Open AccessSpikeGoogle: Spiking Neural Networks with GoogLeNet‐like inception module
Author(s) -
Wang Xuan,
Zhong Minghong,
Cheng Hoiyuen,
Xie Junjie,
Zhou Yingchu,
Ren Jun,
Liu Mengyuan
Publication year - 2022
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/cit2.12082
Subject(s) - convolutional neural network , pooling , computer science , spiking neural network , margin (machine learning) , artificial intelligence , artificial neural network , convolution (computer science) , spike (software development) , layer (electronics) , pattern recognition (psychology) , machine learning , chemistry , software engineering , organic chemistry
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.