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

TL;DR

This study demonstrates that augmenting recurrent neural networks with a cerebellar-inspired module enhances learning speed and performance on temporal tasks, highlighting the benefits of modularity as an architectural bias.

Contribution

Introduces a cortico-cerebellar RNN architecture that improves temporal learning efficiency and performance, emphasizing modularity's role as an inductive bias in neural systems.

Findings

CB-RNN learns faster than fully recurrent baselines.

Freezing the recurrent core after minimal training retains high performance.

Heterogeneous modularity acts as a structural inductive bias.

Abstract

The cerebellum and cerebral cortex form tightly coupled circuits thought to support flexible and efficient temporal processing. How this interaction shapes cortical learning dynamics, and whether such heterogeneous modularity can benefit artificial systems, remains unclear. Here, we augment a recurrent neural network (RNN) with a cerebellar-inspired feedforward module and evaluate the resulting architecture on temporal tasks of varying difficulty. The cortico-cerebellar RNN (CB-RNN) learns faster and reaches higher maximum performance than parameter-matched fully recurrent baselines across a variety of regimes. Crucially, freezing the recurrent core after minimal training and delegating subsequent learning to the cerebellar module preserves superior learning efficiency, suggesting the cerebellar module is a primary driver of efficiency and that the cortical network can largely function as a fixed reservoir. Our results suggest that heterogeneous modular architectures can act as a powerful structural inductive bias in neural systems.