Forward Models in the Cerebellum using Reservoirs and Perturbation Learning


The cerebellum is thought to be able to learn forward models, which allow to predict the sensory consequences of planned movements and adapt behavior accordingly. Although classically considered as a feedforward structure learning in a supervised manner, recent proposals highlighted the importance of the internal recurrent connectivity of the cerebellum to produce rich dynamics (Rössert et al., 2015), as well as the importance of reinforcement-like mechanisms for its plasticity (Bouvier et al. 2019). Based on these models, we propose a neuro-computational model of the cerebellum using an inhibitory reservoir architecture and biologically plausible learning mechanisms based on perturbation learning. The model is trained to predict the position of a simple robotic arm after ballistic movements. Understanding how the cerebellum is able to learn forward models might allow elucidating the biological basis of model-based reinforcement learning.

2019 Conference on Cognitive Computational Neuroscience, Berlin (Germany)