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Artículo

Transition to synchronization in heterogeneous inhibitory neural networks with structured synapses

Urdapilleta, EugenioIcon
Fecha de publicación: 03/2021
Editorial: American Institute of Physics
Revista: Chaos
ISSN: 1054-1500
e-ISSN: 1089-7682
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:
Otras Ciencias Físicas

Resumen

Inhibitory neurons form an extensive network involved in the development of different rhythms in the cerebral cortex. A transition from an incoherent state, where all inhibitory neurons fire unrelated to each other, to a synchronized or locked state, where all or most neurons define a tight firing pattern, is maybe the most salient process to analyze when considering neuronal rhythms. In this work, we analyzed whether different patterns of effective synaptic connectivity may support a first-order-like transition in this path to synchronization. Such an "explosive"phenomenon may be relevant in neural processes, as normal cognitive processing in different tasks and some neurological disorders manifest an increased power in many neuronal rhythms, supported by an extended concerted spiking activity and an abrupt change to this state. Furthermore, we built an adaptive mechanism that supports the generation of this kind of network, which rapidly creates the underlying structure based on the ongoing firing statistics.
Palabras clave: Inhibitory neurons , Synchronization , Oscillations , Synapses
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info:eu-repo/semantics/restrictedAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)
Identificadores
URI: http://hdl.handle.net/11336/167578
URL: https://aip.scitation.org/doi/10.1063/5.0038896
DOI: https://doi.org/10.1063/5.0038896
Colecciones
Articulos(CCT - PATAGONIA NORTE)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - PATAGONIA NORTE
Citación
Urdapilleta, Eugenio; Transition to synchronization in heterogeneous inhibitory neural networks with structured synapses; American Institute of Physics; Chaos; 31; 3; 3-2021; 1-12
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