Learning to Represent Space in the Brain

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Many actions in our daily lives - getting to work, exploring a city or moving around a room - involve navigation. To navigate we need to sense where we are, for which we predominantly use vision. The goal of this proposal is to understand how brain circuits learn to use visual information to support navigation.Navigational areas of brain are known to represent space, including place cells of the hippocampus and grid cells of the entorhinal cortex. This representation of space is supported by inputs from visual cortical areas. The conventional model for processing a representation of space is a hierarchical model, where visual cortical areas build increasingly elaborate representations of visual features over the visual cortical hierarchy, and then feed these representations to navigational areas. Recent results however find a representation of space even in the primary visual cortex, suggesting a more distributed model of processing. This leads to a mystery as to which of the conflicting models is true: hierarchical or distributed. Based on the gradual appearance of spatial signals in the visual cortex, I hypothesize that it is possible for them to be true at different times, and learning transforms the representation from hierarchical to distributed. We will test this central hypothesis using a combination of virtual reality, large scale recordings from multiple brain regions, and inactivations and recordings from specific feedback projections to the visual cortex. Navigation is an essential cognitive ability common to all animals, and is a cognitive archetype, fusing perception, memory, planning and decision making. Understanding how spatial representations are built in the brain therefore offers the opportunity to understand both navigation itself and the wider mechanisms of general cognitive abilities. It thus has the potential to transform the fields of sensory neuroscience, memory research and neuromorphic machine learning.