 The dynamical analysis of non-uniform neocortical network model in up-down state oscillationsAmir Sabouri, Mahdieh Ghasemi and Mahtab Mehrabbeik Chaos, Solitons & Fractals, 2023, vol. 168, issue C Abstract: The brain is the most complex organ of the human body and consists of many excitatory and inhibitory neurons. The neural interactions are through synaptic connections. Such connections are the pathway of information; therefore, the neurons can exhibit various dynamical behaviors based on the active synapses. This paper employs a non-uniform neuronal network model of the brain neocortex to investigate the synchronization of up-down state oscillations. The dynamical analysis is performed using the bifurcation diagrams, Lyapunov exponents’ spectra, phase portraits, and time series as a function of different coupling parameters. The results revealed that the studied model exhibits chaotic behavior in a wide range of the variation of the coupling strengths. Furthermore, when excitatory–excitatory neuronal coupling strength is assumed as the control parameter, some instantaneous periodic windows were detected within the chaotic regions, which is a rare phenomenon of neural dynamics. Keywords: Chaos; Bifurcation; Lyapunov exponent; Up-down state; Neuronal network model (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:168:y:2023:i:c:s0960077923001182 DOI: 10.1016/j.chaos.2023.113217 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 |
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