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
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.