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Routes to Chaos Induced by a Discontinuous Resetting Process in a Hybrid Spiking Neuron Model

机译:混合尖峰神经元模型中的不连续重置过程引起的混沌路径。

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摘要

Several hybrid spiking neuron models combining continuous spike generation mechanisms and discontinuous resetting processes following spiking have been proposed. The Izhikevich neuron model, for example, can reproduce many spiking patterns. This model clearly possesses various types of bifurcations and routes to chaos under the effect of a state-dependent jump in the resetting process. In this study, we focus further on the relation between chaotic behaviour and the state-dependent jump, approaching the subject by comparing spiking neuron model versions with and without the resetting process. We first adopt a continuous two-dimensional spiking neuron model in which the orbit in the spiking state does not exhibit divergent behaviour. We then insert the resetting process into the model. An evaluation using the Lyapunov exponent with a saltation matrix and a characteristic multiplier of the Poincar’e map reveals that two types of chaotic behaviour (i.e. bursting chaotic spikes and near-period-two chaotic spikes) are induced by the resetting process. In addition, we confirm that this chaotic bursting state is generated from the periodic spiking state because of the slow- and fast-scale dynamics that arise when jumping to the hyperpolarization and depolarization regions, respectively.
机译:已经提出了几种混合的尖峰神经元模型,该模型结合了尖峰之后的连续尖峰生成机制和不连续的重置过程。例如,Izhikevich神经元模型可以重现许多尖峰模式。在重置过程中,在依赖状态的跃迁的影响下,该模型显然拥有各种类型的分支和通往混乱的路线。在这项研究中,我们将进一步关注混沌行为与状态相关跳跃之间的关系,通过比较带有或不带有重置过程的尖峰神经元模型版本来接近对象。我们首先采用连续的二维峰值神经元模型,其中峰值状态下的轨道不表现出发散行为。然后,我们将重置过程插入模型。使用带有盐析矩阵和庞加莱图特征乘数的Lyapunov指数进行的评估显示,重置过程会诱发两种类型的混沌行为(即突发的混沌尖峰和近两个混沌尖峰)。另外,我们确认,由于分别跳到超极化和去极化区域时出现的慢速和快速尺度的动力学,这种混沌的爆发状态是由周期性的尖峰状态产生的。

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