title: Diversity and time delays induce resonance in a modular neuronal network.
abstract: This paper focuses on the resonance dynamics of a modular neuronal network consisting of several small-world subnetworks. The considered network is composed of delay-coupled FitzHugh-Nagumo (FHN) neurons, whose characteristic parameters present diversity in the form of quenched noise. Our numerical results indicate that when such a network is subjected to an external subthreshold periodic signal, its collective response is optimized for an intermediate level of diversity, namely, a resonant behavior can be induced by an appropriate level of diversity. How the probabilities of intramodule and intermodule connections, as well as the number of subnetworks influence the diversity-induced resonance are also discussed. Further, conclusive evidences demonstrate the nontrivial role of time-delayed coupling on the diversity-induced resonance properties. Especially, multiple resonance is obviously detected when time delays are located at integer multiples of the oscillation period of the signal. Moreover, the phenomenon of fine-tuned delays in inducing multiple resonance remains when diversity is within an intermediate range. Our findings have implications that neural systems may profit from their generic diversity and delayed coupling to optimize the response to external stimulus.
authors.affiliation: College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, People's Republic of China.
authors.forename: Y B
authors.initials: YB
authors.lastname: Jia
authors.affiliation: College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, People's Republic of China.
authors.forename: X L
authors.initials: XL
authors.lastname: Yang
authors.affiliation: Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany.
authors.forename: J
authors.initials: J
authors.lastname: Kurths
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001683:Biological Clocks / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics