Wang, Zhongrui Joshi, Saumil Saveliev, Sergey Song, Wenhao Midya, Rivu Li, Yunning Rao, Mingyi Yan, Peng Asapu, Shiva Zhuo, Ye Jiang, Hao Lin, Peng Li, Can Yoon, Jung Ho Upadhyay, Navnidhi K. Zhang, Jiaming Hu, Miao Strachan, John Paul Barnell, Mark Wu, Qing Wu, Huaqiang Williams, R.S. Xia, Qiangfei Yang, J. Joshua 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 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. Computational science;Electrical and electronic engineering;Materials for devices;Nanoscale devices;Physical Sciences not elsewhere classified 2018-01-25
    https://repository.lboro.ac.uk/articles/journal_contribution/Fully_memristive_neural_networks_for_pattern_classification_with_unsupervised_learning/9409226