Hippocampal place cells are spatially tuned neurons that serve as elements of a ‘cognitive map’ in the mammalian brain1. To detect the animal’s location, place cells are thought to rely upon two interacting mechanisms: sensing the position of the animal relative to familiar landmarks2,3 and measuring the distance and direction that the animal has travelled from previously occupied locations4,5,6,7. The latter mechanism—known as path integration—requires a finely tuned gain factor that relates the animal’s self-movement to the updating of position on the internal cognitive map, as well as external landmarks to correct the positional error that accumulates8,9. Models of hippocampal place cells and entorhinal grid cells based on path integration treat the path-integration gain as a constant9,10,11,12,13,14, but behavioural evidence in humans suggests that the gain is modifiable15. Here we show, using physiological evidence from rat hippocampal place cells, that the path-integration gain is a highly plastic variable that can be altered by persistent conflict between self-motion cues and feedback from external landmarks. In an augmented-reality system, visual landmarks were moved in proportion to the movement of a rat on a circular track, creating continuous conflict with path integration. Sustained exposure to this cue conflict resulted in predictable and prolonged recalibration of the path-integration gain, as estimated from the place cells after the landmarks were turned off. We propose that this rapid plasticity keeps the positional update in register with the movement of the rat in the external world over behavioural timescales. These results also demonstrate that visual landmarks not only provide a signal to correct cumulative error in the path-integration system4,8,16,17,18,19, but also rapidly fine-tune the integration computation itself.
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The datasets used in this study are available from the corresponding author upon reasonable request.
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We thank B. Nash and B. Quinlan for assistance with constructing the apparatus; M. Ferreyros, M. Breault, N. Lukish, J. Johnson, B. Vagvolgyi and D. GoodSmith for technical assistance in running experiments; and G. Rao, V. Puliyadi, C. Wang, H. Lee, R. Nickl, A. Haith and J. Bohren for discussions and technical advice. This research was supported by National Institutes of Health grants R01 MH079511 (H.T.B., J.J.K.), R21 NS095075 (N.J.C., J.J.K.), and R01 NS102537 (N.J.C., J.J.K., F.S.), a Johns Hopkins University (JHU) Discovery Award (N.J.C., J.J.K.), a JHU Science of Learning Institute Award (J.J.K., N.J.C.), a JHU Kavli NDI Postdoctoral Distinguished Fellowship (M.S.M.) and a JHU Mechanical Engineering Departmental Fellowship (R.P.J.).