Neurally driven synthesis of learned, complex vocalizations

dc.creatorArneodo, Ezequiel Matías
dc.creatorChen, Shukai
dc.creatorBrown, Daril E.
dc.creatorGilja, Vikash
dc.creatorGentner, Timothy Q.
dc.date2021-08
dc.date.accessioned2023-08-31T00:32:35Z
dc.date.available2023-08-31T00:32:35Z
dc.identifierhttp://hdl.handle.net/11336/179036
dc.identifierArneodo, Ezequiel Matías; Chen, Shukai; Brown, Daril E.; Gilja, Vikash; Gentner, Timothy Q.; Neurally driven synthesis of learned, complex vocalizations; Cell Press; Current Biology; 31; 15; 8-2021; 3419-3425
dc.identifier0960-9822
dc.identifierCONICET Digital
dc.identifierCONICET
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8543539
dc.descriptionBrain machine interfaces (BMIs) hold promise to restore impaired motor function and serve as powerful tools to study learned motor skill. While limb-based motor prosthetic systems have leveraged nonhuman primates as an important animal model,1–4 speech prostheses lack a similar animal model and are more limited in terms of neural interface technology, brain coverage, and behavioral study design.5–7 Songbirds are an attractive model for learned complex vocal behavior. Birdsong shares a number of unique similarities with human speech,8–10 and its study has yielded general insight into multiple mechanisms and circuits behind learning, execution, and maintenance of vocal motor skill.11–18 In addition, the biomechanics of song production bear similarity to those of humans and some nonhuman primates.19–23 Here, we demonstrate a vocal synthesizer for birdsong, realized by mapping neural population activity recorded from electrode arrays implanted in the premotor nucleus HVC onto low-dimensional compressed representations of song, using simple computational methods that are implementable in real time. Using a generative biomechanical model of the vocal organ (syrinx) as the low-dimensional target for these mappings allows for the synthesis of vocalizations that match the bird's own song. These results provide proof of concept that high-dimensional, complex natural behaviors can be directly synthesized from ongoing neural activity. This may inspire similar approaches to prosthetics in other species by exploiting knowledge of the peripheral systems and the temporal structure of their output.
dc.descriptionFil: Arneodo, Ezequiel Matías. University of California; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina
dc.descriptionFil: Chen, Shukai. University of California; Estados Unidos
dc.descriptionFil: Brown, Daril E.. University of California; Estados Unidos
dc.descriptionFil: Gilja, Vikash. University of California; Estados Unidos
dc.descriptionFil: Gentner, Timothy Q.. The Kavli Institute For Brain And Mind; Estados Unidos. University of California; Estados Unidos
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherCell Press
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://doi.org/10.1016/j.cub.2021.05.035
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.cub.2021.05.035
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subjectBIOPROSTHETICS
dc.subjectBIRDSONG
dc.subjectBRAIN MACHINE INTERFACES
dc.subjectELECTROPHYSIOLOGY
dc.subjectNEURAL NETWORKS
dc.subjectNONLINEAR DYNAMICS
dc.subjectSPEECH
dc.subjecthttps://purl.org/becyt/ford/1.3
dc.subjecthttps://purl.org/becyt/ford/1
dc.titleNeurally driven synthesis of learned, complex vocalizations
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:ar-repo/semantics/artículo
dc.typeinfo:eu-repo/semantics/publishedVersion


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