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dc.contributor.authorWang, Jun Jieen_US
dc.contributor.authorYu, Qien_US
dc.contributor.authorHu, Shao Gangen_US
dc.contributor.authorLiu, Yanchenen_US
dc.contributor.authorGuo, Ruien_US
dc.contributor.authorChen, Tu Peien_US
dc.contributor.authorYin, Youen_US
dc.contributor.authorLiu, Yangen_US
dc.identifier.citationWang, J. J., Yu, Q., Hu, S. G., Liu, Y., Guo, R., Chen, T. P., . . . Liu, Y. (2019). Winner-takes-all mechanism realized by memristive neural network. Applied Physics Letters, 115(24), 243701-. doi:10.1063/1.5120973en_US
dc.description.abstractWinner-Takes-All (WTA), an important mechanism in neural networks of recurrently connected neurons, is a critical element of many models of cortical processing. However, few WTA neural networks have been realized physically, especially by memristor networks. In this work, we have designed and implemented a neural network with memristor-based synapses to realize the WTA in a neural system. Neuronal self-excitatory, excitatory, and inhibition by other neurons have been demonstrated. Competitions between two neurons, among three neurons, and between two groups of neurons are realized based on the memristive neural network. The winner neuron or winner group can suppress the other neuron(s) or other group(s) of neurons and dominate the neuronal firing. This work paves the way for further realization of complex models of cortical processing with memristive neural networks.en_US
dc.relation.ispartofApplied Physics Lettersen_US
dc.rights© 2019 Author(s). All rights reserved. This paper was published by AIP Publishing in Applied Physics Letters and is made available with permission of 2019 Author(s).en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleWinner-takes-all mechanism realized by memristive neural networken_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.versionPublished versionen_US
dc.subject.keywordsArtificial Neural Networksen_US
dc.subject.keywordsArtificial Intelligenceen_US
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