Department of Bioengineering, Faculty of Engineering, Imperial College London, London, SW7 2AZ, United Kingdom.
Department of Mechanical Engineering, Faculty of Engineering, Imperial College London, London, SW7 2AZ, United Kingdom.
Med Eng Phys. 2024 Aug;130:104197. doi: 10.1016/j.medengphy.2024.104197. Epub 2024 Jul 22.
The neural control of human quiet stance remains controversial, with classic views suggesting a limited role of the brain and recent findings conversely indicating direct cortical control of muscles during upright posture. Conceptual neural feedback control models have been proposed and tested against experimental evidence. The most renowned model is the continuous impedance control model. However, when time delays are included in this model to simulate neural transmission, the continuous controller becomes unstable. Another model, the intermittent control model, assumes that the central nervous system (CNS) activates muscles intermittently, and not continuously, to counteract gravitational torque. In this study, a delayed reinforcement learning algorithm was developed to seek optimal control policy to balance a one-segment inverted pendulum model representing the human body. According to this approach, there was no a-priori strategy imposed on the controller but rather the optimal strategy emerged from the reward-based learning. The simulation results indicated that the optimal neural controller exhibits intermittent, and not continuous, characteristics, in agreement with the possibility that the CNS intermittently provides neural feedback torque to maintain an upright posture.
人体静立姿势的神经控制仍然存在争议，经典观点认为大脑的作用有限，而最近的发现则相反，表明大脑在直立姿势时直接控制肌肉。已经提出了概念性的神经反馈控制模型，并根据实验证据进行了测试。最著名的模型是连续阻抗控制模型。然而，当在该模型中包含时滞以模拟神经传输时，连续控制器变得不稳定。另一个模型，间歇控制模型，假设中枢神经系统（CNS）间歇性地而不是连续地激活肌肉，以抵消重力扭矩。在这项研究中，开发了一种延迟强化学习算法来寻求最佳控制策略，以平衡代表人体的单段倒立摆模型。根据这种方法，控制器没有强加先验策略，而是从基于奖励的学习中产生最佳策略。模拟结果表明，最优神经控制器表现出间歇性而不是连续性的特征，这与 CNS 间歇性地提供神经反馈扭矩以维持直立姿势的可能性一致。
