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

Classification is an important task at which both biological and artificial neural networks
excel,. In machine learning, nonlinear projection into a high-dimensional feature space can …

Molecular neuromorphic devices are composed of a random and extremely dense network
of single-walled carbon nanotubes (SWNTs) complexed with polyoxometalate (POM). Such …

This tutorial covers physical reservoir computing from a computer science perspective. It first
defines what it means for a physical system to compute, rather than merely evolve under the …

The reservoir computing (RC) framework states that any nonlinear, input-driven dynamical
system (the reservoir) exhibiting properties such as a fading memory and input separability …

Biological neuronal networks are the computing engines of the mammalian brain. These
networks exhibit structural characteristics such as hierarchical architectures, small-world …

We explore the effect of connectivity and topology on the dynamical behaviour of Reservoir
Computers. At present, considerable effort is taken to design and hand-craft physical …

The reservoir computing paradigm of information processing has emerged as a natural
response to the problem of training recurrent neural networks. It has been realized that the …

The Reservoir Computing (RC) paradigm utilizes a dynamical system, ie, a reservoir, and a
linear classifier, ie, a read-out layer, to process data from sequential classification tasks. In …

Abstract We overview Reservoir Computing (RC) with physical systems from an
Unconventional Computing (UC) perspective. We discuss challenges present in both fields …