A model of the interaction between long and short term memory

The authors describe a novel connectionist architecture aimed at modeling the interaction between long and short term memory. The model is capable of incrementally storing several items from long term memory in a short term memory. The model combines two different network architectures, bidirectional associative memory (BAM) and mean field theory (MFT), which serve as short term and long term store, respectively. The properties of a BAM system match all the major design criteria of an incremental short term store. When augmented with randomized internal representations (RIR), a BAM system can serve as an autoassociative memory which supports hidden representations and has an enlarged capacity and ability to store correlated patterns. MFT systems can be powerful autoassociators capable of storing very large numbers of correlated patterns, because they can utilize hidden units. Interaction between the two systems is established by means of a common hidden representation