Spiking Neural Networks (SNNs), the third generation NNs, have come under the spotlight
for machine learning based applications due to their biological plausibility and reduced …

Autonomous Driving (AD) related features provide new forms of mobility that are also
beneficial for other kind of intelligent and autonomous systems like robots, smart …

This paper introduces a novel methodology for training an event-driven classifier within a
Spiking Neural Network (SNN) System capable of yielding good classification results when …

Spiking neural networks (SNNs) have shown great potential as a solution for realizing
ultralow-power consumption on neuromorphic hardware, but obtaining deep SNNs is still a …

Deep Learning (DL) has contributed to the success of many applications in recent years.
The applications range from simple ones such as recognizing tiny images or simple speech …

Deep spiking neural networks (SNNs) support asynchronous event-driven computation,
massive parallelism and demonstrate great potential to improve the energy efficiency of its …

Abstract Spiking Neural Network (SNN) is considered more biologically plausible and
energy-efficient on emerging neuromorphic hardware. Recently backpropagation algorithm …

Brain-inspired spiking neural networks (SNNs) have recently drawn more and more
attention due to their event-driven and energy-efficient characteristics. The integration of …

Artificial neural networks (ANNs), a popular path towards artificial intelligence, have
experienced remarkable success via mature models, various benchmarks, open-source …

Over the past few years, Spiking Neural Networks (SNNs) have become popular as a
possible pathway to enable low-power event-driven neuromorphic hardware. However, their …