Perturbative analysis of brain networks

The brain is composed of complex networks of neurons, which underlie its adaptive and versatile functionality. Recent neurotechnological developments have provided new tools for probing network function through optogenetic perturbations, enabling investigators to go beyond observational techniques. The overarching aim of this proposal is to develop a unifying computational framework that can accompany and guide experimental perturbational interrogation of neural circuits. This is pursued by simulating and analysing biologically detailed models of neuronal networks to study how neuronal connectivity, dynamics, and plasticity can be probed with optogenetic perturbations. The models are used to explore and quantify the high-dimensional properties of neural activity space and input-output transformations through parametrized perturbations. The insights obtained from the computational modelling will guide the design of experiments, where it is only feasible to test a small number of parameters. The models will provide hypotheses and predictions, and offer a conceptual framework to interpret and organize the large-scale datasets resulting from perturbation studies. By filling the gap between neurotheory and neurotechnology, this work will take us closer to the fundamental goal of understanding how neuronal networks represent and process information, and pave the way to more effectively probe the functional and dysfunctional properties of brain circuits.