Transition to synchronization in heterogeneous inhibitory neural networks with structured synapses-Reference-Cited by-同舟云学术

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Transition to synchronization in heterogeneous inhibitory neural networks with structured synapses

Published:2021-03 Issue:3 Volume:31 Page:033151
ISSN:1054-1500
Container-title:Chaos: An Interdisciplinary Journal of Nonlinear Science
language:en
Short-container-title:Chaos

Author:

Urdapilleta Eugenio¹^ORCID

Affiliation:

1. Centro Atómico Bariloche and Instituto Balseiro, Comisión Nacional de Energía Atómica (CNEA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Cuyo, Av. E. Bustillo 9500, R8402AGP San Carlos de Bariloche, Río Negro, Argentina

Funder

Consejo Nacional de Investigaciones Científicas y Técnicas

Instituto Balseiro, Universidad Nacional de Cuyo

Publisher

AIP Publishing

Subject

Applied Mathematics,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0038896

Reference72 articles.

1. Synchronization

2. Chemical Oscillations, Waves, and Turbulence

3. The Kuramoto model: A simple paradigm for synchronization phenomena

4. Thermodynamic limit of the first-order phase transition in the Kuramoto model

5. Nonlinear stability of incoherence and collective synchronization in a population of coupled oscillators

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3. Finite-time synchronisation transmission of quantum signals between Jaynes-Cummings models based on coupling technology;Optical and Quantum Electronics;2022-12-26

4. Finite-time synchronization transmission of signal in erbium-doped laser network;Optical Review;2022-11-08

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