Fully memristive neural networks for pattern classification with unsupervised learning

By Zhongrui Wang, S. Joshi, Sergey Savel’ev, Wenhao Song, Rivu Midya, Yunning Li, Mingyi Rao, Peng Yan, Shiva Asapu, Ye Zhuo and 14 more. Nature Electronics, 2018

993 citations recorded by the source corpus.

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. Leaky integrate-and-fire artificial neurons based on diffusive memristors enable unsupervised weight updates of drift-memristor synapses in an integrated convolutional neural network capable of pattern recognition.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/68b8793dc2e730e4b2241e0045065c9c904eb7a8

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

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