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@INPROCEEDINGS{Lohoff:1038043,
      author       = {Lohoff, Jamie and Finkbeiner, Jan and Neftci, Emre},
      title        = {{SNNAX}-{S}piking {N}eural {N}etworks in {JAX}},
      reportid     = {FZJ-2025-01092},
      pages        = {251 - 255},
      year         = {2024},
      abstract     = {Spiking Neural Networks (SNNs) simulators are essential
                      tools to prototype biologically inspired models and
                      neuromorphic hardware architectures and predict their
                      performance. For such a tool, ease of use and flexibility
                      are critical, but so is simulation speed especially given
                      the complexity inherent to simulating SNN. Here, we present
                      SNNAX, a JAX-based framework for simulating and training
                      such models with PyTorch-like intuitiveness and JAX-like
                      execution speed. SNNAX models are easily extended and
                      customized to fit the desired model specifications and
                      target neuromorphic hardware. Additionally, SNNAX offers key
                      features for optimizing the training and deployment of SNNs
                      such as flexible automatic differentiation and just-in-time
                      compilation. We evaluate and compare SNNAX to other commonly
                      used machine learning (ML) frameworks used for programming
                      SNNs. We provide key performance metrics, best practices,
                      documented examples for simulating SNNs in SNNAX, and
                      implement several benchmarks used in the literature.},
      month         = {Jul},
      date          = {2024-07-30},
      organization  = {2024 International Conference on
                       Neuromorphic Systems (ICONS),
                       Arlington, Virginia (USA), 30 Jul 2024
                       - 2 Aug 2024},
      cin          = {PGI-15},
      cid          = {I:(DE-Juel1)PGI-15-20210701},
      pnm          = {5234 - Emerging NC Architectures (POF4-523) / GREENEDGE -
                      Taming the environmental impact of mobile networks through
                      GREEN EDGE computing platforms (953775) / BMBF 03ZU1106CA -
                      NeuroSys: Algorithm-Hardware Co-Design (Projekt C) - A
                      (03ZU1106CA) / BMBF 03ZU1106CB - NeuroSys:
                      Algorithm-Hardware Co-Design (Projekt C) - B
                      (BMBF-03ZU1106CB) / BMBF 16ME0400 - Verbundprojekt:
                      Neuro-inspirierte Technologien der künstlichen Intelligenz
                      für die Elektronik der Zukunft - NEUROTEC II - (16ME0400)},
      pid          = {G:(DE-HGF)POF4-5234 / G:(EU-Grant)953775 /
                      G:(BMBF)03ZU1106CA / G:(DE-Juel1)BMBF-03ZU1106CB /
                      G:(BMBF)16ME0400},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.34734/FZJ-2025-01092},
      url          = {https://juser.fz-juelich.de/record/1038043},
}