Prediction of partial synchronization in delay-coupled nonlinear oscillators, with application to Hindmarsh–Rose neurons

The full synchronization of coupled nonlinear oscillators has been widely studied. In this paper we investigate conditions for which partial synchronization of time-delayed diffusively coupled systems arises. The coupling configuration of the systems is described by a directed graph. As a novel quantitative result we first give necessary and sufficient conditions for the presence of forward invariant sets characterized by partially synchronous motion. These conditions can easily be checked from the eigenvalues and eigenvectors of the graph Laplacian. Second, we perform stability analysis of the synchronized equilibria in a (gain,delay) parameter space. For this analysis the coupled nonlinear systems are linearized around the synchronized equilibria and then the resulting characteristic function is factorized. By such a factorization, it is shown that the relation between the behaviour of different agents at the zero of the characteristic function depends on the structure of the eigenvectors of the weighted Laplacian matrix. By determining the structure of the solutions in the unstable manifold, combined with the characterization of invariant sets, we predict which partially synchronous regimes occur and estimate the corresponding coupling gain and delay values. We apply the obtained results to networks of coupled Hindmarsh–Rose neurons and verify the occurrence of the expected partially synchronous regimes by using a numerical simulation. We also make a comparison with an existing approach based on Lyapunov functionals.

[1]  Eckehard Schöll,et al.  Cluster and group synchronization in delay-coupled networks. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  Erik Steur,et al.  Synchronous behavior in networks of coupled systems : with applications to neuronal dynamics , 2011 .

[3]  Henk Nijmeijer,et al.  Synchronization of delay-coupled nonlinear oscillators: an approach based on the stability analysis of synchronized equilibria. , 2009, Chaos.

[4]  Charles M. Gray,et al.  Synchronous oscillations in neuronal systems: Mechanisms and functions , 1994, Journal of Computational Neuroscience.

[5]  Nikolai F. Rulkov,et al.  Images of synchronized chaos: Experiments with circuits. , 1996, Chaos.

[6]  Silviu-Iulian Niculescu,et al.  Stability of Traffic Flow Behavior with Distributed Delays Modeling the Memory Effects of the Drivers , 2007, SIAM J. Appl. Math..

[7]  M. Bennett,et al.  Electrical Coupling and Neuronal Synchronization in the Mammalian Brain , 2004, Neuron.

[8]  S. Niculescu,et al.  Stability and Stabilization of Time-Delay Systems: An Eigenvalue-Based Approach , 2007 .

[9]  H. Nijmeijer,et al.  Partial synchronization in diffusively time-delay coupled oscillator networks. , 2012, Chaos.

[10]  P. Lancaster,et al.  On the perturbation of analytic matrix functions , 1999 .

[11]  Kunihiko Kaneko,et al.  Relevance of dynamic clustering to biological networks , 1993, chao-dyn/9311008.

[12]  David J. Hill,et al.  Strict Quasipassivity and Ultimate Boundedness for Nonlinear Control Systems , 1998 .

[13]  G. Samaey,et al.  DDE-BIFTOOL v. 2.00: a Matlab package for bifurcation analysis of delay differential equations , 2001 .

[14]  J. Hindmarsh,et al.  A model of neuronal bursting using three coupled first order differential equations , 1984, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[15]  I. Tyukin,et al.  Semi-passivity and synchronization of diffusively coupled neuronal oscillators , 2009, 0903.3535.

[16]  Gang Zhang,et al.  A new method to realize cluster synchronization in connected chaotic networks. , 2006, Chaos.

[17]  S H Strogatz,et al.  Coupled oscillators and biological synchronization. , 1993, Scientific American.

[18]  A. Isidori,et al.  Passivity, feedback equivalence, and the global stabilization of minimum phase nonlinear systems , 1991 .

[19]  Wim Michiels,et al.  Prediction of Partially Synchronous Regimes of Delay-Coupled Nonlinear Oscillators , 2013, NOLCOS.

[20]  A. Schnitzler,et al.  Normal and pathological oscillatory communication in the brain , 2005, Nature Reviews Neuroscience.

[21]  Tianping Chen,et al.  Partial synchronization in linearly and symmetrically coupled ordinary differential systems , 2009 .

[22]  Leon O. Chua,et al.  On a conjecture regarding the synchronization in an array of linearly coupled dynamical systems , 1996 .

[23]  H. Nijmeijer,et al.  Cooperative oscillatory behavior of mutually coupled dynamical systems , 2001 .

[24]  Jinde Cao,et al.  Cluster synchronization in an array of hybrid coupled neural networks with delay , 2009, Neural Networks.

[25]  A. Pogromsky,et al.  A partial synchronization theorem. , 2008, Chaos.

[26]  T. Glad,et al.  On Diffusion Driven Oscillations in Coupled Dynamical Systems , 1999 .

[27]  H. Nijmeijer,et al.  Partial synchronization: from symmetry towards stability , 2002 .

[28]  Henk Nijmeijer,et al.  Networks of diffusively time-delay coupled systems: Conditions for synchronization and its relation to the network topology , 2014 .

[29]  Wim Michiels,et al.  Consensus Problems with Distributed Delays, with Application to Traffic Flow Models , 2009, SIAM J. Control. Optim..

[30]  John R. Terry,et al.  Synchronization of chaos in an array of three lasers , 1999 .