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扫视过程中丘系与小脑功能的分布式模型。

Distributed model of collicular and cerebellar function during saccades.

作者信息

Optican Lance M, Quaia Christian

机构信息

Laboratory of Sensorimotor Research, National Eye Institute, NIH, Bethesda, Maryland 20892, USA.

出版信息

Ann N Y Acad Sci. 2002 Apr;956:164-77. doi: 10.1111/j.1749-6632.2002.tb02817.x.

Abstract

How does the brain tell the eye where to go? Classical models of rapid eye movements are lumped control systems that compute analogs of physical signals such as desired eye displacement, instantaneous error, and motor drive. Components of these lumped models do not correspond well with anatomical and physiological data. We have developed a more brain-like, distributed model (called a neuromimetic model), in which the superior colliculus (SC) and cerebellum (CB) play novel roles, using information about the desired target and the movement context to generate saccades. It suggests that the SC is neither sensory nor motor; rather it encodes the desired sensory consequence of the saccade in retinotopic coordinates. It also suggests a non-computational scheme for motor control by the cerebellum, based on context learning and a novel spatial mechanism, the pilot map. The CB learns to use contextual information to initialize the pilot signal that will guide the saccade to its goal. The CB monitors feedback information to steer and stop the saccade, and thus replaces the classical notion of a displacement integrator. One consequence of this model is that no desired eye movement signal is encoded explicitly in the brain; rather it is distributed across activity in both the SC and CB. Another is that the transformation from spatially coded sensory information to temporally coded motor information is implicit in the velocity feedback loop around the CB. No explicit spatial-to-temporal transformation with a normalization step is needed.

摘要

大脑如何指示眼睛看向何处?快速眼动的经典模型是集中控制系统,可计算诸如期望眼位移、瞬时误差和运动驱动等物理信号的类似物。这些集中模型的组成部分与解剖学和生理学数据不太相符。我们开发了一种更类似大脑的分布式模型(称为神经拟态模型),其中上丘(SC)和小脑(CB)发挥着新的作用,利用有关期望目标和运动背景的信息来产生扫视。这表明上丘既非感觉性的也非运动性的;相反,它在视网膜坐标中编码扫视的期望感觉结果。这还提出了一种小脑运动控制的非计算方案,基于情境学习和一种新的空间机制——引导图。小脑学会利用情境信息来初始化将引导扫视至目标的引导信号。小脑监测反馈信息以引导和停止扫视,从而取代了位移积分器的经典概念。该模型的一个结果是大脑中没有明确编码期望的眼动信号;相反,它分布在上丘和小脑的活动中。另一个结果是,从空间编码的感觉信息到时间编码的运动信息的转换隐含在围绕小脑的速度反馈回路中。不需要带有归一化步骤的明确的空间到时间的转换。

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