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Deep convolutional neural networks are biologically driven models thatresemble the hierarchical structure of primate visual cortex and are thecurrent best predictors of the neural responses measured along the ventralstream. However, the networks lack topographic properties that are present inthe visual cortex, such as orientation maps in primary visual cortex andcategory-selective maps in inferior temporal (IT) cortex. In this work, theminimum wiring cost constraint was approximated as an additional learning rulein order to generate topographic maps of the networks. We found that ourtopographic deep artificial neural networks (ANNs) can reproduce the categoryselectivity maps of the primate IT cortex.

Topographic Deep Artificial Neural Networks (TDANNs) predict face selectivity topography in primate inferior temporal (IT) cortex