A Review: Neural-Inspired Photonic Functional Systems for Dynamic RF Signal Processing

Drawing inspiration from our nature, photonic systems that use light to imitate neural algorithms and behaviors of nature could be effective to solve complex problems in human's civilized society that have been challenging for conventional electronic to tackle. Since neural algorithms are natural designs that have undergone hundreds of million years of evolution and govern the survival of the organism, therefore, those neural algorithms are highly effective for the designated tasks. The goal of this article is to review the recent demonstrations of the photonic implementations of small scale functional neural algorithms for dynamic RF signal processing applications. In this article, two small-scale neural algorithms are reviewed – (i) spike timing dependent plasticity, an algorithm that governs how neural network are connecting together and how learning/adaptation can be achieved in animals, and (ii) jamming avoidance response in Eigenmannia, an algorithm in a gene of electric fish that mitigates frequency jamming between neighboring electric fish. The photonic circuits that are inspired by the two neural algorithms are also presented and the real-life applications of the neural algorithms in human society will be discussed.