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A new method to estimate the Kolmogorov entropy from recurrence plots: its application to neuronal signals

机译:一种从递归图估计Kolmogorov熵的新方法:其在神经元信号中的应用

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摘要

Recurrence plots are a useful graphic tool for studying dynamical systems. A new variable is proposed to quantify their deterministic structure. It allows an easy computation of the KZ entropy since the distance between points needs to be calculated only once instead of every time for each new embedding dimension, and it bypasses some of the Limitations inherent to the classical Grassberger and Procaccia method. It is valuable for biological data, as illustrated here by tests with various dynamical models and its application to experimental signals recorded from neurons. (C) 1998 Elsevier Science B.V. [References: 29]
机译:递归图是研究动力学系统的有用图形工具。提出了一个新变量来量化其确定性结构。由于每个新的嵌入维度只需要计算一次,而不是每次都需要计算点之间的距离,因此可以轻松计算KZ熵,并且它绕过了经典Grassberger和Procaccia方法固有的一些局限性。如前所述,通过各种动力学模型进行的测试及其对神经元记录的实验信号的应用,这对于生物学数据非常有价值。 (C)1998 Elsevier Science B.V. [参考:29]

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