Physical reservoir computing for biomedical applications

Abstract Reservoir computing (RC) is a feedforward computational framework derived from recurrent neural networks that leverages the high-dimensional dynamic behaviors of complex systems for efficient information processing. A wide range of interdisciplinary research has been undertaken in recent years to fully enhance the capabilities of RC, especially with the advent of physical RC (PRC). PRC has demonstrated efficacy in applications for biomedical edge devices with advantages in power consumption, latency, bandwidth and privacy. This article provides a structured review of PRC implementation paradigms in different categories and their applications in biomedical signal processing, including the training methods. Additionally, it discusses the emerging opportunities and outlines existing challenges for the practical industrial applications.