Memory, Light, Spin Glasses, and Deep Classification.

by Marco Leonetti (CNR Sede di Roma)

Monday 4 Mar 2024, 16:00 → 17:00 Europe/Rome

Aula Conversi (Dipartimento di Fisica - Ed. G.Marconi)

Seminario CNR-NANOTEC

Memory serves as the foundation for computing in both artificial and biological systems. The pioneering Hopfield model represents the initial physical-mathematical framework for memory, linking memory components to the synaptic matrix—a mathematical structure housing neural synaptic weights. Recently, a connection has been established between the Hopfield memory model and the physics governing the intensity of light transmitted through a multitude of disorganized light rays [1-2].
In fact, the transmission of light through a disordered, strongly scattering medium can be elucidated using the transmission matrix—a two-dimensional array delineating the attenuation and dephasing of each light mode/ray. This mapping between the optical transmission matrix and the memory synaptic matrix finds various applications.
Primarily, we harnessed this connection to develop a photonic analog optical computer capable of dynamically calculating spin glass system dynamics, presenting an advantage over digital counterparts. 
Additionally, we utilized the intrinsic random memory patterns stored into a scattering medium to create an optical storage system [3]. These random memories can be leveraged to generate higher-hierarchy archetype memories in an emergent manner. In contrast to random memories, archetype memories can be purposefully designed by users to store meaningful information.
Furthermore, we demonstrated the merging of multiple archetype memories to realize a fully optical programmable classifier, providing enhanced efficiency compared to previous architectures.

[1] ML et al: Photonic Stochastic Emergent Storage for deep classification by scattering-intrinsic patterns, Nature Communications 15, 505 (2024).
[2] ML et al: Reference-less wavefront shaping in a Hopfield-like rough intensity landscape, Opt. Express 31, 28987-28998 (2023)
[3] ML et al: Optical computation of a spin glass dynamics with tunable complexity; PNAS May 25, 2021 118 (21) e2015207118;