Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements
Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements
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DOI：
10.1152/jn.2000.84.1.401
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发表时间：
2000-07-01
影响因子：
2.5
通讯作者：
Buzsáki, G
中科院分区：
文献类型：
--
作者：
Harris, KD;Henze, DA;Buzsáki, G
从大量神经元同时记录是理解它们合作行为的先决条件。为了实现这一目标，正在使用各种记录技术和尖峰分离方法。然而，棘波分离所涉及的错误率还没有被量化。我们研究了四极管(四线电极)记录的棘波的分离可靠性，方法是用玻璃移液管在细胞内和用四极管在细胞外同时监测。通过手动棘波排序，我们发现了I型和II型错误之间的权衡，误差通常在0%到30%之间，这取决于细胞的幅度和放电模式、相邻神经元波形的相似性以及操作者的经验。仅使用单线记录的性能明显较低，这表明多点监测技术比单线记录技术具有优势。对于四极管记录，突发活动和细胞同步期间会增加错误率。通过搜索最佳椭球团簇形状来估计可能的最低分离错误率。人类操作员的表现明显低于估计的最佳水平。对误差分布的研究表明，由于算子不能在高维特征空间中准确地标记簇边界，导致了性能次优。因此，我们假设自动尖峰排序算法有可能显著降低错误率。半自动分类系统的实施证实了这一建议，将误差减少到接近估计的最佳水平，在0-8%的范围内。
Simultaneous recording from large numbers of neurons is a prerequisite for understanding their cooperative behavior. Various recording techniques and spike separation methods are being used toward this goal. However, the error rates involved in spike separation have not yet been quantified. We studied the separation reliability of "tetrode" (4-wire electrode) recorded spikes by monitoring simultaneously from the same cell intracellularly with a glass pipette and extracellularly with a tetrode. With manual spike sorting, we found a trade-off between Type I and Type II errors, with errors typically ranging from 0 to 30% depending on the amplitude and firing pattern of the cell, the similarity of the waveshapes of neighboring neurons, and the experience of the operator. Performance using only a single wire was markedly lower, indicating the advantages of multiple-site monitoring techniques over single-wire recordings. For tetrode recordings, error rates were increased by burst activity and during periods of cellular synchrony. The lowest possible separation error rates were estimated by a search for the best ellipsoidal cluster shape. Human operator performance was significantly below the estimated optimum. Investigation of error distributions indicated that suboptimal performance was caused by inability of the operators to mark cluster boundaries accurately in a high-dimensional feature space. We therefore hypothesized that automatic spike-sorting algorithms have the potential to significantly lower error rates. Implementation of a semi-automatic classification system confirms this suggestion, reducing errors close to the estimated optimum, in the range 0-8%.