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@ARTICLE{Knight:1047373,
      author       = {Knight, James C. and Senk, Johanna and Nowotny, Thomas},
      title        = {{A} flexible framework for structural plasticity in
                      {GPU}-accelerated sparse spiking neural networks},
      journal      = {arXiv},
      publisher    = {arXiv},
      reportid     = {FZJ-2025-04262},
      year         = {2025},
      abstract     = {The majority of research in both training Artificial Neural
                      Networks (ANNs) and modeling learning in biological brains
                      focuses on synaptic plasticity, where learning equates to
                      changing the strength of existing connections. However, in
                      biological brains, structural plasticity - where new
                      connections are created and others removed - is also vital,
                      not only for effective learning but also for recovery from
                      damage and optimal resource usage. Inspired by structural
                      plasticity, pruning is often used in machine learning to
                      remove weak connections from trained models to reduce the
                      computational requirements of inference. However, the
                      machine learning frameworks typically used for
                      backpropagation-based training of both ANNs and Spiking
                      Neural Networks (SNNs) are optimized for dense connectivity,
                      meaning that pruning does not help reduce the training costs
                      of ever-larger models. The GeNN simulator already supports
                      efficient GPU-accelerated simulation of sparse SNNs for
                      computational neuroscience and machine learning. Here, we
                      present a new flexible framework for implementing
                      GPU-accelerated structural plasticity rules and demonstrate
                      this first using the e-prop supervised learning rule and
                      DEEP R to train efficient, sparse SNN classifiers and then,
                      in an unsupervised learning context, to learn topographic
                      maps. Compared to baseline dense models, our sparse
                      classifiers reduce training time by up to 10x while the DEEP
                      R rewiring enables them to perform as well as the original
                      models. We demonstrate topographic map formation in
                      faster-than-realtime simulations, provide insights into the
                      connectivity evolution, and measure simulation speed versus
                      network size. The proposed framework will enable further
                      research into achieving and maintaining sparsity in network
                      structure and neural communication, as well as exploring the
                      computational benefits of sparsity in a range of
                      neuromorphic applications.},
      keywords     = {Neural and Evolutionary Computing (cs.NE) (Other) / Neurons
                      and Cognition (q-bio.NC) (Other) / FOS: Computer and
                      information sciences (Other) / FOS: Biological sciences
                      (Other)},
      cin          = {IAS-6},
      cid          = {I:(DE-Juel1)IAS-6-20130828},
      pnm          = {5232 - Computational Principles (POF4-523) / 5234 -
                      Emerging NC Architectures (POF4-523) / HBP SGA3 - Human
                      Brain Project Specific Grant Agreement 3 (945539) / EBRAINS
                      2.0 - EBRAINS 2.0: A Research Infrastructure to Advance
                      Neuroscience and Brain Health (101147319)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)POF4-5234 /
                      G:(EU-Grant)945539 / G:(EU-Grant)101147319},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/arXiv.2510.19764},
      url          = {https://juser.fz-juelich.de/record/1047373},
}