Reservoir Computing with Neuro-memristive Nanowire Networks

被引：0

作者：

Fu, Kaiwei ^{[1
,2
]}

Zhu, Ruomin ^{[1
,2
]}

Loeffler, Alon ^{[1
,2
]}

Hochstetter, Joel ^{[1
,2
]}

Diaz-Alvarez, Adrian ^{[3
]}

Stieg, Adam ^{[3
,4
]}

Gimzewski, James ^{[3
,4
]}

Nakayama, Tomonobu ^{[1
,2
,3
]}

Kuncic, Zdenka ^{[1
,2
,3
]}

机构：

[1] Univ Sydney, Sch Phys, Sydney, NSW 2006, Australia

[2] Univ Sydney, Sydney Nano Inst, Sydney, NSW 2006, Australia

[3] Natl Inst Mat Sci, Int Ctr Mat Nanoarchitecton, Tsukuba, Ibaraki, Japan

[4] Univ Calif Los Angeles, Calif NanoSyst Inst, Los Angeles, CA 90024 USA

来源：

2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2020年

关键词：

Neuromorphic systems; Memristive systems; Nanowire networks; Reservoir computing;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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.

引用

页数：8

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