  Among several approaches to tackle the problem of energy consumption in
modern computing systems, two solutions are currently investigated: one
consists of artificial neural networks (ANNs) based on photonic technologies,
the other is a different paradigm compared to ANNs and it is based on random
networks of nonlinear nanoscale junctions resulting from the assembling of
nanoparticles or nanowires as substrates for neuromorphic computing. These
networks show the presence of emergent complexity and collective phenomena in
analogy with biological neural networks characterized by self-organization,
redundancy, non-linearity. Starting from this background, we propose and
formalize a generalization of the perceptron model to describe a classification
device based on a network of interacting units where the input weights are
nonlinearly dependent. We show that this model, called "receptron", provides
substantial advantages compared to the perceptron as, for example, the solution
of non-linearly separable Boolean functions with a single device. The receptron
model is used as a starting point for the implementation of an all-optical
device that exploits the non-linearity of optical speckle fields produced by a
solid scatterer. By encoding these speckle fields we generated a large variety
of target Boolean functions without the need for time-consuming machine
learning algorithms. We demonstrate that by properly setting the model
parameters, different classes of functions with different multiplicity can be
solved efficiently. The optical implementation of the receptron scheme opens
the way for the fabrication of a completely new class of optical devices for
neuromorphic data processing based on a very simple hardware.
