Classification based on dynamic mode decomposition applied to brain recognition of context

"Local Field Potentials (LFPs) are easy to access electrical signals of the brain that represent the summation in the extracellular space, of currents originated within the neurons. As such, LFPs could contain infor mation about ongoing computations in neuronal circuits and could potentially be...

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Autores principales: Martínez, Sebastián, Silva, Azul, García Violini, Demián, Piriz, Joaquin, Belluscio, Mariano, Sánchez-Peña, Ricardo
Formato: Artículo de Publicación Periódica acceptedVersion
Lenguaje:Inglés
Publicado: 2021
Materias:
CEREBRO
ALGORITMOS
Acceso en línea:https://ri.itba.edu.ar/handle/123456789/4135
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Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-4135
record_format dspace
spelling I32-R138-123456789-41352023-01-06T15:20:01Z Classification based on dynamic mode decomposition applied to brain recognition of context Martínez, Sebastián Silva, Azul García Violini, Demián Piriz, Joaquin Belluscio, Mariano Sánchez-Peña, Ricardo CEREBRO ALGORITMOS "Local Field Potentials (LFPs) are easy to access electrical signals of the brain that represent the summation in the extracellular space, of currents originated within the neurons. As such, LFPs could contain infor mation about ongoing computations in neuronal circuits and could potentially be used to design brain machine interface algorithms. However how brain computations could be decoded from LFPs is not clear. Within this context, a methodology for signal classification is proposed in this study, particularly based on the Dynamic Mode Decomposition method, in conjunction with binary clustering routines based on supervised learning. Note that, although the classification methodology is presented here in the context of a biological problem, it can be applied to a broad range of applications. Then, as a case-study, the proposed method is validated with the classification of LFP-based brain cognitive states. All the analysis, signals, and results shown in this study consider real data measured in the hippocampus, in rats perform ing exploration tasks. Consequently, it is shown that, using the measured LFP, the method infers which context was the animal exploring. Thus, evidence on the spatial codification in LFP signals is consequently provided, which still is an open question in neuroscience." 2021-09 2023-01-04T17:43:29Z 2023-01-04T17:43:29Z 2021-09 Artículo de Publicación Periódica info:eu-repo/semantics/acceptedVersion 0960-0779 https://ri.itba.edu.ar/handle/123456789/4135 en info:eu-repo/grantAgreement/ANPCyT/PICT/2017-2417/AR. Ciudad Autónoma de Buenos Aires info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chaos.2021.111056 application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic CEREBRO
ALGORITMOS
spellingShingle CEREBRO
ALGORITMOS
Martínez, Sebastián
Silva, Azul
García Violini, Demián
Piriz, Joaquin
Belluscio, Mariano
Sánchez-Peña, Ricardo
Classification based on dynamic mode decomposition applied to brain recognition of context
topic_facet CEREBRO
ALGORITMOS
description "Local Field Potentials (LFPs) are easy to access electrical signals of the brain that represent the summation in the extracellular space, of currents originated within the neurons. As such, LFPs could contain infor mation about ongoing computations in neuronal circuits and could potentially be used to design brain machine interface algorithms. However how brain computations could be decoded from LFPs is not clear. Within this context, a methodology for signal classification is proposed in this study, particularly based on the Dynamic Mode Decomposition method, in conjunction with binary clustering routines based on supervised learning. Note that, although the classification methodology is presented here in the context of a biological problem, it can be applied to a broad range of applications. Then, as a case-study, the proposed method is validated with the classification of LFP-based brain cognitive states. All the analysis, signals, and results shown in this study consider real data measured in the hippocampus, in rats perform ing exploration tasks. Consequently, it is shown that, using the measured LFP, the method infers which context was the animal exploring. Thus, evidence on the spatial codification in LFP signals is consequently provided, which still is an open question in neuroscience."
format Artículo de Publicación Periódica
acceptedVersion
author Martínez, Sebastián
Silva, Azul
García Violini, Demián
Piriz, Joaquin
Belluscio, Mariano
Sánchez-Peña, Ricardo
author_facet Martínez, Sebastián
Silva, Azul
García Violini, Demián
Piriz, Joaquin
Belluscio, Mariano
Sánchez-Peña, Ricardo
author_sort Martínez, Sebastián
title Classification based on dynamic mode decomposition applied to brain recognition of context
title_short Classification based on dynamic mode decomposition applied to brain recognition of context
title_full Classification based on dynamic mode decomposition applied to brain recognition of context
title_fullStr Classification based on dynamic mode decomposition applied to brain recognition of context
title_full_unstemmed Classification based on dynamic mode decomposition applied to brain recognition of context
title_sort classification based on dynamic mode decomposition applied to brain recognition of context
publishDate 2021
url https://ri.itba.edu.ar/handle/123456789/4135
work_keys_str_mv AT martinezsebastian classificationbasedondynamicmodedecompositionappliedtobrainrecognitionofcontext
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AT garciaviolinidemian classificationbasedondynamicmodedecompositionappliedtobrainrecognitionofcontext
AT pirizjoaquin classificationbasedondynamicmodedecompositionappliedtobrainrecognitionofcontext
AT bellusciomariano classificationbasedondynamicmodedecompositionappliedtobrainrecognitionofcontext
AT sanchezpenaricardo classificationbasedondynamicmodedecompositionappliedtobrainrecognitionofcontext
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