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Symbolic dynamical unfolding of spike-adding bifurcations in chaotic neuron models

机译:混沌神经元模型中加峰分叉的符号动力学展开

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摘要

We characterize the systematic changes in the topological structure of chaotic attractors that occur as spike-adding and homoclinic bifurcations are encountered in the slow-fast dynamics of neuron models. This phenomenon is detailed in the simple Hindmarsh-Rose neuron model, where we show that the unstable periodic orbits appearing after each spike-adding bifurcation are associated with specific symbolic sequences in the canonical symbolic encoding of the dynamics of the system. This allows us to understand how these bifurcations modify the internal structure of the chaotic attractors. Copyright (C) EPLA, 2015
机译:我们表征了混沌吸引子的拓扑结构的系统变化,这种变化是在神经元模型的快慢动力学中遇到尖峰相加和同斜分叉。此现象在简单的Hindmarsh-Rose神经元模型中进行了详细说明,在该模型中,我们显示了每个加尖峰的分叉之后出现的不稳定周期轨道都与系统动力学的规范符号编码中的特定符号序列相关。这使我们能够理解这些分叉如何改变混沌吸引子的内部结构。版权(C)EPLA,2015年

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