One of the first and most remarkable successes in neuromorphic (brain-inspired) engineering was the development of event cameras, which communicate transients in luminance as events. Here we evaluate the combination of the Channel and Spatial Reliability Tracking (CSRT) algorithm and the LapDepth neural network for the implementation of 3D object tracking with event cameras. We show that following image reconstruction, implemented using the FireNet convolution neural network, visual features are augmented, dramatically increasing tracking performance. We utilized the 3D tracker to neuromorphically represent error-correcting signals. These error-correcting signals can further be used for motion correction in adaptive neurorobotics.