Robotic system reveals how brain signals control movement precision
Decoding Neural Population Dynamics through Robotic Analog
Robotics
Summary
Voluntary movements in animals come from special brain activity patterns called rotational neural dynamics, but how these relate to actual movement was unclear. The authors built a robot with artificial muscles and sensors, controlled by a neural network mimicking brain signals. This robot showed movement patterns and brain-like dynamics similar to animals, helping explain how brain rotations create smooth adjustments during reaching. Their model also found surprising energy rules in the brain and moments of insight during learning, linking biology and robotics.
What this means in practice
- •For robotics engineers: Design robots with neural-inspired control systems that improve movement accuracy and damage resilience using rotational neural dynamics.
- •For prosthetics developers: Create prosthetic limbs that utilize brain-like neural population dynamics for smoother and more flexible voluntary movements.
Authors
Wenhui Chen, Jiyue Tao, Yitao Cheng, Yutong Shi, Feitian Zhang, Xitong Liang, Ke Liu
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.