A memristor is a resistive device with an inherent memory. The theoretical concept of a
memristor was connected to physically measured devices in 2008 and since then there has …

Neuromorphic systems, which emulate neural functionalities of a human brain, are
considered to be an attractive next‐generation computing approach, with advantages of …

In the brain, learning is achieved through the ability of synapses to reconfigure the strength
by which they connect neurons (synaptic plasticity). In promising solid-state synapses called …

A nociceptor is a critical and special receptor of a sensory neuron that is able to detect
noxious stimulus and provide a rapid warning to the central nervous system to start the …

Memristive devices are a class of physical systems with history-dependent dynamics
characterized by signature hysteresis loops in their input–output relations. In the past few …

This paper presents an in-depth review of the memristor from a rigorous circuit-theoretic
perspective, independent of the material the device is made of. From an experimental …

Wearable devices are a fast-growing technology with impact on personal healthcare for both
society and economy. Due to the widespread of sensors in pervasive and distributed …

Spiking neural networks, the most realistic artificial representation of biological nervous
systems, are promising due to their inherent local training rules that enable low-overhead …

In this paper we review several ways of realizing asynchronous Spike-Timing-Dependent-
Plasticity (STDP) using memristors as synapses. Our focus is on how to use individual …

Compact and power-efficient plastic electronic synapses are of fundamental importance to
overcoming the bottlenecks of develo** a neuromorphic chip. Memristor is a strong …