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A good understanding of the world depends on factorizing it into its parts and learning the regularities within it. The brain solves this problem both generally and in specific problem domains. The hippocampal complex -- famous from human studies for its role in constructing memories involving the who, what, when, and where of biographical events – has been a rich playground for understanding the neural circuits underlying these functions. This is because neural activity recorded from the hippocampus and associated areas provides a strikingly explicit representation of abstract low-dimensional variables, especially those that are key to solving cognitive problems but are latent, or not directly specified by sensory input. Such neural representations in the hippocampus were originally discovered in the form of spatial maps recorded from freely behaving rats (work which was awarded the 2014 Nobel prize), and recently several groups have begun to explore such cognitive maps in non-spatial domains. I will 1) highlight the important pieces of the phenomenology of these representations, 2) describe mechanistic circuit models, and 3) briefly summarize recent progress in characterizing the nature of these representations through unsupervised discovery of latent low-dimensional structure from population data. I will conclude with an overview, based on recent experimental work, of how understanding these circuits in the realm of navigation gives insights into their use in non-spatial cognitive representations as well.