Probabilistic population codes for Bayesian decision making.

Published

Journal Article

When making a decision, one must first accumulate evidence, often over time, and then select the appropriate action. Here, we present a neural model of decision making that can perform both evidence accumulation and action selection optimally. More specifically, we show that, given a Poisson-like distribution of spike counts, biological neural networks can accumulate evidence without loss of information through linear integration of neural activity and can select the most likely action through attractor dynamics. This holds for arbitrary correlations, any tuning curves, continuous and discrete variables, and sensory evidence whose reliability varies over time. Our model predicts that the neurons in the lateral intraparietal cortex involved in evidence accumulation encode, on every trial, a probability distribution which predicts the animal's performance. We present experimental evidence consistent with this prediction and discuss other predictions applicable to more general settings.

Full Text

Duke Authors

Cited Authors

  • Beck, JM; Ma, WJ; Kiani, R; Hanks, T; Churchland, AK; Roitman, J; Shadlen, MN; Latham, PE; Pouget, A

Published Date

  • December 26, 2008

Published In

Volume / Issue

  • 60 / 6

Start / End Page

  • 1142 - 1152

PubMed ID

  • 19109917

Pubmed Central ID

  • 19109917

Electronic International Standard Serial Number (EISSN)

  • 1097-4199

Digital Object Identifier (DOI)

  • 10.1016/j.neuron.2008.09.021

Language

  • eng

Conference Location

  • United States