The optimal neural strategy for a stable motor task requires a compromise between level of muscle cocontraction and synaptic gain of afferent feedback.

Increasing joint stiffness by cocontraction of antagonist muscles and compensatory reflexes are neural strategies to minimize the impact of unexpected perturbations on movement. Combining these strategies, however, may compromise steadiness, as elements of the afferent input to motor pools innervating antagonist muscles are inherently negatively correlated. Consequently, a high afferent gain and active contractions of both muscles may imply negatively correlated neural drives to the muscles and thus an unstable limb position. This hypothesis was systematically explored with a novel computational model of the peripheral nervous system and the mechanics of one limb. Two populations of motor neurons received synaptic input from descending drive, spinal interneurons, and afferent feedback. Muscle force, simulated based on motor unit activity, determined limb movement that gave rise to afferent feedback from muscle spindles and Golgi tendon organs. The results indicated that optimal steadiness was achieved with low synaptic gain of the afferent feedback. High afferent gains during cocontraction implied increased levels of common drive in the motor neuron outputs, which were negatively correlated across the two populations, constraining instability of the limb. Increasing the force acting on the joint and the afferent gain both effectively minimized the impact of an external perturbation, and suboptimal adjustment of the afferent gain could be compensated by muscle cocontraction. These observations show that selection of the strategy for a given contraction implies a compromise between steadiness and effectiveness of compensations to perturbations. This indicates that a task-dependent selection of neural strategy for steadiness is necessary when acting in different environments.