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计算分析小型背根神经节神经元的 9D 模型。

Computational analysis of a 9D model for a small DRG neuron.

机构信息

Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, USA.

Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.

出版信息

J Comput Neurosci. 2020 Nov;48(4):429-444. doi: 10.1007/s10827-020-00761-6. Epub 2020 Aug 30.

Abstract

Small dorsal root ganglion (DRG) neurons are primary nociceptors which are responsible for sensing pain. Elucidation of their dynamics is essential for understanding and controlling pain. To this end, we present a numerical bifurcation analysis of a small DRG neuron model in this paper. The model is of Hodgkin-Huxley type and has 9 state variables. It consists of a Na1.7 and a Na1.8 sodium channel, a leak channel, a delayed rectifier potassium, and an A-type transient potassium channel. The dynamics of this model strongly depend on the maximal conductances of the voltage-gated ion channels and the external current, which can be adjusted experimentally. We show that the neuron dynamics are most sensitive to the Na1.8 channel maximal conductance ([Formula: see text]). Numerical bifurcation analysis shows that depending on [Formula: see text] and the external current, different parameter regions can be identified with stable steady states, periodic firing of action potentials, mixed-mode oscillations (MMOs), and bistability between stable steady states and stable periodic firing of action potentials. We illustrate and discuss the transitions between these different regimes. We further analyze the behavior of MMOs. As the external current is decreased, we find that MMOs appear after a cyclic limit point. Within this region, bifurcation analysis shows a sequence of isolated periodic solution branches with one large action potential and a number of small amplitude peaks per period. For decreasing external current, the number of small amplitude peaks is increasing and the distance between the large amplitude action potentials is growing, finally tending to infinity and thereby leading to a stable steady state. A closer inspection reveals more complex concatenated MMOs in between these periodic MMO branches, forming Farey sequences. Lastly, we also find small solution windows with aperiodic oscillations which seem to be chaotic. The dynamical patterns found here-as consequences of bifurcation points regulated by different parameters-have potential translational significance as repetitive firing of action potentials imply pain of some form and intensity; manipulating these patterns by regulating the different parameters could aid in investigating pain dynamics.

摘要

小背根神经节 (DRG) 神经元是主要的伤害感受器,负责感知疼痛。阐明其动力学对于理解和控制疼痛至关重要。为此,本文提出了一种小型 DRG 神经元模型的数值分岔分析。该模型为 Hodgkin-Huxley 型,具有 9 个状态变量。它由 Na1.7 和 Na1.8 钠离子通道、一个漏电流通道、一个延迟整流钾通道和一个 A 型瞬态钾通道组成。该模型的动力学强烈依赖于电压门控离子通道的最大电导和外部电流,这可以通过实验进行调整。我们表明,神经元动力学对 Na1.8 通道最大电导 ([Formula: see text]) 最为敏感。数值分岔分析表明,取决于 [Formula: see text] 和外部电流,可以识别出不同的参数区域,这些区域具有稳定的稳态、动作电位的周期性发放、混合模式振荡 (MMO) 和稳定稳态与稳定动作电位周期性发放之间的双稳性。我们说明了并讨论了这些不同状态之间的转变。我们进一步分析了 MMO 的行为。随着外部电流的减小,我们发现 MMO 出现在循环极限点之后。在这个区域内,分岔分析显示出一系列孤立的周期性解分支,每个周期有一个大的动作电位和多个小幅度峰值。随着外部电流的进一步减小,小幅度峰值的数量增加,大动作电位之间的距离也增大,最终趋于无穷大,从而导致稳定的稳态。更仔细的检查显示,在这些周期性 MMO 分支之间存在更复杂的串接 MMO,形成 Farey 序列。最后,我们还发现了一些具有非周期性振荡的小解窗口,这些窗口似乎是混沌的。这里发现的动力学模式是由不同参数调节的分岔点的结果,它们具有潜在的转化意义,因为动作电位的重复发放意味着某种形式和强度的疼痛;通过调节不同的参数来控制这些模式可能有助于研究疼痛动力学。

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