Computational models and emergent properties of respiratory neural networks.
Computational models and emergent properties of respiratory neural networks.
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DOI：
10.1002/cphy.c110016
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发表时间：
2012-07
影响因子：
5.8
通讯作者：
Smith JC
中科院分区：
文献类型：
--
作者：
Lindsey BG;Rybak IA;Smith JC
哺乳动物呼吸神经控制系统的计算模型提供了一个理论和计算框架，汇集了在各种实验条件下从不同动物制剂中获得的实验数据。其中许多模型是与实验研究并行和迭代开发的，并提供指导新实验的预测。这种数据驱动的建模方法加深了我们对呼吸网络架构和呼吸节律和模式生成背后的神经机制的理解，包括它们在不同生理条件下的功能重组。这里回顾的模型在神经生物学细节和计算复杂性方面有所不同，并且跨越呼吸控制机制的多个时空尺度。最近的模型描述了空间分布在 Bötzinger 和前 Bötzinger 复合体以及包含呼吸中枢模式发生器 (CPG) 核心回路的头端腹外侧延髓内的相互作用的呼吸神经元群体。这些回路内的网络相互作用以及神经元固有的节律发生特性形成了多种节律生成机制的层次结构。这些机制的功能表达由来自其他脑干组件的输入驱动控制，包括梯形后核和脑桥，它们调节核心电路的动态行为。新出现的观点是，脑干呼吸网络在多个层次的回路组织中具有节律能力。这允许在各种生理条件下灵活地、状态依赖地表达不同的神经模式生成机制，从而实现广泛的呼吸行为。一些模型考虑在咳嗽等防御行为期间通过肺部反馈和网络重新配置来控制呼吸 CPG。考虑了呼吸 CPG 建模的未来方向。
Computational models of the neural control system for breathing in mammals provide a theoretical and computational framework bringing together experimental data obtained from different animal preparations under various experimental conditions. Many of these models were developed in parallel and iteratively with experimental studies and provided predictions guiding new experiments. This data-driven modeling approach has advanced our understanding of respiratory network architecture and neural mechanisms underlying generation of the respiratory rhythm and pattern, including their functional reorganization under different physiological conditions. Models reviewed here vary in neurobiological details and computational complexity and span multiple spatiotemporal scales of respiratory control mechanisms. Recent models describe interacting populations of respiratory neurons spatially distributed within the Bötzinger and pre-Bötzinger complexes and rostral ventrolateral medulla that contain core circuits of the respiratory central pattern generator (CPG). Network interactions within these circuits along with intrinsic rhythmogenic properties of neurons form a hierarchy of multiple rhythm generation mechanisms. The functional expression of these mechanisms is controlled by input drives from other brainstem components, including the retrotrapezoid nucleus and pons, which regulate the dynamic behavior of the core circuitry. The emerging view is that the brainstem respiratory network has rhythmogenic capabilities at multiple levels of circuit organization. This allows flexible, state-dependent expression of different neural pattern-generation mechanisms under various physiological conditions, enabling a wide repertoire of respiratory behaviors. Some models consider control of the respiratory CPG by pulmonary feedback and network reconfiguration during defensive behaviors such as cough. Future directions in modeling of the respiratory CPG are considered.
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影响因子：
5.5
作者：
Abdala, A. P. L.;Rybak, I. A.;Paton, J. F. R.
通讯作者：
Paton, J. F. R.
影响因子：
1.9
作者：
BALIS, UJ;MORRIS, KF;LINDSEY, BG
通讯作者：
LINDSEY, BG
影响因子：
2.1
作者：
Best, J;Borisyuk, A;Wechselberger, M
通讯作者：
Wechselberger, M
影响因子：
16.2
作者：
Abbott, L. F.
通讯作者：
Abbott, L. F.
影响因子：
5.5
作者：
Arata, A;Hernandez, YM;Shannon, R
通讯作者：
Shannon, R