Before revision:   In this paper, we propose an efficient and strong unsupervised SNN named BioSNet with high biological plausibility to handle image classification tasks. 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.
After revision:  In this paper, to ease these limitations: 1) We designed a highly parallel multi-pathway network architecture, inspired by the Inception module in the Artificial Neural Network (ANN) literature. 2) We extended the classic vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer so as to reduce information loss during decoding.