Dayhoff J E, Gerstein G L
J Neurophysiol. 1983 Jun;49(6):1334-48. doi: 10.1152/jn.1983.49.6.1334.
Traditional spike-train analysis methods cannot identify patterns of firing that occur frequently but at arbitrary times. It is appropriate to search for recurring patterns because such patterns could be used for information transfer. In this paper, we present two methods for identifying "favored patterns" --patterns that occur more often than is reasonably expected at random. The quantized Monte Carlo method identifies and establishes significance for favored patterns whose detailed timing may vary but that do not have extra or missing spikes. The template method identifies favored patterns whose occurrences may have extra or missing spikes. This method is useful when employed after the results of the first method are known. Studies with simulated spike trains containing known interpolated patterns are used to establish the sensitivity and accuracy of the quantized Monte Carlo method. Certain trends with regard to parameters of the detected patterns and of the analysis methods are described. Application of these methods to neurophysiological data has shown that a large proportion of spike trains have favored patterns. These findings are described in the accompanying paper (3).
传统的脉冲序列分析方法无法识别频繁出现但时间任意的放电模式。寻找重复模式是合适的，因为这样的模式可用于信息传递。在本文中，我们提出了两种识别“偏好模式”的方法——即出现频率高于随机预期的模式。量化蒙特卡罗方法识别并确定偏好模式的显著性，这些模式的详细时间可能不同，但没有额外或缺失的脉冲。模板方法识别出现时可能有额外或缺失脉冲的偏好模式。当在第一种方法的结果已知后使用时，这种方法很有用。使用包含已知插入模式的模拟脉冲序列进行的研究，用于确定量化蒙特卡罗方法的灵敏度和准确性。描述了关于检测到的模式和分析方法的参数的某些趋势。将这些方法应用于神经生理学数据表明，很大一部分脉冲序列具有偏好模式。这些发现将在随附的论文（3）中描述。
