We present a self-supervised framework that learns population-level codes for
intracranial neural recordings at scale, unlocking the benefits of
representation learning for a key neuroscience recording modality. The
Population Transformer (PopT) lowers the amount of data required for decoding
experiments, while increasing accuracy, even on never-before-seen subjects and
tasks. We address two key challenges in developing PopT: sparse electrode
distribution and varying electrode location across patients. PopT stacks on top
of pretrained representations and enhances downstream tasks by enabling learned
aggregation of multiple spatially-sparse data channels. Beyond decoding, we
interpret the pretrained PopT and fine-tuned models to show how it can be used
to provide neuroscience insights learned from massive amounts of data. We
release a pretrained PopT to enable off-the-shelf improvements in multi-channel
intracranial data decoding and interpretability, and code is available at
this https URL

我们提出了一个自主训练框架，以大规模学习人口层级下的颅内神经记录，实现了对关键的神经科学记录模态的表示学习的益处。Population Transformer（PopT）降低了解码实验所需的数据量，同时提高了准确性，即使在以前从未见过的受试者和任务上也能如此。我们解决了开发 PopT 时遇到的两个主要挑战：稀疏电极分布和不同病人之间的电极位置变化。PopT 在预训练表示的基础上增加层级，通过启用多个空间稀疏数据通道的学习聚合来增强下游任务。除了解码，我们使用预训练的 PopT 和微调模型来解释它如何用于提供从大量数据中学习的神经科学见解。我们发布了预训练的 PopT 以实现多通道颅内数据解码和可解释性的即时改进，代码可在此 https URL 中获取。