Ontology type: schema:ScholarlyArticle Open Access: True
2017-04-03
AUTHORSSören Boyn, Julie Grollier, Gwendal Lecerf, Bin Xu, Nicolas Locatelli, Stéphane Fusil, Stéphanie Girod, Cécile Carrétéro, Karin Garcia, Stéphane Xavier, Jean Tomas, Laurent Bellaiche, Manuel Bibes, Agnès Barthélémy, Sylvain Saïghi, Vincent Garcia
ABSTRACTIn the brain, learning is achieved through the ability of synapses to reconfigure the strength by which they connect neurons (synaptic plasticity). In promising solid-state synapses called memristors, conductance can be finely tuned by voltage pulses and set to evolve according to a biological learning rule called spike-timing-dependent plasticity (STDP). Future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Here we report on synapses based on ferroelectric tunnel junctions and show that STDP can be harnessed from inhomogeneous polarization switching. Through combined scanning probe imaging, electrical transport and atomic-scale molecular dynamics, we demonstrate that conductance variations can be modelled by the nucleation-dominated reversal of domains. Based on this physical model, our simulations show that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way, opening the path towards unsupervised learning in spiking neural networks. More... »
PAGES14736
http://scigraph.springernature.com/pub.10.1038/ncomms14736
DOIhttp://dx.doi.org/10.1038/ncomms14736
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PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/28368007
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"type": "PropertyValue",
"value": [
"101528555"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1038/ncomms14736"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1084508653"
]
}
],
"sameAs": [
"https://doi.org/10.1038/ncomms14736",
"https://app.dimensions.ai/details/publication/pub.1084508653"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-10T21:55",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8687_00000608.jsonl",
"type": "ScholarlyArticle",
"url": "https://www.nature.com/articles/ncomms14736"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/ncomms14736'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/ncomms14736'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/ncomms14736'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/ncomms14736'
This table displays all metadata directly associated to this object as RDF triples.
341 TRIPLES
21 PREDICATES
68 URIs
23 LITERALS
12 BLANK NODES