Through the success of artificial neural networks (ANNs) in different domains, intense
research has been recently centered on changing the networks architecture to optimize the …

Reservoir computing (RC) refers to a new class of state-space models with a fixed state
transition structure (the reservoir) and an adaptable readout form the state space. The …

Abstract Machine learning influences numerous aspects of modern society, empowers new
technologies, from Alphago to ChatGPT, and increasingly materializes in consumer products …

Cortical neural connectivity has been shown to exhibit a small-world (SW) network topology.
However, the role of the topology in neural information processing remains unclear. In this …

An echo-state network (ESN) is an effective alternative to gradient methods for training
recurrent neural network. However, it is difficult to determine the structure (mainly the …

Wind turbines are among the most popular sources of renewable energy. The energy
available from wind varies widely because wind energy is highly dependent on continually …

In this paper, we propose a novel computational architecture of memristor-based echo state
network (MESN) with the online least mean square (LMS) algorithm. Newman and Watts …

Self-assembled networks of nanoparticles and nanowires have recently emerged as
promising systems for brain-like computation. Here, we focus on percolating networks of …

Abstract The Echo State Network (ESN) is a representative model for reservoir computing,
which is capable of high-speed model training for machine learning tasks with time series …

Deep belief network (DBN) has attracted many attentions in time series prediction. However,
the DBN-based methods fail to provide favorable prediction results due to the congenital …