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.