  Deep convolutional neural networks are biologically driven models that
resemble the hierarchical structure of primate visual cortex and are the
current best predictors of the neural responses measured along the ventral
stream. However, the networks lack topographic properties that are present in
the visual cortex, such as orientation maps in primary visual cortex and
category-selective maps in inferior temporal (IT) cortex. In this work, the
minimum wiring cost constraint was approximated as an additional learning rule
in order to generate topographic maps of the networks. We found that our
topographic deep artificial neural networks (ANNs) can reproduce the category
selectivity maps of the primate IT cortex.
