The spiking neural network (SNN), as a promising brain-inspired computational model with
binary spike information transmission mechanism, rich spatially-temporal dynamics, and …

Abstract Spiking Neural Networks (SNNs) are promising in neuromorphic hardware owing to
utilizing spatio-temporal information and sparse event-driven signal processing. However, it …

Abstract Spiking Neural Networks (SNNs) have been attached great importance due to the
distinctive properties of low power consumption, biological plausibility, and adversarial …

In recent years, spiking neural networks (SNNs) have received extensive attention in brain-
inspired intelligence due to their rich spatially-temporal dynamics, various encoding …

Abstract Spiking Neural networks (SNNs) represent and transmit information by
spatiotemporal spike patterns, which bring two major advantages: biological plausibility and …

Abstract Spiking Neural Networks (SNNs) have recently emerged as a new generation of
low-power deep neural networks, which is suitable to be implemented on low-power …

Spiking neural networks (SNNs) are shown to be more biologically plausible and energy
efficient over their predecessors. However, there is a lack of an efficient and generalized …

There is an increasing interest in emulating Spiking Neural Networks (SNNs) on
neuromorphic computing devices due to their low energy consumption. Recent advances …

Abstract Spiking Neural Networks (SNNs) are considered a promising alternative to Artificial
Neural Networks (ANNs) for their event-driven computing paradigm when deployed on …

Spiking neural networks (SNNs) have manifested remarkable advantages in power
consumption and event-driven property during the inference process. To take full advantage …