Yang Zhi, Zhao Qi, Liu Wentai
School of Engineering, University of California at Santa Cruz, Santa Cruz, CA 95064, USA.
J Neural Eng. 2009 Aug;6(4):046006. doi: 10.1088/1741-2560/6/4/046006. Epub 2009 Jul 9.
This paper presents spike derivatives as a tool for spike feature extraction to improve the separation of similar neurons. The theoretical framework of neuronal geometry signatures and noise shaping to perform the spike derivative is formulated first, and based on the derivations we show that the first derivative of the spikes manifests the waveform difference contributed by the geometry signatures and also reduces the associated low-frequency noise. Quantitative comparisons of sorting neurons using spikes and their derivatives are performed on spike sequences from a public database, and improved results are observed when using spike derivatives.
本文提出将尖峰导数作为一种尖峰特征提取工具，以改善相似神经元的分离效果。首先阐述了用于执行尖峰导数的神经元几何特征和噪声整形的理论框架，并基于这些推导表明，尖峰的一阶导数体现了由几何特征贡献的波形差异，同时还降低了相关的低频噪声。使用来自公共数据库的尖峰序列对使用尖峰及其导数进行神经元分类的结果进行了定量比较，并且在使用尖峰导数时观察到了更好的结果。
