Harischandra Nalin, Ekeberg Orjan
Computational Biology and Neurocomputing, School of Computer Science and Communication, Royal Institute of Technology, 10044, Stockholm, Sweden.
Biol Cybern. 2008 Aug;99(2):125-38. doi: 10.1007/s00422-008-0243-z. Epub 2008 Jul 22.
Neurophysiological experiments in walking cats have shown that a number of neural control mechanisms are involved in regulating the movements of the hind legs during locomotion. It is experimentally hard to isolate individual mechanisms without disrupting the natural walking pattern and we therefore introduce a different approach where we use a model to identify what control is necessary to maintain stability in the musculo-skeletal system. We developed a computer simulation model of the cat hind legs in which the movements of each leg are produced by eight limb muscles whose activations follow a centrally generated pattern with no proprioceptive feedback. All linear transfer functions, from each muscle activation to each joint angle, were identified using the response of the joint angle to an impulse in the muscle activation at 65 postures of the leg covering the entire step cycle. We analyzed the sensitivity and stability of each muscle action on the joint angles by studying the gain and pole plots of these transfer functions. We found that the actions of most of the hindlimb muscles display inherent stability during stepping, even without the involvement of any proprioceptive feedback mechanisms, and that those musculo-skeletal systems are acting in a critically damped manner, enabling them to react quickly without unnecessary oscillations. We also found that during the late swing, the activity of the posterior biceps/semitendinosus (PB/ST) muscles causes the joints to be unstable. In addition, vastus lateralis (VL), tibialis anterior (TA) and sartorius (SAT) muscle-joint systems were found to be unstable during the late stance phase, and we conclude that those muscles require neuronal feedback to maintain stable stepping, especially during late swing and late stance phases. Moreover, we could see a clear distinction in the pole distribution (along the step cycle) for the systems related to the ankle joint from that of the other two joints, hip or knee. A similar pattern, i.e., a pattern in which the poles were scattered over the s-plane with no clear clustering according to the phase of the leg position, could be seen in the systems related to soleus (SOL) and TA muscles which would indicate that these muscles depend on neural control mechanisms, which may involve supraspinal structures, over the whole step cycle.
对行走的猫进行的神经生理学实验表明,在运动过程中,有多种神经控制机制参与调节后腿的运动。在不破坏自然行走模式的情况下,通过实验很难分离出单个机制,因此我们引入了一种不同的方法,即使用模型来确定在肌肉骨骼系统中维持稳定性所需的控制。我们开发了一个猫后腿的计算机模拟模型,其中每条腿的运动由八条肢体肌肉产生,这些肌肉的激活遵循中央生成的模式,没有本体感觉反馈。利用在覆盖整个步周期的腿部65种姿势下,关节角度对肌肉激活中的脉冲的响应,确定了从每个肌肉激活到每个关节角度的所有线性传递函数。通过研究这些传递函数的增益和极点图,我们分析了每个肌肉动作对关节角度的敏感性和稳定性。我们发现,即使没有任何本体感觉反馈机制的参与,大多数后肢肌肉的动作在迈步过程中也表现出固有的稳定性,并且那些肌肉骨骼系统以临界阻尼的方式起作用,使它们能够快速反应而不会产生不必要的振荡。我们还发现,在摆动后期,肱二头肌/半腱肌(PB/ST)后部肌肉的活动会导致关节不稳定。此外,发现股外侧肌(VL)、胫骨前肌(TA)和缝匠肌(SAT)的肌肉-关节系统在站立后期不稳定,我们得出结论,这些肌肉需要神经元反馈来维持稳定的迈步,特别是在摆动后期和站立后期。此外,我们可以看到,与踝关节相关的系统的极点分布(沿步周期)与其他两个关节,即髋关节或膝关节的极点分布有明显区别。在与比目鱼肌(SOL)和TA肌肉相关的系统中,可以看到类似的模式,即极点在s平面上分散,没有根据腿部位置的阶段进行明显聚类,这表明这些肌肉在整个步周期中依赖于可能涉及脊髓上结构的神经控制机制。