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@MASTERSTHESIS{Schmidt:893681,
      author       = {Schmidt, Marvin},
      othercontributors = {Pinna, Daniele and Zajzon, Barna and Mokrousov, Yuriy and
                          Morrison, Abigail},
      title        = {{I}nvestigating the role of {C}haos and characteristic time
                      scales in {R}eservoir {C}omputing},
      school       = {RWTH Aachen University},
      type         = {Masterarbeit},
      reportid     = {FZJ-2021-02753},
      pages        = {47 p.},
      year         = {2021},
      note         = {Masterarbeit, RWTH Aachen University, 2021},
      abstract     = {Dynamical systems suited for Reservoir Computing (RC)
                      should be able to both retain information for sufficiently
                      long times and exhibit a rich representation of the input
                      driving. However, selecting and tuning system parameters as
                      well as choosing a sufficient input encoding has yet to be
                      standardized as a procedure. This work attempts to make
                      progress in this regard by focusing on the input and
                      dynamical timescales in RC systems. Two qualitatively
                      different models are studied: An adaptation of the
                      Fermi-Pasta-Ulam-Tsingou model made suitable for Reservoir
                      Computing and sparsely connected networks of spiking
                      excitatory/inhibitory neurons. By comparing input injection
                      frequencies to system relaxation timescales, and measuring
                      its effects on the degree of chaos in the dynamical system,
                      a relationship between timescales and the performance on a
                      short term memory and parity-check tasks is established. We
                      find that both systems rely on a close matching of their
                      relaxation timescales with the input frequency in order to
                      memorize and make precise use of the most recent information
                      in the input. This was consistent across both models,
                      implying greater generalizability. Furthermore, we find that
                      a high degree of chaos deprecates memory in the
                      Fermi-Pasta-Ulam-Tsingou model, while at the same time
                      enhancing performance in the parity-check task, suggesting
                      the edge of chaos to be an optimal tradeoff. The networks of
                      spiking neurons show similar performance on the performance
                      tasks, suggesting that nonlinear computations happen on a
                      much faster timescale.},
      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          = {5232 - Computational Principles (POF4-523) / 5234 -
                      Emerging NC Architectures (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)POF4-5234},
      typ          = {PUB:(DE-HGF)19},
      url          = {https://juser.fz-juelich.de/record/893681},
}