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@ARTICLE{KorcsakGorzo:1051609,
      author       = {Korcsak-Gorzo, Agnes and Valverde, Jesús A. Espinoza and
                      Stapmanns, Jonas and Plesser, Hans Ekkehard and Dahmen,
                      David and Bolten, Matthias and van Albada, Sacha J. and
                      Diesmann, Markus},
      title        = {{E}vent-driven eligibility propagation in large sparse
                      networks: efficiency shaped by biological realism},
      reportid     = {FZJ-2026-00532, arXiv:2511.21674},
      year         = {2025},
      abstract     = {Despite remarkable technological advances, AI systems may
                      still benefit from biological principles, such as recurrent
                      connectivity and energy-efficient mechanisms. Drawing
                      inspiration from the brain, we present a biologically
                      plausible extension of the eligibility propagation (e-prop)
                      learning rule for recurrent spiking networks. By translating
                      the time-driven update scheme into an event-driven one, we
                      integrate the learning rule into a simulation platform for
                      large-scale spiking neural networks and demonstrate its
                      applicability to tasks such as neuromorphic MNIST. We extend
                      the model with prominent biological features such as
                      continuous dynamics and weight updates, strict locality, and
                      sparse connectivity. Our results show that biologically
                      grounded constraints can inform the design of
                      computationally efficient AI algorithms, offering
                      scalability to millions of neurons without compromising
                      learning performance. This work bridges machine learning and
                      computational neuroscience, paving the way for sustainable,
                      biologically inspired AI systems while advancing our
                      understanding of brain-like learning.},
      cin          = {IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)INM-10-20170113},
      pnm          = {5234 - Emerging NC Architectures (POF4-523) / JL SMHB -
                      Joint Lab Supercomputing and Modeling for the Human Brain
                      (JL SMHB-2021-2027) / $HiRSE_PS$ - Helmholtz Platform for
                      Research Software Engineering - Preparatory Study
                      $(HiRSE_PS-20220812)$ / BMBF 03ZU1106CB - NeuroSys:
                      Algorithm-Hardware Co-Design (Projekt C) - B
                      (BMBF-03ZU1106CB) / 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) / Brain-Scale Simulations
                      $(jinb33_20220812)$},
      pid          = {G:(DE-HGF)POF4-5234 / G:(DE-Juel1)JL SMHB-2021-2027 /
                      $G:(DE-Juel-1)HiRSE_PS-20220812$ /
                      G:(DE-Juel1)BMBF-03ZU1106CB / G:(EU-Grant)945539 /
                      G:(EU-Grant)101147319 / $G:(DE-Juel1)jinb33_20220812$},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2511.21674},
      howpublished = {arXiv:2511.21674},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2511.21674;\%\%$},
      url          = {https://juser.fz-juelich.de/record/1051609},
}