S-120 | Low dimensional neural dynamics underlying the generation of rhythmic vocal behavior in canaries

Sensory and Motor Systems
Author: Fiamma Liz Leites | Email: fiamma.liz17@gmail.com

Fiamma L. Leites^1°2°, Cecilia T. Herbert^1°2°, Felipe I. Cignoli^1°2°,  Gabriel B. Mindlin^1°2°, Ana Amador^1°2°

^1° Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
^2° Instituto de Física Interdisciplinaria y Aplicada, INFINA-CONICET, Buenos Aires, Argentina

The dynamical features of neural coding of complex learned vocal behavior remain under active investigation. Here we examined multiunit neural activity in the song system nucleus HVC of singing adult male canaries (Serinus canaria). Canaries have a rich repertoire of syllable types and some flexibility in how these are sequentially deployed during song production. Using machine learning techniques to search for concise underlying structure in the data, we find a low dimensional representation of the neural recordings analyzing the modes of the latent space of an auto-encoder. These modes are closely correlated with characteristics of the associated song, such as the rhythm of singing or the shape of syllables in the sound envelope, including characteristics of the underlying motor gestures, air sac pressure patterns. Several syllable types can be represented by one mode. Our results demonstrate a tight link between peripheral and central dynamical patterns of activity during singing.