A low-power artificial spiking neuron based on ionic memristor for modulated frequency coding

被引:1
|
作者
Liu, Yulin [1 ]
Wang, Wei [1 ,2 ,3 ]
He, Shang [1 ,2 ]
Liu, Huiyuan [2 ,3 ,4 ]
Chen, Qilai [1 ]
Li, Gang [1 ]
Duan, Jipeng [2 ,3 ]
Liu, Yanchao [5 ]
He, Lei [5 ]
Xiao, Yongguang [1 ]
Yan, Shaoan [6 ]
Zhu, Xiaojian [2 ,3 ]
Li, Run-Wei [2 ,3 ]
Tang, Minghua [1 ]
机构
[1] Xiangtan Univ, Sch Mat Sci & Engn, Xiangtan 411105, Peoples R China
[2] Chinese Acad Sci, Ningbo Inst Mat Technol & Engn, CAS Key Lab Magnet Mat & Devices, Ningbo 315201, Peoples R China
[3] Chinese Acad Sci, Ningbo Inst Mat Technol & Engn, Key Lab Magnet Mat & Applicat Technol, Ningbo 315201, Peoples R China
[4] Univ Sci & Technol China, Nano Sci & Technol Inst, Suzhou 215123, Peoples R China
[5] Xiangtan Univ, Shi Changxu Class 2021 Sch Mat Sci & Engn, Xiangtan 411105, Peoples R China
[6] Xiangtan Univ, Sch Mech Engn & Mech, Xiangtan 411105, Hunan, Peoples R China
基金
中国国家自然科学基金;
关键词
memristive spiking neuron; ionic dynamics; spike frequency; strength-modulated frequency;
D O I
10.1088/1402-4896/ad317a
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Neurons encode information through firing spikes with rich spatiotemporal dynamics. Using artificial neuron hardware based on memristors to emulate neuronal firing is of great significance for advancing the development of brain-like computing and artificial intelligence. However, it is still challenging to achieve low power frequency coding in memristive artificial neurons. Here, a low-power ionic memristor based on Pt/HfO2/Ag is reported for artificial spiking neurons. The device is driven by a low bias current and the filament dynamically ruptures and forms, producing oscillated voltage spikes that resemble neuronal spikes. The oscillation frequency increases from 0.5 Hz to similar to 2.18 Hz with the stimulation current increasing from 1 nA to 5 nA, enabling the emulation of neuronal frequency-coding function. The low power consumption of similar to 70 pJ per pulse indicates that the device is promising for energy-efficient neuromorphic computing applications. In addition, the device is found to be capable of simulating the phasic,adaptive, and burst firing modes of neurons.
引用
收藏
页数:8
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