Counting Boolean networks are universal approximators

A Boolean neural model is presented, where fuzzy reasoning emerges as a macroscopic property from individual neuron Boolean counting operations and random inter-neuron connections. The main objective of this work is to demonstrate that such networks are Universal Approximators. This is achieved through well known properties of non parametric techniques (Parzen Window estimators) to estimate any probability density function.