Modularity In Motor Control From Muscle Synergies To Cognitive Action Representation
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Modularity in Motor Control: From Muscle Synergies to Cognitive Action Representation
Author: Andrea d'Avella
language: en
Publisher: Frontiers Media SA
Release Date: 2016-04-21
Mastering a rich repertoire of motor behaviors, as humans and other animals do, is a surprising and still poorly understood outcome of evolution, development, and learning. Many degrees-of-freedom, non-linear dynamics, and sensory delays provide formidable challenges for controlling even simple actions. Modularity as a functional element, both structural and computational, of a control architecture might be the key organizational principle that the central nervous system employs for achieving versatility and adaptability in motor control. Recent investigations of muscle synergies, motor primitives, compositionality, basic action concepts, and related work in machine learning have contributed to advance, at different levels, our understanding of the modular architecture underlying rich motor behaviors. However, the existence and nature of the modules in the control architecture is far from settled. For instance, regularity and low-dimensionality in the motor output are often taken as an indication of modularity but could they simply be a byproduct of optimization and task constraints? Moreover, what are the relationships between modules at different levels, such as muscle synergies, kinematic invariants, and basic action concepts? One important reason for the new interest in understanding modularity in motor control from different viewpoints is the impressive development in cognitive robotics. In comparison to animals and humans, the motor skills of today’s best robots are limited and inflexible. However, robot technology is maturing to the point at which it can start approximating a reasonable spectrum of isolated perceptual, cognitive, and motor capabilities. These advances allow researchers to explore how these motor, sensory and cognitive functions might be integrated into meaningful architectures and to test their functional limits. Such systems provide a new test bed to explore different concepts of modularity and to address the interaction between motor and cognitive processes experimentally. Thus, the goal of this Research Topic is to review, compare, and debate theoretical and experimental investigations of the modular organization of the motor control system at different levels. By bringing together researchers seeking to understand the building blocks for coordinating many muscles, for planning endpoint and joint trajectories, and for representing motor and behavioral actions in memory we aim at promoting new interactions between often disconnected research areas and approaches and at providing a broad perspective on the idea of modularity in motor control. We welcome original research, methodological, theoretical, review, and perspective contributions from behavioral, system, and computational motor neuroscience research, cognitive psychology, and cognitive robotics.
Modularity in Motor Control: from Muscle Synergies to Cognitive Action Representation
Mastering a rich repertoire of motor behaviors, as humans and other animals do, is a surprising and still poorly understood outcome of evolution, development, and learning. Many degrees-of-freedom, non-linear dynamics, and sensory delays provide formidable challenges for controlling even simple actions. Modularity as a functional element, both structural and computational, of a control architecture might be the key organizational principle that the central nervous system employs for achieving versatility and adaptability in motor control. Recent investigations of muscle synergies, motor primitives, compositionality, basic action concepts, and related work in machine learning have contributed to advance, at different levels, our understanding of the modular architecture underlying rich motor behaviors. However, the existence and nature of the modules in the control architecture is far from settled. For instance, regularity and low-dimensionality in the motor output are often taken as an indication of modularity but could they simply be a byproduct of optimization and task constraints? Moreover, what are the relationships between modules at different levels, such as muscle synergies, kinematic invariants, and basic action concepts? One important reason for the new interest in understanding modularity in motor control from different viewpoints is the impressive development in cognitive robotics. In comparison to animals and humans, the motor skills of today's best robots are limited and inflexible. However, robot technology is maturing to the point at which it can start approximating a reasonable spectrum of isolated perceptual, cognitive, and motor capabilities. These advances allow researchers to explore how these motor, sensory and cognitive functions might be integrated into meaningful architectures and to test their functional limits. Such systems provide a new test bed to explore different concepts of modularity and to address the interaction between motor and cognitive processes experimentally. Thus, the goal of this Research Topic is to review, compare, and debate theoretical and experimental investigations of the modular organization of the motor control system at different levels. By bringing together researchers seeking to understand the building blocks for coordinating many muscles, for planning endpoint and joint trajectories, and for representing motor and behavioral actions in memory we aim at promoting new interactions between often disconnected research areas and approaches and at providing a broad perspective on the idea of modularity in motor control. We welcome original research, methodological, theoretical, review, and perspective contributions from behavioral, system, and computational motor neuroscience research, cognitive psychology, and cognitive robotics.
Fundamentals of Neuromechanics
Author: Francisco J. Valero-Cuevas
language: en
Publisher: Springer
Release Date: 2015-09-07
This book provides a conceptual and computational framework to study how the nervous system exploits the anatomical properties of limbs to produce mechanical function. The study of the neural control of limbs has historically emphasized the use of optimization to find solutions to the muscle redundancy problem. That is, how does the nervous system select a specific muscle coordination pattern when the many muscles of a limb allow for multiple solutions? I revisit this problem from the emerging perspective of neuromechanics that emphasizes finding and implementing families of feasible solutions, instead of a single and unique optimal solution. Those families of feasible solutions emerge naturally from the interactions among the feasible neural commands, anatomy of the limb, and constraints of the task. Such alternative perspective to the neural control of limb function is not only biologically plausible, but sheds light on the most central tenets and debates in the fields of neural control, robotics, rehabilitation, and brain-body co-evolutionary adaptations. This perspective developed from courses I taught to engineers and life scientists at Cornell University and the University of Southern California, and is made possible by combining fundamental concepts from mechanics, anatomy, mathematics, robotics and neuroscience with advances in the field of computational geometry. Fundamentals of Neuromechanics is intended for neuroscientists, roboticists, engineers, physicians, evolutionary biologists, athletes, and physical and occupational therapists seeking to advance their understanding of neuromechanics. Therefore, the tone is decidedly pedagogical, engaging, integrative, and practical to make it accessible to people coming from a broad spectrum of disciplines. I attempt to tread the line between making the mathematical exposition accessible to life scientists, and convey the wonder and complexity of neuroscience to engineers and computational scientists. While no one approach can hope to definitively resolve the important questions in these related fields, I hope to provide you with the fundamental background and tools to allow you to contribute to the emerging field of neuromechanics.