Browsing by Subject "Birdsong"

The ability to learn and to modify complex vocal sequences requires extensive practice coupled with performance evaluation through auditory feedback. An efficient solution to the challenge of vocal learning, stemming from reinforcement learning theory, proposes that an “actor” learns correct vocal behavior through the instructive guidance of an auditory “critic.” However, the neural circuit mechanisms supporting performance evaluation and even how “actor” and “critic” circuits are instantiated in biological brains are fundamental mysteries. Here, I use a songbird model to dissociate “actor” and “critic” circuits and uncover biological mechanisms for vocal learning.

First, I employ closed-loop optogenetic methods in singing birds to identify two inputs to midbrain dopamine neurons that operate in an opponent fashion to guide vocal learning. Next, I employ electrophysiological methods to establish a microcircuit architecture underlying this opponent mechanism. Notably, I show that disrupting activity in these midbrain dopamine inputs precisely when auditory feedback is processed impairs learning, showing that they function as “critics.” Conversely, I show that disrupting activity in a downstream premotor region prior to vocal production prevents learning, consistent with an “actor” role. Taken together, these experiments dissociate discrete “actor” and “critic” circuits in the songbird’s brain and elucidate neural circuit and microcircuit mechanisms by which “actors” and “critics” working cooperatively enable vocal 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.