Jump to ContentJump to Main Navigation
Bayesian BrainProbabilistic Approaches to Neural Coding$
Users without a subscription are not able to see the full content.

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

Show Summary Details
Page of

PRINTED FROM MIT PRESS SCHOLARSHIP ONLINE (www.mitpress.universitypressscholarship.com). (c) Copyright The MIT Press, 2022. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use.date: 27 January 2022

Neural Models of Bayesian Belief Propagation

Neural Models of Bayesian Belief Propagation

(p.238) (p.239) 11 Neural Models of Bayesian Belief Propagation
Bayesian Brain

Rajesh P. N. Rao

The MIT Press

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

MIT Press Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs, and if you can't find the answer there, please contact us.