Understanding human motor control and learning - a mathematical perspective

点击翻译按钮获取中文摘要

How does human learn to control the body to perform a motion task? This seemingly trivial, but fundamentally important question, remains open. A number of theories have been proposed, e.g., the bottom-up approach (using physiological modelling from first principles) and top-down framework (using statistical data driven modelling methods). However, the aforementioned two approaches are largely separated, the interaction and integration of which - that could have addressed a number of key limitations in both methods - is missing. This project aims to bridge this gap exploiting the recent developments in machine learning (particularly in interpretable deep reinforcement learning) and intelligent control systems (especially iterative learning control) to create a novel theory to explain human motor control and learning. Underpinning this theory is the recent progress in optimisation methods, particular for very high dimensional problems that have been driven by recent years' surging demand of big data research and new AI algorithms.This interdisciplinary project will improve the neuroscientist's understanding of how human brain learns and controls the body for motion tasks. This understanding will further feedback into the research of machine learning and intelligent control, enabling us to develop more efficient algorithms. This improved understanding will also help to develop better rehabilitation technologies helping patients re-learn their motion movements in a more natural and effective way.