title: Inference and computation with population codes.
abstract: In the vertebrate nervous system, sensory stimuli are typically encoded through the concerted activity of large populations of neurons. Classically, these patterns of activity have been treated as encoding the value of the stimulus (e.g., the orientation of a contour), and computation has been formalized in terms of function approximation. More recently, there have been several suggestions that neural computation is akin to a Bayesian inference process, with population activity patterns representing uncertainty about stimuli in the form of probability distributions (e.g., the probability density function over the orientation of a contour). This paper reviews both approaches, with a particular emphasis on the latter, which we see as a very promising framework for future modeling and experimental work.
authors.affiliation: Department of Brain and Cognitive Sciences, Meliora Hall, University of Rochester, Rochester, NY 14627, USA. alex@bcs.rochester.edu
authors.forename: Alexandre
authors.initials: A
authors.lastname: Pouget
authors.forename: Peter
authors.initials: P
authors.lastname: Dayan
authors.forename: Richard S
authors.initials: RS
authors.lastname: Zemel
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D006801:Humans; D008959:Models, Neurological; D009042:Motivation; D009415:Nerve Net; D009424:Nervous System Physiological Phenomena; D016571:Neural Networks, Computer; D009474:Neurons / Q000145:classification / Q000502:physiology*; D009949:Orientation; D011601:Psychophysics