Complexity of brain-like signals in self-organised nanoscale networks.

Steel, JK; Wagner, F; Galli, E; Acharya, SK; Mallinson, JB; Bones, PJ; Arnold, MD; Brown, SA

Complexity of brain-like signals in self-organised nanoscale networks.

Steel, JK Wagner, F Galli, E Acharya, SK Mallinson, JB Bones, PJ Arnold, MD Brown, SA

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Neural Netw, 2026, 193, pp. 108031
Issue Date:: 2026-01

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Field	Value	Language
dc.contributor.author	Steel, JK
dc.contributor.author	Wagner, F
dc.contributor.author	Galli, E
dc.contributor.author	Acharya, SK
dc.contributor.author	Mallinson, JB
dc.contributor.author	Bones, PJ
dc.contributor.author	Arnold, MD
dc.contributor.author	Brown, SA
dc.date.accessioned	2026-02-18T03:53:55Z
dc.date.available	2025-08-23
dc.date.available	2026-02-18T03:53:55Z
dc.date.issued	2026-01
dc.identifier.citation	Neural Netw, 2026, 193, pp. 108031
dc.identifier.issn	0893-6080
dc.identifier.issn	1879-2782
dc.identifier.uri	http://hdl.handle.net/10453/193556
dc.description.abstract	The biological brain is comprised of a complex, interconnected, self-assembled network of neurons and synapses. This network enables efficient and accurate information processing, unsurpassed by any other known computational system. Percolating networks of nanoparticles (PNNs) are complex, interconnected, self-assembled systems that exhibit many emergent brain-like characteristics. Notably, neuron-like spiking patterns from PNNs have been shown to be critical, similar to signals from the cortex. PNNs are therefore an appealing candidate for neuromorphic computational systems. Here, the inherent complexity of the patterns of switching events generated by PNNs is explored using several different measures. We begin by defining qualitative measures of spatial, temporal, and spatio-temporal complexity, and then investigate a quantitative measure of complexity that was developed for analysis of patterns of spikes from neurons in the cortex. We discuss adaptations of the method that are required for data from the electronic devices of interest and the impact of various pre-processing procedures on the analysis. Through these measures, it is shown that the neuron-like spiking patterns from PNNs are indeed complex and are clearly distinct from random and ordered data.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Neural Netw
dc.relation.isbasedon	10.1016/j.neunet.2025.108031
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4611 Machine learning
dc.subject.classification	4905 Statistics
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Brain
dc.subject.mesh	Neurons
dc.subject.mesh	Models, Neurological
dc.subject.mesh	Animals
dc.subject.mesh	Humans
dc.subject.mesh	Action Potentials
dc.subject.mesh	Nerve Net
dc.subject.mesh	Nanotechnology
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Synapses
dc.subject.mesh	Brain
dc.subject.mesh	Nerve Net
dc.subject.mesh	Neurons
dc.subject.mesh	Synapses
dc.subject.mesh	Animals
dc.subject.mesh	Humans
dc.subject.mesh	Action Potentials
dc.subject.mesh	Nanotechnology
dc.subject.mesh	Models, Neurological
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Brain
dc.subject.mesh	Neurons
dc.subject.mesh	Models, Neurological
dc.subject.mesh	Animals
dc.subject.mesh	Humans
dc.subject.mesh	Action Potentials
dc.subject.mesh	Nerve Net
dc.subject.mesh	Nanotechnology
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Synapses
dc.title	Complexity of brain-like signals in self-organised nanoscale networks.
dc.type	Journal Article
utslib.citation.volume	193
utslib.location.activity	United States
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.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-02-18T03:53:50Z
pubs.publication-status	Published
pubs.volume	193

Abstract:

The biological brain is comprised of a complex, interconnected, self-assembled network of neurons and synapses. This network enables efficient and accurate information processing, unsurpassed by any other known computational system. Percolating networks of nanoparticles (PNNs) are complex, interconnected, self-assembled systems that exhibit many emergent brain-like characteristics. Notably, neuron-like spiking patterns from PNNs have been shown to be critical, similar to signals from the cortex. PNNs are therefore an appealing candidate for neuromorphic computational systems. Here, the inherent complexity of the patterns of switching events generated by PNNs is explored using several different measures. We begin by defining qualitative measures of spatial, temporal, and spatio-temporal complexity, and then investigate a quantitative measure of complexity that was developed for analysis of patterns of spikes from neurons in the cortex. We discuss adaptations of the method that are required for data from the electronic devices of interest and the impact of various pre-processing procedures on the analysis. Through these measures, it is shown that the neuron-like spiking patterns from PNNs are indeed complex and are clearly distinct from random and ordered data.

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http://hdl.handle.net/10453/193556