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dc.contributor.author
Cohn, Brian A.
dc.contributor.author
Szedlák, May
dc.contributor.author
Gärtner, Bernd
dc.contributor.author
Valero-Cuevas, Francisco J.
dc.date.accessioned
2018-10-04T13:37:20Z
dc.date.available
2018-10-04T01:47:18Z
dc.date.available
2018-10-04T13:37:20Z
dc.date.issued
2018-09-11
dc.identifier.issn
1662-5188
dc.identifier.other
10.3389/fncom.2018.00062
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/293155
dc.identifier.doi
10.3929/ethz-b-000293155
dc.description.abstract
We present Feasibility Theory, a conceptual and computational framework to unify today's theories of neuromuscular control. We begin by describing how the musculoskeletal anatomy of the limb, the need to control individual tendons, and the physics of a motor task uniquely specify the family of all valid muscle activations that accomplish it (its ‘feasible activation space'). For our example of producing static force with a finger driven by seven muscles, computational geometry characterizes—in a complete way—the structure of feasible activation spaces as 3-dimensional polytopes embedded in 7-D. The feasible activation space for a given task is the landscape where all neuromuscular learning, control, and performance must occur. This approach unifies current theories of neuromuscular control because the structure of feasible activation spaces can be separately approximated as either low-dimensional basis functions (synergies), high-dimensional joint probability distributions (Bayesian priors), or fitness landscapes (to optimize cost functions).
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Frontiers Research Foundation
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
feasibility
en_US
dc.subject
neuromechanics
en_US
dc.subject
motor control
en_US
dc.subject
tendon-driven
en_US
dc.subject
dimensionality
en_US
dc.subject
synergies
en_US
dc.subject
optimization
en_US
dc.subject
forces
en_US
dc.title
Feasibility Theory Reconciles and Informs Alternative Approaches to Neuromuscular Control
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
Frontiers in Computational Neuroscience
ethz.journal.volume
12
en_US
ethz.journal.abbreviated
Front. comput. neurosci.
ethz.pages.start
62
en_US
ethz.size
18 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Lausanne
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02643 - Institut für Theoretische Informatik / Inst. Theoretical Computer Science::03457 - Welzl, Emo / Welzl, Emo
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02643 - Institut für Theoretische Informatik / Inst. Theoretical Computer Science::03457 - Welzl, Emo / Welzl, Emo
ethz.date.deposited
2018-10-04T01:48:33Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2018-10-04T13:37:27Z
ethz.rosetta.lastUpdated
2020-02-15T15:16:43Z
ethz.rosetta.versionExported
true
ethz.COinS
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