Artificial neural networks have been used as a powerful processing tool in various areas
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …

Spiking neural networks (SNNs) are promising in a bio-plausible coding for spatio-temporal
information and event-driven signal processing, which is very suited for energy-efficient …

The best performing learning algorithms devised for event cameras work by first converting
events into dense representations that are then processed using standard CNNs. However …

How to effectively and efficiently deal with spatio-temporal event streams, where the events
are generally sparse and non-uniform and have the us temporal resolution, is of great value …

Abstract Configuring deep Spiking Neural Networks (SNNs) is an exciting research avenue
for low power spike event based computation. However, the spike generation function is non …

Developing neuromorphic intelligence on event-based datasets with Spiking Neural
Networks (SNNs) has recently attracted much research attention. However, the limited size …

Abstract Spiking Neural Networks (SNNs) have recently emerged as a new generation of
low-power deep neural networks due to sparse, asynchronous, and binary event-driven …

Different from traditional video cameras, event cameras capture asynchronous events
stream in which each event encodes pixel location, trigger time, and the polarity of the …

Event cameras, offering extremely high temporal resolution and high dynamic range, have
brought a new perspective to addressing common object detection challenges (eg, motion …

Event cameras are bio-inspired sensors that respond to per-pixel brightness changes in the
form of asynchronous and sparse “events”. Recently, pattern recognition algorithms, such as …