reward-modulation of sensory receptive fields, GABAergic effects on saccadic movements, and risk aversion in decisions under uncertainty.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 23
Subtitle of host publication24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010
PublisherNeural Information Processing Systems
ISBN (Print)9781617823800
StatePublished - 2010
Externally publishedYes
Event24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010 - Vancouver, BC, Canada
Duration: Dec 6 2010Dec 9 2010

Publication series

NameAdvances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

Conference

Conference24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010
Country/TerritoryCanada
CityVancouver, BC
Period12/6/1012/9/10

ASJC Scopus subject areas

  • Information Systems

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