SOURCE-LINKED INTELLIGENCE
Error- and Prediction-Driven Motor Learning in the Cortico-Cerebellar Loop
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 with
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-24T15:07:10.000Z
First collected: 2026-09-25T06:12:46.948Z. This is not the publication date.