| Summary: | In this paper we propose an extension of the Recursive Auto-Associative Memory (RAAM) by Pollack. This extension, the Labeling RAAM (LRAAM), is able to encode labeled graphs with cycles by representing pointers explicitly. A theoretical analysis of the constraints imposed on the weights by the learning task under the hypothesis of perfect learning and linear output units is presented. Cycles and confluent pointers result to be particularly effective in imposing constraints on the weights. Some technical problems encountered in the RAAM, such as the termination problem in the learning and decoding processes, are solved more naturally in the LRAAM framework. The representations developed for the pointers seem to be robust to recurrent decoding along a cycle. Data encoded in a LRAAM can be accessed by pointer as well as by content. The direct access by content can be achieved by transforming the encoder network of the LRAAM in a Bidirectional Associative Memory (BAM). Different access pro.
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