The complete understanding of the mammalian brain requires exact knowledge of the function of each neuron subpopulation 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.

中文翻译：

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电生理学和转录组学特征揭示了皮质神经元的循环分类

对哺乳动物大脑的完整了解需要准确了解组成其各部分的每个神经元亚群的功能。为了实现这一目标，应该定义一个详尽的、精确的、可重复的和稳健的神经元分类法。在本文中，提出了一种基于转录组特征和新的电生理特征的新循环分类法。该方法通过分析来自 Allen 细胞类型数据库的不同小鼠视觉皮层神经元的 1850 多个电生理信号而得到验证。该研究在两个不同的层面上进行：神经元及其细胞类型聚集到 Cre 细胞系中。在神经元水平上，已经用一个有前途的模型提取了电生理特征，该模型已经证明了其在神经元动力学中的价值。在 Cre Line 水平上，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相结合。通过前两个主要成分的简单变换揭示了具有循环顺序的分类法，从而可以表征不同的 Cre 系。此外，所提出的方法在分类学中定位了不具有可用转录组特征的其他 Cre 系。最后，通过机器学习方法验证了分类法，该方法能够通过所提出的电生理学特征来区分不同的神经元类型。