Active locomotion predictively rescues head direction attractor dynamics in head-fixed mice

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Abstract

Head direction (HD) cells in the anterodorsal thalamic nuclei form the brain’s internal compass, and are often modeled as a ring attractor maintaining azimuth coding by leveraging continuous visual and inertial sensory input. Here, we test how the common experimental preparation of head-fixed animals alters this code. Complete head-fixation that creates vestibular conflict disrupts both unit and population encoding of head direction, while selectively constraining head-on-body movements either in real or virtual reality uniquely impairs HD population activity. More specifically, attractor dynamics is altered in head-restrained mice during periods of immobility, but remarkably recover several hundred milliseconds prior to locomotion onset. The rescue preceding movement onset suggests that an efference copy or prediction of a re-afferent signal is necessary to maintain HD network activity during head restraint. A computational model recapitulates these effects by perturbing lateral connectivity among HD neurons. More generally, the results indicate that the HD network is a context- and state-dependent predictive estimator, stabilized by forthcoming self-motion signals. The classic ring-attractor models should be revised to integrate context-dependent dynamics with prospective motor signals, offering a more complete account of how the brain’s compass remains stable across both naturalistic and constrained conditions.
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Abstract Head direction (HD) cells in the anterodorsal thalamic nuclei form the brain’s internal compass, and are often modeled as a ring attractor maintaining azimuth coding by leveraging continuous visual and inertial sensory input. Here, we test how the common experimental preparation of head-fixed animals alters this code. Complete head-fixation that creates vestibular conflict disrupts both unit and population encoding of head direction, while selectively constraining head-on-body movements either in real or virtual reality uniquely impairs HD population activity. More specifically, attractor dynamics is altered in head-restrained mice during periods of immobility, but remarkably recover several hundred milliseconds prior to locomotion onset. The rescue preceding movement onset suggests that an efference copy or prediction of a re-afferent signal is necessary to maintain HD network activity during head restraint. A computational model recapitulates these effects by perturbing lateral connectivity among HD neurons. More generally, the results indicate that the HD network is a context- and state-dependent predictive estimator, stabilized by forthcoming self-motion signals. The classic ring-attractor models should be revised to integrate context-dependent dynamics with prospective motor signals, offering a more complete account of how the brain’s compass remains stable across both naturalistic and constrained conditions. Competing Interest Statement The authors have declared no competing interest.

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last seen: 2026-05-20T01:45:00.602351+00:00