Artificial Neural Network modelling for studying posture

人工神经网络(ANN)应用面临的主要挑战 在姿势控制中考察运动-感觉关系的模型是 将神经网络的输入和输出联系起来的神经突触权重是混沌的 在自然界中。在通过理论分析进行的早期研究中，数值模拟 和实验测试，PI和她的同事发现这些重量是 相互依存。这些权重的乘积是统计稳定变量，并且 可以用来量化人工神经网络的输入输出关系，称为Q值。这个 这项拟议研究的目标是将上述工作扩展到以下领域 人类的姿势控制。具体地说，我们将探索Q值是否 人工神经网络中的概念可以用来量化经典的运动-感觉关系 姿势控制任务-当支撑底座处于 突然以脚趾向上的方向旋转。我们将构建一个人工神经网络模型， 包括两个输出和七个输入。这两个输出是来自 踝关节背屈肌和跟伸肌对支撑基座启动的响应 旋转。这七个输入是平均眼睛-目标距离(距眼睛中心的距离 视觉目标)、头部加速度(线性和角度)、脚踝关节旋转， 踝关节旋转速度和地面反作用力(法向和剪切) 在脚下。这些输入表示对视觉的机械刺激， 前庭系统和躯体感觉系统。这些输入和输出 变量将直接从两组老年受试者中测量： 外周神经病和正常，非外周神经病。到时候我们会的 通过反向传播训练确定神经网络模型中的权值 例程，以及将每一输出与每一输入相关联的对应Q值。 我们将对多个感觉输入之间的Q值进行统计比较 在每一组内。我们假设在这个实验条件下，Q 体感输入与姿势肌肉活动之间的关联值为：(1) 显著高于与其他感觉输入有关的Q值(例如 视觉和前庭输入)在正常的、非神经性的受试者中；和(2) 显著低于神经病理性疾病中与其他感觉输入有关的Q值 研究对象。希望这项研究将有助于我们理解如何 感觉信息被用来控制姿势肌肉活动，以及如何 运动-感觉关系的改变可以提高姿势的稳定性 或者摔倒。

A major challenge with the application of artificial neural network (ANN) modeling in examining the motor-sensory relation in postural control is that the neural synaptic weights that relate the inputs to the outputs of the ANN are chaotic in nature. In an earlier study through theoretical analysis, numerical simulations and experimental tests, the PI and her colleague have found that these weights are interdependent. The product of these weights is a statistically stable variable and can be used to quantify the input-output relation of the ANN, called Q value. The objective of this proposed research is to extend the above work to the area of human postural control. Specifically, we will explore whether or not a Q value concept in an ANN can be used to quantify the motor-sensory relation in a classical postural control task - maintaining upright balance when the supporting base is suddenly rotated in a toes up direction. We will construct an ANN model that includes two outputs and seven inputs. The two outputs are the EMG signals from ankle dorsiflexor and plantartlexor in response to the onset of the supporting base rotation. The seven inputs are average eye-target distance (distance from eye center to a visual target), head acceleration (both linear and angular), ankle joint rotation, ankle joint rotation speed, and ground reaction forces (both normal and shear) under feet. These inputs represent the mechanical stimulation to the visual, vestibular, and somatosensory systems, respectively. These inputs and outputs variables will be measured directly from two groups of elderly subjects: peripherally neuropathic and normal, non-peripherally neuropathic. We will then determine the weights in the ANN model by a backward-propagation training routine, and the corresponding Q values relating each output to each of the inputs. We will statistically compare the Q values among the multiple sensory inputs within each group. We hypothesize that under this experimental condition, the Q values relating postural muscle activities to the somatosensory inputs would be: (1) significantly higher than the Q values relating to other sensory inputs (such as visual and vestibular inputs) in normal, non-neuropathic subjects; and (2) significantly lower than the Q values relating to other sensory inputs in neuropathic subjects. It is hoped that this study will contribute to our understanding of how sensory information is used to control postural muscle activities, and how a modification in the motor-sensory relation can result in increased postural stability or falls.