Photonic Implementation of Spike-Timing-Dependent Plasticity and Learning Algorithms of Biological Neural Systems

Abstract

The neurobiological learning algorithm, spike-timing-dependent plasticity (STDP), is demonstrated in a simple photonic system using the cooperative nonlinear effects of cross gain modulation and nonlinear polarization rotation, and supervised and unsupervised learning using photonic neuron principles are examined. An STDP-based supervised learning scheme is presented which is capable of mimicking a desirable spike pattern through learning and adaptation. Furthermore, unsupervised learning is illustrated by a principal component analysis system operating under similar learning rules. Finally, a photonic-distributed processing network capable of STDP-based unsupervised learning is theoretically explored.