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 scal. 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.