神经元脉冲序列中序列依赖性的无分布图形和统计分析。

Distribution-free graphical and statistical analysis of serial dependence in neuronal spike trains.

作者信息

Rhoades B K, Weil J C, Kowalski J M, Gross G W

机构信息

Center for Network Neuroscience, University of North Texas, Denton 76203, USA.

出版信息

J Neurosci Methods. 1996 Jan;64(1):25-37. doi: 10.1016/0165-0270(95)00074-7.

DOI:10.1016/0165-0270(95)00074-7

PMID:8869481

Abstract

Two-dimensional 'joint' interval distributions of sequential interspike (ISIs) are a commonly used tool in neuronal spike train analysis. We present and evaluate here a modification of the joint interval plot using ranked ISIs. This modification provides clearer graphical evaluation of serial dependence in ISI sequences, a distribution-free basis for isolating changes in serial dependence across experimental treatments from changes in ISI distributions, and a basis for unambiguous statistical tests of serial dependence and stationarity. To validate this method and illustrate the advantages of its use we have applied it to both single-neuron spike trains recorded from cultured mammalian spinal cord neurons and artificial spike trains generated by stochastic models with defined burst envelopes and serial dependencies.

摘要

连续峰峰间期（ISI）的二维“联合”间期分布是神经元放电序列分析中常用的工具。我们在此展示并评估一种使用排序后的ISI对联合间期图的改进。这种改进为ISI序列中的序列依赖性提供了更清晰的图形评估，为从ISI分布的变化中分离出跨实验处理的序列依赖性变化提供了无分布基础，也为序列依赖性和平稳性的明确统计检验提供了基础。为了验证该方法并说明其使用优势，我们将其应用于从培养的哺乳动物脊髓神经元记录的单神经元放电序列以及由具有定义的爆发包络和序列依赖性的随机模型生成的人工放电序列。