Balancing Inhibition and Sparsity for Stable, Accurate Cerebellar Learning

Abstract

The cerebellums structured circuitry supports learning across motor and cognitive domains, yet the coding strategies in granule cells that enable this versatility remain unclear. Using a theoretical cerebellar model, we examined how feedforward inhibition (FFI) and feedback inhibition (FBI) shape granule cell activation patterns to optimize learning in two tasks: complex trace learning and pattern identification. For trace learning, performance depends on spatiotemporally ordered granule cell activity shaped by FBI, with temporal sparsity emerging as the key determinant of accuracy. For pattern identification, both pathways support high accuracy, with only slight sensitivity to pathway choice. In both tasks, spatial sparsity becomes critical in incremental learning to prevent memory interference, a role reinforced by advanced synaptic plasticity strategies. These findings identify sparse granule cell activation as a unifying principle for cerebellar learning and reveal task-dependent roles of inhibitory pathways, providing a mechanistic framework for understanding stability-plasticity trade-offs in cerebellar learning.