The extension of the master stability function (MSF) to analyse stability of generalized synchronization for coupled nearly identical oscillators is discussed. The nearly identical nature of the coupled oscillators is due to some parameter mismatch while the dynamical equations are the same for all the oscillators. From the stability criteria of the MSF, we construct optimal networks with better synchronization property, i.e., the synchronization is stable for widest possible range of coupling parameters. In the optimized networks the nodes with parameter value at one extreme are selected as hubs. The pair of nodes with larger parameter difference are preferred to create links in the optimized networks, and the optimized networks are found to be disassortative in nature, i.e., the nodes with high degree tend to connect with nodes with low degree.

We investigate the effects of symmetry-preserving and symmetry-breaking interactions n a drive–response system with the driving-induced bistability. The basins of attraction on the initial conditions plane are observed for the driving-induced bistability. The basins are dependent on the interaction between the driven and the driving system. The coexisting attractors display both in-phase as well as antiphase synchrony.

A large number of studies have recently been carried out on the early signatures of regime shifts in a number of dynamical systems, e.g., ecosystems, the climate, fish and wildlife populations, financial markets, complex diseases and gene circuits. The underlying model in most cases is that of the fold-bifurcation in which a sudden regime shift occurs at a bifurcation point. The shift involves a discontinuous jump from one type of stable steady state to another. The dynamics of natural systems have both deterministic and stochastic components. The early signatures of abrupt regime shifts include the critical slowing down as a transition point is approached, rising variance and the lag-1 autocorrelation function, increased skewness of the steady-state probability distribution and the ratio of two mean first passage times for the exits from the stable steady states as the bifurcation point is approached. Noise-induced regime shifts are also possible for which the vicinity of the bifurcation point is not essential. In this paper, we review examples of regime shifts in natural systems and the associated early signatures. We further discuss how such approaches provide useful insights on a cell biological process involving the fold-bifurcation.

A design of coupling is proposed to control partial synchronization in two chaotic oscillators in a driver–response mode. A control of synchrony between one response variables is made possible (a transition from a complete synchronization to antisynchronization via amplitude death and vice versa without loss of synchrony) keeping the other pairs of variables undisturbed in their pre-desired states of coherence. Further, one of the response variables can be controlled so as to follow the dynamics of an external signal (periodic or chaotic) while keeping the coherent status of other variables unchanged. The stability of synchronization is established using the Hurwitz matrix criterion. Numerical example of an ecological foodweb model is presented. The control scheme is demonstrated in an electronic circuit of the Sprott system.

In this paper, we review and extend the results from our recently published work [Scientific Reports (Nature) 4, 4308] on taming explosive growth in spatially extended systems. Specifically, we consider collections of relaxation oscillators, which are relevant to modelling phenomena ranging from engineering to biology, under varying coupling topologies. We find that the system witnesses unbounded growth under regular connections on a ring, for sufficiently strong coupling strengths. However, when a fraction of the regular connections are dynamically rewired to random links, this blow-up is suppressed. We present the critical value of random links necessary for successful prevention of explosive growth in the oscillators for varying network rewiring time-scales. Further, we outline our analysis on the possible mechanisms behind the occurrence of catastrophes and how the switching of links helps to suppress them.

We report the existence of chimera states in an assembly of identical nonlinear oscillators that are globally linked to each other in a simple planar cross-coupled form. The rotational symmetry breaking of the coupling term appears to be responsible for the emergence of these collective states that display a characteristic coexistence of coherent and incoherent behaviour. The finding, observed in both a collection of van der Pol oscillators and chaotic Rössler oscillators, further simplifies the existence criterion for chimeras, thereby broadens the range of their applicability to real-world situations.

We study the role of mean-field diffusive coupling on suppression of oscillations for systems of limit cycle oscillators. We show that this coupling scheme not only induces amplitude death (AD) but also oscillation death (OD) in coupled identical systems. The suppression of oscillations in the parameter space crucially depends on the value of mean-field diffusion parameter. It is also found that the transition from oscillatory solutions to OD in conjugate coupling case is different from the case when the coupling is through similar variable. We rationalize our study using linear stability analysis.

We investigate the influence of diversity on the temporal regularity of spiking in a ring of coupled model neurons. We find diversity-induced coherence in the spike events, with an optimal amount of parametric heterogeneity at the nodal level yielding the greatest regularity in the spike train. Further, we investigate the system under random spatial connections, where the links are both dynamic and quenched, and in all the cases we observe marked diversity-induced coherence. We quantitatively find the effect of coupling strength and random rewiring probability, on the optimal coherence that can be achieved under diversity. Our results indicate that the largest coherence in the spike events emerge when the coupling strength is high, and when the underlying connections are mostly random and dynamically changing.

