Photonic Stochastic Emergent Storage for deep classification by scattering-intrinsic patterns.

ABSTRACT: Disorder is a pervasive characteristic of natural systems, offering a wealth of non-repeating patterns. In this study, we present a novel storage method that harnesses naturally-occurring random structures to store an arbitrary pattern in a memory device. This method, the Stochastic Emergent Storage (SES), builds upon the concept of emergent archetypes, where a training set of imperfect examples (prototypes) is employed to instantiate an archetype in a Hopfield-like network through emergent processes. We demonstrate this non-Hebbian paradigm in the photonic domain by utilizing random transmission matrices, which govern light scattering in a white-paint turbid medium, as prototypes. Through the implementation of programmable hardware, we successfully realize and experimentally validate the

SUBMITTER: Leonetti M 

PROVIDER: S-EPMC10787794 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature