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呼吸节律产生在动态状态下的稳健性。

Robustness of respiratory rhythm generation across dynamic regimes.

机构信息

Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.

Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.

出版信息

PLoS Comput Biol. 2019 Jul 30;15(7):e1006860. doi: 10.1371/journal.pcbi.1006860. eCollection 2019 Jul.

Abstract

A central issue in the study of the neural generation of respiratory rhythms is the role of the intrinsic pacemaking capabilities that some respiratory neurons exhibit. The debate on this issue has occurred in parallel to investigations of interactions among respiratory network neurons and how these contribute to respiratory behavior. In this computational study, we demonstrate how these two issues are inextricably linked. We use simulations and dynamical systems analysis to show that once a conditional respiratory pacemaker, which can be tuned across oscillatory and non-oscillatory dynamic regimes in isolation, is embedded into a respiratory network, its dynamics become masked: the network exhibits similar dynamical properties regardless of the conditional pacemaker node's tuning, and that node's outputs are dominated by network influences. Furthermore, the outputs of the respiratory central pattern generator as a whole are invariant to these changes of dynamical properties, which ensures flexible and robust performance over a wide dynamic range.

摘要

在研究呼吸节律的神经产生的过程中,一个核心问题是某些呼吸神经元所表现出的固有起搏能力的作用。关于这个问题的争论与对呼吸网络神经元之间的相互作用的研究以及这些相互作用如何促成呼吸行为同时发生。在这项计算研究中,我们展示了这两个问题是如何紧密相连的。我们使用模拟和动力系统分析来表明,一旦一个条件性呼吸起搏器被嵌入到一个呼吸网络中,它可以在孤立的振荡和非振荡动态范围内进行调整,那么它的动力学就会被掩盖:无论条件性起搏器节点的调整如何,网络都会表现出相似的动力学特性,并且该节点的输出会受到网络影响的支配。此外,呼吸中枢模式发生器的整体输出对这些动力学特性的变化是不变的,这确保了在广泛的动态范围内具有灵活和稳健的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85c3/6697358/c1dddb0cd5a6/pcbi.1006860.g001.jpg

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