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

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 …

Spiking neural networks (SNNs), which are composed of biologically plausible spiking
neurons, and combined with bio-physically realistic auditory periphery models, offer a …

Spiking neural networks (SNNs) have undergone continuous development and extensive
study for decades, leading to increased biological plausibility and optimal energy efficiency …

Neuromorphic object recognition with spiking neural networks (SNNs) is the cornerstone of
low-power neuromorphic computing. However, existing SNNs suffer from significant latency …

Introduction The spiking neural network (SNN) is a bionic model that is energy-efficient
when implemented on neuromorphic hardwares. The non-differentiability of the spiking …

In the era of AI at the edge, self-driving cars, and climate change, the need for energy-
efficient, small, embedded AI is growing. Spiking Neural Networks (SNNs) are a promising …

The training method of Spiking Neural Networks (SNNs) is an essential problem, and how to
integrate local and global learning is a worthy research interest. However, the current …

The brain topology highly reflects the complex cognitive functions of the biological brain after
million-years of evolution. Learning from these biological topologies is a smarter and easier …