Dynamical model for the neural activity of singing Serinus canaria

HERBERT, CECILIA T.; BOARI, SANTIAGO; MINDLIN, GABRIEL B.; AMADOR, ANA

CHAOS AN INTERDISCIPLINARY JR OF NONLINEAR SCIENCE

AMER INST PHYSICS

Año: 2020 vol. 30

1054-1500

Vocal production in songbirds is a key topic regarding the motor control of a complex, learned behavior. Birdsong is the result of the inter-action between the activity of an intricate set of neural nuclei specifically dedicated to song production and learning (known as the ?songsystem?), the respiratory system and the vocal organ. These systems interact and give rise to precise biomechanical motor gestures whichresult in song production. Telencephalic neural nuclei play a key role in the production of motor commands that drive the periphery, andwhile several attempts have been made to understand their coding strategy, difficulties arise when trying to understand neural activity in theframe of the song system as a whole. In this work, we report neural additive models embedded in an architecture compatible with the songsystem to provide a tool to reduce the dimensionality of the problem by considering the global activity of the units in each neural nucleus.This model is capable of generating outputs compatible with measurements of air sac pressure during song production in canaries (Serinuscanaria). In this work, we show that the activity in a telencephalic nucleus required by the model to reproduce the observed respiratorygestures is compatible with electrophysiological recordings of single neuron activity in freely behaving animals.