Volitional activation of remote place representations with a hippocampal brain‐machine interface

<strong>Overview</strong> This repository is associated with the following paper: <strong>Lai C, Tanaka S, Harris TD, Lee AK. Volitional activation of remote place representations with a hippocampal brain‐machine interface. Science, 2023 (in press).</strong> This dataset demonstrates the ability of animals to activate remote place representations within the hippocampus when they aren't physically present at those locations. Such remote activations serve as a fundamental capability underpinning memory recall, mental simulation/planning, imagination, and reasoning. By employing a hippocampal map-based brain-machine interface (BMI), we designed two specific tasks to test whether animals can intentionally control their hippocampal activity in a flexible, goal-directed, and model-based manner. Our results show that animals can perform both tasks in real-time and in single trials. This dataset provides the neural and behavior data of these two tasks. The details of the tasks and results are described in the paper. <strong>Dataset, pre-trained model and code access:</strong> Unzip the <code>data.7z</code> to get a <code>data</code> folder. The <code>data</code> folder contains three subfolders: <strong>1. Running</strong>: This folder has two subfolders: <strong>run_before_jumper</strong>: Contains data files for the Running task performed before the Jumper task. <strong>run_before_jedi</strong>: Contains data files for the Running task performed before the Jedi task. <strong>2. Jumper</strong>: Contains data files for the Jumper task. <strong>2. Jedi</strong>: Contains data files for the Jedi task. Unzip the <code>model.7z</code> to get a <code>pretrained_model</code> folder, which contains all 6 pretrained models (<code>pth</code> files) trained using the data from the <code>Running</code> tasks, 3 used in <code>Jumper</code> tasks and 3 used in the <code>Jedi</code> tasks. Unzip the <code>code.7z</code>