Complete synchronization analysis of neocortical network model

• Published in: The European physical journal. ST, Special topics Vol. 231; no. 22-23; pp. 4037 - 4048
• Main Authors: Kang, Jian, Ramadoss, Janarthanan, Wang, Zhen, Ali, Ahmed M. Ali
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2022; Springer Nature B.V
• Subjects: Atomic; Classical and Continuum Physics; Collective Behavior of Nonlinear Dynamical Oscillators; Condensed Matter Physics; Materials Science; Measurement Science and Instrumentation; Molecular; Neural networks; Neurons; Optical and Plasma Physics; Physics; Physics and Astronomy; Regular Article; Synchronism
• ISSN: 1951-6355; 1951-6401
• DOI: 10.1140/epjs/s11734-022-00630-6

The brain is a complex network consisting of excitatory and inhibitory neurons. The connections between excitatory and inhibitory neurons lead to different dynamical behaviors. The synchronization is a significant behavior among these neurons. In this paper, the synchronization is analyzed by considering a simple neural network model for up-to-down-state oscillation of the cortical network. This neural network model includes a group of excitatory and inhibitory neurons coupled with each other. Synchronization of two neural models is analyzed, and it is revealed that it depends on the coupling of the excitatory neurons rather than the inhibitory ones. The network of neural models is also investigated by considering a one-dimensional and also two-layer structure. The results represent the formation of different dynamical behaviors such as imperfect synchronization, chimera state, and complete synchronization in the networks.