Section: New Results

The motor loop

Sensorimotor learning represents a challenging problem for artificial and natural systems. Several computational models try to explain the neural mechanisms at play in the brain to implement such learning. These models have several common components: a motor control model, a sensory system and a learning architecture. In S. Pagliarini's PhD, our challenge is to build a biologically plausible model for song learning in birds including neuro-anatomical and developmental constraints.

We made a review on a specific type of sensorimotor learning referred to as imitative vocal learning and examplified by song learning in birds or human complex vocalizations[35]. Sensorimotor learning represents a challenging problem for natural and artificial systems. Several computational models have been proposed to explain the neural and cognitive mechanisms at play in the brain [34]. In general, these models can be decomposed in three common components: a sensory system, a motor control device and a learning framework. The latter includes the architecture, the learning rule or optimisation method, and the exploration strategy used to guide learning. In this review, we focus on imitative vocal learning, that is exemplified in song learning in birds and speech acquisition in humans. We aim to synthesise, analyse and compare the various models of vocal learning that have been proposed, highlighting their common points and differences. We first introduce the biological context, including the behavioural and physiological hallmarks of vocal learning and sketch the neural circuits involved. Then, we detail the different components of a vocal learning model and how they are implemented in the reviewed models.

On this topic, X. Hinaut is also collaborating with Catherine del Negro’s team (CNRS, NeuroPSI, Orsay) on the representation of syntax in songbird brains. In particular, the project aims at (1) linking the neural activity of a sensori-motor area (HVC) to syntax elements in the songs of domestic canaries ; (2) analysing the audio files and transcripts of canary songs in order to find syntax cues and higher order representations (graph properties of songs, evaluate Markovian forward and backward transition probabilities of various orders). The song transcription and analyses has been done by M1 intern (in Neuroscience) Juliette Giraudon, and preliminary work on song segmentation and classification has been done by L3 intern (ENS) Pierre Marcus. In December, Aurore Cazala (student in the NeuroPSI collaborator team since 2014) defended her PhD at the University of Paris-Saclay on "Codage neuronal de l’ordre des signaux acoustiques dans le chant des oiseaux chanteurs"; X. Hinaut participated in studies done in this PhD.