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

Autor: Paroli B; CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via G. Celoria 16, 20133, Milan, Italy. Electronic address: bruno.paroli@unimi.it., Martini G; CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via G. Celoria 16, 20133, Milan, Italy. Electronic address: gianluca.martini@unimi.it., Potenza MAC; CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via G. Celoria 16, 20133, Milan, Italy. Electronic address: marco.potenza@unimi.it., Siano M; CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via G. Celoria 16, 20133, Milan, Italy. Electronic address: mirko.siano@unimi.it., Mirigliano M; CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via G. Celoria 16, 20133, Milan, Italy. Electronic address: matteo.mirigliano@unimi.it., Milani P; CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via G. Celoria 16, 20133, Milan, Italy. Electronic address: paolo.milani@mi.infn.it.
Jazyk: angličtina
Zdroj: Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2023 Sep; Vol. 166, pp. 634-644. Date of Electronic Publication: 2023 Aug 09.
DOI: 10.1016/j.neunet.2023.08.001
Abstrakt: 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 non-linear 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, and 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 non-linearly 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. 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.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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