Structured Physical Primitives for Robust Neural Decoding

The main challenge of neural decoding in practical applications, especially in brain-computer interfaces (BCIs), lies in maintaining stable performance in the presence of noise and missing data. Current methods either learn dynamics directly from data or impose statistical constraints, making them prone to degradation when data quality declines. Physical motion, however, possesses intrinsic geometric structure with motion-specific primitives, such as translation and rotation in rodent planar motion, which are independent of neural data quality and offer a stable source of prior knowledge when neural evidence is unreliable. Motivated by this, we propose Structured Physical Primitives for Neural Decoding (MaPIC), a novel framework comprising two complementary components: a neural evidence encoder for observation likelihood and a physical primitive model for dynamic Bayesian priors. By maximizing the ELBO, MaPIC continuously integrates neural evidence with physical priors, where the KL divergence term serves as a structured regularizer guided by physical motion primitives. Notably, MaPIC learns the posterior uncertainty of each primitive via variational inference, enabling adaptive weighting among primitives. Our framework is causal and plug-and-play, requiring no modifications to the base architecture. Experiments on simulated dynamical systems and real rat hippocampal recordings demonstrate that even when $80\%$ of neurons is randomly dropped, MaPIC surpasses state-of-the-art methods, significantly reducing position decoding error and correcting directional phase delays at turning points.