BAM Learning in High Level of Connection Sparseness

Bidirectional Associative Memories (BAMs) are artificial neural networks that can learn and recall various types of associations. Although BAMs have shown great promise at modeling human cognitive processes, these models have often been investigated under optimal conditions in which the network is fully connected. Whereas some BAM models have shown to be robust to connection sparseness, those particular models could not handle highly sparse connectivity, unlike the human brain. This paper shows that a particular type of BAM can perform learning and recall under higher levels of sparse connectivity by increasing input dimensionality. This study provides a better understanding of the conditions impacting the convergence of the learning in BAM models and introduces a new avenue of research in learning in biological levels of sparseness, namely network dimensionality.