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Neuromorphic systems are engineering solutions that take inspiration from biological neural systems. They use spike-or event-based representation and codification of the information. This codification allows performing complex computations, filters, classifications and learning in a pseudo-simultaneous way. Small incremental processing is done per event, which shows useful results with very low latencies...
Correcting digital images tilt needs huge quantities of memory, high computational resources, and use to take a considerable amount of time. This demonstration shows how a spikes-based silicon retina dynamic vision sensor (DVS) tilt can corrected in real time using a commercial accelerometer. DVS output is a stream of spikes codified using the address-event representation (AER). Event-based processing...
Neuromorphic engineering tries to mimic biological information processing. Address-event representation (AER) is a neuromorphic communication protocol for spiking neurons between different chips. We present a new way to drive robotic platforms using spiking neurons. We have simulated spiking control models for DC motors, and developed a mobile robot (Eddie) controlled only by spikes. We apply AER...
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