Asymptotic behavior and synchronizability characteristics of a class of recurrent neural networks.
We propose an approach to the analysis of the influence of the topology of a neural network on its synchronizability in the sense of equal output activity rates given by a particular neural network model. The model we introduce is a variation of the Zhang model. We investigate the time-asymptotic behavior of the corresponding dynamical system (in particular, the conditions for the existence of an invariant compact asymptotic set) and apply the results of the synchronizability analysis on a class of random scale free networks and to the classical random networks with Poisson connectivity distribution.
Cebulla
Christof
C
Institute for Applied Mathematics, Universität Bonn, Bonn, Germany. cebulla@wiener.iam.uni-bonn.de
D000818:Animals; D001519:Behavior / Q000502:physiology*; D003198:Computer Simulation; D005246:Feedback; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted