The quantity of sensor nodes within current computing systems is rapidly increasing in
tandem with the sensing data. The presence of a bottleneck in data transmission between …

Biologically inspired spiking neural networks are increasingly popular in the field of artificial
intelligence due to their ability to solve complex problems while being power efficient. They …

Abstract Spiking Neural Networks (SNNs) have attracted enormous research interest due to
temporal information processing capability, low power consumption, and high biological …

In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …

Artificial neural networks have been used as a powerful processing tool in various areas
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …

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 …

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

Abstract Deep Convolutional Neural Network (CNN)-based image classification systems are
often susceptible to noise interruption, ie, minor image noise may significantly impact the …

The problem of training spiking neural networks (SNNs) is a necessary precondition to
understanding computations within the brain, a field still in its infancy. Previous work has …

Abstract Spiking Neural Networks (SNNs) have shown substantial promise in processing
spatio-temporal data, mimicking biological neuronal mechanisms, and saving computational …