The memristor has been extensively used to facilitate the synaptic online learning of brain-
inspired spiking neural networks (SNNs). However, the current memristor-based work can …

Different memristive devices have different characteristic curves; how to describe and
simulate various kinds of memristive devices with a unified model is still a significant work. In …

The Bayesian Confidence Propagation Neural Network (BCPNN) has been implemented in
a way that allows mapping to neural and synaptic processes in the human cortexandhas …

Neural networks that can capture key principles underlying brain computation offer exciting
new opportunities for developing artificial intelligence and brain-like computing algorithms …

Recently, memristors have been widely used to implement Spiking Neural Networks (SNNs),
which is promising in edge computing scenarios. However, most memristor-based SNN …

The brain achieves complex information processing and cognitive functions leveraging
synaptic learning mechanisms that are local, asynchronous, online and Hebbian in nature …

Evaluating the computational accuracy of Spiking Neural Network (SNN) implemented as in-
situ learning on large-scale memristor crossbars remains a challenge due to the lack of a …

We present a CMOS-Memristor hybrid analog design of a neuromorphic crossbar array with
integrated inference and training. Each crosspoint on the crossbar includes a memristor to …

Memristors have been widely studied as artificial synapses in neuromorphic circuits, due to
their functional similarity with biological synapses, low operating power, and high integration …

Nowadays, neuromorphic systems based on Spiking Neural Networks (SNNs) attract
attentions of many researchers. There are many studies to improve performances of …