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@INPROCEEDINGS{Dick:1015246,
      author       = {Dick, Michael and Alexander, van Meegen and Helias, Moritz},
      title        = {{L}inking {N}etwork and {N}euron {L}evel {C}orrelations via
                      {R}enormalized {F}ield {T}heory},
      reportid     = {FZJ-2023-03601},
      year         = {2023},
      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 analogue 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.},
      month         = {Sep},
      date          = {2023-09-26},
      organization  = {Bernstein Conference 2023, Berlin
                       (Germany), 26 Sep 2023 - 30 Sep 2023},
      subtyp        = {After Call},
      cin          = {PGI-1 / INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)PGI-1-20110106 / I:(DE-Juel1)INM-6-20090406 /
                      I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)INM-10-20170113},
      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 491111487
                      - Open-Access-Publikationskosten / 2022 - 2024 /
                      Forschungszentrum Jülich (OAPKFZJ) (491111487) / MSNN -
                      Theory of multi-scale neuronal networks
                      (HGF-SMHB-2014-2018)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(EU-Grant)945539 /
                      G:(DE-Juel-1)BMBF-01IS19077A / G:(GEPRIS)491111487 /
                      G:(DE-Juel1)HGF-SMHB-2014-2018},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2023-03601},
      url          = {https://juser.fz-juelich.de/record/1015246},
}