The researchers report that across multiple macaque motor datasets, behavioral pretraining improved trajectory decoding by an 11% R2 increase on center-out and 8% on random-target tasks, that robotic-trajectory pretraining achieved performance comparable to macaque pretraining, and that only 10% calibration was needed to match training from scratch. This study is a preprint and has not been peer reviewed.
Preprint: NeuroPB Scales Neural Decoding with Pretrained Behavioral Representations
Summary
A preprint introduces NeuroPB, a framework that scales neural decoding by transferring pretrained behavioral representations: a motion encoder is first pretrained on large-scale motor behavior data, then limited paired neural-behavior recordings align neural activity to the behavioral representation space, and a neural encoder with a lightweight motor decoder reconstructs continuous movement. The researchers report that behavioral pretraining lifted center-out trajectory decoding R² by 11% and random-target task performance by 8%, that pretraining on robotic trajectories matched macaque-trajectory performance, and that only 10% calibration was needed to match training from scratch. The study is a preprint and has not been peer-reviewed.
Why it matters
The telling result is that surrogate behavior data — robotic trajectories — can stand in for scarce paired neural recordings, opening a route to high-performance BCIs in data-limited settings.
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arxiv.org 2026-08-05