Title |
A Hierarchical Neuronal Model for Generation and Online Recognition of Birdsongs
|
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Published in |
PLoS Computational Biology, December 2011
|
DOI | 10.1371/journal.pcbi.1002303 |
Pubmed ID | |
Authors |
Izzet B. Yildiz, Stefan J. Kiebel |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 17% |
Spain | 1 | 17% |
Unknown | 4 | 67% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 67% |
Science communicators (journalists, bloggers, editors) | 2 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Switzerland | 3 | 3% |
United States | 3 | 3% |
United Kingdom | 2 | 2% |
Germany | 1 | 1% |
Netherlands | 1 | 1% |
Russia | 1 | 1% |
Mexico | 1 | 1% |
Unknown | 78 | 87% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 28 | 31% |
Researcher | 19 | 21% |
Professor | 11 | 12% |
Student > Master | 9 | 10% |
Student > Bachelor | 5 | 6% |
Other | 10 | 11% |
Unknown | 8 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 25 | 28% |
Neuroscience | 17 | 19% |
Computer Science | 9 | 10% |
Psychology | 7 | 8% |
Engineering | 5 | 6% |
Other | 18 | 20% |
Unknown | 9 | 10% |