Characterizing the complexity of brain and mind networks

Autores
Zamora López, Gorka; Russo, Eleonora; Gleiser, Pablo Martin; Zhou, Changsong; Kurths, Jürgen
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Recent studies of brain connectivity and language with methods of complex networks have revealed common features of organization. These observations open a window to better understand the intrinsic relationship between the brain and the mind by studying how information is either physically stored or mentally represented. In this paper, we review some of the results in both brain and linguistic networks, and we illustrate how modelling approaches can serve to comprehend the relationship between the structure of the brain and its function. On the one hand, we show that brain and neural networks display dynamical behaviour with optimal complexity in terms of a balance between their capacity to simultaneously segregate and integrate information. On the other hand, we show how principles of neural organization can be implemented into models of memory storage and recognition to reproduce spontaneous transitions between memories, resembling phenomena of memory association studied in psycholinguistic experiments.
Fil: Zamora López, Gorka. Humboldt-Universität zu Berlin; Alemania. Bernstein Center for Computational Neuroscience; Alemania
Fil: Russo, Eleonora. Scuola Internazionale Superiore Di Studi Avanzati (sissa);
Fil: Gleiser, Pablo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina
Fil: Zhou, Changsong. The Beijing–Hong Kong–Singapore Joint Centre for Nonlinear and Complex Systems; China. Hong Kong Baptist University; China
Fil: Kurths, Jürgen. Humboldt-Universität zu Berlin; Alemania. Universita Zu Berlin. Universita Postdam; Alemania
Materia
Brain networks
Segregation
Integration
Complexity
Semantic networks
Memory latching
Free-association
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:11336/281625
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/281625
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network_name_str CONICET Digital (CONICET)
spelling Characterizing the complexity of brain and mind networksZamora López, GorkaRusso, EleonoraGleiser, Pablo MartinZhou, ChangsongKurths, JürgenBrain networksSegregationIntegrationComplexitySemantic networksMemory latchingFree-associationhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Recent studies of brain connectivity and language with methods of complex networks have revealed common features of organization. These observations open a window to better understand the intrinsic relationship between the brain and the mind by studying how information is either physically stored or mentally represented. In this paper, we review some of the results in both brain and linguistic networks, and we illustrate how modelling approaches can serve to comprehend the relationship between the structure of the brain and its function. On the one hand, we show that brain and neural networks display dynamical behaviour with optimal complexity in terms of a balance between their capacity to simultaneously segregate and integrate information. On the other hand, we show how principles of neural organization can be implemented into models of memory storage and recognition to reproduce spontaneous transitions between memories, resembling phenomena of memory association studied in psycholinguistic experiments.Fil: Zamora López, Gorka. Humboldt-Universität zu Berlin; Alemania. Bernstein Center for Computational Neuroscience; AlemaniaFil: Russo, Eleonora. Scuola Internazionale Superiore Di Studi Avanzati (sissa);Fil: Gleiser, Pablo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; ArgentinaFil: Zhou, Changsong. The Beijing–Hong Kong–Singapore Joint Centre for Nonlinear and Complex Systems; China. Hong Kong Baptist University; ChinaFil: Kurths, Jürgen. Humboldt-Universität zu Berlin; Alemania. Universita Zu Berlin. Universita Postdam; AlemaniaThe Royal Society2011-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/281625Zamora López, Gorka; Russo, Eleonora; Gleiser, Pablo Martin; Zhou, Changsong; Kurths, Jürgen; Characterizing the complexity of brain and mind networks; The Royal Society; Philosophical Transactions of the Royal Society A - Mathematical Physical and Engineering Sciences; 369; 1952; 3-2011; 3730-37471364-503XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://royalsocietypublishing.org/rsta/article-abstract/369/1952/3730/114489/Characterizing-the-complexity-of-brain-and-mindinfo:eu-repo/semantics/altIdentifier/doi/10.1098/rsta.2011.0121info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-05-13T11:13:59Zoai:ri.conicet.gov.ar:11336/281625instacron: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:34982026-05-13 11:14:00.266CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Characterizing the complexity of brain and mind networks
title Characterizing the complexity of brain and mind networks
spellingShingle Characterizing the complexity of brain and mind networks
