A large body of research has led to statistical models showing how movement velocity is encoded in the motor cortex. However, forces also need to be controlled in harmony with motion when interacting with objects and research has rarely focused on how the motor system coordinates both together. The simultaneous variation of force and motion is incorporated in the definition of impedance. Our current neural models do not describe impedance encoding, which limits our understanding of object interaction, an important aspect of human behavior. The proposed research will develop new models of motor cortical impedance encoding during object interaction. Using these new models to decode ongoing impedance signaling, we will substantiate an advanced theory of impedance control used by the motor system to produce accurate object displacement in response to the forces applied by the hand. This research bridges the expertise of Dr. Schwartz in neurophysiology and of Dr. Hogan in robot control. Monkey subjects will perform tasks with real and virtual tools that naturally encourage the use of impedance control. We will record the activity of motor cortical neurons during these tasks and develop new mathematical models to describe the relation between neural activity and force, motion and impedance. Results from electromyography recordings, joint angle measurements and torque calculations, together with the neural models, will be used to better understand how impedance is regulated at the level of muscles and joints. Contributions of stretch reflexes to impedance will be studied and compared to the predictive impedance signaling decoded from motor cortex. This work promises to extend our understanding of the neural control principles governing the way we use our arms and hands to interact with our surroundings. These principles can be used to build new theories of the cognitive processes used to predict and effect changes in the world around us. At the same time, elucidation of the neural and mechanical details of forceful interaction will lead to new rehabilitative and neural prosthetic approaches to paralysis.
In recent years, much has been discovered about how the motor cortex enables reaching movements, but the bulk of this research has not yet included neurophysiological studies of how we interact with objects such as tools. Results of this project could provide valuable insight into the fundamental principles of this control and advance our understanding of the way we act on our surroundings. The decoding algorithms developed in this work will be used for neural prostheses that restore the ability of paralyzed subjects to manipulate objects in their surroundings.
