A Spiking Neural Network Model of Rodent Head Direction Calibrated With Landmark Free Learning
Maintaining a stable estimate of head direction requires both self-motion (idiothetic) information and environmental (allothetic) anchoring. In unfamiliar or dark environments idiothetic drive can maintain a rough estimate of heading but is subject to inaccuracy, visual information is required to st...
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Published in: | Frontiers in neurorobotics Vol. 16; p. 867019 |
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Main Authors: | , , |
Format: | Journal Article |
Language: | English |
Published: |
Switzerland
Frontiers Research Foundation
26-05-2022
Frontiers Media S.A |
Subjects: | |
Online Access: | Get full text |
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Summary: | Maintaining a stable estimate of head direction requires both self-motion (idiothetic) information and environmental (allothetic) anchoring. In unfamiliar or dark environments idiothetic drive can maintain a rough estimate of heading but is subject to inaccuracy, visual information is required to stabilize the head direction estimate. When learning to associate visual scenes with head angle, animals do not have access to the 'ground truth' of their head direction, and must use egocentrically derived imprecise head direction estimates. We use both discriminative and generative methods of visual processing to learn these associations without extracting explicit landmarks from a natural visual scene, finding all are sufficiently capable at providing a corrective signal. Further, we present a spiking continuous attractor model of head direction (SNN), which when driven by idiothetic input is subject to drift. We show that head direction predictions made by the chosen model-free visual learning algorithms can correct for drift, even when trained on a small training set of estimated head angles self-generated by the SNN. We validate this model against experimental work by reproducing cue rotation experiments which demonstrate visual control of the head direction signal. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: James Courtney Knight, University of Sussex, United Kingdom; Dan Turner-Evans, University of California, Santa Cruz, United States Edited by: Leslie Samuel Smith, University of Stirling, United Kingdom |
ISSN: | 1662-5218 1662-5218 |
DOI: | 10.3389/fnbot.2022.867019 |