  The hippocampal-entorhinal complex plays a major role in the organization of
memory and thought. The formation of and navigation in cognitive maps of
arbitrary mental spaces via place and grid cells can serve as a representation
of memories and experiences and their relations to each other. The multi-scale
successor representation is proposed to be the mathematical principle
underlying place and grid cell computations. Here, we present a neural network,
which learns a cognitive map of a semantic space based on 32 different animal
species encoded as feature vectors. The neural network successfully learns the
similarities between different animal species, and constructs a cognitive map
of 'animal space' based on the principle of successor representations with an
accuracy of around 30% which is near to the theoretical maximum regarding the
fact that all animal species have more than one possible successor, i.e.
nearest neighbor in feature space. Furthermore, a hierarchical structure, i.e.
different scales of cognitive maps, can be modeled based on multi-scale
successor representations. We find that, in fine-grained cognitive maps, the
animal vectors are evenly distributed in feature space. In contrast, in
coarse-grained maps, animal vectors are highly clustered according to their
biological class, i.e. amphibians, mammals and insects. This could be a
possible mechanism explaining the emergence of new abstract semantic concepts.
Finally, even completely new or incomplete input can be represented by
interpolation of the representations from the cognitive map with remarkable
high accuracy of up to 95%. We conclude that the successor representation can
serve as a weighted pointer to past memories and experiences, and may therefore
be a crucial building block for future machine learning to include prior
knowledge, and to derive context knowledge from novel input.
