A neural architecture for the identification of number sequences

This paper describes an architecture based on spatio-temporal networks that identifies sequences of numbers. This architecture incorporates an input layer that transforms (by means of a mathematical function) the system's input into a normalized vector that will be applied in a second step to a spatio-temporal network. Finally, the architecture is completed by an output layer using Grossberg's outstar units (1982). We have appreciated our system's complexity to be lower than any other existent method developed to solve problems of this type. By means of this architecture we have implemented a system that reminds a user of a telephone number, out of a given list, if the user enter the number wrongly, i.e. partially or with a series of exchanged digits.