Toward an improvement of the analysis of neural coding

Autores
Alegre Cortés, Javier; Soto Sánchez, Cristina; Albarracin, Ana Lia; Farfan, Fernando Daniel; Val Calvo, Mikel; Ferrandez, José M.; Fernandez, Eduardo
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields of research as speech recognition, behavioral states classification, or LFP recordings. However, despite the huge technological advances in neural data reduction of dimensionality, pattern selection, and clustering during the last years, there has not been a proportional development of the analytical tools used for Time–Frequency (T–F) analysis in neuroscience. Bearing this in mind, we introduce the convenience of using non-linear, non-stationary tools, EMD algorithms in particular, for the transformation of the oscillatory neural data (EEG, EMG, spike oscillations…) into the T–F domain prior to its analysis with machine learning tools. We support that to achieve meaningful conclusions, the transformed data we analyze has to be as faithful as possible to the original recording, so that the transformations forced into the data due to restrictions in the T–F computation are not extended to the results of the machine learning analysis. Moreover, bioinspired computation such as brain–machine interface may be enriched from a more precise definition of neuronal coding where non-linearities of the neuronal dynamics are considered.
Fil: Alegre Cortés, Javier. Universidad de Miguel Hernández; España
Fil: Soto Sánchez, Cristina. Universidad de Alicante; España. Universidad de Miguel Hernández; España. Centro de Redes de Investigación Biomédica en Bioingeniería, Biomateriales y Nanomedicina; España
Fil: Albarracin, Ana Lia. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina
Fil: Farfan, Fernando Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología; Argentina
Fil: Val Calvo, Mikel. Universidad Politécnica de Cartagena; España
Fil: Ferrandez, José M.. Universidad Politécnica de Cartagena; España
Fil: Fernandez, Eduardo. Centro de Redes de Investigación Biomédica en Bioingeniería, Biomateriales y Nanomedicina; España. Universidad de Miguel Hernández; España
Materia
NEURAL CODING
NON-LINEAR SIGNALS
NA-MEMD
MACHINE LEARNING CLASSIFICATION
SINGLE TRIAL CLASSIFICATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/101936
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Toward an improvement of the analysis of neural codingAlegre Cortés, JavierSoto Sánchez, CristinaAlbarracin, Ana LiaFarfan, Fernando DanielVal Calvo, MikelFerrandez, José M.Fernandez, EduardoNEURAL CODINGNON-LINEAR SIGNALSNA-MEMDMACHINE LEARNING CLASSIFICATIONSINGLE TRIAL CLASSIFICATIONhttps://purl.org/becyt/ford/1.7https://purl.org/becyt/ford/1Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields of research as speech recognition, behavioral states classification, or LFP recordings. However, despite the huge technological advances in neural data reduction of dimensionality, pattern selection, and clustering during the last years, there has not been a proportional development of the analytical tools used for Time–Frequency (T–F) analysis in neuroscience. Bearing this in mind, we introduce the convenience of using non-linear, non-stationary tools, EMD algorithms in particular, for the transformation of the oscillatory neural data (EEG, EMG, spike oscillations…) into the T–F domain prior to its analysis with machine learning tools. We support that to achieve meaningful conclusions, the transformed data we analyze has to be as faithful as possible to the original recording, so that the transformations forced into the data due to restrictions in the T–F computation are not extended to the results of the machine learning analysis. Moreover, bioinspired computation such as brain–machine interface may be enriched from a more precise definition of neuronal coding where non-linearities of the neuronal dynamics are considered.Fil: Alegre Cortés, Javier. Universidad de Miguel Hernández; EspañaFil: Soto Sánchez, Cristina. Universidad de Alicante; España. Universidad de Miguel Hernández; España. Centro de Redes de Investigación Biomédica en Bioingeniería, Biomateriales y Nanomedicina; EspañaFil: Albarracin, Ana Lia. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; ArgentinaFil: Farfan, Fernando Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología; ArgentinaFil: Val Calvo, Mikel. Universidad Politécnica de Cartagena; EspañaFil: Ferrandez, José M.. Universidad Politécnica de Cartagena; EspañaFil: Fernandez, Eduardo. Centro de Redes de Investigación Biomédica en Bioingeniería, Biomateriales y Nanomedicina; España. Universidad de Miguel Hernández; EspañaFrontiers Research Foundation2018-01-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/101936Alegre Cortés, Javier; Soto Sánchez, Cristina; Albarracin, Ana Lia; Farfan, Fernando Daniel; Val Calvo, Mikel; et al.; Toward an improvement of the analysis of neural coding; Frontiers Research Foundation; Frontiers in Neuroinformatics; 11; 77; 10-1-2018; 1-61662-51961662-5196CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://journal.frontiersin.org/article/10.3389/fninf.2017.00077/fullinfo:eu-repo/semantics/altIdentifier/doi/10.3389/fninf.2017.00077info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:24:01Zoai:ri.conicet.gov.ar:11336/101936instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:24:02.099CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Toward an improvement of the analysis of neural coding
title Toward an improvement of the analysis of neural coding
spellingShingle Toward an improvement of the analysis of neural coding
Alegre Cortés, Javier
NEURAL CODING
NON-LINEAR SIGNALS
NA-MEMD
MACHINE LEARNING CLASSIFICATION
SINGLE TRIAL CLASSIFICATION
title_short Toward an improvement of the analysis of neural coding
title_full Toward an improvement of the analysis of neural coding
title_fullStr Toward an improvement of the analysis of neural coding
title_full_unstemmed Toward an improvement of the analysis of neural coding
title_sort Toward an improvement of the analysis of neural coding
dc.creator.none.fl_str_mv Alegre Cortés, Javier
Soto Sánchez, Cristina
Albarracin, Ana Lia
Farfan, Fernando Daniel
Val Calvo, Mikel
Ferrandez, José M.
