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

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. 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 re-anchor 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., , # Dataset Description

This dataset contains raw electrophysiological and behavioral data from 3 experimental cohorts comprising 17 mice.

## File Organization

The experimental data are organized in subdirectories corresponding to individual recording sessions. File names typically include the recording session identifier (e.g., `jp486-18032023-0108`) as the basename. The example session `jp486-18032023-0108` represents an electrophysiological recording session with associated behavioral data.

## Session-Level Files

**{session}.desel**

* Specifies the brain region targeted by each probe
* Each line corresponds to one shank (Neuronexus/Cambridge probes were used)

**{session}.desen**

* Contains environment codes for each recording trial
* Random foraging trials: \"circ80\"
* Rest periods: \"rest\"
* AutoPI task trials: \"autopi\"

**{session}.environmentFamiliarity**

* Indicates whether the environment was novel or familiar
* All environments in this dataset are familiar, denoted as \"fam...,