| Autor: |
Chau G, Wang C, Talukder S, Subramaniam V, Soedarmadji S, Yue Y, Katz B, Barbu A |
| Jazyk: |
angličtina |
| Zdroj: |
ArXiv [ArXiv] 2024 Oct 09. Date of Electronic Publication: 2024 Oct 09. |
| Abstrakt: |
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. 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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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