Memristor-based cellular nonlinear networks with belief propagation inspired algorithm

Neural Networks trained with the Belief Propagation Inspired (BPI) algorithm are able to learn a number of associations close to the theoretical limit in time that is sublinear in the number of input. Using binary synapses, implemented by a memristor, a single layer perceptron with BPI has been proposed. It well know that perceptrons with step function type nonlinearity can be implemented by a suitable class of Cellular Neural/Nonlinear Networks. This paper aims to present a statistical analysis on the learning efficiency of Memristor-based Cellular Nonlinear Networks (M-CNNs) with Belief Propagation Inspired (BPI) algorithm. Monte Carlo simulations permit to assess that the learning efficiency of M-CNNs with BPI is not regardless of the input signals given to train the perceptron.