Computing muscle mechanical state variables from combined proprioceptive sensory feedback

Proprioceptive sensory feedback is crucial for the control of movement. In many ways, sensorimotor control loops in the neuromuscular system act as state feedback controllers. These controllers combine input commands and sensory feedback regarding the mechanical state of the muscle, joint or limb to modulate the mechanical output of the muscles. To understand how these control circuits function, it is necessary to understand fully the mechanical state variables that are signalled by proprioceptive sensory (propriosensory) afferents. Using new computational approaches, we demonstrate how combinations of group Ia and II muscle spindle afferent feedback can allow for tuned responses to force and the rate of force (or length and velocity) and how combinations of muscle spindle and Golgi tendon organ feedback can parse external and internal (self-generated) force. These models suggest that muscle spindle feedback might be used to monitor and control muscle forces in addition to length and velocity and, when combined with tendon organ feedback, can distinguish self-generated from externally imposed forces. Given that these models combine feedback from different sensory afferent types, they emphasize the utility of analysing muscle propriosensors as an integrated population, rather than independently, to gain a better understanding of propriosensory-motor control. Furthermore, these models propose a framework that links neural connectivity in the spinal cord with neuromechanical control. Although considerable work has been done on propriosensory-motor pathways in the CNS, our aim is to build upon this work by emphasizing the mechanical context.