Error- and Prediction-Driven Motor Learning in the Cortico-Cerebellar Loop

2026-09-24

AI Summary

This study addresses the fundamental challenge of robust control under delayed sensory feedback, where conventional models struggle to simultaneously achieve rapid online correction and dynamic adaptation. To overcome these limitations, this work proposes a cerebellum-inspired neural control framework that reveals the inherent constraints of relying on a single predictive signal. By leveraging a synergistic mechanism that multiplexes predictive representations with internal feedback loops and integrates them with motor control algorithms, the proposed approach unifies online correction and rapid learning within a single architecture. Experimental results demonstrate that this framework significantly accelerates system adaptation, reducing learning time by an order of magnitude while substantially enhancing control precision in delayed environments. Ultimately, this research establishes a novel paradigm for biologically inspired robust control.

Abstract

Robust control under delayed sensory feedback remains a key challenge in both robotics and neuroscience. Classical cerebellar models explain delay compensation through forward prediction but fail to account for fast online corrections and rapid adaptation observed in biological systems. We propose a cerebellum-inspired control framework that combines multiplexed predictive representations with internal feedback. By jointly encoding kinematic variables and task-relevant error signals, the model enables accurate online correction despite delayed feedback. Furthermore, incorporating feedback within the cerebellar loop significantly accelerates adaptation, reducing learning time by an order of magnitude. Our results show that single-signal predictions are insufficient under delay, while multiplexing and feedback together provide a unified mechanism for online control and rapid learning.

Problem

• delayed sensory feedback
• motor learning
• cortico-cerebellar loop
• online correction
• rapid adaptation

Innovation

• cerebellar-inspired control
• multiplexed predictive representations
• internal feedback
• delay compensation
• rapid motor learning