  Feedforward convolutional neural networks are the prevalent model of core
object recognition. For challenging conditions, such as occlusion,
neuroscientists believe that the recurrent connectivity in the visual cortex
aids object recognition. In this work we investigate if and how artificial
neural networks can also benefit from recurrent connectivity. For this we
systematically compare architectures comprised of bottom-up (B), lateral (L)
and top-down (T) connections. To evaluate performance, we introduce two novel
stereoscopic occluded object datasets, which bridge the gap from classifying
digits to recognizing 3D objects. The task consists of recognizing one target
object occluded by multiple occluder objects. We find that recurrent models
perform significantly better than their feedforward counterparts, which were
matched in parametric complexity. We show that for challenging stimuli, the
recurrent feedback is able to correctly revise the initial feedforward guess of
the network. Overall, our results suggest that both artificial and biological
neural networks can exploit recurrence for improved object recognition.
