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动眼整合器:神经网络模型的测试

The oculomotor integrator: testing of a neural network model.

作者信息

Arnold D B, Robinson D A

机构信息

Department of Ophthalmology, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.

出版信息

Exp Brain Res. 1997 Jan;113(1):57-74. doi: 10.1007/BF02454142.

Abstract

An important part of the vestibulo-ocular reflex is a group of cells in the caudal pons, known as the neural integrator, that converts eye-velocity commands, from the semicircular canals for example, to eye-position commands for the motoneurons of the extraocular muscles. Previously, a recurrently connected neural network model was developed by us that learns to simulate the signal processing done by the neural integrator, but it uses an unphysiological learning algorithm. We describe here a new network model that can learn the same task by using a local, Hebbian-like learning algorithm that is physiologically plausible. Through the minimization of a retinal slip error signal the model learns, given randomly selected initial synaptic weights, to both integrate simulated push-pull semicircular canal afferent signals and compensate for orbital mechanics as well. Approximately half of the model's 14 neurons are inhibitory, half excitatory. After learning, inhibitory cells tend to project contralaterally, thus forming an inhibitory commissure. The network can, of course, recover from lesions. The mature network is also able to change its gain by simulating abnormal visual-vestibular interactions. When trained with a sine wave at a single frequency, the network changed its gain at and near the training frequency but not at significantly higher or lower frequencies, in agreement with previous experimental observations. Commissural connections are essential to the functioning of this model, as was the case with our previous model. In order to determine whether a commissure plays a similar role in the real neural integrator, a series of electrical perturbations were performed on the midlines of awake, behaving juvenile rhesus monkeys and the effects on the monkeys' eye movements were examined. Eye movements were recorded using the coil system before, during, and after electrical stimulation in the midline of the pons just caudal to the abducens nuclei, which reversibly made the integrator leaky. Eye movements were also recorded from two of the monkeys before and after a midline electrolytic lesion was made at the location where stimulation produced a leaky integrator. This lesion disabled the integrator irreversibly. The eye movements that were produced by the monkeys as a result of these perturbations were then compared with eye movements produced by the model after analogous perturbations. The results are compatible with the hypothesis that integration comes about by positive feedback through lateral inhibition effected by an inhibitory commissure.

摘要

前庭眼反射的一个重要部分是位于脑桥尾部的一组细胞,称为神经整合器,它将例如来自半规管的眼速度指令转换为眼外肌运动神经元的眼位置指令。此前,我们开发了一个循环连接的神经网络模型,该模型学习模拟神经整合器所进行的信号处理,但它使用的是一种非生理性的学习算法。我们在此描述一种新的网络模型,它可以通过使用一种局部的、类似赫布型的、具有生理合理性的学习算法来学习相同的任务。通过最小化视网膜滑动误差信号,该模型在给定随机选择的初始突触权重的情况下,学习整合模拟的推挽式半规管传入信号并补偿眼球运动力学。该模型的14个神经元中约一半是抑制性的,一半是兴奋性的。学习后,抑制性细胞倾向于向对侧投射,从而形成一个抑制性连合。该网络当然能够从损伤中恢复。成熟的网络还能够通过模拟异常的视觉 - 前庭相互作用来改变其增益。当用单一频率的正弦波进行训练时,网络在训练频率及其附近改变了增益,但在显著更高或更低的频率下没有改变,这与先前的实验观察结果一致。连合连接对于该模型的功能至关重要,就像我们之前的模型一样。为了确定连合在真实的神经整合器中是否发挥类似作用,对清醒、行为活跃的幼年恒河猴的中线进行了一系列电扰动,并检查了对猴子眼动的影响。在脑桥中线、展神经核尾侧进行电刺激之前、期间和之后,使用线圈系统记录眼动,该刺激可逆地使整合器产生泄漏。在中线电解损伤位于刺激产生泄漏整合器的位置之前和之后,也对两只猴子记录了眼动。该损伤不可逆地使整合器失效。然后将猴子因这些扰动而产生的眼动与模型在类似扰动后产生的眼动进行比较。结果与以下假设相符,即整合是通过抑制性连合产生的侧向抑制的正反馈实现的。

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