Decoding the complexity of the brain requires an understanding of the architecture, function, and development of its neuronal circuits. Neuronal classifications that group neurons based on specific features/behaviors have become essential to further analyze the different subtypes in a systematic and reproducible way. A comprehensive taxonomic framework, accounting for multiple defining and quantitative features, will provide the reference to infer generalized rules for cells ascribed to the same neuronal type, and eventually predict cellular behaviors, even in the absence of experimental measures. Technologies that enable cell-type classification in the nervous system are rapidly evolving in scalability and resolution. While these approaches depict astonishing diversity in neuronal morphology, electrophysiology, and gene expression, a robust metric of the coherence between different profiling modalities leading to a unified classification is still largely missing. Focusing on GABAergic neurons of the cerebral cortex, Gouwens . pioneered the first integrated cell-type classification based on the simultaneous analysis of the transcriptional networks, the recording of intrinsic electrophysiological properties, and the reconstruction of 3D morphologies of the same cell. Their comprehensive and high-quality data provide a new framework to shed light on what may be considered a "neuronal cell type." Copyright: © 2022 Faculty Opinions Ltd.