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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/47939
Ver los metadatos del registro completo
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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 |
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1844613026847653888 |
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13.070432 |