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基于主成分分析的尖峰分类算法的性能评估。

Performance evaluation of PCA-based spike sorting algorithms.

作者信息

Adamos Dimitrios A, Kosmidis Efstratios K, Theophilidis George

机构信息

Laboratory of Animal Physiology, School of Biology, Aristotle University of Thessaloniki, 54 124 Thessaloniki, Greece.

出版信息

Comput Methods Programs Biomed. 2008 Sep;91(3):232-44. doi: 10.1016/j.cmpb.2008.04.011. Epub 2008 Jun 18.

DOI:10.1016/j.cmpb.2008.04.011
PMID:18565614
Abstract

Deciphering the electrical activity of individual neurons from multi-unit noisy recordings is critical for understanding complex neural systems. A widely used spike sorting algorithm is being evaluated for single-electrode nerve trunk recordings. The algorithm is based on principal component analysis (PCA) for spike feature extraction. In the neuroscience literature it is generally assumed that the use of the first two or most commonly three principal components is sufficient. We estimate the optimum PCA-based feature space by evaluating the algorithm's performance on simulated series of action potentials. A number of modifications are made to the open source nev2lkit software to enable systematic investigation of the parameter space. We introduce a new metric to define clustering error considering over-clustering more favorable than under-clustering as proposed by experimentalists for our data. Both the program patch and the metric are available online. Correlated and white Gaussian noise processes are superimposed to account for biological and artificial jitter in the recordings. We report that the employment of more than three principal components is in general beneficial for all noise cases considered. Finally, we apply our results to experimental data and verify that the sorting process with four principal components is in agreement with a panel of electrophysiology experts.

摘要

从多单元噪声记录中解读单个神经元的电活动对于理解复杂的神经系统至关重要。一种广泛使用的尖峰排序算法正在针对单电极神经干记录进行评估。该算法基于主成分分析(PCA)进行尖峰特征提取。在神经科学文献中,通常认为使用前两个或最常见的三个主成分就足够了。我们通过评估该算法在模拟动作电位序列上的性能来估计基于PCA的最佳特征空间。对开源nev2lkit软件进行了一些修改,以便能够系统地研究参数空间。我们引入了一种新的度量来定义聚类误差,考虑到对于我们的数据,实验人员认为过聚类比欠聚类更有利。程序补丁和度量都可在线获取。叠加相关和白色高斯噪声过程以考虑记录中的生物和人为抖动。我们报告说,对于所考虑的所有噪声情况,使用三个以上的主成分通常是有益的。最后,我们将结果应用于实验数据,并验证使用四个主成分的排序过程与一组电生理专家的意见一致。

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