Phase Diagram and Storage Capacity of Sequence-Storing Neural Networks

We solve the dynamics of Hopfield-type neural networks which store sequences ofpatterns, close to saturation. The asymmetry of the interaction matrix in such models leads to violation of detailed balance, ruling out an equilibrium statistical mechanical analysis. Using generating functional methods we derive exact closed equations for dynamical order parameters, viz.the sequence overlap and correlation and response functions.