A spiking neural network (SNN) is a type of biological plausibility model that performs
information processing based on spikes. Training a deep SNN effectively is challenging due …

Address event representation (AER) image sensors represent the visual information as a
sequence of events that denotes the luminance changes of the scene. In this paper, we …

We increasingly rely on deep learning algorithms to process colossal amount of
unstructured visual data. Commonly, these deep learning algorithms are deployed as …

Spiking Neural Networks (SNNs) are an emerging domain of biologically inspired neural
networks that have shown promise for low-power AI. A number of methods exist for building …

事件相机作为一种新型动态视觉传感器, 通过各个像素点独立检测光照强度变化并异步输出“
事件流” 信号, 具有数据量小, 延迟低, 动态范围高等优秀特性, 给机器人控制带来新的可能 …

A method of event-based down sampling includes receiving multiple sensor events
corresponding to addresses and time stamps. The method further includes spatially down …

We propose a biologically plausible computational system using spiking neural networks for
object recognition, which processes the data from a temporal contrast address event …

One of the applications of AI lies in developing intelligent systems for safe on-road driving,
other than building and perfecting self-driving vehicles, and many others. Driving Scene …

the human retina in contrast, is an example of an event-based vision sensor. That is,
individual neurons in the retina are sensitive to a portion of the visual field such that each …

(57) ABSTRACT A method of processing asynchronous event-driven input samples of a
continuous time signal, includes calculating a convolutional output directly from the event …