Zahra Aminzare, Eduardo D. Sontag
This paper proves that contractive ordinary differential equation systems remain contractive when diffusion is added. Thus, diffusive instabilities, in the sense of the Turing phenomenon, cannot arise for such systems. An important biochemical system is shown to satisfy the required conditions.
Zahra Aminzare, Eduardo D. Sontag
In this note, we present a condition which guarantees spatial uniformity for the asymptotic behavior of the solutions of a reaction-diffusion PDE with Neumann boundary conditions in one dimension, using the Jacobian matrix of the reaction term and the first Dirichlet eigenvalue of the Laplacian operator on the given spatial domain. We also derive an analog of this PDE result for the synchronization of a network of identical ODE models coupled by diffusion terms.
Zahra Aminzare
In [1], we showed contractivity of reaction-diffusion PDE: \frac{\partial u}{\partial t}(ω,t) = F(u(ω,t)) + DΔu(ω,t) with Neumann boundary condition, provided μ_{p,Q}(J_F (u)) < 0 (uniformly on u), for some 1 \leq p \leq \infty and some positive, diagonal matrix Q, where J_F is the Jacobian matrix of F. This note extends the result for Q weighted L_2 norms, where Q is a positive, symmetric (not merely diagonal) matrix and Q^2D+DQ^2>0.
Zahra Aminzare, Biswadip Dey, Elizabeth N. Davison et al.
Finding the conditions that foster synchronization in networked oscillatory systems is critical to understanding a wide range of biological and mechanical systems. However, the conditions proved in the literature for synchronization in nonlinear systems with linear coupling, such as has been used to model neuronal networks, are in general not strict enough to accurately determine the system behavior. We leverage contraction theory to derive new sufficient conditions for cluster synchronization in terms of the network structure, for a network where the intrinsic nonlinear dynamics of each node may differ. Our result requires that network connections satisfy a cluster-input-equivalence condition, and we explore the influence of this requirement on network dynamics. For application to networks of nodes with neuronal spiking dynamics, we show that our new sufficient condition is tighter than those found in previous analyses which used nonsmooth Lyapunov functions. Improving the analytical conditions for when cluster synchronization will occur based on network configuration is a significant step toward facilitating understanding and control of complex oscillatory systems.