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前庭系统中的内部模型和神经计算。

Internal models and neural computation in the vestibular system.

机构信息

Dépt. de Physiologie, Université de Montréal, 2960 Chemin de la Tour, Rm. 4141, Montreal, QC H3T 1J4, Canada.

出版信息

Exp Brain Res. 2010 Jan;200(3-4):197-222. doi: 10.1007/s00221-009-2054-4.

DOI:10.1007/s00221-009-2054-4
PMID:19937232
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2853943/
Abstract

The vestibular system is vital for motor control and spatial self-motion perception. Afferents from the otolith organs and the semicircular canals converge with optokinetic, somatosensory and motor-related signals in the vestibular nuclei, which are reciprocally interconnected with the vestibulocerebellar cortex and deep cerebellar nuclei. Here, we review the properties of the many cell types in the vestibular nuclei, as well as some fundamental computations implemented within this brainstem-cerebellar circuitry. These include the sensorimotor transformations for reflex generation, the neural computations for inertial motion estimation, the distinction between active and passive head movements, as well as the integration of vestibular and proprioceptive information for body motion estimation. A common theme in the solution to such computational problems is the concept of internal models and their neural implementation. Recent studies have shed new insights into important organizational principles that closely resemble those proposed for other sensorimotor systems, where their neural basis has often been more difficult to identify. As such, the vestibular system provides an excellent model to explore common neural processing strategies relevant both for reflexive and for goal-directed, voluntary movement as well as perception.

摘要

前庭系统对于运动控制和空间自身运动感知至关重要。来自耳石器官和半规管的传入神经与视动、躯体感觉和运动相关信号在前庭核中汇聚,前庭核与前庭小脑皮质和深部小脑核相互联系。在这里,我们回顾了前庭核中许多细胞类型的特性,以及该脑干小脑回路中实现的一些基本计算。这些包括用于反射生成的感觉运动转换、用于惯性运动估计的神经计算、主动和被动头部运动的区分,以及用于身体运动估计的前庭和本体感觉信息的整合。解决这些计算问题的一个共同主题是内部模型的概念及其神经实现。最近的研究为解决这些计算问题提供了新的见解,这些问题与其他感觉运动系统中提出的组织原则非常相似,而这些系统的神经基础通常更难确定。因此,前庭系统为探索与反射性和目标导向的、自愿运动以及感知相关的共同神经处理策略提供了一个极好的模型。

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