Séminaire DIC-ISC-CRIA – 12 janvier 2023 par Xavier HINAUT

Xavier HINAUT – 12 janvier 2023

Titre : Sensorimotor Interaction of Language and Symbol Embodiment

Résumé :

Language involves several hierarchical levels of abstraction. Most models focus on a particular level of abstraction, making them unable to model bottom-up and top-down processes. It is not yet known how the brain grounds symbols to perceptions and how these symbols emerge throughout development. Experimental evidence suggests that perception and action shape one another (e.g., motor areas activated during speech perception) but the precise mechanisms involved in this action-perception shaping at various levels of abstraction are still largely unknown. My work includes modelling language comprehension, language acquisition from a robotic perspective, sensorimotor function and extended models of Reservoir Computing. I will also present general results on reservoir computing, and why it is an interesting framework to model cognitive processes, such as working memory.

Bio :

Xavier HINAUT is a Researcher in the Mnemosyne team at Inria in Bordeaux. His work focusses mainly on Recurrent Neural Network modelling (especially prefrontal cortex), language acquisition (applied to Robotics) and the brain codes of bird song syntax. The common thread is the neural coding and the modelling of complex sequence processing, “chunking,” learning and production, for “syntax-based” sequences, to be applied to robotics (for eventual embodiment). He manages the development of a new Reservoir Computing library in Python: https://github.com/reservoirpy/reservoirpy

Xavier HINAUT est chercheur dans l’équipe Mnemosyne à Inria à Bordeaux. Ses travaux portent principalement sur la modélisation des réseaux de neurones récurrents (en particulier le cortex préfrontal), l’acquisition du langage (appliqué à la robotique) et les codes cérébraux de la syntaxe des chants d’oiseaux. Le fil conducteur est le codage neuronal et la modélisation du traitement de séquences complexes, le « chunking », l’apprentissage et la production, pour des séquences « basées sur la syntaxe », à appliquer à la robotique (pour une éventuelle réalisation). Il dirige le développement d’une bibliothèque Python sur le Reservoir Computing: https://github.com/reservoirpy/reservoirpy

Références

Trouvain, N., Rougier, N., & Hinaut, X. (2022). Create Efficient and Complex Reservoir Computing Architectures with ReservoirPy. In International Conference on Simulation of Adaptive Behavior, pp. 91-102.

Pagliarini, S., Leblois, A., & Hinaut, X. (2021). Canary Vocal Sensorimotor Model with RNN Decoder and Low-dimensional GAN Generator. In 2021 IEEE International Conference on Development and Learning (ICDL), pp. 1-8.

Pagliarini, S., Leblois, A., & Hinaut, X. (2020). Vocal imitation in sensorimotor learning models: a comparative review. IEEE Transactions on Cognitive and Developmental Systems13(2), 326-342.

Strock, A., Hinaut, X., & Rougier, N. P. (2020). A robust model of gated working memory. Neural Computation, 32(1), 153-181.

Hinaut, X., & Dominey, P. F. (2013). Real-time parallel processing of grammatical structure in the fronto-striatal system: A recurrent network simulation study using reservoir computingPloS one8(2), e52946.

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