Probabilistic Population Codes for Bayesian Decision Making

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). pp. 1142-1152. ISSN 08966273 (ISSN)

DOI: 10.1016/j.neuron.2008.09.021


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. © 2008 Elsevier Inc. All rights reserved.

Item Type: Paper
Uncontrolled Keywords: SYSNEURO sysneuro article Bayes theorem brain nerve cell decision making electroencephalogram nerve cell network parietal lobe priority journal probability reliability Action Potentials Animals Computer Simulation Haplorhini Humans Models, Neurological Motion Perception Neural Networks computer Neurons Nonlinear Dynamics Photic Stimulation Reaction Time Time Factors
Subjects: organism description > animal behavior > decision making
CSHL Authors:
Communities: CSHL labs > Churchland lab
Depositing User: CSHL Librarian
Date: 2008
Date Deposited: 20 Mar 2012 14:58
Last Modified: 08 May 2013 15:40
PMCID: PMC2742921
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