Tracking neurons recorded from tetrodes across time
Tracking neurons recorded from tetrodes across time
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DOI：
10.1016/j.jneumeth.2003.12.022
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发表时间：
2004-05-30
影响因子：
3
通讯作者：
Miller, KD
中科院分区：
文献类型：
--
作者：
Emondi, AA;Rebrik, SP;Miller, KD
Tetrodes允许通过聚集尖峰信号在单个记录部位隔离多个神经元。由于电极漂移，也许是由于时变神经元特性。簇的位置和形状随时间变化。由于数据通常以顺序文件的形式收集，因此要跨文件跟踪神经元，必须确定来自不同文件的哪些聚类属于同一个神经元。我们报告的半自动神经元跟踪程序，使用计算的平均尖峰波形的集群之间的相似性。具有最相似波形的聚类被分配给相同的神经元，只要它们的相似性超过阈值。为了设置这个阈值，我们计算两个分布：文件内相似性的分布。以及相邻文件相似性中的最佳匹配。阈值设置为最佳分离两个分布的值。我们比较不同的相似性度量（度量），通过它们区分这些分布的能力。我们发现，这些指标并没有显着不同的性能，但考虑到跨信道噪声相关性显着提高所有指标的性能。我们还演示了一个独立的数据集上的方法，并表明，神经元，分配的程序，具有一致的生理特性跨文件。(C)2004 Elsevier B.V.保留所有权利。
Tetrodes allow isolation Of Multiple neurons at a single recording site by clustering spikes. Due to electrode drift and perhaps due to time-varving neuronal properties. positions and shapes of clusters change in time. As data is typically collected in sequential files, to track neurons across files one has to decide which clusters from different files belong to the same neuron. We report on a semi-automated neuron tracking procedure that uses computed similarities between the mean spike waveforms of the clusters. The clusters with the most similar waveforms are assigned to the same neuron, provided their similarity exceeds a threshold. To set this threshold, we calculate two distributions: of within-file similarities. and of best matches in the across adjacent file similarities. The threshold is set to the value that optimally separates the two distributions. We compare different measures of similarity (metrics) by their ability to separate these distributions. We find that these metrics do not differ drastically in their performance, but that taking into account the cross-channel noise Correlation significantly improves performance of all metrics. We also demonstrate the method on an independent dataset and show that neurons, as assigned by the procedure, have consistent physiological properties across files. (C) 2004 Elsevier B.V. All rights reserved.