Abstract
The phenomenon of synchronous firings is investigated in excitable small-world networks (ESWNs) of 2D lattices. Two sharply different types of patterns, wavelet turbulence (WT) patterns and synchronous firing (SF) patterns, and the associated transitions and hysteresis are found in wide parameter regions and in different excitable models. The WT state is maintained by wavelet defects while the SF state is due to iterative excitations between majority nodes and minority nodes where defects do not play essential roles. Moreover, a dominant phase-advanced driving method is applied to explain how self-sustained SFs can be maintained in ESWN and why SF and WT states show distinctive characteristic features. Since excitability of node and small-world network structure are two essential ingredients of some neural subsystems and SFs are important for many neural functions, the results in this paper are thus expected to be instructive for understanding the dynamics of some neural networks.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
