Journal
NATURE ELECTRONICS
Volume 1, Issue 2, Pages 137-145Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41928-018-0023-2
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Funding
- US Air Force Research Laboratory (AFRL) [FA8750-15-2-0044]
- Defense Advanced Research Projects Agency (DARPA) [D17PC00304]
- Intelligence Advanced Research Projects Activity (IARPA) [2014-14080800008]
- National Science Foundation (NSF) [ECCS-1253073]
- Beijing Advanced Innovation Center for Future Chip (ICFC)
- NSFC [61674089, 61674092]
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Neuromorphic computers consisting 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 non-volatile memristive synapses to build fully memristive artificial neural networks. With these integrated networks, we experimentally demonstrate unsupervised synaptic weight updating and pattern classification.
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