多电极尖峰分类方法的最新进展。
Recent progress in multi-electrode spike sorting methods.
作者信息
Lefebvre Baptiste, Yger Pierre, Marre Olivier
机构信息
Sorbonne Universités, UPMC Univ Paris 06, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012 Paris, France; Laboratoire de Physique Statistique, UPMC-Sorbonne Universités, CNRS, ENS-PSL Research University, 24 rue Lhomond, 75005 Paris, France.
Sorbonne Universités, UPMC Univ Paris 06, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012 Paris, France.
出版信息
J Physiol Paris. 2016 Nov;110(4 Pt A):327-335. doi: 10.1016/j.jphysparis.2017.02.005. Epub 2017 Mar 2.
In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms.
近年来，已开发并制造出细胞外电极阵列，用于同时从数百个高密度排列的电极进行记录。借助信号处理算法，这些记录应能让神经科学家重建在这些电极附近产生动作电位的神经元的个体活动。算法需要解决一个源分离问题，也称为峰电位分类。然而，这些新设备对传统的峰电位分类方法提出了挑战。在此，我们回顾为从这些大规模记录中分类峰电位而开发的不同方法。我们描述了这些算法的共同特性及其主要差异。最后，我们概述了未来峰电位分类算法仍有待解决的问题。
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