This work is aimed to study experimental and theoretical approaches for searching effective
local training rules for unsupervised pattern recognition by high-performance memristor …

Neuromorphic systems consisting of artificial neurons and memristive synapses could
provide a much better performance and a significantly more energy-efficient approach to the …

On metrics of density and power efficiency, neuromorphic technologies have the potential to
surpass mainstream computing technologies in tasks where real-time functionality …

Spiking neural network, inspired by the human brain, consisting of spiking neurons and
plastic synapses, is a promising solution for highly efficient data processing in neuromorphic …

Emerging brain-inspired architectures call for devices that can emulate the functionality of
biological synapses in order to implement new efficient computational schemes able to …

Recent advances in memristor technology lead to the feasibility of large-scale neuromorphic
systems by leveraging the similarity between memristor devices and synapses. For instance …

Recent research in nanotechnology has led to the practical realization of nanoscale devices
that behave as memristors, a device that was postulated in the seventies by Chua based on …

Memristors have recently emerged as promising circuit elements to mimic the function of
biological synapses in neuromorphic computing. The fabrication of reliable nanoscale …

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 …

Recent theoretical studies have shown that probabilistic spiking can be interpreted as
learning and inference in cortical microcircuits. This interpretation creates new opportunities …