Probabilistic population codes and the exponential family of distributions.

Published

Journal Article (Review)

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.

Full Text

Duke Authors

Cited Authors

  • Beck, J; Ma, WJ; Latham, PE; Pouget, A

Published Date

  • 2007

Published In

Volume / Issue

  • 165 /

Start / End Page

  • 509 - 519

PubMed ID

  • 17925267

Pubmed Central ID

  • 17925267

International Standard Serial Number (ISSN)

  • 0079-6123

Digital Object Identifier (DOI)

  • 10.1016/S0079-6123(06)65032-2

Language

  • eng

Conference Location

  • Netherlands