对哺乳动物大脑的完全理解需要对组成其各个部分的每个神经元的功能有确切的了解。为实现此目标，应定义一个详尽，准确，可重现且健壮的神经元分类法。本文提出了一种基于转录组特征和新的电生理特征的新的圆形分类法。通过分析来自Allen Cell Types Database的1850多种不同的小鼠视觉皮层神经元的电生理信号，验证了该方法的有效性。该研究在两个不同的水平上进行：神经元及其细胞类型聚集到Cre系中。在神经元水平上，已经用一种有前途的模型提取了电生理特征，该模型已经在神经元动力学中证明了其价值。在Cre Line级别，电生理学和转录组学特征与具有可用遗传信息的细胞类型结合在一起。通过对前两个主成分的简单转换即可显示出具有循环顺序的分类法，从而可以表征不同的Cre线。此外，所提出的方法可以在分类法中找到其他Cre谱系，这些谱系没有可用的转录组学特征。最后，通过机器学习方法验证了分类法，该方法能够区分具有建议的电生理特征的不同神经元类型。所提出的方法可以在分类法中找到其他Cre谱系，这些谱系没有可用的转录组学特征。最后，通过机器学习方法验证了分类法，该方法能够区分具有建议的电生理特征的不同神经元类型。所提出的方法可以在分类法中找到其他Cre谱系，这些谱系没有可用的转录组学特征。最后，通过机器学习方法验证了分类法，该方法能够区分具有建议的电生理特征的不同神经元类型。
The complete understanding of the mammalian brain requires exact knowledge of the function of each of the neurons composing its parts. To achieve this goal, an exhaustive, precise, reproducible, and robust neuronal taxonomy should be defined. In this paper, a new circular taxonomy based on transcriptomic features and novel electrophysiological features is proposed. The approach is validated by analysing more than 1850 electrophysiological signals of different mouse visual cortex neurons proceeding from the Allen Cell Types Database. The study is conducted on two different levels: neurons and their cell-type aggregation into Cre Lines. At the neuronal level, electrophysiological features have been extracted with a promising model that has already proved its worth in neuronal dynamics. At the Cre Line level, electrophysiological and transcriptomic features are joined on cell types with available genetic information. A taxonomy with a circular order is revealed by a simple transformation of the first two principal components that allow the characterization of the different Cre Lines. Moreover, the proposed methodology locates other Cre Lines in the taxonomy that do not have transcriptomic features available. Finally, the taxonomy is validated by Machine Learning methods which are able to discriminate the different neuron types with the proposed electrophysiological features.
