Automatic sorting of multiple unit neuronal signals in the presence of anisotropic and non-Gaussian variability
Automatic sorting of multiple unit neuronal signals in the presence of anisotropic and non-Gaussian variability
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DOI：
10.1016/s0165-0270(96)00050-7
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发表时间：
1996-11-01
影响因子：
3
通讯作者：
Kleinfeld, D
中科院分区：
文献类型：
--
作者：
Fee, MS;Mitra, PP;Kleinfeld, D
神经元噪声源和尖峰形状的系统可变性限制了将从神经组织记录的多个单位波形分类为它们的单个神经元成分的能力。在这里，我们提出了一个程序，以有效地排序尖峰存在的噪声是各向异性的，即，由特定频率支配，并且其幅度分布可以是非高斯的，例如当尖峰波形是尖峰间间隔的函数时发生的。我们的算法使用层次聚类方案。首先，多个单元记录被排序到一个过于庞大的集群的递归二分法。第二，这些集群逐步聚合成一个最小的一组推定的单一单位的基础上的尖峰形状的相似性，以及尖峰到达时间的统计，如施加的不应期。我们应用该算法记录的波形与长期植入的微丝立体声电极从行为大鼠的新皮层。该算法的自然扩展可用于聚类来自具有许多输入通道的记录的尖峰波形，诸如用四极管和多站点光学技术获得的那些。
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 extensions 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.