Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial
neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal …

The ambition of brain-inspired Spiking Neural Networks (SNNs) is to become a low-power
alternative to traditional Artificial Neural Networks (ANNs). This work addresses two major …

This study aims to select the appropriate renewable energy alternatives for the efficiency of
hybrid energy systems to increase energy transition performance. For this purpose, a novel …

Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models.
Compared to conventional deep Artificial Neural Networks (ANNs), SNNs exhibit superior …

Abstract Spiking Neural Networks (SNNs) are naturally suited to process sequence tasks
such as NLP with low power, due to its brain-inspired spatio-temporal dynamics and spike …

Compared to traditional Artificial Neural Network (ANN), Spiking Neural Network (SNN) has
garnered widespread academic interest for its intrinsic ability to transmit information in a …

The aim of this paper is to present a category of novel pattern classification algorithms
inspired by the phenomenon of the firing rate based stochastic resonance in a noisy leaky …

On-device computing, or edge computing, is becoming increasingly important for remote
sensing, particularly in applications like deep network-based perception on on-orbit …

Neuromorphic computing systems promise high energy efficiency and low latency. In
particular, when integrated with neuromorphic sensors, they can be used to produce …

Brain-inspired Spiking Neural Network (SNN) has demonstrated its effectiveness and
efficiency in vision, natural language, and speech understanding tasks, indicating their …