title: Hippocampal replay captures the unique topological structure of a novel environment.
abstract: Hippocampal place-cell replay has been proposed as a fundamental mechanism of learning and memory, which might support navigational learning and planning. An important hypothesis of relevance to these proposed functions is that the information encoded in replay should reflect the topological structure of experienced environments; that is, which places in the environment are connected with which others. Here we report several attributes of replay observed in rats exploring a novel forked environment that support the hypothesis. First, we observed that overlapping replays depicting divergent trajectories through the fork recruited the same population of cells with the same firing rates to represent the common portion of the trajectories. Second, replay tended to be directional and to flip the represented direction at the fork. Third, replay-associated sharp-wave-ripple events in the local field potential exhibited substructure that mapped onto the maze topology. Thus, the spatial complexity of our recording environment was accurately captured by replay: the underlying neuronal activities reflected the bifurcating shape, and both directionality and associated ripple structure reflected the segmentation of the maze. Finally, we observed that replays occurred rapidly after small numbers of experiences. Our results suggest that hippocampal replay captures learned information about environmental topology to support a role in navigation.
authors.affiliation: Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205.
authors.forename: Xiaojing
authors.initials: X
authors.lastname: Wu
authors.forename: David J
authors.initials: DJ
authors.lastname: Foster
mesh_terms: D000465:Algorithms; D000818:Animals; D055724:Electrophysiological Phenomena / Q000502:physiology; D004777:Environment; D005071:Evoked Potentials / Q000502:physiology; D006624:Hippocampus / Q000033:anatomy & histology* / Q000502:physiology*; D007858:Learning / Q000502:physiology; D016014:Linear Models; D008297:Male; D018782:Maze Learning / Q000502:physiology; D009434:Neural Pathways / Q000033:anatomy & histology / Q000502:physiology; D009474:Neurons / Q000502:physiology; D051381:Rats; D020318:Rats, Long-Evans; D012201:Reward; D013037:Spatial Behavior / Q000502:physiology