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

TL;DR

This paper introduces Topographic Deep Artificial Neural Networks (TDANNs) that incorporate wiring cost constraints to produce topographic maps, successfully mimicking face selectivity topography in primate IT cortex.

Contribution

The study develops a novel topographic deep neural network model that integrates wiring cost constraints to replicate cortical topography.

Findings

TDANNs reproduce category selectivity maps of primate IT cortex.

Wiring cost constraints enable the emergence of topographic organization.

The model advances understanding of neural topography in visual cortex.

Abstract

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.