How single node dynamics enhances synchronization in neural networks with electrical coupling. (April 2016)

• Record Type: Journal Article
• Authors: Bonacini, E.; Burioni, R.; di Volo, M.; Groppi, M.; Soresina, C.; Vezzani, A.
• Is Part Of: Chaos, solitons and fractals. Volume 85(2016)
• Page Start: 32
• Page End: 43
• Publication Date: 2016-04
• Subjects: Synchronization -- Master Stability Function -- Connection Graph Stability -- Neural network; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2016.01.009
• Languages: English
• ISSNs: 0960-0779

Abstract

Abstract: The stability of the completely synchronous state in neural networks with electrical coupling is analytically investigated applying both the Master Stability Function approach (MSF), developed by Pecora and Carroll (1998), and the Connection Graph Stability method (CGS) proposed by Belykh et al. (2004). The local dynamics is described by Morris–Lecar model for spiking neurons and by Hindmarsh–Rose model in spike, burst, irregular spike and irregular burst regimes. The combined application of both CGS and MSF methods provides an efficient estimate of the synchronization thresholds, namely bounds for the coupling strength ranges in which the synchronous state is stable. In all the considered cases, we observe that high values of coupling strength tend to synchronize the system. Furthermore, we observe a correlation between the single node attractor and the local stability properties given by MSF. The analytical results are compared with numerical simulations on a sample network, with excellent agreement.