Learning accurate path integration in ring attractor models of the head direction system
- California Institute of Technology, United States
- Charité - Universitätsmedizin Berlin, Germany
- Humboldt-Universität zu Berlin, Germany
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, 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.
Data availability
All code used in this work is available at https://github.com/panvaf/LearnPI. The files required to reproduce the figures can be found at https://gin.g-node.org/pavaf/LearnPI.
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A Connectome of the Adult Drosophila Central BrainneuPrint https://neuprint.janelia.org/.
Article and author information
Author details
Funding
German Research Foundation (SFB 1315 - project-ID 327654276)
- David Owald
- Richard Kempter
Emmy Noether Programme (282979116)
- David Owald
German Federal Ministry for Education and Research (01GQ1705)
- Richard Kempter
Onassis Foundation Scholarship
- Pantelis Vafidis
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2022, Vafidis et al.
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.
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Learning accurate path integration in ring attractor models of the head direction system
eLife 11:e69841.
https://doi.org/10.7554/eLife.69841
