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| Home > Publications database > Event-driven eligibility propagation in large sparse networks: efficiency shaped by biological realism > print |
| 001 | 1051609 | ||
| 005 | 20260119203214.0 | ||
| 024 | 7 | _ | |a arXiv:2511.21674 |2 arXiv |
| 037 | _ | _ | |a FZJ-2026-00532 |
| 088 | _ | _ | |a arXiv:2511.21674 |2 arXiv |
| 100 | 1 | _ | |a Korcsak-Gorzo, Agnes |0 P:(DE-Juel1)176282 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Event-driven eligibility propagation in large sparse networks: efficiency shaped by biological realism |
| 260 | _ | _ | |c 2025 |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1768822941_31203 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 5234 - Emerging NC Architectures (POF4-523) |0 G:(DE-HGF)POF4-5234 |c POF4-523 |f POF IV |x 0 |
| 536 | _ | _ | |a JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027) |0 G:(DE-Juel1)JL SMHB-2021-2027 |c JL SMHB-2021-2027 |x 1 |
| 536 | _ | _ | |a HiRSE_PS - Helmholtz Platform for Research Software Engineering - Preparatory Study (HiRSE_PS-20220812) |0 G:(DE-Juel-1)HiRSE_PS-20220812 |c HiRSE_PS-20220812 |x 2 |
| 536 | _ | _ | |a BMBF 03ZU1106CB - NeuroSys: Algorithm-Hardware Co-Design (Projekt C) - B (BMBF-03ZU1106CB) |0 G:(DE-Juel1)BMBF-03ZU1106CB |c BMBF-03ZU1106CB |x 3 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 4 |
| 536 | _ | _ | |a EBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319) |0 G:(EU-Grant)101147319 |c 101147319 |f HORIZON-INFRA-2022-SERV-B-01 |x 5 |
| 536 | _ | _ | |a Brain-Scale Simulations (jinb33_20220812) |0 G:(DE-Juel1)jinb33_20220812 |c jinb33_20220812 |f Brain-Scale Simulations |x 6 |
| 588 | _ | _ | |a Dataset connected to arXivarXiv |
| 700 | 1 | _ | |a Valverde, Jesús A. Espinoza |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Stapmanns, Jonas |0 P:(DE-Juel1)171475 |b 2 |
| 700 | 1 | _ | |a Plesser, Hans Ekkehard |0 P:(DE-Juel1)169781 |b 3 |u fzj |
| 700 | 1 | _ | |a Dahmen, David |0 P:(DE-Juel1)156459 |b 4 |u fzj |
| 700 | 1 | _ | |a Bolten, Matthias |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a van Albada, Sacha J. |0 P:(DE-Juel1)138512 |b 6 |u fzj |
| 700 | 1 | _ | |a Diesmann, Markus |0 P:(DE-Juel1)144174 |b 7 |u fzj |
| 909 | C | O | |o oai:juser.fz-juelich.de:1051609 |p openaire |p VDB |p ec_fundedresources |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)176282 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)169781 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)156459 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 6 |6 P:(DE-Juel1)138512 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 7 |6 P:(DE-Juel1)144174 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5234 |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Computational and Systems Neuroscience |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-10-20170113 |k INM-10 |l Jara-Institut Brain structure-function relationships |x 1 |
| 980 | _ | _ | |a preprint |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-10-20170113 |
| 980 | _ | _ | |a UNRESTRICTED |
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