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  A hierarchical neuronal model for generation and online recognition of birdsongs

Yildiz, I. B., & Kiebel, S. J. (2011). A hierarchical neuronal model for generation and online recognition of birdsongs. PLoS Computational Biology, 7(12): e1002303. doi:10.1371/journal.pcbi.1002303.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0012-10E1-E Version Permalink: http://hdl.handle.net/21.11116/0000-0002-4C99-D
Genre: Journal Article

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 Creators:
Yildiz, Izzet Burak1, Author              
Kiebel, Stefan J.1, Author              
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              

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Free keywords: Birdsong; Hierarchy; Model; Generation; Recognition; Bayesian; Free energy; HVC; RA; Syrinx; Oscillation; Dynamical
 Abstract: The neuronal system underlying learning, generation and recognition of song in birds is one of the best-studied systems in the neurosciences. Here, we use these experimental findings to derive a neurobiologically plausible, dynamic, hierarchical model of birdsong generation and transform it into a functional model of birdsong recognition. The generation model consists of neuronal rate models and includes critical anatomical components like the premotor song-control nucleus HVC (proper name), the premotor nucleus RA (robust nucleus of the arcopallium), and a model of the syringeal and respiratory organs. We use Bayesian inference of this dynamical system to derive a possible mechanism for how birds can efficiently and robustly recognize the songs of their conspecifics in an online fashion. Our results indicate that the specific way birdsong is generated enables a listening bird to robustly and rapidly perceive embedded information at multiple time scales of a song. The resulting mechanism can be useful for investigating the functional roles of auditory recognition areas and providing predictions for future birdsong experiments.

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Language(s): eng - English
 Dates: 2011-06-202011-10-292011-12-15
 Publication Status: Published online
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 Identifiers: DOI: 10.1371/journal.pcbi.1002303
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 7 (12) Sequence Number: e1002303 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: /journals/resource/1000000000017180_1