Brainlike Networks of Nanowires and Nanoparticles: A Change of Perspective

Daniels, RK; Arnold, MD; Heywood, ZE; Mallinson, JB; Bones, PJ; Brown, SA

Brainlike Networks of Nanowires and Nanoparticles: A Change of Perspective

Daniels, RK Arnold, MD Heywood, ZE Mallinson, JB Bones, PJ Brown, SA

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Publisher:: American Physical Society (APS)
Publication Type:: Journal Article
Citation:: Physical Review Applied, 2023, 20, (3), pp. 034021
Issue Date:: 2023-09-01

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Field	Value	Language
dc.contributor.author	Daniels, RK
dc.contributor.author	Arnold, MD
dc.contributor.author	Heywood, ZE
dc.contributor.author	Mallinson, JB
dc.contributor.author	Bones, PJ
dc.contributor.author	Brown, SA
dc.date.accessioned	2024-01-16T02:18:24Z
dc.date.available	2024-01-16T02:18:24Z
dc.date.issued	2023-09-01
dc.identifier.citation	Physical Review Applied, 2023, 20, (3), pp. 034021
dc.identifier.issn	2331-7043
dc.identifier.issn	2331-7019
dc.identifier.uri	http://hdl.handle.net/10453/174547
dc.description.abstract	The connectivity of self-assembled networks of nanowires and nanoparticles is believed to strongly influence their performance in brainlike (neuromorphic) computing applications. Here we present a new perspective on the connectivity of these networks in which their neuronlike active elements are viewed in the same way as the nodes in artificial and biological neuronal networks. We consider two-dimensional and quasi-three-dimensional networks of nanowires and percolating networks of nanoparticles and show that, from this new perspective, they all have similar small-world characteristics. Other characteristics which may impact the computational performance of the networks are also investigated, including their assortativity and the scalefree nature of the nanoparticle networks. Taken together, these results allow comparison of key network characteristics for a variety of self-assembled nanoscale networks, and provide a basis for detailed investigations of computational performance.
dc.language	en
dc.publisher	American Physical Society (APS)
dc.relation.ispartof	Physical Review Applied
dc.relation.isbasedon	10.1103/PhysRevApplied.20.034021
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	© American Physical Society (APS) [Brainlike Networks of Nanowires and Nanoparticles: A Change of Perspective. Physical Review Applied 20, 3 (2023)]
dc.subject	02 Physical Sciences, 09 Engineering
dc.subject.classification	40 Engineering
dc.subject.classification	51 Physical sciences
dc.title	Brainlike Networks of Nanowires and Nanoparticles: A Change of Perspective
dc.type	Journal Article
utslib.citation.volume	20
utslib.for	02 Physical Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access	*
dc.date.updated	2024-01-16T02:18:21Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	20
utslib.citation.issue	3

Abstract:

The connectivity of self-assembled networks of nanowires and nanoparticles is believed to strongly influence their performance in brainlike (neuromorphic) computing applications. Here we present a new perspective on the connectivity of these networks in which their neuronlike active elements are viewed in the same way as the nodes in artificial and biological neuronal networks. We consider two-dimensional and quasi-three-dimensional networks of nanowires and percolating networks of nanoparticles and show that, from this new perspective, they all have similar small-world characteristics. Other characteristics which may impact the computational performance of the networks are also investigated, including their assortativity and the scalefree nature of the nanoparticle networks. Taken together, these results allow comparison of key network characteristics for a variety of self-assembled nanoscale networks, and provide a basis for detailed investigations of computational performance.

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http://hdl.handle.net/10453/174547