HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Rao, R. P. N. Search for Related Content PubMed PubMed Citation Articles by Rao, R. P. N.

Bayesian Computation in Recurrent Neural Circuits

rao{at}cs.washington.edu, Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, U.S.A.

A large number of human psychophysical results have been successfully explained in recent years using Bayesian models. However, the neural implementation of such models remains largely unclear. In this article, we show that a network architecture commonly used to model the cerebral cortex can implement Bayesian inference for an arbitrary hidden Markov model. We illustrate the approach using an orientation discrimination task and a visual motion detection task. In the case of orientation discrimination, we show that the model network can infer the posterior distribution over orientations and correctly estimate stimulus orientation in the presence of significant noise. In the case of motion detection, we show that the resulting model network exhibits direction selectivity and correctly computes the posterior probabilities over motion direction and position. When used to solve the well-known random dots motion discrimination task, the model generates responses that mimic the activities of evidence-accumulating neurons in cortical areas LIP and FEF. The framework we introduce posits a new interpretation of cortical activities in terms of log posterior probabilities of stimuli occurring in the natural world.

This article has been cited by other articles:

				B. Epshtein, I. Lifshitz, and S. Ullman Image interpretation by a single bottom-up top-down cycle PNAS, September 23, 2008; 105(38): 14298 - 14303. [Abstract] [Full Text] [PDF]

				Y. Sato, T. Toyoizumi, and K. Aihara Bayesian Inference Explains Perception of Unity and Ventriloquism Aftereffect: Identification of Common Sources of Audiovisual Stimuli. Neural Comput., December 1, 2007; 19(12): 3335 - 3355. [Abstract] [Full Text] [PDF]

				S. Deneve, J.-R. Duhamel, and A. Pouget Optimal Sensorimotor Integration in Recurrent Cortical Networks: A Neural Implementation of Kalman Filters J. Neurosci., May 23, 2007; 27(21): 5744 - 5756. [Abstract] [Full Text] [PDF]

				U. Ernst, D. Rotermund, and K. Pawelzik Efficient Computation Based on Stochastic Spikes Neural Comput., May 1, 2007; 19(5): 1313 - 1343. [Abstract] [Full Text] [PDF]

				J. M. Beck and A. Pouget Exact Inferences in a Neural Implementation of a Hidden Markov Model Neural Comput., May 1, 2007; 19(5): 1344 - 1361. [Abstract] [Full Text] [PDF]

				S. Wu and S.-i. Amari Computing with Continuous Attractors: Stability and Online Aspects Neural Comput., October 1, 2005; 17(10): 2215 - 2239. [Abstract] [Full Text] [PDF]

				J. M. Wallace, L. S. Stone, and G. S. Masson Object Motion Computation for the Initiation of Smooth Pursuit Eye Movements in Humans J Neurophysiol, April 1, 2005; 93(4): 2279 - 2293. [Abstract] [Full Text] [PDF]