Reservoir Computing with Neuromemristive Nanowire Networks

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...

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Bibliographic Details
Published in2020 International Joint Conference on Neural Networks (IJCNN) pp. 1 - 8
Main Authors Fu, Kaiwei, Zhu, Ruomin, Loeffler, Alon, Hochstetter, Joel, Diaz-Alvarez, Adrian, Stieg, Adam, Gimzewski, James, Nakayama, Tomonobu, Kuncic, Zdenka
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2020
Subjects
Computational modeling
Junctions
Mathematical model
Memristive systems
Nanobioscience
Nanowire networks
Neuromorphic systems
Reservoir computing
Reservoirs
Task analysis
Training
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Summary: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.
ISSN:2161-4407
DOI:10.1109/IJCNN48605.2020.9207727