论文标题
尖峰火车同步和方向性的度量
Measures of spike train synchrony and directionality
论文作者
论文摘要
尖峰列车同步的度量已成为实验和理论神经科学的重要工具。三种称为ISI距离，尖峰距离和尖峰同步的度量已经成功地应用于许多不同的情况下。这些度量是独立的，因为它们认为所有时间尺度同样重要。但是，在实际数据中，通常对最小的时间尺度感兴趣，并且需要一种更适应性的方法。因此，在本章的第一部分中，我们最近介绍了这三种措施的概括，这些措施逐渐忽略了较小的时间尺度的差异。除了相似性，尖峰火车的另一个非常相关的属性是尖峰的时间顺序。在本章的第二部分中，我们介绍了该属性，并描述了最近提出的算法，该算法量化了一组Spike火车内的方向性。这种多元方法将从领导者到追随者的多个尖峰列车分类，并量化了传播模式的一致性。最后，本章中描述的所有措施均可自由下载。
Measures of spike train synchrony have become important tools in both experimental and theoretical neuroscience. Three time-resolved measures called the ISI-distance, the SPIKE-distance, and SPIKE-synchronization have already been successfully applied in many different contexts. These measures are time scale independent, since they consider all time scales as equally important. However, in real data one is typically less interested in the smallest time scales and a more adaptive approach is needed. Therefore, in the first part of this Chapter we describe recently introduced generalizations of the three measures, that gradually disregard differences in smaller time-scales. Besides similarity, another very relevant property of spike trains is the temporal order of spikes. In the second part of this chapter we address this property and describe a very recently proposed algorithm, which quantifies the directionality within a set of spike train. This multivariate approach sorts multiple spike trains from leader to follower and quantifies the consistency of the propagation patterns. Finally, all measures described in this chapter are freely available for download.
