Feedback control of recurrent dynamics constrains learning timescales during motor adaptation

Summary Neural activity has been observed to lie on low-dimensional manifolds, constraining what behaviors are easier or harder to learn. We propose that beyond this geometric constraint, learning on fast timescales is limited by how neural activity can flow over time within these manifolds—i.e., by the system’s underlying dynamics. In primary motor cortex (M1), these neural dynamics are shaped not only by internal recurrence but also by sensory feedback inputs that can continually update cortical activity and compensate for external perturbations. By extending recurrent neural network modeling with adaptive feedback controllers in a brain-computer interface (BCI) task, we show that feedback-driven dynamics determine not just the robustness but also the flexibility of motor output, and constrain what movements can be efficiently learned. Through a control-theoretic approach, we quantitatively link learning speed and success for individual BCI decoders to underlying network properties, demonstrating that input-driven dynamical structure limits the class of decoders that can be learned on fast timescales. Crucially, this dynamical systems perspective explains a continuous form of experimentally-observed learning variability that has previously not been addressed. We also make a testable prediction that rapid adaptation to new BCI decoders depends on upstream input plasticity, such as remapping of sensory feedback, beyond local plasticity within M1. Overall, our work identifies potential network mechanisms for fast but limited motor learning, and clarifies how constraints on learning arise from both the geometry of neural activity and its underlying dynamical structure.