SpikeGoogle: Spiking Neural Networks with GoogLeNet-like inception module

Abstract

Spiking Neural Network is known as the third-generation artificial neural network whose development has great potential. With the help of Spike Layer Error Reassignment in Time for error back-propagation, this work presents a new network called SpikeGoogle, which is implemented with GoogLeNet-like inception module. In this inception module, different convolution kernels and max-pooling layer are included to capture deep features across diverse scales. Experiment results on small NMNIST dataset verify the results of the authors’ proposed SpikeGoogle, which outperforms the previous Spiking Convolutional Neural Network method by a large margin.