Cerebellar control of saccadic adaptation using a spiking neural network model integrated into the neurorobotics platform

The cerebellum is essential for motor learning and adaptation since it can change the relationship between sensory input and motor output. Cerebellar activity contributes to the motor execution with the aim of optimizing the resulting performance. This optimization is driven by the error information, that the cerebellum receives through the sensory feedback. In this study, we aim at studying the cerebellar contribution in the saccadic adaptation, fine and rapid control of eye movements. We set up a virtual experiment where an iCub robot, controlled by a cerebellar-inspired spiking neural network, was challenged in a saccadic adaptation task. The virtual robot, at first, made inaccurate saccades and therefore attempted to adjust them, trial after trial, based on sensory error feedback. The spiking neural network model was equipped with plasticity mechanisms, that allowed the robot to change and adapt the amplitude of the saccade movement after a few trials. Interestingly, we observed that for this task a single plasticity mechanism was sufficient to produce a fast adaptation, while in other cerebellar-driven tasks, the addition of more plasticities granted an improvement of the adaptation performances.