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dc.contributor.authorShan, Linbo
dc.contributor.authorWang, Zongwei
dc.contributor.authorBao, Lin
dc.contributor.authorBao, Shengyu
dc.contributor.authorQin, Yabo
dc.contributor.authorLing, Yaotian
dc.contributor.authorBai, Guandong
dc.contributor.authorRobertson, John
dc.contributor.authorCai, Yimao
dc.contributor.authorHuang, Ru
dc.date.accessioned2022-03-09T09:00:35Z
dc.date.available2022-03-09T09:00:35Z
dc.date.issued2022-03-08
dc.date.submitted2021-12-16
dc.identifier.issn2640-4567
dc.identifier.otheraisy202100264
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334803
dc.description.abstractArtificial neurons are the fundamental elements in neuromorphic computing systems. Studies have revealed neuronal spike‐rate adaptation owing to intrinsic plasticity that neurons will adapt to the spiking patterns and store the events in the background spiking through clustered neuronal spiking. The event can be reactivated by specific retrieval clues instead of solely relying on synaptic plasticity. However, the neural adaptation, as well as the interactive adaptations of neuronal activity for information processing, have not been implemented. Herein, an artificial adaptive neuron via in materia modulation of the VO2/HfO2 based dual‐mode memristor is demonstrated. By changing the conductance of the HfO2 layer, the firing threshold can be modulated, thus the excitability and inhibition can be adjusted according to the previous stimuli without any complex peripherals, showing an adaptive firing rate even under the same stimuli. The artificial neuron clusters can emulate the concept of neuronal memory and neural adaptation, demonstrating spatiotemporal encoding capabilities via the correlated neural firing patterns. This conceptual work provides an alternative way to expand the computation power of spiking neural networks by exploiting the neural adaptation and could be enlightenment to maximize the synergy across both synapse and neuron in neuromorphic computing systems.
dc.languageen
dc.publisherWiley
dc.subjectResearch Article
dc.subjectResearch Articles
dc.subjectartificial adaptive neuron
dc.subjectintrinsic plasticity
dc.subjectneuromorphic computing systems
dc.subjectspike-rate adaptation
dc.titleIn Materia Neuron Spiking Plasticity for Sequential Event Processing Based on Dual-Mode Memristor
dc.typeArticle
dc.date.updated2022-03-09T09:00:34Z
prism.publicationNameADVANCED INTELLIGENT SYSTEMS
dc.identifier.doi10.17863/CAM.82233
rioxxterms.versionofrecord10.1002/aisy.202100264
rioxxterms.versionAO
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidWang, Zongwei [0000-0001-6297-2700]
dc.contributor.orcidCai, Yimao [0000-0002-6854-8211]
dc.identifier.eissn2640-4567
pubs.funder-project-idNational Key Research and Development Program (2019YFB2205401)
pubs.funder-project-idNational Natural Science Foundation of China (61834001, 62025401, 61904003, 61927901)
pubs.funder-project-id“111” Project (B18001)
pubs.funder-project-idBeijing Natural Science Foundation (4212049)
pubs.funder-project-idPKU-Baidu Fund (2020BD022)
cam.issuedOnline2022-03-08


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