Preserved Neural Dynamics across Arm- and Brain-controlled Movements

The neural response for motor control is complex and dynamic; it has been found to dramatically transit from planning to executing movements. As brain-machine interfaces (BMIs) can directly connect the brain and the external world by yielding comparable motor outcomes with artificial apparatus, a central question remains whether the neural dynamics underlying brain-control movement share characteristics or mechanism in natural neural motor control. To enable a systematic comparison, we developed a feedforward BMI framework that enables rapid cursor control to intercept moving targets. This BMI allowed monkeys to voluntarily initiate neural states which controlled the direction and timing of a launched ballistic movement, like skeet shooting. Based on this, we found similar neural representation patterns and computational structure across brain- and arm-control conditions. Notably, in addition to resembling the rotational structure in natural reaching, the neural population dynamics during open loop control also shared preserved manifold with those during arm interception. These findings suggest a fundamental principle and reveal a set of basic computational motifs, for neural control of movement in an abstract hierarchy independent of constraints from actuators. This study thus has potential to reshape the consideration of how BMIs assist paralyzed patients in interacting with dynamic environments and promote next-generation BMI systems.