  We propose a new generic type of stochastic neurons, called $q$-neurons, that
considers activation functions based on Jackson's $q$-derivatives with
stochastic parameters $q$. Our generalization of neural network architectures
with $q$-neurons is shown to be both scalable and very easy to implement. We
demonstrate experimentally consistently improved performances over
state-of-the-art standard activation functions, both on training and testing
loss functions.
