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小脑学习中下橄榄核神经元的自适应耦合。

Adaptive coupling of inferior olive neurons in cerebellar learning.

机构信息

Department of Mechanical Engineering, Ritsumeikan University, Shiga, Japan.

出版信息

Neural Netw. 2013 Nov;47:42-50. doi: 10.1016/j.neunet.2012.12.006. Epub 2012 Dec 28.

Abstract

In the cerebellar learning hypothesis, inferior olive neurons are presumed to transmit high fidelity error signals, despite their low firing rates. The idea of chaotic resonance has been proposed to realize efficient error transmission by desynchronized spiking activities induced by moderate electrical coupling between inferior olive neurons. A recent study suggests that the coupling strength between inferior olive neurons can be adaptive and may decrease during the learning process. We show that such a decrease in coupling strength can be beneficial for motor learning, since efficient coupling strength depends upon the magnitude of the error signals. We introduce a scheme of adaptive coupling that enhances the learning of a neural controller for fast arm movements. Our numerical study supports the view that the controlling strategy of the coupling strength provides an additional degree of freedom to optimize the actual learning in the cerebellum.

摘要

在小脑学习假说中,尽管下橄榄核神经元的放电率较低,但它们被认为传递着高度保真的误差信号。混沌共振的思想已经被提出,通过在下橄榄核神经元之间适度的电耦合诱导去同步的尖峰活动,实现有效的误差传递。最近的一项研究表明,下橄榄核神经元之间的耦合强度可能是自适应的,并可能在学习过程中降低。我们表明,这种耦合强度的降低对运动学习是有益的,因为有效的耦合强度取决于误差信号的大小。我们引入了一种自适应耦合的方案,该方案增强了快速手臂运动神经控制器的学习。我们的数值研究支持这样一种观点,即耦合强度的控制策略为优化小脑的实际学习提供了一个额外的自由度。

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