Yu, Z, Chen, F and Liu, JK orcid.org/0000-0002-5391-7213 (2020) Sampling-Tree Model: Efficient Implementation of Distributed Bayesian Inference in Neural Networks. IEEE Transactions on Cognitive and Developmental Systems, 12 (3). pp. 497-510. ISSN 2379-8920
Abstract
Experimental observations from neuroscience have suggested that the cognitive process of human brain is realized as probabilistic reasoning and further modeled as Bayesian inference. However, it remains unclear how Bayesian inference could be implemented by network of neurons in the brain. Here a novel implementation of neural circuit, named the sampling-tree model, is proposed to fulfill this aim. By using a deep tree structure to implement sampling with simple and stackable basic neural network motifs for any given Bayesian networks, 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 intensive iteration and scale-limitation. As a result, this model utilizes the structure benefit of neuronal system, i.e., neuronal abundance and multihierarchy, to perform fast inference in an extendable way.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 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; Neurons; Biological neural networks; Computational modeling; Brain modeling; Probabilistic logic; Integrated circuit modeling |
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 09:33 |
Last Modified: | 14 Jul 2021 09:33 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/tcds.2019.2927808 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:176161 |