As the research on artificial intelligence booms, there is broad interest in brain‐inspired
computing using novel neuromorphic devices. The potential of various emerging materials …

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 …

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic
chips with high energy efficiency by introducing neural dynamics and spike properties. As …

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

Abstract Configuring deep Spiking Neural Networks (SNNs) is an exciting research avenue
for low power spike event based computation. However, the spike generation function is non …

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 …

Spiking neural networks (SNNs) are a promising energy-efficient alternative to artificial
neural networks (ANNs) due to their rich dynamics, capability to process spatiotemporal …

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 …

Class imbalance is often a problem in various real-world data sets, where one class (ie the
minority class) contains a small number of data points and the other (ie the majority class) …

Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …