Xiang Yongqing, Yakushin Sergei B, Cohen Bernard, Raphan Theodore
Department of Computer and Information Science, Brooklyn College of CUNY, 2900 Bedford Avenue, Brooklyn, NY 11210, USA.
J Neurophysiol. 2006 Dec;96(6):3349-61. doi: 10.1152/jn.00430.2006. Epub 2006 Sep 13.
A neural network model was developed to explain the gravity-dependent properties of gain adaptation of the angular vestibuloocular reflex (aVOR). Gain changes are maximal at the head orientation where the gain is adapted and decrease as the head is tilted away from that position and can be described by the sum of gravity-independent and gravity-dependent components. The adaptation process was modeled by modifying the weights and bias values of a three-dimensional physiologically based neural network of canal-otolith-convergent neurons that drive the aVOR. Model parameters were trained using experimental vertical aVOR gain values. The learning rule aimed to reduce the error between eye velocities obtained from experimental gain values and model output in the position of adaptation. Although the model was trained only at specific head positions, the model predicted the experimental data at all head positions in three dimensions. Altering the relative learning rates of the weights and bias improved the model-data fits. Model predictions in three dimensions compared favorably with those of a double-sinusoid function, which is a fit that minimized the mean square error at every head position and served as the standard by which we compared the model predictions. The model supports the hypothesis that gravity-dependent adaptation of the aVOR is realized in three dimensions by a direct otolith input to canal-otolith neurons, whose canal sensitivities are adapted by the visual-vestibular mismatch. The adaptation is tuned by how the weights from otolith input to the canal-otolith-convergent neurons are adapted for a given head orientation.
开发了一种神经网络模型，以解释角前庭眼反射（aVOR）增益适应的重力依赖性特性。增益变化在增益适应的头部方向处最大，并随着头部从该位置倾斜而减小，并且可以由与重力无关和与重力有关的分量之和来描述。通过修改驱动aVOR的管-耳石汇聚神经元的三维生理神经网络的权重和偏差值，对适应过程进行建模。使用实验性垂直aVOR增益值训练模型参数。学习规则旨在减少从实验增益值获得的眼速度与适应位置处的模型输出之间的误差。尽管该模型仅在特定头部位置进行训练，但该模型在三维空间中预测了所有头部位置的实验数据。改变权重和偏差的相对学习率改善了模型与数据的拟合度。三维模型预测与双正弦函数的预测相比具有优势，双正弦函数是一种在每个头部位置最小化均方误差的拟合，并且作为我们比较模型预测的标准。该模型支持以下假设：aVOR的重力依赖性适应是通过耳石直接输入到管-耳石神经元在三维空间中实现的，其管敏感性通过视觉-前庭不匹配进行适应。适应是通过调整从耳石输入到管-耳石汇聚神经元的权重如何适应给定的头部方向来进行调节的。
