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@ARTICLE{Boutaib:906138,
      author       = {Boutaib, Youness and Bartolomaeus, Wiebke and Nestler,
                      Sandra and Rauhut, Holger},
      title        = {{P}ath classification by stochastic linear recurrent neural
                      networks},
      journal      = {Advances in continuous and discrete models},
      volume       = {2022},
      number       = {1},
      issn         = {2731-4235},
      address      = {London . BioMed Central},
      reportid     = {FZJ-2022-01251},
      pages        = {13},
      year         = {2022},
      abstract     = {We investigate the functioning of a classifying biological
                      neural network from the perspective of statistical learning
                      theory, modelled, in a simplified setting, as a
                      continuous-time stochastic recurrent neural network (RNN)
                      with the identity activation function. In the purely
                      stochastic (robust) regime, we give a generalisation error
                      bound that holds with high probability, thus showing that
                      the empirical risk minimiser is the best-in-class
                      hypothesis. We show that RNNs retain a partial signature of
                      the paths they are fed as the unique information exploited
                      for training and classification tasks. We argue that these
                      RNNs are easy to train and robust and support these
                      observations with numerical experiments on both synthetic
                      and real data. We also show a trade-off phenomenon between
                      accuracy and robustness.},
      cin          = {INM-6 / IAS-6 / INM-10},
      ddc          = {510},
      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) / neuroIC002 - Recurrence and
                      stochasticity for neuro-inspired computation
                      (EXS-SF-neuroIC002)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(DE-HGF)POF4-5232 /
                      G:(DE-HGF)POF4-5234 / G:(DE-82)EXS-SF-neuroIC002},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000867408700002},
      doi          = {10.1186/s13662-022-03686-9},
      url          = {https://juser.fz-juelich.de/record/906138},
}