Sargolzaei Arman, Abdelghani Mohamed, Yen Kang K, Sargolzaei Saman
Department of Electrical and Computer Engineering, Florida International University, Miami, FL, 33174, USA.
Department of Mathematics and Statistics, University of Alberta, Edmonton, 30332, Canada.
BMC Bioinformatics. 2016 Jul 25;17 Suppl 7(Suppl 7):245. doi: 10.1186/s12859-016-1098-2.
The predictive nature of the primate sensorimotor systems, for example the smooth pursuit system and their ability to compensate for long delays have been proven by many physiological experiments. However, few theoretical models have tried to explain these facts comprehensively. Here, we propose a sensorimotor learning and control model that can be used to (1) predict the dynamics of variable time delays and current and future sensory states from delayed sensory information; (2) learn new sensorimotor realities; and (3) control a motor system in real time.
This paper proposed a new time-delay estimation method and developed a computational model for a predictive control solution of a sensorimotor control system under time delay. Simulation experiments are used to demonstrate how the proposed model can explain a sensorimotor system's ability to compensate for delays during online learning and control. To further illustrate the benefits of the proposed time-delay estimation method and predictive control in sensorimotor systems a simulation of the horizontal Vestibulo-Ocular Reflex (hVOR) system is presented. Without the proposed time-delay estimation and prediction, the hVOR can be unstable and could be affected by high frequency oscillations. These oscillations are reminiscent of a fast correction mechanism, e.g., a saccade to compensate for the hVOR delays. Comparing results of the proposed model with those in literature, it is clear that the hVOR system with impaired time-delay estimation or impaired sensory state predictor can mimic certain outcomes of sensorimotor diseases. Even more, if the control of hVOR is augmented with the proposed time-delay estimator and the predictor for eye position relative to the head, then hVOR control system can be stabilized.
Three claims with varying degrees of experimental support are proposed in this paper. Firstly, the brain or any sensorimotor system has time-delay estimation circuits for the various sensorimotor control systems. Secondly, the brain continuously estimates current/future sensory states from the previously sensed states. Thirdly, the brain uses predicted sensory states to perform optimal motor control.
灵长类动物感觉运动系统的预测特性，例如平稳跟踪系统及其补偿长时间延迟的能力，已被许多生理学实验所证实。然而，很少有理论模型试图全面解释这些事实。在此，我们提出一种感觉运动学习与控制模型，该模型可用于：（1）根据延迟的感觉信息预测可变时间延迟以及当前和未来感觉状态的动态变化；（2）学习新的感觉运动实际情况；（3）实时控制运动系统。
本文提出了一种新的时间延迟估计方法，并开发了一个计算模型，用于解决存在时间延迟的感觉运动控制系统的预测控制问题。仿真实验用于证明所提出的模型如何能够解释感觉运动系统在在线学习和控制过程中补偿延迟的能力。为了进一步说明所提出的时间延迟估计方法和预测控制在感觉运动系统中的优势，本文给出了水平前庭眼反射（hVOR）系统的仿真。如果没有所提出的时间延迟估计和预测，hVOR可能会不稳定，并可能受到高频振荡的影响。这些振荡让人联想到一种快速校正机制，例如用于补偿hVOR延迟的扫视。将所提出模型的结果与文献中的结果进行比较，可以清楚地看到，时间延迟估计受损或感觉状态预测器受损的hVOR系统可以模拟感觉运动疾病的某些结果。甚至，如果用所提出的时间延迟估计器和相对于头部的眼睛位置预测器来增强hVOR的控制，那么hVOR控制系统可以得到稳定。
本文提出了三个具有不同程度实验支持的观点。首先，大脑或任何感觉运动系统具有用于各种感觉运动控制系统的时间延迟估计电路。其次，大脑从先前感知的状态持续估计当前/未来的感觉状态。第三，大脑使用预测的感觉状态来执行最优的运动控制。
