Background: Songbirds are a preeminent animal model for understanding the neural basis underlying the development and evolution of a complex learned behavior, bird song. However, only a few quantitative methods exist to analyze these species-specific sequential behaviors in multiple species using the same calculation method.
New method: We report a method of analysis that focuses on calculating the frequency of characteristic syllable transitions in songs. This method comprises two steps: The first step involves forming correlation matrices of syllable similarity scores, named syllable similarity matrices (SSMs); these are obtained by calculating the round-robin comparison of all the syllables in two songs, while maintaining the sequential order of syllables in the songs. In the second step, each occurrence rate of three patterns of binarized "2 rows x 2 columns" cells in the SSMs is calculated to extract information on the characteristic syllable transitions.
Results: The SSM analysis method allowed obtaining species-specific features of song patterns and intraspecies individual variability simultaneously. Furthermore, it enabled quantitative tracking of the developmental trajectory of the syllable sequence patterns.
Comparison with existing method: This method enables us to extract the species-specific song patterns and dissect the regulation of song syntax development without human-biased procedures for syllable identification. This method can be adapted to study the acoustic communication systems in several animal species, such as insects and mammals.
Conclusions: This present method provides a comprehensive qualitative approach for understanding the regulation of species specificity and its development in vocal learning. (C) 2016 Elsevier B.V. All rights reserved.