Cortico-cerebellar modularity as an architectural inductive bias for efficient temporal learning

The cerebellum and cerebral cortex establish closely integrated loops that are involved in temporal processing; however, the manner in which their distinct circuit architectures influence learning remains ambiguous. This study investigates whether the integration of recurrent dynamics with a cerebellar-inspired feedforward pathway alters the learning of temporal computations. We enhance a recurrent neural network (RNN) by incorporating an expansion–compression module that feeds back into the recurrent dynamics, with both pathways being trained concurrently towards the same task objective. In the context of curriculum-trained temporal cognition tasks, this cortico-cerebellar RNN (CB-RNN) demonstrates more efficient and adaptive learning compared to parameter-matched recurrent networks across single-task, multitask, and task-switching scenarios. Limiting the plasticity of recurrent weights retains much of this learning advantage, suggesting that learning can increasingly be carried by the feedforward pathway while recurrent dynamics provide a substrate for computation. However, causal ablation indicates that the resulting recurrent representations remain reliant on cerebellar input. Mechanistic analyses reveal that this interaction is organised differently depending on task demands. Collectively, these findings illustrate that recurrent–feedforward modularity can enhance temporal learning without imposing a rigid functional decomposition between pathways, thereby allowing a task-dependent division of labour to emerge through joint optimisation.