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@ARTICLE{Dahmen:1050720,
      author       = {Dahmen, David and Hutt, Axel and Indiveri, Giacomo and
                      Kennedy, Ann and Lefebvre, Jeremie and Mazzucato, Luca and
                      Motter, Adilson E. and Narayanan, Rishikesh and Payvand,
                      Melika and Planert, Henrike and Gast, Richard},
      title        = {{H}ow heterogeneity shapes dynamics and computation in the
                      brain},
      journal      = {Neuron},
      volume       = {-},
      number       = {-},
      issn         = {0896-6273},
      address      = {[Cambridge, Mass.]},
      publisher    = {Cell Press},
      reportid     = {FZJ-2026-00465},
      pages        = {-},
      year         = {2025},
      abstract     = {Much effort has been spent clustering neurons into
                      transcriptomic or functional cell types and
                      characterizingthe differences between them. Beyond
                      subdividing neurons into categories, we must recognize that
                      no twoneurons are identical and that graded physiological or
                      transcriptomic properties exist within cells of agiven type.
                      This often overlooked ‘‘within-type’’ heterogeneity
                      is a specific neuronal implementation ofwhat statistical
                      physics refers to as ‘‘disorder’’ and exhibits rich
                      computational properties, the identificationof which may
                      shed crucial insights into theories of brain function. In
                      this perspective article, we address thisgap by highlighting
                      theoretical frameworks for the study of neural tissue
                      heterogeneity and discussing thebenefits and implications of
                      within-type heterogeneity for neural network dynamics,
                      computation, andself-organization.},
      cin          = {IAS-6},
      ddc          = {610},
      cid          = {I:(DE-Juel1)IAS-6-20130828},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / 5232 -
                      Computational Principles (POF4-523) / DFG project
                      G:(GEPRIS)430157073 - Evolutinäre Konvergenz hierarchischer
                      Informationsverarbeitung (430157073)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(DE-HGF)POF4-5232 /
                      G:(GEPRIS)430157073},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1016/j.neuron.2025.11.023},
      url          = {https://juser.fz-juelich.de/record/1050720},
}