Low-dimensional neural dynamics underlying rhythmic vocal behavior in songbirds

Abstract

Birdsong is a complex learned behavior that requires millisecond-scale precision in the coordinated activation of respiratory and vocal muscles to generate sound. Canary song consists of sequences of syllables organized into phrases, in which each syllable type is repeated at a characteristic rate, giving rise to a well-defined rhythmic vocal behavior. Here, we analyze neural population activity in the telencephalic song system nucleus HVC of singing adult male canaries (Serinus canaria), in relation to both vocal output and the underlying respiratory motor gestures. To uncover structure in these high-dimensional neural recordings, we used an unsupervised autoencoder. We found that a three-dimensional latent space was sufficient to reconstruct the data with minimal information loss, revealing a low-dimensional representation of HVC population activity. The oscillation frequencies of the latent modes closely matched both the syllabic repetition rate and the corresponding respiratory motor patterns. These results show that multiunit activity in HVC captures key rhythmic features of song at the population level, providing a dynamical representation of behaviorally relevant motor structure. More broadly, our findings highlight how data-driven dimensionality reduction can reveal structured, low-dimensional neural dynamics underlying complex learned motor behaviors.