  An artificial neuron is modelled as a weighted summation followed by an
activation function which determines its output. A wide variety of activation
functions such as rectified linear units (ReLU), leaky-ReLU, Swish, MISH, etc.
have been explored in the literature. In this short paper, we explore what
happens when the activation function of each neuron in an artificial neural
network is learned natively from data alone. This is achieved by modelling the
activation function of each neuron as a small neural network whose weights are
shared by all neurons in the original network. We list our primary findings in
the conclusions section. The code for our analysis is available at:
https://github.com/amina01/Learning-Neural-Activations.
