Continuous attractors in recurrent neural networks and phase space learning

Abstract

Recurrent networks can be used as associative memories where the stored memories represent fixed points to which the dynamics of the network converges. These networks, however, also can present continuous attractors, as limit cycles and chaotic attractors. The use of these attractors in recurrent networks for the construction of associative memories is argued. We provide a training algorithm for continuous attractors and present some numerical results of the learning method which involves genetic algorithms.