Fee M S, Mitra P P, Kleinfeld D
AT & T Bell Laboratories, Murray Hill, NJ 07974, USA.
J Neurosci Methods. 1996 Nov;69(2):175-88. doi: 10.1016/S0165-0270(96)00050-7.
Neuronal noise sources and systematic variability in the shape of a spike limit the ability to sort multiple unit waveforms recorded from nervous tissue into their single neuron constituents. Here we present a procedure to efficiently sort spikes in the presence of noise that is anisotropic, i.e., dominated by particular frequencies, and whose amplitude distribution may be non-Gaussian, such as occurs when spike waveforms are a function of interspike interval. Our algorithm uses a hierarchical clustering scheme. First, multiple unit records are sorted into an overly large number of clusters by recursive bisection. Second, these clusters are progressively aggregated into a minimal set of putative single units based on both similarities of spike shape as well as the statistics of spike arrival times, such as imposed by the refractory period. We apply the algorithm to waveforms recorded with chronically implanted micro-wire stereotrodes from neocortex of behaving rat. Natural extension of the algorithm may be used to cluster spike waveforms from records with many input channels, such as those obtained with tetrodes and multiple site optical techniques.
神经元噪声源以及尖峰形状的系统变异性限制了将从神经组织记录的多个单元波形分类为单个神经元成分的能力。在此，我们提出一种程序，用于在存在各向异性噪声（即由特定频率主导且其幅度分布可能是非高斯分布，例如当尖峰波形是尖峰间隔的函数时出现的情况）的情况下有效地对尖峰进行分类。我们的算法使用分层聚类方案。首先，通过递归二分法将多个单元记录分类为数量过多的簇。其次，基于尖峰形状的相似性以及尖峰到达时间的统计信息（例如由不应期所施加的），将这些簇逐步聚合为最小的一组假定单个单元。我们将该算法应用于用长期植入的微线立体电极从行为大鼠的新皮层记录的波形。该算法的自然扩展可用于对来自具有许多输入通道的记录（例如用四极管和多部位光学技术获得的记录）的尖峰波形进行聚类。
