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BEEMO: Decoupled Behavioral Pretraining Scales Neural Motor Decoding

Researchers introduce BEEMO, a multi-session framework that decouples behavioral and neural representation learning for neural motor decoding. It trains a behavior decoder on abundant unpaired behavioral data and incorporates neural activity via cross-attention when paired recordings are available. Experiments on nonhuman primate datasets show that adding unpaired behavioral data improves decoding performance, especially for transfer to new tasks, and behavior-only pretraining matches models pretrained on larger neural datasets.