Neural network equations and symbolic dynamics
In this paper we provide an up-to-date survey on the study of the complexity of the mosaic solutions on neural network equations. Three types of equations, namely, cellular neural networks (CNNs), multi-layer CNN (MCNNs) and inhomogeneous CNNs (ICNNs) are discuss herein. Such topic strong related to the learning algorithm and training process on neural network equations. Each neural network produces different mosaic solution space, and each mosaic solution space induces an different symbolic dynamics. To understand the complexity (spatial entropy) of the mosaic solution space for a given neural network equation, we need to identify which the underlying symbolic space is, then using the established knowledge of symbolic dynamical systems to compute its spatial entropy. Recently there has been substantial progress in this field. This paper is a comprehensive survey of this field. It provides a summary of the interesting results in this field. It is our hope that the paper will provide a good overview of major results and techniques, and a friendly entry point for anyone who is interested in studying problems in this field.
cellular neural networks multi-layer cnn inhomogeneous cnn separation property topological entropy