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Mixed-mode bursting oscillations: Dynamics created by a slow passage through spike-adding canard explosion in a square-wave burster

机译:混合模式爆发振荡:由方波爆发器中缓慢通过添加尖峰的鸭形爆炸所产生的动力学

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This article concerns the phenomenon of Mixed-Mode Bursting Oscillations (MMBOs). These are solutions of fast-slow systems of ordinary differential equations that exhibit both small-amplitude oscillations (SAOs) and bursts consisting of one or multiple large-amplitude oscillations (LAOs). The name MMBO is given in analogy to Mixed-Mode Oscillations, which consist of alternating SAOs and LAOs, without the LAOs being organized into burst events. In this article, we show how MMBOs are created naturally in systems that have a spike-adding bifurcation or spike-adding mechanism, and in which the dynamics of one (or more) of the slow variables causes the system to pass slowly through that bifurcation. Canards are central to the dynamics of MMBOs, and their role in shaping the MMBOs is two-fold: saddle-type canards are involved in the spike-adding mechanism of the underlying burster and permit one to understand the number of LAOs in each burst event, and folded-node canards arise due to the slow passage effect and control the number of SAOs. The analysis is carried out for a prototypical fourth-order system of this type, which consists of the third-order Hindmarsh-Rose system, known to have the spike-adding mechanism, and in which one of the key bifurcation parameters also varies slowly. We also include a discussion of the MMBO phenomenon for the Morris-Lecar-Terman system. Finally, we discuss the role of the MMBOs to a biological modeling of secreting neurons.
机译:本文涉及混合模式突发振荡(MMBO)的现象。这些是常微分方程快慢系统的解,该系统同时显示小振幅振荡(SAO)和由一个或多个大振幅振荡(LAO)组成的突发。 MMBO的名称类似于混合模式振荡,它由交替的SAO和LAO组成,而LAO没有组织成突发事件。在本文中,我们展示了如何在具有加尖峰分叉或加尖峰机制的系统中自然创建MMBO,并且其中一个(或多个)慢变量的动力学导致系统缓慢通过该分叉。卡纳德斯对MMBO的动力学至关重要,并且它们在塑造MMBOs中的作用有两个方面:鞍式卡纳德涉及基础突波器的尖峰添加机制,并允许人们了解每个突波事件中LAO的数量。 ,并且通过缓慢的通道效应会出现折叠节式鸭嘴,并控制SAO的数量。对这种典型的四阶系统进行分析,该系统由已知具有尖峰添加机制的三阶Hindmarsh-Rose系统组成,并且其中一个重要的分叉参数也缓慢变化。我们还讨论了Morris-Lecar-Terman系统的MMBO现象。最后,我们讨论了MMBO在分泌神经元生物学模型中的作用。

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