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出 处:《湖北大学学报(自然科学版)》2009年第4期409-414,共6页Journal of Hubei University:Natural Science
基 金:广西大学科研基金资助项目(X081024)
摘 要:采用近似熵(approximate entropy,ApEn)的新统计方法衡量神经元不同自发放电活动时间序列数据的规律性和复杂度,对多电极阵列上培养的海马神经元网络自发活动的复杂度进行研究.结果表明不同自发放电活动的近似熵动态变化曲线有明显差别.静息期时近似熵值范围1.0-1.2;典型爆发活动时近似熵值呈现迅速下降而后上升再下降小幅振荡(0.2-0.6);而伪爆发活动时近似熵值在0.2-0.7范围,沿平行时间轴的某一直线上下波动;连续发放锋电位时近似熵值在0.8-0.9范围;而随机单发锋电位时近似熵值0.6-0.8范围.以上分析结果说明近似熵动态变化曲线能够体现爆发活动和锋电位发放过程的规律性和复杂度变化,并可以有效地识别培养神经元网络自发的不同电生理信号,因而在神经元电信号分析中有着潜在的应用价值.To investigate the complexity of spontaneous activities of hippocampal neuronal network which cultured on multi-electrode arrays (MEA) substrate. The approximate entropy (ApEn),a new statistic, was introduced to quantify the amount of regularity and complexity in time-series data of neuronal different spontaneous firing patterns. The results indicated that the changes with time approximate entropy dynamic curves were distinction for different spontaneous firing patterns time- domain waveform. The ApEn value range of dynamic curves was 1.0--1.2 in periods of quiescence; the ApEn value was 0.2--0.6 during typical bursts firing pattern, and the dynamic curves trend was first decrease and then increase, a small oscillation final; the ApEn value was 0.2--0.7 during pseudo- burst firing pattern, but the dynamic curvrs were fluctuating along a line which parallels the time coordinate; the ApEn value was 0.8--0. 9 during continuous single spike firing pattern; the ApEn value was 0.6--0.8 at random single spike firing pattern. This result showed that the approximate entropy could be used to effectively identify the different electrophysiological signals from the spontaneous activity of cultured neural network, and the ApEn dynamic curves also could be used to reflect the regularity and complexity change of the bursts and spikes firing process. Thus, it indicated that the ApEn algorithm had a potential wide applicability to analyze the neuronal signal.
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