Probabilistic population codes for Bayesian decision making.
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.
Beck
Jeffrey M
JM
Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627, USA.
Ma
Wei Ji
WJ
Kiani
Roozbeh
R
Hanks
Tim
T
Churchland
Anne K
AK
Roitman
Jamie
J
Shadlen
Michael N
MN
Latham
Peter E
PE
Pouget
Alexandre
A
D000200:Action Potentials / Q000502:physiology; D000818:Animals; D001499:Bayes Theorem; D003198:Computer Simulation; D003657:Decision Making / Q000502:physiology*; D000882:Haplorhini; D006801:Humans; D008959:Models, Neurological; D009039:Motion Perception / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D010775:Photic Stimulation; D011930:Reaction Time; D013997:Time Factors