Optical Soliton Neural Networks

Abstract

The chapter describes the realization of photonic integrated circuits based on photorefractive solitonic waveguides. In particular, it has been shown that X-junctions formed by soliton waveguides can learn information by switching their state. X junctions can perform both supervised and unsupervised learning. In doing so, complex networks of interconnected waveguides behave like a biological neural network, where information is stored as preferred trajectories within the network. In this way, it is possible to create “episodic” psycho-memories, able to memorize information bit-by-bit, and subsequently use it to recognize unknown data. Using optical systems, it is also possible to create more advanced dense optical networks, capable of recognizing keywords within information packets (procedural psycho-memory) and possibly comparing them with the stored data (semantic psycho-memory). In this chapter, we shall describe how Solitonic Neural Networks work, showing the close parallel between biological and optical systems.