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@INPROCEEDINGS{Helias:889320,
      author       = {Helias, Moritz},
      title        = {{T}ransient chaotic dimensionality expansion by recurrent
                      networks},
      reportid     = {FZJ-2021-00212},
      year         = {2020},
      abstract     = {Transient chaotic dimensionality expansion by recurrent
                      networksMoritz HeliasINM-6, Juelich Research CentreFaculty
                      of Physics, RWTH Aachen UniversityCortical neurons
                      communicate with spikes, which are discrete events intime
                      and value. They often show optimal computational performance
                      close toa transition to rate-chaos; chaos that is driven by
                      local and smooth averagesof the discrete activity.We here
                      analyze microscopic and rate chaos in discretely-coupled
                      networksof binary neurons by a model-independent field
                      theory. We find a stronglynetwork size-dependent transition
                      to microscopic chaos and a chaoticsubmanifold that spans
                      only a finite fraction of the entire activity space.Rate
                      chaos is shown to be impossible in these networks.Applying
                      stimuli to a strongly microscopically chaotic binary
                      networkthat acts as a reservoir, one observes a transient
                      expansion of thedimensionality of the representing neuronal
                      space. Crucially, the numberof dimensions corrupted by noise
                      lags behind the informative dimensions.This translates to a
                      transient peak in the networks' classification
                      performanceeven deeply in the chaotic regime, extending the
                      view that computationalperformance is always optimal near
                      the edge of chaos. Classificationperformance peaks rapidly
                      within one activation per neuron, demonstratingfast
                      event-based computation. The generality of this mechanism
                      isunderlined by simulations of spiking networks of leaky
                      integrate-and fireneurons.1. Keup, Kuehn, Dahmen, Helias
                      (2020) Transient chaotic dimensionality expansion by
                      recurrent networks. arXiv:2002.11006 [cond-mat.dis-nn]},
      organization  = {WWTH seminar series, Paris (France)},
      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)},
      pid          = {G:(DE-HGF)POF3-571 / G:(DE-HGF)POF3-574},
      typ          = {PUB:(DE-HGF)31},
      url          = {https://juser.fz-juelich.de/record/889320},
}