Representations of continuous attractors of discrete-time Cellular Neural Networks

To describe the encoding of continuous stimuli in neural networks, continuous attractors have been recognized as promising models. A continuous attractor is a set of connected stable equilibrium points. It exhibits interesting dynamical properties in many recurrent neural networks. This paper studies the continuous attractors of discrete-time cellular neural networks (DCNNs). The main contribution is that the representations of continuous attractors for DCNNs are obtained under some conditions. Such important results provide clear and complete descriptions to the continuous attractors of DCNNs.