TY  - EJOUR
AU  - Korcsak-Gorzo, Agnes
AU  - Valverde, Jesús A. Espinoza
AU  - Stapmanns, Jonas
AU  - Plesser, Hans Ekkehard
AU  - Dahmen, David
AU  - Bolten, Matthias
AU  - van Albada, Sacha J.
AU  - Diesmann, Markus
TI  - Event-driven eligibility propagation in large sparse networks: efficiency shaped by biological realism
IS  - arXiv:2511.21674
M1  - FZJ-2026-00532
M1  - arXiv:2511.21674
PY  - 2025
AB  - 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.
LB  - PUB:(DE-HGF)25
UR  - https://juser.fz-juelich.de/record/1051609
ER  -