  How does the mind organize thoughts? The hippocampal-entorhinal complex is
thought to support domain-general representation and processing of structural
knowledge of arbitrary state, feature and concept spaces. In particular, it
enables the formation of cognitive maps, and navigation on these maps, thereby
broadly contributing to cognition. It has been proposed that the concept of
multi-scale successor representations provides an explanation of the underlying
computations performed by place and grid cells. Here, we present a neural
network based approach to learn such representations, and its application to
different scenarios: a spatial exploration task based on supervised learning, a
spatial navigation task based on reinforcement learning, and a non-spatial task
where linguistic constructions have to be inferred by observing sample
sentences. In all scenarios, the neural network correctly learns and
approximates the underlying structure by building successor representations.
Furthermore, the resulting neural firing patterns are strikingly similar to
experimentally observed place and grid cell firing patterns. We conclude that
cognitive maps and neural network-based successor representations of structured
knowledge provide a promising way to overcome some of the short comings of deep
learning towards artificial general intelligence.
