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Modeling the Encoding of Saccade Kinematic Metrics in the Purkinje Cell Layer of the Cerebellar Vermis

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Specialty Biology
Date 2019 Jan 29
PMID 30687055
Citations 2
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Abstract

Recent electrophysiological observations related to saccadic eye movements in rhesus monkeys, suggest a prediction of the sensory consequences of movement in the Purkinje cell layer of the cerebellar oculomotor vermis (OMV). A definite encoding of real-time motion of the eye has been observed in simple-spike responses of the combined burst-pause Purkinje cell populations, organized based upon their complex-spike directional tuning. However, the underlying control mechanisms that could lead to such action encoding are still unclear. We propose a saccade control model, with emphasis on the structure of the OMV and its interaction with the extra-cerebellar components. In the simulated bilateral organization of the OMV, each caudal fastigial nucleus is arranged to receive incoming projections from combined burst-pause Purkinje cell populations. The OMV, through the caudal fastigial nuclei, interacts with the brainstem to provide adaptive saccade gain corrections that minimize the visual error in reaching a given target location. The simulation results corroborate the experimental Purkinje cell population activity patterns and their relation with saccade kinematic metrics. The Purkinje layer activity that emerges from the proposed organization, precisely predicted the speed of the eye at different target eccentricities. Simulated granular layer activity suggests no separate dynamics with respect to shaping the bilateral Purkine layer activity. We further examine the validity of the simulated OMV in maintaining the accuracy of saccadic eye movements in the presence of signal dependent variabilities, that can occur in extra-cerebellar pathways.

Citing Articles

Dual STDP processes at Purkinje cells contribute to distinct improvements in accuracy and speed of saccadic eye movements.

Fruzzetti L, Kalidindi H, Antonietti A, Alessandro C, Geminiani A, Casellato C PLoS Comput Biol. 2022; 18(10):e1010564.

PMID: 36194625 PMC: 9565489. DOI: 10.1371/journal.pcbi.1010564.


A generative learning model for saccade adaptation.

Cassanello C, Ostendorf F, Rolfs M PLoS Comput Biol. 2019; 15(8):e1006695.

PMID: 31398185 PMC: 6703699. DOI: 10.1371/journal.pcbi.1006695.

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