  Recurrent connectivity in the visual cortex is believed to aid object
recognition for challenging conditions such as occlusion. Here we investigate
if and how artificial neural networks also benefit from recurrence. We compare
architectures composed of bottom-up, lateral and top-down connections and
evaluate their performance using two novel stereoscopic occluded object
datasets. We find that classification accuracy is significantly higher for
recurrent models when compared to feedforward models of matched parametric
complexity. Additionally we show that for challenging stimuli, the recurrent
feedback is able to correctly revise the initial feedforward guess.
