Information dynamics in neuromorphic nanowire networks

Abstract Neuromorphic systems comprised of self-assembled nanowires exhibit a range of neural-like dynamics arising from the interplay of their synapse-like electrical junctions and their complex network topology. Additionally, various information processing tasks have been demonstrated with neuromo...

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Autores principales:	Ruomin Zhu, Joel Hochstetter, Alon Loeffler, Adrian Diaz-Alvarez, Tomonobu Nakayama, Joseph T. Lizier, Zdenka Kuncic
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c3b16c600ec94682981a86951a830b1b
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spelling	oai:doaj.org-article:c3b16c600ec94682981a86951a830b1b2021-12-02T17:14:30ZInformation dynamics in neuromorphic nanowire networks10.1038/s41598-021-92170-72045-2322https://doaj.org/article/c3b16c600ec94682981a86951a830b1b2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92170-7https://doaj.org/toc/2045-2322Abstract Neuromorphic systems comprised of self-assembled nanowires exhibit a range of neural-like dynamics arising from the interplay of their synapse-like electrical junctions and their complex network topology. Additionally, various information processing tasks have been demonstrated with neuromorphic nanowire networks. Here, we investigate the dynamics of how these unique systems process information through information-theoretic metrics. In particular, Transfer Entropy (TE) and Active Information Storage (AIS) are employed to investigate dynamical information flow and short-term memory in nanowire networks. In addition to finding that the topologically central parts of networks contribute the most to the information flow, our results also reveal TE and AIS are maximized when the networks transitions from a quiescent to an active state. The performance of neuromorphic networks in memory and learning tasks is demonstrated to be dependent on their internal dynamical states as well as topological structure. Optimal performance is found when these networks are pre-initialised to the transition state where TE and AIS are maximal. Furthermore, an optimal range of information processing resources (i.e. connectivity density) is identified for performance. Overall, our results demonstrate information dynamics is a valuable tool to study and benchmark neuromorphic systems.Ruomin ZhuJoel HochstetterAlon LoefflerAdrian Diaz-AlvarezTomonobu NakayamaJoseph T. LizierZdenka KuncicNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ruomin Zhu Joel Hochstetter Alon Loeffler Adrian Diaz-Alvarez Tomonobu Nakayama Joseph T. Lizier Zdenka Kuncic Information dynamics in neuromorphic nanowire networks
description	Abstract Neuromorphic systems comprised of self-assembled nanowires exhibit a range of neural-like dynamics arising from the interplay of their synapse-like electrical junctions and their complex network topology. Additionally, various information processing tasks have been demonstrated with neuromorphic nanowire networks. Here, we investigate the dynamics of how these unique systems process information through information-theoretic metrics. In particular, Transfer Entropy (TE) and Active Information Storage (AIS) are employed to investigate dynamical information flow and short-term memory in nanowire networks. In addition to finding that the topologically central parts of networks contribute the most to the information flow, our results also reveal TE and AIS are maximized when the networks transitions from a quiescent to an active state. The performance of neuromorphic networks in memory and learning tasks is demonstrated to be dependent on their internal dynamical states as well as topological structure. Optimal performance is found when these networks are pre-initialised to the transition state where TE and AIS are maximal. Furthermore, an optimal range of information processing resources (i.e. connectivity density) is identified for performance. Overall, our results demonstrate information dynamics is a valuable tool to study and benchmark neuromorphic systems.
format	article
author	Ruomin Zhu Joel Hochstetter Alon Loeffler Adrian Diaz-Alvarez Tomonobu Nakayama Joseph T. Lizier Zdenka Kuncic
author_facet	Ruomin Zhu Joel Hochstetter Alon Loeffler Adrian Diaz-Alvarez Tomonobu Nakayama Joseph T. Lizier Zdenka Kuncic
author_sort	Ruomin Zhu
title	Information dynamics in neuromorphic nanowire networks
title_short	Information dynamics in neuromorphic nanowire networks
title_full	Information dynamics in neuromorphic nanowire networks
title_fullStr	Information dynamics in neuromorphic nanowire networks
title_full_unstemmed	Information dynamics in neuromorphic nanowire networks
title_sort	information dynamics in neuromorphic nanowire networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c3b16c600ec94682981a86951a830b1b
work_keys_str_mv	AT ruominzhu informationdynamicsinneuromorphicnanowirenetworks AT joelhochstetter informationdynamicsinneuromorphicnanowirenetworks AT alonloeffler informationdynamicsinneuromorphicnanowirenetworks AT adriandiazalvarez informationdynamicsinneuromorphicnanowirenetworks AT tomonobunakayama informationdynamicsinneuromorphicnanowirenetworks AT josephtlizier informationdynamicsinneuromorphicnanowirenetworks AT zdenkakuncic informationdynamicsinneuromorphicnanowirenetworks
_version_	1718381284674240512

Information dynamics in neuromorphic nanowire networks

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