Two-color optically-addressed spatial light modulator as generic spatio-temporal systems

TL;DR

This paper demonstrates how a transmissive, optically-addressed spatial light modulator can be tuned to exhibit different bifurcation behaviors, enabling the simulation of various nonlinear spatio-temporal systems relevant to physics and neural networks.

Contribution

It introduces a versatile optical platform capable of continuously transitioning between different normal forms of bifurcations through parameter tuning.

Findings

Successfully derived normal form equations analytically.

Confirmed bifurcation control via numerical simulations.

Device characterized using experimental parameters.

Abstract

Nonlinear spatio-temporal systems are the basis for countless physical phenomena in such diverse fields as ecology, optics, electronics and neuroscience. The canonical approach to unify models originating from different fields is the normal form description, which determines the generic dynamical aspects and different bifurcation scenarios. Realizing different types of dynamical systems via one experimental platform that enables continuous transition between normal forms through tuning accessible system parameters is therefore highly relevant. Here, we show that a transmissive, optically-addressed spatial light modulator under coherent optical illumination and optical feedback coupling allows tuning between pitchfork, transcritical and saddle-node bifurcations of steady states. We demonstrate this by analytically deriving the system's normal form equations and confirm these results via extensive numerical simulations. Our model describes a nematic liquid crystal device using nano-dimensional dichalcogenide (a-As$_2$S$_3$) glassy thin-films as photo sensors and alignment layers, and we use device parameters obtained from experimental characterization. Optical coupling, for example using diffraction, holography or integrated unitary maps allow implementing a variety of system topologies of technological relevance for neural networks and potentially XY-Hamiltonian models with ultra low energy consumption.