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Fascinating and puzzling phenomena, such as landmark vector cells, splittercells, and event-specific representations to name a few, are regularlydiscovered in the hippocampus. Without a unifying principle that can explainthese divergent observations, each experiment seemingly discovers a new anomalyor coding type. Here, we provide a unifying principle that the mentalrepresentation of space is an emergent property of latent higher-order sequencelearning. Treating space as a sequence resolves myriad phenomena, and suggeststhat the place-field mapping methodology where sequential neuron responses areinterpreted in spatial and Euclidean terms might itself be a source ofanomalies. Our model, called Clone-structured Causal Graph (CSCG), uses aspecific higher-order graph scaffolding to learn latent representations bymapping sensory inputs to unique contexts. Learning to compress sequential andepisodic experiences using CSCGs result in the emergence of cognitive maps -mental representations of spatial and conceptual relationships in anenvironment that are suited for planning, introspection, consolidation, andabstraction. We demonstrate that over a dozen different hippocampal phenomena,ranging from those reported in classic experiments to the most recent ones, aresuccinctly and mechanistically explained by our model.

Space is a latent sequence: Structured sequence learning as a unified theory of representation in the hippocampus