PEM-UDE for Neural Mass Models
Scientific machine learning has proven effective in deriving equations for complex dynamical systems but faces challenges with chaotic systems, particularly in biological systems with incomplete theories and noisy data. We present a new approach combining universal differential equations with the prediction-error method from optimization to successfully learn neural system dynamics from simulated and real spiking neural networks.