  Diversity conveys advantages in nature, yet homogeneous neurons typically
comprise the layers of artificial neural networks. Here we construct neural
networks from neurons that learn their own activation functions, quickly
diversify, and subsequently outperform their homogeneous counterparts on image
classification and nonlinear regression tasks. Sub-networks instantiate the
neurons, which meta-learn especially efficient sets of nonlinear responses.
Examples include conventional neural networks classifying digits and
forecasting a van der Pol oscillator and physics-informed Hamiltonian neural
networks learning H\'enon-Heiles stellar orbits and the swing of a video
recorded pendulum clock. Such \textit{learned diversity} provides examples of
dynamical systems selecting diversity over uniformity and elucidates the role
of diversity in natural and artificial systems.
