Yu, Z, Huang, T and Liu, JK orcid.org/0000-0002-5391-7213 (2018) Implementation of Bayesian Inference In Distributed Neural Networks. In: 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP). 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), 21-23 Mar 2018, Cambridge, UK. IEEE , pp. 666-673. ISBN 978-1-5386-4976-3
Abstract
Numerous neuroscience experiments have suggested that the cognitive process of human brain is realized as probability reasoning and further modeled as Bayesian inference. It is still unclear how Bayesian inference could be implemented by neural underpinnings in the brain. Here we present a novel Bayesian inference algorithm based on importance sampling. By distributed sampling through a deep tree structure with simple and stackable basic motifs for any given neural circuit, one can perform local inference while guaranteeing the accuracy of global inference. We show that these task-independent motifs can be used in parallel for fast inference without iteration and scale-limitation. Furthermore, experimental simulations with a small-scale neural network demonstrate that our distributed sampling-based algorithm, consisting with our theoretical analysis, can approximate Bayesian inference. Taken all together, we provide a proofof- principle to use distributed neural networks to implement Bayesian inference, which gives a road-map for large-scale Bayesian network implementation based on spiking neural networks with computer hardwares, including neuromorphic chips.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Bayes methods, Mathematical model, Inference algorithms, Monte Carlo methods, Biological neural networks, Hidden Markov models |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Jul 2021 10:24 |
Last Modified: | 14 Jul 2021 10:38 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/pdp2018.2018.00111 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:176132 |