In this paper we review several ways of realizing asynchronous Spike-Timing-Dependent-
Plasticity (STDP) using memristors as synapses. Our focus is on how to use individual …

Inspired by biology, neuromorphic systems have been trying to emulate the human brain for
decades, taking advantage of its massive parallelism and sparse information coding …

Neuromorphic computing systems comprise networks of neurons that use asynchronous
events for both computation and communication. This type of representation offers several …

Deep spiking neural networks (SNNs) hold the potential for improving the latency and
energy efficiency of deep neural networks through data-driven event-based computation …

Deep neural networks such as Convolutional Networks (ConvNets) and Deep Belief
Networks (DBNs) represent the state-of-the-art for many machine learning and computer …

A striking difference between brain-inspired neuromorphic processors and current von
Neumann processor architectures is the way in which memory and processing is organized …

In this paper, we describe the design of Neurogrid, a neuromorphic system for simulating
large-scale neural models in real time. Neuromorphic systems realize the function of …

Event-driven visual sensors have attracted interest from a number of different research
communities. They provide visual information in quite a different way from conventional …

Dynamic Vision Sensors (DVS) have recently appeared as a new paradigm for vision
sensing and processing. They feature unique characteristics such as contrast coding under …

This article reports on two databases for event-driven object recognition using a Dynamic
Vision Sensor (DVS). The first, which we call Poker-DVS and is being released together with …