title: A hierarchical neuronal model for generation and online recognition of birdsongs.
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.
authors.affiliation: Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. yildiz@cbs.mpg.de
authors.forename: Izzet B
authors.initials: IB
authors.lastname: Yildiz
authors.forename: Stefan J
authors.initials: SJ
authors.lastname: Kiebel
mesh_terms: D000818:Animals; D001245:Association Learning / Q000502:physiology; D001499:Bayes Theorem; D003198:Computer Simulation; D008959:Models, Neurological; D009434:Neural Pathways / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D020308:Songbirds / Q000502:physiology*; D014828:Vocalization, Animal / Q000502:physiology*