Computational models of neuromotor recovery after a stroke might help to unveil the underlying physiological mechanisms and might suggest how to make recovery faster and more effective. At least in principle, these models could serve: (i) To provide testable hypotheses on the nature of recovery; (ii) To predict the recovery of individual patients; (iii) To design patient-specific “optimal” therapy, by setting the treatment variables for maximizing the amount of recovery or for achieving a better generalization of the learned abilities across different tasks. Here we review the state of the art of computational models for neuromotor recovery through exercise, and their implications for treatment. We show that to properly account for the computational mechanisms of neuromotor recovery, multiple levels of description need to be taken into account. The review specifically covers models of recovery at central, functional and muscle synergy level.

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Motor skill learning has different components. When we acquire a new motor skill we have both to learn a reliable action-value map to select a highly rewarded action (task model) and to develop an internal representation of the novel dynamics of the task environment, in order to execute properly the action previously selected (internal model). Here we focus on a 'pure' motor skill learning task, in which adaptation to a novel dynamical environment is negligible and the problem is reduced to the acquisition of an action-value map, only based on knowledge of results. Subjects performed point-to-point movement, in which start and target positions were fixed and visible, but the score provided at the end of the movement depended on the distance of the trajectory from a hidden viapoint. Subjects did not have clues on the correct movement other than the score value. The task is highly redundant, as infinite trajectories are compatible with the maximum score. Our aim was to capture the strategies subjects use in the exploration of the task space and in the exploitation of the task redundancy during learning. The main findings were that (i) subjects did not converge to a unique solution; rather, their final trajectories are determined by subject-specific history of exploration. (ii) with learning, subjects reduced the trajectory's overall variability, but the point of minimum variability gradually shifted toward the portion of the trajectory closer to the hidden via-point.

It is commonly acknowledged that movement performance is determined by a trade-off between accuracy requirements and energetic expenditure. However, their relative weights are subjective and depend on the perceived benefit (or cost) associated to successful movement completion. A deeper knowledge on how this trade-off affects motor behavior may suggest ways to manipulate it in pathologies, like Parkinson's disease, in which the mechanisms underlying the selection of motor response are believed to be defective. In this preliminary study, we associate a monetary incentive to successful completion of a full-body reaching task and look at the determinants of motor performance. Our preliminary results suggest that motor performance (measured as the absolute average acceleration of hand movements) increases with movement amplitude/target elevation. Overall, performance also increases with the amount of monetary incentive and with the average reward experienced in previous trials. In addition, subjects with a greater sensitivity to incentive exhibit a low sensitivity to the average reward. In contrast, subjects with a negative sensitivity to incentive exhibit a smaller sensitivity to the average reward. These results suggest that motor performance has a complex relation with its perceived benefits, and this relation is probably subject-dependent.

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