| Popis: |
The design of control systems for limb prostheses seems likely to benefit from an understanding of how sensorimotor integration is achieved in the intact system. Traditional BMIs guess what movement parameters are encoded by brain activity and then decode them to drive prostheses directly. Modeling the known structure and emergent properties of the biological decoder itself is likely to be more effective in bridging from normal brain activity to functionally useful limb movement. In this study, we have extended a model of spinal circuitry (termed SLR for spinal-like regulator; see Raphael, G., Tsianos, G. A.,Loeb G. E. 2010, Spinal-like regulator facilitates control of a two-degree-of-freedom wrist. The Journal of Neuroscience, 30(28), 9431-9444.) to a planar elbow-shoulder system to investigate how the spinal cord contributes to the control of a musculoskeletal system with redundant and multiarticular musculature and interaction (Coriolis) torques, which are common control problems for multisegment linkages throughout the body. The SLR consists of a realistic set of interneuronal pathways (monosynaptic Ia-excitatory, reciprocal Ia-inhibitory, Renshaw inhibitory, Ib-inhibitory, and propriospinal) that are driven by unmodulated step commands with learned amplitudes. We simulated the response of a planar arm to a brief, oblique impulse at the hand and investigated the role of cocontraction in learning to resist it. Training the SLR without cocontraction led to generally poor performance that was significantly worse than training with cocontraction. Further, removing cocontraction from the converged solutions and retraining the system achieved better performance than the SLR responses without cocontraction. Cocontraction appears to reshape the solution space, virtually eliminating the probability of entrapment in poor local minima. The local minima that are entered during learning with cocontraction are favorable starting points for learning to perform the task when cocontraction is abruptly removed. Given the control system's ability to learn effectively and rapidly, we hypothesize that it will generalize more readily to the wider range of tasks that subjects must learn to perform, as opposed to BMIs mapped to outputs of the musculoskeletal system. |
