Adaptive Cerebellar Feedback Enables Multi-Task Learning Through Flexible Reorganization of Cortical Dynamics

Accumulating evidence suggests that high-dimensional cortical population activity is organized along low-dimensional latent dynamics that support flexible representations and computations. However, existing theoretical models often rely on explicit architectural constraints, such as low-rank recurrent connectivity, to induce structured dynamics, leaving unresolved the question of how such low-dimensional organization may emerge through biologically plausible mechanisms. Inspired by experimental advances in cortico-cerebellar interactions, we propose a cerebellar adaptive feedback mechanism that enables the emergence of low-dimensional cortical dynamics without imposing constraints on recurrent connectivity. We develop a cerebro-cerebellar coordination framework in which a frozen full-rank cortical recurrent neural network (RNN) preserves high-dimensional representational capacity, while an adaptive cerebellar pathway provides task-dependent feedback to regulate cortical dynamics through biologically plausible local plasticity. This interaction reorganizes cortical activity into low-dimensional and separable neural manifolds, reducing representational interference during multi-task learning. We evaluate this framework on cognitive rule learning and handwritten-letter trajectory prediction tasks, demonstrating that adaptive cerebellar feedback effectively improves multi-task learning performance. These findings reveal a biologically grounded computational principle for organizing flexible neural dynamics.