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 …

In this paper, we reviewed the recent trends on neuromorphic computing using emerging
memory technologies. Two representative learning algorithms used to implement a …

Implementing compact, low-power artificial neural processing systems with real-time on-line
learning abilities is still an open challenge. In this paper we present a full-custom mixed …

We propose a learning approach to corner detection for event-based cameras that is stable
even under fast and abrupt motions. Event-based cameras offer high temporal resolution …

Neural recording systems are a central component of Brain-Machince Interfaces (BMIs). In
most of these systems the emphasis is on faithful reproduction and transmission of the …

In the brain, information is encoded, transmitted and used to inform behaviour at the level of
timing of action potentials distributed over population of neurons. To implement neural-like …

In this paper, we introduce a framework for dynamic gesture recognition with background
suppression operating on the output of a moving event-based camera. The system is …

Bio-inspired asynchronous event-based vision sensors are currently introducing a paradigm
shift in visual information processing. These new sensors rely on a stimulus-driven principle …

Event cameras are bio-inspired vision sensors measuring brightness changes (referred to
as an 'event') for each pixel independently, instead of capturing brightness images at a fixed …

This paper gives an overview of recent progress on 1) online learning algorithms with
spiking neurons 2) neuromorphic platforms that efficiently run these algorithms with a focus …