CAREER: Adaptive Control in Biological and Man-made Systems

Marquette University, Milwaukee WI

Investigators

Abstract

0238442 Scheidt Engineers have long drawn inspiration and insight from the study of biological systems. Some of the most intriguing parallels between biological and man-made systems have arisen in the study of information processing and control within biological nervous systems. Early explorations into control and communication in animals and machines have influenced the development of diverse engineering disciplines such as artificial intelligence, computational neuroscience and the mathematical modeling of logic and the mind. Particularly intriguing are studies of learning and adaptation in nervous systems; such studies have influenced the development and analysis of artificial neural networks and have in turn been influenced by advances in adaptive control theory. The goals of the proposed research are to identify the neural mechanisms mediating adaptive improvements in motor performance, and to test the hypothesis that abnormal sensory feedback gain degrades motor coordination in a relatively common clinical population. The PI will develop a novel, single-degree-of-freedom, robotic manipulandum for use with both event-related functional magnetic resonance imaging (ER-fMRI) and systems identification techniques to characterize, model and locate within the brain the adaptive mechanisms mediating an important form of motor learning known as motor adaptation. Neurologically unimpaired subjects and autistic children will make goal-directed hand movements while holding the handle of a novel robot. ER-fMRI techniques will be used to quantify brain activity while the robot perturbs the moving hand. Linear (and if needed, nonlinear) systems identification techniques will be used to identify which brain region(s) are most likely involved in the adaptive neural representation of the hand's mechanical environment. The proposal outlines the PI's role in developing Marquette University's new Biocomputer Engineering curriculum. The PI will train up to 40 engineers per year in the processes, methodologies, technologies and physiological aspects of engineering microcontroller-based systems in the regulated medical electronics industry. The PI will integrate state-of-the-art knowledge of adaptive algorithms and their validation into the new undergraduate curriculum. The PI will also integrate the proposed research and device development activities into his existing Neuromotor Control course. Finally, the proposal describes an engineering curriculum development process that draws from industrial best practices in product design and quality control. Curriculum development will incorporate industrial advisory feedback from periodic curriculum design reviews to ensure that state-of-the-art design processes, methodologies and technologies are instructed.