Sloan/Swartz Centers for Theoretical Neurobiology
Annual Summer Meeting 2003

Abstract

Rajesh P. N. Rao
Dept of Computer Science and Engineering
University of Washington

Probabilistic Computation in Recurrent Neural Circuits

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 talk, we discuss how a network architecture commonly used to model the cerebral cortex can implement Bayesian inference for an arbitrary Markov model. We illustrate the suggested approach using a visual motion detection task. Our simulation results show that the model network exhibits direction selectivity and correctly computes the posterior probabilities for motion direction. 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. In addition, the model predicts reaction time distributions that are similar to those obtained in human psychophysical experiments that manipulate the prior probabilities of targets and task urgency.