具有同态可塑性的全集成膜霍奇金-赫胥黎神经元

IF 4.1 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Yue Yang;Xumeng Zhang;Pei Chen;Lingli Cheng;Yanting Ding;Chao Li;Jie Yu;Qi Liu
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引用次数: 0

摘要

基于霍奇金-赫胥黎(H-H)模型的人工神经元可以模仿最丰富的发射模式,在构建高智能系统方面显示出巨大潜力。与传统晶体管相比,基于氧化铌的阈值开关器件等新兴器件在构建 H-H 神经元电路方面具有更多优势。然而,忆阻 H-H 神经元电路的片上集成仍有待探索,限制了其在硬件中的实际应用。在这里,我们设计并制造了一个完全集成的忆阻性 H-H 神经元电路,并实现了全有或全无、折射期、积分器、1 级兴奋、强直性尖峰、阈下振荡、强直性猝发和混合模式发射行为。我们还展示了基于集成 H-H 神经元的同态可塑性,具体来说,当接收到过强的输入时,神经元会自发地提高阈值,以避免神经元过度兴奋。这项工作验证了构建集成记忆性 H-H 神经元的可行性,为构建高仿生神经形态系统奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fully Integrated Memristive Hodgkin-Huxley Neurons With Homeostatic Plasticity
Artificial neurons based on the Hodgkin-Huxley (H-H) models could mimic the richest firing patterns, showing great potential in building high-intelligent systems. Emerging devices, such as NbO2-based threshold-switching devices, exhibit more advantages in constructing H-H neuron circuits compared to conventional transistors. However, the on-chip integration of the memristive H-H neuron circuit remains unexplored, limiting its practical applications in hardware. Here, we design and fabricate a fully integrated memristive H-H neuron circuit and achieve all-or-nothing, refractory period, integrator, class 1 excitation, tonic spiking, subthreshold oscillation, tonic bursting, and mixed-mode firing behaviors. We also demonstrate the homeostatic plasticity based on integrated H-H neuron, specifically, the neuron increases threshold spontaneously when receiving an excessively strong input to avoid the superexcitation in the neuron. This work verifies the feasibility of building an integrated memristive H-H neuron and lays the foundation for building high-bionic neuromorphic systems.
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来源期刊
IEEE Electron Device Letters
IEEE Electron Device Letters 工程技术-工程:电子与电气
CiteScore
8.20
自引率
10.20%
发文量
551
审稿时长
1.4 months
期刊介绍: IEEE Electron Device Letters publishes original and significant contributions relating to the theory, modeling, design, performance and reliability of electron and ion integrated circuit devices and interconnects, involving insulators, metals, organic materials, micro-plasmas, semiconductors, quantum-effect structures, vacuum devices, and emerging materials with applications in bioelectronics, biomedical electronics, computation, communications, displays, microelectromechanics, imaging, micro-actuators, nanoelectronics, optoelectronics, photovoltaics, power ICs and micro-sensors.
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