State University of New York Downstate/New York University-Poly Joint Biomedical Engineering Program, Brooklyn, New York 11203, USA.
J Neurosci. 2011 Nov 9;31(45):16398-409. doi: 10.1523/JNEUROSCI.4053-11.2011.
Technological advances in electrode construction and digital signal processing now allow recording simultaneous extracellular action potential discharges from many single neurons, with the potential to revolutionize understanding of the neural codes for sensory, motor, and cognitive variables. Such studies have revealed the importance of ensemble neural codes, encoding information in the dynamic relationships among the action potential spike trains of multiple single neurons. Although the success of this research depends on the accurate classification of extracellular action potentials to individual neurons, there are no widely used quantitative methods for assessing the quality of the classifications. Here we describe information theoretic measures of action potential waveform isolation applicable to any dataset that have an intuitive, universal interpretation, that are not dependent on the methods or choice of parameters for single-unit isolation, and that have been validated using a dataset of simultaneous intracellular and extracellular neuronal recordings from Sprague Dawley rats.
电极结构和数字信号处理方面的技术进步现在可以实现同时记录来自多个单个神经元的细胞外动作电位放电,有可能彻底改变对感觉、运动和认知变量的神经编码的理解。此类研究表明了集合神经编码的重要性,即通过多个单个神经元的动作电位尖峰序列之间的动态关系来编码信息。尽管这项研究的成功取决于对单个神经元的细胞外动作电位的准确分类,但目前还没有广泛使用的定量方法来评估分类的质量。在这里,我们描述了适用于任何数据集的动作电位波形隔离的信息论度量方法,这些方法具有直观、通用的解释,不依赖于单单元隔离的方法或参数选择,并且已经使用来自 Sprague Dawley 大鼠的同时细胞内和细胞外神经元记录数据集进行了验证。