Muscle synergy space: learning model to create an optimal muscle synergy
Muscle redundancy allows the central nervous system (CNS) to choose a suitable combination of muscles from a number of options. This flexibility in muscle combinations allows for efficient behaviors to be generated in daily life. The computational mechanism of choosing muscle combinations, however,...
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Published in | Frontiers in computational neuroscience Vol. 7; p. 136 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Research Foundation
15.10.2013
Frontiers Media S.A |
Subjects | |
Online Access | Get full text |
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Summary: | Muscle redundancy allows the central nervous system (CNS) to choose a suitable combination of muscles from a number of options. This flexibility in muscle combinations allows for efficient behaviors to be generated in daily life. The computational mechanism of choosing muscle combinations, however, remains a long-standing challenge. One effective method of choosing muscle combinations is to create a set containing the muscle combinations of only efficient behaviors, and then to choose combinations from that set. The notion of muscle synergy, which was introduced to divide muscle activations into a lower-dimensional synergy space and time-dependent variables, is a suitable tool relevant to the discussion of this issue. The synergy space defines the suitable combinations of muscles, and time-dependent variables vary in lower-dimensional space to control behaviors. In this study, we investigated the mechanism the CNS may use to define the appropriate region and size of the synergy space when performing skilled behavior. Two indices were introduced in this study, one is the synergy stability index (SSI) that indicates the region of the synergy space, the other is the synergy coordination index (SCI) that indicates the size of the synergy space. The results on automatic posture response experiments show that SSI and SCI are positively correlated with the balance skill of the participants, and they are tunable by behavior training. These results suggest that the CNS has the ability to create optimal sets of efficient behaviors by optimizing the size of the synergy space at the appropriate region through interacting with the environment. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: David Hansel, University of Paris, France This article was submitted to the journal Frontiers in Computational Neuroscience. Reviewed by: Omri Barak, Technion, Israel; Katsunori Kitano, Ritsumeikan University, Japan |
ISSN: | 1662-5188 1662-5188 |
DOI: | 10.3389/fncom.2013.00136 |