Engineering and Technology

A framework for the general design and computation of hybrid neural networks

R. Zhao, Z. Yang, et al.

This innovative research introduces a groundbreaking framework for hybrid neural networks (HNNs), merging the capabilities of spiking neural networks and artificial neural networks. By utilizing hybrid units, the framework effectively integrates different information flows, demonstrated through case studies in sensing, modulation, and reasoning networks. This exciting work was conducted by a team of talented researchers including Rong Zhao, Zheyu Yang, and others.

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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

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