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dc.contributor.author
Lipkind, Dina
dc.contributor.author
Zai, Anja T.
dc.contributor.author
Hanuschkin, Alexander
dc.contributor.author
Marcus, Gary F.
dc.contributor.author
Tchernichovski, Ofer
dc.contributor.author
Hahnloser, Richard H.R.
dc.date.accessioned
2017-12-06T15:52:23Z
dc.date.available
2017-11-10T04:02:07Z
dc.date.available
2017-12-06T15:52:23Z
dc.date.issued
2017
dc.identifier.other
10.1038/s41467-017-01436-0
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/206829
dc.identifier.doi
10.3929/ethz-b-000206829
dc.description.abstract
While acquiring motor skills, animals transform their plastic motor sequences to match desired targets. However, because both the structure and temporal position of individual gestures are adjustable, the number of possible motor transformations increases exponentially with sequence length. Identifying the optimal transformation towards a given target is therefore a computationally intractable problem. Here we show an evolutionary workaround for reducing the computational complexity of song learning in zebra finches. We prompt juveniles to modify syllable phonology and sequence in a learned song to match a newly introduced target song. Surprisingly, juveniles match each syllable to the most spectrally similar sound in the target, regardless of its temporal position, resulting in unnecessary sequence errors, that they later try to correct. Thus, zebra finches prioritize efficient learning of syllable vocabulary, at the cost of inefficient syntax learning. This strategy provides a non-optimal but computationally manageable solution to the task of vocal sequence learning.
en_US
dc.language.iso
en
en_US
dc.publisher
Nature Publishing Group
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Songbirds work around computational complexity by learning song vocabulary independently of sequence
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2017-11-01
ethz.journal.title
Nature Communications
ethz.journal.volume
8
en_US
ethz.journal.issue
1
en_US
ethz.pages.start
1247
en_US
ethz.size
11 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Departement Informationstechnologie und Elektrotechnik / Department of Information Technology and Electrical Engineering::02533 - Institut für Neuroinformatik (INI) / Institute of Neuroinformatics (INI)::03774 - Hahnloser, Richard H.R.
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Departement Informationstechnologie und Elektrotechnik / Department of Information Technology and Electrical Engineering::02533 - Institut für Neuroinformatik (INI) / Institute of Neuroinformatics (INI)::03774 - Hahnloser, Richard H.R.
ethz.date.deposited
2017-11-10T04:02:10Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-12-06T15:52:34Z
ethz.rosetta.lastUpdated
2018-02-01T10:02:04Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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