A network of chaotic elements for information processing

A Globally Coupled Map (GCM) model is a network of chaotic elements that are globally coupled with each other. In this paper, rst, a modi ed GCM model called the \Globally Coupled Map using the Symmetric map (S-GCM)" is proposed. The SGCM is designed for information-processing applications. The S-GCM has attractors called \cluster frozen attractors," each of which is taken to represent information. This paper also describes the following characteristics of the S-GCM which are important to information-processing applications: (a) The S-GCM falls into one of the cluster frozen attractors over a wide range of parameters. This means that the information representation is stable over parameters; (b) Represented information can be preserved or broken by controlling parameters; (c) The cluster partitioning is restricted, i.e., the representation of information has a limitation. Finally, our techniques for applying the S-GCM to information processing are shown, considering these characteristics. Two associative memory systems are proposed and their performance is compared with that of the Hop eld network.