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dc.contributor.authorLin, Hairong
dc.contributor.authorWang, Chunhua
dc.contributor.authorChen, Chengjie
dc.contributor.authorSun, Yichuang
dc.contributor.authorZhou, Chao
dc.contributor.authorXu, Cong
dc.contributor.authorHong , Qinghui
dc.date.accessioned2021-06-14T10:00:01Z
dc.date.available2021-06-14T10:00:01Z
dc.date.issued2021-06-02
dc.identifier.citationLin , H , Wang , C , Chen , C , Sun , Y , Zhou , C , Xu , C & Hong , Q 2021 , ' Neural Bursting and Synchronization Emulated by Neural Networks and Circuits ' , IEEE Transactions on Circuits and Systems I: Regular Papers , pp. 1-14 . https://doi.org/10.1109/TCSI.2021.3081150
dc.identifier.issn1549-8328
dc.identifier.otherPURE: 25147880
dc.identifier.otherPURE UUID: 39183534-538d-4266-ae10-0f21042e5aa3
dc.identifier.otherScopus: 85107387945
dc.identifier.urihttp://hdl.handle.net/2299/24580
dc.description© 2021 IEEE - All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TCSI.2021.3081150
dc.description.abstractNowadays, research, modeling, simulation and realization of brain-like systems to reproduce brain behaviors have become urgent requirements. In this paper, neural bursting and synchronization are imitated by modeling two neural network models based on the Hopfield neural network (HNN). The first neural network model consists of four neurons, which correspond to realizing neural bursting firings. Theoretical analysis and numerical simulation show that the simple neural network can generate abundant bursting dynamics including multiple periodic bursting firings with different spikes per burst, multiple coexisting bursting firings, as well as multiple chaotic bursting firings with different amplitudes. The second neural network model simulates neural synchronization using a coupling neural network composed of two above small neural networks. The synchronization dynamics of the coupling neural network is theoretically proved based on the Lyapunov stability theory. Extensive simulation results show that the coupling neural network can produce different types of synchronous behaviors dependent on synaptic coupling strength, such as anti-phase bursting synchronization, anti-phase spiking synchronization, and complete bursting synchronization. Finally, two neural network circuits are designed and implemented to show the effectiveness and potential of the constructed neural networks.en
dc.format.extent13
dc.language.isoeng
dc.relation.ispartofIEEE Transactions on Circuits and Systems I: Regular Papers
dc.titleNeural Bursting and Synchronization Emulated by Neural Networks and Circuitsen
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionCommunications and Intelligent Systems
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.description.statusPeer reviewed
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1109/TCSI.2021.3081150
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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