Event-driven eligibility propagation in large sparse networks: efficiency shaped by biological realism
The research adapted eligibility propagation for large, sparse networks by switching from time-driven updates to event-driven ones. This change computes synaptic weight changes only when a spike occurs at a synapse, significantly reducing computation and improving scalability for neuromorphic systems. The resulting model incorporates biological realism to create more efficient AI algorithms.