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Article: A Tantalum Disulfide Charge-Density-Wave Stochastic Artificial Neuron for Emulating Neural Statistical Properties

TitleA Tantalum Disulfide Charge-Density-Wave Stochastic Artificial Neuron for Emulating Neural Statistical Properties
Authors
Keywords1T-tantalum disulfide (1T-TaS ) 2
brain emulation
charge-density-wave
stochastic artificial neurons
Issue Date2021
Citation
Nano Letters, 2021, v. 21, n. 8, p. 3465-3472 How to Cite?
AbstractArtificial neuronal devices that functionally resemble biological neurons are important toward realizing advanced brain emulation and for building bioinspired electronic systems. In this Communication, the stochastic behaviors of a neuronal oscillator based on the charge-density-wave (CDW) phase transition of a 1T-TaS2 thin film are reported, and the capability of this neuronal oscillator to generate spike trains with statistical features closely matching those of biological neurons is demonstrated. The stochastic behaviors of the neuronal device result from the melt-quench-induced reconfiguration of CDW domains during each oscillation cycle. Owing to the stochasticity, numerous key features of the Hodgkin-Huxley description of neurons can be realized in this compact two-terminal neuronal oscillator. A statistical analysis of the spike train generated by the artificial neuron indicates that it resembles the neurons in the superior olivary complex of a mammalian nervous system, in terms of its interspike interval distribution, the time-correlation of spiking behavior, and its response to acoustic stimuli.
Persistent Identifierhttp://hdl.handle.net/10722/335414
ISSN
2023 Impact Factor: 9.6
2023 SCImago Journal Rankings: 3.411
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLiu, Hefei-
dc.contributor.authorWu, Tong-
dc.contributor.authorYan, Xiaodong-
dc.contributor.authorWu, Jiangbin-
dc.contributor.authorWang, Nan-
dc.contributor.authorDu, Zhonghao-
dc.contributor.authorYang, Hao-
dc.contributor.authorChen, Buyun-
dc.contributor.authorZhang, Zhihan-
dc.contributor.authorLiu, Fanxin-
dc.contributor.authorWu, Wei-
dc.contributor.authorGuo, Jing-
dc.contributor.authorWang, Han-
dc.date.accessioned2023-11-17T08:25:43Z-
dc.date.available2023-11-17T08:25:43Z-
dc.date.issued2021-
dc.identifier.citationNano Letters, 2021, v. 21, n. 8, p. 3465-3472-
dc.identifier.issn1530-6984-
dc.identifier.urihttp://hdl.handle.net/10722/335414-
dc.description.abstractArtificial neuronal devices that functionally resemble biological neurons are important toward realizing advanced brain emulation and for building bioinspired electronic systems. In this Communication, the stochastic behaviors of a neuronal oscillator based on the charge-density-wave (CDW) phase transition of a 1T-TaS2 thin film are reported, and the capability of this neuronal oscillator to generate spike trains with statistical features closely matching those of biological neurons is demonstrated. The stochastic behaviors of the neuronal device result from the melt-quench-induced reconfiguration of CDW domains during each oscillation cycle. Owing to the stochasticity, numerous key features of the Hodgkin-Huxley description of neurons can be realized in this compact two-terminal neuronal oscillator. A statistical analysis of the spike train generated by the artificial neuron indicates that it resembles the neurons in the superior olivary complex of a mammalian nervous system, in terms of its interspike interval distribution, the time-correlation of spiking behavior, and its response to acoustic stimuli.-
dc.languageeng-
dc.relation.ispartofNano Letters-
dc.subject1T-tantalum disulfide (1T-TaS ) 2-
dc.subjectbrain emulation-
dc.subjectcharge-density-wave-
dc.subjectstochastic artificial neurons-
dc.titleA Tantalum Disulfide Charge-Density-Wave Stochastic Artificial Neuron for Emulating Neural Statistical Properties-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1021/acs.nanolett.1c00108-
dc.identifier.pmid33835802-
dc.identifier.scopuseid_2-s2.0-85104909357-
dc.identifier.volume21-
dc.identifier.issue8-
dc.identifier.spage3465-
dc.identifier.epage3472-
dc.identifier.eissn1530-6992-
dc.identifier.isiWOS:000645560000016-

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