Conventional neuro-computing architectures and artificial neural networks have often been
developed with no or loose connections to neuroscience. As a consequence, they have …

Inspired by biology, neuromorphic systems have been trying to emulate the human brain for
decades, taking advantage of its massive parallelism and sparse information coding …

As the era of big data approaches, conventional digital computers face increasing difficulties
in performance and power efficiency due to their von Neumann architecture. As a result …

Memristors, promising nanoelectronic devices with in-memory resistive switching behavior
that is assembled with a physically integrated core processing unit (CPU) and memory unit …

The memristor is a resistive switch where its resistive state is programable based on the
applied voltage or current. Memristive devices are thus capable of storing and computing …

This article provides a review of current development and challenges in brain-inspired
computing with memristors. We review the mechanisms of various memristive devices that …

The memristor is a promising candidate to implement high-density memory and
neuromorphic computing. Based on the characteristic retention time, memristors are …

The design and simulation of a scalable neural chip with synaptic electronics using
nanoscale memristors fully integrated with complementary metal–oxide–semiconductor …

In the era of information, characterized by an exponential growth in data volume and an
escalating level of data abstraction, there has been a substantial focus on brain‐like chips …

A memristor is a two-terminal electronic device whose conductance can be precisely
modulated by charge or flux through it. Here we experimentally demonstrate a nanoscale …