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Core and Shell Song Systems Unique to the Parrot Brain

Overview of attention for article published in PLOS ONE, June 2015
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Title
Core and Shell Song Systems Unique to the Parrot Brain
Published in
PLOS ONE, June 2015
DOI 10.1371/journal.pone.0118496
Pubmed ID
Authors

Mukta Chakraborty, Solveig Walløe, Signe Nedergaard, Emma E. Fridel, Torben Dabelsteen, Bente Pakkenberg, Mads F. Bertelsen, Gerry M. Dorrestein, Steven E. Brauth, Sarah E. Durand, Erich D. Jarvis

Abstract

The ability to imitate complex sounds is rare, and among birds has been found only in parrots, songbirds, and hummingbirds. Parrots exhibit the most advanced vocal mimicry among non-human animals. A few studies have noted differences in connectivity, brain position and shape in the vocal learning systems of parrots relative to songbirds and hummingbirds. However, only one parrot species, the budgerigar, has been examined and no differences in the presence of song system structures were found with other avian vocal learners. Motivated by questions of whether there are important differences in the vocal systems of parrots relative to other vocal learners, we used specialized constitutive gene expression, singing-driven gene expression, and neural connectivity tracing experiments to further characterize the song system of budgerigars and/or other parrots. We found that the parrot brain uniquely contains a song system within a song system. The parrot "core" song system is similar to the song systems of songbirds and hummingbirds, whereas the "shell" song system is unique to parrots. The core with only rudimentary shell regions were found in the New Zealand kea, representing one of the only living species at a basal divergence with all other parrots, implying that parrots evolved vocal learning systems at least 29 million years ago. Relative size differences in the core and shell regions occur among species, which we suggest could be related to species differences in vocal and cognitive abilities.

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Geographical breakdown

Country Count As %
Austria 3 3%
Hungary 1 <1%
Germany 1 <1%
Chile 1 <1%
Netherlands 1 <1%
France 1 <1%
Denmark 1 <1%
Unknown 104 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 24%
Researcher 17 15%
Student > Bachelor 16 14%
Student > Master 14 12%
Other 7 6%
Other 19 17%
Unknown 13 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 40 35%
Neuroscience 22 19%
Biochemistry, Genetics and Molecular Biology 10 9%
Linguistics 4 4%
Computer Science 3 3%
Other 18 16%
Unknown 16 14%