A bio-inspired model towards vocal gesture learning in songbird

Silvia Pagliarini 1 Xavier Hinaut 1 Arthur Leblois 2
1 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives [Bordeaux]
Abstract : The paper proposes a bio-inspired model for an imitative sensorimotor learning, which aims at building a map between the sensory representations of gestures (sensory targets) and their underlying motor pattern through random exploration of the motor space. An example of such learning process occurs during vocal learning in humans or birds, when young subjects babble and learn to copy previously heard adult vocalizations. Previous work has suggested that a simple Hebbian learning rule allows perfect imitation when sensory feedback is a purely linear function of the motor pattern underlying movement production. We aim at generalizing this model to the more realistic case where sensory responses are sparse and non-linear. To this end, we explore the performance of various learning rules and nor-malizations and discuss their biological relevance. Importantly, the proposed model is robust whatever normalization is chosen. We show that both the imitation quality and the convergence time are highly dependent on the sensory selectivity and dimension of the motor representation.
Type de document :
Communication dans un congrès
2018 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), Sep 2018, Tokyo, Japan
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https://hal.inria.fr/hal-01906459
Contributeur : Silvia Pagliarini <>
Soumis le : vendredi 26 octobre 2018 - 17:50:39
Dernière modification le : jeudi 7 février 2019 - 16:55:51
Document(s) archivé(s) le : dimanche 27 janvier 2019 - 19:04:08

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  • HAL Id : hal-01906459, version 1

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Silvia Pagliarini, Xavier Hinaut, Arthur Leblois. A bio-inspired model towards vocal gesture learning in songbird. 2018 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), Sep 2018, Tokyo, Japan. 〈hal-01906459〉

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