Connectome-scale self-supervised representation learning reveals neuronal organization beyond canonical labels

Authors

Shi, T., Chen, Y., Liu, C., Zhang, R.

Abstract

Dense electron-microscopy connectomes provide synaptic-resolution maps of neuronal structure and wiring, but learning scalable representations that integrate structure and connectivity for connectome discovery with minimal human intervention remains difficult. Here we present a self-supervised framework for structure-connectivity representation learning in dense connectomes. A hierarchical graph neural network with skeleton decomposition enables contrastive learning from finely sampled FlyWire neuronal skeletons, showing that fine skeletons preserve substantially richer identity information than coarse representations. Coordinate-free topology reduces developmental and geometric confounds, improving clustering and label-efficient inference. We then use learned structural embeddings as continuous descriptors of synaptic partners to construct structure-driven connectivity representations, improving subtype discrimination without predefined partner-type labels. Iterative multi-hop learning further reveals higher-order organization, including hemispheric connectivity lateralization and connectivity-defined subgroups. Attention analysis links these differences to specific synaptic partners. Together, these results establish a self-supervised and scalable framework for discovering neuronal identity and connectome organization in a large-scale dense connectome.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 05 Jul 2026.

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