Neuromotor synergies as a basis for coordinated intentional action.

By Wynne A. Lee. 1984

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Active learning of inverse models with intrinsically motivated goal exploration in robots (2013, positive): “…(lower time scale) considers the goal-directed active choice and active exploration of lower-level actions to be taken to reach the goals selected at the higher level, and depending on local measures of interest related to the evolution of the quality of learned inverse and/or forward models;”
A limited set of muscle synergies for force control during a postural task. (2005, background): “A different approach regards a synergy as a fixed temporal sequence of bursts of activity across groups of motoneurons.”
Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets. (2006, methodology): “In the context of principal component analysis, d ជ represents the data to be decomposed, the w ជ i values constitute the principal components, and the c i values are the component scores.”
Intrinsically Motivated Learning of Real-World Sensorimotor Skills with Developmental Constraints (2013, methodology): “Unlike R-IAC that was made for active learning of forward models, we show that this new algorithm allows for efficient learning of inverse models in redundant robots by leveraging the lower-level dimension of the task space.”