Neural Models of Bayesian Belief Propagation
Neural Models of Bayesian Belief Propagation
This chapter discusses neural models of Bayesian inference through belief propagation. It analyses neural implementations of the belief propagation algorithm based on linear recurrent networks, nonlinear networks, and noisy spiking neurons, and then describes how these networks perform Bayesian inference. The chapter concludes with recent models of inference in neural circuits and suggests directions in future research.
Keywords: Bayesian inference, belief propagation, linear recurrent networks, nonlinear networks, noisy spiking neurons, inference, neural circuits
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