峰间间隔的多重分形趋势波动分析及局部尺度指数

Tamara Ceranic, T. Lončar-Turukalo, László Négyessy, E. Procyk, D. Bajić
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引用次数: 0

摘要

本研究分析了清醒猕猴皮层神经元集合的非趋势波动(DFA)。采用原始的DFA方法分析不同阶次拟合趋势的方差波动。计算了局部尺度指数谱,考察了不同尺度区域的存在。结果表明,单一标度系数不足以描述放电模式动态,短期α2标度系数和长期α2标度系数均能较好地拟合放电模式动态。使用阶段随机替代的验证程序提供了更可靠的局部尺度指数估计。广义DFA分析揭示了ISI时间序列存在多重分形。结果表明,多重分形部分是由于广泛的概率分布函数,部分是由于存在远程相关性。采用等分布替代数据检验广义赫斯特指数谱的意义和多重分形行为的起源。综上所述,DFA及其多重分形展开都揭示了ISI时间序列的长程相关性,表明神经元放电模式中存在记忆。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multifractal detrended fluctuation analysis and local scale exponents of inter spike intervals
The study presents the analysis of detrended fluctuations (DFA) in inter spike intervals (ISI) of neuronal ensemble from cortex of awake behaving macaque monkeys. The original DFA method was applied to analyze fluctuation of variances from fitted trends of different order. The spectrum of local scale exponent was calculated to investigate the presence of different scaling regions. It was observed that the single scaling exponent is insufficient to describe the firing pattern dynamics, the better fit is achieved using both short-term a1 and long-term α2 scaling coefficients. The validation procedure using phase randomized surrogates provided more reliable local scale exponents' estimates. Generalized DFA analysis revealed the presence of multifractality in ISI time series. Results indicate that multifractality is partly due to the broad probability distribution function and partly due to the presence of long-range correlations. Isodistributional surrogate data were used to test the significance of generalized Hurst exponent spectrum and origin of multifractal behavior. In conclusion, both DFA and its multifractal expansion reveal the presence of long-range correlation in ISI time series indicating the presence of memory in the neuronal firing pattern.
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