articleTop 1% cited
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
Nano Letters · 2010 · Vol. 10(4) · pp. 1297–1301
Sung Hyun Jo✉(University of Michigan–Ann Arbor)Ting‐Chang Chang(University of Michigan–Ann Arbor)Idongesit E. Ebong(University of Michigan–Ann Arbor)Bhavitavya B. Bhadviya(University of Michigan–Ann Arbor)Pinaki Mazumder(University of Michigan–Ann Arbor)Wei Lü(University of Michigan–Ann Arbor)
Abstract
A memristor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. Here we experimentally demonstrate a nanoscale silicon-based memristor device and show that a hybrid system composed of complementary metal-oxide semiconductor neurons and memristor synapses can support important synaptic functions such as spike timing dependent plasticity. Using memristors as synapses in neuromorphic circuits can potentially offer both high connectivity and high density required for efficient computing.
Advanced Memory and Neural ComputingNeuroscience and Neural EngineeringPhotoreceptor and optogenetics researchMemristorNeuromorphic engineeringNanoscopic scaleMaterials scienceConductanceMemistorSynapseElectronic circuitNanotechnologyResistive random-access memory
MeSH terms
NeuronsSemiconductorsSiliconSilverSynapsesNeural Networks, ComputerNanotechnology
Funding
- Defense Advanced Research Projects Agency
Citations
4,080
FWCI
132.69
field-weighted impact
References
28
Percentile
100%
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References
Competitive Hebbian learning through spike-timing-dependent synaptic plasticity
Nature Neuroscience · 2000 · 2,738 citations
Memristive model of amoeba learning
Physical Review E · 2009 · 483 citations
Memristive switching mechanism for metal/oxide/metal nanodevices
Nature Nanotechnology · 2008 · 2,920 citations
A VLSI Array of Low-Power Spiking Neurons and Bistable Synapses With Spike-Timing Dependent Plasticity
IEEE Transactions on Neural Networks · 2006 · 988 citations
Culturing hippocampal neurons
Nature Protocols · 2006 · 1,716 citations
Memristive devices and systems
Proceedings of the IEEE · 1976 · 2,537 citations
Nanoionics-based resistive switching memories
Nature Materials · 2007 · 4,759 citations
The Organization of Behavior; A Neuropsychological Theory
The American Journal of Psychology · 1950 · 4,857 citations
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