NeuroMoCo: A Neuromorphic Momentum Contrast Learning Method for Spiking Neural Networks
<|reference_start|>NeuroMoCo: A Neuromorphic Momentum Contrast Learning Method for Spiking Neural Networks: Recently, brain-inspired spiking neural networks (SNNs) have attracted great research attention owing to their inherent bio-interpretability, event-triggered properties and powerful perception of spatiotemporal information, which is beneficial to handling event-based neuromorphic datasets. In contrast to conventional static image datasets, event-based neuromorphic datasets present heightened complexity in feature extraction due to their distinctive time series and sparsity characteristics, which influences their classification accuracy. To overcome this challenge, a novel approach termed Neuromorphic Momentum Contrast Learning (NeuroMoCo) for SNNs is introduced in this paper by extending the benefits of self-supervised pre-training to SNNs to effectively stimulate their potential. This is the first time that self-supervised learning (SSL) based on momentum contrastive learning is realized in SNNs. In addition, we devise a novel loss function named MixInfoNCE tailored to their temporal characteristics to further increase the classification accuracy of neuromorphic datasets, which is verified through rigorous ablation experiments. Finally, experiments on DVS-CIFAR10, DVS128Gesture and N-Caltech101 have shown that NeuroMoCo of this paper establishes new state-of-the-art (SOTA) benchmarks: 83.6% (Spikformer-2-256), 98.62% (Spikformer-2-256), and 84.4% (SEW-ResNet-18), respectively.<|reference_end|>
@article{ma2024neuromoco:,
    title={NeuroMoCo: A Neuromorphic Momentum Contrast Learning Method for Spiking
  Neural Networks},
    author={Yuqi Ma and Huamin Wang and Hangchi Shen and Xuemei Chen and Shukai
  Duan and Shiping Wen},
    journal={arXiv preprint arXiv:2406.06305},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06305},
    primaryClass={cs.CV cs.AI}
}