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© 2022, Vafidis et al. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Ring attractor models for angular path integration have received strong experimental support. To function as integrators, head direction circuits require precisely tuned connectivity, but it is currently unknown how such tuning could be achieved. Here, we propose a network model in which a local, biologically plausible learning rule adjusts synaptic efficacies during development, guided by supervisory allothetic cues. Applied to the Drosophila head direction system, the model learns to path-integrate accurately and develops a connectivity strikingly similar to the one reported in experiments. The mature network is a quasi-continuous attractor and reproduces key experiments in which optogenetic stimulation controls the internal representation of heading in flies, and where the network remaps to integrate with different gains in rodents. Our model predicts that path integration requires self-supervised learning during a developmental phase, and proposes a general framework to learn to path-integrate with gain-1 even in architectures that lack the physical topography of a ring.

Details

Title
Learning accurate path integration in ring attractor models of the head direction system
Author
Vafidis Pantelis; Owald, David; D'Albis Tiziano; Kempter, Richard
University/institution
U.S. National Institutes of Health/National Library of Medicine
Publication year
2022
Publication date
2022
Publisher
eLife Sciences Publications Ltd.
e-ISSN
2050084X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2696819505
Copyright
© 2022, Vafidis et al. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.