Measuring burstiness and regularity in oscillatory spike trains
Measuring burstiness and regularity in oscillatory spike trains
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DOI：
10.1016/j.jneumeth.2011.08.013
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发表时间：
2011-10-15
影响因子：
3
通讯作者：
Schneider, Gaby
中科院分区：
文献类型：
--
作者：
Bingmer, Markus;Schiemann, Julia;Schneider, Gaby
神经元发出不同的放电模式（如脉冲或振荡）的能力对大脑的信息处理很重要。在多巴胺能神经元中，突出的模式包括重复的、振荡的爆发、规律的起搏器和不规则的具有非平稳特性的尖峰序列。为了描述和测量这些模式的可变性，我们在单个模型框架中描述了突发性和规律性。我们提出了一个双随机尖峰序列模型，在该模型中，具有独立和正态分布间隔的背景振荡可以产生具有高斯发射强度的单尖峰或突发尖峰事件。五个容易解释的参数允许分类为突发或单峰和不规则或有规律的振荡发射模式。这种分类主要基于自相关直方图的特征，这些特征通常通过目测定性地研究。目前的模型提供了一个定量和客观的分类方案，并将这些特征直接与潜在的过程联系起来。此外，置信区间将参数估计的不确定性和分类精度可视化。我们将该模型应用于从体内细胞外记录的单个多巴胺能黑质神经元获得的数据集。该模型能够代表经验观察到的多种放电模式，并且分类与目视检查非常吻合。此外，还可以定量地研究参数的变化，包括与破裂行为有关的性质。因此，该模型可用于描述神经元放电模式，并研究其随细胞和实验条件的动态变化。(C) 2011 Elsevier B.V.版权所有
The ability of neurons to emit different firing patterns such as bursts or oscillations is important for information processing in the brain. In dopaminergic neurons, prominent patterns include repetitive, oscillatory bursts, regular pacemakers, and irregular spike trains with nonstationary properties. In order to describe and measure the variability of these patterns, we describe burstiness and regularity in a single model framework.We present a doubly stochastic spike train model in which a background oscillation with independent and normally distributed intervals gives rise to either single spikes or bursty spike events with Gaussian firing intensities. Five easily interpretable parameters allow a classification into bursty or single spike and irregularly or regularly oscillating firing patterns. This classification is based primarily on features of the autocorrelation histogram which are usually studied qualitatively by visual inspection. The present model provides a quantitative and objective classification scheme and relates these features directly to the underlying processes. In addition, confidence intervals visualize the uncertainty of parameter estimation and classification precision.We apply the model to a data set obtained from single dopaminergic substantia nigra neurons recorded extracellularly in vivo. The model is able to represent a high variety of discharge patterns observed empirically, and the classification agrees closely with visual inspection. In addition, changes in the parameters can be studied quantitatively, including also the properties related to bursting behavior. Thus, the proposed model can be used for the description of neuronal firing patterns and the investigation of their dynamical changes with cellular and experimental conditions. (C) 2011 Elsevier B.V. All rights reserved.