Bidirectional Associative Memory for Short-term Memory Learning

Previous research has shown that Bidirectional Associative Memories (BAM), a special type of artificial neural network, can perform various types of associations that human beings are able to perform with little effort. However, considering a simple association problem, such as associating faces with names, iterative type BAM networks usually take hundreds and sometimes thousands of learning trials to encode such associations correctly, whereas humans in some conditions learn much faster. The present study therefore proposes an adjustment to a particular type of BAM network that increases its performance in a rapid learning condition while processing memory capacity is limited. Results show that the modification to the original learning rule of the BHM leads to improved performance when rapid learning is required. Moreover, the model preserves its high memory load capacity in standard learning. This study could lead to improved cognitive models that can adapt their behavior in function of the contextual conditions.