Effects of bias current and control of multistability in 3D hopfield neural network.
Bias current
Bursting oscillation
Hopfield neural networks
Microcontroller implementation
Multistability control
Journal
Heliyon
ISSN: 2405-8440
Titre abrégé: Heliyon
Pays: England
ID NLM: 101672560
Informations de publication
Date de publication:
Feb 2023
Feb 2023
Historique:
received:
16
08
2022
revised:
11
01
2023
accepted:
12
01
2023
entrez:
16
2
2023
pubmed:
17
2
2023
medline:
17
2
2023
Statut:
epublish
Résumé
This work studies the dynamics of a three dimensional Hopfield neural network focusing on the impact of bias terms. In the presence of bias terms, the models displays an odd symmetry and experiences typical behaviors including period doubling, spontaneous symmetry breaking, merging crisis, bursting oscillation, coexisting attractors and coexisting period-doubling reversals as well. Multistability control is investigated by employing the linear augmentation feedback strategy. We numerically prove that the multistable neural system can be adjusted to experience only a single attractor behavior when the coupling coefficient is gradually monitored. Experimental results from a microcontroller based realization of the underlined neural system are consistent with the theoretical analysis.
Identifiants
pubmed: 36793969
doi: 10.1016/j.heliyon.2023.e13034
pii: S2405-8440(23)00241-4
pmc: PMC9922829
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e13034Informations de copyright
© 2023 The Authors.
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.
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