慢负反馈增强了方波爆破的鲁棒性。

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Sushmita Rose John, Bernd Krauskopf, Hinke M Osinga, Jonathan E Rubin
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引用次数: 2

摘要

方波爆发是多种神经元和内分泌细胞模型中常见的一种活动模式,与呼吸和其他生理功能的中枢模式产生有关。许多显示方波爆破的简化数学模型的产量转变为一种可选择的伪平台爆破模式,参数变化很小。这种对活动变化的易感性可能在由尖峰产生触发的释放事件是功能所必需的设置中代表一个有问题的特征。在这项工作中,我们分析了模型破裂和其他活动模式如何随着与快速内向电流电导相关的时间尺度的变化而变化。具体来说,使用数值模拟和动态系统方法,如快慢分解和分岔和相平面分析,我们展示并解释了这些模型中与快速内向电流逐渐减少相关的缓慢负反馈的存在如何有助于在爆发的活跃阶段保持峰值的存在。因此,虽然这样的负反馈对突发产生不是必需的,但我们发现它的存在产生了对函数可能很重要的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Slow negative feedback enhances robustness of square-wave bursting.

Slow negative feedback enhances robustness of square-wave bursting.

Square-wave bursting is an activity pattern common to a variety of neuronal and endocrine cell models that has been linked to central pattern generation for respiration and other physiological functions. Many of the reduced mathematical models that exhibit square-wave bursting yield transitions to an alternative pseudo-plateau bursting pattern with small parameter changes. This susceptibility to activity change could represent a problematic feature in settings where the release events triggered by spike production are necessary for function. In this work, we analyze how model bursting and other activity patterns vary with changes in a timescale associated with the conductance of a fast inward current. Specifically, using numerical simulations and dynamical systems methods, such as fast-slow decomposition and bifurcation and phase-plane analysis, we demonstrate and explain how the presence of a slow negative feedback associated with a gradual reduction of a fast inward current in these models helps to maintain the presence of spikes within the active phases of bursts. Therefore, although such a negative feedback is not necessary for burst production, we find that its presence generates a robustness that may be important for function.

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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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