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...

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Veröffentlicht in:	Neural networks Jg. 179; S. 106499
Hauptverfasser:	Li, Yang, Zhao, Feifei, Zhao, Dongcheng, Zeng, Yi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.11.2024
Schlagworte:	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-Zugang:	Volltext
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