Abstract
The hippocampus has long been observed to encode a representation of an animal's position in space. Recent evidence suggests that the nature of this representation is somewhat predictive and can be modeled by learning a successor representation (SR) between distinct positions in an environment. However, this discretization of space is subjective making it difficult to formulate predictions about how some environmental manipulations should impact the hippocampal representation. Here, we present a model of place and grid cell firing as a consequence of learning a SR from a basis set of known neurobiological features—boundary vector cells (BVCs). The model describes place cell firing as the successor features of the SR, with grid cells forming a low‐dimensional representation of these successor features. We show that the place and grid cells generated using the BVC‐SR model provide a good account of biological data for a variety of environmental manipulations, including dimensional stretches, barrier insertions, and the influence of environmental geometry on the hippocampal representation of space.
Highlights
The hippocampal formation plays a central role in the ability of humans and other mammals to navigate physical space (Morris, Garrud, Rawlins, & O'Keefe, 1982; Scoville & Milner, 1957)
Following Stachenfeld and colleagues (Stachenfeld et al, 2017), we propose that the hippocampus encodes the boundary vector cells (BVCs) successor features ψ~ to facilitate decision making during spatial navigation
The model presented here links the BVC model of place cell firing with a successor representation (SR) to provide an efficient platform for using reinforcement learning (RL) to navigate space
Summary
The hippocampal formation plays a central role in the ability of humans and other mammals to navigate physical space (Morris, Garrud, Rawlins, & O'Keefe, 1982; Scoville & Milner, 1957). Consistent with behavioral findings, electrophysiological studies in rodents have uncovered a range of spatially modulated neurons—yielding important insights into how the brain represents space—including place cells (O'Keefe & Dostrovsky, 1971), grid cells (Hafting, Fyhn, Molden, Moser, & Moser, 2005), head direction cells (Taube, Muller, & Ranck, 1990), and boundary vector cells (BVCs) (Barry et al, 2006; Lever, Burton, Jeewajee, O'Keefe, & Burgess, 2009; Solstad, Boccara, Kropff, Moser, & Moser, 2008) How these neural representations combine to facilitate flexible and efficient goal-directed navigation, such as that observed in mammals (Etienne & Jeffery, 2004), remains an open question. A quantity often used in RL is the value V of a state s in the environment which is defined as the expected cumulative reward R, exponentially discounted into the future by a discount parameter γ [0, 1]
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