As a new brain-inspired computational model of the artificial neural network, a spiking
neural network encodes and processes neural information through precisely timed spike …

In today's era of big‐data, a new computing paradigm beyond today's von‐Neumann
architecture is needed to process these large‐scale datasets efficiently. Inspired by the …

Objective. Electroencephalography (EEG) motor imagery classification has been widely
used in healthcare applications such as mobile assistive robots and post-stroke …

Neuromorphic vision sensors, whose pixels output events/spikes asynchronously with a
high temporal resolution according to the scene radiance change, are naturally appropriate …

Most of the associate memory circuits are at the simulation stage. If these designs are to be
realized in hardware, they pose substantial requirements in terms of experimental …

In recent years, numerous attempts have been made to integrate sliding mode control (SMC)
and neural networks (NN) in order to leverage the advantages of both methods while …

Spiking neural networks (SNNs) are considered as a potential candidate to overcome
current challenges, such as the high-power consumption encountered by artificial neural …

Spiking neurons are becoming increasingly popular owing to their biological plausibility and
promising computational properties. Unlike traditional rate-based neural models, spiking …

Artificial neural networks have shown promising results in a variety of natural language
understanding (NLU) tasks. Despite their successes, conventional neural-based NLU …

Spikes play an essential role in information transmission in central nervous system, but how
neurons learn from them remains a challenging question. Most algorithms studied how to …