A cascade associative memory model with a hierarchical memory structure

The introduction of a hierarchical memory structure into a cascade associative memory model for storing hierarchically correlated patterns improves the storage capacity and the size of the basins of attraction remarkably. A learning algorithm groups descendants (second-level patterns) according to their ancestors (first-level ones), and organizes the memory structure in a weight matrix where the groups are memorized separately. The weight matrix is, thus, in the form of a pile of covariance matrices, each of which is responsible for recalling only the descendants of each ancestor. Putting it simply, the model is multiplex associative memory. The recalling process proceeds as follows: the model first recalls the ancestor of a target descendant. Then, the dynamics with dynamic threshold combines the ancestor and the weight matrix to activate the covariance matrix for recalling only the descendants of the ancestor. This mechanism suppresses the cross-talk noise generated by the descendants of the other ancestors, and the recalling ability is enhanced.