  In this work, we formulate NEWRON: a generalization of the McCulloch-Pitts
neuron structure. This new framework aims to explore additional desirable
properties of artificial neurons. We show that some specializations of NEWRON
allow the network to be interpretable with no change in their expressiveness.
By just inspecting the models produced by our NEWRON-based networks, we can
understand the rules governing the task. Extensive experiments show that the
quality of the generated models is better than traditional interpretable models
and in line or better than standard neural networks.
