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Learning accurate path integration in a ring attractor model of the head direction system

Vafidis, Pantelis and Owald, David and D’Albis, Tiziano and Kempter, Richard (2021) Learning accurate path integration in a ring attractor model of the head direction system. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210419-171909265

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Abstract

Ring attractor models for angular path integration have recently received strong experimental support. To function as integrators, head-direction (HD) 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 HD 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. Our model predicts that path integration requires supervised learning during a developmental phase. The model setting is general and also applies to architectures that lack the physical topography of a ring, like the mammalian HD system.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/2021.03.12.435035DOIDiscussion Paper
ORCID:
AuthorORCID
Vafidis, Pantelis0000-0002-9768-0609
Owald, David0000-0001-7747-7884
D’Albis, Tiziano0000-0003-1585-1433
Kempter, Richard0000-0002-5344-2983
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. This version posted March 12, 2021. We would like to thank Raquel Suárez-Grimalt and Marcel Heim for fruitful discussions. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation; SFB 1315 – project-ID 327654276 to R.K. and D.O.; and the Emmy Noether Programme 282979116 to D.O.), the German Federal Ministry for Education and Research (BMBF; Grant 01GQ1705 to R.K.), and the Onassis Foundation (P.V.). Author Contributions: P.V., T.D., and R.K. conceived the study. P.V. performed analyses and wrote the initial draft of the manuscript. T.D. contributed to analyses. D.O., T.D., and R.K. supervised the research. All authors wrote the manuscript. The authors have declared no competing interest.
Funders:
Funding AgencyGrant Number
Deutsche Forschungsgemeinschaft (DFG)SFB 1315
Deutsche Forschungsgemeinschaft (DFG)327654276
Deutsche Forschungsgemeinschaft (DFG)282979116
Bundesministerium für Bildung und Forschung (BMBF)01GQ1705
Onassis FoundationUNSPECIFIED
Subject Keywords:path integration, head direction cells, recurrent neural networks, synaptic plasticity, compartmentalized neuron, coincidence detection, supervised learning, error correction, predictive coding, navigation
Record Number:CaltechAUTHORS:20210419-171909265
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210419-171909265
Official Citation:Learning accurate path integration in a ring attractor model of the head direction system. Pantelis Vafidis, David Owald, Tiziano D’Albis, Richard Kempter. bioRxiv 2021.03.12.435035; doi: https://doi.org/10.1101/2021.03.12.435035
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108763
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:22 Apr 2021 17:26
Last Modified:22 Apr 2021 17:26

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