Sun, X., Yue, S. and Mangan, M. orcid.org/0000-0002-0293-8874 (2020) A decentralised neural model explaining optimal integration of navigational strategies in insects. eLife, 9. ISSN 2050-084X
Abstract
Insect navigation arises from the coordinated action of concurrent guidance systems but the neural mechanisms through which each functions, and are then coordinated, remains unknown. We propose that insects require distinct strategies to retrace familiar routes (route-following) and directly return from novel to familiar terrain (homing) using different aspects of frequency encoded views that are processed in different neural pathways. We also demonstrate how the Central Complex and Mushroom Bodies regions of the insect brain may work in tandem to coordinate the directional output of different guidance cues through a contextually switched ring-attractor inspired by neural recordings. The resultant unified model of insect navigation reproduces behavioural data from a series of cue conflict experiments in realistic animal environments and offers testable hypotheses of where and how insects process visual cues, utilise the different information that they provide and coordinate their outputs to achieve the adaptive behaviours observed in the wild.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Authors. This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | computational biology; none; systems biology |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Science Research Council EP/S030964/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 Jul 2020 08:17 |
Last Modified: | 24 May 2024 14:07 |
Status: | Published |
Publisher: | eLife Sciences Publications, Ltd |
Refereed: | Yes |
Identification Number: | 10.7554/elife.54026 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:162675 |