title: A model of the cerebellum in adaptive control of saccadic gain. II. Simulation results.
abstract: A large, realistic cerebellar neural network has been incorporated into a previously developed saccade model. Using this model, in the present paper, we simulate the complex spatiotemporal behavior of the neuronal subpopulations implicated in adaptive saccadic control. Our simulation results are in good agreement with neurophysiological and behavioral data. Furthermore, we suggest several new experiments to test the validity of our predictions on adaptive saccadic control.
authors.affiliation: Center for Neural Engineering, University of Southern California, Los Angeles 90089-2520, USA. nicolas@hip.atr.co.jp
authors.forename: N
authors.initials: N
authors.lastname: Schweighofer
authors.forename: M A
authors.initials: MA
authors.lastname: Arbib
authors.forename: P F
authors.initials: PF
authors.lastname: Dominey
mesh_terms: D000222:Adaptation, Physiological; D000818:Animals; D002531:Cerebellum / Q000502:physiology*; D003198:Computer Simulation; D003491:Cybernetics; D004594:Electrophysiology; D006801:Humans; D007858:Learning / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D012438:Saccades / Q000502:physiology*