Efficient learning and intrinsic noise filtering in recurrent spiking neural networks trained with e-prop
Davide Noè, Hideaki Yamamoto, Yuichi Katori, Shigeo Sato
This study examines how e-prop learning performs in recurrent spiking neural networks under varying connectivity and noise levels, finding that sparsity improves learning by enhancing activation diversity.
bioRxiv · May 25, 2025 · PreprintNeuroscience