# Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders

arXiv: https://arxiv.org/abs/2110.13911 · HTML: https://mlsanity.com/paper/2110.13911/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 64 references that are in the map.
- First posted on arXiv 25 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Age Progression/Regression by Conditional Adversarial Autoencoder](https://mlsanity.com/paper/1702.08423/) (2017)
- [Density Modeling of Images using a Generalized Normalization Transformation](https://mlsanity.com/paper/1511.06281/) (2015)
- [Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Brain's Ventral Visual Pathway](https://mlsanity.com/paper/1411.6422/) (2014)
- [11K Hands: Gender recognition and biometric identification using a large dataset of hand images](https://mlsanity.com/paper/1711.04322/) (2017)
- [Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs](https://mlsanity.com/paper/2003.01262/) (2020)

## Built on by (most cited first, 10 of 10)

- [Traveling Waves Encode the Recent Past and Enhance Sequence Learning](https://mlsanity.com/paper/2309.08045/) (2023)
- [TopoLM: brain-like spatio-functional organization in a topographic language model](https://mlsanity.com/paper/2410.11516/) (2024)
- [Poisson Variational Autoencoder](https://mlsanity.com/paper/2405.14473/) (2024)
- [Inferring response times of perceptual decisions with Poisson variational autoencoders](https://mlsanity.com/paper/2511.11480/) (2025)
- [Discovering Functionally Selective Brain Regions with a Deep Topographic Multimodal Model](https://mlsanity.com/paper/2606.09770/) (2026)
- [Modeling biological face recognition with deep convolutional neural networks](https://mlsanity.com/paper/2208.06681/) (2022)
- [Language learning shapes visual category-selectivity in deep neural networks](https://mlsanity.com/paper/2502.16456/) (2025)
- [Beyond topography: Topographic regularization improves robustness and reshapes representations in convolutional neural networks](https://mlsanity.com/paper/2508.00043/) (2025)
- [Model-Guided Microstimulation Steers Primate Visual Behavior](https://mlsanity.com/paper/2510.03684/) (2025)
- [Do traveling waves make good positional encodings?](https://mlsanity.com/paper/2511.11668/) (2025)

## Abstract (arXiv)

Category-selectivity in the brain describes the observation that certain spatially localized areas of the cerebral cortex tend to respond robustly and selectively to stimuli from specific limited categories. One of the most well known examples of category-selectivity is the Fusiform Face Area (FFA), an area of the inferior temporal cortex in primates which responds preferentially to images of faces when compared with objects or other generic stimuli. In this work, we leverage the newly introduced Topographic Variational Autoencoder to model the emergence of such localized category-selectivity in an unsupervised manner. Experimentally, we demonstrate our model yields spatially dense neural clusters selective to faces, bodies, and places through visualized maps of Cohen's d metric. We compare our model with related supervised approaches, namely the Topographic Deep Artificial Neural Network (TDANN) of Lee et al., and discuss both theoretical and empirical similarities. Finally, we show preliminary results suggesting that our model yields a nested spatial hierarchy of increasingly abstract categories, analogous to observations from the human ventral temporal cortex.