Showing papers by "Sarika Jalan published in 2013"
TL;DR: In this article, the role of delay in phase synchronization and phenomena responsible for cluster formation in delayed coupled maps on various networks was investigated using numerical simulations, showing that the presence of delay may change the mechanism of the unit to unit interaction.
Abstract: We study the role of delay in phase synchronization and phenomena responsible for cluster formation in delayed coupled maps on various networks. Using numerical simulations, we demonstrate that the presence of delay may change the mechanism of the unit to unit interaction. At weak coupling values, the same parity delays are associated with the same phenomenon of cluster formation and exhibit similar dynamical evolution. Intermediate coupling values yield rich delay-induced driven cluster patterns. A Lyapunov function analysis sheds light on the robustness of the driven clusters observed for delayed bipartite networks. Our results reveal that delay may lead to a completely different relation, between dynamical and structural clusters, than that observed for the undelayed case.
22 citations
TL;DR: It is found that the largest real part of eigenvalues of a network, which accounts for the stability of an underlying system, decreases linearly as a function of inhibitory connection probability up to a particular threshold value, after which it exhibits rich behaviors with the distribution manifesting generalized extreme value statistics.
Abstract: Inspired by the importance of inhibitory and excitatory couplings in the brain, we analyze the largest eigenvalue statistics of random networks incorporating such features. We find that the largest real part of eigenvalues of a network, which accounts for the stability of an underlying system, decreases linearly as a function of inhibitory connection probability up to a particular threshold value, after which it exhibits rich behaviors with the distribution manifesting generalized extreme value statistics. Fluctuations in the largest eigenvalue remain somewhat robust against an increase in system size but reflect a strong dependence on the number of connections, indicating that systems having more interactions among its constituents are likely to be more unstable.
15 citations
TL;DR: In this article, the effects of delay in diffusively coupled logistic maps on the Cayley tree networks were studied and the importance of results to understand conflicts and cooperations observed in family business was discussed.
Abstract: We study effects of delay in diffusively coupled logistic maps on the Cayley tree networks. We find that smaller coupling values exhibit sensitiveness to value of delay, and lead to different cluster patterns of self-organized and driven types. Whereas larger coupling strengths exhibit robustness against change in delay values, and lead to stable driven clusters comprising nodes from last generation of the Cayley tree. Furthermore, introduction of delay exhibits suppression as well as enhancement of synchronization depending upon coupling strength values. To the end we discuss the importance of results to understand conflicts and cooperations observed in family business.
2 citations
01 Jan 2013
TL;DR: The symbolic dynamics defined by such partition has several practical applications, one of which is the detection of global synchrony in coupled systems, which uses short time series and is hence computationally fast.
Abstract: Symbolic dynamics based on specific partitions prevents the occurrence of certain symbolic sequences that are characteristics of the dynamical function. Such partitions lead to a maximal difference in the permutation entropy of a chaotic and the corresponding random system. The symbolic dynamics defined by such partition has several practical applications, one of which is the detection of global synchrony in coupled systems. The synchronized state is detected by observing the complete absence or at least low frequency of particular symbol sequences. The method uses short time series and is hence computationally fast. Also, because it compares the symbol sequence of one single unit in the network with some model behavior, it does not depend on the size of the network and is robust against external noise.
2 citations