基于图谱的神经元模型中的尖峰模式与混沌

Piotr Bartłomiejczyk, Frank Llovera Trujillo, Justyna Signerska-Rynkowska
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引用次数: 0

摘要

摘要 本论文研究了库尔巴奇、内科金和弗多文于 2007 年提出的著名神经元动力学基于映射的模型,该模型因其在医学上的各种应用而具有重要意义。我们还回顾并扩展了有关 β 变换和(扩展)洛伦兹映射的一些现有成果。然后,我们将它们用于推导 CNV 模型生成的尖峰脉冲串的重要属性,并解释它们对神经元行为的影响。特别是,利用洛伦兹样图的最新旋转理论定理,我们对该模型中的周期性尖峰模式进行了分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spike Patterns and Chaos in a Map–Based Neuron Model
Abstract The work studies the well-known map-based model of neuronal dynamics introduced in 2007 by Courbage, Nekorkin and Vdovin, important due to various medical applications. We also review and extend some of the existing results concerning β-transformations and (expanding) Lorenz mappings. Then we apply them for deducing important properties of spike-trains generated by the CNV model and explain their implications for neuron behaviour. In particular, using recent theorems of rotation theory for Lorenz-like maps, we provide a classification of periodic spiking patterns in this model.
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