Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis

Autores
Baglietto, Gabriel; Gigante, Guido; Del Giudice, Paolo
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the ‘mean-shift’ algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters’ centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network’s state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.
Fil: Baglietto, Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. Italian National Institute for Nuclear Research; Italia
Fil: Gigante, Guido. Italian Institute of Health; Italia. Mperience; Italia
Fil: Del Giudice, Paolo. Italian National Institute for Nuclear Research; Italia. Italian Institute of Health; Italia
Materia
NEUROSCIENCE
DIMENSIONAL REDUCTION
INFERENCE
COMPLEXITY ANALYSIS
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:11336/47939

id CONICETDig_641deeae113ac1d6bbf09f2cac61726d
oai_identifier_str oai:ri.conicet.gov.ar:11336/47939
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysisBaglietto, GabrielGigante, GuidoDel Giudice, PaoloNEUROSCIENCEDIMENSIONAL REDUCTIONINFERENCECOMPLEXITY ANALYSIShttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the ‘mean-shift’ algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters’ centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network’s state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.Fil: Baglietto, Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. Italian National Institute for Nuclear Research; ItaliaFil: Gigante, Guido. Italian Institute of Health; Italia. Mperience; ItaliaFil: Del Giudice, Paolo. Italian National Institute for Nuclear Research; Italia. Italian Institute of Health; ItaliaPublic Library of Science2017-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/47939Baglietto, Gabriel; Gigante, Guido; Del Giudice, Paolo; Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis; Public Library of Science; Plos One; 12; 4; 4-2017; 1-25; e0174911932-6203CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pone.0174918info:eu-repo/semantics/altIdentifier/url/http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174918info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:33:25Zoai:ri.conicet.gov.ar:11336/47939instacron: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 09:33:25.397CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis
title Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis
spellingShingle Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis
Baglietto, Gabriel
NEUROSCIENCE
DIMENSIONAL REDUCTION
INFERENCE
COMPLEXITY ANALYSIS
title_short Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis
title_full Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis
title_fullStr Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis
title_full_unstemmed Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis
title_sort Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis
dc.creator.none.fl_str_mv Baglietto, Gabriel
Gigante, Guido
Del Giudice, Paolo
author Baglietto, Gabriel
author_facet Baglietto, Gabriel
Gigante, Guido
Del Giudice, Paolo
author_role author
author2 Gigante, Guido
Del Giudice, Paolo
author2_role author
author
dc.subject.none.fl_str_mv NEUROSCIENCE
DIMENSIONAL REDUCTION
INFERENCE
COMPLEXITY ANALYSIS
topic NEUROSCIENCE
DIMENSIONAL REDUCTION
INFERENCE
COMPLEXITY ANALYSIS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the ‘mean-shift’ algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters’ centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network’s state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.
Fil: Baglietto, Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. Italian National Institute for Nuclear Research; Italia
Fil: Gigante, Guido. Italian Institute of Health; Italia. Mperience; Italia
Fil: Del Giudice, Paolo. Italian National Institute for Nuclear Research; Italia. Italian Institute of Health; Italia
description Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the ‘mean-shift’ algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters’ centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network’s state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.
publishDate 2017
dc.date.none.fl_str_mv 2017-04
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/11336/47939
Baglietto, Gabriel; Gigante, Guido; Del Giudice, Paolo; Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis; Public Library of Science; Plos One; 12; 4; 4-2017; 1-25; e017491
1932-6203
CONICET Digital
CONICET
url http://hdl.handle.net/11336/47939
identifier_str_mv Baglietto, Gabriel; Gigante, Guido; Del Giudice, Paolo; Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis; Public Library of Science; Plos One; 12; 4; 4-2017; 1-25; e017491
1932-6203
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pone.0174918
info:eu-repo/semantics/altIdentifier/url/http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174918
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
dc.publisher.none.fl_str_mv Public Library of Science
publisher.none.fl_str_mv Public Library of Science
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
_version_ 1844613026847653888
score 13.070432