As one of the energy-efficient alternatives of conventional neural networks (CNNs), spiking
neural networks (SNNs) have gained more and more interest recently. To train the deep …

Abstract Spiking Neural Networks (SNNs) as one of the biology-inspired models have
received much attention recently. It can significantly reduce energy consumption since they …

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

Abstract Spiking Neural Networks (SNNs) have attracted significant attention due to their
energy-efficient properties and potential application on neuromorphic hardware. State-of-the …

The Spiking Neural Network (SNN), as one of the biologically inspired neural network
infrastructures, has drawn increasing attention recently. It adopts binary spike activations to …

Spiking Neural Networks (SNNs), known for their biologically plausible architecture, face the
challenge of limited performance. The self-attention mechanism, which is the cornerstone of …

This paper presents a new methodology to alleviate the fundamental trade-off between
accuracy and latency in spiking neural networks (SNNs). The approach involves decoding …

Brain-inspired spiking neural networks (SNNs) have gained prominence in the field of
neuromorphic computing owing to their low energy consumption during feedforward …

Spiking Neural Networks (SNNs) have garnered considerable attention due to their energy
efficiency and unique biological characteristics. However, the widely adopted Leaky …

Spiking Neural Networks (SNNs) have received widespread attention in academic
communities due to their superior spatio-temporal processing capabilities and energy …