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dc.contributor.author
Noel, Gabriel David
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Mugno, Lionel E.
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Andres, Daniela Sabrina
dc.date.available
2023-10-17T18:43:22Z
dc.date.issued
2023-08
dc.identifier.citation
Noel, Gabriel David; Mugno, Lionel E.; Andres, Daniela Sabrina; From signals to music: a bottom-up approach to the structure of neuronal activity; Frontiers Media; Frontiers in Systems Neuroscience; 17; 8-2023; 1-9
dc.identifier.uri
http://hdl.handle.net/11336/215245
dc.description.abstract
Introduction: The search for the “neural code” has been a fundamental quest in neuroscience, concerned with the way neurons and neuronal systems process and transmit information. However, the term “code” has been mostly used as a metaphor, seldom acknowledging the formal definitions introduced by information theory, and the contributions of linguistics and semiotics not at all. The heuristic potential of the latter was suggested by structuralism, which turned the methods and findings of linguistics to other fields of knowledge. For the study of complex communication systems, such as human language and music, the necessity of an approach that considers multilayered, nested, structured organization of symbols becomes evident. We work under the hypothesis that the neural code might be as complex as these human-made codes. To test this, we propose a bottom-up approach, constructing a symbolic logic in order to translate neuronal signals into music scores. Methods: We recorded single cells’ activity from the rat’s globus pallidus pars interna under conditions of full alertness, blindfoldedness and environmental silence. We analyzed the signals with statistical, spectral, and complex methods, including Fast Fourier Transform, Hurst exponent and recurrence plot analysis. Results: The results indicated complex behavior and recurrence graphs consistent with fractality, and a Hurst exponent >0.5, evidencing temporal persistence. On the whole, these features point toward a complex behavior of the time series analyzed, also present in classical music, which upholds the hypothesis of structural similarities between music and neuronal activity. Furthermore, through our experiment we performed a comparison between music and raw neuronal activity. Our results point to the same conclusion, showing the structures of music and neuronal activity to be homologous. The scores were not only spontaneously tonal, but they exhibited structure and features normally present in human-made musical creations. Discussion: The hypothesis of a structural homology between the neural code and the code of music holds, suggesting that some of the insights introduced by linguistic and semiotic theory might be a useful methodological resource to go beyond the limits set by metaphoric notions of “code.”
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Frontiers Media
dc.relation
https://ri.conicet.gov.ar/handle/11336/215244
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
FRACTALS
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LINGUISTICS
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LÉVI-STRAUSS
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MUSIC
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NEURAL CODE
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STRUCTURAL HEARING
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STRUCTURALISM
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Otras Ciencias Médicas
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Otras Ciencias Médicas
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CIENCIAS MÉDICAS Y DE LA SALUD
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Antropología, Etnología
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Sociología
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CIENCIAS SOCIALES
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Matemática Aplicada
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Matemáticas
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CIENCIAS NATURALES Y EXACTAS
dc.title
From signals to music: a bottom-up approach to the structure of neuronal activity
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-10-17T18:39:51Z
dc.identifier.eissn
1662-5137
dc.journal.volume
17
dc.journal.pagination
1-9
dc.journal.pais
Suiza
dc.journal.ciudad
Lausana
dc.description.fil
Fil: Noel, Gabriel David. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martín. Instituto de Altos Estudios Sociales; Argentina
dc.description.fil
Fil: Mugno, Lionel E.. Conservatorio "Alfredo Luis Schiuma"; Argentina
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Fil: Andres, Daniela Sabrina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Tecnologias Emergentes y Ciencias Aplicadas. - Universidad Nacional de San Martin. Instituto de Tecnologias Emergentes y Ciencias Aplicadas.; Argentina. Universidad Nacional de San Martin. Escuela de Ciencia y Tecnologia. Laboratorio de Neuroingenieria.; Argentina
dc.journal.title
Frontiers in Systems Neuroscience
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fnsys.2023.1171984/full
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.3389/fnsys.2023.1171984
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