A Plasticity Model of Cerebellar Regulation of Cortical Dynamical Stability and Task-Associated Representations

Experiments indicate that cerebellar regulation helps stabilize cortical activity, but the mechanisms that support this effect remain unclear. We construct a biologically motivated cortical–cerebellar model to study how cerebellar learning can regulate an active cortical circuit. A recurrent neural network (RNN) with fixed connections and active/chaotic dynamics represents the cortical circuit, while continuous online learning acquires cerebellar feedback. Differential Hebbian plasticity at the input stage associates cortical pattern activity with category-related cues (context) supplied by an auxiliary cortical population; the output stage uses eligibility traces and a teaching signal $q$ that reflects the history of internal activity changes to learn feedback. In a five-category pattern task, removing context after training leaves cerebellar feedback able to reduce trajectory speed and repeated-stimulus perturbation distance while increasing category separation at the final state. Structural replacement and plasticity ablations show that the expanded sparse feedforward cerebellar pathway and coordinated adaptation at the input and output sites jointly support response stabilization and category organization. When category cues are mismatched, part of the dynamical control is preserved but category separation is impaired; unlearned categories evoke stronger $q$ responses and weaker cumulative perturbation contraction. These results suggest that the cerebellum can learn to regulate effective recurrent drive by sensing and integrating internal cortical changes, while associative plasticity allows stabilization to retain task-related pattern structure.