From logical reasoning to mental simulation, biological and artificial neural systems possess
an incredible capacity for computation. Such neural computers offer a fundamentally novel …

Reservoir computing is a machine learning paradigm that transforms the transient dynamics
of high-dimensional nonlinear systems for processing time-series data. Although the …

Reservoir computing (RC) systems are powerful models for online computations on input
sequences. They consist of a memoryless readout neuron that is trained on top of a …

This manuscript serves a specific purpose: to give readers from fields such as material
science, chemistry, or electronics an overview of implementing a reservoir computing (RC) …

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

To learn and mimic how the brain processes information has been a major research
challenge for decades. Despite the efforts, little is known on how we encode, maintain and …

Reservoir Computing is a novel technique which employs recurrent neural networks while
circumventing difficult training algorithms. A very recent trend in Reservoir Computing is the …

Reservoir computers are powerful machine learning algorithms for predicting nonlinear
systems. Unlike traditional feedforward neural networks, they work on small training data …

Reservoir computing (RC) is a powerful computational paradigm that allows high versatility
with cheap learning. While other artificial intelligence approaches need exhaustive …

Randomly connected recurrent neural circuits have proven to be very powerful models for
online computations when a trained memoryless re adout function is appended. Such …