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Most deep neural networks use simple, fixed activation functions, such assigmoids or rectified linear units, regardless of domain or network structure.We introduce differential equation units (DEUs), an improvement to modernneural networks, which enables each neuron to learn a particular nonlinearactivation function from a family of solutions to an ordinary differentialequation. Specifically, each neuron may change its functional form duringtraining based on the behavior of the other parts of the network. We show thatusing neurons with DEU activation functions results in a more compact networkcapable of achieving comparable, if not superior, performance when is comparedto much larger networks.

Learning Compact Neural Networks Using Ordinary Differential Equations as Activation Functions