On the access by content capabilities of the LRAAM

The labeling RAAM (LRAAM) is a neural network able to encode data structures in fixed size patterns, thus allowing the application of neural networks to structured domains. Moreover, the structures stored into an LRAAM can be accessed both by pointer and by content. In this paper we briefly discuss basic and generalized associative access procedures for the LRAAM. Basic procedures are obtained by transforming the LRAAM network into a BAM. Different constrained versions of the BAM are used depending on the key(s) used to retrieve information. Generalized procedures are implemented by generalized Hopfield networks (GHN) which are built both by composing the subset of weights compounding the LRAAM and according to the query used to retrieve information. Some examples for generalized procedures are given