Stability of Hopfield neural network with resistive and magnetic coupling

Fuqiang Wu, Ting Kang, Yan Shao and Qingyun Wang

Chaos, Solitons & Fractals, 2023, vol. 172, issue C

Abstract: Inspired by the interplay between both electrical and chemical synapses, we propose an analog electronic synapse-like model to characterize biological synaptic properties. By introducing the resistive and magnetic coupling, we consider hierarchical interconnection and the mixed couplings with two different kinds of coupling mechanisms. Meanwhile, based on Lyapunov function of the variable gradient method, stability of the Hopfield neural network with resistive and magnetic couplings was derived as the interconnection is symmetric and asymmetric, respectively. Furthermore, corresponding examples are calculated by using the numerical approach. The obtained results can be helpful to further develop brain-like systems based on the hierarchical Hopfield neural network with timing-dependent synaptic plasticity.

Keywords: Hopfield neural network; Stability analysis; Lyapunov theorems; Magnetic coupling (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077923004708
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:172:y:2023:i:c:s0960077923004708

DOI: 10.1016/j.chaos.2023.113569

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().