Humans and other animals are capable of remarkable range of intricate motor behaviors. Although the brain systems involved in motor control are mostly known, the way in which these systems function interactivley in motor control are mostly known, the way in which these systems function interactivley in motor control is still only partially understood. The investigators on this project have recetly developed a model of cerebellar control in which they propose that the cerebellum implements servo (feedback based) control of limb movements through the processing of "wave variables" (Massaquoi & Slotine, 1996). Wave variables are special linear combinations of command and senory signals that ensure stability of servo systems despite delays in signal transmission. The previously developed simple wave-variable-based cerebellar control model displays single-joint movement control of two-joint horizontal planar arm movement and also produces several realistic internal signals. The model includes the roles of the intermediate and lateral cerebellum and parts of the cerebrum, spinal cord, peripheral nerve and muscles. The current project is designed to verify and futher develop the cerebellar model by attempting to correlate signals observed in active experimental primates with those predicted by the model, and to account for motor behavior of healthy human subjects and humans suffering from cerebellar dysfunction. The performance of the model will be analyzed from the perspective of robot balance and leg control during ambulation. The investigating team seeks to develop a mdoel of human (primate) cerebellar system function which is physiologically, neurroanatomically and quantitatively accurate, and also fully comprehensible in engineering terms. It is anticipated that this will contribute significantly to the understanding of the mechanisms, capacities and limitations of human and animal motor control in health and disease. The project should also provide nsights into design principles for intelligent executive systems in general, both natural (brain-based) and artificial (robotic). Anticipated applications of this line of investigation include more precise and specific interpretation of functional neuroimaging data, improved rational design of neuroprosthetic devices and neurosurgical interventions, and the design of more behaviorally adaptable, well-coordinated and agile robots.
