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 …

Neural networks, one of the key artificial intelligence technologies today, have the
computational power and learning ability similar to the brain. However, implementation of …

Diverse models have been proposed over the past years to explain the exhibiting behavior
of memristors, the fourth fundamental circuit element. The models varied in complexity …

With the arrival of the Internet of Things (IoT) and the challenges arising from Big Data,
neuromorphic chip concepts are seen as key solutions for coping with the massive amount …

Memristor crossbar arrays are used in a wide range of in-memory and neuromorphic
computing applications. However, memristor devices suffer from non-idealities that result in …

A recurrent spiking neural network is proposed that implements planning as probabilistic
inference for finite and infinite horizon tasks. The architecture splits this problem into two …

Resistive random access memory is very well known for its potential application in in-
memory and neural computing. However, they often have different types of device-to-device …

Biological neural networks outperform current computer technology in terms of power
consumption and computing speed while performing associative tasks, such as pattern …

Synaptic devices with bipolar analog resistive switching behavior are the building blocks for
memristor-based neuromorphic computing. In this work, a fully complementary metal-oxide …

We propose a memristive interface consisting of two FitzHugh–Nagumo electronic neurons
connected via a metal–oxide (Au/Zr/ZrO2 (Y)/TiN/Ti) memristive synaptic device. We create a …