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小脑区 C3 的自适应滤波器模型能否作为安全肢体控制的基础?

An adaptive filter model of cerebellar zone C3 as a basis for safe limb control?

机构信息

P. Dean: Department of Psychology, University of Sheffield, Western Bank, Sheffield S10 2TP, UK.

出版信息

J Physiol. 2013 Nov 15;591(22):5459-74. doi: 10.1113/jphysiol.2013.261545. Epub 2013 Jul 8.

Abstract

The review asks how the adaptive filter model of the cerebellum might be relevant to experimental work on zone C3, one of the most extensively studied regions of cerebellar cortex. As far as features of the cerebellar microcircuit are concerned, the model appears to fit very well with electrophysiological discoveries concerning the importance of molecular layer interneurons and their plasticity, the significance of long-term potentiation and the striking number of silent parallel fibre synapses. Regarding external connectivity and functionality, a key feature of the adaptive filter model is its use of the decorrelation algorithm, which renders it uniquely suited to problems of sensory noise cancellation. However, this capacity can be extended to the avoidance of sensory interference, by appropriate movements of, for example, the eyes in the vestibulo-ocular reflex. Avoidance becomes particularly important when painful signals are involved, and as the climbing fibre input to zone C3 is extremely responsive to nociceptive stimuli, it is proposed that one function of this zone is the avoidance of pain by, for example, adjusting movements of the body to avoid self-harm. This hypothesis appears consistent with evidence from humans and animals concerning the role of the intermediate cerebellum in classically conditioned withdrawal reflexes, but further experiments focusing on conditioned avoidance are required to test the hypothesis more stringently. The proposed architecture may also be useful for automatic self-adjusting damage avoidance in robots, an important consideration for next generation 'soft' robots designed to interact with people.

摘要

这篇综述探讨了小脑的自适应滤波器模型如何与小脑皮质中研究最广泛的区域之一——C3 区的实验工作相关。就小脑微电路的特征而言,该模型似乎非常符合有关分子层中间神经元及其可塑性、长时程增强的重要性以及惊人数量的沉默平行纤维突触的电生理学发现。关于外部连接和功能,自适应滤波器模型的一个关键特征是其使用去相关算法,这使其非常适合解决感官噪声消除问题。然而,这种能力可以通过适当的运动(例如在前庭眼反射中移动眼睛)扩展到避免感官干扰。当涉及到疼痛信号时,避免变得尤为重要,由于 C3 区的 climbing 纤维输入对伤害性刺激极为敏感,因此有人提出该区域的一个功能是通过例如调整身体运动来避免伤害,从而避免疼痛。这一假设与人类和动物关于中间小脑在经典条件反射退缩反射中的作用的证据一致,但需要进一步的聚焦于条件性回避的实验来更严格地检验该假设。所提出的架构对于自动自我调整的损伤回避机器人也可能有用,这是下一代旨在与人交互的“软”机器人的一个重要考虑因素。

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