Resistive random access memory (RRAM) devices are receiving increasing extensive
attention due to their enhanced properties such as fast operation speed, simple device …

The spiking neural network (SNN), as a promising brain-inspired computational model with
binary spike information transmission mechanism, rich spatially-temporal dynamics, and …

As one of the energy-efficient alternatives of conventional neural networks (CNNs), spiking
neural networks (SNNs) have gained more and more interest recently. To train the deep …

Humanoid robots, intelligent machines resembling the human body in shape and functions,
cannot only replace humans to complete services and dangerous tasks but also deepen the …

Here we provide a perspective concept of neurohybrid memristive chip based on the
combination of living neural networks cultivated in microfluidic/microelectrode system, metal …

Breast cancer is one of the most significant causes of death for women around the world.
Breast thermography supported by deep convolutional neural networks is expected to …

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

The modern-day computing technologies are continuously undergoing a rapid changing
landscape; thus, the demands of new memory types are growing that will be fast, energy …

The design of modern convolutional artificial neural networks (ANNs) composed of formal
neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy …

In this study, we fabricate and characterize a Ti/TiO2/Si device with different dopant
concentrations on a silicon surface for neuromorphic systems. We verify the device stack …