Grid cells accurately track movement during path integration-based navigation despite switching reference frames

Abstract

Grid cells, with their periodic firing fields, are fundamental units in neural networks that perform path integration. It is widely assumed that grid cells encode movement in a single, global reference frame. In this study, by recording grid cell activity in mice performing a self-motion-based navigation task, we discovered that grid cells did not have a stable grid pattern during the task. Instead, grid cells track the animal movement in multiple reference frames within single trials. Specifically, grid cells reanchor to a task-relevant object through a translation of the grid pattern. Additionally, the internal representation of movement direction in grid cells drifted during self-motion navigation, and this drift predicted the mouse’s homing direction. Our findings reveal that grid cells do not operate as a global positioning system but rather estimate position within multiple local reference frames. Grid cells do not maintain a stable pattern during a self-motion-based task, but track animal movement in multiple local reference frames and reanchor to task-relevant objects, thus estimating local rather than global position.