Filamentary resistive switching (RS) devices are not only considered as promising building
blocks for brain-inspired computing architectures but also realize an unprecedented …

This paper investigates the synchronization problem for master‐slave stochastic memristive
neural networks (SMNNs) with time‐delay via event‐triggered impulsive control (ETIC) …

Memristive devices hold promise to improve the scale and efficiency of machine learning
and neuromorphic hardware, thanks to their compact size, low power consumption, and the …

In this paper, we prepared and investigated the electrical switching behaviors of Cu 3 P/Ag 2
S heterojunction in the absence/presence of light/heat excitation. The structure exhibited …

Human brains image complicated scenes when reading a novel. Replicating this
imagination is one of the ultimate goals of AI-Generated Content (AIGC). However, current …

Based on the similarity between the working mechanism of memristors and the synapses of
neural networks, a design scheme of perceptron neural networks based on memristors is …

Digital twins, the cornerstone of Industry 4.0, replicate real-world entities through computer
models, revolutionising fields such as manufacturing management and industrial …

The transistor is broadly used to address memristor networks, but its three-terminal structure
can impose limitations on fully exploiting the potential of efficient integration that a two …

The co-location of memory and processing is a core principle of neuromorphic computing. A
local memory device for synaptic weight storage has long been recognized as an enabling …

The execution of deep neural network (DNN) algorithms suffers from significant bottlenecks
due to the separation of the processing and memory units in traditional computer systems …