NEURAL NETWORK MODELING OF ASSOCIATIVE MEMORY - BEYOND THE HOPFIELD MODEL

A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying dynamics are used to store and associatively recall information, are described. In the first class of models, a hierarchical structure is used to store an exponentially large number of strongly correlated memories. The second class of models uses limit cycles to store and retrieve individual memories. A neurobiologically plausible network that generates low-amplitude periodic variations of activity, similar to the oscillations observed in electro-encephalographic recordings, is also described. Results obtained from analytic and numerical studies of the properties of these networks are discussed.