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综述了混合神经网络（Hybrid Neural Networks, HNNs）的起源、概念、生物学视角、构建框架及其支持系统，并对HNNs的潜在研究方向提供了见解和建议。HNNs作为类脑智能计算（Brain-inspired Computing, BIC）的一个重要实例，通过整合计算机科学导向的人工神经网络（Artificial Neural Networks, ANNs）和神经科学导向的脉冲神经网络（Spiking Neural Networks, SNNs），展现出在感知、认知和学习等多样智能任务中的独特优势。文章强调了HNNs在处理数据异构性、硬件异构性和神经科学建模方面的应用潜力，并探讨了如何通过混合芯片、软件和系统基础设施来支持HNNs的高效部署和应用。HNNs的发展不仅为实现人工通用智能（Artificial General Intelligence, AGI）提供了新的途径，也为跨学科研究和智能硬件设计提供了新的视角和方法，对推动智能科技领域的进步具有深远影响。文章发表在2024年5月5日《National Science Review》（IF=20.6）

Liu, F., Zheng, H., Ma, S., Zhang, W., Liu, X., Chua, Y., ... & Zhao, R. (2024). Advancing brain-inspired computing with Hybrid Neural networks. National Science Review, nwae066.

       Brain-inspired computing, drawing inspiration from the fundamental structure and information-processing mechanisms of the human brain, has gained significant momentum in recent years. It has emerged as a research paradigm centered on brain–computer dual-driven and multi-network integration. One noteworthy instance of this paradigm is the hybrid neural network (HNN), which integrates computer-science-oriented artificial neural networks (ANNs) with neuroscience-oriented spiking neural networks (SNNs). HNNs exhibit distinct advantages in various intelligent tasks, including perception, cognition and learning. This paper presents a comprehensive review of HNNs with an emphasis on their origin, concepts, biological perspective, construction framework and supporting systems. Furthermore, insights and suggestions for potential research directions are provided aiming to propel the advancement of the HNN paradigm.