Decoding by Dynamics: Reframing Neural Decoding as Stable Control Inference with Behavior Priors

Abstract

Continuous neural decoding is fragile under nonstationary neural recordings because unconstrained sequence regressors can turn small mapping errors into temporally inconsistent and physically implausible motion. We propose Neural State-Space Dynamic Movement Primitives (Neural SS-DMP), which shifts the inductive bias from the neural encoder to the decoded output space: instead of directly predicting kinematics, the model infers low-dimensional movement-primitive controls and realizes them through a differentiable second-order dynamical generator. This reframes decoding as structured control inference, shrinking the set of admissible trajectories while retaining expressivity through learned forcing inputs. Because a universal motor prior cannot capture subject-specific movement dynamics, we form a personalized generator by blending the base DMP dynamics with behavior-derived subject dynamics estimated solely from training kinematics. Across ECoG and multi-session spiking benchmarks, Neural SS-DMP improves strong offline baselines in accuracy, consistently improves trajectory smoothness, and shows slower degradation on chronologically held-out sessions under an offline window-causal protocol.