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Fully memristive neural networks for pattern classification with unsupervised learning

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journal contribution
posted on 25.01.2018 by Zhongrui Wang, Saumil Joshi, Sergey Saveliev, Wenhao Song, Rivu Midya, Yunning Li, Mingyi Rao, Peng Yan, Shiva Asapu, Ye Zhuo, Hao Jiang, Peng Lin, Can Li, Jung Ho Yoon, Navnidhi K. Upadhyay, Jiaming Zhang, Miao Hu, John Paul Strachan, Mark Barnell, Qing Wu, Huaqiang Wu, R.S. Williams, Qiangfei Xia, J. Joshua Yang
Neuromorphic computers comprised of artificial neurons and synapses could provide a more efficient approach to implementing neural network algorithms than traditional hardware. Recently, artificial neurons based on memristors have been developed, but with limited bio-realistic dynamics and no direct interaction with the artificial synapses in an integrated network. Here we show that a diffusive memristor based on silver nanoparticles in a dielectric film can be used to create an artificial neuron with stochastic leaky integrate-and-fire dynamics and tunable integration time, which is determined by silver migration alone or its interaction with circuit capacitance. We integrate these neurons with nonvolatile memristive synapses to build fully memristive artificial neural networks. With these integrated networks, we experimentally demonstrate unsupervised synaptic weight updating and pattern classification.

History

School

  • Science

Department

  • Physics

Published in

Nature Electronics

Volume

1

Issue

2

Pages

137 - 145

Citation

WANG, Z. ...et al., 2018. Fully memristive neural networks for pattern classification with unsupervised learning. Nature Electronics, 1, pp.137–145.

Publisher

Nature Publishing Group

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

11/01/2018

Publication date

2018-02-08

Notes

This paper was accepted for publication in the journal Nature Electronics and the definitive published version is available at https://doi.org/10.1038/s41928-018-0023-2.

ISSN

2520-1131

eISSN

2520-1131

Language

en

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