CAAI Transactions on Intelligence Technology (Sep 2022)

SpikeGoogle: Spiking Neural Networks with GoogLeNet‐like inception module

  • Xuan Wang,
  • Minghong Zhong,
  • Hoiyuen Cheng,
  • Junjie Xie,
  • Yingchu Zhou,
  • Jun Ren,
  • Mengyuan Liu

DOI
https://doi.org/10.1049/cit2.12082
Journal volume & issue
Vol. 7, no. 3
pp. 492 – 502

Abstract

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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.

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