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@ARTICLE{Keup:885628,
      author       = {Keup, Christian and Kühn, Tobias and Dahmen, David and
                      Helias, Moritz},
      title        = {{T}ransient chaotic dimensionality expansion by recurrent
                      networks},
      reportid     = {FZJ-2020-03969},
      pages        = {2002.11006},
      year         = {2020},
      abstract     = {Neurons communicate with spikes, which are discrete events
                      in time. Functional network models often employ rate units
                      that are continuously coupled by analog signals. Is there a
                      benefit of discrete signaling? By a unified mean-field
                      theory we show that large random networks of rate and binary
                      units have identical second order statistics. Yet their
                      stimulus processing properties are radically different: We
                      discover a chaotic sub-manifold in binary networks that does
                      not exist in rate models. Its dimensionality increases with
                      time after stimulus onset and reaches a fixed point
                      depending on the synaptic coupling strength. Low dimensional
                      stimuli are transiently expanded into higher-dimensional
                      representations within this manifold. High noise resilience
                      persists not only near the edge of chaos, but throughout the
                      chaotic regime. In rate models of spiking activity, the
                      effective spiking noise suppresses chaos, severely impairing
                      classification performance. Chaotic rate networks without
                      effective spiking noise also show the transient performance
                      boost. The transitions to chaos in the two models do not
                      coincide and have qualitatively different causes. Our theory
                      mechanistically explains these observations. These findings
                      have several implications. 1) Discrete state networks reach
                      optimal performance with weaker synapses; implying lower
                      energetic costs for synaptic transmission. 2) The
                      classification mechanism is robust to noise, compatible with
                      fluctuations in biophysical systems. 3) Optimal performance
                      is reached after only a single activation per participating
                      neuron; demonstrating event-based computation with short
                      latencies. 4) The chaotic sub-manifold predicts a transient
                      increase of variability after stimulus onset. Our results
                      thus provide a hitherto unknown link between recurrent and
                      chaotic dynamics of functional networks, neuronal
                      variability, and dimensionality of neuronal responses.},
      cin          = {INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / MSNN -
                      Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018)
                      / neuroIC002 - Recurrence and stochasticity for
                      neuro-inspired computation (EXS-SF-neuroIC002) / PhD no
                      Grant - Doktorand ohne besondere Förderung
                      (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-Juel1)HGF-SMHB-2014-2018 /
                      G:(DE-82)EXS-SF-neuroIC002 /
                      G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)25},
      url          = {https://juser.fz-juelich.de/record/885628},
}