基于混沌降噪的神经元放电峰峰间期序列分析

ANALYZING INTERSPIKE INTERVALS OF NEURONFIRINGS BASED ON CHAOTIC NOISE REDUCTION

围绕如何来消除神经元峰峰间期序列中随机噪声影响从而提取出决定不规则性的确定性动力学关系这个问题，本文首先简要介绍峰峰间期序列样本的制备，然后着重讨论一个简单可行的混沌时间序列降噪方法的原理和算法实现，最终将该方法运用到神经元放电活动数值模拟和实验记录到的峰峰间期时间序列样本分析中。本文分析结果再次证明神经放电活动中确实存在着不规则混沌运动。

The aim of this paper is to suppress negative effect of noise on complex firing patterns of neurons and extract deterministic dynamical relationship underlying irregular interspike intervals (ISI). We first give a brief introduction to ISI data to be analyzed. Then a simple nonlinear noise reduction method based on chaos theory is presented. The method is finally used to analyze ISI data both from a theoretical model and experiments. Our results prove that chaotic dynamics does exist in irregular firings of neurons. Furthermore, one-hump map with piece-wise smoothing structure underlying irregular ISI time series recorded in neural electrophysiological experiments is for the first time made clear.