  A simply implemented activation function with even cubic nonlinearity is
introduced that increases the accuracy of neural networks without substantial
additional computational resources. This is partially enabled through an
apparent tradeoff between convergence and accuracy. The activation function
generalizes the standard RELU function by introducing additional degrees of
freedom through optimizable parameters that enable the degree of nonlinearity
to be adjusted. The associated accuracy enhancement is quantified in the
context of the MNIST digit data set through a comparison with standard
techniques.
