  In this paper, we discuss the outer-synchronization of the asymmetrically
connected recurrent time-varying neural networks. By both centralized and
decentralized discretization data sampling principles, we derive several
sufficient conditions based on diverse vector norms that guarantee that any two
trajectories from different initial values of the identical neural network
system converge together. The lower bounds of the common time intervals between
data samples in centralized and decentralized principles are proved to be
positive, which guarantees exclusion of Zeno behavior. A numerical example is
provided to illustrate the efficiency of the theoretical results.
