Tree-like hierarchical associative memory structures.
In this letter we explore an alternative structural representation for Steinbuch-type binary associative memories. These networks offer very generous storage capacities (both asymptotic and finite) at the expense of sparse coding. However, the original retrieval prescription performs a complete search on a fully-connected network, whereas only a small fraction of units will eventually contain desired results due to the sparse coding requirement. Instead of modelling the network as a single layer of neurons we suggest a hierarchical organization where the information content of each memory is a successive approximation of one another. With such a structure it is possible to enhance retrieval performance using a progressively deepening procedure. To backup our intuition we provide collected experimental evidence alongside comments on eventual biological plausibility.
INESC-ID Lisboa and Instituto Superior Técnico, Technical University of Lisbon, Av. Prof. Dr. Aníbal Cavaco Silva, 2744-016 Porto Salvo, Portugal. joao.sacramento@ist.utl.pt
João
J
Sacramento
Andreas
A
Wichert
D001245:Association Learning / Q000502:physiology*; D006801:Humans; D008568:Memory / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology