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Bayesian computation in recurrent cortical circuits
Rajesh Rao
Dept of Computer Science and Engineering &
Neurobiology and Behavior Program
University of Washington, Seattle
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.