Revisiting memristor properties

Chen, L; He, Z; Li, C; Wen, S; Chen, Y

Revisiting memristor properties

Chen, L He, Z Li, C Wen, S

Chen, Y

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Publisher:: World Scientific Pub Co Pte Lt
Publication Type:: Journal Article
Citation:: International Journal of Bifurcation and Chaos, 2020, 30, (12)
Issue Date:: 2020-09-30

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Field	Value	Language
dc.contributor.author	Chen, L
dc.contributor.author	He, Z
dc.contributor.author	Li, C
dc.contributor.author	Wen, S https://orcid.org/0000-0002-5048-0319
dc.contributor.author	Chen, Y
dc.date.accessioned	2021-04-16T03:23:00Z
dc.date.available	2021-04-16T03:23:00Z
dc.date.issued	2020-09-30
dc.identifier.citation	International Journal of Bifurcation and Chaos, 2020, 30, (12)
dc.identifier.issn	0218-1274
dc.identifier.issn	1793-6551
dc.identifier.uri	http://hdl.handle.net/10453/148167
dc.description.abstract	Memristor is a natural synapse because of its nanoscale and memory property, which influences the performance of memristive artificial neural networks. A three-variable memristor model is simplified with 15 kinds of properties, including the learning experience, the forgetting curve, the spiking time-dependent plasticity (STDP), the spiking rate dependent plasticity (SRDP), and the integration property. Through the analysis of the model, one more unobserved property called pseudo-polarity reversibility property is predicted by assuming the long-term memory is independent of memductance.
dc.language	en
dc.publisher	World Scientific Pub Co Pte Lt
dc.relation.ispartof	International Journal of Bifurcation and Chaos
dc.relation.isbasedon	10.1142/S0218127420501722
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0103 Numerical and Computational Mathematics, 0913 Mechanical Engineering
dc.subject.classification	Fluids & Plasmas
dc.title	Revisiting memristor properties
dc.type	Journal Article
utslib.citation.volume	30
utslib.for	0102 Applied Mathematics
utslib.for	0103 Numerical and Computational Mathematics
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-04-16T03:22:59Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	30
utslib.citation.issue	12

Abstract:

Memristor is a natural synapse because of its nanoscale and memory property, which influences the performance of memristive artificial neural networks. A three-variable memristor model is simplified with 15 kinds of properties, including the learning experience, the forgetting curve, the spiking time-dependent plasticity (STDP), the spiking rate dependent plasticity (SRDP), and the integration property. Through the analysis of the model, one more unobserved property called pseudo-polarity reversibility property is predicted by assuming the long-term memory is independent of memductance.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/148167