  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.
