P. Lichtsteiner

TL;DR: CAVIAR is a massively parallel hardware implementation of a spike-based sensing-processing-learning-actuating system inspired by the physiology of the nervous system that achieves millisecond object recognition and tracking latencies.

TL;DR: It is shown that matching on the timing of the visual events provides a new solution to the real-time computation of 3-D objects when combined with geometric constraints using the distance to the epipolar lines.

TL;DR: This demonstration shows how a pair of spike-based silicon retina dynamic vision sensors (DVS) is used to provide fast visual feedback for controlling an actuated table to balance an ordinary pencil.

TL;DR: A general-purpose solution in the form of a peripheral component interconnect (PCI) board (the PCI-AER board) supported by software is implemented and a reconfigurable multichip neuromorphic system for feature selectivity which models orientation tuning properties of cortical neurons is demonstrated.

TL;DR: A hybrid neuromorphic-procedural system consisting of an address-event silicon retina, a computer, and a servo motor can be used to implement a fast sensory-motor reactive controller to track and block balls shot at a goal.