Studies on human perception of well known complex tonal patterns suggest that acoustic memory is based on an abstract representation of the signal rather than a trace representation. In this report we present a model for these abstract representations interpreted as chains of stable states in a dynamic system. The dynamic system is realized by a recurrent neural network composed of several modules. Simulations demonstrate aspects of system performance and point to the need for learning algorithms that control network dynamics rather than trajectories in the activation state space.