Temporal Structure in Zebra Finch Song: Implications for Motor Coding

Abstract

Adult zebra finch songs consist of stereotyped sequences of syllables. Although some behavioral and physiological data suggest that songs are structured hierarchically, there is also evidence that they are driven by nonhierarchical, clock-like bursting in the premotor nucleus HVC (used as a proper name). In this study, we developed a semiautomated template-matching algorithm to identify repeated sequences of syllables and a modified dynamic time-warping algorithm to make fine-grained measurements of the temporal structure of song. We find that changes in song length are expressed across the song as a whole rather than resulting from an accumulation of independent variance during singing. Song length changes systematically over the course of a day and is related to the general level of bird activity as well as the presence of a female. The data also show patterns of variability that suggest distinct mechanisms underlying syllable and gap lengths: as tempo varies, syllables stretch and compress proportionally less than gaps, whereas syllable-syllable and gap-gap correlations are significantly stronger than syllable-gap correlations. There is also increased temporal variability at motif boundaries and especially strong positive correlations between the same syllables sung in different motifs. Finally, we find evidence that syllable onsets may have a special role in aligning syllables with global song structure. Generally, the timing data support a hierarchical view in which song is composed of smaller syllable-based units and provide a rich set of constraints for interpreting the results of physiological recordings.

Differential Influence of Frequency, Timing, and Intensity Cues in a Complex Acoustic Categorization Task

“…Analyses in other papers indicate that pitch can vary by 1-3% (0.1-0.4 octaves) (Kao and Brainard 2006), while duration can vary by 1-4% (Cooper and Goller 2006;Glaze and Troyer 2006). Variation within the normal range for individual birds should not affect the bird's classification of an individual's song in an operant classification task, whereas variation beyond that range might or might not, depending on the importance of the parameter to recognition and classification.…”

“…Duration, like pitch, was multiplied and divided by 100, 101, 102, 104, 108, 116, 132, and 164% (equivalent to durations ranging from 61 to 164% of the original song length). The duration of individual notes varies by 1-4% in natural song (Cooper and Goller 2006;Glaze and Troyer 2006;Kao and Brainard 2006). Our manipulations of duration and pitch thus spanned an equivalent range, both in terms of percent change and in comparison to the range of natural within-individual variability in each parameter.…”

“…A bird's song is a high-dimensional stimulus not unlike a human voice with structure that can be parameterized in many ways. Although a bird's song is recognizable, each rendition is different from the last: its volume will depend on the distance between singer and listener; background noises-often other songs-will corrupt the signal (Zann 1996); and the pitch and duration of individual notes will vary slightly (Glaze and Troyer 2006;Kao and Brainard 2006). To recognize a bird by his song, then, females must be able both to discriminate between the songs of different individuals and to ignore the variations in a single bird's song.…”

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

“…Therefore, instead of studying individual behavioral events, it became possible to study the distributions of continuous features over many sounds, and it became easier to investigate multiple time scales of song learning-from moment to moment (7,47), over cycles of night sleep and morning singing (48), and over the entire learning trajectory (8). The analysis of the stereotyped adult song gained as well from analytic approaches based on large data sets of continuous features, revealing details of fine structure and variation that were not detectable via classical analysis (49,50).…”

“…We can thus observe the dynamics of song development at multiple time scales. For example, the performance of song is affected by various factors, such as time of day (48,49) and behavioral state [presence of female (24,58) or hormonal levels (59, 60)]. These effects can be pinned down by quantifying the changes in the features and the variability of song syllables, which can be easily done by tracking changes in the location and size of their corresponding clusters in the feature distributions (Fig.…”