CONICET | Buscador de Institutos y Recursos Humanos

Vocal production in songbirds is a key topic regarding the motor control of a complex, learned behavior. Birdsong is the result of the interaction between the activity of an intricate set of neural nuclei specifically dedicated to song production and learning (known as the ?song system?), the respiratory system and the vocal organ. These systems interact and give rise to precise biomechanical motor gestures which result in song production. Telencephalic neural nuclei play a key role in the production of motor commands that drive the periphery, and while several attempts have been made to understand their coding strategy, difficulties arise when trying to understand neural activity in the frame of the song system as a whole. In this work, we report neural additive models embedded in an architecture compatible with the song system 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 (Serinus canaria). In this work, we show that the activity in a telencephalic nucleus required by the model to reproduce the observed respiratory gestures is compatible with electrophysiological recordings of single neuron activity in freely behaving animals.Songbirds produce their song by articulating the neural activity of areas in the brain dedicated to song production with the respiratory system and the vocal organ. Functional studies of the song system in the avian brain have been greatly focused on single units, i.e., trying to understand how this system works by measuring the activity of many individual neurons. From a technical standpoint, it is not yet possible to measure such large ensembles from multiple brain areas simultaneously, as this would involve voltage recordings of hundreds of thousands of neurons simultaneously. In this work, we present a different approach. Using a model of the neural architecture involved, we found which global neural activity patterns give rise to an output compatible with motor commands used during song production. We then recorded neural activity of individual neurons in singing birds in a particular brain region and found that the neural activity patterns provided by the model were compatible with the experimental results. In this way, models constitute a framework that could guide further experiments in the field to elucidate, from a macroscopic perspective, how different parts of the song system articulate to give rise to a delicate vocal behavior.