Neural dynamic transitions caused by changes of synaptic strength in heterogeneous networks

Bang-Lin Xu, Jian-Fang Zhou, Rui Li, En-Hua Jiang and Wu-Jie Yuan

Physica A: Statistical Mechanics and its Applications, 2023, vol. 617, issue C

Abstract: Sleep-dependent memory consolidation (SDMC) is an unaddressed and challenging functional issue regarding neural dynamics. Based on experimental findings, the synaptic homeostasis hypothesis for understanding SDMC implies a link between changes of synaptic strength and transitions of neural dynamics (including tonic and bursting activities). However, the causality of the link has been unclear. Recently, it has been found that, the synaptic changes can cause the dynamical transitions and so can produce the slow-wave activity (SWA) similar to that observed during sleep in a homogeneous network (Zhou et al., 2021). Since many real neural networks are heterogeneous in topology, we herein further investigated the transitions and the SWA driven by the synaptic changes in heterogeneous networks. It was found that synaptic changes can also cause the dynamical transitions and the SWA. Differently, the transitions in heterogeneous networks are hierarchical for neurons with different degrees, whether in electrically or chemically coupled networks. The critical synaptic strengths related to the transitions for neurons depend strongly on their degrees. The larger the degree, the smaller the critical synaptic strength. We showed that, they obey power-law relations, both in electrically coupled networks and in chemically coupled networks in the presence of inhibitory synapses. Particularly, it was found that the networked critical synaptic strength depends only on the networked maximum degree in electrically coupled networks. We showed, both numerically and analytically, that they also satisfy a power-law function. In general, our study revealed a possible causal relationship between changes of synaptic strength and transitions of neural dynamics in heterogeneous networks. Further interesting and challenging investigations are briefly discussed as well.

Keywords: Heterogeneous networks; Hindmarsh–Rose neural networks; Dynamical transitions; Slow-wave activity; Synaptic changes (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437123002182
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:617:y:2023:i:c:s0378437123002182

DOI: 10.1016/j.physa.2023.128663

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 ().