Event-based cameras are attracting significant interest as they provide rich edge information, high dynamic range, and high temporal resolution.

Event cameras are neuromorphic sensors that summarize the evolving world as a stream of events . Each event describes the pixel coordinates, time, and polarity of an intensity change.

During training, RNN-based learning algorithms are forced to unfold through the time axis, as explainedinFigure1andSection2. Asaresult,thecorresponding numberofoperations isatleast O(TMN), where Tisthe total time steps and M, N are the number of input and output neurons. On the other hand, event-based learning algorithms only have to deal with cases where a certain neuron fires aspike, and record the relevant information.