Deep Classification from Scattered Light

Abstract

Photonic Stochastic Emergent Learning (PSEL) represents an innovative paradigm rooted in mathematical brain modelling and emergent memories. In this study, we explore the intersection of these concepts to address memory storage and classification tasks. Leveraging optical computing principles and random projections, PSEL constructs memory representations from the inherent randomness in nature. Specifically, we select a set of highly similar random states generated by coherent light scattered from a diffusive medium. Classification is performed by organizing the memories spatially into different classes and comparing inputs to those stored memories. The results demonstrate the efficacy of PSEL in memory construction and parallel classification, emphasizing its potential applications in high-performance computing and artificial intelligence systems.