Spiking neural networks (SNNs) are inspired by information processing in biology, where
sparse and asynchronous binary signals are communicated and processed in a massively …

In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …

Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference
because the neurons in the networks are sparsely activated and computations are event …

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …

Spiking Neural Networks (SNNs), as bio-inspired energy-efficient neural networks, have
attracted great attentions from researchers and industry. The most efficient way to train deep …

Event-based cameras have recently drawn the attention of the Computer Vision community
thanks to their advantages in terms of high temporal resolution, low power consumption and …

Spiking neural networks (SNNs) have received significant attention for their biological
plausibility. SNNs theoretically have at least the same computational power as traditional …

The problem of training spiking neural networks (SNNs) is a necessary precondition to
understanding computations within the brain, a field still in its infancy. Previous work has …

Event-based visual, a new visual paradigm with bio-inspired dynamic perception and μ s
level temporal resolution, has prominent advantages in many specific visual scenarios and …

Abstract Spiking Neural Networks (SNNs) are well known as a promising energy-efficient
alternative to conventional artificial neural networks. Subject to the preconceived impression …