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Among several approaches to tackle the problem of energy consumption inmodern computing systems, two solutions are currently investigated: oneconsists of artificial neural networks (ANNs) based on photonic technologies,the other is a different paradigm compared to ANNs and it is based on randomnetworks of nonlinear nanoscale junctions resulting from the assembling ofnanoparticles or nanowires as substrates for neuromorphic computing. Thesenetworks show the presence of emergent complexity and collective phenomena inanalogy with biological neural networks characterized by self-organization,redundancy, non-linearity. Starting from this background, we propose andformalize a generalization of the perceptron model to describe a classificationdevice based on a network of interacting units where the input weights arenonlinearly dependent. We show that this model, called "receptron", providessubstantial advantages compared to the perceptron as, for example, the solutionof non-linearly separable Boolean functions with a single device. The receptronmodel is used as a starting point for the implementation of an all-opticaldevice that exploits the non-linearity of optical speckle fields produced by asolid scatterer. By encoding these speckle fields we generated a large varietyof target Boolean functions without the need for time-consuming machinelearning algorithms. We demonstrate that by properly setting the modelparameters, different classes of functions with different multiplicity can besolved efficiently. The optical implementation of the receptron scheme opensthe way for the fabrication of a completely new class of optical devices forneuromorphic data processing based on a very simple hardware.

Solving classification tasks by a receptron based on nonlinear optical speckle fields