CASIA OpenIR  > 类脑智能研究中心
Diverse Neuron Type Selection for Convolutional Neural Networks
Guibo Zhu1; Zhaoxiang Zhang1,2,3; Xu-Yao Zhang1; Cheng-Lin Liu1,2,3
2017
Conference NameInternational Joint Conference on Artificial Intelligence
Conference DateAugust 19-25, 2017
Conference PlaceAustralia
Abstract
The activation function for neurons is a prominent element in the deep learning architecture for obtaining high performance. Inspired by neuroscience findings, we introduce and define two types of neurons with different activation functions for artificial neural networks: excitatory and inhibitory neurons, which can be adaptively selected by selflearning. Based on the definition of neurons, in the paper we not only unify the mainstream activation functions, but also discuss the complementariness among these types of neurons. In addition, through the cooperation of excitatory and inhibitory neurons, we present a compositional activation function that leads to new state-of-the-art performance comparing to rectifier linear units. Finally, we hope that our framework not only gives a basic unified framework of the existing activation neurons to provide guidance for future design, but also contributes neurobiological explanations which can be treated as a window to bridge the gap between biology and computer science.
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20453
Collection类脑智能研究中心
Corresponding AuthorZhaoxiang Zhang
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Center for Excellence in Brain Science and Intelligence Technology, CAS
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Guibo Zhu,Zhaoxiang Zhang,Xu-Yao Zhang,et al. Diverse Neuron Type Selection for Convolutional Neural Networks[C],2017.
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