Bayesian inference with probabilistic population codes.

Published

Journal Article

Recent psychophysical experiments indicate that humans perform near-optimal Bayesian inference in a wide variety of tasks, ranging from cue integration to decision making to motor control. This implies that neurons both represent probability distributions and combine those distributions according to a close approximation to Bayes' rule. At first sight, it would seem that the high variability in the responses of cortical neurons would make it difficult to implement such optimal statistical inference in cortical circuits. We argue that, in fact, this variability implies that populations of neurons automatically represent probability distributions over the stimulus, a type of code we call probabilistic population codes. Moreover, we demonstrate that the Poisson-like variability observed in cortex reduces a broad class of Bayesian inference to simple linear combinations of populations of neural activity. These results hold for arbitrary probability distributions over the stimulus, for tuning curves of arbitrary shape and for realistic neuronal variability.

Full Text

Duke Authors

Cited Authors

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

Published Date

  • November 2006

Published In

Volume / Issue

  • 9 / 11

Start / End Page

  • 1432 - 1438

PubMed ID

  • 17057707

Pubmed Central ID

  • 17057707

International Standard Serial Number (ISSN)

  • 1097-6256

Digital Object Identifier (DOI)

  • 10.1038/nn1790

Language

  • eng

Conference Location

  • United States