Probabilistic population codes and the exponential family of distributions

J. Beck, W. J. Ma, P. E. Latham, A. Pouget

Research output: Contribution to journalArticle

Abstract

Many experiments have shown that human behavior is nearly Bayes optimal in a variety of tasks. This implies that neural activity is capable of representing both the value and uncertainty of a stimulus, if not an entire probability distribution, and can also combine such representations in an optimal manner. Moreover, this computation can be performed optimally despite the fact that observed neural activity is highly variable (noisy) on a trial-by-trial basis. Here, we argue that this observed variability is actually expected in a neural system which represents uncertainty. Specifically, we note that Bayes' rule implies that a variable pattern of activity provides a natural representation of a probability distribution, and that the specific form of neural variability can be structured so that optimal inference can be executed using simple operations available to neural circuits.

Original languageEnglish (US)
Pages (from-to)509-519
Number of pages11
JournalProgress in Brain Research
Volume165
DOIs
StatePublished - 2007

Keywords

  • bayes
  • inference
  • neural coding
  • noise

ASJC Scopus subject areas

  • Neuroscience(all)

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