Spiking Neural Network (SNN), as a brain-inspired approach, is attracting attentions due to its potential to produce ultra-high-energy-efficient hardware. Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a popular method to train  unsupervised SNN. However, previous unsupervised SNNs trained through this method are limited to shallow networks with only one learnable layer and can't achieve satisfactory results when compared with multi-layer SNNs. In this paper, we ease this limitation by: 1)We propose Spiking Inception (Sp-Inception) module, inspired by the Inception module in  Artificial Neural Network (ANN) literature. This module is trained through STDP- based competitive learning and outperforms  baseline modules on learning capability, learning efficiency, and robustness ; 2)We propose Pooling-Reshape-Activate (PRA) layer to make  Sp-Inception module stackable ; 3)We stack multiple Sp-Inception modules to construct multi-layer SNNs. Our method greatly exceeds baseline methods on image classification tasks and reaches state-of-the-art results on  MNIST dataset among  existing unsupervised SNNs.
Spiking Neural Network (SNN), as a brain-inspired approach, is attracting attention due to its potential to produce ultra-high-energy-efficient hardware. Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a popular method to train an unsupervised SNN. However, previous unsupervised SNNs trained through this method are limited to a shallow network with only one learnable layer and cannot achieve satisfactory results when compared with multi-layer SNNs. In this paper, we eased this limitation by: 1)We proposed a Spiking Inception (Sp-Inception) module, inspired by the Inception module in the Artificial Neural Network (ANN) literature. This module is trained through STDP-based competitive learning and outperforms the baseline modules on learning capability, learning efficiency, and robustness . 2)We proposed a Pooling-Reshape-Activate (PRA) layer to make the Sp-Inception module stackable . 3)We stacked multiple Sp-Inception modules to construct multi-layer SNNs. Our algorithm outperforms the baseline algorithms on the hand-written digit classification task, and reaches state-of-the-art results on the MNIST dataset among the existing unsupervised SNNs.
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a shallow network
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method greatly exceeds baseline methods on image classification tasks
algorithm outperforms the baseline algorithms on the hand-written digit classification task,
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