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Article: Artificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications

TitleArtificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications
Authors
Keywordsartificial neurons
brain emulation
neuromorphic computing
sensory neurons
spiking neural networks
Issue Date2023
Citation
Advanced Materials, 2023, v. 35, n. 37, article no. 2205047 How to Cite?
AbstractArtificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed.
Persistent Identifierhttp://hdl.handle.net/10722/335445
ISSN
2023 Impact Factor: 27.4
2023 SCImago Journal Rankings: 9.191
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLiu, Hefei-
dc.contributor.authorQin, Yuan-
dc.contributor.authorChen, Hung Yu-
dc.contributor.authorWu, Jiangbin-
dc.contributor.authorMa, Jiahui-
dc.contributor.authorDu, Zhonghao-
dc.contributor.authorWang, Nan-
dc.contributor.authorZou, Jingyi-
dc.contributor.authorLin, Sen-
dc.contributor.authorZhang, Xu-
dc.contributor.authorZhang, Yuhao-
dc.contributor.authorWang, Han-
dc.date.accessioned2023-11-17T08:25:57Z-
dc.date.available2023-11-17T08:25:57Z-
dc.date.issued2023-
dc.identifier.citationAdvanced Materials, 2023, v. 35, n. 37, article no. 2205047-
dc.identifier.issn0935-9648-
dc.identifier.urihttp://hdl.handle.net/10722/335445-
dc.description.abstractArtificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed.-
dc.languageeng-
dc.relation.ispartofAdvanced Materials-
dc.subjectartificial neurons-
dc.subjectbrain emulation-
dc.subjectneuromorphic computing-
dc.subjectsensory neurons-
dc.subjectspiking neural networks-
dc.titleArtificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/adma.202205047-
dc.identifier.pmid36609920-
dc.identifier.scopuseid_2-s2.0-85150526495-
dc.identifier.volume35-
dc.identifier.issue37-
dc.identifier.spagearticle no. 2205047-
dc.identifier.epagearticle no. 2205047-
dc.identifier.eissn1521-4095-
dc.identifier.isiWOS:000945738200001-

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