Data-Driven Analysis of Zebra Finch Song Copying and Learning

Children learn crucial skills like speech by imitating the behavior of skilled adults. Similarly, juvenile zebra finches learn to sing by learning to imitate adults. This song learning process enables laboratory study of juvenile imitative learning. But it also poses behavioral quantification challenges. Zebra finches produce hundreds of thousands of complex vocalizations during vocal development. These undergo learned changes with respect to acoustic features that are relevant to the animal but experimentally unknown \textit{a priori}. Recent developments in machine learning provide tools to reduce the dimensionality of complex behaviors, plausibly simplifying this inference challenge. These tools have not been validated on or applied to song learning problems.

Here, I validate the use of an autoencoder to extract copying-relevant features from zebra finch song. Then, I develop tools to quantify developmental song change with respect to extracted features. In particular, I generate forward models that quantify developmental changes in syllable acoustic distributions. I also develop a method to score syllable maturity on a rendition-by-rendition basis. Both these techniques reveal circadian behavioral patterns that differ between normally developing and untutored juveniles, suggesting that tutoring not only sets target song acoustics; it directly affects intrinsic features of practice behavior. Critically, these tools enable making concrete predictions from otherwise abstract song learning theories.