Lab of Computational Neuroscience
Universidad San Sebastián
What do we do?
The Computational Neuroscience Laboratory at Universidad San Sebastián brings together experimental and computational approaches to understand brain dynamics across scales, from neural oscillations and large-scale brain networks to cognition, aging, and neurodegeneration. Our work integrates computational modeling, brain stimulation, translational EEG research, and data science to study how brain activity supports behavior and how these mechanisms change in health and disease, with the goal of designing interventions to improve diagnosis, monitoring, and rehabilitation for neurological and psychiatric conditions.
We are particularly interested in the dynamics of brain oscillations, the organization of functional networks, the effects of rhythmic and non-invasive stimulation on neural activity, and the development of EEG-based markers for cognitive decline, dementia, language-related dysfunction, hearing loss, and other brain disorders. We also work on multicentre EEG harmonization and scalable analytical pipelines that support reproducible neuroscience and translational applications.
Who are we?
Research lines
Brain oscillations and neural dynamics
We study the role of neural oscillations in cognition and behavior, with a special focus on entrainment and large-scale coordination in the human brain. This includes experimental and computational work on periodic stimulation and oscillatory brain responses.
Computational modeling of brain networks
We develop models of brain activity at different spatial and temporal scales to understand how coherent network dynamics emerge and how they can be modulated. Our work includes connectome-based modeling, network control, and computational approaches to non-invasive brain stimulation.
Multicentre neuroinformatics and translational data science
We build analytical tools and harmonization strategies for EEG, enabling the development of computational pipelines to identify interpretable, scalable biomarkers to support earlier detection and improved phenotyping of age-related neurodegenerative conditions.
