Barbara Feulner

This study shows that feedback-based motor control and motor adaptation share a common implementation in M1 circuits, as demonstrated through a recurrent neural network trained with error feedback.

The paper uses a network model to show that small, correlated changes in synaptic connectivity can explain motor learning without disrupting neural covariance, aligning with experimental observations.

This study shows that humans rely more on previous motor actions than sensory input when forming spatial priors during sensorimotor learning.

This study shows that recurrent weight changes in a computational model can explain within- and outside-manifold learning differences without altering the neural manifold.