  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 an
Hopfield-like network through emergent processes.
  We demostrate 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 capability to
store an arbitrary archetype and perform classification at the speed of light.
Leveraging the vast number of modes excited by mesoscopic diffusion, our
approach enables the simultaneous storage of thousands of memories without
requiring any additional fabrication efforts. Similar to a content addressable
memory, all stored memories can be collectively assessed against a given
pattern to identify the matching element. Furthermore, by organizing memories
spatially into distinct classes, they become features within a higher-level
categorical (deeper) optical classification layer.