Fernandez, Eduardo
author Alegre Cortés, Javier
author_facet Alegre Cortés, Javier
Soto Sánchez, Cristina
Albarracin, Ana Lia
Farfan, Fernando Daniel
Val Calvo, Mikel
Ferrandez, José M.
Fernandez, Eduardo
author_role author
author2 Soto Sánchez, Cristina
Albarracin, Ana Lia
Farfan, Fernando Daniel
Val Calvo, Mikel
Ferrandez, José M.
Fernandez, Eduardo
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv NEURAL CODING
NON-LINEAR SIGNALS
NA-MEMD
MACHINE LEARNING CLASSIFICATION
SINGLE TRIAL CLASSIFICATION
topic NEURAL CODING
NON-LINEAR SIGNALS
NA-MEMD
MACHINE LEARNING CLASSIFICATION
SINGLE TRIAL CLASSIFICATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.7
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields of research as speech recognition, behavioral states classification, or LFP recordings. However, despite the huge technological advances in neural data reduction of dimensionality, pattern selection, and clustering during the last years, there has not been a proportional development of the analytical tools used for Time–Frequency (T–F) analysis in neuroscience. Bearing this in mind, we introduce the convenience of using non-linear, non-stationary tools, EMD algorithms in particular, for the transformation of the oscillatory neural data (EEG, EMG, spike oscillations…) into the T–F domain prior to its analysis with machine learning tools. We support that to achieve meaningful conclusions, the transformed data we analyze has to be as faithful as possible to the original recording, so that the transformations forced into the data due to restrictions in the T–F computation are not extended to the results of the machine learning analysis. Moreover, bioinspired computation such as brain–machine interface may be enriched from a more precise definition of neuronal coding where non-linearities of the neuronal dynamics are considered.
Fil: Alegre Cortés, Javier. Universidad de Miguel Hernández; España
Fil: Soto Sánchez, Cristina. Universidad de Alicante; España. Universidad de Miguel Hernández; España. Centro de Redes de Investigación Biomédica en Bioingeniería, Biomateriales y Nanomedicina; España
Fil: Albarracin, Ana Lia. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina
Fil: Farfan, Fernando Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología; Argentina
Fil: Val Calvo, Mikel. Universidad Politécnica de Cartagena; España
Fil: Ferrandez, José M.. Universidad Politécnica de Cartagena; España
Fil: Fernandez, Eduardo. Centro de Redes de Investigación Biomédica en Bioingeniería, Biomateriales y Nanomedicina; España. Universidad de Miguel Hernández; España
description Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields of research as speech recognition, behavioral states classification, or LFP recordings. However, despite the huge technological advances in neural data reduction of dimensionality, pattern selection, and clustering during the last years, there has not been a proportional development of the analytical tools used for Time–Frequency (T–F) analysis in neuroscience. Bearing this in mind, we introduce the convenience of using non-linear, non-stationary tools, EMD algorithms in particular, for the transformation of the oscillatory neural data (EEG, EMG, spike oscillations…) into the T–F domain prior to its analysis with machine learning tools. We support that to achieve meaningful conclusions, the transformed data we analyze has to be as faithful as possible to the original recording, so that the transformations forced into the data due to restrictions in the T–F computation are not extended to the results of the machine learning analysis. Moreover, bioinspired computation such as brain–machine interface may be enriched from a more precise definition of neuronal coding where non-linearities of the neuronal dynamics are considered.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/101936
Alegre Cortés, Javier; Soto Sánchez, Cristina; Albarracin, Ana Lia; Farfan, Fernando Daniel; Val Calvo, Mikel; et al.; Toward an improvement of the analysis of neural coding; Frontiers Research Foundation; Frontiers in Neuroinformatics; 11; 77; 10-1-2018; 1-6
1662-5196
1662-5196
CONICET Digital
CONICET
url http://hdl.handle.net/11336/101936
identifier_str_mv Alegre Cortés, Javier; Soto Sánchez, Cristina; Albarracin, Ana Lia; Farfan, Fernando Daniel; Val Calvo, Mikel; et al.; Toward an improvement of the analysis of neural coding; Frontiers Research Foundation; Frontiers in Neuroinformatics; 11; 77; 10-1-2018; 1-6
1662-5196
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.frontiersin.org/article/10.3389/fninf.2017.00077/full
info:eu-repo/semantics/altIdentifier/doi/10.3389/fninf.2017.00077
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Frontiers Research Foundation
publisher.none.fl_str_mv Frontiers Research Foundation
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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