Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path
for implementing massively-parallel and highly energy-efficient neuromorphic computing …

Coupled oscillators are highly complex dynamical systems, and it is an intriguing concept to
use this oscillator dynamics for computation. The idea is not new, but is currently the subject …

Spiking neural networks (SNNs) are a promising energy-efficient alternative to artificial
neural networks (ANNs) due to their rich dynamics, capability to process spatiotemporal …

Neuromorphic computing has emerged as a promising avenue towards building the next
generation of intelligent computing systems. It has been proposed that memristive devices …

In order to understand how the mammalian neocortex is performing computations, two
things are necessary; we need to have a good understanding of the available neuronal …

The volume, veracity, variability, and velocity of data produced from the ever increasing
network of sensors connected to Internet pose challenges for power management …

In this paper, the recent progress of synaptic electronics is reviewed. The basics of biological
synaptic plasticity and learning are described. The material properties and electrical …

Memristors are memory resistors that promise the efficient implementation of synaptic
weights in artificial neural networks. Whereas demonstrations of the synaptic operation of …

Spin-transfer torque magnetic memory (STT-MRAM) is currently under intense academic
and industrial development, since it features non-volatility, high write and read speed and …

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 …