Mesoscopic Segregation of Excitation and Inhibition in a Brain Network Model

Neurons in the brain are known to operate under a careful balance of excitation and inhibition, which maintains neural microcircuits within the proper operational range. How this balance is played out at the mesoscopic level of neuronal populations is, however, less clear. In order to address this i...

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Detalles Bibliográficos
Autores: Malagarriga Guasch, Daniel, Villa, Alessandro, García Ojalvo, Jordi|||0000-0002-3716-7520, Pons Rivero, Antonio Javier|||0000-0002-1481-8159
Tipo de recurso: artículo
Fecha de publicación:2015
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/26402
Acceso en línea:https://hdl.handle.net/2117/26402
https://dx.doi.org/10.1371/journal.pcbi.1004007
Access Level:acceso abierto
Palabra clave:Brain
Dynamics
Cervell
Dinàmica
Àrees temàtiques de la UPC::Física
Descripción
Sumario:Neurons in the brain are known to operate under a careful balance of excitation and inhibition, which maintains neural microcircuits within the proper operational range. How this balance is played out at the mesoscopic level of neuronal populations is, however, less clear. In order to address this issue, here we use a coupled neural mass model to study computationally the dynamics of a network of cortical macrocolumns operating in a partially synchronized, irregular regime. The topology of the network is heterogeneous, with a few of the nodes acting as connector hubs while the rest are relatively poorly connected. Our results show that in this type of mesoscopic network excitation and inhibition spontaneously segregate, with some columns actingmainly in an excitatory manner while some others have predominantly an inhibitory effect on their neighbors.We characterize the conditions under which this segregation arises, and relate the character of the different columns with their to- pological role within the network. In particular, we show that the connector hubs are preferentially inhibitory, the more so the larger the node's connectivity. These results suggest a potential mesoscale organization of the excitation-inhibition balance in brain networks.