Distilling Neural Population Geometry into Transformers and GRUs for Motor Control

We introduce neuro-distillation (Neuro-Dist), a technique designed to directly transfer computationally useful neural population structure from biological brains into artificial controllers. Using three macaque motor-cortical datasets as neuro-distillation teachers, we align controller internal states with cortical neural population geometry during training; controller deployment requires no neural data. We evaluate gated recurrent unit (GRU) networks and Transformers controlling a simulated arm. Neuro-Dist achieves the lowest median error among five methods, including distillation from movement kinematics, in 10 of 18 comparisons across trained, student-held-out, and new target directions. Seed-split aggregate statistical comparisons significantly favor Neuro-Dist over all six controls on trained and new targets, and five on student-held-out targets (Holm-corrected familywise $p<0.05$). We furthermore find that Neuro-Dist can substantially improve controller robustness to sensory feedback noise. Mechanistically, Neuro-Dist induced low tangling of trajectories in internal controller state space—a functionally important characteristic of motor cortex, demonstrating successful transfer of a known motor-cortical population geometry feature. These findings establish neuro-distillation as a complement to behavioral distillation and point to a novel class of neuro-interfaces that tap biological neural population structure as a computational resource for improving the control of goal-directed behavior in robotics and embodied AI.