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 |