Method for stationarity-segmentation of spike train data with application to the Pearson cross-correlation.
Method for stationarity-segmentation of spike train data with application to the Pearson cross-correlation.
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DOI：
10.1152/jn.00186.2013
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发表时间：
2013-05
影响因子：
2.5
通讯作者：
Claudio S. Quiroga-Lombard;Joachim Hass;D. Durstewitz
中科院分区：
文献类型：
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作者：
Claudio S. Quiroga-Lombard;Joachim Hass;D. Durstewitz
神经元间的相互关系在神经系统的计算和信息编码中起着重要的作用。从经验上看，神经元之间的功能相互作用最常用相互关联函数来评估。最近的研究表明，两两相关可能确实足以捕获神经相互作用中存在的大部分信息。然而，相关函数的许多应用隐含地倾向于假设底层过程是平稳的。这一假设通常不适用于在体内记录的真实神经元，因为它们在行为任务中的活动受到刺激、运动或认知相关过程以及更一般的过程（如缓慢振荡或警觉状态变化）的严重影响。为了解决非平稳性问题，我们引入了一种经验评估平稳性的方法，然后根据弱感知平稳性的统计定义将尖峰列车“切片”成平稳段。我们检查了平稳和非平稳条件下的成对Pearson互相关（PCCs），并确定了另一个协方差的来源，它可以与尖峰时间的协方差区分开来，并在切片过程后作为残余非平稳性的结果出现：在每个片段上定义的射击率的协方差。在此基础上，提出了一种考虑分割影响的PCC校正方法。我们在模拟数据集和行为大鼠前额叶皮层的体内记录上对这些方法进行了探讨。而不是去除非平稳性，本方法也可用于检测重大事件的尖峰列车。
Correlations among neurons are supposed to play an important role in computation and information coding in the nervous system. Empirically, functional interactions between neurons are most commonly assessed by cross-correlation functions. Recent studies have suggested that pairwise correlations may indeed be sufficient to capture most of the information present in neural interactions. Many applications of correlation functions, however, implicitly tend to assume that the underlying processes are stationary. This assumption will usually fail for real neurons recorded in vivo since their activity during behavioral tasks is heavily influenced by stimulus-, movement-, or cognition-related processes as well as by more general processes like slow oscillations or changes in state of alertness. To address the problem of nonstationarity, we introduce a method for assessing stationarity empirically and then "slicing" spike trains into stationary segments according to the statistical definition of weak-sense stationarity. We examine pairwise Pearson cross-correlations (PCCs) under both stationary and nonstationary conditions and identify another source of covariance that can be differentiated from the covariance of the spike times and emerges as a consequence of residual nonstationarities after the slicing process: the covariance of the firing rates defined on each segment. Based on this, a correction of the PCC is introduced that accounts for the effect of segmentation. We probe these methods both on simulated data sets and on in vivo recordings from the prefrontal cortex of behaving rats. Rather than for removing nonstationarities, the present method may also be used for detecting significant events in spike trains.