TY - JOUR T1 - Grid Cells, Place Cells, and Geodesic Generalization for Spatial Reinforcement Learning A1 - Gustafson, Nicholas J. A1 - Daw, Nathaniel D. Y1 - 2011/10/27 N2 - Author Summary The central problem of learning is generalization: how to apply what was discovered in past experiences to future situations, which will inevitably be the same in some respects and different in others. Effective learning requires generalizing appropriately: to situations which are similar in relevant respects, though of course the trick is determining what is relevant. In this article, we quantify and investigate relevant generalization in the context of a particular learning problem often studied in the laboratory: learning to navigate in a spatial maze. In particular, we consider whether the brain's well-characterized systems for representing an organism's location in space generalize appropriately for this task. Our simulations of learning verify that to generalize effectively, these representations should treat nearby locations similarly (that is, neurons should fire similarly when an animal occupies nearby locations)—but, more subtly, that to enable successful learning, “nearby” must be defined in terms of paths around obstacles, rather than in absolute space “as the crow flies.” These considerations suggest new principles for understanding these spatial representations and why they appear warped and distorted in environments, such as mazes, with barriers and obstacles. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 7 IS - 10 UR - https://doi.org/10.1371/journal.pcbi.1002235 SP - e1002235 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1002235 ER -