Combined State and Parameter Reduction for Nonlinear Systems with an Application in Neuroscience

This work investigates two complementary methods for combined state and parameter reduction of nonlinear systems. First, a system-theoretic approach using empirical gramians and second, an iterative method utilizing the greedy algorithm. The presented methods are applied in the context of connectivity analysis of neuroimaging data, in which these nonlinear model reduction techniques are demonstrated to accelerate the solution of large-scale inverse problems enabling the data-driven exploration of more complex neuronal networks for example in the human brain.