Geometry-Aware Self-Supervised Electrophysiology Representation Learning

Precise localization of recording electrodes is fundamental to systems neuroscience; yet, current methods depend on labor-intensive post-hoc histology, which is incompatible with real-time feedback or chronic implants. We test the hypothesis that the electrical signals being recorded themselves carry sufficient anatomical information to localize their recording site in 3D atlas coordinates, without the need for histology. To probe this, we introduce a self-supervised framework that learns geometry-aware representations of single-channel local field potentials (LFP), leveraging probe channel geometry as a self-supervisory signal. We compare speech-based SSL objectives (e.g., masked prediction) against geometry-aware objectives that constrain the embedding space to mirror physical channel layout along the probe. These representations outperform masked-prediction baselines, and the gains compound at larger data scales. We test downstream analysis with 3D coordinate regression and brain region classification, benchmarking three SSL backbones (Wav2Vec 2.0, Whisper, and Data2Vec) against supervised baselines (AnyNet, ViT, and classical spectral classifiers) across datasets, laboratories, species, and probe technologies. Our method achieves state-of-the-art results in brain region classification and 3D coordinate regression from raw single-channel LFP, outperforming all baselines, affirming our hypothesis.