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dc.contributor.authorWang, Frank Z.
dc.contributor.authorHelian, Na
dc.contributor.authorWu, Sining
dc.contributor.authorYang, Xiao
dc.contributor.authorGuo, Yike
dc.contributor.authorLim, Guan
dc.contributor.authorRashid, Md Mamunur
dc.identifier.citationWang , F Z , Helian , N , Wu , S , Yang , X , Guo , Y , Lim , G & Rashid , M M 2012 , ' Delayed switching applied to memristor neural networks ' , Journal of Applied Physics , vol. 111 , no. 7 , 07E317 .
dc.identifier.otherPURE: 690134
dc.identifier.otherPURE UUID: 4b095654-b8ff-4e34-a0d7-7c09ff79606e
dc.identifier.otherScopus: 84861738154
dc.identifier.otherORCID: /0000-0001-6687-0306/work/64003378
dc.description.abstractMagnetic flux and electric charge are linked in a memristor. We reported recently that a memristor has a peculiar effect in which the switching takes place with a time delay because a memristor possesses a certain inertia. This effect was named the “delayed switching effect.” In this work, we elaborate on the importance of delayed switching in a brain-like computer using memristor neural networks. The effect is used to control the switching of a memristor synapse between two neurons that fire together (the Hebbian rule). A theoretical formula is found, and the design is verified by a simulation. We have also built an experimental setup consisting of electronic memristive synapses and electronic neuronsen
dc.relation.ispartofJournal of Applied Physics
dc.titleDelayed switching applied to memristor neural networksen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionBiocomputation Research Group
dc.description.statusPeer reviewed
rioxxterms.typeJournal Article/Review

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