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首页> 美国卫生研究院文献>Journal of Neurophysiology >A comparison of computational methods for detecting bursts in neuronal spike trains and their application to human stem cell-derived neuronal networks
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A comparison of computational methods for detecting bursts in neuronal spike trains and their application to human stem cell-derived neuronal networks

机译：检测神经元突波序列爆发的计算方法的比较及其在人类干细胞衍生的神经元网络中的应用

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Accurate identification of bursting activity is an essential element in the characterization of neuronal network activity. Despite this, no one technique for identifying bursts in spike trains has been widely adopted. Instead, many methods have been developed for the analysis of bursting activity, often on an ad hoc basis. Here we provide an unbiased assessment of the effectiveness of eight of these methods at detecting bursts in a range of spike trains. We suggest a list of features that an ideal burst detection technique should possess and use synthetic data to assess each method in regard to these properties. We further employ each of the methods to reanalyze microelectrode array (MEA) recordings from mouse retinal ganglion cells and examine their coherence with bursts detected by a human observer. We show that several common burst detection techniques perform poorly at analyzing spike trains with a variety of properties. We identify four promising burst detection techniques, which are then applied to MEA recordings of networks of human induced pluripotent stem cell-derived neurons and used to describe the ontogeny of bursting activity in these networks over several months of development. We conclude that no current method can provide “perfect” burst detection results across a range of spike trains; however, two burst detection techniques, the MaxInterval and logISI methods, outperform compared with others. We provide recommendations for the robust analysis of bursting activity in experimental recordings using current techniques.
机译：准确识别爆发活动是表征神经元网络活动的基本要素。尽管如此，还没有一种技术被广泛采用来识别尖峰轮系中的脉冲。取而代之的是，已经开发了许多方法来分析突发活动，通常是临时性的。在这里，我们对这些方法中的八种方法在一系列尖峰脉冲序列中检测突发的有效性进行了公正的评估。我们提出了理想的突发检测技术应具备的功能列表，并使用合成数据来评估关于这些属性的每种方法。我们进一步采用每种方法来重新分析来自小鼠视网膜神经节细胞的微电极阵列（MEA）记录，并检查其与人类观察者检测到的爆发的相干性。我们表明，几种常见的突发检测技术在分析具有各种属性的尖峰序列时表现不佳。我们确定了四种有前途的爆发检测技术，然后将其应用于人类诱导的多能干细胞衍生神经元网络的MEA记录，并用于描述这些网络在过去几个月的发展中爆发活动的本体。我们得出的结论是，目前没有一种方法可以在一系列尖峰序列中提供“完美”的脉冲串检测结果。但是，与其他方法相比，MaxInterval和logISI方法是两种突发检测技术的佼佼者。我们提供了使用当前技术对实验记录中的爆发活动进行鲁棒分析的建议。

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