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@INPROCEEDINGS{Tetko:873632,
      author       = {State, Laura and Vilimelis Aceituno, Pau},
      editor       = {Tetko, Igor V. and Kůrková, Věra and Karpov, Pavel and
                      Theis, Fabian},
      title        = {{T}raining {D}elays in {S}piking {N}eural {N}etworks},
      reportid     = {FZJ-2020-00875},
      isbn         = {978-3-030-30486-7 (print)},
      year         = {2019},
      abstract     = {Spiking Neural Networks (SNNs) are a promising
                      computational paradigm, both to understand biological
                      information processing and for low-power, embedded chips.
                      Although SNNs are known to encode information in the precise
                      timing of spikes, conventional artificial learning
                      algorithms do not take this into account directly. In this
                      work, we implement the spike timing by training the synaptic
                      delays in a single layer SNN. We use two different
                      approaches: a classical gradient descent and a direct
                      algebraic method that is based on a complex-valued encoding
                      of the spikes. Both algorithms are equally able to correctly
                      solve simple detection tasks. Our work provides new
                      optimization methods for the data analysis of highly
                      time-dependent data and training methods for neuromorphic
                      chips.},
      month         = {Sep},
      date          = {2019-09-17},
      organization  = {ICANN 2019: Artificial Neural Networks
                       and Machine Learning – ICANN 2019:
                       Theoretical Neural Computation pp,
                       Munich (Germany), 17 Sep 2019 - 19 Sep
                       2019},
      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) /
                      Smartstart - SMARTSTART Training Program in Computational
                      Neuroscience (90251)},
      pid          = {G:(DE-HGF)POF3-574 / G:(EU-Grant)90251},
      typ          = {PUB:(DE-HGF)1 / PUB:(DE-HGF)3},
      UT           = {WOS:000546494000054},
      doi          = {10.1007/978-3-030-30487-4_54},
      url          = {https://juser.fz-juelich.de/record/873632},
}