Transition between Functional Regimes in an Integrate-And-Fire Network Model of the Thalamus

The thalamus is a key brain element in the processing of sensory information. During the sleep and awake states, this brain area is characterized by the presence of two distinct dynamical regimes: in the sleep state activity is dominated by spindle oscillations (7 − 15 Hz) weakly affected by externa...

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Detalles Bibliográficos
Autores: Barardi, Alessandro, García Ojalvo, Jordi, Mazzoni, Alberto
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2016
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/28174
Acceso en línea:http://hdl.handle.net/10230/28174
http://dx.doi.org/10.1371/journal.pone.0161934
Access Level:acceso abierto
Palabra clave:Neurons
Neural networks
Action potentials
Single neuron function
Thalamus
Gamma-aminobutyric acid
Membrane potential
Network analysis
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oai_identifier_str oai:recercat.cat:10230/28174
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Transition between Functional Regimes in an Integrate-And-Fire Network Model of the Thalamus
title Transition between Functional Regimes in an Integrate-And-Fire Network Model of the Thalamus
spellingShingle Transition between Functional Regimes in an Integrate-And-Fire Network Model of the Thalamus
Barardi, Alessandro
Neurons
Neural networks
Action potentials
Single neuron function
Thalamus
Gamma-aminobutyric acid
Membrane potential
Network analysis
title_short Transition between Functional Regimes in an Integrate-And-Fire Network Model of the Thalamus
title_full Transition between Functional Regimes in an Integrate-And-Fire Network Model of the Thalamus
title_fullStr Transition between Functional Regimes in an Integrate-And-Fire Network Model of the Thalamus
title_full_unstemmed Transition between Functional Regimes in an Integrate-And-Fire Network Model of the Thalamus
title_sort Transition between Functional Regimes in an Integrate-And-Fire Network Model of the Thalamus
dc.creator.none.fl_str_mv Barardi, Alessandro
García Ojalvo, Jordi
Mazzoni, Alberto
author Barardi, Alessandro
author_facet Barardi, Alessandro
García Ojalvo, Jordi
Mazzoni, Alberto
author_role author
author2 García Ojalvo, Jordi
Mazzoni, Alberto
author2_role author
author
dc.subject.none.fl_str_mv Neurons
Neural networks
Action potentials
Single neuron function
Thalamus
Gamma-aminobutyric acid
Membrane potential
Network analysis
topic Neurons
Neural networks
Action potentials
Single neuron function
Thalamus
Gamma-aminobutyric acid
Membrane potential
Network analysis
description The thalamus is a key brain element in the processing of sensory information. During the sleep and awake states, this brain area is characterized by the presence of two distinct dynamical regimes: in the sleep state activity is dominated by spindle oscillations (7 − 15 Hz) weakly affected by external stimuli, while in the awake state the activity is primarily driven by external stimuli. Here we develop a simple and computationally efficient model of the thalamus that exhibits two dynamical regimes with different information-processing capabilities, and study the transition between them. The network model includes glutamatergic thalamocortical (TC) relay neurons and GABAergic reticular (RE) neurons described by adaptive integrate-and-fire models in which spikes are induced by either depolarization or hyperpolarization rebound. We found a range of connectivity conditions under which the thalamic network composed by these neurons displays the two aforementioned dynamical regimes. Our results show that TC-RE loops generate spindle-like oscillations and that a minimum level of clustering (i.e. local connectivity density) in the RE-RE connections is necessary for the coexistence of the two regimes. We also observe that the transition between the two regimes occurs when the external excitatory input on TC neurons (mimicking sensory stimulation) is large enough to cause a significant fraction of them to switch from hyperpolarization-rebound-driven firing to depolarization-driven firing. Overall, our model gives a novel and clear description of the role that the two types of neurons and their connectivity play in the dynamical regimes observed in the thalamus, and in the transition between them. These results pave the way for the development of efficient models of the transmission of sensory information from periphery to cortex.
publishDate 2016
dc.date.none.fl_str_mv 2016
2017
2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/28174
http://dx.doi.org/10.1371/journal.pone.0161934
url http://hdl.handle.net/10230/28174
http://dx.doi.org/10.1371/journal.pone.0161934
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv PLoS ONE. 2016;11(9):20161934
info:eu-repo/grantAgreement/EC/FP7/289146
info:eu-repo/grantAgreement/ES/1PE/FIS2015-66503-C3-1-P1
dc.rights.none.fl_str_mv https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Public Library of Science (PLoS)
publisher.none.fl_str_mv Public Library of Science (PLoS)
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Transition between Functional Regimes in an Integrate-And-Fire Network Model of the ThalamusBarardi, AlessandroGarcía Ojalvo, JordiMazzoni, AlbertoNeuronsNeural networksAction potentialsSingle neuron functionThalamusGamma-aminobutyric acidMembrane potentialNetwork analysisThe thalamus is a key brain element in the processing of sensory information. During the sleep and awake states, this brain area is characterized by the presence of two distinct dynamical regimes: in the sleep state activity is dominated by spindle oscillations (7 − 15 Hz) weakly affected by external stimuli, while in the awake state the activity is primarily driven by external stimuli. Here we develop a simple and computationally efficient model of the thalamus that exhibits two dynamical regimes with different information-processing capabilities, and study the transition between them. The network model includes glutamatergic thalamocortical (TC) relay neurons and GABAergic reticular (RE) neurons described by adaptive integrate-and-fire models in which spikes are induced by either depolarization or hyperpolarization rebound. We found a range of connectivity conditions under which the thalamic network composed by these neurons displays the two aforementioned dynamical regimes. Our results show that TC-RE loops generate spindle-like oscillations and that a minimum level of clustering (i.e. local connectivity density) in the RE-RE connections is necessary for the coexistence of the two regimes. We also observe that the transition between the two regimes occurs when the external excitatory input on TC neurons (mimicking sensory stimulation) is large enough to cause a significant fraction of them to switch from hyperpolarization-rebound-driven firing to depolarization-driven firing. Overall, our model gives a novel and clear description of the role that the two types of neurons and their connectivity play in the dynamical regimes observed in the thalamus, and in the transition between them. These results pave the way for the development of efficient models of the transmission of sensory information from periphery to cortex.This work was supported by the European Commission under ITN project NETT (FP7 contract 289146), by the Spanish Ministry of Economy and Competitiveness and FEDER (project FIS2015-66503-C3-1-P1), and by the Generalitat de Catalunya (project 2014SGR0947). A.M. was supported by the NEBIAS European project (EUFP7-ICT-611687), by the PRIN/ HandBot Italian project (CUP: B81J12002680008; prot.: 20102YF2RY), and by the Italian Ministry of Foreign Affairs and International Cooperation, Directorate General for Country Promotion (Economy, Culture and Science) Unit for Scientific and Technological Cooperation, via the Italy-Sweden bilateral research project on "Brain network mechanisms for integration of natural tactile input patterns". J.G.O. also acknowledges support from the ICREA Academia programme and from the "Maria de Maeztu" Programme for Units of Excellence in R&D (Spanish Ministry of Economy and Competitiveness, MDM-2014-0370). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Public Library of Science (PLoS)201720172016info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/28174http://dx.doi.org/10.1371/journal.pone.0161934reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésPLoS ONE. 2016;11(9):20161934info:eu-repo/grantAgreement/EC/FP7/289146info:eu-repo/grantAgreement/ES/1PE/FIS2015-66503-C3-1-P1© 2016 Barardi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/281742026-05-29T05:05:01Z
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