  The human brain possesses the extraordinary capability to contextualize the
information it receives from our environment. The entorhinal-hippocampal plays
a critical role in this function, as it is deeply engaged in memory processing
and constructing cognitive maps using place and grid cells. Comprehending and
leveraging this ability could significantly augment the field of artificial
intelligence. The multi-scale successor representation serves as a good model
for the functionality of place and grid cells and has already shown promise in
this role. Here, we introduce a model that employs successor representations
and neural networks, along with word embedding vectors, to construct a
cognitive map of three separate concepts. The network adeptly learns two
different scaled maps and situates new information in proximity to related
pre-existing representations. The dispersion of information across the
cognitive map varies according to its scale - either being heavily
concentrated, resulting in the formation of the three concepts, or spread
evenly throughout the map. We suggest that our model could potentially improve
current AI models by providing multi-modal context information to any input,
based on a similarity metric for the input and pre-existing knowledge
representations.
