记忆电阻器
神经形态工程学
实现(概率)
人工神经元
人工神经网络
计算机科学
人工智能
物理神经网络
电子工程
工程类
循环神经网络
人工神经网络的类型
数学
统计
作者
Yihao Chen,Yu Wang,Yuhao Luo,Xinwei Liu,Yuqi Wang,Fei Gao,Jianguang Xu,Ertao Hu,Subhranu Samanta,Xiang Wan,Xiaojuan Lian,Jian Xiao,Yi Tong
标识
DOI:10.1109/led.2019.2936261
摘要
Artificial neurons and synapses are critical units for processing intricate information in brain-inspired neuromorphic systems. Memristors are frequently engineered as artificial synapses due to their simple structures, nonlinear dynamics, and high-density integration. However, the development of artificial neurons on memristors has less progress. In this letter, we propose a rich dynamics-driven artificial neuron based on two-dimensional materials MXene. Partial essential neural features of neural processing, including leaky integration, automatic threshold-driven fire, and self-recovery, were successfully emulated in a unified manner. The space-charge-limited current (SCLC) model accompanied by electrochemical metallization effect was used to explain electrical characteristics. This work will provide a useful guideline for designing and manipulating memristor as artificial neurons for brain-inspired systems.
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