  We demonstrate that the dynamics of neural networks trained with gradient
descent and the dynamics of scalar fields in a flat, vacuum energy dominated
Universe are structurally profoundly related. This duality provides the
framework for synergies between these systems, to understand and explain neural
network dynamics and new ways of simulating and describing early Universe
models. Working in the continuous-time limit of neural networks, we
analytically match the dynamics of the mean background and the dynamics of
small perturbations around the mean field, highlighting potential differences
in separate limits. We perform empirical tests of this analytic description and
quantitatively show the dependence of the effective field theory parameters on
hyperparameters of the neural network. As a result of this duality, the
cosmological constant is matched inversely to the learning rate in the gradient
descent update.
