Comb-based photonic neural population for parallel and nonlinear processing

TL;DR

This paper introduces a scalable comb-based photonic neural population that leverages ultrabroad bandwidth for parallel, nonlinear processing, demonstrating effective pattern classification in real-time.

Contribution

It presents a novel comb-based photonic neural population with carrier coupling, enabling scalable, parallel, and nonlinear neural processing.

Findings

Demonstrated nonlinear response curves and population activity coding

Successfully classified three input patterns in real-time

Showed the system's potential for scalable photonic neural networks

Abstract

It is believed that neural information representation and processing relies on the neural population instead of a single neuron. In neuromorphic photonics, photonic neurons in the form of nonlinear responses have been extensively studied in single devices and temporal nodes. However, to construct a photonic neural population (PNP), the process of scaling up and massive interconnections remain challenging considering the physical complexity and response latency. Here, we propose a comb-based PNP interconnected by carrier coupling with superior scalability. Two unique properties of neural population are theoretically and experimentally demonstrated in the comb-based PNP, including nonlinear response curves and population activities coding. A classification task of three input patterns with dual radio-frequency (RF) tones is successfully implemented in a real-time manner, which manifests the comb-based PNP can make effective use of the ultra-broad bandwidth of photonics for parallel and nonlinear processing.