Journal of Applied Mathematics
Volume 2011 (2011), Article ID 301204, 34 pages
Research Article

Encoding Static and Temporal Patterns with a Bidirectional Heteroassociative Memory

1School of Psychology, University of Ottawa, 136 Jean-Jacques Lussier, Ottawa, ON, Canada K1N 6N5
2Département d'informatique, Université du Québec à Montréal, Case postale 8888, Succursale Centre-ville, Montréal, QC, Canada H3C 3P8

Received 15 September 2010; Accepted 8 December 2010

Academic Editor: Haydar Akca

Copyright © 2011 Sylvain Chartier and Mounir Boukadoum. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Brain-inspired, artificial neural network approach offers the ability to develop attractors for each pattern if feedback connections are allowed. It also exhibits great stability and adaptability with regards to noise and pattern degradation and can perform generalization tasks. In particular, the Bidirectional Associative Memory (BAM) model has shown great promise for pattern recognition for its capacity to be trained using a supervised or unsupervised scheme. This paper describes such a BAM, one that can encode patterns of real and binary values, perform multistep pattern recognition of variable-size time series and accomplish many-to-one associations. Moreover, it will be shown that the BAM can be generalized to multiple associative memories, and that it can be used to store associations from multiple sources as well. The various behaviors are the result of only topological rearrangements, and the same learning and transmission functions are kept constant throughout the models. Therefore, a consistent architecture is used for different tasks, thereby increasing its practical appeal and modeling importance. Simulations show the BAM's various capacities, by using several types of encoding and recall situations.