Directly training temporal Spiking Neural Network with sparse surrogate gradient

Brain-inspired Spiking Neural Networks (SNNs) have attracted much attention due to their event-based computing and energy-efficient features. However, the spiking all-or-none nature has prevented direct training of SNNs for various applications. The surrogate gradient (SG) algorithm has recently ena...

Full description

Bibliographic Details

Published in:	Neural networks Vol. 179; p. 106499
Main Authors:	Li, Yang, Zhao, Feifei, Zhao, Dongcheng, Zeng, Yi
Format:	Journal Article
Language:	English
Published:	United States Elsevier Ltd 01.11.2024
Subjects:	Action Potentials - physiology Algorithms Brain - physiology Direct Training Humans Models, Neurological Neural Networks, Computer Neurons - physiology Sparse Surrogate Gradient Spiking Neural Network Temporally Weighted Output Sparse Surrogate Gradient Temporally Weighted Output Spiking Neural Network Direct Training
ISSN:	0893-6080, 1879-2782, 1879-2782
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!