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@INPROCEEDINGS{Benmeziane:1025399,
      author       = {Benmeziane, Hadjer and Ounnoughene, Amine Ziad and
                      Hamzaoui, Imane and Bouhadjar, Younes},
      title        = {{S}kip {C}onnections in {S}piking {N}eural {N}etworks: {A}n
                      {A}nalysis of {T}heir {E}ffect on {N}etwork {T}raining},
      publisher    = {IEEE},
      reportid     = {FZJ-2024-02858},
      pages        = {790-794},
      year         = {2023},
      abstract     = {Spiking neural networks (SNNs) have gained attention as a
                      promising alternative to traditional artificial neural
                      networks (ANNs) due to their potential for energy efficiency
                      and their ability to model spiking behavior in biological
                      systems. However, the training of SNNs is still a
                      challenging problem, and new techniques are needed to
                      improve their performance. In this paper, we study the
                      impact of skip connections on SNNs and propose a
                      hyperparameter optimization technique that adapts models
                      from ANN to SNN. We demonstrate that optimizing the
                      position, type, and number of skip connections can
                      significantly improve the accuracy and efficiency of SNNs by
                      enabling faster convergence and increasing information flow
                      through the network. Our results show an average $+8\%$
                      accuracy increase on CIFAR-10-DVS and DVS128 Gesture
                      datasets adaptation of multiple state-of-the-art models.},
      month         = {May},
      date          = {2023-05-15},
      organization  = {IEEE International Parallel and
                       Distributed Processing Symposium
                       Workshops (IPDPSW), St. Petersburg
                       (USA), 15 May 2023 - 19 May 2023},
      cin          = {PGI-15 / PGI-7},
      cid          = {I:(DE-Juel1)PGI-15-20210701 / I:(DE-Juel1)PGI-7-20110106},
      pnm          = {5234 - Emerging NC Architectures (POF4-523) / BMBF
                      16ME0398K - Verbundprojekt: Neuro-inspirierte Technologien
                      der künstlichen Intelligenz für die Elektronik der Zukunft
                      - NEUROTEC II - (BMBF-16ME0398K) / BMBF 16ME0399 -
                      Verbundprojekt: Neuro-inspirierte Technologien der
                      künstlichen Intelligenz für die Elektronik der Zukunft -
                      NEUROTEC II - (BMBF-16ME0399)},
      pid          = {G:(DE-HGF)POF4-5234 / G:(DE-82)BMBF-16ME0398K /
                      G:(DE-82)BMBF-16ME0399},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:001055030700095},
      doi          = {10.1109/IPDPSW59300.2023.00132},
      url          = {https://juser.fz-juelich.de/record/1025399},
}