Before revision:  In BioSNet, we propose a new biomimetic spiking neuron model named MRON inspired by 'recognition memory' in the human brain, design an efficient and robust network architecture corresponding to biological characteristics of the human brain as well, and extend the traditional voting mechanism to the Vote-for-All (VFA) decoding layer so as to reduce information loss during decoding. 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.
After revision:  In BioSNet, we propose a new biomimetic spiking neuron model named MRON inspired by 'recognition memory' in the human brain, design an efficient and robust network architecture corresponding to biological characteristics of the human brain as well, and extend the traditional voting mechanism to the Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding. 3) We proposed to use adaptive repolarization (i.e. resetting) in the spiking neuron model to further accelerate the network's learning. We evaluated our contributions on the hand-written digit/letter classification tasks. The experimental 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.