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@ARTICLE{Schiffer:1048160,
      author       = {Schiffer, Christian and Boztoprak, Zeynep and Kropp,
                      Jan-Oliver and Thönnißen, Julia and Berr, Katia and
                      Spitzer, Hannah and Amunts, Katrin and Dickscheid, Timo},
      title        = {{C}yto{N}et: {A} {F}oundation {M}odel for the {H}uman
                      {C}erebral {C}ortex},
      publisher    = {arXiv},
      reportid     = {FZJ-2025-04528},
      year         = {2025},
      abstract     = {To study how the human brain works, we need to explore the
                      organization of the cerebral cortex and its detailed
                      cellular architecture. We introduce CytoNet, a foundation
                      model that encodes high-resolution microscopic image patches
                      of the cerebral cortex into highly expressive feature
                      representations, enabling comprehensive brain analyses.
                      CytoNet employs self-supervised learning using spatial
                      proximity as a powerful training signal, without requiring
                      manual labelling. The resulting features are anatomically
                      sound and biologically relevant. They encode general aspects
                      of cortical architecture and unique brain-specific traits.
                      We demonstrate top-tier performance in tasks such as
                      cortical area classification, cortical layer segmentation,
                      cell morphology estimation, and unsupervised brain region
                      mapping. As a foundation model, CytoNet offers a consistent
                      framework for studying cortical microarchitecture,
                      supporting analyses of its relationship with other
                      structural and functional brain features, and paving the way
                      for diverse neuroscientific investigations.},
      keywords     = {Neurons and Cognition (q-bio.NC) (Other) / Artificial
                      Intelligence (cs.AI) (Other) / Machine Learning (cs.LG)
                      (Other) / FOS: Biological sciences (Other) / FOS: Computer
                      and information sciences (Other) / I.2.6; I.2.10; I.4.7;
                      I.5.1; I.5.4 (Other)},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / EBRAINS 2.0 - EBRAINS 2.0: A Research
                      Infrastructure to Advance Neuroscience and Brain Health
                      (101147319) / JL SMHB - Joint Lab Supercomputing and
                      Modeling for the Human Brain (JL SMHB-2021-2027) / HIBALL -
                      Helmholtz International BigBrain Analytics and Learning
                      Laboratory (HIBALL) (InterLabs-0015) / X-BRAIN
                      (ZT-I-PF-4-061) / DFG project G:(GEPRIS)501864659 -
                      NFDI4BIOIMAGE - Nationale Forschungsdateninfrastruktur für
                      Mikroskopie und Bildanalyse (501864659)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(EU-Grant)101147319 /
                      G:(DE-Juel1)JL SMHB-2021-2027 / G:(DE-HGF)InterLabs-0015 /
                      G:(DE-HGF)ZT-I-PF-4-061 / G:(GEPRIS)501864659},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2511.01870},
      url          = {https://juser.fz-juelich.de/record/1048160},
}