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Brain-inspired model for early vocal learning and correspondence matching using free-energy optimization
We propose a developmental model inspired by the cortico-basal system (CX-BG) for vocal learning in babies and for solving the correspondence mismatch problem they face when they hear unfamiliar voices, with different tones and pitches. This model is based on the neural architecture INFERNO standing...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7891699/ https://www.ncbi.nlm.nih.gov/pubmed/33600482 http://dx.doi.org/10.1371/journal.pcbi.1008566 |
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author | Pitti, Alexandre Quoy, Mathias Boucenna, Sofiane Lavandier, Catherine |
author_facet | Pitti, Alexandre Quoy, Mathias Boucenna, Sofiane Lavandier, Catherine |
author_sort | Pitti, Alexandre |
collection | PubMed |
description | We propose a developmental model inspired by the cortico-basal system (CX-BG) for vocal learning in babies and for solving the correspondence mismatch problem they face when they hear unfamiliar voices, with different tones and pitches. This model is based on the neural architecture INFERNO standing for Iterative Free-Energy Optimization of Recurrent Neural Networks. Free-energy minimization is used for rapidly exploring, selecting and learning the optimal choices of actions to perform (eg sound production) in order to reproduce and control as accurately as possible the spike trains representing desired perceptions (eg sound categories). We detail in this paper the CX-BG system responsible for linking causally the sound and motor primitives at the order of a few milliseconds. Two experiments performed with a small and a large audio database show the capabilities of exploration, generalization and robustness to noise of our neural architecture in retrieving audio primitives during vocal learning and during acoustic matching with unheared voices (different genders and tones). |
format | Online Article Text |
id | pubmed-7891699 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-78916992021-02-25 Brain-inspired model for early vocal learning and correspondence matching using free-energy optimization Pitti, Alexandre Quoy, Mathias Boucenna, Sofiane Lavandier, Catherine PLoS Comput Biol Research Article We propose a developmental model inspired by the cortico-basal system (CX-BG) for vocal learning in babies and for solving the correspondence mismatch problem they face when they hear unfamiliar voices, with different tones and pitches. This model is based on the neural architecture INFERNO standing for Iterative Free-Energy Optimization of Recurrent Neural Networks. Free-energy minimization is used for rapidly exploring, selecting and learning the optimal choices of actions to perform (eg sound production) in order to reproduce and control as accurately as possible the spike trains representing desired perceptions (eg sound categories). We detail in this paper the CX-BG system responsible for linking causally the sound and motor primitives at the order of a few milliseconds. Two experiments performed with a small and a large audio database show the capabilities of exploration, generalization and robustness to noise of our neural architecture in retrieving audio primitives during vocal learning and during acoustic matching with unheared voices (different genders and tones). Public Library of Science 2021-02-18 /pmc/articles/PMC7891699/ /pubmed/33600482 http://dx.doi.org/10.1371/journal.pcbi.1008566 Text en © 2021 Pitti et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Pitti, Alexandre Quoy, Mathias Boucenna, Sofiane Lavandier, Catherine Brain-inspired model for early vocal learning and correspondence matching using free-energy optimization |
title | Brain-inspired model for early vocal learning and correspondence matching using free-energy optimization |
title_full | Brain-inspired model for early vocal learning and correspondence matching using free-energy optimization |
title_fullStr | Brain-inspired model for early vocal learning and correspondence matching using free-energy optimization |
title_full_unstemmed | Brain-inspired model for early vocal learning and correspondence matching using free-energy optimization |
title_short | Brain-inspired model for early vocal learning and correspondence matching using free-energy optimization |
title_sort | brain-inspired model for early vocal learning and correspondence matching using free-energy optimization |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7891699/ https://www.ncbi.nlm.nih.gov/pubmed/33600482 http://dx.doi.org/10.1371/journal.pcbi.1008566 |
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