Emergent behavior in a Kuramoto coupled oscillator model of the brain
Cabral et al. (2022) demonstrated metastable oscillatory modes (MOMs) emerging from a Stuart-Landau brain model with time delay and performed an analysis of the emergent behavior, and characterizing the MOMs in particular. Here, we use a simple, time-delayed Kuramoto brain model to reproduce their r...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2024 |
| 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:2117/412012 |
| Acceso en línea: | https://hdl.handle.net/2117/412012 |
| Access Level: | acceso abierto |
| Palabra clave: | Neurosciences Artificial intelligence Machine learning neuroscience computational neuroscience small world networks Kuramoto model time delay metastability coupled oscillators phase oscillators brain model connectome biological simulation artificial intelligence machine learning Neurociències Intel·ligència artificial Aprenentatge automàtic Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Sumario: | Cabral et al. (2022) demonstrated metastable oscillatory modes (MOMs) emerging from a Stuart-Landau brain model with time delay and performed an analysis of the emergent behavior, and characterizing the MOMs in particular. Here, we use a simple, time-delayed Kuramoto brain model to reproduce their results, with particular focus on analyzing the emergent behavior of this coupled network. We examine the impact of the global coupling strength, the mean conduction delay, and the structural matrix itself on the model's behavior. We find that increasing the global coupling strength always leads to increases of synchrony levels in the system. While mean conduction delays are essential for the emergence of metastability in the system in general, the specific impact in a system is highly dependent on the coupling strength. Finally, we find that while metastability may emerge in network structures other than that of the original connectome, they are not as numerous, diverse, or sustained. From these findings, we discuss the relevance of small world networks and their implications for artificial intelligence, for example in connection to machine learning networks and multi-agent systems. |
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