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@ARTICLE{vanMeegen:902076,
      author       = {van Meegen, Alexander and Kühn, Tobias and Helias, Moritz},
      title        = {{L}arge-{D}eviation {A}pproach to {R}andom {R}ecurrent
                      {N}euronal {N}etworks: {P}arameter {I}nference and
                      {F}luctuation-{I}nduced {T}ransitions},
      journal      = {Physical review letters},
      volume       = {127},
      number       = {15},
      issn         = {0031-9007},
      address      = {College Park, Md.},
      publisher    = {APS},
      reportid     = {FZJ-2021-04016},
      pages        = {158302},
      year         = {2021},
      abstract     = {We here unify the field-theoretical approach to neuronal
                      networks with large deviations theory. For a prototypical
                      random recurrent network model with continuous-valued units,
                      we show that the effective action is identical to the rate
                      function and derive the latter using field theory. This rate
                      function takes the form of a Kullback-Leibler divergence
                      which enables data-driven inference of model parameters and
                      calculation of fluctuations beyond mean-field theory.
                      Lastly, we expose a regime with fluctuation-induced
                      transitions between mean-field solutions.},
      cin          = {INM-6 / IAS-6 / INM-10},
      ddc          = {530},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / MSNN -
                      Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018)
                      / HBP SGA2 - Human Brain Project Specific Grant Agreement 2
                      (785907) / RenormalizedFlows - Transparent Deep Learning
                      with Renormalized Flows (BMBF-01IS19077A) / SDS005 - Towards
                      an integrated data science of complex natural systems
                      (PF-JARA-SDS005) / PhD no Grant - Doktorand ohne besondere
                      Förderung (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(DE-Juel1)HGF-SMHB-2014-2018 /
                      G:(EU-Grant)785907 / G:(DE-Juel-1)BMBF-01IS19077A /
                      G:(DE-Juel-1)PF-JARA-SDS005 /
                      G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {34678014},
      UT           = {WOS:000705650600007},
      doi          = {10.1103/PhysRevLett.127.158302},
      url          = {https://juser.fz-juelich.de/record/902076},
}