Eroğlu, DenizTopal, IremEroglu, Deniz2023-10-192023-10-19202330031-90071079-7114https://doi.org/10.1103/PhysRevLett.130.117401https://hdl.handle.net/20.500.12469/5079Reconstructing network dynamics from data is crucial for predicting the changes in the dynamics of complex systems such as neuron networks; however, previous research has shown that the reconstruction is possible under strong constraints such as the need for lengthy data or small system size. Here, we present a recovery scheme blending theoretical model reduction and sparse recovery to identify the governing equations and the interactions of weakly coupled chaotic maps on complex networks, easing unrealistic constraints for real-world applications. Learning dynamics and connectivity lead to detecting critical transitions for parameter changes. We apply our technique to realistic neuronal systems with and without noise on a real mouse neocortex and artificial networks.eninfo:eu-repo/semantics/openAccessArticle11130WOS:00095480310001310.1103/PhysRevLett.130.1174012-s2.0-85151297304Q1Q137001085