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Event-based cameras are neuromorphic sensors capable of efficiently encodingvisual information in the form of sparse sequences of events. Beingbiologically inspired, they are commonly used to exploit some of thecomputational and power consumption benefits of biological vision. In thispaper we focus on a specific feature of vision: visual attention. We proposetwo attentive models for event based vision: an algorithm that tracks eventsactivity within the field of view to locate regions of interest and afully-differentiable attention procedure based on DRAW neural model. Wehighlight the strengths and weaknesses of the proposed methods on fourdatasets, the Shifted N-MNIST, Shifted MNIST-DVS, CIFAR10-DVS and N-Caltech101collections, using the Phased LSTM recognition network as a baseline referencemodel obtaining improvements in terms of both translation and scale invariance.

Attention Mechanisms for Object Recognition with Event-Based Cameras