The role of coupling factors on the emergence of synchronization and chimera patterns in network of non-locally coupled pancreatic β-cells

Abstract

The study of the pancreatic β-cells and their collective behavior is of great importance from the fluctuating insulin perspective. In this paper, a network of non-locally coupled β-cells is considered. It has been frequently reported that bursting of β-cells and their synchronous oscillations are associated with gap junctions. Therefore, a linear coupling between the membrane potentials of the clustered cells is used to model the gap junctions. The effects of coupling strength and number of neighbouring connections on the behavior of the cells is also investigated. Various collective patterns including chimera state, cluster synchronization and complete synchronization are observed. It is attained that the dynamic of the cells, e.g., being chaotic or periodic, has a significant role in their behavior. The observed collective behaviors not only is related to the network's parameters, but also heavily depends on its underlying topology. Detailed analytical description and extensive numerical simulations are provided to support the claims.

Chimera states in a neuronal network under the action of an electric field

“…Next, we vary N and also different schemes of application of the external field. Last, we analyze the effect of the electrical coupling on the network since both (electrical and chemical coupling) are physiologically important [31,32]. In the study, in the absence of an electric field, we found new phenomena such as traveling multicluster chimeras breathers, multicluster chimera breathers, which are properly described in Sections III.…”

A primer on modelling pancreatic islets: from models of coupled β-cells to multicellular islet models

“…Next in complexity are the 1-D arrays of more than two β-cells, ranging in size from triplets 72 to open-ended lines 67 , 68 or closed rings 73 including tens or hundreds of cells (see Figure 2a ). The main drawback of the 1D models is that each cell has only two neighbors, and, therefore, the number of interactions between cells is considerably limited.…”