POYO: A Unified, Scalable Framework for Neural Population Decoding

POYO (Azabou et al 2023, NeurIPS) introduces a new transformer-based framework for neural population decoding, designed to adapt rapidly to new, unseen sessions with minimal labels, leveraging large-scale neural recordings. Read here for a high-level intro to POYO.

This repository contains code from the POYO paper, which is a part of the Neuro-Galaxy project.

Installation Instructions

In a clean virtual environment, follow these steps:

git clone https://github.com/neuro-galaxy/poyo.git
cd poyo
pip install -e .

Link to Papers

For an in-depth understanding of our framework, refer to our paper. Please cite as:

@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},
}