Autonomous dynamical systems based on hardware implementations of delay-reservoir computers

Reservoir computing (RC) is a machine learning technique allowing for novel approaches to realize trainable autonomous nonlinear oscillators. Here we employ delay-based echo state networks with output feedback, simple yet powerful implementations of neuromorphic systems, to reproduce the dynamical behavior of a Rössler chaotic system. Our hardware implementation relies on a delay-based RC topology, and consists of two main elements: an analog Mackey Glass nonlinearity and a Raspberry Pi board. We demonstrate the capacity of our experiment to generate chaotic time-traces in an autonomous manner, and we prove that noise can play a constructive role in the training process, when realizing nonlinear oscillators based on closed-loop operation. We use phase-space reconstruction of the chaotic attractor and the comparison of the frequency spectra, along with recurrence quantification analysis (RQA), to perform a nonlinear data analysis with the aim of comparing the autonomous operation and the original time-series in more detail.

• Full paper

Related research projects

IDEA

Improving data DEcoding in optical communication networks All-optically using neuro-inspired photonic systems

P.I.: Miguel C. Soriano, Ingo Fischer, Claudio Mirasso

Novel technologies related to optical communications, sensing, the Internet of Things (IoT) and artificial intelligence have been generating unique opportunities and potential to enhance our quality-of-life, and to provide new services for …