Complex Oscillatory Dynamics in Neural Populations Using Map-Based Models

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dc.contributor.advisorBilleke Bobadilla, Pablo
dc.contributor.advisorRaveau, María Paz
dc.contributor.advisorSoto-Icaza, Patricia
dc.contributor.authorMárquez Rodríguez, Víctor
dc.date.accessioned2025-08-06T20:42:41Z
dc.date.available2025-08-06T20:42:41Z
dc.date.issued2025
dc.descriptionThesis Submitted to the Facultad de Gobierno Universidad del Desarrollo to pursue the Degree of Doctor in Ciencias de la Complejidad Social
dc.description.abstractThis thesis explores the emergence of complex oscillatory dynamics in neuronal populations, focusing on key phenomena such as chimera states, phase-amplitude coupling (PAC), and metastability. Neural oscillations play a fundamental role in coordinating brain functions, facilitating information integration, and supporting cognitive processes. Understanding how these oscillatory dynamics emerge requires investigating both the causal interactions shaping neural activity and the influence of structural organization on complex oscillatory features. To address this inquiry, we have employed a two-stage computational modeling approach. In the first stage, a system of two populations of map-based neurons interacting through mean-field coupling was developed to analyze synchronization states and quantify information flow within chimera states. The second stage introduced a more biologically realistic model by incorporating synaptic interactions and a connectivity network derived from cortical tissue, allowing the exploration of how these structural factors influenced the emergence of complex oscillatory features such as PAC and metastability. We have found that chimera states exhibited a well defined directional flow of information, from the desynchronized population to the synchronized one, offering insights into conditions such as epilepsy and autism. Furthermore, we have observed that incorporating realistic cortical connectivity enhanced PAC and metastability, emphasizing the role of cortical layer organization in shaping oscillatory frequency bands and adaptive brain dynamics. By integrating computational modeling with map-based neuron models and experimental insights, this work aims to contribute to a better understanding of the dynamic oscillatory features of neural activity and their relevance to both normal and pathological brain states.
dc.format.extent52 p.
dc.identifier.doihttps://doi.org/10.52611/11447/10159
dc.identifier.urihttps://hdl.handle.net/11447/10159
dc.language.isoen
dc.publisherUniversidad del Desarrollo. Facultad de Gobierno
dc.subjectModelos de poblaciones de neuronas
dc.subjectNeurociencia
dc.subjectSistemas complejos
dc.subject060005S
dc.subjectNeuronal populations
dc.titleComplex Oscillatory Dynamics in Neural Populations Using Map-Based Models
dc.typeThesis

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