Fully memristive neural networks for pattern classification with unsupervised learning

Fully memristive neural networks for pattern classification with unsupervised learning

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...

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Bibliographic Details
Main Authors: 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
Format: Default Article
Published: 2018
Subjects:
Other physical sciences not elsewhere classified
Computational science
Electrical and electronic engineering
Materials for devices
Nanoscale devices
Physical Sciences not elsewhere classified
Online Access:https://hdl.handle.net/2134/28261
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https://hdl.handle.net/2134/28261

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