Abstract
The analysis of network dynamics is oftentimes restricted to networks with one-dimensional internal dynamics. Here, we show how symmetry explains the relation between behavior of systems with one-dimensional internal dynamics and with higher dimensional internal dynamics, when the network topology is the same. Fundamental networks of homogeneous coupled cell systems (B. Rink, J. Sanders. Coupled Cell Networks and Their Hidden Symmetries. SIAM J. Math. Anal. 46.2 (2014)) can be expressed in terms of monoid representations, which uniquely decompose into indecomposable subrepresentations. In the high-dimensional internal dynamics case, these subrepresentations are isomorphic to multiple copies of those one computes in the one-dimensional case. We describe the implications of this observation on steady state and Hopf bifurcations in -parameter families of network vector fields. The main results are that (1) generic one-parameter steady state bifurcations are qualitatively independent of the dimension of the internal dynamics and that, (2) in order to observe all generic -parameter bifurcations that may occur for internal dynamics of any dimension, the internal dynamics has to be at least -dimensional for steady state bifurcations and -dimensional for Hopf bifurcations. Furthermore, we illustrate how additional structure in the network can be exploited to obtain understanding beyond qualitative statements about the collective dynamics. One-parameter steady state bifurcations in feedforward networks exhibit an unusual amplification in the asymptotic growth rates of individual cells, when these are one-dimensional (S. von der Gracht, E. Nijholt, B. Rink. Amplified steady state bifurcations in feedforward networks. Nonlinearity 35.4 (2022)). We prove that (3) the same cells exhibit this amplifying effect with the same growth rates when the internal dynamics is high-dimensional.
Sören von der Gracht
Mathematician researching dynamical systems
Research in network dynamical systems and its applications.