One-shot entorhinal maps enable flexible navigation in novel environments - part 2
Description
Neuropixels 1.0 and 2.0 were used to collect electrophysiological data from head-fixed mice navigating virtual reality linear environments or from freely moving mice randomly foraging in open field environments. This dataset is part 2 of 2 and contains data from two tasks: 1. The Hidden Reward Track; 2. The sessions where animals first ran head-fixed in darkness and then foraged freely in an open field. More details can be found at the preprint here: https://www.biorxiv.org/content/10.1101/2023.09.07.556744v1. A link to a peer-reviewed publication will be provided once available. This dataset contains the same variables in the .mat files as in dataset part 1 of 2, with the following exceptions. In this dataset, all but four of the VR sessions (Saline Only or Muscimol Only) are associated with Neuropixels data collected while animals ran on VR tracks (see the supplementary data table in the paper above for more details). In the four sessions where Neuropixels data were not collected, trial_starts is of length n_blocks + 1, where the final value is one greater than the total number of trials in that session. Finally, data collected from animal AJ2 on the hidden reward task took place on a 240cm VR track instead of a 200cm track. Neuropixels 2.0 probes were chronically implanted in mice that first ran head-fixed in darkness and then freely foraging in an open field. Each session is associated with four files: 1. A .mat file containing the same variables described in dataset part 1 of 2. 2. A "_position.txt" file, which contains behaviorally relevant variables, such as position, heading direction, and speed for each observation. See the header in the file for more information. 3. A "_arena.txt" file, which contains the boundaries of the open field environment needed to run the script "calculate_and_plot_open_field.ipynb" included in the code repository: https://github.com/GiocomoLab/mec-rapid-learning. 4. A "_spikes.txt" file, which contains the concatenated spikes from the head-fixed run and the open field run. This file can be loaded with the script "VR_and_OF_spikes.py" that is also included in the code repository.