Fang, Y, Yu, Z, Liu, JK orcid.org/0000-0002-5391-7213 et al. (1 more author) (2019) A unified neural circuit of causal inference and multisensory integration. Neurocomputing, 358. pp. 355-368. ISSN 0925-2312
Abstract
Causal inference and multisensory integration are two fundamental processes of perception. It is generally believed that there should be one unified neural circuit in the brain to realize these two processes in an optimal way. However, there is no solution yet due to the complicated neural implementation for posterior probability computation. In this study, we propose a unified neural network by solving the complicated posterior probability computation. A unified theoretical framework is presented from the viewpoint of expectation. In addition, a biologically realistic neural circuit is proposed with the combination of importance sampling and probabilistic population coding. Theoretical analyses and simulation results manifest that our proposed neural circuit can implement both causal inference and multisensory integration. Taken together, our framework provides a new perspective of how different perceptual tasks can be performed by the same neural circuit.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Elsevier B.V. All rights reserved. This is an author produced version of an article published in Neurocomputing. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Multisensory integration; Causal inference; Unified neural circuit; Importance sampling; Probabilistic population codes |
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: | 13 Jul 2021 12:35 |
Last Modified: | 14 Jul 2021 10:31 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.neucom.2019.05.067 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:176129 |