Perceptrons with Hebbian learning based on wave ensembles in spatially patterned potentials

• Published in: Physical review letters Vol. 114; no. 11; p. 118101
• Main Authors: Espinosa-Ortega, T, Liew, T C H
• Format: Journal Article
• Language: English
• Published: United States 20-03-2015
• Subjects: Hardware; Learning; Lithography; Mathematical analysis; Models, Theoretical; Neural networks; Neural Networks (Computer); Nonlinear Dynamics; Nuclear spin; Optics and Photonics - methods; Reciprocal space; Schroedinger equation
• ISSN: 0031-9007; 1079-7114
• DOI: 10.1103/PhysRevLett.114.118101

A general scheme to realize a perceptron for hardware neural networks is presented, where multiple interconnections are achieved by a superposition of Schrödinger waves. Spatially patterned potentials process information by coupling different points of reciprocal space. The necessary potential shape is obtained from the Hebbian learning rule, either through exact calculation or construction from a superposition of known optical inputs. This allows implementation in a wide range of compact optical systems, including (1) any nonlinear optical system, (2) optical systems patterned by optical lithography, and (3) exciton-polariton systems with phonon or nuclear spin interactions.