Efficient Spiking Neural Networks with Biologically Similar Lithium-Ion Memristor Neurons

神经形态工程学 尖峰神经网络 记忆电阻器 计算机科学 材料科学 突触 人工神经网络 人工智能 模式识别(心理学) 神经科学 电子工程 生物 工程类
作者
Shanwu Ke,Yanqin Pan,Yaoyao Jin,Jiahao Meng,Yongyue Xiao,Siqi Chen,Zihao Zhang,Ruiqi Li,Fangjiu Tong,Bei Jiang,Zhitang Song,Min Zhu,Cong Ye
出处
期刊:ACS Applied Materials & Interfaces [American Chemical Society]
卷期号:16 (11): 13989-13996 被引量:10
标识
DOI:10.1021/acsami.3c19261
摘要

Benefiting from the brain-inspired event-driven feature and asynchronous sparse coding approach, spiking neural networks (SNNs) are becoming a potentially energy-efficient replacement for conventional artificial neural networks. However, neuromorphic devices used to construct SNNs persistently result in considerable energy consumption owing to the absence of sufficient biological parallels. Drawing inspiration from the transport nature of Na+ and K+ in synapses, here, a Li-based memristor (LixAlOy) was proposed to emulate the biological synapse, leveraging the similarity of Li as a homologous main group element to Na and K. The Li-based memristor exhibits ∼8 ns ultrafast operating speed, 1.91 and 0.72 linearity conductance modulation, and reproducible switching behavior, enabled by lithium vacancies forming a conductive filament mechanism. Moreover, these memristors are capable of simulating fundamental behaviors of a biological synapse, including long-term potentiation and long-term depression behaviors. Most importantly, a threshold-tunable leaky integrate-and-fire (TT-LIF) neuron is built using LixAlOy memristors, successfully integrating synaptic signals from both temporal and spatial levels and achieving an optimal threshold of SNNs. A computationally efficient TT-LIF-based SNN algorithm is also implemented for image recognition schemes, featuring a high recognition rate of 90.1% and an ultralow firing rate of 0.335%, which is 4 times lower than those of other memristor-based SNNs. Our studies reveal the ion dynamics mechanism of the LixAlOy memristor and confirm its potential in rapid switching and the construction of SNNs.
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