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Tree-like hierarchical associative memory structures
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DOI：10.1016/j.neunet.2010.09.012.png)
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Abstract
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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. (C) 2010 Elsevier Ltd. All rights reserved.
Keywords:
Associative memory
Steinbuch model
Structural representation
Hierarchical neural network
Sparse coding
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