Juvenile zebra finch syllables for data-driven analysis of development

Learning skilled behaviors requires intensive practice over days, months, or years. Behavioral hallmarks of practice include exploratory variation and long-term improvements, both of which can be impacted by circadian processes. During weeks of vocal practice, the juvenile male zebra finch transforms highly variable and simple song into a stable and precise copy of an adult tutor's complex song. Song variability and performance in juvenile finches also exhibit circadian structure that could influence this long-term learning process. In fact, one influential study reported juvenile song regresses towards immature performance overnight, while another suggested a more complex pattern of overnight change. However, neither of these studies thoroughly examined how circadian patterns of variability may structure the production of more or less mature songs. Here we relate the circadian dynamics of song maturation to circadian patterns of song variation, leveraging a combination of data-driven approaches. In particular we analyze juvenile singing in learned feature space that supports both data-driven measures of song maturity and generative developmental models of song production. These models reveal that circadian fluctuations in variability lead to especially regressive morning variants even without overall overnight regression, and highlight the utility of data-driven generative models for untangling these contributions.

掌握熟练行为需历经数日、数月乃至数年的密集练习。练习的典型行为特征包含探索性变异与长期性能提升，二者均会受到昼夜节律过程（circadian processes）的影响。在数周的鸣唱练习过程中，幼年雄性斑胸草雀（juvenile male zebra finch）会将高度多变且结构简单的鸣曲，转化为对成年导师复杂鸣曲（adult tutor's complex song）的稳定且精准的复刻。幼年斑胸草雀的鸣唱变异性与表现同样呈现昼夜节律结构，该结构或可对其长期学习过程产生影响。事实上，一项颇具影响力的研究曾指出，幼年鸣曲会在夜间向未成熟状态退化；而另一项研究则提出了更为复杂的夜间变化模式。然而，这两项研究均未深入探究昼夜节律性的变异模式如何塑造不同成熟度鸣曲的生成机制。本研究将鸣唱成熟的昼夜节律动态与鸣唱变异的昼夜节律模式相关联，综合运用多种数据驱动方法展开研究。具体而言，我们在学习得到的特征空间中对幼年鸣唱进行分析，该空间既可支持鸣唱成熟度的数据驱动度量，也可用于鸣唱生成的生成式发育模型（generative developmental models）的构建。上述模型揭示，即便不存在整体的夜间退化现象，鸣唱变异的昼夜节律波动仍会导致晨间鸣唱变异呈现出尤为显著的退化倾向；同时也凸显了数据驱动的生成模型在厘清这些复杂贡献方面的应用价值。