油藏计算
神经形态工程学
纳米线
混乱的
水准点(测量)
正弦波
非线性系统
计算机科学
正弦
吸引子
拓扑(电路)
电导
人工神经网络
转化(遗传学)
网络拓扑
电压
物理
材料科学
人工智能
循环神经网络
纳米技术
数学
工程类
电气工程
计算机网络
地质学
数学分析
化学
凝聚态物理
生物化学
几何学
大地测量学
量子力学
基因
作者
Kaiwei Fu,Ruomin Zhu,Alon Loeffler,Joel Hochstetter,Adrian Diaz‐Alvarez,Adam Z. Stieg,James K. Gimzewski,Tomonobu Nakayama,Zdenka Kuncic
标识
DOI:10.1109/ijcnn48605.2020.9207727
摘要
We present simulations based on a model of self- assembled nanowire networks with memristive junctions and neural network-like topology. We analyze the dynamical voltage distribution in response to an applied bias and explain the network conductance fluctuations observed in our previous experimental studies. We then demonstrate the potential of neuromorphic nanowire networks as a physical reservoir by performing benchmark reservoir computing tasks. The tasks include sine wave nonlinear transformation, sine wave auto- generation and forecasting the Mackey-Glass chaotic time series.
科研通智能强力驱动
Strongly Powered by AbleSci AI