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肢体间振荡的神经控制。II. 两足和四足步态及分岔

Neural control of interlimb oscillations. II. Biped and quadruped gaits and bifurcations.

作者信息

Pribe C, Grossberg S, Cohen M A

机构信息

Center for Adaptive Systems, Boston University, MA 02215, USA.

出版信息

Biol Cybern. 1997 Aug;77(2):141-52. doi: 10.1007/s004220050375.

Abstract

Behavioral data concerning animal and human gaits and gait transitions are simulated as emergent properties of a central pattern generator (CPG) model. The CPG model is a version of the Ellias-Grossberg oscillator. Its neurons obey Hodgkin-Huxley type equations whose excitatory signals operate on a faster time scale than their inhibitory signals in a recurrent on-center off-surround anatomy. A descending command or GO signal activates the gaits and triggers gait transitions as its amplitude increases. A single model CPG can generate both in-phase and anti-phase oscillations at different GO amplitudes. Phase transitions from either in-phase to anti-phase oscillations or from anti-phase to in-phase oscillations can occur in different parameter ranges, as the GO signal increases. Quadruped vertebrate gaits, including the amble, the walk, all three pairwise gaits (trot, pace, and gallop), and the pronk are simulated using this property. Rapid gait transitions are simulated in the order--walk, trot, pace, and gallop--that occurs in the cat, along with the observed increase in oscillation frequency. Precise control of quadruped gait switching uses GO-dependent modulation of inhibitory interactions, which generates a different functional anatomy at different arousal levels. The primary human gaits (the walk and the run) and elephant gaits (the amble and the walk) are simulated, without modulation, by oscillations with the same phase relationships but different waveform shapes at different GO signal levels, much as the duty cycles of the feet are longer in the walk than in the run. Relevant neural data from spinal cord, globus pallidus, and motor cortex, among other structures, are discussed.

摘要

关于动物和人类步态及步态转换的行为数据被模拟为中枢模式发生器(CPG)模型的涌现特性。CPG模型是埃利亚斯 - 格罗斯伯格振荡器的一个版本。其神经元遵循霍奇金 - 赫胥黎类型的方程,在循环的中心兴奋外周抑制解剖结构中,其兴奋性信号的运行时间尺度比抑制性信号更快。一个下行指令或启动信号会激活步态,并随着其幅度增加触发步态转换。单个模型CPG可以在不同的启动信号幅度下产生同相和反相振荡。随着启动信号增加,在不同参数范围内会发生从同相振荡到反相振荡或从反相振荡到同相振荡的相位转换。利用这一特性模拟了四足脊椎动物的步态,包括缓行、行走、所有三种两两组合的步态(小跑、侧行和疾驰)以及跳跃。按照猫中出现的顺序——行走、小跑、侧行和疾驰——模拟了快速步态转换,同时观察到振荡频率增加。四足步态切换的精确控制使用依赖于启动信号的抑制性相互作用调制,这在不同的唤醒水平下产生不同的功能解剖结构。主要的人类步态(行走和跑步)以及大象的步态(缓行和行走)在未进行调制的情况下,通过在不同启动信号水平下具有相同相位关系但不同波形形状的振荡来模拟,就像行走时脚的占空比比跑步时更长一样。还讨论了来自脊髓、苍白球和运动皮层等结构的相关神经数据。

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