记忆电阻器
神经形态工程学
人工肌肉
MNIST数据库
突触
纳米技术
计算机科学
兴奋性突触后电位
制作
材料科学
人工智能
长时程增强
导电体
钥匙(锁)
突触后电位
物理神经网络
电阻式触摸屏
深度学习
人工神经网络
突触后电流
工程类
电压
简单(哲学)
作者
Linqing Zhou,Li Song,Junqing Wei,Yaodong Liu,Shaojie Fan,Kuibo Lan,Yulin Feng,Fang Wang,Kailiang Zhang
标识
DOI:10.1021/acs.jpcb.5c05699
摘要
Organic polymer-based memristors attract attention in emerging artificial synapses thanks to the simple fabrication process, low cost, and biocompatibility, which can be used for reliable reservoir computing (RC). This article introduces an effective strategy of construction of an organic artificial neural synapse by using conductive polymer PEDOT:PSS and polysaccharide-pectin as a functional layer. After receiving an electrical stimulation, metal ions (Cu2+, from the top electrode) can gain electrons and form uniform and controllable conductive filaments through the PEDOT:PSS-pectin layer. Especially, the as-fabricated Cu/PEDOT:PSS-pectin/ITO (CPPI) device simulates abundant functions like neural synapses, such as long-term potentiation (LTP), long-term depression (LTD), paired-pulse facilitation (PPF), paired-pulse depression (PPD), excitatory postsynaptic current (EPSC), spike-timing-dependent plasticity (STDP), and the transition from short-term to long-term plasticity. Furthermore, a physical RC system is constructed, achieving a recognition rate of 99.0% for machine-written numbers (from “0” to “9”). Then, a physical model of a CPPI device is built and integrated with the RC system, achieving a recognition rate of 91.1% for MNIST handwritten digits. This study advances the development of organic polymers in artificial synapses and provides a novel material platform for designing memristors tailored to next-generation brain-inspired computing.
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