Although people can typically maintain balance on moving trains, or press the appropriate button on an elevator with little conscious effort, the apparent ease of these sensorimotor tasks is courtesy of neural mechanisms that continuously interpret many sensory input signals to activate muscles throughout the body. The overall hypothesis of this work is that motor behaviors emerge from the interacting constraints and features of the nervous and musculoskeletal systems. The nervous system may simplify the control problem by recruiting muscles in groups called muscle synergies rather than individually. Because muscles cannot be recruited individually, muscle synergies may represent a neural constraint on behavior. However, the constraints of the musculoskeletal system and environment may also contribute to determining motor behaviors, and so must be considered in order to identify and interpret muscle synergies.

Here, I integrated techniques from musculoskeletal modeling, control systems engineering, and data analysis to identify neural and biomechanical constraints that determine the muscle activity and ground reaction forces during the automatic postural response (APR) in cats. First, I quantified the musculoskeletal constraints on force production during postural tasks in a detailed, 3D musculoskeletal model of the cat hindlimb. I demonstrated that biomechanical constraints on force production in the isolated hindlimb do not uniquely determine the characteristic patterns of force activity observed during the APR. However, when I constrained the muscles in the model to activate in a few muscle synergies based on experimental data, the force production capability drastically changed, exhibiting a characteristic rotation with the limb axis as the limb posture was varied that closely matched experimental data. Finally, after extending the musculoskeletal model to be quadrupedal, I simulated the optimal feedforward control of individual muscles or muscle synergies to regulate the center of mass (CoM) during the postural task. I demonstrated that both muscle synergy control and optimal muscle control reproduced the characteristic force patterns observed during postural tasks. These results are consistent with the hypothesis that the nervous system may use a low-dimension control scheme based on muscle synergies to approximate the optimal motor solution for the postural task given the constraints of the musculoskeletal system.

One primary contribution of this work was to demonstrate that the influences of biomechanical mechanisms in determining motor behaviors may be unclear in reduced models, a factor that may need to be considered in other studies of motor control. The biomechanical constraints on force production in the isolated hindlimb did not predict the stereotypical forces observed during the APR unless a muscle synergy organization was imposed, suggesting that neural constraints were critical in resolving musculoskeletal redundancy during the postural task. However, when the model was extended to represent the quadrupedal system in the context of the task, the optimal control of the musculoskeletal system predicted experimental force patterns in the absence of neural constraints.

A second primary contribution of this work was to test predictions concerning muscle synergies developed in theoretical neuromechanical models in the context of a natural behavior, suggesting that these concepts may be generally useful for understanding motor control. It has previously been shown in abstract neuromechanical models that low-dimension motor solutions such as muscle synergies can emerge from the optimal control of individual muscles. This work demonstrates for the first time that low-dimension motor solutions can emerge from optimal muscle control in the context of a natural behavior and a realistic musculoskeletal model. This work also represents the first explicit comparison of muscle synergy control and optimal muscle control during a natural behavior. It demonstrates that an explicit low-dimension control scheme based on muscle synergies is competent for performance of the postural task across biomechanical conditions, and in fact, may approximate the motor solution predicted by optimal muscle control.

This work advances our understanding how the constraints and features of the nervous and musculoskeletal systems interact to produce motor behaviors. In the future, this understanding may inform improved clinical interventions, prosthetic applications, and the general design of distributed, hierarchal systems.