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@INPROCEEDINGS{Helias:889321,
      author       = {Helias, Moritz},
      title        = {{C}orrelations, chaos, and criticality in neural networks},
      reportid     = {FZJ-2021-00213},
      year         = {2020},
      abstract     = {Correlations, chaos, and criticality in neural
                      networksMoritz HeliasINM-6 Juelich Research CentreTheory of
                      condensed matter physicsRWTH AachenThe remarkable properties
                      of information-processing of biological andof artificial
                      neuronal networks alike arise from the interaction oflarge
                      numbers of neurons. A central quest is thus to
                      characterizetheir collective states. The directed coupling
                      between pairs ofneurons and their continuous dissipation of
                      energy, moreover, causedynamics of neuronal networks outside
                      thermodynamic equilibrium.Tools from non-equilibrium
                      statistical mechanics and field theory arethus instrumental
                      to obtain a quantitative understanding. We herepresent
                      progress with this recent approach [1].On the experimental
                      side, we show how correlations betweenpairs of neurons are
                      informative on the dynamics ofcortical networks: they are
                      poised near a transition to chaos [2].Close to this
                      transition, we find prolongued sequential memoryfor past
                      signals [3]. In the chaotic regime, networks
                      offerrepresentations of information whose dimensionality
                      expands with time.We show how this mechanism aids
                      classification performance [3].Together these works
                      illustrate the fruitful interplay betweentheoretical
                      physics, neuronal networks, and neural
                      informationprocessing.1. Helias, Dahmen (2020) Statistical
                      field theory for neural networks.Springer lecture notes in
                      physics.2. Dahmen D, Grün S, Diesmann M, Helias M (2019).
                      Second type of criticality in the brain uncovers rich
                      multiple-neuron dynamics. PNAS 116 (26) 13051-130603.
                      Schuecker J, Goedeke S, Helias M (2018). Optimal sequence
                      memory in driven random networks. Phys Rev X 8, 0410294.
                      Keup, Kuehn, Dahmen, Helias (2020) Transient chaotic
                      dimensionality expansion by recurrent networks.
                      arXiv:2002.11006 [cond-mat.dis-nn]},
      organization  = {Heidelberg (Germany)},
      subtyp        = {Invited},
      cin          = {INM-6 / IAS-6},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828},
      pnm          = {571 - Connectivity and Activity (POF3-571) / 574 - Theory,
                      modelling and simulation (POF3-574) / 5232 - Computational
                      Principles (POF4-523) / 5231 - Neuroscientific Foundations
                      (POF4-523) / 5234 - Emerging NC Architectures (POF4-523)},
      pid          = {G:(DE-HGF)POF3-571 / G:(DE-HGF)POF3-574 /
                      G:(DE-HGF)POF4-5232 / G:(DE-HGF)POF4-5231 /
                      G:(DE-HGF)POF4-5234},
      typ          = {PUB:(DE-HGF)31},
      url          = {https://juser.fz-juelich.de/record/889321},
}