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小脑在人类伸手动作中的作用。II. 中间小脑的神经模型。

Role of the cerebellum in reaching movements in humans. II. A neural model of the intermediate cerebellum.

作者信息

Schweighofer N, Spoelstra J, Arbib M A, Kawato M

机构信息

Centre for Neural Engineering, University of Southern California, Los Angeles 90089-2520, USA.

出版信息

Eur J Neurosci. 1998 Jan;10(1):95-105. doi: 10.1046/j.1460-9568.1998.00007.x.

DOI:10.1046/j.1460-9568.1998.00007.x
PMID:9753117
Abstract

The cerebellum is essential for the control of multijoint movements; when the cerebellum is lesioned, the performance error is more than the summed errors produced by single joints. In the companion paper (Schweighofer et al., 1998), a functional anatomical model for visually guided arm movement was proposed. The model comprised a basic feedforward/feedback controller with realistic transmission delays and was connected to a two-link, six-muscle, planar arm. In the present study, we examined the role of the cerebellum in reaching movements by embedding a novel, detailed cerebellar neural network in this functional control model. We could derive realistic cerebellar inputs and the role of the cerebellum in learning to control the arm was assessed. This cerebellar network learned the part of the inverse dynamics of the arm not provided by the basic feedforward/feedback controller. Despite realistically low inferior olive firing rates and noisy mossy fibre inputs, the model could reduce the error between intended and planned movements. The responses of the different cell groups were comparable to those of biological cell groups. In particular, the modelled Purkinje cells exhibited directional tuning after learning and the parallel fibres, due to their length, provide Purkinje cells with the input required for this coordination task. The inferior olive responses contained two different components; the earlier response, locked to movement onset, was always present and the later response disappeared after learning. These results support the theory that the cerebellum is involved in motor learning.

摘要

小脑对于多关节运动的控制至关重要;当小脑受损时,表现误差大于单个关节产生的误差总和。在配套论文(施韦霍费尔等人,1998年)中,提出了一个视觉引导手臂运动的功能解剖模型。该模型包括一个具有实际传输延迟的基本前馈/反馈控制器,并与一个双关节、六条肌肉的平面手臂相连。在本研究中,我们通过在这个功能控制模型中嵌入一个新颖、详细的小脑神经网络,研究了小脑在伸手运动中的作用。我们可以得出实际的小脑输入,并评估小脑在学习控制手臂中的作用。这个小脑网络学习了基本前馈/反馈控制器未提供的手臂逆动力学部分。尽管下橄榄核的放电率实际较低且苔藓纤维输入存在噪声,但该模型可以减少预期运动和计划运动之间的误差。不同细胞群的反应与生物细胞群的反应相当。特别是,模拟的浦肯野细胞在学习后表现出方向调谐,并且平行纤维由于其长度,为浦肯野细胞提供了这种协调任务所需的输入。下橄榄核的反应包含两个不同的成分;较早的反应与运动开始同步,总是存在,而较晚的反应在学习后消失。这些结果支持了小脑参与运动学习的理论。

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