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

The growing demands of brain science and artificial intelligence create an urgent need for
the development of artificial neural networks (ANNs) that can mimic the structural, functional …

Semiconductor memories are essential ingredients of modern electronic devices. Resistive
Random-Access Memories (RRAMs) have emerged as better alternatives for conventional …

The emergence of new nonvolatile memory (NVM) technologies—such as phase change
memory, resistive, and spin-torque-transfer magnetic RAM—has been motivated by exciting …

A mathematical model of the prototype of memristor, manufactured in 2008 in Hewlett-
Packard Labs, is described in the paper. It is shown that the hitherto published approaches …

Abstract Design of artificial neural networks based on memristor has attracted increasing
attentions from researchers. However, there are no reports on the discrete memristor based …

The need for reliable models that take into account the nonlinear kinetics of dopants is
nowadays of paramount importance, particularly with the physical dimensions of electron …

Cellular nonlinear/neural network (CNN) has been recognized as a powerful massively
parallel architecture capable of solving complex engineering problems by performing …

Novel information processing techniques are being actively explored to overcome
fundamental limitations associated with CMOS scaling. A new paradigm of adaptive …

There has been a surge of interest in Non-Volatile Memory (NVM) in recent years. With
many advantages, such as density and power consumption, NVM is carving out a place in …