  Deep convolutional neural networks (CNNs) trained for object classification
have a number of striking similarities with the primate ventral visual stream.
In particular, activity in early, intermediate, and late layers is closely
related to activity in V1, V4, and the inferotemporal cortex (IT). This study
further compares activity in late layers of object-classification CNNs to
activity patterns reported in the IT electrophysiology literature. There are a
number of close similarities, including the distributions of population
response sparseness across stimuli, and the distribution of size tuning
bandwidth. Statisics of scale invariance, responses to clutter and occlusion,
and orientation tuning are less similar. Statistics of object selectivity are
quite different. These results agree with recent studies that highlight strong
parallels between object-categorization CNNs and the ventral stream, and also
highlight differences that could perhaps be reduced in future CNNs.
