Schematic description of the sensorimotor adaptation task and the model.

<p><b>A.</b> The rotation task. From left to right: 1) A circular target (red circle) of radius appears on the screen at direction (here ) to instruct the subject where to move the cursor. 2) The subject moves the cursor, which is invisible to him, toward the target (blue arrow). The only information available to the subject on his performance is the reward, delivered only if the cursor falls within the target. 3) A perturbation is introduced: the cursor is rotated by an angle <i>γ</i> with respect to the direction of the subject's hand movement (black arrow). 4) A learning phase follows where the subject progressively adapts to the perturbation, reducing the distance between the cursor endpoint and the target. <b>B.</b> Schematic description of the model. When the target appears, the activity profile of the input layer (red neurons) peaks around the target direction. The parameter <i>ρ</i> controls the width of the activity profile. The connectivity matrix between the input and the output (blue neurons) layers is denoted by . A Gaussian noise with zero mean and a standard deviation of is added to the output layer of the network. The two-dimensional output vector rotated by the matrix represents the cursor endpoint. A reward is delivered if the distance between the cursor endpoint and the center of the target is smaller than . The connectivity matrix is then changed according to a reward-modulated plasticity rule (see Eq(8)).