Probabilistic Population Codes for Bayesian Decision Making

Jeffrey M. Beck, Wei Ji Ma, Roozbeh Kiani, Tim Hanks, Anne K. Churchland, Jamie Roitman, Michael N. Shadlen, Peter E. Latham, Alexandre Pouget

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1142-1152
Number of pages11
JournalNeuron
Volume60
Issue number6
DOIs
StatePublished - Dec 26 2008

Keywords

  • SYSNEURO

ASJC Scopus subject areas

  • Neuroscience(all)

Fingerprint Dive into the research topics of 'Probabilistic Population Codes for Bayesian Decision Making'. Together they form a unique fingerprint.

  • Cite this

    Beck, J. M., Ma, W. J., Kiani, R., Hanks, T., Churchland, A. K., Roitman, J., Shadlen, M. N., Latham, P. E., & Pouget, A. (2008). Probabilistic Population Codes for Bayesian Decision Making. Neuron, 60(6), 1142-1152. https://doi.org/10.1016/j.neuron.2008.09.021