The possibility of using a dynamic environment to achieve and optimize phase synchronization in a network of self-excited cells with free-end boundary conditions is addressed in this paper. The dynamic environment is an oscillatory bath coupled linearly to a network of four cells. The boundaries of the stable solutions of the dynamical states as well as the ranges of coupling parameters leading to stability and instability of synchronization are determined. Numerical simulations are used to check the accuracy and to complement the result obtained from analytical treatment. The robustness of synchronization strategy is tested using a local and global injection of Gaussian white noise in the network. The control gain parameter of the bath coupling can modulate the occurrence of synchronization in the network without prior requirement of direct coupling among all the cells. The process of synchronization obtained through local injection is independent of the node at which noise is injected into the system. As compared to local injection, the global injection scheme increases the range of noise amplitude for which synchronization occurs in the network.

Dissemination of information has been one of the prime needs in almost every kind of communication network. The existing algorithms for this service, try to maximize the coverage, i.e., the number of distinct nodes to which a given piece of information could be conveyed under the constraints of time and energy. However, the problem becomes challenging for unstructured and decentralized environments. Due to its simplicity and adaptability, random walk (RW) has been a very useful tool for such environments. Different variants of this technique have been studied. In this paper, we study a history-based non-uniform proliferating random strategy where new walkers are dynamically introduced in the sparse regions of the network. Apart from this, we also study the breadth-first characteristics of the random walk-based algorithms through an appropriately designed metrics.

Random matrix theory, initially proposed to understand the complex interactions in nuclear spectra, has demonstrated its success in diverse domains of science ranging from quantum chaos to galaxies. We demonstrate the applicability of random matrix theory for networks by providing a new dimension to complex systems research. We show that in spite of huge differences these interaction networks, representing real-world systems, posses from random matrix models, the spectral properties of the underlying matrices of these networks follow random matrix theory bringing them into the same universality class. We further demonstrate the importance of randomness in interactions for deducing crucial properties of the underlying system. This paper provides an overview of the importance of random matrix framework in complex systems research with biological systems as examples.

The Penner interaction known in studies of moduli space of punctured Riemann surfaces is introduced and studied in the context of random matrix model of homo RNA. An analytic derivation of the generating function is given and the corresponding partition function is derived numerically. An additional dependence of the structure combinatorics factor on 𝑁 (related to the size of the matrix and the interaction strength) is obtained. This factor has a strong effect on the structure combinatorics in the low 𝑁 regime. Databases are scanned for real ribonucleic acid (RNA) structures and pairing information for these RNA structures is computationally extracted. Then the genus is calculated for every structure and plotted as a function of length. The genus distribution function is compared with the prediction from the nonlinear (NL) model. The specific heat and distribution of structure with temperature calculated from the NL model shows that the NL inter-action is biased towards planar structures. The second derivative of specific heat changes phase from a double peaked function for small 𝑁 to a single peak for large 𝑁. Detailed analysis reveals the presence of the double peak only for genus 0 structures, the higher genii behave normally with 𝑁. Comparable behaviour is found in studies involving interactions of RNA with osmolytes and monovalent cations in unfolding experiments.

Probability distribution (𝑃(𝑟)) of the level spacing ratios has been introduced recently and is used to investigate many-body localization as well as to quantify the distance from integrability on finite size lattices. In this paper, we study the distribution of the ratio of consecutive level spacings using one-body plus two-body random matrix ensembles for finite interacting many-fermion and many-boson systems. 𝑃(𝑟) for these ensembles move steadily from the Poisson to the Gaussian orthogonal ensemble (GOE) form as the two-body interaction strength 𝜆 is varied. Other related quantities are also used in the analysis to obtain critical strength 𝜆_c for the transition. The 𝜆_c values deduced using the 𝑃(𝑟) analysis are in good agreement with the results obtained using the nearest neighbour spacing distribution (NNSD) analysis.

We apply random matrix theory (RMT) to investigate the structure of cross-correlation in 20 global financial time series after the global financial crisis of 2008. We find that the largest eigenvalue deviates from the RMT prediction and is sensitive to the financial crisis. We find that the components of eigenvectors corresponding to the second largest eigenvalue changes sign in response to the crisis. We show that 20 global financial indices exhibit multifractality. We find that the origin of multifractality is due to the long-range correlations as well as broad probability function in the financial indices, with the exception of the index of Taiwan, as in all other indices the multifractal degree for shuffled and surrogate series is weaker than the original series. We fit the binomial multifractal model to the global financial indices.