CytoNet: A Foundation Model for the Human Cerebral Cortex at Cellular Resolution

[2511.01870] CytoNet: A Foundation Model for the Human Cerebral Cortex at Cellular Resolution Skip to main content Search Submit Donate Log in Quantitative Biology > Neurons and Cognition arXiv:2511.01870 (q-bio) [Submitted on 21 Oct 2025 ( v1 ), last revised 25 Aug 2026 (this version, v3)] Title: CytoNet: A Foundation Model for the Human Cerebral Cortex at Cellular Resolution Authors: Christian Schiffer , Zeynep Boztoprak , Jan-Oliver Kropp , Julia Thönnißen , Katia Berr , Hannah Spitzer , Mathis Bode , Thomas Lippert , Katrin Amunts , Timo Dickscheid View PDF HTML (experimental) Abstract: Studying the cellular architecture of the human cerebral cortex is essential for understanding how the brain is organized from the micro to the macro level, and how it functions. However, investigating complex texture patterns in histological images using automatic methods that can be scaled across whole brains remains a challenge. Here we introduce CytoNet, a foundation model trained on 1 million unlabeled microscopic image patches from over 4,000 histological sections from nine postmortem brains, and evaluated on over 2,000 sections from five additional brains excluded from self-supervised pretraining. Using co-localization in the cortical sheet for self-supervision, CytoNet learns to encode complex cellular patterns into expressive and anatomically meaningful feature representations. CytoNet supports multiple downstream applications, including area classification, laminar segmentation, quantification of microarchitectural variation, and exploratory mapping of cortical subdivisions. Functional parcellation analyses provided parcellation-dependent evidence for links between cytoarchitecture and macroscale functional organization. Together, these results establish CytoNet as a unified framework for scalable analysis of cortical microarchitecture and for testing links between cellular architecture and structure-function organization in the human cerebral cortex. Comments: 59 pages, 15 figures, 10 tables. Revised version Subjects: Neurons and Cognition (q-bio.NC) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) ACM classes: I.2.6; I.2.10; I.4.7; I.5.1; I.5.4 Cite as: arXiv:2511.01870 [q-bio.NC] (or arXiv:2511.01870v3 [q-bio.NC] for this version) https://doi.org/10.48550/arXiv.2511.01870 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Christian Schiffer [ view email ] [v1] Tue, 21 Oct 2025 11:39:23 UTC (28,083 KB) [v2] Thu, 5 Mar 2026 13:45:17 UTC (27,604 KB) [v3] Tue, 25 Aug 2026 09:08:12 UTC (26,572 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: q-bio.NC < prev | next > new | recent | 2025-11 Change to browse by: cs cs.AI cs.LG q-bio References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from (opens in new tab) (opens in new tab) (opens in new tab)

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