Neuromorphic Functions of Light in Parity-Time-Symmetric Systems

Abstract

As an elementary processor of neural networks, a neuron performs exotic dynamic functions, such as bifurcation, repetitive firing, and oscillation quenching. To achieve ultrafast neuromorphic signal processing, the realization of photonic equivalents to neuronal dynamic functions has attracted considerable attention. However, despite the nonconservative nature of neurons due to energy exchange between intra- and extra-cellular regions through ion channels, the critical role of non-Hermitian physics in the photonic analogy of a neuron has been neglected. Here, a neuromorphic non-Hermitian photonic system ruled by parity-time symmetry is presented. For a photonic platform that induces the competition between saturable gain and loss channels, dynamical phases are classified with respect to parity-time symmetry and stability. In each phase, unique oscillation quenching functions and nonreciprocal oscillations of light fields are revealed as photonic equivalents of neuronal dynamic functions. The proposed photonic system for neuronal functionalities will become a fundamental building block for light-based neural signal processing.