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@ARTICLE{Tiberi:911044,
      author       = {Tiberi, Lorenzo and Stapmanns, Jonas and Kühn, Tobias and
                      Luu, Thomas and Dahmen, David and Helias, Moritz},
      title        = {{G}ell-{M}ann–{L}ow {C}riticality in {N}eural {N}etworks},
      journal      = {Physical review letters},
      volume       = {128},
      number       = {16},
      issn         = {0031-9007},
      address      = {College Park, Md.},
      publisher    = {APS},
      reportid     = {FZJ-2022-04370},
      pages        = {168301},
      year         = {2022},
      abstract     = {Criticality is deeply related to optimal computational
                      capacity. The lack of a renormalized theory of critical
                      brain dynamics, however, so far limits insights into this
                      form of biological information processing to mean-field
                      results. These methods neglect a key feature of critical
                      systems: the interaction between degrees of freedom across
                      all length scales, required for complex nonlinear
                      computation. We present a renormalized theory of a
                      prototypical neural field theory, the stochastic
                      Wilson-Cowan equation. We compute the flow of couplings,
                      which parametrize interactions on increasing length scales.
                      Despite similarities with the Kardar-Parisi-Zhang model, the
                      theory is of a Gell-Mann–Low type, the archetypal form of
                      a renormalizable quantum field theory. Here, nonlinear
                      couplings vanish, flowing towards the Gaussian fixed point,
                      but logarithmically slowly, thus remaining effective on most
                      scales. We show this critical structure of interactions to
                      implement a desirable trade-off between linearity, optimal
                      for information storage, and nonlinearity, required for
                      computation.},
      cin          = {INM-6 / INM-10 / IAS-6 / IAS-4 / IKP-3},
      ddc          = {530},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)INM-10-20170113 /
                      I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)IAS-4-20090406 /
                      I:(DE-Juel1)IKP-3-20111104},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / HBP SGA3 -
                      Human Brain Project Specific Grant Agreement 3 (945539) /
                      RenormalizedFlows - Transparent Deep Learning with
                      Renormalized Flows (BMBF-01IS19077A)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(EU-Grant)945539 /
                      G:(DE-Juel-1)BMBF-01IS19077A},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35522522},
      UT           = {WOS:000804565600003},
      doi          = {10.1103/PhysRevLett.128.168301},
      url          = {https://juser.fz-juelich.de/record/911044},
}