Complex Systems

Synchronization in Electrically Coupled Neural Networks Download PDF

Rajesh G. Kavasseri
Electronic mail address:
Department of Electrical and Computer Engineering,
North Dakota State University, Fargo, ND 58105

Radhakrishnan Nagarajan
Department of Biostatistics,
University of Arkansas for Medical Sciences,
629 Jack Stephens Drive, Little Rock, AR 72205


This report investigates the synchronization of temporal activity in an electrically coupled neural network model. The electrical coupling is established by homotypic static gap-junctions (connexin-43). Two distinct network topologies, namely: sparse random network (SRN) and fully connected network (FCN), are used to establish the connectivity. The strength of connectivity in the FCN is governed by the mean gap-junctional conductance (μ). In the SRN case, the overall strength of connectivity is governed by the density of connections (δ) and the connection strength between two neurons (S0). The synchronization of the network with increasing gap-junctional strength and varying population sizes is investigated. It is observed that the network abruptly makes a transition from a weakly synchronized to a well synchronized regime when (δ) or (μ) exceeds a critical value. It was also observed that the (δ, μ) values used to achieve synchronization decrease with increasing network size.