a hierarchical neuronal model for generation and online recognition of birdsongs层次神经元模型生成和在线识别的鸟.pdfVIP

A Hierarchical Neuronal Model for Generation and Online Recognition of Birdsongs Izzet B. Yildiz*, Stefan J. Kiebel Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany 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. Citation: Yildiz IB, Kiebel SJ (2011) A Hierarchical Neuronal Model for Generation and Online Recognition of Birdsongs. PLoS Comput Biol 7(12): e1002303. doi:10.1371/journal.pcbi.1002303 Editor: Olaf Sporns, Indiana University, United States of America Received June 20, 2011; Accepted October 29, 2011; Published December 15, 2011 Copyright: 2011 Yildiz, Kiebel. This is an open-access article distributed under the terms of the Creative Commons Attribution License, wh