Cross-species representation learning aligns mouse and human neural dynamics and tracks clinical drug efficacy
Preclinical models poorly predict human drug efficacy, particularly in neurological disorders. Neural activity offers a uniquely rich source of translational information because it captures high-dimensional variation in nervous-system function that can be measured in both animals and humans. However, its high dimensionality makes it difficult to distinguish conserved disease-related features from variation arising from species, recording modality and experimental context. Here, we test whether shared neural dynamics can be identified directly from electrophysiology data by learning representations organized by biological state rather than species. We develop a dual-rule contrastive learni...
| Authors | Marko Tvrdic, Justin Richmond Domingo, Jae Ann Buenaluz et al. |
|---|---|
| Primary category | q-bio.QM |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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