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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/101936
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T09:54:42Zoai: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:34982025-09-29 09:54:42.339CONICET 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 |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.070432 |