Abstract
Continuous attractor networks require calibration. Computational models of the head direction (HD) system of the rat usually assume that the connections that maintain HD neuron activity are pre-wired and static. Ongoing activity in these models relies on precise continuous attractor dynamics. It is currently unknown how such connections could be so precisely wired, and how accurate calibration is maintained in the face of ongoing noise and perturbation. Our adaptive attractor model of the HD system that uses symmetric angular head velocity (AHV) cells as a training signal shows that the HD system can learn to support stable firing patterns from poorly-performing, unstable starting conditions. The proposed calibration mechanism suggests a requirement for symmetric AHV cells, the existence of which has previously been unexplained, and predicts that symmetric and asymmetric AHV cells should be distinctly different (in morphology, synaptic targets and/or methods of action on postsynaptic HD cells) due to their distinctly different functions.
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Notes
The authors wish to thank an anonymous reviewer for suggesting this interpretation of the calibration algorithm.
This experiment assumes that slow rotation of the visual environment induces a slow rotation of head direction representation. The alternative outcome, that the animal simply becomes disoriented, could possibly be avoided by using prominent visual cues and an extended training period, prior to testing, during which there is no cue rotation.
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The authors wish to thank two anonymous reviewers for suggestions which greatly improved the quality of this manuscript.
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Stratton, P., Wyeth, G. & Wiles, J. Calibration of the head direction network: a role for symmetric angular head velocity cells. J Comput Neurosci 28, 527–538 (2010). https://doi.org/10.1007/s10827-010-0234-7
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DOI: https://doi.org/10.1007/s10827-010-0234-7