Modeling the neural network responsible for song learningmars 2021 Directeur(s) de thèse : Xavier Hinaut & Arthur Leblois Résumé de thèse
Humans learn to speak in a similar way as how songbirds learn to sing.
Both learn to speak/sing by imitation from an early age going through the same stages of development. First, they listen to their parents’ vocalizations, then they try to reproduce them: initially babbling until their vocal output mimics those of their parents.
Songbirds have dedicated brain circuits for vocal learning, making them an ideal model for exploring the representation of imitative vocal learning.
My research project aims to build a bio-inspired model to describe imitative vocal learning.
This model consists in a perceptual-motor loop where a sensory evaluation mechanism drives learning.
The sound production is obtained from real recordings, using recent developments in artificial intelligence.
This project, in between computer science and neuroscience, may help to better understand imitative vocal learning, and more generally sensorimotor learning.
Jury composition Mme. DESAINTE-CATHERINE, Myriam, University of Bordeaux (Examinatrice) M. HAHNLOSER, Richard, ETH Zurich (Rapporteur) M. SCHWARTZ, Jean-Luc, CNRS (Rapporteur) Mme. WARLAUMONT, Anne, University of California, Los Angeles (Examinatrice)