A spiking neural network model of rodent head direction calibrated with landmark free learning

Stentiford, Rachael; Knowles, Thomas C; Pearson, Martin J

doi:10.3389/fnbot.2022.867019

A spiking neural network model of rodent head direction calibrated with landmark free learning

Stentiford, Rachael; Knowles, Thomas C; Pearson, Martin J

Authors

Rachael Stentiford

Thomas C Knowles

Martin Pearson Martin.Pearson@uwe.ac.uk
Senior Lecturer

Abstract

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.

Citation

Stentiford, R., Knowles, T. C., & Pearson, M. J. (2022). A spiking neural network model of rodent head direction calibrated with landmark free learning. Frontiers in Neurorobotics, 16, -. https://doi.org/10.3389/fnbot.2022.867019

Journal Article Type	Article
Acceptance Date	Apr 19, 2022
Online Publication Date	May 26, 2022
Publication Date	May 26, 2022
Deposit Date	May 26, 2022
Publicly Available Date	May 26, 2022
Journal	Frontiers in Neurorobotics
Electronic ISSN	1662-5218
Publisher	Frontiers Media
Peer Reviewed	Peer Reviewed
Volume	16
Article Number	867019
Pages	-
Series ISSN	1662-5218
DOI	https://doi.org/10.3389/fnbot.2022.867019
Keywords	spiking neural network; pyNEST; head direction; predictive coding; localization; continuous attractor
Public URL	https://uwe-repository.worktribe.com/output/9572634