The Neuroethology of Birdsong (eBook)

Vocal signals are central for social communication across a wide range of vertebrate species; consequently, it is critical to understand the mechanisms underlying the learning, control, and evolution of vocal communication. Songbirds are at the forefront of research into such neural mechanisms. Indeed, songbirds provide a particularly important model system for this endeavor because of the many parallels between birdsong and human speech. Specifically, (1) songbirds are one of the few vertebrate species that, like humans, learn their vocal signals during development, (2) the processes of song learning and control in songbirds shares many parallels with the process of speech acquisition in humans, and (3) there exist deep homologies between the circuits for the learning, control, and processing of vocal signals across songbirds and humans. In addition, because of the diversity of songbirds and song learning strategies, songbirds offer a powerful model system to use the comparative method to reveal mechanisms underlying the evolution of song learning and production. Taken together, research on songbirds can not only reveal general principles underlying vertebrate vocal communication but can also provide insight into potential mechanisms underlying the learning, control, and processing of speech. 
This volume will cover a range of topics in birdsong spanning multiple level of analysis.  Chapters will be authored by the world's leading experts on birdsong and will provide comprehensive reviews of the processes underlying song learning, of the neural circuits for song learning and control as well as for the extraction and processing of song information, of the selection pressures underlying song evolution, and of genetic and molecular mechanisms underlying the learning and evolution of song. The primary goals of this volume are to provide comprehensive, integrative, and comparative perspectives on birdsong and to underscore the importance of birdsong to biomedical research, evolutionary biology, and behavioral, systems, and computational neuroscience.