Role of sleep in birdsong learning During our daily life, we use a variety of gradually learned motor skills, such as riding a bike, playing guitar, or even speaking. Speech is a fundamental behavior acquired through sensorimotor learning during which a baby discovers the sets of action to execute for saying a new word through motor exploration and perception of her own actions. Previous research indicates that the basal ganglia (BG) are a likely candidate which could evaluate the discrepancy between expected and actual outcome and guide trial-and-error learning. While the BG drive behavioral adaptations that minimize errors early during learning, these adaptations are slowly incorporated in the downstream premotor cortical networks. It was proposed that the BG, through its subcortico-cortical loop, stabilize these adaptations by training premotor cortical areas. We hypothesize that BG-cortical stabilization might occur during sleep through active processes, such as coordination of large neuronal assemblies between BG-cortical areas, which is a putative neurophysiological substrate allowing the long-term imprinting of recently acquired skills. To disentangle the BG-cortical dynamics during motor skill learning and the impact of sleep on these dynamics, we use the songbird model and study the acquisition of their song learning behavior. Song learning is a natural form of sensorimotor learning akin to human speech acquisition during which a juvenile learns his song by imitating an adult bird (a tutor). Sleep in songbirds include mammalian-like features and is crucial for song learning. Moreover, songbirds have a set of interconnected brain nuclei dedicated to song, including a BG-thalamocortical loop, and that shares analogies with brain areas involved in human language. Songbirds thus represent an outstanding animal model for understanding the neurobiological bases of motor skill learning and the role of sleep. The main hypothesis is that sleep plays a critical role in the dynamical reorganization of the BG-cortical network and the consolidation of song. We perform large-scale electrophysiological recordings in freely moving birds (with Neuropixel probes) allowing the simultaneous recording of up to 384 channels. The master student will be required to establish a user-friendly analysis pipeline of the acquired data from the raw recordings to a fully processed and exploitable dataset with up-to-date computational tools. Data to be analyzed include both neuronal (spikes, local field potential) and acoustic (birdsong) signals from awake and sleeping birds. Programming skills in Python or Matlab are mandatory for this project. The master student must also be comfortable with experiments involving live animals. Project supervisor : Giret Nicolas nicolas.giret@universite-paris-saclay.fr 00331.69.82.63.68 Laboratory/ Institute : Institut des Neurosciences Paris SaclayUMR9197 CNRS Université Paris Saclay, 151, route de la Rotonde 91400 Saclay https://neuropsi.cnrs.fr