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Abstract
This paper proposes a framework for designing and computing hybrid neural networks (HNNs) by combining spiking neural networks (SNNs) and artificial neural networks (ANNs). The framework uses hybrid units (HUs) as an interface to integrate the strengths of both paradigms while maintaining flexibility and efficiency. HUs are designed to handle hybrid information flows, and the framework's capabilities are demonstrated through three case studies: a hybrid sensing network, a hybrid modulation network, and a hybrid reasoning network, showcasing its potential for various intelligent tasks.
Publisher
Nature Communications
Published On
Jun 14, 2022
Authors
Rong Zhao, Zheyu Yang, Hao Zheng, Yujie Wu, Faqiang Liu, Zhenzhi Wu, Lukai Li, Feng Chen, Seng Song, Jun Zhu, Wenli Zhang, Haoyu Huang, Mingkun Xu, Kaifeng Sheng, Qianbo Yin, Jing Pei, Guoqi Li, Youhui Zhang, Mingguo Zhao, Luping Shi
Tags
hybrid neural networks
spiking neural networks
artificial neural networks
hybrid units
intelligent tasks
information flows
case studies

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