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Tracking the positions of objects in local space is a core function of animal brains. We do not yet understand how it is done with limited neural resources. The challenges of spatial cognition are discussed under the criteria: (a) scaling of computational costs; (b) feature binding; (c) precise calculation of spatial displacements; (d) fast learning of invariant patterns; and (e) exploiting the strong Bayesian prior of object constancy. The leading current models of spatial cognition are Hierarchical Bayesian models of vision, and Deep Neural Nets. These are typically fully distributed models, which compute using direct communication links between a set of modular knowledge sources, and no other essential components. Their distributed nature leads to difficulties with the criteria (a) - (e). I discuss an alternative model of spatial cognition, which uses a single central position aggregator to store estimated locations of each object or feature, and applies constraints on locations in an iterative cycle between the aggregator and the knowledge sources. This model has advantages in addressing the criteria (a) - (e). If there is an aggregator in mammalian brains, there are reasons to believe that it is in the thalamus. I outline a possible neural realisation of the aggregator function in the thalamus.