Abstract
This paper discusses a class of discrete time recurrent neural networks with multivalued neurons (MVN) with complex-valued weights and an activation function defined as a function of the argument of a weighted sum. Complementing state-of-the-art of such networks, this paper focuses on the convergence analysis of such networks in synchronous update mode. One theorem is presented and simulation results are used to illustrate the theory.