The expression “neuromorphic computing” strikingly shows that the contemporary scientific narrative compares brains to computers [1]. Computer architectures are created by designers and deterministically assembled, whereas biological neural networks are based on self-organization so that structure and function co-evolve. Both process data using a huge number of simple equivalent components linked by a complex physical pattern of connections, however it remains quite undefined to what extent the use of the brain-computer metaphor can be useful for the design of computers that perform as brains.

Self-assembled nanoparticle or nanowire networks have recently come under the spotlight as systems able to obtain brain-like data processing performances by exploiting the memristive character and the wiring of the junctions connecting the nanostructured network building blocks [2]. The role of the wiring in biological and artificial systems has been originally recognized by Rosenblatt with the Perceptron model [3], a more complex model was proposed by Hopfield who pointed out the importance of the non-linearity of the input-output relationship and of the use of nonlinear logical operations [4]. These aspects are crucial for the design and operation of data processing networks of nanoobjects.

Recently we demonstrated that nanostructured Au films, fabricated by the assembling of gold clusters produced in the gas phase, have non-linear and non-local electric conduction properties caused by the extremely high density of grain boundaries and the resulting complex arrangement of nanojunctions [5]. Starting from the characterization of this system, we proposed and formalized a generalization of the Perceptron model to describe a classification device based on a network of interacting units where the input weights are non-linearly dependent [6]. 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 [6].

Here we present and discuss the practical application of the Receptron model to the realization of electronic components for the classification of Boolean function without previous training in view of the fabrication of arithmetic logic unit circuits.

We will also show that the Receptron model can be used for the implementation of an all-optical device exploiting the non-linearity of optical speckle fields produced by a solid scatterer. A single layer optical Receptron network can efficiently be used for pattern recognition without previous training. 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.