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Bayesian BrainProbabilistic Approaches to Neural Coding$
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Kenji Doya, Shin Ishii, Alexandre Pouget, and Rajesh P.N. Rao

Print publication date: 2006

Print ISBN-13: 9780262042383

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262042383.001.0001

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Neural Models of Bayesian Belief Propagation

Neural Models of Bayesian Belief Propagation

Chapter:
(p.238) (p.239) 11 Neural Models of Bayesian Belief Propagation
Source:
Bayesian Brain
Author(s):

Rajesh P. N. Rao

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262042383.003.0011

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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