NTAC: Neuronal type assignment from connectivity

Recent advances in electron microscopy and computer vision now allow the reconstruction of complete wiring diagrams, or connectomes, of animal brains. This creates an urgent need for methods that can automatically identify neuronal cell types directly from these large connectivity datasets. Here we show that synaptic connectivity alone can be used to assign neurons to cell types with high accuracy. We introduce NTAC (Neuronal Type Assignment from Connectivity), which groups neurons based only on connectivity. NTAC has two forms: a semi-supervised one that leverages a small fraction of labeled neurons to infer the types of all others, and an unsupervised one that requires no labels at all. Applied to multiple state-of-the-art fruit fly brain connectomes, NTAC achieves high accuracy within only minutes on a laptop, demonstrating that connectivity provides a powerful and scalable basis for classifying neuronal cell types across the brain. In this study, the authors develop NTAC, Neuronal Type Assignment from Connectivity, using synaptic connectivity alone to identify cell types with high accuracy within minutes on a standard CPU.