Decoding Neural Population Dynamics through Robotic Analog

2026-10-07

AI Summary

This study investigates the causal mechanisms by which rotational neural population dynamics in the motor cortex enable precise voluntary movement. Methodologically, we construct an embodied robotic system integrating artificial muscles with multimodal sensing, employing deep reinforcement learning to train controllers that replicate biological kinematic signatures. We elucidate the physical principles through which neural rotation optimizes trajectories via orthogonal oscillations, uncovering counterintuitive energy scaling laws under redundant degrees of freedom alongside emergent learning insight phenomena. The proposed framework achieves high-precision, robust motor control, empirically validating the critical contribution of neural dynamics to movement flexibility. Ultimately, this work offers a novel perspective for designing animal-like intelligent robots grounded in biologically plausible neural computation.

Abstract

Animal evidence shows that precise voluntary movements arise from rotational neural population dynamics in motor cortex, but their physical effects remain unknown. We developed a robotic analog of biological motor systems with artificial muscles, multimodal sensors, and a neural network controller trained via reinforcement learning. The robotic analog exhibited accurate movements, robustness to damage, and neural population dynamics akin to animals. This task-driven, embodied model illuminates the causal link between neural population dynamics and motor outcomes. We discovered that neural rotations generate oscillatory maneuvers orthogonal to the reaching direction, optimizing trajectory adjustments, which is confirmed by primate neural data. The model also revealed counterintuitive neural energy principles under sensor and motor redundancies, and striking Eureka moments during motor learning, bridging biological and artificial systems. These findings provide new perspectives on how neural dynamics contribute to accurate and flexible movement, inspiring future intelligent robots with animal-like mobility.

Problem

• neural population dynamics
• motor cortex
• voluntary movement
• embodied model
• motor control

Innovation

• Neural Population Dynamics
• Robotic Analog
• Reinforcement Learning
• Embodied Model
• Motor Learning