 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.
<clarity>  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.
 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 proposed a Spiking Inception (Sp-Inception) module, inspired by the Inception module in Artificial Neural Network (ANN) literature.