Improvements in computing performance have significantly slowed down over the past few
years owing to the intrinsic limitations of computing hardware. However, the demand for data …

Neuromorphic computing approaches become increasingly important as we address future
needs for efficiently processing massive amounts of data. The unique attributes of quantum …

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 …

Artificial neurons and synapses are critical units for processing intricate information in
neuromorphic systems. Memristors are frequently engineered as artificial synapses due to …

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 …

Biorealistic spiking neural networks (SNN) are believed to hold promise for further energy
improvement over artificial neural networks (ANNs). However, it is difficult to implement …

Resistance random access memories (RRAM) or memristors with an analog change of
conductance are widely explored as an artificial synapse, eg, Pr 0.7 Ca 0.3 MnO 3 (PCMO) …

We present and experimentally validate two minimal compact memristive models for spiking
neuronal signal generation using commercially available low-cost components. The first …

With rapid advancement and deep integration of artificial intelligence and the internet-of-
things, artificial intelligence of things has emerged as a promising technology changing …

The unstructured data such as visual information, natural language, and human behaviors
opens up a wide array of opportunities in the field of artificial intelligence (AI). The memory …