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 Network (SNN) is a promising energy-efficient AI model when
implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs …

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

Spiking neural networks (SNNs) are regarded as a promising candidate to deal with the
major challenges of current machine learning techniques, including the high energy …

Spiking neural networks (SNNs) are well known as the brain-inspired models with high
computing efficiency, due to a key component that they utilize spikes as information units …

Abstract Spiking Neural Networks (SNNs) have received extensive academic attention due
to the unique properties of low power consumption and high-speed computing on …

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

The field of Deep Learning (DL) has seen a remarkable series of developments with
increasingly accurate and robust algorithms. However, the increase in performance has …

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 …

Abstract Recently, Spiking Neural Networks (SNNs), enjoying extreme energy efficiency,
have drawn much research attention on 2D visual recognition and shown gradually …