A Model for Hierarchical Associative Memories via Dynamically Coupled GBSB Neural Networks

Abstract. Many approaches have emerged in the attempt to explain the memory process. One of which is the Theory of Neuronal Group Selection (TNGS), proposed by Edelman [1]. In the present work, inspired by Edelman ideas, we design and implement a new hierarchically coupled dynamical system consisting of GBSB neural networks. Our results show that, for a wide range of the system parameters, even when the networks are weakly coupled, the system evolve towards an emergent global associative memory resulting from the correlation of the lowest level memories