arXiv: cs.AI preprintsInternational6 November 2026
Scaling Neural Motor Decoding via Decoupled Behavioral Pretraining
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arXiv:2611.08263v1 Announce Type: cross Abstract: Recent neural decoding approaches achieve strong performance by training on large collections of paired neural-behavioral recordings, but such datasets are expensive, invasive, and difficult to scale. In contrast, behavioral data is abundant and easy to collect, raising the question of whether neural decoding can benefit from decoupling behavioral and neural representation learning. We introduce BEEMO, a flexible multi-session framework that enables training with arbitrary mixtures of paired neural-behavioral recordings and unpaired behavioral
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