POYO 🧠

Official codebase for POYO published at NeurIPS 2023 [project page] [arxiv]

Installation

First, install uv by following the steps here. Then, create your Python environment:

uv venv venv -p 3.11
source venv/bin/activate
uv pip install -r requirements.txt

Training POYO-MP

To train POYO-MP you first need to download and process the perich_miller_population_2018 data using brainsets.

brainsets config --raw-dir ./data/raw --processed-dir ./data/processed
brainsets prepare perich_miller_population_2018

Then you can train POYO-MP by running:

python train.py --config-name train_poyo_mp.yaml

Checkout configs/base.yaml and configs/train_poyo_mp.yaml for all configurations available.

Training POYO-1

To train POYO-1 you first need to download all datasets using brainsets.

brainsets config --raw-dir ./data/raw --processed-dir ./data/processed
brainsets prepare perich_miller_population_2018
brainsets prepare churchland_shenoy_neural_2012
brainsets prepare flint_slutzky_accurate_2012
brainsets prepare odoherty_sabes_nonhuman_2017

Then you can train POYO-1 by running:

python train.py --config-name train_poyo_1.yaml

Checkout configs/base.yaml and configs/train_poyo_1.yaml for all configurations available.

Cite

Please cite our paper if you use this code in your own work:

@inproceedings{
    azabou2023unified,
    title={A Unified, Scalable Framework for Neural Population Decoding},
    author={Mehdi Azabou and Vinam Arora and Venkataramana Ganesh and Ximeng Mao and Santosh Nachimuthu and Michael Mendelson and Blake Richards and Matthew Perich and Guillaume Lajoie and Eva L. Dyer},
    booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
    year={2023},
}