Coupling internal cerebellar models enhances online adaptation and supports offline consolidation in sensorimotor tasks

Abstract

The cerebellum is thought to mediate sensori motor adaptation through the acquisition of internal models of the body-environment interaction.These representations can beof two types, identified as forward and inverse models.The first predicts the sensory consequence so factions, while the second provides the correct commands to achieve desired state transitions. In this paper, we propose a composite architecture consisting of multiple cerebellar internal models to account for the adaptation performance of humans during seorimotor learning. The proposed model takes inspiration from the cerebellar microcomplex circuit, and employs spiking neurons to process information. We investigate the intrinsic properties of the cerebellar circuitry subserving efficient adaptation properties, and weassess the complementary contributions of internal representations by simulating our model in a procedural adaptationt ask. Our simulation results suggest that the coupling of internal models enhances learning performances ignificantly (compared with in dependent for Ward and inverse models), and it allows for the reproduction of human adaptation capabilities. Further more, we provide a computational explanation for the performance improvement observed after one night of sleep in a wide range of sensorimotor tasks. We predict that in ternal model coupling is a necessary condition for the offline consolidation of procedural memories.