Zamora López, Gorka
Brain networks
Segregation
Integration
Complexity
Semantic networks
Memory latching
Free-association
title_short Characterizing the complexity of brain and mind networks
title_full Characterizing the complexity of brain and mind networks
title_fullStr Characterizing the complexity of brain and mind networks
title_full_unstemmed Characterizing the complexity of brain and mind networks
title_sort Characterizing the complexity of brain and mind networks
dc.creator.none.fl_str_mv Zamora López, Gorka
Russo, Eleonora
Gleiser, Pablo Martin
Zhou, Changsong
Kurths, Jürgen
author Zamora López, Gorka
author_facet Zamora López, Gorka
Russo, Eleonora
Gleiser, Pablo Martin
Zhou, Changsong
Kurths, Jürgen
author_role author
author2 Russo, Eleonora
Gleiser, Pablo Martin
Zhou, Changsong
Kurths, Jürgen
author2_role author
author
author
author
dc.subject.none.fl_str_mv Brain networks
Segregation
Integration
Complexity
Semantic networks
Memory latching
Free-association
topic Brain networks
Segregation
Integration
Complexity
Semantic networks
Memory latching
Free-association
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Recent studies of brain connectivity and language with methods of complex networks have revealed common features of organization. These observations open a window to better understand the intrinsic relationship between the brain and the mind by studying how information is either physically stored or mentally represented. In this paper, we review some of the results in both brain and linguistic networks, and we illustrate how modelling approaches can serve to comprehend the relationship between the structure of the brain and its function. On the one hand, we show that brain and neural networks display dynamical behaviour with optimal complexity in terms of a balance between their capacity to simultaneously segregate and integrate information. On the other hand, we show how principles of neural organization can be implemented into models of memory storage and recognition to reproduce spontaneous transitions between memories, resembling phenomena of memory association studied in psycholinguistic experiments.
Fil: Zamora López, Gorka. Humboldt-Universität zu Berlin; Alemania. Bernstein Center for Computational Neuroscience; Alemania
Fil: Russo, Eleonora. Scuola Internazionale Superiore Di Studi Avanzati (sissa);
Fil: Gleiser, Pablo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina
Fil: Zhou, Changsong. The Beijing–Hong Kong–Singapore Joint Centre for Nonlinear and Complex Systems; China. Hong Kong Baptist University; China
Fil: Kurths, Jürgen. Humboldt-Universität zu Berlin; Alemania. Universita Zu Berlin. Universita Postdam; Alemania
description Recent studies of brain connectivity and language with methods of complex networks have revealed common features of organization. These observations open a window to better understand the intrinsic relationship between the brain and the mind by studying how information is either physically stored or mentally represented. In this paper, we review some of the results in both brain and linguistic networks, and we illustrate how modelling approaches can serve to comprehend the relationship between the structure of the brain and its function. On the one hand, we show that brain and neural networks display dynamical behaviour with optimal complexity in terms of a balance between their capacity to simultaneously segregate and integrate information. On the other hand, we show how principles of neural organization can be implemented into models of memory storage and recognition to reproduce spontaneous transitions between memories, resembling phenomena of memory association studied in psycholinguistic experiments.
publishDate 2011
dc.date.none.fl_str_mv 2011-03
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/11336/281625
Zamora López, Gorka; Russo, Eleonora; Gleiser, Pablo Martin; Zhou, Changsong; Kurths, Jürgen; Characterizing the complexity of brain and mind networks; The Royal Society; Philosophical Transactions of the Royal Society A - Mathematical Physical and Engineering Sciences; 369; 1952; 3-2011; 3730-3747
1364-503X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/281625
identifier_str_mv Zamora López, Gorka; Russo, Eleonora; Gleiser, Pablo Martin; Zhou, Changsong; Kurths, Jürgen; Characterizing the complexity of brain and mind networks; The Royal Society; Philosophical Transactions of the Royal Society A - Mathematical Physical and Engineering Sciences; 369; 1952; 3-2011; 3730-3747
1364-503X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://royalsocietypublishing.org/rsta/article-abstract/369/1952/3730/114489/Characterizing-the-complexity-of-brain-and-mind
info:eu-repo/semantics/altIdentifier/doi/10.1098/rsta.2011.0121
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 The Royal Society
publisher.none.fl_str_mv The Royal Society
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_ 1865200503250485248
score 13.115601

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