Reconnect - Data-driven network dynamics reconstruction to predicting critical transitions: a random dynamics approach

'Reconnect' addresses the problem of reconstructing complex systems' network dynamics from data to understand and predict critical transitions. Using the framework of complex systems, it pioneers methods for characterising the network dynamics that can feature in diverse fields such as geology, climate science, chemistry, and neuroscience by blending tools from the fields of Dynamical Systems (DS) and Machine Learning (ML). Interactions between network components often lead to unexpected behaviour. For example, the functioning of an individual neuron is relatively well understood, yet the behaviour of neural networks and their emergent collective dynamics remain elusive. Data-driven reconstruction of network structure facilitates the prediction of switching behaviour in such complex systems from data. This proposal will investigate mathematical brain networks.