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@ARTICLE{Keup:893385,
      author       = {Keup, Christian and Kühn, Tobias and Dahmen, David and
                      Helias, Moritz},
      title        = {{T}ransient {C}haotic {D}imensionality {E}xpansion by
                      {R}ecurrent {N}etworks},
      journal      = {Physical review / X},
      volume       = {11},
      number       = {2},
      issn         = {2160-3308},
      address      = {College Park, Md.},
      publisher    = {APS},
      reportid     = {FZJ-2021-02726},
      pages        = {021064},
      year         = {2021},
      abstract     = {Neurons in the brain communicate with spikes, which are
                      discrete events in time and value. Functional network models
                      often employ rate units that are continuously coupled by
                      analog signals. Is there a qualitative difference implied by
                      these two forms of signaling? We develop a unified
                      mean-field theory for large random networks to show that
                      first- and second-order statistics in rate and binary
                      networks are in fact identical if rate neurons receive the
                      right amount of noise. Their response to presented stimuli,
                      however, can be radically different. We quantify these
                      differences by studying how nearby state trajectories evolve
                      over time, asking to what extent the dynamics is chaotic.
                      Chaos in the two models is found to be qualitatively
                      different. In binary networks, we find a
                      network-size-dependent transition to chaos and a chaotic
                      submanifold whose dimensionality expands stereotypically
                      with time, while rate networks with matched statistics are
                      nonchaotic. Dimensionality expansion in chaotic binary
                      networks aids classification in reservoir computing and
                      optimal performance is reached within about a single
                      activation per neuron; a fast mechanism for computation that
                      we demonstrate also in spiking networks. A generalization of
                      this mechanism extends to rate networks in their respective
                      chaotic regimes.},
      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          = {5232 - Computational Principles (POF4-523) / 5231 -
                      Neuroscientific Foundations (POF4-523) / MSNN - Theory of
                      multi-scale neuronal networks (HGF-SMHB-2014-2018) / GRK
                      2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur
                      Aufklärung neuronaler multisensorischer Integration
                      (368482240) / neuroIC002 - Recurrence and stochasticity for
                      neuro-inspired computation (EXS-SF-neuroIC002) / SDS005 -
                      Towards an integrated data science of complex natural
                      systems (PF-JARA-SDS005)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)POF4-5231 /
                      G:(DE-Juel1)HGF-SMHB-2014-2018 / G:(GEPRIS)368482240 /
                      G:(DE-82)EXS-SF-neuroIC002 / G:(DE-Juel-1)PF-JARA-SDS005},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000667073000001},
      doi          = {10.1103/PhysRevX.11.021064},
      url          = {https://juser.fz-juelich.de/record/893385},
}