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@INPROCEEDINGS{Nestler:892802,
      author       = {Nestler, Sandra and Keup, Christian and Dahmen, David and
                      Gilson, Matthieu and Rauhut, Holger and Helias, Moritz},
      title        = {{U}nfolding recurrence by {G}reen’s functions for
                      optimized reservoir computing},
      reportid     = {FZJ-2021-02359},
      year         = {2021},
      abstract     = {Cortical networks are strongly recurrent, and neurons have
                      intrinsic temporal dynamics. This sets them apart from deep
                      feed-forward networks. Despite the tremendous progress in
                      the application of feed-forward networks and their
                      theoretical understanding, it remains unclear how the
                      interplay of recurrence and non-linearities in recurrent
                      cortical networks contributes to their function. The purpose
                      of this work is to present a solvable recurrent network
                      model that links to feed forward networks. By perturbative
                      methods we transform the time-continuous,recurrent dynamics
                      into an effective feed-forward structure of linear and
                      non-linear temporal kernels. The resulting analytical
                      expressions allow us to build optimal time-series
                      classifiers from random reservoir networks. Firstly, this
                      allows us to optimize not only the readout vectors, but also
                      the input projection, demonstrating a strong potential
                      performance gain. Secondly, the analysis exposes how the
                      second order stimulus statistics is a crucial element that
                      interacts with the non-linearity of the dynamics and boosts
                      performance.},
      organization  = {RNN seminar talk (online), Mortimer B.
                       Zuckerman Mind Brain Behavior Institute
                       (USA)},
      subtyp        = {Invited},
      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          = {5231 - Neuroscientific Foundations (POF4-523) / 5232 -
                      Computational Principles (POF4-523) / 5234 - Emerging NC
                      Architectures (POF4-523) / MSNN - Theory of multi-scale
                      neuronal networks (HGF-SMHB-2014-2018) / HBP SGA2 - Human
                      Brain Project Specific Grant Agreement 2 (785907) / HBP SGA3
                      - Human Brain Project Specific Grant Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(DE-HGF)POF4-5232 /
                      G:(DE-HGF)POF4-5234 / G:(DE-Juel1)HGF-SMHB-2014-2018 /
                      G:(EU-Grant)785907 / G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)31},
      url          = {https://juser.fz-juelich.de/record/892802},
}