Sorry, but I think I disagree...

If I understand the PM neuron, it's outputs are boolean. This means there's no backprop signal, despite differentiability, since the outputs are constant (thus, gradient zero) in any neighborhood, which in turns zeros out any learning signal you would want to backprop through them. So you need a different learning strategy than backprop to use them.

(see also: the 'dead neuron' problem/phenomenon with ReLU activations.)