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未能识别多个神经元活动中的重叠尖峰信号会导致人为的相关性。

Failure in identification of overlapping spikes from multiple neuron activity causes artificial correlations.

作者信息

Bar-Gad I, Ritov Y, Vaadia E, Bergman H

机构信息

Center for Neural Computation, The Hebrew University, Jerusalem, Israel.

出版信息

J Neurosci Methods. 2001 May 30;107(1-2):1-13. doi: 10.1016/s0165-0270(01)00339-9.

Abstract

Recording of multiple neurons from a single electrode is common practice during extra-cellular recordings. Separation and sorting of spikes originating from the different neurons can be performed either on-line or off-line using multiple methods for pattern matching. However, all spike sorting techniques fail either fully or partially in identifying spikes from multiple neurons when they overlap due to occurrence within a short time interval. This failure, that we termed the 'shadowing effect', causes the well-known phenomenon of decreased cross-correlation at zero offset. However, the shadowing effect also causes other artifacts in the auto and cross-correlation of the recorded neurons. These artifacts are significant mainly in brain areas with high firing rate or increased firing synchrony leading to a high probability of spike overlap. Cross correlation of cells recorded from the same electrodes tends to reflect the autocorrelation functions of the two cells, even when there are no functional interactions between the cells. Therefore, the cross-correlation function tends to have a short-term (about the length of the refractory period) peak. A long-term (hundreds of milliseconds to a few seconds) trough in the cross-correlation can be seen in cells with bursting and pausing activities recorded from the same electrode. Even the autocorrelation functions of the recorded neurons feature firing properties of other neurons recorded from the same electrode. Examples of these effects are given from our recordings in the globus pallidus of behaving primates and from the literature. Results of simulations of independent simple model neurons exhibit the same properties as the recorded neurons. The effect is analyzed and can be estimated to enable better evaluation of the underlying firing patterns and the actual synchronization of neighboring neurons recorded by a single electrode.

摘要

在细胞外记录过程中,从单个电极记录多个神经元是常见的做法。源于不同神经元的尖峰的分离和分类可以使用多种模式匹配方法在线或离线进行。然而,当多个神经元的尖峰由于在短时间间隔内出现而重叠时,所有的尖峰分类技术在识别这些尖峰时都会全部或部分失败。这种我们称之为“阴影效应”的失败会导致在零偏移时互相关降低这一众所周知的现象。然而,阴影效应还会在记录的神经元的自相关和互相关中引起其他伪迹。这些伪迹主要在具有高放电率或放电同步性增加的脑区中很显著,这会导致尖峰重叠的可能性很高。即使细胞之间没有功能相互作用,从同一电极记录的细胞的互相关也倾向于反映这两个细胞的自相关函数。因此,互相关函数往往会有一个短期(大约是不应期的长度)峰值。在从同一电极记录的具有爆发和暂停活动的细胞中,可以看到互相关中有一个长期(数百毫秒到几秒)的低谷。甚至记录的神经元的自相关函数也具有从同一电极记录的其他神经元的放电特性。我们从行为灵长类动物苍白球的记录以及文献中给出了这些效应的例子。独立简单模型神经元的模拟结果表现出与记录的神经元相同的特性。对这种效应进行了分析并可以进行估计,以便更好地评估潜在的放电模式以及单个电极记录的相邻神经元的实际同步情况。

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