Quantification of developmental birdsong learning from the subsyllabic scale to cultural evolution

Abstract

Quantitative analysis of behavior plays an important role in birdsong neuroethology, serving as a common denominator in studies spanning molecular to system-level investigation of sensory-motor conversion, developmental learning, and pattern generation in the brain. In this review, we describe the role of behavioral analysis in facilitating cross-level integration. Modern sound analysis approaches allow investigation of developmental song learning across multiple time scales. Combined with novel methods that allow experimental control of vocal changes, it is now possible to test hypotheses about mechanisms of vocal learning. Further, song analysis can be done at the population level across generations to track cultural evolution and multigenerational behavioral processes. Complementing the investigation of song development with noninvasive brain imaging technology makes it now possible to study behavioral dynamics at multiple levels side by side with developmental changes in brain connectivity and in auditory responses.B irdsong neuroethology is a small but diverse and influential subfield of neuroscience, studied in ∼100 laboratories, and covering a range of research areas spanning the molecular to the organismal levels of investigation. The quantification of singing behavior has been a common denominator across most of those studies. Over the past four decades, birdsong neuroethology has made tremendous gains by incorporating existing tools for representing and analyzing sound (1). The sonogram (Fig. 1A) (2-4) provides an excellent descriptive model of singing behavior, and over the past decade several analysis approaches were developed that allow automatic segmentation categorization and comparison of vocal sounds (5-9). In this review, we describe the role of behavioral analysis in the progress made in birdsong neuroethology research.The past 20 y or so have seen rapid progress in understanding the neural and molecular processes involved in song. However, this progress was preceded by a long tradition of quantifying singing behavior (10, 11). Birdsong is uniquely amenable and attractive for behavioral analysis for a number of reasons: adult song is a structured behavior, repeated with a high degree of stereotypy (Fig. 1A). This makes it relatively easy to detect and characterize song structure and components (e.g., notes, syllables, motifs, and phrases) to align song renditions to each other, to assess the degree of similarity between songs, and, ultimately, to relate singing behavior to neuronal events and thus elucidate underlying neural mechanisms. We start by reviewing the role of behavioral quantification in achieving integration across levels of investigation. We then present a short outline of sound analysis techniques currently used for the tracking of developmental song learning, and an experimental approach for hypothesis-driven investigation of song development. Finally, we present a generalization of behavioral analysis to the population and cross-generation levels, and propose some ideas about n...

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