Reward tuning in motor cortex

This repository contains code for figure 3 of the following paper:

Lee C, Harkin EF, Yin X, Naud R, Chen S. Cell-type specific responses to associative learning in the primary motor cortex. eLife. 2022;11:e72549.

Open access available here.

Setup

Paste the shell snippet below into a terminal to download the source code, install required python packages, and set up the required directory structure. It is recommended to do this in a virtual environment (eg, a new Anaconda environment) to avoid polluting your python installation.

mkdir -p interneuron-reward-project/interneuron-reward-data/{processed,raw} && \
    cd interneuron-reward-project && \
    git clone --depth 1 https://github.com/nauralcodinglab/interneuron-reward.git && \
    cd interneuron-reward && \
    pip install -r requirements.txt && \
    pip install -e .

Next, download the raw data and place it in the interneuron-reward-data/raw directory that was just created.

Set up a database that is compatible with the Python SQLAlchemy package (eg, SQLite or MySQL) and create a new environment variable called SQLALCHEMY_ENGINE_URL containing a URL that can be used to access it (more info in the SQLAlchemy docs). Once this is done, the raw data can be loaded into the database by running the scripts in src.

Finally, launch a Jupyter notebook server and run the Python and R notebooks under notebooks to reproduce results.

License

If you use this code in a publication, please cite our paper!

@article{lee2022cell,
  title={Cell-type specific responses to associative learning in the primary motor cortex},
  author={Lee, Candice and Harkin, Emerson F and Yin, Xuming and Naud, Richard and Chen, Simon X},
  journal={eLife},
  volume={11},
  pages={e72549},
  year={2022},
  publisher={eLife Sciences Publications Limited}
}

This work is licensed under a Creative Commons Attribution 4.0 International License.

This software is provided "as-is" in the spirit of the CRAPL academic-strength open-source license.