  In this study we want to connect our previously proposed context-relevant
topographical maps with the deep learning community. Our architecture is a
classifier with hidden layers that are hierarchical two-dimensional
topographical maps. These maps differ from the conventional self-organizing
maps in that their organizations are influenced by the context of the data
labels in a top-down manner. In this way bottom-up and top-down learning are
combined in a biologically relevant representational learning setting. Compared
to our previous work, we are here specifically elaborating the model in a more
challenging setting compared to our previous experiments and to advance more
hidden representation layers to bring our discussions into the context of deep
representational learning.
