The content discusses the challenges of training Spiking Neural Networks (SNNs) in an event-driven manner and introduces two new algorithms, STD-ED and MPD-ED. These algorithms are evaluated on various datasets, showcasing superior performance compared to existing methods.
The authors highlight the importance of addressing over-sparsity and gradient reversal issues in SNN training. They propose innovative solutions through the AFT-IF neuron model in STD-ED and the AFT-LIF model in MPD-ED. These models adaptively adjust firing thresholds to optimize learning efficiency.
Extensive experiments on static and neuromorphic datasets demonstrate the effectiveness of the proposed event-driven learning methods. The results show significant improvements in energy efficiency and performance compared to traditional backpropagation methods.
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by Wenjie Wei,M... at arxiv.org 03-04-2024
https://arxiv.org/pdf/2403.00270.pdfDeeper Inquiries