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

Neuronal diversity can improve machine learning for physics and beyond