The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

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 …

Abstract Spiking Neural Networks (SNNs) have recently become more popular as a
biologically plausible substitute for traditional Artificial Neural Networks (ANNs). SNNs are …

Emerged as a biology-inspired method, Spiking Neural Networks (SNNs) mimic the spiking
nature of brain neurons and have received lots of research attention. SNNs deal with binary …

Spiking Neural Networks (SNNs) have emerged as energy-efficient alternatives to Artificial
Neural Networks (ANNs) when deployed on neuromorphic chips. While recent studies have …

Abstract Spiking Neural Networks (SNN) are recognised as well-suited for processing
spatiotemporal information with ultra-low energy consumption. However, proposals based …

Spiking neural networks (SNNs) are a promising avenue for machine learning with superior
energy efficiency compared to traditional artificial neural networks (ANNs). Recent advances …

Event cameras, with their high dynamic range and temporal resolution, are ideally suited for
object detection, especially under scenarios with motion blur and challenging lighting …

Robotic technologies have been an indispensable part for improving human productivity
since they have been helping humans in completing diverse, complex, and intensive tasks …