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A hierarchical neuronal model for generation and online recognition of birdsongs

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Yildiz,  Izzet Burak
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Kiebel,  Stefan J.
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Yildiz_2011.PDF
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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0012-10E1-E
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.