Finite-time divergence in Chialvo hyperneuron model of nilpotent matrices

Rasa Smidtaite and Minvydas Ragulskis

Chaos, Solitons & Fractals, 2024, vol. 179, issue C

Abstract: The Chialvo hyperneuron model is introduced as the extension of the scalar Chialvo neuron model in this paper. The complexity of the model is increased not by adding another spatial variable but by replacing scalar nodal variables with square matrices of iterative variables. It is shown that such an extension does yield the effect of the divergence if the matrices of iterative variables are nilpotent matrices and the Lyapunov exponent of the scalar Chialvo neuron model is positive. Different regimes of divergence are classified into the finite-time and the explosive divergence of the hyperneuron. Analytical and computational simulations are used to illustrate the complex dynamical behavior of the Chialvo hyperneuron not observable in the scalar Chialvo neuron model.

Keywords: Chialvo neuron model; Nilpotent matrix; Finite-time divergence; Wada boundary (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S096007792400033X
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:179:y:2024:i:c:s096007792400033x

DOI: 10.1016/j.chaos.2024.114482

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. ().