Li Zhiyuan, Tang Wei, Zhang Beining, Yang Rui, Miao Xiangshui
School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China.
Hubei Yangtze Memory Laboratories, Wuhan, China.
Sci Technol Adv Mater. 2023 Apr 19;24(1):2188878. doi: 10.1080/14686996.2023.2188878. eCollection 2023.
Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful information processing units and can achieve highly complex nonlinear computations. By leveraging the switching dynamic characteristics of memristive device, memristive neurons show rich spiking behaviors with simple circuit. This report reviews the memristive neurons and their applications in neuromorphic sensing and computing systems. The switching mechanisms that endow memristive devices with rich dynamics and nonlinearity are highlighted, and subsequently various nonlinear spiking neuron behaviors emulated in these memristive devices are reviewed. Then, recent development is introduced on neuromorphic system with memristive neurons for sensing and computing. Finally, we discuss challenges and outlooks of the memristive neurons toward high-performance neuromorphic hardware systems and provide an insightful perspective for the development of interactive neuromorphic electronic systems.
受生物神经系统原理的启发,神经形态工程为智能计算带来了一种具有高能效和低功耗的有前景的替代方法。作为神经形态系统的关键组件,人工脉冲神经元是强大的信息处理单元,能够实现高度复杂的非线性计算。通过利用忆阻器件的开关动态特性,忆阻神经元通过简单的电路展现出丰富的脉冲行为。本报告回顾了忆阻神经元及其在神经形态传感和计算系统中的应用。重点介绍了赋予忆阻器件丰富动力学和非线性的开关机制,随后回顾了在这些忆阻器件中模拟的各种非线性脉冲神经元行为。然后,介绍了具有忆阻神经元的神经形态系统在传感和计算方面的最新进展。最后,我们讨论了忆阻神经元在高性能神经形态硬件系统方面面临的挑战和前景,并为交互式神经形态电子系统的发展提供了有见地的观点。