Neuromorphic Downsampling of Event-Based Camera Output

Abstract

In this work, we address the problem of training a neuromorphic agent to work on data from event-based cameras. Although event-based camera data is much sparser than standard video frames, the sheer number of events can make the observation space too complex to effectively train an agent. We construct multiple neuromorphic networks that downsample the camera data so as to make training more effective. We then perform a case study of training an agent to play the Atari Pong game by converting each frame to events and downsampling them. The final network combines both the downsampling and the agent. We discuss some practical considerations as well.