ArticlePatterns (New York, N.Y.)2024
DSM: Deep sequential model for complete neuronal morphology representation and feature extraction.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Applied Nanobiotechnology for Vector-borne Disease Management: Drug Delivery, Diagnostics, and Sustainable Vector Control.Applied biochemistry and biotechnology · 2026Review
- NTAC: Neuronal type assignment from connectivity.Nature communications · 2026Article
- Reconstruction of a connectome of single neurons in mouse brains by cross-validating multi-scale multi-modality data.Nature methods · 2025Article
- Connectivity Is All You Need: Inferring Neuronal Types with NTAC.bioRxiv : the preprint server for biology · 2025Article
- NeuroXiv: AI-powered open databasing and dynamic mining of brain-wide neuron morphometry.Nature methods · 2025Article
- A Multi-Scale Neuron Morphometry Dataset from Peta-voxel Mouse Whole-Brain Images.Scientific data · 2025Article
- Learning meaningful representation of single-neuron morphology via large-scale pre-training.Bioinformatics (Oxford, England) · 2024Article
- DSM: Deep sequential model for complete neuronal morphology representation and feature extraction.Patterns (New York, N.Y.) · 2024Article
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Authors and funding
8 authors.
Funding
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Abstract
The full morphology of single neurons is indispensable for understanding cell types, the basic building blocks in brains. Projecting trajectories are critical to extracting biologically relevant information from neuron morphologies, as they provide valuable information for both connectivity and cell identity. We developed an artificial intelligence method, deep sequential model (DSM), to extract concise, cell-type-defining features from projections across brain regions. DSM achieves more than 90% accuracy in classifying 12 major neuron projection types without compromising performance when spatial noise is present. Such remarkable robustness enabled us to efficiently manage and analyze several major full-morphology data sources, showcasing how characteristic long projections can define cell identities. We also succeeded in applying our model to both discovering previously unknown neuron subtypes and analyzing exceptional co-expressed genes involved in neuron projection circuits.
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Registered trials
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