1 Department of Biomedical Engineering, University of Connecticut, 260 Glenbrook Road, Storrs CT 06269-3247, USA.
Int J Neural Syst. 2018 Apr;28(3):1750050. doi: 10.1142/S0129065717500502. Epub 2017 Oct 20.
A linear homeomorphic eye movement model that produces 3D saccadic eye movements consistent with anatomical and physiological evidence is introduced in this second part of a two-paper sequence. Central to the model is the implementation of a time-optimal neural control strategy involving six linear muscle models that faithfully represent the dynamic characteristics of 3D saccades. The muscle is modeled as a parallel combination of viscosity [Formula: see text] and series elasticity [Formula: see text], connected to the parallel combination of active-state tension generator [Formula: see text], viscosity element [Formula: see text], and length tension elastic element [Formula: see text]. The neural input for each muscle is separately maintained while the effective pulling direction is modulated by its respective pulley. The results demonstrate that a time-optimal, 2D commutative neural controller, together with the pulley system, actively functions to implement Listing's law during both static and dynamic simulations and provide an excellent match with the experimental data. The parameters and neural input to the muscles are estimated using a time domain system identification technique from saccade data, with an excellent match between the model estimates and the data. A total of 20 horizontal, 5 vertical and 62 oblique saccades are analyzed.
在这篇由两部分组成的论文的第二部分中，介绍了一种线性同胚眼动模型，该模型产生的 3D 扫视眼动与解剖学和生理学证据一致。该模型的核心是实施一种时间最优的神经控制策略，涉及六个线性肌肉模型，这些模型忠实地再现了 3D 扫视的动态特性。肌肉被建模为粘性[公式：见正文]和串联弹性[公式：见正文]的并联组合，与主动状态张力发生器[公式：见正文]、粘性元件[公式：见正文]和长度张力弹性元件[公式：见正文]的并联组合相连。在保持每个肌肉的单独神经输入的同时，通过各自的滑轮来调节有效牵拉方向。结果表明，一个时间最优的、2D 可交换神经控制器，以及滑轮系统，在静态和动态模拟中积极地作用，以实施 Listing 法则，并与实验数据非常吻合。肌肉的参数和神经输入使用来自扫视数据的时域系统辨识技术进行估计，模型估计值与数据之间具有极好的吻合度。总共分析了 20 次水平扫视、5 次垂直扫视和 62 次斜向扫视。
