A triple distinction of cerebellar function for oculomotor learning and fatigue compensation

Masselink, Jana; Cheviet, Alexis; Froment-Tilikete, Caroline; Pélisson, Denis; Lappe, Markus

Abstract

The cerebellum implements error-based motor learning via synaptic gain adaptation of an inverse model, i.e. the mapping of a spatial movement goal onto a motor command. Recently, we modeled the motor and perceptual changes during learning of saccadic eye movements, showing that learning is actually a threefold process. Besides motor recalibration of (1) the inverse model, learning also comprises perceptual recalibration of (2) the visuospatial target map and (3) of a forward dynamics model that estimates the saccade size from corollary discharge. Yet, the site of perceptual recalibration remains unclear. Here we dissociate cerebellar contributions to the three stages of learning by modeling the learning data of eight cerebellar patients and eight healthy controls. Results showed that cerebellar pathology restrains short-term recalibration of the inverse model while the forward dynamics model is well informed about the reduced saccade change. Adaptation of the visuospatial target map trended in learning direction only in control subjects, yet without reaching significance. Moreover, some patients showed a tendency for uncompensated oculomotor fatigue caused by insufficient upregulation of saccade duration. According to our model, this could induce long-term perceptual compensation, consistent with the overestimation of target eccentricity found in the patients’ baseline data. We conclude that the cerebellum mediates short-term adaptation of the inverse model, especially by control of saccade duration, while the forward dynamics model was not affected by cerebellar pathology.

Citation

Masselink, J., Cheviet, A., Froment-Tilikete, C., Pélisson, D., & Lappe, M. (2023). A triple distinction of cerebellar function for oculomotor learning and fatigue compensation. PLoS Computational Biology, 19(8), Article e1011322. https://doi.org/10.1371/journal.pcbi.1011322

• Journal Article Type: Article
• Acceptance Date: Jul 2, 2023
• Online Publication Date: Aug 4, 2023
• Publication Date: Aug 4, 2023
• Deposit Date: Feb 14, 2024
• Publicly Available Date: Feb 14, 2024
• Journal: PLOS Computational Biology
• Print ISSN: 1553-734X
• Electronic ISSN: 1553-7358
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 8
• Article Number: e1011322
• DOI: https://doi.org/10.1371/journal.pcbi.1011322
• Keywords: Computational Theory and Mathematics; Cellular and Molecular Neuroscience; Genetics; Molecular Biology; Ecology; Modeling and Simulation; Ecology, Evolution, Behavior and Systematics
• Public URL: https://durham-repository.worktribe.com/output/2255808