Before revision:  Our SNN not only achieves comparable results with the state-of-the-art unsupervised SNNs (95.64\%/80.11\% accuracy on the MNIST/EMNISE dataset) , but also shows superior learning efficiency and robustness against hardware damage.
After revision:  Our SNN not only achieves comparable results with the state-of-the-art unsupervised SNNs (95.64\%/80.11\% accuracy on the MNIST/EMNISE dataset) while having superior learning efficiency and robustness against hardware damage.