Spiking Neural Network (SNN) is a brain-inspired, event-driven machine learning algorithm that has recognized potential in producing ultra-high-energy-efficient hardware. Among existing SNNs, unsupervised SNNs are based on synaptic plasticity and considered to have more potential in imitating the learning process of  biological brain. Most unsupervised SNNs are trained through competitive learning with Spike-Timing-Dependent Plasticity (STDP). However, the STDP-based SNNs are limited by slow learning speed and/or constrained learning capability. In this paper, to overcome these limitations , we : 1) designed a high-parallelism network architecture, inspired by the Inception module in the Artificial Neural Network (ANN)literature ; 2) extended a widely used vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding ; 3) proposed to use adaptive repolarization ( i.e. resetting) in the spiking neuron model to enhance the spiking activities and thus further accelerate the network' s learning . We evaluated our contributions on the two established benchmark datasets (MNIST/EMNIST) . Our experimental results show that our architecture exhibits superior performance than widely used Fully-Connected (FC ) and Locally-Connected (LC) architectures. 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. Our SNN trained with only hundreds of iterations can achieve a great classification accuracy , and random destruction of large numbers of synapses and neurons only leads to negligible performance degradation.
Spiking Neural Networks (SNNs) are brain-inspired, event-driven machine learning algorithms that have been widely recognized in producing ultra-high-energy-efficient hardware. Among existing SNNs, unsupervised SNNs based on synaptic plasticity , especially Spike-Timing-Dependent Plasticity (STDP), are considered to have great potential in imitating the learning process of the biological brain. Nevertheless, the existing STDP-based SNNs have limitations in constrained learning capability and/or slow learning speed. Most STDP-based SNNs adopted a slow-learning Fully-Connected (FC) architecture and used a sub-optimal vote-based scheme for spike decoding. In this paper, we overcome these limitations with : 1) a design of high-parallelism network architecture, inspired by the Inception module in Artificial Neural Networks (ANNs) ; 2) use of a Vote-for-All (VFA) decoding layer as a replacement to the standard vote-based spike decoding scheme, to reduce the information loss in spike decoding and, 3) a proposed adaptive repolarization ( resetting) mechanism that accelerates SNNs' learning by enhancing spiking activities. Our experimental results on two established benchmark datasets (MNIST/EMNIST) show that our network architecture resulted in superior performance compared to the widely used FC architecture and a more advanced Locally-Connected (LC) architecture, and that our SNN achieved competitive results with 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. Our SNN achieved great classification accuracy with only hundreds of training iterations , and random destruction of large numbers of synapses or neurons only led to negligible performance degradation.
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