呼吸神经元模型中斜坡爆发的动力学。

IF 2 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Journal of Computational Neuroscience Pub Date : 2022-05-01 Epub Date: 2021-10-26 DOI:10.1007/s10827-021-00800-w
Muhammad U Abdulla, Ryan S Phillips, Jonathan E Rubin
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引用次数: 5

摘要

密集的计算和理论工作导致了哺乳动物脑干pre-Bötzinger复合体(pre-BötC)中呼吸神经元破裂的多种数学模型的发展。尽管如此,这些先前的模型并没有捕捉到这些神经元活动模式的吸气前斜坡方面,在这种模式中,相对缓慢的紧张性尖峰逐渐发展到更快的尖峰和全面爆发,并伴随相应的潜在平台电位的逐渐发展。在这项工作中,我们表明,将细胞外钾离子浓度的动力学纳入pre-BötC神经元破裂的现有模型,并进行一些参数调整,足以诱导这种斜坡行为。利用快慢分解,我们证明了这种活动可以被认为是抛物型爆发的一种形式,但爆发终止在同斜分岔处,而不是SNIC分岔处。我们还研究了这些解决方案的参数依赖性,并表明所提出的模型比文献中其他模型产生的突发频率,持续时间和占空比的动态范围更大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamics of ramping bursts in a respiratory neuron model.

Intensive computational and theoretical work has led to the development of multiple mathematical models for bursting in respiratory neurons in the pre-Bötzinger Complex (pre-BötC) of the mammalian brainstem. Nonetheless, these previous models have not captured the pre-inspiratory ramping aspects of these neurons' activity patterns, in which relatively slow tonic spiking gradually progresses to faster spiking and a full-blown burst, with a corresponding gradual development of an underlying plateau potential. In this work, we show that the incorporation of the dynamics of the extracellular potassium ion concentration into an existing model for pre-BötC neuron bursting, along with some parameter adjustments, suffices to induce this ramping behavior. Using fast-slow decomposition, we show that this activity can be considered as a form of parabolic bursting, but with burst termination at a homoclinic bifurcation rather than as a SNIC bifurcation. We also investigate the parameter-dependence of these solutions and show that the proposed model yields a greater dynamic range of burst frequencies, durations, and duty cycles than those produced by other models in the literature.

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来源期刊
CiteScore
2.00
自引率
8.30%
发文量
32
审稿时长
3 months
期刊介绍: The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods yielding insights into the function of the nervous system, are also welcomed (in this case, methodological papers should include an application of the new method, exemplifying the insights that it yields).It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience.
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