Reservoir computing is a computational framework suited for temporal/sequential data
processing. It is derived from several recurrent neural network models, including echo state …

Delay-based reservoir computing has gained a lot of attention due to the relative simplicity
with which this concept can be implemented in hardware. However, unnecessary constraints …

We present a fully automatic and fast ECG arrhythmia classifier based on a simple brain-
inspired machine learning approach known as Echo State Networks. Our classifier has a low …

Photonic neuromorphic computing is of particular interest due to its significant potential for
ultrahigh computing speed and energy efficiency. The advantage of photonic computing …

Reservoir computing is a relatively recent computational paradigm that originates from a
recurrent neural network and is known for its wide range of implementations using different …

In the reservoir computing literature, the information processing capacity is frequently used
to characterize the computing capabilities of a reservoir. However, it remains unclear how …

We propose a new approach to dynamical system forecasting called data-informed-reservoir
computing (DI-RC) that, while solely being based on data, yields increased accuracy …

The concept of reservoir computing emerged from a specific machine learning paradigm
characterized by a three-layered architecture (input, reservoir, and output), where only the …

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural
network in which neurons are randomly connected. Once initialized, the connection …

Abstract Machine learning (ML) and neuromorphic computing have been enforcing problem-
solving in many applications. Such approaches found fertile ground in optical …