Before revision:  Simulation results show that BioSNet not only achieves  state-of-the-art unsupervised classification accuracy on MNIST/EMNIST data sets , but also exhibits superior learning efficiency and high robustness. Specifically, the BioSNet trained with only dozens of samples per class can achieve a favorable classification accuracy over 80\% and randomly deleting even 95\% of synapses or neurons in the BioSNet only leads to slight performance degradation.
After revision:  Simulation results show that BioSNet not only achieves state-of-the-art unsupervised classification accuracy on MNIST/EMNIST data sets , but also exhibits superior learning efficiency and robustness. Our SNN trained with only dozens of samples per class can achieve a favorable classification accuracy over 80\% and randomly deleting even 95\% of synapses or neurons in the BioSNet only leads to slight performance degradation.