In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks.
Journal
Nature materials
ISSN: 1476-4660
Titre abrégé: Nat Mater
Pays: England
ID NLM: 101155473
Informations de publication
Date de publication:
02 2022
02 2022
Historique:
received:
03
02
2021
accepted:
09
08
2021
pubmed:
6
10
2021
medline:
18
3
2022
entrez:
5
10
2021
Statut:
ppublish
Résumé
Neuromorphic computing aims at the realization of intelligent systems able to process information similarly to our brain. Brain-inspired computing paradigms have been implemented in crossbar arrays of memristive devices; however, this approach does not emulate the topology and the emergent behaviour of biological neuronal circuits, where the principle of self-organization regulates both structure and function. Here, we report on in materia reservoir computing in a fully memristive architecture based on self-organized nanowire networks. Thanks to the functional synaptic connectivity with nonlinear dynamics and fading memory properties, the designless nanowire complex network acts as a network-wide physical reservoir able to map spatio-temporal inputs into a feature space that can be analysed by a memristive resistive switching memory read-out layer. Computing capabilities, including recognition of spatio-temporal patterns and time-series prediction, show that the emergent memristive behaviour of nanowire networks allows in materia implementation of brain-inspired computing paradigms characterized by a reduced training cost.
Identifiants
pubmed: 34608285
doi: 10.1038/s41563-021-01099-9
pii: 10.1038/s41563-021-01099-9
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
195-202Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer Nature Limited.
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