Scientific Machine Learning of Chaotic Systems Learns Reduced-Order Equations for Neural Populations
PEM-UDE, a method that combines prediction-error methodology with universal differential equations to discover governing equations from limited, noise-corrupted observations, yields a multi-scale neural mass model that ties single-neuron parameters to macroscopic network dynamics and predicts a relationship between connection density, dominant oscillation frequency, and synchrony.
Anthony G. Chesebro, David Hofmann, V. Dixit et al.
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