Understanding how the brain controls even the simplest movement is a major challenge due to the bewildering complexity of the sensorimotor and musculoskeletal system. For example, the large number of joints and muscles provide the human musculoskeletal system with numerous degrees of freedom. As such, any motor task can be achieved via an infinite number of different muscle activation patterns. Yet, experimental studies show very consistent and stereotypical patterns of kinematics and muscle activation. Furthermore, to adequately control and execute movements, the brain needs accurate information about the state of the musculoskeletal system and about (its relationship with) the world around it. The brain receives information from the many sensory systems in the human body. However, this information is corrupted with noise that is inherent to processes in any biological (nervous) system.
The proposed research is aimed at testing two influential theories in human sensorimotor control – ‘Optimal Control Theory’ and ‘The Uncontrolled Manifold Theory’- that try to explain how the brain deals with the many degrees of freedom. This will be done through a combination of computer simulations with a state-of-the-art neuro-musculoskeletal model of the human arm and new experimental designs using a unique exoskeleton robot. Furthermore, Bayesian Decision Theory has a great amount of success in explaining various phenomena in sensory-motor neuroscience. In the proposed research, this theory will be integrated with Optimal Control in combination with a detailed model of the neuromuscular plant. Specific predictions will be tested using a novel experimental design.
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