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

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) …

Over the past two decades, the feedforward neural network (FNN) optimization has been a
key interest among the researchers and practitioners of multiple disciplines. The FNN …

Neural networks, as powerful tools for data mining and knowledge engineering, can learn
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …

Echo State Networks and Liquid State Machines introduced a new paradigm in artificial
recurrent neural network (RNN) training, where an RNN (the reservoir) is generated …

An echo state network (ESN) consists of a large, randomly connected neural network, the
reservoir, which is driven by an input signal and projects to output units. During training, only …

Reservoir Computing (RC) is a paradigm of understanding and training Recurrent Neural
Networks (RNNs) based on treating the recurrent part (the reservoir) differently than the …

The connection patterns of neural circuits form a complex network. How signaling in these
circuits manifests as complex cognition and adaptive behaviour remains the central question …

The frontal cortex and the basal ganglia interact via a relatively well understood and
elaborate system of interconnections. In the context of motor function, these interconnections …

Populations of neurons display an extraordinary diversity in the behaviors they affect and
display. Machine learning techniques have recently emerged that allow us to create …