Signal-Dependent Planning Noise Reduces Task Interference by Assisting in the Formation of Stable Motor Primitives in a Neural Network Model of Muscle Coordination Learning

In human motor coordination, learning to coactivate multiple muscles at once to achieve distinct target combinations of forces or tasks remains a fundamental area of study. Task interference, where training on one task degrades performance on previously learned tasks, can slow motor learning. However, the neural mechanisms that reduce interference are not fully understood. We hypothesized that the structure of planning noise, specifically its signal-dependent nature, significantly shapes learning dynamics and limits interference within motor learning systems that rely on variability for exploration. To test this hypothesis, we developed a three-layer neural network model of muscle coordination informed by key neuroanatomical and neurophysiological principles and simulated learning for producing various combinations of muscle forces. Synaptic weights were stochastically altered from trial to trial with either fixed-variance planning noise (FVPN), where each connection's variance was fixed during learning, or signal-dependent planning noise (SDPN), where noise variance depended on the neuron population activity. Weights were reinforced when they reduced output error relative to target forces. An execution noise term, applied to the motor output, modeled peripheral motor variability. However, the learning rule was not informed about how much of the output corresponded to peripheral or central variability. Our results showed that SDPN improved both the rate and accuracy of multitask learning by reducing task interference compared to FVPN across network sizes, training schedules, and execution noise levels. SDPN achieved this by concentrating neural plasticity within the neuron populations engaged by the current task rather than modifying the entire network. This signal-dependent plasticity allowed multiple motor primitives to form, stabilize, and be reused for new tasks. The model replicated the well-documented benefit of interleaved versus blocked training in motor learning. As a computational proof of concept, this work suggests that SDPN can benefit multitask motor training by facilitating the formation of motor primitives.