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 energy-efficient models for
neuromorphic computing. For training the non-differentiable SNN models, the …

Spiking neural networks (SNNs) are a promising energy-efficient alternative to artificial
neural networks (ANNs) due to their rich dynamics, capability to process spatiotemporal …

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 …

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) with biologically inspired spatio-temporal dynamics
demonstrate superior energy efficiency on neuromorphic architectures. Error …

Spiking neural networks (SNN), also often referred to as the third generation of neural
networks, carry the potential for a massive reduction in memory and energy consumption …

Recently, brain-inspired spiking neural networks (SNNs) have demonstrated promising
capabilities in solving pattern recognition tasks. However, these SNNs are grounded on …

Mode decomposition methods are the current workhorse for the analysis of non-stationary
signals. However, current attempts at these methods mainly focus on improving accuracy …

The identification of sensory cues associated with potential opportunities and dangers is
frequently complicated by unrelated events that separate useful cues by long delays. As a …