Population Transformer: Learning Population-level Representations of Neural Activity
<|reference_start|>Population Transformer: Learning Population-level Representations of Neural Activity: We present a self-supervised framework that learns population-level codes for arbitrary ensembles of neural recordings at scale. We address two key challenges in scaling models with neural time-series data: sparse and variable electrode distribution across subjects and datasets. The Population Transformer (PopT) stacks on top of pretrained representations and enhances downstream decoding by enabling learned aggregation of multiple spatially-sparse data channels. The pretrained PopT lowers the amount of data required for downstream decoding experiments, while increasing accuracy, even on held-out subjects and tasks. Compared to end-to-end methods, this approach is computationally lightweight and more interpretable, while still retaining competitive performance. We further show how our framework is generalizable to multiple time-series embeddings and neural data modalities. Beyond decoding, we interpret the pretrained PopT and fine-tuned models to show how they can be used to extract neuroscience insights from massive amounts of data. We release our code as well as a pretrained PopT to enable off-the-shelf improvements in multi-channel intracranial data decoding and interpretability.<|reference_end|>
@article{chau2024population,
    title={Population Transformer: Learning Population-level Representations of
  Neural Activity},
    author={Geeling Chau, Christopher Wang, Sabera Talukder, Vighnesh Subramaniam,
  Saraswati Soedarmadji, Yisong Yue, Boris Katz, and Andrei Barbu},
    journal={arXiv preprint arXiv:2406.03044},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03044},
    primaryClass={cs.LG q-bio.NC}
}