An interpretable deep learning framework for classifying neuronal morphologies using topology and graph neural networks
Journal:
bioRxiv
Published Date:
May 4, 2026
Abstract
Neuronal shape determines how neurons process and integrate information, yet a consistent and objective classi cation of neuronal morphologies remains elusive. Current approaches rely heavily on subjective expert views or on prede ned features, limiting reproducibility and interpretability. Here, we present an interpretable deep learning framework that uni es topological data analysis, graph neural networks, and traditional morphometrics to classify neuronal morphologies objectively and transparently. Our framework compares complementary mathematical representations of neurons to capture geometric, topological, and graph-structural information. Then it benchmarks their performance against expert-labeled datasets. We show that topology- and graph-based models achieve accuracies comparable to human experts, revealing that both global branching invariants and local connectivity patterns are essential to de ne morphological cell types. Using explainable articial intelligence methods, we identify structural features driving each classi cation decision, bridging computational and neuroanatomical interpretations. This open source and reproducible approach provides a foundation for scalable, interpretable and biologically meaningful neuronal taxonomy, enabling consistent comparisons between data sets and species.
