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@ARTICLE{Dick:1030128,
      author       = {Dick, Michael and Meegen, Alexander van and Helias, Moritz},
      title        = {{L}inking network- and neuron-level correlations by
                      renormalized field theory},
      journal      = {Physical review research},
      volume       = {6},
      number       = {3},
      issn         = {2643-1564},
      address      = {College Park, MD},
      publisher    = {APS},
      reportid     = {FZJ-2024-05234},
      pages        = {033264},
      year         = {2024},
      abstract     = {It is frequently hypothesized that cortical networks
                      operate close to a critical point. Advantages of criticality
                      include rich dynamics well suited for computation and
                      critical slowing down, which may offer a mechanism for
                      dynamic memory. However, mean-field approximations, while
                      versatile and popular, inherently neglect the fluctuations
                      responsible for such critical dynamics. Thus, a renormalized
                      theory is necessary. We consider the
                      Sompolinsky-Crisanti-Sommers model which displays a well
                      studied chaotic as well as a magnetic transition. Based on
                      the analog of a quantum effective action, we derive
                      self-consistency equations for the first two renormalized
                      Greens functions. Their self-consistent solution reveals a
                      coupling between the population level activity and single
                      neuron heterogeneity. The quantitative theory explains the
                      population autocorrelation function, the single-unit
                      autocorrelation function with its multiple temporal scales,
                      and cross correlations.},
      cin          = {IAS-6 / PGI-1 / INM-6},
      ddc          = {530},
      cid          = {I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)PGI-1-20110106 /
                      I:(DE-Juel1)INM-6-20090406},
      pnm          = {5232 - Computational Principles (POF4-523) / HBP SGA3 -
                      Human Brain Project Specific Grant Agreement 3 (945539) /
                      RenormalizedFlows - Transparent Deep Learning with
                      Renormalized Flows (BMBF-01IS19077A) / DFG project
                      G:(GEPRIS)491111487 - Open-Access-Publikationskosten / 2022
                      - 2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(EU-Grant)945539 /
                      G:(DE-Juel-1)BMBF-01IS19077A / G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001310522300005},
      doi          = {10.1103/PhysRevResearch.6.033264},
      url          = {https://juser.fz-juelich.de/record/1030128},
}