Dependence on the local dynamics of a network phase synchronization process

E.B.S.A. Cambraia, J.V.V. Flauzino, T.L. Prado and S.R. Lopes

Physica A: Statistical Mechanics and its Applications, 2023, vol. 619, issue C

Abstract: Partial phase synchronization, also reported as neuron cooperation, is a pivotal behavior of the brain and related to its main features, such as memory. The excess or even the lack of phase synchronization are associated with brain disorders like epilepsy and Parkinson’s disease. These diseases may be related to malfunctioning of the synchronization process of the neurons, triggered by changes of the local dynamics of the neurons influenced by parameters such as the ion-channel conductance. In fact, it is common to change the local neuron dynamics, using drugs to block or activate specific channels, changing the conductance and bringing the synchronization process to some desired behavior. Here we show that there are two distinct mechanisms leading to network phase synchronized states. The first one is strongly affected by the individual neuron dynamics. In this case, the synchronous state of the network may occur for low values of the coupling, regardless of the network topology. The second synchronized state is induced by the network coupling, in this case the network coupling strength promotes synchronized global states, we say it is a network driver synchronization. We report here how individual characteristics of the local dynamics of the neurons, such as their linear stability, when coupled in a network may be a fundamental player in the phase synchronization process as a function of the coupling strength. Global and small-world topologies are considered for a Hindmarsh–Rose-neuron network. For both coupling schemes, the effects of the local dynamics are clear, inducing early or retarding the occurrence of partial phase synchronization of the network when the coupling strength is varied. In this scenario, we discuss the effect of the local dynamics of the neuron, showing it may be of fundamental importance to understand and control the process of the network phase synchronization. The study also brings useful information to the general understanding of network-phase-synchronization processes.

Keywords: Neural networks; Phase synchronization; Hindmarsh–Rose neuron model (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437123003059
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

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:phsmap:v:619:y:2023:i:c:s0378437123003059

DOI: 10.1016/j.physa.2023.128750

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().