| The performance of motor tasks requires the coordinated control and continuous adjustment of myriad individual muscles. The basic commands for the successful performance of a sensorimotor task originate in "higher" centers such as the motor cortex, but the actual muscle activation and resulting torques and motion are considerably shaped by the integrative function of the spinal interneurons. The relative contributions of brain and spinal cord are less clear for reaching movements than for automatic tasks such as locomotion. We have modeled a two-axis, four-muscle wrist joint with realistic musculoskeletal mechanics and proprioceptors and a network of spinal circuitry based on the classical types of interneurons (propriospinal, monosynaptic Ia-excitatory, reciprocal Ia-inhibitory, Renshaw inhibitory and Ib-inhibitory pathways) and their supraspinal control (via biasing activity, presynaptic inhibition and fusimotor gain). The modeled system has a very large number of control inputs, not unlike the real spinal cord that the brain must learn to control to produce desired behaviors. We then programmed this model to emulate actual performance in four very different but well-described behaviors: (1) stabilizing responses to force perturbations; (2) rapid movement to position target; (3) isometric force to a target level; (4) adaptation to viscous curl force fields. We found that relatively simple and generally intuitive step-changes in a small subset of descending controls could reproduce each of these behaviors. We then optimized the control inputs using a gradient descent algorithm. Even though the algorithm started with random values for the control inputs, the model converged rapidly to produce physiologically realistic outputs. Our general hypothesis is that the real task of the brain is to configure the spinal circuitry to minimize interventions by the brain during the task. |
