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Table 1 Organization of this review

From: Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Introduction
 Nature of the problem and definition of computational neurorehabilitation
 How will computational models of neurorehabilitation be useful?
Review
I. Model elements for computational neurorehabilitation
 A. Inputs: Sensorimotor Activity
 B. Innards: Modeling activity-dependent plasticity
 C. Outputs: Functional outcomes and kinematics
II. The Current Modeling Benchmark: Prognostic Regression Models
 A. Predicting outcome post stroke with baseline behavioral measures
 B. Predicting outcome post-stroke with brain imaging measures
 C. Predicting treatment effects
III. Computational neurorehabilitation models
 A. Reaching the threshold for recovery in bilateral hand use
 B. Recovering from weakness via reinforcement learning
 C. Robot assistance, retention, and learning predicts recovery
 D. Understanding interactions between function and use
 E. Modeling the effect of assistance-as-needed
 F. Patient-trainer dynamics as an optimization
Conclusions