Spiking networks for Bayesian inference and choice.

Published

Journal Article (Review)

Systems neuroscience traditionally conceptualizes a population of spiking neurons as merely encoding the value of a stimulus. Yet, psychophysics has revealed that people take into account stimulus uncertainty when performing sensory or motor computations and do so in a nearly Bayes-optimal way. This suggests that neural populations do not encode just a single value but an entire probability distribution over the stimulus. Several such probabilistic codes have been proposed, including one that utilizes the structure of neural variability to enable simple neural implementations of probabilistic computations such as optimal cue integration. This approach provides a quantitative link between Bayes-optimal behaviors and specific neural operations. It allows for novel ways to evaluate probabilistic codes and for predictions for physiological population recordings.

Full Text

Duke Authors

Cited Authors

  • Ma, WJ; Beck, JM; Pouget, A

Published Date

  • April 2008

Published In

Volume / Issue

  • 18 / 2

Start / End Page

  • 217 - 222

PubMed ID

  • 18678253

Pubmed Central ID

  • 18678253

International Standard Serial Number (ISSN)

  • 0959-4388

Digital Object Identifier (DOI)

  • 10.1016/j.conb.2008.07.004

Language

  • eng

Conference Location

  • England