PubMed1999
The Willshaw model is asymptotically the most efficient neural associative memory (NAM), but its finite version is hampered by high retrieval errors. Iterative retrieval has been proposed in a large number of different models to improve performance in auto-association tasks. In this paper, bidirectional retrieval for the hetero-associative memory task is considered: we define information efficiency as a general performance measure for bidirectional associative memory (BAM) and determine its asymptotic bound for the bidirectional Willshaw model. For the finite Willshaw model, an efficient new bidirectional retrieval strategy is proposed, the appropriate combinatorial model analysis is derived, and implications of the proposed sparse BAM for applications and brain theory are discussed. The distribution of the dendritic sum in the finite Willshaw model given by Buckingham and Willshaw [Buckingham, J., & Willshaw, D. (1992). Performance characteristics of associative nets. Network, 3, 407-414] allows no fast numerical evaluation. We derive a combinatorial formula with a highly reduced evaluation time that is used in the improved error analysis of the basic model and for estimation of the retrieval error in the naive model extension, where bidirectional retrieval is employed in the hetero-associative Willshaw model. The analysis rules out the naive BAM extension as a promising improvement. A new bidirectional retrieval algorithm - called crosswise bidirectional (CB) retrieval - is presented. The cross talk error is significantly reduced without employing more complex learning procedures or dummy augmentation in the pattern coding, as proposed in other refined BAM models [Wang, Y. F., Cruz, J. B., & Mulligan, J. H. (1990). Two coding strategies for bidirectional associative memory. IEEE Trans. Neural Networks, 1(1), 81-92; Leung, C.-S., Chan, L.-W., & Lai, E. (1995). Stability, capacity and statistical dynamics of second-order bidirectional associative memory. IEEE Trans. Syst. Man Cybern., 25(10), 1414-1424]. The improved performance of CB retrieval is shown by a combinatorial analysis of the first step and by simulation experiments: it allows very efficient hetero-associative mapping, as well as auto-associative completion for sparse patterns - the experimentally achieved information efficiency is close to the asymptotic bound. The different retrieval methods in the hetero-associative Willshaw matrix are discussed as Boolean linear optimization problems. The improved BAM model opens interesting new perspectives, for instance, in information retrieval it allows efficient data access providing segmentation of ambiguous user input, relevance feedback and relevance ranking. Finally, we discuss BAM models as functional models for reciprocal cortico-cortical pathways, and the implication of this for a more flexible version of Hebbian cell-assemblies.