Data, models and transitions in computational neuroscience: Bottom-up and top-down approaches

This thesis is concerned with building and analyzing mathematical models in computational neuroscience using bottom-up and top-down approaches. Models are constructed using biophysical principles to understand the pathophysiology of cerebral ischemia at different spatial and temporal scales. Data-driven techniques in conjunction with machine learning are used to build compact parameter-dependent models from high-dimensional data. Finally, model maps are introduced to explain the generic unfolding of a newly observed bifurcation.