title: Spiking networks for Bayesian inference and choice.
abstract: 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.
authors.affiliation: Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627, USA.
authors.forename: Wei Ji
authors.initials: WJ
authors.lastname: Ma
authors.forename: Jeffrey M
authors.initials: JM
authors.lastname: Beck
authors.forename: Alexandre
authors.initials: A
authors.lastname: Pouget
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002755:Choice Behavior / Q000502:physiology*; D003463:Cues; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*